IJACSA Volume 11 Issue 10 - thesai.org

IJACSA Volume 11 Issue 10

Copyright Statement: This is an open access publication licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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Paper 1: Performance Analysis for Mining Images of Deep Web

Abstract: In this paper, advancing web scale knowledge extraction and alignment by integrating few sources has been considered by exploring different methods of aggregation and attention in order to focus on image information. An improved model, namely, Wrapper Extraction of Image using DOM and JSON (WEIDJ) has been proposed to extract images and the related information in fastest way. Several models, such as Document Object Model (DOM), Wrapper using Hybrid DOM and JSON (WHDJ), WEIDJ and WEIDJ (no-rules) are been discussed. The experimental results on real world websites demonstrate that our models outperform others, such as Document Object Model (DOM), Wrapper using Hybrid DOM and JSON (WHDJ) in terms of mining in a higher volume of web data from a various types of image format and taking the consideration of web data extraction from deep web.

Author 1: Ily Amalina Ahmad Sabri

Author 2: Mustafa Man

Keywords: Data extraction; Document Object Model; web data extraction; Wrapper using Hybrid DOM and JSON; Wrapper Extraction of Image using DOM and JSON

Paper 2: Alzheimer’s Disease Detection using Neighborhood Components Analysis and Feature Selection

Abstract: In this paper, we propose a Computer Aided Diagnosis (CAD) system in order to assist the physicians in the early detection of Alzheimer’s Disease (AD) and ensure an effective diagnosis. The proposed framework is designed to be fully-automated upon the capture of the brain structure using Magnetic Resonance Imaging (MRI) scanners. The Voxel-Based Morphometry (VBM) analysis is a key element in the proposed detection process as it is intended to investigate the Gray Matter (GM) tissues in the captured MRI images. In other words, the feature extraction phase consists in encoding the voxel properties in the MRI images into numerical vectors. The resulting feature vectors are then fed into a Neighborhood Component Analysis and Feature Selection (NCFS) algorithm coupled with K-Nearest Neighbor (KNN) algorithm in order to learn a classification model able to recognize AD cases. The feature selection based on NCFS algorithm improved the overall classification performance.

Author 1: Mohamed Maher Ben Ismail

Author 2: Reema Alabdullatif

Author 3: Ouiem Bchir

Keywords: Alzheimer detection; classification; feature selection

Paper 3: Support Kernel Classification: A New Kernel-Based Approach

Abstract: In this paper, we introduce a new classification approach that learns class dependent Gaussian kernels and the belongingness likelihood of the data points with respect to each class. The proposed Support Kernel Classification (SKC) is designed to characterize and discriminate between the data instances from the different classes. It relies on the maximization of the intra-class distances and the minimization of the intra-class distances to learn the optimal Gaussian parameters. In fact, a novel objective function is proposed to model each class using one Gaussian function. The experiments conducted using synthetic datasets demonstrated the effectiveness of the proposed algorithm. Moreover, the results obtained using real datasets proved that the proposed classifier outperforms the relevant state of the art approaches.

Author 1: Ouiem Bchir

Author 2: Mohamed M. Ben Ismail

Author 3: Sara Algarni

Keywords: Supervised learning; classification; kernel based learning

Paper 4: e-Lifestyle Confirmatory of Consumer Generation Z

Abstract: The development of information technology has changed daily life patterns that tend towards digital. Differences across generations will result in understanding different behaviors and lifestyles, which are a challenge in this research. Lifestyle is needed in determining market segments of consumer behavior. Lifestyle understanding in Generation Z is expected to provide valuable information in various fields of socio-economic life. These findings are expected to provide an overview for marketers targeting the market in this segment. Understanding lifestyles can be an ingredient in developing marketing strategies according to the intended segment, especially Generation Z, which has identified a lifestyle following information technology or digital development. The research aimed to confirm e-lifestyle factors among Generation Z, especially university students, as members of the academic environment's dominant academic community. Specifically, the aim is to identify the pattern of e-lifestyle formation in Generation Z, especially among students and the information or social media used by Generation Z. This type of research is a survey. This research was initiated through empirical field observations. The study population used in this study was university students in Yogyakarta-Indonesia. The sampling technique uses a simple random sampling technique. The data used are primary: the response given by research subjects related to e-lifestyle factors. Data was collected through a survey using a questionnaire. The data analysis technique in this study uses a Confirmatory Factor Analysis (CFA). The results showed that the motives that became the basis of e-lifestyle in the Z generation corresponded to four factors, namely, e-activities, e-interests, e-opinions, and e-values. Information or social media are often used by Generation Z, namely, Instagram, Youtube, Line, Facebook, Twitter, Discard, Pinterest, Spotify, and Telegram. The purpose of using the information or social media is communication, entertainment, consumption or shopping, and community activities.

Author 1: Tony Wijaya

Author 2: Arum Darmawati

Author 3: Andreas M Kuncoro

Keywords: e-Lifestyle; consumer; Generation Z; social media; information technology

Paper 5: A Novel Chaotic System for Text Encryption Optimized with Genetic Algorithm

Abstract: With meteoric developments in communication systems and data storage technologies, the need for secure data transmission is more crucial than ever. The level of security provided by any cryptosystem relies on the sensitivity of the private key, size of the key space as well as the trapdoor function being used. In order to satisfy the aforementioned constraints, there has been a growing interest over the past few years, in studying the behavior of chaotic systems and their applications in various fields such as data encryption due to characteristics like randomness, unpredictability and sensitivity of the generated sequence to the initial value and its parameters. This paper utilizes a novel 2D chaotic function that displays a uniform bifurcation over a large range of parameters and exhibits high levels of chaotic behavior to generate a random sequence that is used to encrypt the input data. The proposed method uses a genetic algorithm to optimize the parameters of the map to enhance security for any given textual data. Various analyses demonstrate an adequately large key space and the existence of multiple global optima indicating the necessity of the proposed system and the security provided by it.

Author 1: Unnikrishnan Menon

Author 2: Anirudh Rajiv Menon

Author 3: Atharva Hudlikar

Keywords: Chaotic map; genetic algorithm; encryption; bifurcation diagram; Lyapunov exponent

Paper 6: Optimizing the C4.5 Decision Tree Algorithm using MSD-Splitting

Abstract: We propose an optimization of Dr. Ross Quin-lan’s C4.5 decision tree algorithm, used for data mining and classification. We will show that by discretizing and binning a data set’s continuous attributes into four groups using our novel technique called MSD-Splitting, we can significantly improve both the algorithm’s accuracy and efficiency, especially when applied to large data sets. We applied both the standard C4.5 algorithm and our optimized C4.5 algorithm to two data sets obtained from UC Irvine’s Machine Learning Repository: Census Income and Heart Disease. In our initial model, we discretized continuous attributes by splitting them into two groups at the point with the minimum expected information requirement, in accordance with the standard C4.5 algorithm. Using five-fold cross-validation, we calculated the average accuracy of our initial model for each data set. Our initial model yielded a 75.72% average accuracy across both data sets. The average execution time of our initial model was 1,541.57 s for the Census Income data set and 50.54 s for the Heart Disease data set. We then optimized our model by applying MSD-Splitting, which discretizes continuous attributes by splitting them into four groups using the mean and the two values one standard deviation away from the mean as split points. The accuracy of our model improved by an average of 5.11%across both data sets, while the average execution time reduced by an average of 96.72% for the larger Census Income data set and 46.38% for the Heart Disease data set.

Author 1: Patrick Rim

Author 2: Erin Liu

Keywords: C4.5 Algorithm; decision tree; data mining; machine learning; classification

Paper 7: Extraction of Keywords for Retrieval from Paper Documents and Drawings based on the Method of Determining the Importance of Knowledge by the Analytic Hierarchy Process: AHP

Abstract: Extraction method of keywords for retrieval from paper documents and drawings based on the method of determining the importance of knowledge by the Analytic Hierarchy Process: AHP method is proposed. The method allows distinguish the documents into three categories, letter, form and drawing types of documents, then the most appropriate knowledge about keyword for retrievals, font size, location, frequency of the words etc. are selected for each document type. Production rules are created with more than five of the knowledge on keywords for retrievals. Traditional production system employs isolated knowledge so that it is not easy to take overall suitability of the knowledge. In order to overcome this situation, AHP is employed in the proposed system. Through experiments with 100 documents and diagrams, 98% success rate is achieved, and it is found that appropriate candidates for keywords with likelihood or certainty factor can be extracted with the proposed system. The proposed production system shows 50% of improvement on success rate of the keywords extraction from documents and diagrams compared to the existing production system without AHP.

Author 1: Kohei Arai

Keywords: AHP method; extraction keywords; production rule system; document/diagram recognitions; certainty factor

Paper 8: Data Retrieval Method based on Physical Meaning and its Application for Prediction of Linear Precipitation Zone with Remote Sensing Satellite Data and Open Data

Abstract: Data retrieval method based on physical meaning is proposed together with its application for prediction of linear precipitation zone with remote sensing satellite data and open data. The linear precipitation zone causes extremely severe storm and flood damage, landslide, and so on. Linear precipitation zone is formed in the case that the warm, moist air must continuously flow in, the force to lift this air up and it is often in collisions with mountain slopes or cold fronts, the atmospheric conditions are unstable, and there is a certain direction of wind above the sky. These conditions can be monitored by remote sensing satellite data. The proposed method is intended to attempt for prediction of the linear precipitation zone for disaster mitigation. There are water vapor data, cloud liquid data, cloud fraction data, and upper atmospheric wind data derived from the remote sensing satellite-based mission instruments. Through experiment in the case of the linear precipitation zone which was occurred in northern Kyushu, Japan in the begging of July 2020, a possibility to detect the linear precipitation zone was confirmed. Also, flooding damages and other disasters occurred in the northern Kyushu, in same time period caused by the detected linear precipitation zone is detected with Sentinel-1 of SAR data.

Author 1: Kohei Arai

Keywords: Linear precipitation zone; remote sensing satellite data; water vapor; cloud liquid; upper atmospheric wind; disaster mitigation

Paper 9: Ranking Beauty Clinics in Riyadh using Lexicon-Based Sentiment Analysis and Multiattribute-Utility Theory

Abstract: In recent years, the amount of beauty-related user-created content has steadily increased. Digital beauty-clinic reviews have major impact on user preferences. In supporting user selection decisions, ranking beauty clinics via online reviews is a valuable study subject, although research on this problem is still fairly limited. Sentiment analysis is a very important subject in the research community to evaluate a predefined sentiment from online texts written in a natural language on a particular topic. Recently, research on sentiment analysis for the Arabic language has become popular, since the language has become the fastest-growing language on the web. However, most sentiment-analysis tools are designed for the Modern Standard Arabic language, which is not widely used on social-media platforms. Moreover, the number of lexicons designed to handle the informal Arabic language is restricted, especially in the beauty-clinic-related domain. Besides, numerous sentiment-analysis studies have concentrated on improving the accuracy of sentiment classifiers. Studies about choosing the right company or product on the basis of the results of sentiment analysis are still missing. In decision-analysis domain, the multiattribute-utility theory has been extensively used in selecting the best option among a set of alternatives. Thus, this research aims to propose a systematic methodology that can develop a beauty-clinic-domain-related sentiment lexicon in Saudi dialect, perform sentiment analysis on online reviews of 10 beauty clinics in Riyadh based on the built lexicon, and feed the lexicon-based sentiment analysis results to the multiattribute-utility theory method to evaluate and rank the beauty clinics. Results showed that the Abdelazim Bassam Clinic is Riyadh’s best beauty clinic on the basis of the proposed method. The research not only impacts data analysts regarding how to rate beauty clinics on the basis of lexicon-based sentiment-analysis results, but also directs users toward selecting the best beauty clinic.

Author 1: Zuhaira M. Zain

Author 2: Aya A. Alhajji

Author 3: Norah S. Alotaibi

Author 4: Najwa B. Almutairi

Author 5: Alaa D. Aldawas

Author 6: Muneerah M. Almazrua

Author 7: Atheer M. Alhammad

Keywords: Arabic language; beauty clinics; ranking; Lexicon-based; machine learning; sentiment analysis; multiattribute-utility theory

Paper 10: Blockchain Network Model to Improve Supply Chain Visibility based on Smart Contract

Abstract: Due to the increasing complexity of supply chains over the past years, many factors significantly contribute to lowering the supply chains performance. Poor visibility is one of the major challenging factors that lowers supply chains performance. This paper proposes a Blockchain-based supply chain network model to improve the supply chain visibility. The model focuses in improving the visibility measurements properties: information sharing, traceability, and inventory visibility. The proposed model consists of information sharing, traceability, and inventory visibility platforms based on Blockchain technology smart contract. The model built with Hyperledger platform and extend the Hyperledger Composer Supply Chain Network (HCSC) model. The research is designed to three main phases. First phase: the preliminary phase which is the literature review phase to identify the existing challenges in the domain. The second phase: the design and implementation phase which is the development steps of the proposed research model. The third phase: the evaluation phase which represent the performance evaluation of the proposed model and the comparisons between the proposed model and the existing models. In the evaluation performance, the common performance metrics Lead time and average inventory levels will be compared in the proposed model, Cloud-based information system, and the traditional supply chain. These proposed platforms offer an end-to-end visibility of products, orders, and stock levels for supply chain practitioners and customers within supply chain networks. Which helps managers’ access key information that support critical business decisions and offers essential criteria for competitiveness and therefore, enhance supply chain performance.

Author 1: Arwa Mukhtar

Author 2: Awanis Romli

Author 3: Noorhuzaimi Karimah Mohd

Keywords: Supply chain management; supply chain visibility; blockchain; smart contact; information sharing; traceability; inventory visibility

Paper 11: Development of Relay Selection Method for Audio Transmission in Cooperative Ad Hoc Networks

Abstract: The quality of service parameters, such as Latency and Bit error rate for audio transmission in IEEE 802.11b Wireless ad hoc network are analyzed in this paper. The issue addressed here is that the quality of the audio, when transmitted directly from source to destination in ad hoc network is low. This can be improved by incorporating a relay between source and destination, where the relay uses decode and forward technique before forwarding the information to the destination. Destination applies Maximal ratio combining (MRC), to combine the signal received from the source and the relay. This concept is called Cooperative communication. A location aware channel estimation based relay selection strategy is proposed in this paper for a wireless ad hoc network. Audio is transmitted using 16 QAM modulation scheme over a Rayleigh fading channel in the presence of additive white Gaussian noise (AWGN). This paper focusses on the relay selection method for audio transmission in cooperative ad hoc networks where best relay is selected based on the average channel strength supported between the source and the destination. Audio quality at the destination is observed for the cases of relay presence and relay absence. Results showed that the measured audio quality with the presence of relay was far better than the measured quality in the absence of relay which stresses the importance of cooperative communication.

Author 1: Usha Padma

Author 2: H.V.Kumaraswamy

Author 3: S.Ravishankar

Keywords: Cooperative communication; channel strength; ad hoc networks; maximal ratio combining; IEEE 802.11b; G.711 Codec; Mean Openion Score (MOS)

Paper 12: KadOLSR: An Efficient P2P Overlay for Mobile Ad Hoc Networks

Abstract: P2P and MANET are self-organized, decentralized, and dynamic networks. Although both networks have common characteristics, they are used for different purposes and operate at different layers. P2P provides the ability for peers to store and locate services in the network, while MANET provides an underlying routing capability to reach other mobile nodes. Thus, P2P and MANET could complement each other. However, P2P is originally designed to operate over the Internet, which provides rich routing capabilities compared to MANET. Therefore, deploying P2P over MANET must come with careful consideration of how to adjust P2P approaches to better suit MANET. In this paper, a novel system called KadOLSR is proposed to better develop an efficient P2P over MANET. The structure of the well-known Kademlia is used along with the OLSR. KadOLSR optimizes the similarities between P2P and MANET to reduce overlay management communication overhead and hence deploys a lightweight and efficient P2P over MANET. The network layer routing information is shared with the overlay to achieve the optimization. A cross-layer channel is constructed between the network layer and the overlay layer to exchange relevant routing information. The proposed system is designed, and its performance is evaluated using a network simulator. The performance of KadOLSR is also compared to one of the recent P2P for MANET systems. The simulation results show that KadOLSR performs well across all different network sizes and mobility speeds.

Author 1: Mohammad Al Mojamed

Keywords: Peer-To-Peer P2P; Mobile ad hoc Networks MANET; cross layering; Kademlia; Optimized Link State Routing OLSR; KadOLSR

Paper 13: Identification of Student-Teachers Groups’ Needs in Physical Education and Sport for Designing an Open Distance Learning on the Model of Small Private Online Courses

Abstract: Currently, there are witnessing several distance-learning offerings: FOAD (Open Distance Learning) MOOCs (Massive Open Online Course) and SPOCS (Small Private Online Courses) in various intervention sectors including education and training. However, little research has dealt with analyzing the needs of participants before implementing SPOCs in higher education. This study aims to identify needs in order to design and guide a technopedagogical device in SPOCs’ form for teacher training.The results showed that more than 70% of interviewees declared that SPOC reduces participants’ travel time, 87% aimed at developing professional competence in planning learning, 77% wanted students’ evaluation and more than 60% wanted to know the disciplinary knowledge relating to physical and sporting activities (PSA) and their Learning activities’ management.In addition, 64.3% of participants preferred, as device’s form and design, the four modalities at the same time: text structured in title, video capsules, images and sound recording. In terms of educational tutoring ,more than 75% of participants declared their need to understand certain concepts in the course. These results will guide us to focus attention on three basic professional skills: planning, management and evaluation of learning as a priority training module in the envisaged SPOC with technical and pedagogical support both audiovisual and textual.

Author 1: Mostafa HAMSE

Author 2: Said LOTFI

Author 3: Mohammed TALBI

Keywords: Needs; physical education and sport; professional training; SPOC; teachers

Paper 14: A Classification Javanese Letters Model using a Convolutional Neural Network with KERAS Framework

Abstract: One of the essential things in research engaged in the field of Computer Vision is image classification, wherein previous studies models were used to classify an image. Javanese Letters, in this case, is a basis of a sentence that uses the Javanese language. The problem is that Javanese sentences are often found in Yogyakarta, especially the use of name tourist attractions, making it difficult for tourists to translate these Javanese sentences. Therefore, in this study, we try to create a Javanese character classification model hoping that this model will later be used as a basis for developing research into the next stage. One of the most popular methods lately for dealing with image classification problems is to use Deep Learning techniques, namely using the Convolutional Neural Network (CNN) method using the KERAS framework. The simplicity of the training model and dataset used in this work brings the advantage of computation weight and time. The model has an accuracy of 86.68% using 1000 datasets and conducted for 50 epochs based on the results. The average inference time with the same specification mentioned above is 0.57 seconds, and again the fast inference time is because of the simplicity of the model and dataset toolbar. This model's advantages with fast and light computation time bring the possibility to use this model on devices with limited computation resources such as mobile devices, familiar web server interface, and internet-of-things devices.

Author 1: Yulius Harjoseputro

Keywords: Javanese letters; deep learning; convolutional neural network; epoch; framework KERAS

Paper 15: Debris Run-Out Modeling Without Site-Specific Data

Abstract: Recent population growth and actions near hilly areas increase the vulnerability of occurring landslides. The effects of climate change further increase the likelihood of landslide danger. Therefore, accurate analysis of unstable slope behavior is crucial to prevent loss of life and destruction to property. Predicting landslide flow path is essential in identifying the route of debris, and it is essential necessary component in hazard mapping. However, current methodologies of determining the flow direction of landslides require costly site-specific data such as surface soil type, categories of underground soil layers, and other related field characteristics. This paper demonstrates an approach to predict the flow direction without site-specific data, taking a large landslide incident in Sri Lanka at Araranyaka region in the district of Kegalle as a case study. Spreading area assessment was based on deterministic eight-node (D8) and Multiple Direction Flow (MDF) flow directional algorithms. Results acquired by the model were compared with the real Aranayaka landslide data set and the landslide hazard map of the area. Debris paths generated from the proof of concept software tool using the D8 algorithm showed greater than 76% agreement, and MDF showed greater than 87% agreement with the actual flow paths and other related statistics such as maximum width of the slide, run-out distance, and slip surface area.

Author 1: NMT De Silva

Author 2: Prasad Wimalaratne

Keywords: Landslide flow path; route of debris; hazard mapping; D8 Algorithm; multiple direction flow algorithm

Paper 16: Improved Selected Mapping Technique for Reduction of PAPR in OFDM Systems

Abstract: High peak to average power ratio (PAPR) is a limiting factor towards the performance of an OFDM system. Selected Mapping (SLM) is a popular peak to average power ratio (PAPR) reduction scheme used with the OFDM systems. In this technique, U set of candidate sequences are generated that leads to improvement in the PAPR reduction ability of the OFDM systems. The major concern of the conventional SLM is that as the number of candidates is increased, there is a proportional rise in the inverse fast Fourier transforms (IFFT) computations of the systems. In our article we have proposed a scheme in which we increase the number of candidate sequence as (U+U2/4) that leads to improved PAPR performance of the OFDM systems without any equivalent rise in the IFFT computations. It has been demonstrated that both the simulation and analytical results are well-approximated in our proposed schemes. We also estimate the threshold value of PAPR at a fixed value of complementary cumulative distribution function (CCDF) for different number of subcarriers and candidate sequences. Results demonstrate that our proposed scheme outperforms in terms of PAPR reduction ability of the OFDM signal and obtains effective PAPR threshold values with negligible loss in BER performance of the system.

Author 1: Saruti Gupta

Author 2: Ashish Goel

Keywords: Orthogonal Frequency Division Multiplexing (OFDM); Peak to Average Power Ratio (PAPR); Selected Mapping (SLM); Complementary Cumulative Distribution Function (CCDF); PAPR threshold

Paper 17: Efficient DWT based Fusion Algorithm for Improving Contrast and Edge Preservation

Abstract: The main principle of infrared (IR) image is that it captures thermal radiation of light. The objects that are captured in low light, fog, and snow conditions can be detected clearly in IR image. But the major drawback of IR image is that it provides poor resolution and low texture information. Due to that humans are unable to understand overall scene information present in IR image. Nowadays for the detection of objects in poor weather conditions with improved texture information, the result of visible (VI) and IR image fusion is used. It is mostly used in military, surveillance, and remote sensing applications. The efficient DWT based fusion algorithm for improving contrast and edge preservation is presented in this paper. First morphology hat transform is applied on source images for improving contrast. DWT on decomposition produces low frequency and high frequency sub-bands. A novel mean weighted fusion rule is introduced in this paper for fusing low frequency sub-bands. Its aim is to improve the visual quality of final fused image. The max fusion rule has used for fusing high frequency sub-bands to improve edge information. The final fused image is reconstructed by using IDWT. In this paper, the proposed fusion algorithm has produced improved results both subjectively and as well as objectively when compared to existing fusion methods.

Author 1: Sumanth Kumar Panguluri

Author 2: Laavanya Mohan

Keywords: Visible image; infrared image; Discrete Wavelet Transform (DWT); Inverse Discrete Wavelet Transform (IDWT); novel mean-weighted fusion rule; max fusion rule

Paper 18: A Comparison of Classification Models to Detect Cyberbullying in the Peruvian Spanish Language on Twitter

Abstract: Cyberbullying is a social problem in which bullies’ actions are more harmful than in traditional forms of bullying as they have the power to repeatedly humiliate the victim in front of an entire community through social media. Nowadays, multiple works aim at detecting acts of cyberbullying via the analysis of texts in social media publications written in one or more languages; however, few investigations target the cyberbullying detection in the Spanish language. In this work, we aim to compare four traditional supervised machine learning methods performances in detecting cyberbullying via the identification of four cyberbullying-related categories on Twitter posts written in the Peruvian Spanish language. Specifically, we trained and tested the Naive Bayes, Multinomial Logistic Regression, Support Vector Machines, and Random Forest classifiers upon a manually annotated dataset with the help of human participants. The results indicate that the best performing classifier for the cyberbullying detection task was the Support Vector Machine classifier.

Author 1: Ximena M. Cuzcano

Author 2: Victor H. Ayma

Keywords: Cyberbullying detection; machine learning; natural language processing; feature extraction

Paper 19: An Investigative Study of Genetic Algorithms to Solve the DNA Assembly Optimization Problem

Abstract: This paper aims to highlight the motivations for investigating genetic algorithms to solve the DNA Fragments Assembly problem (DNA_FA). DNA_FA is an optimization problem that attempts to reconstruct the original DNA sequence by finding the shortest DNA sequence from a given set of fragments. We showed that the DNA_FA optimization problem is a special case of the two well-known optimization problems: The Traveling Salesman Problem (TSP) and the Quadratic Assignment Problem (QAP). TSP and QAP are important problems in the field of combinatorial optimization and for which there exists an abundant literature. Genetic Algorithms (GA) applied to these problems have led to very satisfactory results in practice. In the perspective of designing efficient genetic algorithms to solve DNA_FA we showed the existence of a polynomial-time reduction of DNA-FA into TSP and QAP enabling us to point out some technical similarities in terms of solutions and search space complexity. We then conceptually designed a genetic algorithm platform for solving the DNA-FA problem inspired from the existing efficient genetic algorithms in the literature solving TSP and QAP problems. This platform offers several ingredients enabling us to create several variants of GA solvers for the DNA assembly optimization problems.

Author 1: Hachemi Bennaceur

Author 2: Meznah Almutairy

Author 3: Nora Alqhtani

Keywords: Genetic Algorithms; Traveling Salesman Problem; Quadratic Assignment Problem; DNA fragments assembly problem

Paper 20: A Planar Antenna on Flexible Substrate for Future 5G Energy Harvesting in Malaysia

Abstract: This article presents a planar monopole antenna on flexible substrate for middle band 5G (3.5 GHz) application in Malaysia. The antenna has been designed and optimized for its gain and efficiency with an improved performance in contrast of flexible substrate-based other antennas. The antenna resonates at 3.53 GHz and it has a -10dB bandwidth of 545 MHz. The bending effects of this antenna on the S-parameter and gain have also been investigated. The antenna is able to suppress all the other frequency bands until 20 GHz. The designed antenna has been utilized with a newly designed rectifier to act as an rectenna at 3.5 GHz for RF energy harvesting applications. A reasonable amount of DC output voltage of 930 mV, and a Power Conversion Efficiency of 43.5% have been obtained while 0 dBm RF input power is delivered to the rectifier input terminal. Apart from the utilization as an energy harvester being connected with the proposed rectifier, the designed antenna on flexible substrate can also be employed to biomedical and sensor applications.

Author 1: A. K. M. Zakir Hossain

Author 2: Nurulhalim Bin Hassim

Author 3: S. M. Kayser Azam

Author 4: Md Shazzadul Islam

Author 5: Mohammad Kamrul Hasan

Keywords: Planar monopole; flexible substrate; 3G; bending effect; rectenna; RF rectifier; energy harvesting

Paper 21: Rule-based Text Normalization for Malay Social Media Texts

Abstract: Malay social media text is a text written on social media networks like Twitter. Commonly, this text comprises non-standard words, filled with dialects, foreign languages, word abbreviations, grammatical neglect, spelling errors, and many more. It is well known that this type of text is difficult to process due to its high noise and distinct text structure. Such problems can be resolved using rigorous text normalization, which is critical before any technique can be implemented and evaluated on social media text. In this paper, an improved normalization method towards Malay social media text was proposed by converting non-standard Malay words using a rule-based model. The method normalizes common language words often used by Malaysian users, such as non-standard Malay (like dialect and slangs), Romanized Arabic, and English words. Thus, a Malay text normalizer was proposed using a set of rules that extend across different domains of natural language processing (NLP) and is expected to address the challenges of processing Malay social media text. This study implements the proposed Malay text normalizer in a Part-of-Speech (POS) tagging application to evaluate the normalizer’s performance. The implementation demonstrates a substantial improvement in the POS tagging efficiency over several pre-processing stages, with an improvement of accuracy up to 31.8%. The increase of accuracy in the POS tagging indicates two main points. First, the Malay text normalizer’s rules improve the performance of a Malay text normalizer on social media text. Second, our proposed Malay text normalizer has successfully improved the POS tagging percentage and demonstrates the importance of normalized pre-processing in any NLP application.

Author 1: Siti Noor Allia Noor Ariffin

Author 2: Sabrina Tiun

Keywords: Malay normalization; Malay text normalization; informal Malay text; Malay tweets; rule-based normalizer

Paper 22: Distributed Beam Forming Techniques for Dual-hop Decode-and-Forward based Cooperative Relay Networks

Abstract: In this paper, it has been proved that the transmission rate can be increased substantially by alleviating co-channel interference by use of beamforming techniques at relay stations. In this setup, the downlink transmission segment is taken into consideration from the Base Station (BS) to two Mobile Stations (MS). The data is transmitted concurrently through two Relay Stations (RS) using the same frequency channel. It is assumed that the RSs use decode-and-forward (DF) strategy. In this technique of beamforming, pre-coding vectors are used at the RS to alleviate co-channel interferences. Due to this strategy, each user will be able to get its own data sans interference. Two pre-coding techniques which incarnate two different transmission protocols have been proposed. Simulations results show that such type of schemas outperforms their counterpart brethren schemas.

Author 1: Zahoor Ahmed

Author 2: Zuhaibuddin Bhutto

Author 3: Syed Muhammad Shehram Shah

Author 4: Ramesh Kumar

Author 5: Ayaz Hussain

Keywords: Beamforming; base station; decode and forward; mobile station; relay station

Paper 23: An M/M/1 Preemptive Queue based Priority MAC Protocol for WBSN to Transmit Pilgrims’ Data

Abstract: Every year during Hajj in Saudi Arabia and Kumbh Mela in India, many pilgrims suffering from different medical emergencies thus need real-time and fast healthcare services. Quick healthcare can be facilitated by setting up Wireless Body Sensor Network (WBSN) on pilgrims because of its suitability for a wide range of medical applications. However, higher delay, data loss and excessive energy consumption may occur in the network when multiple emergency data aggregate at the coordinator for accessing the data communication channel simultaneously. In this context, for low delay and energy-efficient data transmission, an M/M/1 preemptive queue technique is proposed and minimal backoff period is considered to develop a priority Medium Access Control (MAC) protocol for WBSN. Our proposed MAC is designed based on IEEE802.15.6 standard that supports modified MAC superframe structure for heterogeneous traffic. The proposed priority MAC protocol has been simulated using the Castalia simulator to analyze the results. In the first scenario considering varying nodes, the delay is calculated as 13 ms and 33 ms for the emergency, and the normal medical condition. Besides, for emergency and normal medical condition energy consumption per bit is calculated at around 0.12 µj and 0.19 µj. In the second scenario, we consider variation in traffic size. For 16 bytes traffic size, delay of extremely very high critical traffic is 5.8 ms and 14.5 ms for extremely low critical traffic. Similarly, extremely very high critical traffic consumes 0.035 µj energy per bit, whereas extremely low critical traffic consumes 0.37 µj. in the third scenario, the delay, data loss rate, average energy consumption and throughput for the proposed priority MAC are analyzed. Result demonstrates our proposed priority MAC protocol outperforms the state-of-the-art protocols.

Author 1: Shah Murtaza Rashid Al Masud

Author 2: Mahmood ul Hassan

Author 3: Khalid Mahmood

Author 4: Muhammad Akram

Keywords: Wireless body sensor network; medium access control protocol; preemptive queue; priority; heterogeneous traffic

Paper 24: Hybridized Machine Learning based Fractal Analysis Techniques for Breast Cancer Classification

Abstract: The usefulness of Fractal Analysis (FA) is not limited to a particular area. It is applied in variety of fields and has shown its efficiency towards irregular objects. Fractal dimension is the best measure of the roughness for natural elements and hence, it can be treated as a feature of the natural object. Breast masses are irregular and divers from a malignant tumor to benign; hence breast can be treated as one of the best areas where fractal geometry can be applied. It gives a scope where fractal geometry concept can be used as a feature extraction technique in mammogram. On the other hand, the support vector machine is an emerging technique for classification. The survey shows that few works have done on breast mass classification using support vector machine. In our work two most effective techniques are used in separate operations, FA: Box Count Method (BCM) and Support Vector Machine (SVM) that result well in their fields. Feature extraction is done through Box Count Method. The extracted feature, “fractal dimension”, measures the complexity of the input data set of 42 images. For the next segment, the resulting Fractal Dimensions (FD) are processed under the support vector machine classifier to classify benign and malignant cells. The result analysis shows that the combination of SVM and FD yielded the highest with 98.13% accuracy.

Author 1: Munmun Swain

Author 2: Sumitra Kisan

Author 3: Jyotir Moy Chatterjee

Author 4: Mahadevan Supramaniam

Author 5: Sachi Nandan Mohanty

Author 6: NZ Jhanjhi

Author 7: Azween Abdullah

Keywords: Mammography; feature extraction; fractal dimension; box-counting method; classification; support vector machine

Paper 25: Detection of Anomalous In-Memory Process based on DLL Sequence

Abstract: The use of Computer systems to keep track of day to day activities for single-user systems as well as the implementation of business logic in enterprises is the demand of the hour. As it plays a vital role in making available information on one click as well as impacts improvement in business and influences the profit or loss. There is always a possible threat from unauthorized users as well as untrusted or unknown applications. Trivially a host is intended to run with a list of known or trusted applications based on user’s preference. Any application beyond the trusted list can be called as untrusted or unknown application, which is not expected to run on that host. Untrusted applications becomes available to a host from sources like websites, emails, external storage devices etc. Such untrusted programs may be malicious or non-malicious in nature but the presence must be detected, as it is not a trusted program from user’s view point. All such programs may target the system either to steal valuable information or to decrease the system performance without the knowledge of the user of the system. Antimalware vendors provide support to defend the system from malicious programs. They do not include users trusted program list in to consideration. It is also true that new instances of attacks are found very frequently. Hence there is a need for a system which can be self-defending from anomalous activities on the system with reference to a trusted program list. In this paper design of an “Anomalous In-Memory Process detector based on the use of the DLL (Dynamic Link Library) sequence” is proposed, which does accountability of trusted programs intended to run on a particular host and create a knowledgebase of classes of processes with TF-IDF (Term Frequency-Inverse Document Frequency) multinomial logistic regression based learning approach. This knowledgebase becomes useful to map a suspected In-memory process to a class of processes using loaded DLL’s of it. With a cross-validation approach, the suspected process and processes of its predicted class are used to conclude whether it is a trusted, variant of the trusted or untrusted process for that host. Not necessarily the untrusted program is a malware but it may be a program not listed in the trusted program list for the specific host. Hence this work aims to detect anomaly in concern with list of trusted applications based on user’s preference by doing a dynamic analysis on In-memory processes.

Author 1: Binayak Panda

Author 2: Satya Narayan Tripathy

Keywords: Anomalous In-memory Process; dynamic analysis; DLL hijacking; DLL injection; TF-IDF multinomial logistic regression

Paper 26: Implementation of Random Direction-3D Mobility Model to Achieve Better QoS Support in MANET

Abstract: Mobile Ad hoc Networks (MANETs) provides changing network topology due to the mobility of nodes. The complexity of the network increases because of dynamic topology of nodes. In a MANET, nodes communicate with each other without the help of any infrastructure. Therefore, achieving QoS in MANET becomes a little difficult. The movement of mobile nodes is represented through mobility models. These models have great impact on QoS in MANET. We have proposed a mobility model which is a 3D implementation of existing Random Direction (RD) mobility model. We have done a simulation on AODV with QoS metrics throughput, delay and PDR, using NS-3 and performed analysis of the proposed mobility models with other 3D mobility models, namely Random Way Point (RWP) and Gauss Markov (GM). It is concluded that our proposed model gives better throughput, delay and PDR for AODV routing protocol in comparing to RWP and GM mobility models. This paper is for students and researchers who are involved in wireless technology and MANET. It will help them to understand how a mobility model impacts the entire network and how its enhancement improves the QoS in MANET.

Author 1: Munsifa Firdaus Khan

Author 2: Indrani Das

Keywords: Ad Hoc On Demand Distance Vector (AODV); random direction; mobility model; Quality-of-Service (QoS); Mobile Ad Hoc Networks (MANETs); NS-3

Paper 27: An Empirical Analysis of BERT Embedding for Automated Essay Scoring

Abstract: Automated Essay Scoring (AES) is one of the most challenging problems in Natural Language Processing (NLP). The significant challenges include the length of the essay, the presence of spelling mistakes affecting the quality of the essay and representing essay in terms of relevant features for the efficient scoring of essays. In this work, we present a comparative empirical analysis of Automatic Essay Scoring (AES) models based on combinations of various feature sets. We use 30-manually extracted features, 300-word2vec representation, and 768-word embedding features using BERT model and forms different combinations for evaluating the performance of AES models. We formulate an automated essay scoring problem as a rescaled regression problem and quantized classification problem. We analyzed the performance of AES models for different combinations. We compared them against the existing ensemble approaches in terms of Kappa Statistics and Accuracy for rescaled regression problem and quantized classification problem respectively. A combination of 30-manually extracted features, 300-word2vec representation, and 768-word embedding features using BERT model results up to 77.2 ± 1.7 of Kappa statistics for rescaled regression problem and 75.2 ± 1.0 of accuracy value for Quantized Classification problem using a benchmark dataset consisting of about 12,000 essays divided into eight groups. The reporting results provide directions to the researchers in the field to use manually extracted features along with deep encoded features for developing a more reliable AES model.

Author 1: Majdi Beseiso

Author 2: Saleh Alzahrani

Keywords: Automated Essay Scoring (AES); BERT; deep learning; neural network; language model

Paper 28: Tracking Coronavirus Pandemic Diseases using Social Media: A Machine Learning Approach

Abstract: With the increasing use of social media, a growing need exists for systems that can extract useful information from huge amounts of data. While, People post personal data interactively, an outbreak of an epidemic event can be noticed from these data. The issue of detecting the route of pandemic diseases is addressed. The main objective of this research work is to use a dual machine learning approach to evaluate current and future data of Covid-19 cases based on published social media information in specific geographical region and show how the disease spreads geographically over the time. The dual machine learning approach used based on traditional data mining methods to estimate disease cases found in social media related to specific geographical region. On other hand, sentiment analysis is conducted to assess the public perception of the disease awareness on the same region.

Author 1: Nuha Noha Fakhry

Author 2: Evan Asfoura

Author 3: Gamal Kassam

Keywords: Pandemic diseases; outbreak detection; social media; sentiment analysis; machine learning; text mining; geo-located data; CRISP-DM

Paper 29: Modified K-nearest Neighbor Algorithm with Variant K Values

Abstract: In Machine Learning K-nearest Neighbor is a renowned supervised learning method. The traditional KNN has the unlike requirement of specifying ‘K’ value in advance for all test samples. The earlier solutions of predicting ‘K’ values are mainly focused on finding optimal-k-values for all samples. The time complexity to obtain the optimal-k-values in the previous method is too high. In this paper, a Modified K-Nearest Neighbor algorithm with Variant K is proposed. The KNN algorithm is divided in the training and testing phase to find K value for every test sample. To get the optimal K value the data is trained for various K values with Min-Heap data structure of 2*K size. K values are decided based on the percentage of training data considered from every class. The Indian Classical Music is considered as a case study to classify it in different Ragas. The Pitch Class Distribution features are input to the proposed algorithm. It is observed that the use of Min-Heap has reduced the space complexity nonetheless Accuracy and F1-score for the proposed method are increased than traditional KNN algorithm as well as Support Vector Machine, Decision Tree Classifier for Self-Generated Dataset and Comp-Music Dataset.

Author 1: Kalyani C. Waghmare

Author 2: Balwant A. Sonkamble

Keywords: Classification; K-nearest Neighbor (KNN) classification algorithm; Indian Classical Music; Performance measures; Heap data structure

Paper 30: Binary Operating Antenna Array Elements by using Hausdorff and Euclidean Metric Distance

Abstract: In this paper, the linear antenna array is designed by the usage of Woodward–Lawson method. The design procedure fits antenna array radiation pattern to a predefined/required radiation mask. In this study will be investigated the possibility of powering off some antenna elements without modifying the behavior and power ratio of the elements which remains on. The aim of powering off antenna elements is to reduce power consumption of the designed array antenna and to reduce power dissipation problems on modern full digital beam forming architecture. The choice of binary operation of antenna element (on/off) reduces computational effort required for complex beam forming techniques. The results can be stored on look-up tables, in order to be recalled on demand by antenna operator. There are used two different metrics to identify how close, to the required design, is the modified antenna pattern. Euclidean and Hausdorff distances are both used as score of the modified array performance. The obtained solutions shows the applicability of binary operations on existing antenna array and the metric can be effectively used as ranking solutions.

Author 1: Elson Agastra

Author 2: Julian Imami

Author 3: Olimpjon Shurdi

Keywords: Antenna array; Hausdorff distance; Woodward-Lawson

Paper 31: Environmental Sustainability Coding Techniques for Cloud Computing

Abstract: Cloud Computing (CC) has recently received substantial attention, as a promising approach for providing various information and communication technologies (ICT) services. Running enormously complicated and sophisticated software on the cloud requires more energy and most of the energy is wasted. It is required to explore opportunities to reduce emission of carbon in the CC environment that causes global warming. Global warming affects environment and it is required to lessen greenhouse gas emissions which can be achieved by adopting energy-efficient technologies that largely need to reduce the energy consumption caused during computations, for storing data and during communications to achieve Green computing. In literature, most of the energy-saving techniques focus on hardware aspects. Many improvements can be done in regard to energy efficiency from the software perspective by considering and paying attention to the energy consumption aspects of software for environmental sustainability. The aim of the research is to propose energy-aware profiling environmental sustainability techniques based on parameterized development technique to reduce the energy that has been spent and measure its efficiency inured to support software developers to enhance their code development process in terms of reducing energy. Experiments have been carried out and results prove that the suggested techniques have enabled in achieving energy consumption and achieve environmental sustainability.

Author 1: Shakeel Ahmed

Keywords: Environmental sustainability; cloud computing; energy-efficient; software development life cycle; parameterized development; green computing

Paper 32: High Priority Requests in Grid Environment

Abstract: Grid computing is an enhanced technology consisting of a pool of connected machines that belong to multiple organizations in different sites to form a distributed system. This system can be used to deal with complex scientific or business problems. It is developed to help share distributed resources that may be diverted in nature and solve many computing problems. The typical decentralized grid computing model faces many challenges, such as; different systems and software architectures, quickly handling the enormous amount of grid requesters, and finding the appropriate resources for the grid users. Some of the grid requests might need to get significant attention and fast response to the other requests. Usually, the Grid Broker (GB) works as a third party or mediator between grid service providers and grid service requesters. This paper introduced a new automated system that can help exploit the grid power, improve its functionality, and enhance its performance. This research presents a new architecture for the Grid Broker that can assist with high priority requests and be processed first. This system is also used to monitor and provision the grid providers' work during the job running. It uses a multi-agent system to facilitates its work and accomplish its tasks. The proposed approach is evaluated using the Jade simulator. The results show that using the proposed approach can enhance the way of dealing with high priority requests coming to the grid.

Author 1: Tariq Alwadan

Author 2: Salah Alghyaline

Author 3: Azmi Alazzam

Keywords: Grid computing; multi-agents system; grid broker; static priority mechanism; distributed resources

Paper 33: A Hybrid Approach for Single Channel Speech Enhancement using Deep Neural Network and Harmonic Regeneration Noise Reduction

Abstract: This paper presents a hybrid approach for single channel speech enhancement using deep neural network (DNN) and harmonic regeneration noise reduction (HRNR). The DNN was used as a supervised algorithm to predict new target mask such as constrained Wiener Filter (cWF) target mask from noisy mixture signal that was transformed into gammatone filter bank features. Meanwhile, HRNR algorithm was applied in the post-filtering strategy to eliminate residual noise. The DNN algorithm is an emerging supervised speech enhancement to overcome heavy nonstationary noise and low signal-to-noise ratio (SNR) issues. To validate the proposed algorithm with new target mask, 600 Malay utterances combining male and female speakers were used in a training session while 120 Malay utterances were used in a prediction session. The short time objective intelligibility (STOI) and perceptual evaluation of speech quality (PESQ) scores were calculated as the performance metrics. In this work, the proposed target mask outperformed other baseline target masks. Thus, PESQ and STOI scores for the hybrid speech enhancement algorithm is 1.17 and 0.79, respectively, at - 5 dB babble noise SNR.

Author 1: Norezmi Jamal

Author 2: N. Fuad

Author 3: MNAH. Shaabani

Keywords: Speech enhancement; single channel microphone; deep neural network; constrained Wiener Filter; post-filtering

Paper 34: Improving the Performance of Various Privacy Preserving Databases using Hybrid Geometric Data Perturbation Classification Model

Abstract: As the size of the privacy preserving databases is increasing, it is difficult to improve the privacy and accuracy of these databases due to dimensionality and runtime. However, most of the traditional privacy preserving models are independent of privacy and runtime. Also, it is essential to preserve the privacy of the large sensitive attributes before publishing it to the third-party servers. As a result, a novel framework is required to improve the privacy as well as accuracy on the high dimensional privacy preserving data with less runtime. In order to improve the privacy, accuracy and runtime of the traditional privacy preserving models, a hybrid perturbation based privacy preserving classification model is proposed on the multiple databases. In this work, a new data transformation approach, hybrid geometrical perturbation approach and hybrid boosting classifier are proposed in order to enhance the overall efficiency of the model on the privacy preserving databases. In this work, a hybrid geometric perturbation approach is used to enhance the privacy preserving on the sensitive attributes. Initially, a pre-processing method is applied on the input dataset in order to remove the noise in the feature values. A hybrid machine learning classifier is proposed to predict the privacy preserving class label based on the training data. Experimental results represents the proposed hybrid geometric perturbation based boosting classifier has better statistical accuracy, recall, precision and runtime than the conventional models.

Author 1: Sk. Mohammed Gouse

Author 2: G.Krishna Mohan

Keywords: Privacy preserving databases; machine learning; perturbation; high dimensionality; data filtering; data classification

Paper 35: High-Level Description of Robot Architecture

Abstract: Architectural Description (AD) is the backbone that facilitates the implementation and validation of robotic systems. In general, current high-level ADs reflect great variation and lead to various difficulties, including mixing ADs with implementation issues. They lack the qualities of being systematic and coherent, as well as lacking technical-related forms (e.g., icons of faces, computer screens). Additionally, a variety of languages exist for eliciting requirements, such as object-oriented analysis methods susceptible to inconsistency (e.g., those using multiple diagrams in UML and SysML). In this paper, we orient our research toward a more generic conceptualization of ADs in robotics. We apply a new modeling methodology, namely the Thinging Machine (TM), to describe the architecture in robotic systems. The focus of such an application is on high-level specification, which is one important aspect for realizing the design and implementation in such systems. TM modeling can be utilized in documentation and communication and as the first step in the system’s design phase. Accordingly, sample robot architectures are re-expressed in terms of TM, thus developing (1) a static model that captures the robot’s atemporal aspects, (2) a dynamic model that identifies states, and (3) a behavioral model that specifies the chronology of events in the system. This result shows a viable approach in robot modeling that determines a robot system’s behavior through its static description.

Author 1: Sabah Al-Fedaghi

Author 2: Manar AlSaraf

Keywords: Conceptual model; robot architectural specification; robot behavior; static diagram; dynamism

Paper 36: Enhancing Acceptance Test Driven Development Model with Combinatorial Logic

Abstract: In the Software Development Life Cycle, modelling plays a most significant role in designing and developing software efficiently. Acceptance Test-Driven Development (ATDD) is a powerful agile software development model where a customer provides user acceptance test suits as a part of Software Requirements Specifications. A design has to develop a system so that User Acceptance Tests will be successful. In some systems, the Combinatorial Logic and Combinatorial Testing play a very crucial role. The authors have proposed a novel approach to enhance the existing Acceptance Test Driven Development model to Combinatorial Logic Oriented-ATDD model by incorporating combinatorial logic. Refinement with respect to combinatorial logic needs to be incorporated in all the stages of Software Development Life Cycle, i.e. starting from Software Requirement Specifications to User Acceptance Tests. This comprehensive approach derives the acceptance tests from user requirements effectively and efficiently. In this paper, the existing Indian Railway Reservation System is considered as a case study, and it was fully implemented as per proposed Combinatorial Logic Oriented-ATDD model.

Author 1: Subhash Tatale

Author 2: V. Chandra Prakash

Keywords: Software requirements specification; software development life cycle; acceptance test driven development; combinatorial logic; combinatorial testing; user acceptance tests; railway reservation system

Paper 37: A Comprehensive Study of Blockchain Services: Future of Cryptography

Abstract: Cryptography is the process of protecting information from intruders and letting only the intended users’ access and understand it. It is a technique originated in 2000 BC where simple methods were used in earlier times to keep the information in a way that is not understandable by everyone. Only the intended receiver knows how to decode the information. Later, as technology advances, many sophisticated techniques were used to protect the message so that no intrusion can invade the information. Many mathematically complex algorithms like AES, RSA are used to encrypt and decrypt the data. Due to the advancements in the computer science field, recently, cryptography is used in the development of cryptographic currencies of cryptocurrencies. Blockchain technology, a distributed ledger technology identified to be the foundation of Bitcoin cryptocurrency, implements a high-level cryptographic technique like public-key cryptography, Hash Functions, Merkle Trees, Digital signatures like Elliptic curve digital signatures, etc. These advanced cryptographic techniques are used to provide security to blockchain data and for the secure transmission of information, thereby making Blockchain more popular and demandable. Blockchain applies cryptography in various phases, and some of the techniques used in Blockchain are advanced in cryptographic sciences. This paper intends to provide a brief introduction to cryptography and Blockchain Technology and discusses how both technologies can be integrated to provide the best of the security to the data. This paper reviews the various cryptographic attacks in Blockchain and the various security services offered in Blockchain. The challenges of blockchain security are also analyzed and presented briefly in this paper.

Author 1: Sathya AR

Author 2: Barnali Gupta Banik

Keywords: Cryptography; cryptocurrencies; blockchain; bitcoin

Paper 38: Prototyping with Raspberry Pi in Healthcare Domain

Abstract: The objective of this paper is to conduct a bibliometric study on the use of Raspberry Pi in the medical field. In the past several decades healthcare advancements have played a major role and Raspberry Pi being the charm with its extensive features and low cost, it is of interest to know whether the development in health care technologies with respect to Raspberry Pi has created an impact or not. A platform known as Biblioshiny has been used to collect statistical information and perform the analysis. A total of 154 journal articles have been collected from PubMed, a free full-text archive of biomedical and life sciences journal literature at the U.S. National Institutes of Health's National Library of Medicine (NIH/NLM) and analysis has been made on various parameters such as top authors, countries and affiliations etc. The conclusions drawn help us to understand the usage of Raspberry Pi in the healthcare domain. The Bibliometric Analysis done indicates that there has been an increase in the research over the years and the authors from various countries have been working elaborately indicating that there has been a good amount of usage of Raspberry Pi in the healthcare domain. Overall, our results demonstrate the trending topics the authors currently working on and collaborations amongst authors and countries. Finally, this paper identifies that there are no motor themes but displays the budding keywords (or the ideas where authors have worked on) in the health care domain emerging with the prototyping of Raspberry Pi.

Author 1: Hari Kishan Kondaveeti

Author 2: Sruti Raman

Author 3: Praveen Raj

Keywords: Information extraction; bibliometric study; prototype; Raspberry Pi; healthcare

Paper 39: Effect of Route Length and Signal Attenuation on Energy Consumption in V2V Communication

Abstract: Simulation of Vehicle-to Vehicle (V2V) communication and connectivity is carried out. The main objective of the carried out V2Vcommunication simulation is to study the effect of route length, number of hops per route, attenuation related parameter and message size on energy consumption of transmitted bits per sent message. Mathematical modeling (using the original radio energy model), and analysis, is carried out to quantify and approximate the effect of attenuation related parameter (α) and Route Length (LR) on energy consumption of transmitted Basic Safety Message (BSM) for both, 256 Bytes and 320 Bytes size. The original energy radio model is expanded to include not only message size, but also the effect of number of hops on energy consumption. The work successfully proved the critical effect of α and number of hops on energy consumption for a fixed BSM size and the effect of α on the transitional characteristics of routes as a function of number of hops. It is clear from the simulation that α has an adverse effect on consumed energy for transmitted BSMs and also has a marked effect on routes, where any value of α above 3 will lead to energy depletion among, other negative effects on communicating devices.

Author 1: Mahmoud Zaki Iskandarani

Keywords: Intelligent transportation systems; routing; connected vehicles; energy; V2V; multipath fading; BSM

Paper 40: Handwritten Numeric Image Classification with Quantum Neural Network using Quantum Computer Circuit Simulator

Abstract: Quantum Computer is a computer machine using principles of quantum mechanics in doing its computation. The Quantum Computer Machine itself is still in the development stage and has not been deployed yet, however TensorFlow provides a library for hybrid quantum-classical machine learning called TensorFlow Quantum (TFQ). One of the quantum computing models is the Quantum Neural Network (QNN). QNN is adapted from classical neural networks capable of processing qubit data and passing quantum circuits. QNN is a machine learning model that allows quantum computers to classify image data. The image data used is classical data, but classical data cannot reach a superposition state. So in order to carry out this protocol, the data must be readable into a quantum device that provides superposition. QNN uses a supervised learning method to predict image data. Quantum Neural Network (QNN) with a supervised learning method for classifying handwritten numeric image data is implemented using a quantum computer circuit simulation using the Python 3.6 programming language. The quantum computer circuit simulation is designed using library Cirq and TFQ. The classification process is carried out on Google Colab. The results of training on the QNN model obtained a value of 0.337 for the loss value and 0.3427 for the validation loss value. Meanwhile, the hinge accuracy value from the training results is 0.8603 for the hinge accuracy value with training data and 0.8669 for the hinge accuracy validation value. Model testing is done by providing 100 handwritten number images that are tested, with 53 image data of number three and 47 image data of number six. The results obtained for the percentage of testing accuracy are 100% for the number three image and 100% for the number six image. Thus, the total percentage of testing is 100%.

Author 1: Achmad Benny Mutiara

Author 2: Muhammad Amir Slamet

Author 3: Rina Refianti

Author 4: Yusuf Sutanto

Keywords: Image classification; quantum neural network; quantum computer; TensorFlow

Paper 41: A Novel Approach to Mammogram Classification using Spatio-Temporal and Texture Feature Extraction using Dictionary based Sparse Representation Classifier

Abstract: Cancer is a chronic disease and increasing rapidly worldwide. Breast cancer is one of the most crucial cancer which affects the women health and causes death of the women. In order to predict the breast cancer, mammogram is considered as a promising technique which helps to identify the early stages of cancer. However, several schemes have been developed during last decade to overcome the performance related issues but achieving the desired performance is still challenging task. To overcome this issue, we introduce a novel and robust approach of feature extraction and classification. According to the proposed approach, first of all, we apply pre-processing stage where image binarization is applied using Niblack’s method and later Region of Interest (ROI) extraction and segmentation schemes are applied. In the next phase of work, we developed a mixed strategy of feature extraction where we consider Gray Level Co-occurrence Matrix (GLCM), Histogram of oriented Gradients (HoG) with Principal Component Analysis (PCA) for dimension reduction, Scale-invariant Feature Transform (SIFT), and non-parametric Discrete Wavelet Transform (DWT) features are extracted. Finally, we present K-Singular value decomposition (SVD) based dictionary learning scheme and applied the Sparse representation classifier (SRC) classification approach and performance is evaluated using MATLAB tool. An extensive experimental study is carried out which shows that the proposed approach achieves classification accuracy as 98.13%, Precision as 97.58%, Recall as 98.36%, and F-Score 97.95%. The performance of proposed approach is compared with the state-of-art techniques which shows that the proposed approach gives better performance.

Author 1: Vaishali D. Shinde

Author 2: B. Thirumala Rao

Keywords: Mammogram; segmentation; classification; feature extraction

Paper 42: A Hybrid POS Tagger for Khasi, an Under Resourced Language

Abstract: Khasi is an Austro-Asiatic language spoken mainly in the state of Meghalaya, India, and can be considered as an under resourced and under studied language from the natural language processing perspective. Part-of-speech (POS) tagging is one of the major initial requirements in any natural language processing tasks where part of speech is assigned automatically to each word in a sentence. Therefore, it is only natural to initiate the development of a POS tagger for Khasi and this paper presents the construction of a Hybrid POS tagger for Khasi. The tagger is developed to address the tagging errors of a Khasi Hidden Markov Model (HMM) POS tagger by integrating conditional random fields (CRF). This integration incorporates language features which are otherwise not feasible in an HMM POS tagger. The results of the Hybrid Khasi tagger have shown significant improvement in the tagger’s accuracy as well as substantially reducing most of the tagging confusion of the HMM POS tagger.

Author 1: Medari Janai Tham

Keywords: Khasi corpus; BIS tagset; Khasi POS tagger; conditional random fields (CRF); Hidden Markov Model (HMM)

Paper 43: Robust Drowsiness Detection for Vehicle Driver using Deep Convolutional Neural Network

Abstract: Drowsiness detection during driving is still an unsolved research problem which needs to be addressed to reduce road accidents. Researchers have been trying to solve this problem using various methods where most of these solution lacks behind in accuracy, real-time performance, costly, complex to build, and has a higher computational cost with low frame rate. This research proposes robust method for drowsiness detection of vehicle drivers based on head pose estimation and pupil detection by extracting facial region initially. Proposed method used frame aggregation strategy in case of face region cannot be extracted in any frame due to shortcomings, i.e. light reflection, shadow. In order to improve identification under highly varying lighting conditions, proposed research used cascade of regressors cutting edge method where each regression refers estimation of facial landmarks. Proposed method used deep convolutional neural network (DCNN) for accurate pupil detection to learn non linear data pattern. In this context, challenges of varying illumination, blurring and reflections for robust pupil detection are overcome by using batch normalization for stabilizing distributions of internal activations during training phase which makes overall methodology less influenced by parameter initialization. Proposed research performed extensive experimentation where accuracy rate of 98.97% was achieved using frame rate of 35 fps which is higher comparing with previous research results. Experimental results reveal the effectiveness of the proposed methodology.

Author 1: A F M Saifuddin Saif

Author 2: Zainal Rasyid Mahayuddin

Keywords: Drowsiness detection; convolutional neural network; face region extraction; pupil detection

Paper 44: Towards a Multi-Agent based Network Intrusion Detection System for a Fleet of Drones

Abstract: The objective of this research work is to propose a new model of intrusion detection system for a fleet of UAVs deployed with an ad hoc communication architecture. The security of a drone fleet is rarely addressed by the scientific community, and most research has focused on routing protocols and battery autonomy, while ignoring the security aspect. The multi-agent paradigm is considered the most adequate and appropriate solution to model an effective intrusion detection system capable of detecting intrusions targeting a drone fleet. Multi-agent systems can perfectly address the security problem of a drone fleet, given the mobility, autonomy, cooperation and distribution characteristics present in the network linking the different nodes of the fleet. The proposed model consists of a set of cooperative, autonomous, communicating, learning and intelligent agents that collaborate with each other to carry out intrusion and suspicious activity detection missions that can target the network of a fleet of drones. Our system is autonomous and can detect known and unknown cyber attacks in real time without the need for human experts, who generally design the signatures of known attacks for conventional intrusion detection systems.

Author 1: Said OUIAZZANE

Author 2: Fatimazahra BARRAMOU

Author 3: Malika ADDOU

Keywords: Fleet of drones; drone; intrusion detection; multi agent system; security; intrusion detection system; autonomy; distribution; UAV; unmanned aerial vehicle; unknown attacks; known attacks

Paper 45: MSTD: Moroccan Sentiment Twitter Dataset

Abstract: With the proliferation of social media and Internet accessibility, a massive amount of data has been produced. In most cases, the textual data available through the web comes mainly from people expressing their views in informal words. The Arabic language is one of the hardest Semitic languages to deal with because of its complex morphology. In this paper, a new contribution to the Arabic resources is presented as a large Moroccan dataset retrieved from Twitter and carefully annotated by native speakers. For the best of our knowledge, this dataset is the largest Moroccan dataset for sentiment analysis. It is distinguished by its size, its quality given by the commitment of annotators, and its accessibility for the research community. Furthermore, the MSTD (Moroccan Sentiment Twitter Dataset) is benchmarked through experiments carried out for 4-way classification as well as polarity classification (positive, negative). Various machine-learning algorithms are combined to feature extraction techniques to reach optimal settings. This work also presents the effect of stemming and lemmatization on the improvement of the obtained accuracies.

Author 1: Soukaina MIHI

Author 2: Brahim AIT BEN ALI

Author 3: Ismail EL BAZI

Author 4: Sara AREZKI

Author 5: Nabil LAACHFOUBI

Keywords: Sentiment analysis; Moroccan dialect; machine-learning; stemming; lemmatization; feature extraction

Paper 46: Load Balancing Problem on Hyper Hexa Cell Interconnection Network

Abstract: Dynamic load balancing techniques prevents computer nodes from overloading unevenly while leaving other idle. It is considered as one of the most challenging topics in parallel computing. Moreover, it is essential for increasing the efficiency of highly parallel systems especially in solving multitask problems with unpredictable load estimates. Particularly, over each processor in the parallel systems and interconnection networks. This paper focuses on developing an efficient algorithm for load balancing on Hyper Hexa Cell (HHC) interconnection network, namely, HHCLB algorithm. Basically, the Dimension Exchange Method (DEM) approach is used in this paper to construct a new load balancing approach on the network of HHC interconnections. Thus, an algorithm was introduced and simulated using java threads, where the performance of the algorithm is evaluated both analytically and experimentally. The evaluation was in terms of various performance metrics, including, execution time, load balancing accuracy, communication cost. By implementing the proposed load balancing algorithm to the HHC network, a high degree of accuracy and minimal execution time was achieved. It is important to highlight that the algorithm recorded small gap between the execution time for small number of processors and large number of processors. For instance, the algorithm achieved 0.14 seconds for balancing the load of 6 processors while 0.59 seconds for balancing the load of 3072 processors. This proves how effective the algorithm is in balancing the load for different network sizes from small to large number of processors, with a slight difference in execution time.

Author 1: Aryaf Al-Adwan

Author 2: Basel A. Mahafzah

Author 3: Anaam Aladwan

Keywords: Parallel computing; load balancing; Hyper Hexa-Cell; interconnection network; Dimension Exchange Method (DEM)

Paper 47: Attribute-based Encryption for Fine-Grained Access Control on Secure Hybrid Clouds

Abstract: In the present scenario, the proliferation of cloud computing services allows hospitals and institutions to move their healthcare data to the cloud, enabling global access to data and on-demand high-quality services at a lower cost. Healthcare data has sensitive attributes to be shielded from leakage due to inference attacks by a curious intruder, either directly or indirectly. A hybrid cloud is a mix of both private and public clouds proposed for the storage of health data. Carefully distributing data between private and public clouds to provide protection. While there has been ample work for the delivery of health data for some time now, it does not appear to be more effective in terms of both data retrieval and consideration for fine-grained access control of the data. This work suggests a cordial approach for a more reliable delivery of data using geometric data disruption of health data over hybrid clouds. It is focused on an in-depth review of the results. The distribution enforces fine-grained data access control using attribute-based encryption. In addition, the approach also addresses a method to effectively extract relevant information from hybrid clouds.

Author 1: Sridhar Reddy Vulapula

Author 2: Srinivas Malladi

Keywords: Secure hybrid cloud; geometric data perturbation; efficiency; fine-grained access control; attribute-based encryption

Paper 48: Gabor Capsule Network for Plant Disease Detection

Abstract: Crop diseases contribute significantly to food insecurity, malnutrition, and poverty in Africa where the majority of the population is into Agriculture. Manual plant disease recognition methods are widespread but limited, ineffective, costly, and time-consuming making the need to search for automatic and efficient methods of recognition more crucial. Machine learning and Convolutional Neural Networks have been applied in other jurisdictions in an attempt to solve these problems. They have achieved impressive results in this domain but tend to be ‘data-hungry‘, invariant, and vulnerable to attacks that can easily lead to misclassifications. Capsule Networks, on the other hand, avoids the weaknesses of CNNs and has not been widely used in this area. This article, therefore, proposes the use of Gabor and Capsule network to recognize blurred, deformed, and unseen tomato and citrus disease images. Experimental results show that the proposed model can achieve a 98.13% test accuracy which is comparable to the performance of state-of-the-art CNN models in the literature. Also, the proposed model outperformed two state-of-the-art deep learning models (which were implemented as baseline models) in terms of robustness, flexibility, fast converges, and having fewer parameters. This work can be extended to other crops and may well serve as a useful tool for the recognition of unseen plant diseases under bad weather and bad illumination conditions.

Author 1: Patrick Mensah Kwabena

Author 2: Benjamin Asubam Weyori

Author 3: Ayidzoe Abra Mighty

Keywords: Convolutional neural networks; capsule network; gabor filters; crop diseases; machine learning

Paper 49: Comparison Performance of Lymphocyte Classification for Various Datasets using Deep Learning

Abstract: Analyzing and classifying five types of Lymphocyte White Blood Cell (WBC) is important to monitor the lack or excessive amount of cell in human body. These harmful amount of cell must be detected early for the early treatment can be run to the patient. However, the process may be tedious and time consuming as it is done manually by the experts. Other than that, it may yield inaccurate result as it depends on the pathologist skill and experience. This work presents a way that can be the second opinion to the experts using computer aided system as a solution. Convolutional Neural Network (CNN) is applied to the system to avoid complex structure and to eliminate the features extraction process. Three CNN models of mobilenet, resnet and VGG-16 is experimented on three different datasets which are kaggle, LISC and IDB-2. Kaggle, LISC and IDB-2 dataset consist of 6000, 242 and 260 images respectively. The result is divided into two parts which are dataset and model. As for IDB-2 dataset, the best model is VGG with training and validation accuracy of 0.9721 and 0.7913 respectively. While for kaggle and LISC dataset, the best model is resnet as it achieved training accuracy of 0.9713 and 0.9771 respectively. The highest validation accuracy for kaggle is 0.5955 and 0.5781 for LISC. Lastly, the best database that is most suitable for all model is IDB-2 database. It obtained highest training and validation accuracy for all model of mobilenet, resnet and VGG-16.

Author 1: Syadia Nabilah Mohd Safuan

Author 2: Mohd Razali Md Tomari

Author 3: Wan Nurshazwani Wan Zakaria

Author 4: Nor Surayahani Suriani

Keywords: Convolutional neural network; Google colab; training accuracy; validation accuracy; white blood cell

Paper 50: Supervised Hyperspectral Image Classification using SVM and Linear Discriminant Analysis

Abstract: Hyperspectral images are used to recognize and determine the objects on the earth’s surface. This image contains more number of spectral bands and classifying the image becoming a difficult task. Problems of higher number of spectral dimensions are addressed through feature extraction and reduction. However, accuracy and computational time are the important challenges involved in the classification of hyperspectral images. Hence in this paper, a supervised method has been developed to classify the hyperspectral image using support vector machine (SVM) and linear discriminant analysis (LDA). In this work, spectral features of the images are extracted and reduced using LDA. Spectral features of hyperspectral images are classified using SVM with RBF kernel like buildings, vegetation fields, etc. The simulation results show that the SVM algorithm combined with LDA has good accuracy and less computational time. Furthermore, the accuracy of classification is enhanced by incorporating the spatial features using edge-preserving filters.

Author 1: Shambulinga M

Author 2: G. Sadashivappa

Keywords: Linear discriminant analysis; support vector machine; guided image filtering; bilateral filter

Paper 51: Manar: An Arabic Game-based Application Aimed for Teaching Cybersecurity using Image Processing

Abstract: People use the Internet for various activities, including exchanging money, playing games, and shopping. However, this powerful network came at a cost: the features that provide the Internet with these capabilities are also what make it vulnerable. The need for cybersecurity tools and practices was recognized, especially for children, since they tend to be naive and can be easily tricked. Aimed at children from 6 to 12 years old, Manar is an Arabic smartphone game that seeks to build a generation who are well-informed about cybersecurity issues. It teaches them about notable cybersecurity topics such as social engineering and cryptography, it also has a very appealing theme to attract children to play the game. The theme being “pirates and islands”, each level will be represented as an island and a moving pirate ship will navigate between the levels. The application introduces the technology of image processing in a unique way, allowing children to move around and look for objects, which makes the game as interactive as possible to attract children’s attention.

Author 1: Afnan Alsadhan

Author 2: Asma Alotaibi

Author 3: Lulu Altamran

Author 4: Majd Almalki

Author 5: Moneera Alfulaij

Author 6: Tarfa Almoneef

Keywords: Cybersecurity; image processing; social engineering; cryptography

Paper 52: A Machine Learning Approach to Identifying Students at Risk of Dropout: A Case Study

Abstract: The increase in students’ dropout rate is a huge concern for institutions of higher learning. In this article, classification techniques are applied to determine students “at-risk” of dropping out of their registered qualifications. Being able to identify such students timeously will be beneficial to both the students and the institutions with which they are registered. This study makes use of Random Forest, Support Vector Machines, Decision Trees, Naïve Bayes, K-Nearest Neighbor, and Logistic Regression for classification purposes. The selected algorithms were applied on a dataset of 4419 student records obtained from the institutional database related to Diploma students enrolled in the Faculty of Information, Communication and Technology. The results reveal that the overall accuracy rate of Random Forest (94.14%) was better than the other algorithms in identifying students at risk of dropout.

Author 1: Roderick Lottering

Author 2: Robert Hans

Author 3: Manoj Lall

Keywords: EDM; student dropout; binary classification; ensemble method; KDD

Paper 53: Customized BERT with Convolution Model : A New Heuristic Enabled Encoder for Twitter Sentiment Analysis

Abstract: The Twitter messaging service has turned out to be a domain for news consumers and patrons to convey their sentiments. Capturing these emotions or sentiments in an accurate manner remains a major challenge for analysts. Moreover, the Twitter data include both spam and authentic contents that often affects accurate sentiment categorization. This paper introduces a new customized BERT (Bidirectional Encoder Representations from Transformers) based sentiment classification. The proposed work consists on pre-processing and tokenization step followed by a customized BERT based classification via optimization concept. Initially, the collected raw tweets are pre-processed via "stop word removal, stemming and blank space removal". Prevailing semantic words are acquired, from which the tokens (meaningful words) are extracted in the tokenization phase. Subsequently, these extracted tokens will be subjected to classification via optimized BERT, which weights and biases are optimally tuned by Standard Lion Algorithm (LA). In addition, the maximum sequence length of BERT encoder is updated with standard LA. Finally, the performance of the proposed work is compared over other state-of-the-art models with respect to different performance measures.

Author 1: Fatima-ezzahra LAGRARI

Author 2: Youssfi ELKETTANI

Keywords: Twitter data; sentiment analysis; tokenization; optimized BERT; Lion Algorithm

Paper 54: Empirical Oversampling Threshold Strategy for Machine Learning Performance Optimisation in Insurance Fraud Detection

Abstract: Insurance fraud is one of the most practiced frauds in the sectors of the economy. Faced with increasingly imaginative underwriters to create fraud scenarios and the emergence of organized crime groups, the fraud detection process based on artificial intelligence remains one of the most effective approaches. Real world datasets are usually unbalanced and are mainly composed of "no-fraudulent" class with a very small percentage of "fraudulent" examples to train our model, thus prediction models see their performance severely degraded when the target class appears so poorly represented. Therefore, the present work aims to propose an approach that improves the relevance of the results of the best-known machine learning algorithms and deals with imbalanced classes in classification problems for prediction against insurance fraud. We use one of the most efficient approaches to re-balance training data: SMOTE. We adopted the supervised method applied to automobile claims dataset "carclaims.txt". We compare the results of the different measurements and question the results and relevance of the measurements in the field of study of unbalanced and labeled datasets. This work shows that the SMOTE Method with the KNN Algorithm can achieve better classifier performance in a True Positive Rate than the previous research. The goal of this work is to lead a study of algorithm selections and performance evaluation among different ML classification algorithms, as well as to propose a new approach TH-SMOTE for performance improvement using the SMOTE method by defining the optimum oversampling threshold according to the G-mean measure.

Author 1: Bouzgarne Itri

Author 2: Youssfi Mohamed

Author 3: Bouattane Omar

Author 4: Qbadou Mohamed

Keywords: Machine learning; oversampling; SMOTE; insurance fraud

Paper 55: Design of an Electro-Stimulator Controlled by Bluetooth for the Improvement of Muscular Atrophy

Abstract: Muscle stimulation consists of a muscle's work when it is in constant exercise or contraction. This article presents an electro-stimulator design that generates electrical pulses in muscle cells through two electrodes positioned in an area of the body, causing the response of said cells to improve muscle atrophy. The steps that were followed were the construction of the block diagram, the design, and development of the circuit, the design and development of the control based on the pic12F683 and pic 16F690 microcontrollers, finally, the development of the software of the mobile application that controls the equipment using Bluetooth signals, based on the standard of IEC 60601-1 for basic safety and essential equipment performance. It was possible to obtain control of the frequency, application time, and amplitude of the duty cycle to have better results when applying therapy in specific areas of the body through the mobile application. Finally, the design is developed to respond to the user's parameters, using the Bluetooth of a mobile device and allowing the generation of electrical pulses in the muscle cells to improve muscle atrophy. The team can be part of therapeutic sessions for people with quadriplegia, improving the physiotherapy sessions performed on patients.

Author 1: Paul Portilla Achata

Author 2: Raúl Sulla Torres

Author 3: Juan Carlos Copa Pineda

Author 4: Agueda Munoz del Carpio Toia

Author 5: Jose Sulla-Torres

Keywords: Design; electro-stimulator; microcontroller; electric pulses; muscular atrophy

Paper 56: Dual Annular Ring Coupled Stacked Psi Shape Patch Antenna for Wireless Applications

Abstract: The paper aims to design and analyze an annular ring coupled stacked Psi shaped patch antenna with coplanar waveguide (CPW) feed technique operating for dual band frequency applications. The proposed model comprises of stacked Psi shapes resonating for lower order frequency. The second order resonating band was obtained through capacitively coupled overlapped annular rings. The geometrical dimensions of the proposed model are 3.5*2 (L*W) based on lower order resonating band. The design and simulations were performed using DS CST Microwave Studio suite. The model achieves dual resonant bands, (2.19- 2.68) GHz with impedance bandwidth 490 MHz and (5.569 – 6.09) GHz with 530 MHz impedance bandwidth. The center frequencies are 2.42 GHz and 5.815 GHz with return loss -28.97 dB and -28.99 dB respectively. The design exhibited a maximum gain of 5 dB with bidirectional and omni directional patterns in E and H- planes. The axial ratio at the two resonating bands was less than 3 dB. Parametric analysis was performed for reflection co-efficient on geometric variables like ground width, ground length and permittivity. From (3-5) GHz frequency range, a perfect notch band was also exhibited. Simulated and measured results have shown a good concurrence. The model was suitable for WLAN (Wireless Local Area Network) and ISM (Industrial, Scientific and Medical) Band applications.

Author 1: K. Mahesh Babu

Author 2: T.V. Rama Krishna

Keywords: Annular ring; stacked psi shape; WLAN; ISM band

Paper 57: Secure Communication across the Internet by Encrypting the Data using Cryptography and Image Steganography

Abstract: Sharing the information has become a facile task nowadays just like one-tap which can take the information to any component of the world. This whole thing transpired over the evolution of the cyber world, which avails to stay connected with the entire world. Due to the wide-spread utilization of the cyber world, it leads a peril of data breaching by some incognito or unauthorized people while it is being sent from one utilizer to another. Unauthorized people can get access to the data and extract utilizable information from it. The confidential data being sent through the web which may get tampered while reaching the other end-utilizer. So, to dispense this data breaching, we can encrypt the data being sent and the receiver can only decrypt the message so that we can conceal the data. It routes a tremendous way to do this, the most popular one is cryptography, and another is steganography. Anteriorly there subsist many ways in these techniques like Image Steganography, Secret key Cryptography, LSB method, and so on which are being used to encrypt data and secure communication. One of the algorithms of cryptography is utilized along with Image Steganography to encrypt the data to ascertain more security which resembles the two-step verification process. In proposed paper we utilized new Huffman coding algorithm in step of the Image Steganography to ascertain that even an astronomically immense data can fit into a minute image. The ciphertext is compressed utilizing Huffman Coding and then it gets embedded into an image utilizing LSB method of Image Steganography in which the least paramount bits of the image are superseded with the data from the antecedent step. We implemented the analytical using python and it shows better compression results with large volumes of data to transfer easily through network.

Author 1: P Rajesh

Author 2: Mansoor Alam

Author 3: Mansour Tahernezhadi

Author 4: T Ravi Kumar

Author 5: Vikram Phaneendra Rajesh

Keywords: Cryptography; image steganography; least significant bits; secure communication; Huffman coding; data encryption; data compression

Paper 58: White Blood Cells Detection using YOLOv3 with CNN Feature Extraction Models

Abstract: There are several types of blood cancer. One of them is Leukaemia. This is due to leukocyte or white blood cell (WBCs) production problem in the bone marrow. Detection at earlier stage is important so that the patient is able to get a proper treatment. The conventional detection and blood count method is less efficient and it is done manually by pathologist. Thus, there will be a long line to wait for the results and also delay the treatment. A faster detection procedure and technique will have high impact on the real time diagnostic. Fortunately, these problems are able to overcome by making the blood test procedures automatic. One of the effort is the development of deep learning for WBCs detection and classification. In computer aided WBCs detection, the You Only Look Once (YOLO) based platform present a promising outcome. However, the investigation of optimal YOLO structure remains vague. This paper investigate the effect of the deep learning based WBCs detection using You Only Look Once version 3 (YOLOv3) with different pretrained Convolutional Neural Network (CNN) model. The models that been tested are the Alexnet, Visual Geometry Group 16 (VGG16), Darknet19 and the existing YOLOv3 feature extraction model, the Darknet53. The architecture consist of the bounding box for class prediction, feature extraction, and additional convolutional layers. It was trained with 242 WBCs images from Local Initiatives Support Corporation (LISC) dataset. The final outcome shows that the YOLOv3 architecture with Alexnet as its feature extractor produced the highest mean average precision of 98% and have better performance than the other models.

Author 1: Nurasyeera Rohaziat

Author 2: Mohd Razali Md Tomari

Author 3: Wan Nurshazwani Wan Zakaria

Author 4: Nurmiza Othman

Keywords: Alexnet; darknet19; darknet53; detection; VGG16; white blood cells; YOLO

Paper 59: A Novel Solution for Distributed Database Problems

Abstract: Distributed Databases Systems (DDBS) are a set of logically networked computer databases, managed by different sites, locations and accessible to the user as a single database. DDBS is an emerging technology that is useful in data storage and retrieval purposes. Still, there are some problems and issues that degrade the performance of distributed databases. The Aim of this paper is to provide a novel solution to distributed database problems that is based on distributed database challenges collected in one diagram and on the relationship among DDB challenges in another diagram. This solution presents two methodologies for Distributed Databases management Systems: deep learning-based fragmentation and allocation, and blockchain technology-based security provisioning. The contribution of this paper is twofold. First, it summarizes major issues and challenges in the distributed database. Additionally, it reviews the research efforts presented to resolve the issues. Secondly, this paper presents a distributed database solution that resolves the major issue of distributed database technology. This paper also highlights the future research directions that are appropriate for distributed database technology after the implementation in a large-scale environment and recommended the technologies that can be used to ensure the best implementation of the proposed solution.

Author 1: Bishoy Sameeh Matta Sawiris

Author 2: Manal A. Abdel-Fattah

Keywords: Distributed database; database challenges; deep learning; fragmentation; blockchain; security

Paper 60: Secure Software Defined Networks Controller Storage using Intel Software Guard Extensions

Abstract: The SDN controller is the core of the software-defined network (SDN), which provides important network operations that needs to be protected from all type of threats. Many researches have been focusing on different layers of security regarding the SDN controller such as Anti-DDOS system or enforcement of TLS connection between the controller and the Open-vswitches. One of the major security threats targeting any program is the environment execution itself (e.g. Operating system and the hardware itself). Intel's Software Guard Extension (SGX) offers a sloid layer of security applied to applications by creating a Trusted execution environment. SDN controller relay on a storage module to keep sensitive data such as Flow Rules, users’ credentials and configuration files. Protecting this side of the SDN controller is a must in term of security. To date, no work has been conducted considering SDN controller storage security using Intel SGX. This paper introduces an SGX enabled SDN controller. The new controller ensures the integrity and the confidentiality in a trusted execution environment by leveraging a recent hardware technology called intel SGX. This technology provides a trusted and secure enclave. Enclaves are sealed and unsealed by intel SGX attestation mechanisms to protect the executed code and data inside live memory and disk from being altered by any unauthorized access. High privileged codes such as the OS itself is kept from altering data inside enclaves. We implemented the Intel SGX using the Floodlight SDN controller running a real enabled Intel SGX hardware. Our evaluation shows that the SGX enabled SDN controller introduces a slightly observable performance overhead to the floodlight controller compared to advantages in term of security.

Author 1: Qasmaoui Youssef

Author 2: Maleh Yassine

Author 3: Abdelkrim Haqiq

Keywords: Software defined networks; software guard extensions; storage; integrity; confidentiality

Paper 61: Dynamics of Organizational Change

Abstract: In this research, the evolution of change in an organization, due to continuous changes in the market, is disclosed in a qualitative and quantitative way. The changes developed in the organizational structure were aimed at the search for a flexible, dynamic and agile organization, which would allow adapting to the demands of increasingly informed customers, this engine of change called customer required the company to develop a flexible organizational structure. For this, the key concepts were reviewed, such as: systems, processes, activities, modeling and the use of the Systems Dynamics tool for the elaboration of the causal diagram and flow diagram, which allowed to identify, analyze and evaluate the variables that affected each stage of organizational change. The evolution of the change that the organization developed was carried out in an unplanned sequential manner, in the following stages: first a vertical organization, second an organization by processes, third a focused organization, fourth a modular organization and later a flexible organization, which allowed adapt to changes in customer orders, orders that each time increased in the characteristics of the product model, but decreased in quantities. The changes developed by the organization allowed to increase the response speed in order delivery by 43%.

Author 1: Maximo Flores-Cabezas

Author 2: Desiree Flores-Moya

Author 3: Brian Meneses-Claudio

Keywords: System dynamics; diagram; vertical organization; process organization; focused organization; modular organization; flexible organization

Paper 62: Impact of Change in Business IT Alignment: Evaluation with CBITA Tool

Abstract: Organizations introduce changes to adapt to an agile context in a turbulent environment. These change often have an impact on business and information technology. In most cases, the change impacts organizational elements that make the adaptation not well defined, leaving out elements that can lead to misalignment. The change is realized in this article as a project, which will impact all the Business-IT alignment elements. It is important to know the scope of the impact in order to make a complete adaptation. By reviewing the literature, we found that there is no work dealing with this aspect. To fill this gap, we determine the impact of the project on the organization by considering of Business-IT alignment and we proceed with a comparison of the AS IS model of the organization with the TO BE model which is the target to be implemented keeping the system aligned. Accordingly, we propose a metamodel for change by the impact of the project on the Business-IT alignment and a set of rules and algorithms to predict the impact and adaptation. To make these contributions operational, the implementation of Change Business-IT Alignment Tool and demonstrate its applicability through a case study of an urban agency.

Author 1: Imgharene Kawtar

Author 2: Doumi Karim

Author 3: Baina Salah

Keywords: Agility; business IT alignment; change; project

Paper 63: Enhancing VoIP BW Utilization over ITTP Protocol

Abstract: The revolution of Voice over Internet Protocol (VoIP) technology has propagated everywhere and replaced the conventional telecommunication technology (e.g. landline). Nevertheless, several enhancements need to be done on VoIP technology to improve its performance. One of the main issues is to improve the VoIP network bandwidth (BW) utilization. VoIP packet payload compression is one of the key approaches to do that. This paper proposes a new method to compress VoIP packet payload. The suggested method works over internet telephony transport protocol (ITTP) and named Delta-ITTP method. The core idea of the Delta-ITTP method is to find and transmit the delta between the successive VoIP packet payloads, which is typically smaller than the original VoIP packet payload. The suggested Delta-ITTP method implements VoIP packet payload compression at the sender side and decompression at the receiver side. During the compression process, the Delta-ITTP method needs to keep some values to restore the original VoIP packet payload at the receiver side. For this, the Delta-ITTP method utilizes some of the IP protocol fields and no additional header is needed. The Delta-ITTP method has been deployed and compared with the traditional ITTP protocol without compression. The result showed that up to 19% BW saving was achieved in the tested cases leading to the desired enhancement in the VoIP network BW utilization.

Author 1: AbdelRahman H. Hussein

Author 2: Mahran Al-Zyoud

Author 3: Kholoud Nairoukh

Author 4: Sumaya N. Al-Khatib

Keywords: Voice over IP (VoIP); VoIP protocols; Internet Telephony Transport Protocol (ITTP); payload compression; bandwidth utilization

Paper 64: High-Security Image Steganography Technique using XNOR Operation and Fibonacci Algorithm

Abstract: Since the number of internet users is increasing and sensitive information is exchanging continuously, data security has become a problem. Image steganography is one of the ways to exchange secret data securely using images. However, several issues need to be mitigated, especially in the imperceptibility (security) aspect, which is the process of embedding secret data in the images that can be vulnerable to attacks. This paper focuses on developing a secure method for hiding secret messages in an image, based on the standard Least Significant Bit (LSB). Before proceeding with the embedding stage, the secret message's size is reduced by compression using the Huffman algorithm, followed by two operations, which are the Boolean operation Exclusive-NOR (XNOR) operation and the Fibonacci algorithm when selecting pixels to embed the secret message. As a result of these processes, a stego-image is created with two secret keys. We obtained promising results against standard images with higher Peak Signal-to-Noise Ratio (PSNR) values of 66.6170, 65.8928, and 65.9386 dB for Lena.bmp, Baboon.bmp, and Pepper.bmp, respectively, as compared to other state-of-the-art schemes. The evaluation stage proves the increasing level of security as well as imperceptibility.

Author 1: Ali Abdulzahra Almayyahi

Author 2: Rossilawati Sulaiman

Author 3: Faizan Qamar

Author 4: Abdulwahhab Essa Hamzah

Keywords: Image steganography; Huffman algorithm; XNOR operation; Fibonacci algorithm; LSB; PSNR

Paper 65: Very Deep Neural Networks for Extracting MITE Families Features and Classifying them based on DNA Scalograms

Abstract: DNA sequencing has recently generated a very large volume of data in digital format. These data can be compressed, processed and classified only by using automatic tools which have been employed in biological experiments. In this work, we are interested in the classification of particular regions in C. Elegans Genome, a recently described group of transposable elements (TE) called Miniature Inverted-repeat Transposable Elements (MITEs). We particularly focus on the four MITE families (Cele1, Cele2, Cele14, and Cele42). These elements have distinct chromosomal distribution patterns and specific number conserved on the six autosomes of C. Elegans. Thus, it is necessary to define specific chromosomal domains and the potential relationship between MITEs and Tc / mariner elements, which makes it difficult to determine the similarities between MITES and TC classes. To solve this problem and more precisely to identify these TEs, these data are classified and compressed, in this study, using an efficient classifier model. The application of this model consists of four steps. First, the DNA sequence are mapped in a scalogram’s form. Second, the characteristic motifs are extracted in order to obtain a genomic signature. Third, MITE database is randomly divided into two data sets: 70% for training and 30%for tests. Finally, these scalograms are classified using Transfer Learning Approach based on pre-trained models like VGGNet. The introduced model is efficient as it achieved the highest accuracy rates thanks to the recognition of the correct characteristic patterns and the overall accuracy rate reached 97.11% for these TEs samples classification. Our approach allowed also classifying and identifying the MITES Classes compared to the TC class despite their strong similarity. By extracting the features and the characteristic patterns, the volume of massive data was considerably reduced.

Author 1: Mael SALAH JRAD

Author 2: Afef ELLOUMI OUESLATI

Author 3: Zied LACHIRI

Keywords: DNA scalograms; genomic signature; classification; deep learning; transfer learning; VGGNET; accuracy

Paper 66: The Prediction of Outpatient No-Show Visits by using Deep Neural Network from Large Data

Abstract: Patients’ no-show is one of the leading causes of increasing financial burden for healthcare organizations and is an indicator of healthcare systems' quality and performance. Patients' no-show affects healthcare delivery, workflow, and resource planning. The study aims to develop a prediction model predict no-show visits using a machine learning approach. A large volume of data was extracted from electronic health records of patient visits in outpatient clinics under the umbrella of large medical cities in Saudi Arabia. The data consists of more than 33 million visits, with an 85% no-show rate. A total of 29 features were utilized based on demographic, clinical, and appointment characteristics. Nine features were an original data element, while data elements derived 20 features. This study used and compared three machine learning algorithms; Deep Neural Network (DNN), AdaBoost, and Naive Bayes (NB). Results revealed that the DNN performed better in comparison to NB and AdaBoost. DNN achieved a weighted average of 98.2% and 94.3% of precision and recall, respectively. This study shows that machine learning has the potential to improve the efficiency and effectiveness of healthcare. The results are considered promising, and the model can be an excellent candidate for implementation.

Author 1: Riyad Alshammari

Author 2: Tahani Daghistani

Author 3: Abdulwahhab Alshammari

Keywords: No-show; outpatients; machine learning; prediction model introduction

Paper 67: Virtual Simulation for Entertainment using Genetic Information

Abstract: The genetic information has been researched to predict the disease and to discover the clue of biological causes, especially in the medical field. However, based on the reliability of genetic information, it also gives a powerful realistic experience with VR devices. In this paper, we developed a dating simulation game that users can meet celebrities. The human personality of the celebrity, the favorability feedback depends on each choice of the conversation and a proper choice creation is based on the genetic information of the user and a celebrity. Besides, a method for utilizing genetic DNA information for virtual simulations is proposed. In this study, we have established that DNA information is related to human relationships both in the real and virtual worlds. Also, we concluded that the DNA information contributes to the development of interpersonal and mental health. However, a person's personality or preference for a specific situation or object, etc. are not determined only through genes. Therefore, more quantitative and multifaceted studies will need to be conducted on the effect of genes on personal preferences. We only experimented with virtual characters in virtual reality. It would be meaningful to proceed with the human experiment not only with a virtual character in the virtual environment, but also another human in the virtual environment. Eventually, the result of the experiment between virtual characters and humans should be compared.

Author 1: Jin Woo Kim

Author 2: Hun Lim

Author 3: Taeho You

Author 4: Mee Young Sung

Keywords: Virtual simulation; entertainment; genetic information; DeoxyriboNucleic Acid (DNA) matching; celebrity; favorability

Paper 68: Level of Depression in Positive Patients for COVID-19 in the Puente Piedra District of North Lima, 2020

Abstract: Depression in the positive patient for COVID-19 is one of the emotional confrontations that must endure during isolation and quarantine, therefore, the objective of the research study is to determine the Level of Depression in positive patients for COVID-19 in the district of Puente Piedra in North Lima, 2020. This is a quantitative, non-experimental, descriptive and cross-sectional study, making a flow chart in relation to home care by the nursing professional in a population of 23 Positive patient for COVID-19 from the Puente Piedra district of North Lima, who answered a questionnaire with Sociodemographic data and the self-assessment scale for Zung's depression. In the results where we can observe, with respect to the level of depression in patients positive for COVID-19, where 14 patients represent 60.9% of the total are with a normal level of depression, 9 patients represent 39.1% of the total are slightly depressed. In conclusion, it is necessary to intervene in the psychological aspect according to the characteristics of each patient such as gender and age, in the future, it is recommended to carry out more research at the national level, as it will allow researchers to go into more detail about the mental health of the patient during and after the COVID-19 pandemic.

Author 1: Rosa Perez-Siguas

Author 2: Eduardo Matta-Solis

Author 3: Hernan Matta-Solis

Author 4: Anika Remuzgo-Artezano

Author 5: Lourdes Matta-Zamudio

Author 6: Melissa Yauri-Machaca

Keywords: Depression; coronavirus; patients; home care

Paper 69: Blockchain and Internet of Things for Business Process Management: Theory, Challenges, and Key Success Factors

Abstract: The combination of business process management (BPM) and emerging technologies is a logical step. The evolving number of advanced technologies shows that contributions are with a high value for business processes. From a BPM point of view, value creation from the modern technologies such as Internet of things and Blockchain technology is pivotal on a progressively higher scale and affect the business process in different aspects. However, current research in this area still at the beginning. In order to close this research gap and the lack of experience in this area, the topic of integrating Blockchain and IoT technologies with BPM will play an essential role in a corporate context, particularly in the context of inter/intra-organizational information systems and their diverse design options. This review paper aims to survey the impact of tow emerging technologies: Internet of Things and Blockchain technology on BPM and illustrates the current state of the art in this research domain. Each technology was investigated through a design science research approach to provide as a descriptive theoretical overview, characterize its theoretical background, challenges, and key success factors.

Author 1: Mabrook S. Al-Rakhami

Author 2: Majed Al-Mashari

Keywords: Business process management; BPM; Internet of things; IoT; blockchain technology

Paper 70: A Trust-Based Collaborative Filtering Approach to Design Recommender Systems

Abstract: Collaborative Filtering (CF) is one of the most fre-quently used recommendation techniques to design recommender systems that improve accuracy in terms of recommendation, coverage, and rating prediction. Although CF is a well-established and popular algorithm, it suffers with issues like black-box recommendation, data sparsity, cold-start, and limited content problems that hamper its performance. Moreover, CF is fragile and it is not suitable to find similar users. The existing literatures on CF show that integrating users’ social information with a recommender system can handle the above-mentioned issues effectively. Recently, trustworthiness among users is considered as one such social information that has been successfully combined with CF to predict ratings of the unrated items. In this paper, we propose a trust-based recommender system, TrustRER, which integrates users’ trusts into an existing user-based CF algorithm for rating prediction. It uses both ratings and textual information of the items to generate a trust network for users and derives the trust scores. For trust score, we have defined three novel trust statements based on user rating values, emotion values, and re-view helpfulness votes. To generate a trust network, we have used trust propagation metrics to compute trust scores between those users who are not directly connected. The proposed TrustRER is experimentally evaluated over three datasets related to movie, music, and hotel and restaurant domains, and it performs significantly better in comparison to nine standard baselines and one state-of-the-art recommendation method. TrustRER is also able to effectively deal with the cold-start problem because it improves the rating prediction accuracy for cold-start users in comparison to baselines and state-of-the-art method.

Author 1: Vineet K. Sejwal

Author 2: Muhammad Abulaish

Keywords: Recommender system; collaborative filtering; cold-start; trust; rating prediction

Paper 71: A Game-Based Learning Approach to Improve Students’ Spelling in Thai

Abstract: The problem of misspelled Thai words written in social media is increasing rapidly by youth in Thailand. To decrease the number of misspelled Thai words and improve the learning achievement for Thai youth, a firstperson 3D mobile game was developed. The game is run on an Android smartphone applying a gyroscope sensor. This game has 3 levels in 5 stages. The learning achievement is evaluated from 37 players’ pre and posttest scores, who are bachelor’s degree students of Animation and Game, College of Arts, Media and Technology, Chiang Mai University, Thailand. The data were statistically analysed by a paired sample ttest. Pre and posttest scores were weakly and positively correlated (r = 0.666, p < 0.001). There was a significant average difference between pre and posttest scores (t36 = 11.776, p < 0.001). On average, posttest scores were 15.027 points higher than pretest scores (95% CI [17.615, 12.439]). The results of the research show that the gamebased learning approach significantly improved players’ learning achievement in misspelled written Thai words.

Author 1: Krittiya Saksrisathaporn

Keywords: Game for learning; gamebased learning; mobile game; paired sample ttest; Thai; misspelled words

Paper 72: Physical Parameter Estimation of Linear Voltage Regulators using Model-based Approach

Abstract: Electronic systems are becoming increasingly so-phisticated due to the emergence of advanced technology, which can produce robust integrated circuits by reducing the dimensions of transistors to just a few nanometers. Furthermore, most elec-tronic systems nowadays are in the form of system-on-chip and thus require stable voltage specifications. One of the critical elec-tronic components is the linear voltage regulator (LVR). LVRs are types of power converter used to maintain a stable and constant DC voltage to the load. Therefore, LVR stability is an essential aspect of voltage regulator design. The main factor influencing the stability of LVRs is the load disturbance. In general, disturbances such as a sudden change in load current can be compensated for by an output capacitor, which, contains a parasitic element known as equivalent series resistance (ESR). Therefore, the ESR and output capacitor specified in the datasheet is essential to compensate for load disturbance. However, LVR manufacturers typically do not provide detailed information, such as the internal physical parameters associated with the LVR in the datasheet. This situation leads to difficulties in identifying the behavior and stability of LVR. Therefore, this study aims to develop a method for estimating the internal physical parameters of LVR circuits that are difficult to measure directly by using a model-based approach (MBA). In this study, the MBA estimates the LVR model transfer function by analyzing the input and output signals via a linear regression method. Simulations through MATLAB and OrCAD Capture CIS software verify the estimated LVR model transfer function. Results show that the MBA has an excellent performance in estimating the physical parameters of LVRs and determining their stability.

Author 1: Ng Len Luet

Author 2: Mohd Hairi Mohd Zaman

Author 3: Asraf Mohamed Moubark

Author 4: M Marzuki Mustafa

Keywords: Linear voltage regulator; stability; capacitor; equivalent series resistance; physical parameter; model-based approach

Paper 73: Artificial Bee Colony Algorithm Optimization for Video Summarization on VSUMM Dataset

Abstract: This paper attempts to prove that the Artificial Bee Colony algorithm can be used as an optimization algorithm in sparse-land setup to solve Video Summarization. The critical challenge in doing quasi(real-time) video summarization is still time-consuming with ANN-based methods, as these methods require training time. By doing video summarization in a quasi (real-time), we can solve other challenges like anomaly detection and Online Video Highlighting. A simple threshold function is tested to see the reconstruction error of the current frame given the previous 50 frames from the dictionary. The frames with higher threshold errors form the video summarization. In this work, we have used Image histogram, HOG, HOOF, and Canny edge features as features to the ABC algorithm. We have used Matlab 2014a for doing the feature extraction and ABC algorithm for VS. The results are compared to the existing methods. The evaluation scores are calculated on the VSUMM dataset for all the 50 videos against the two user summaries. This research answers how the ABC algorithm can be used in a sparse-land setup to solve video summarization. Further studies are required to understand the performance evaluation scores as we change the threshold function.

Author 1: Vinsent Paramanantham

Author 2: S. SureshKumar

Keywords: Artificial Bee Colony optimization; video summarization; online video highlighting; sparse-land; anomaly detection; image histogram; HOG; HOOF; canny edge

Paper 74: Continuous Human Activity Recognition in Logistics from Inertial Sensor Data using Temporal Convolutions in CNN

Abstract: Human activity recognition has been an important task for the research community. With the introduction of deep learning architectures, the performance of activity recognition algorithms has improved significantly. However, most of the research in this area has focused on activity recognition for health/assisted living with other applications being given less attention. This paper considers continuous activity recognition in logistics (order picking and packing operations) using a convolutional neural network with temporal convolutions on inertial measurement sensor data from the recently released LARa dataset. Four variants of the popular CNN-IMU are experimented upon and a discussion of the results is provided. The results indicate that temporal convolutions are able to achieve satisfactory performance for some activities (hand center and cart) whereas they perform poorly for the activities of stand and hand up.

Author 1: Abbas Shah Syed

Author 2: Zafi Sherhan Syed

Author 3: Areez Khalil Memon

Keywords: Convolutional Neural Networks; deep learning; Human Activity Recognition (HAR); inertial sensors; LARa dataset

Paper 75: Design of Multi-View Graph Embedding for Features Selection and Remotely Sensing Signal Classification

Abstract: Now-a-days, signal processing remains an intensive challenging area of research. In fact, various strategies have been suggested to address semi-supervised, feature selection and unlabeled samples challenges. The most frequent achievement was dedicated to exploit a single kind of feature/view from the original data. Recently, advanced techniques aimed to explore signals from different views and to, properly, integrate divergent kinds of interdependent features. In this paper, we propose a novel design of a multi-View Graph Embedding for features selection allowing a convenient integration of complementary weighted features. The proposed framework combines the singular properties of each feature space to accomplish a physically meaningful cooperative low-dimensional selection of input data. This allows us not only to perform a semi-supervised classification, but also to propagates narrow class information to unlabeled sample when only partial labeling knowledge is available. This paper makes the following contributions: (i) a feature selection schema for data refinement; and (ii) the adaptation of a multi-view graph-based approach by a better tackling of semi-supervised and dimensionality issues. Our experimental results, conducted by using a mixture of complementary features and aerial images datasets, demonstrate the effectiveness of the proposed framework without significantly increasing computational complexity.

Author 1: Abdullah Alhumaidi Alotaibi

Author 2: Sattam Alotaibi

Keywords: Signal processing; remote sensing images; features selection; graph embedding; unlabeled samples

Paper 76: How Images Defects in Street Scenes Affect the Performance of Semantic Segmentation Algorithms

Abstract: Semantic segmentation methods are used in au-tonomous car development to label pixels of road images (e.g. street, building, pedestrian, car, and so on). DeepLabv3+ and PSPNet are two of the best performance semantic segmentation methods according to Cityscapes benchmark. Although these methods achieved a very high performance with clear road images, yet these two methods are not tested under severe imaging conditions. In this work, we provided new Cityscapes datasets with severe imaging conditions: foggy, rainy, blurred, and noisy datasets. We evaluated the performance of DeepLabv3+ and PSPNet using our datasets. Our work demonstrated that although these models have high performance with clear images, they show very weak performance among the different imaging challenges. We proved that the road semantic segmentation methods must be evaluated using different kinds of severe imaging conditions to ensure the robustness of these methods in autonomous driving.

Author 1: Hoda Imam

Author 2: Bassem A. Abdullah

Author 3: Hossam E. Abd El Munim

Keywords: Semantic segmentation; deep learning; cityscapes; DeepLabv3+; PSPNet

Paper 77: Emotion Analysis of Arabic Tweets during COVID-19 Pandemic in Saudi Arabia

Abstract: Social media has emerged as an effective platform to investigate people’s opinion and feeling towards crisis situations. Along with Coronavirus crisis, range of different emotions reveal, including anger, sadness, fear, trust, and anticipation. In this paper, we investigate public’s emotional responses associated with this pandemic using Twitter as platform to perform our analysis. We investigate how emotional perspective vary regarding lockdown ending in Saudi Arabia. We develop an emotion detection method to classify tweets into standard eight emotions. Furthermore, we present insights into the changes in the intensity of the emotions over time. Our finding shows that joy and anticipation are the most dominant among all emotions. While people express positive emotions, there are tones of fear, anger, and sadness revealed. Moreover, this research might help to better understand public behaviors to gain insight and make the proper decisions.

Author 1: Huda Alhazmi

Author 2: Manal Alharbi

Keywords: Emotion analysis; Arabic tweets; COVID-19; Twitter; Lexicon-based

Paper 78: HPSOGWO: A Hybrid Algorithm for Scientific Workflow Scheduling in Cloud Computing

Abstract: Virtualization is one of the key features of cloud computing, where the physical machines are virtually divided into several virtual machines in the cloud. The user’s tasks are run on these virtual resources as per the requirements. When the user requests the services to the cloud, the user’s tasks are allotted to the virtual resources depending on their needs. An efficient scheduling mechanism is required for optimizing the involved parameters. Scientific workflows deals with a large amount of data with dependency constraints and is used to simplify the applications in the diverse scientific domains. Scheduling the workflow in cloud computing is a well-known NP-hard problem. Deploying such data- and compute-intensive workflow on the cloud needs an efficient scheduling algorithm. In this paper, we have proposed a multi-objective model based hybrid algorithm (HPSOGWO), which combines the desirable characteristics of two well-known algorithms, particle swarm optimization (PSO), and grey wolf optimization (GWO). The results are analyzed under complex real-world scientific workflows such as Montage, CyberShake, Inspiral, and Sipht. We have considered the two essential parameters: total execution time and total execution cost while working in the cloud environment. The simulation results show that the proposed algorithm performs well compared to other state-of-the-art algorithms such as round-robin (RR), ant colony optimization (ACO), heterogeneous earliest time first (HEFT), and particle swarm optimization (PSO).

Author 1: Neeraj Arora

Author 2: Rohitash Kumar Banyal

Keywords: Cloud computing; hybrid algorithms; metaheuristic algorithms; optimization; workflow scheduling

Paper 79: Evaluating the Effect of Multiple Filters in Automatic Language Identification without Lexical Knowledge

Abstract: The classical language identification architecture would require a collection of languages independent text and speech information for training by the system before it can identify the languages correctly. This paper also address language identification framework however with data has been downsized considerably from general language identification architecture. The system goal is to identify the type of language being spoken to the system based on a series of trained speech with sound file features and without any language text data or lexical knowledge of the spoken language. The system is also expected to be able to be deployed in mobile platform in future. This paper is specifically about measuring the performance optimisation of audio filters on a CNN model integration for the language identification system. There are several metric to gauge the performance identification system for a classification problem. Precision, recall and F1 Scores is presented for the performance evaluation with different combination of filters together with CNN model as the framework of the language identification system. The goal is not to get the best filter for noise, instead to identify the filter that is a good fit to develop language model with environmental noise for a robust language identification system. Our experiments manage to identify the best combination of filters to increase the accuracy of language identification using short speech. This resulting us to modify our pre-processing phase in the overall language identification system.

Author 1: Guan-Lip Soon

Author 2: Nur-Hana Samsudin

Author 3: Dennis Lim

Keywords: Language identification; speech recognition; speech filters; minimal language data; minimal lexical information; optimal performance

Paper 80: Classification of Common and Uncommon Tones by P300 Feature Extraction and Identification of Accurate P300 Wave by Machine Learning Algorithms

Abstract: An event-related potential (ERP) is a measure of brain response to a specific sensory, cognitive, or motor event. One common ERP technique used in cognition research is the oddball paradigm where the brain’s response to common and uncommon stimuli is compared. The neurologic response to the oddball paradigm produces a P300 ERP which is one of the major visual/auditory sensory ERP components. The purpose of this study to classify ERP responses to common and uncommon tones by extracting the P300 feature from ERP epochs and identify the accurate shape of the P300 wave. For recording ERP data, and OpenBCI system is used. P300 features are extracted using EEGlab which is a mathematical tool of MATLAB. Finally, various types of machine learning models are used for identifying the accurate shape of a P300 wave and then classifying common and uncommon auditory tones. For stimuli classification, all of the algorithms evaluated performed efficiently and built a consistent model with 93.75% to 99.1% evaluation accuracy. Also, for P300 shape detection, NN model showed the best performance with 94.95% accuracy. These findings have the potential to add useful machine learning-based methods to the clinical application of ERPs.

Author 1: Rafia Akhter

Author 2: Kehinde Lawal

Author 3: Md. Tanvir Rahman

Author 4: Shamim Ahmed Mazumder

Keywords: Event Related Potential (ERP); classification; P300; machine-learning; oddball-paradigm

Paper 81: A Perception Centered Self-Driving System without HD Maps

Abstract: Building a fully autonomous self-driving system has been discussed for more than 20 years yet remains unsolved. Previous systems have limited ability to scale. Their localization subsystem needs labor-intensive map recording for running in a new area, and the accuracy decreases after the changes occur in the environment. In this paper, a new localization method is proposed to solve the scalability problems, with a new method for detecting and making sense of diverse traffic lines. Like the way human drives, a self-driving system should not rely on an exact position to travel in most scenarios. As a result, without HD Maps, GPS or IMU, the proposed localization subsystem relies only on detecting driving-related features around (like lane lines, stop lines, and merging lane lines). For spotting and reasoning all these features, a new line detector is proposed and tested against multiple datasets.

Author 1: Alan Sun

Keywords: Self-driving; lane lines detection; traffic lines detection; visual localization; HD Maps

Paper 82: Static vs. Dynamic Modelling of Acoustic Speech Features for Detection of Dementia

Abstract: Dementia is a chronic neurological disease that causes cognitive disabilities and significantly impacts daily ac-tivities of affected individuals. It is known that early detection of dementia can improve the quality of life of patients through a specialized care program. Recently, there has been a growing interest in speech-based screening of neurological diseases such as dementia. The focus is on continuous monitoring of changes in speech of dementia patients, aiming to identify the early onset of the disease which could facilitate development of preventative treatment care. In this work, we propose a dynamic (temporal) modeling of acoustic speech characteristics aiming at identifying the signs of dementia. The classification performance of the proposed framework is compared with a baseline static modeling of acoustic speech features. Experimental results show that the proposed dynamic approach outperforms the static method. It achieves the classification accuracy of 74.55% compared to 66.92% obtained using the static models.

Author 1: Muhammad Shehram Shah Syed

Author 2: Zafi Sherhan Syed

Author 3: Elena Pirogova

Author 4: Margaret Lech

Keywords: Dementia detection; speech classification; neural networks; recurrent neural networks

Paper 83: Multi-Objective Evolutionary Programming for Developing Recommender Systems based on Collaborative Filtering

Abstract: In the era of internet, several online platforms offer many items to users. Users could spend a lot of time to find (or not) some items they are interested, sometimes, they will probably not find the desired items. An effective strategy to overcome this problem is a recommender system, one of the most popular applications of machine learning. Recommender systems select most appropriate items to an specific user based on previous information between items and users, and they are developed using diffeent approaches. One of the most successful approach for developing recommender systems is collaborative filtering, which can filter out items that a user might like based on reactions of users with similar profiles. Often, traditional recommender systems only consider precision as evaluation metric of performance, however, others metrics (like recall, diversity, novelty, etc) are also important. Unfortunately, some metrics are conflicting, e.g., precision impacts negatively on other metrics. This paper presents a multi-objective evolutionary programming method for developing a recommender system, which is based on a new collaborative filtering technique, while maximizes the recall for a given precision, The new collaborative filtering technique uses three components for recommending an item to a user: 1) clustering of users; 2) a previous memory-based prediction; and 3) five decimal parameters (threshold average clustering, threshold penalty, threshold incentive, weight attached to average clustering and weight attached to Pearson correlation). The multiobjective evolutionary programming optimizes the clustering of users and the five decimal parameters, while, it searches maximizes both similarity precision and recall objectives. A comparison between the proposed method and a previous nonevolutionary method shows that the proposed method improves precision and recall metric on a benchmark database.

Author 1: Edward Hinojosa-Cardenas

Author 2: Edgar Sarmiento-Calisaya

Author 3: Cesar A. Martinez-Salinas

Author 4: Lehi Quincho-Mamani

Author 5: Jair F. Huaman-Canqui

Keywords: Collaborative filtering; clustering; evolutionary programming; multi-objective; recommender systems

Paper 84: Face Verification across Aging using Deep Learning with Histogram of Oriented Gradients

Abstract: One of the complex procedures which affect man’s face shape and texture is facial aging. These changes tend to deteriorate the efficacy of systems that automatically verify faces. It seems that CNN (also known as Convolutional Neural Networks) are thought to be one of the most common deep learning approaches where multiple layers are trained robustly while maintaining the minimum number of learned parameters to improve system performance. In this paper, a deeper model of convolutional neural network is fitted with Histogram of Oriented Gradients (HOG) descriptor to handle feature extraction and classification of two face images with the age gap is proposed. Furthermore, the model has been trained and tested in the MORPH and FG-NET datasets. Experiments on FG-NET achieve a state of the arts accuracy (reaching 100%) while results on MORPH dataset have significant improvements in accuracy of 99.85%.

Author 1: Areeg Mohammed Osman

Author 2: Serestina Viriri

Keywords: Facial aging; verify faces; Convolutional Neural Networks (CNN); Histogram of Oriented Gradients (HOG)

Paper 85: Multi-Target Energy Disaggregation using Convolutional Neural Networks

Abstract: Non-Intrusive Load Monitoring (NILM) has be-come popular for smart meters in recent years due to its low cost installation and maintenance. However, it requires efficient and robust machine learning models to disaggregate the respective electrical appliance energy from the mains. This study investigated NILM in the form of direct point-to-point multiple and single target regression models using convolutional neural networks. Two model architectures have been utilized and compared using five different metrics on two benchmarking datasets (ENERTALK and REDD). The experimental results showed that multi-target disaggregation setting is more complex than single-target disaggregation. For multi-target setting of ENERTALK dataset, the highest individual F1-score is 95.37%and the overall average F1-score is 75.00%. Better results were obtained for the multi-target setting of the other dataset with higher overall average F1-score of 83.32%. Additionally, the robustness and knowledge transfer capability of the models through cross-appliance and cross-domain disaggregation was demonstrated by training for a specific appliance on a specific data, and testing for a different appliance, house and dataset. The proposed models can also disaggregate simultaneous operating appliances with higher F1-scores.

Author 1: Mohammed Ayub

Author 2: El-Sayed M. El-Alfy

Keywords: Energy disaggregation; smart meters; load monitoring; ENERTALK dataset; multi-target disaggregation; multi-target regression; NILM knowledge transfer

Paper 86: Multi-Label Arabic Text Classification: An Overview

Abstract: There is a massive growth of text documents on the web. This led to the increasing need for methods that can organize and classify electronic documents (instances) automati-cally. Multi-label classification task is widely used in real-world problems and it has been applied on di˙erent applications. It assigns multiple labels for each document simultaneously. Few and insuÿcient research studies have investigated the multi-label text classification problem in the Arabic language. Therefore, this survey paper aims to present an extensive review of the existing multi-label classification methods and techniques that can deal with multi-label problem. Besides, we focus on Arabic language by covering the relevant applications of multi-label classification on the Arabic text, and identify the main challenges faced by these studies. Furthermore, this survey presents an experimental comparisons of di˙erent multi-label classification methods applied for the Arabic context and points out some baseline results. We found that further investigations are also needed to improve the multi-label classification task in the Arabic language, especially the hierarchical classification task.

Author 1: Nawal Aljedani

Author 2: Reem Alotaibi

Author 3: Mounira Taileb

Keywords: Machine learning; text classification; multi-label classification; Arabic natural language processing; hierarchical classification; Lexicon approach

Paper 87: Census Estimation using Histogram Representation of 3D Surfaces: A Case Study Focusing on the Karak Region

Abstract: National and regional infrastructure planning is founded on the use of many factors, of which population size can be argued to be the most fundamental. Population size is typically acquired through a census. However, manual census collection is an expensive and resource intensive process; especially in regions that are poorly connected. Computer-aided population estimation, when done accurately, therefore offers significant benefit. This paper presents a comprehensive framework for estimating the population size of a region of interest by applying classification techniques to terrain data. Central to the proposed framework is a novel histogram representation technique designed to support the generation of appropriate and effective classifiers central to the operation of the framework. The presented work uses the Karak region, in Jordan, as a case study for population size estimation. The proposed framework and the representation technique have been evaluated using a variety of classification mechanisms and parameter settings. The reported evaluation of the proposed representation technique demonstrates that good results can be obtained with regard to estimate the population size.

Author 1: Subhieh El-Salhi

Author 2: Safaa Al-Haj Saleh

Author 3: Frans Coenen

Keywords: Histogram representation; Geographic Information System (GIS); population estimation; 3D surface; satellite images; data mining; classification technique; Karak region

Paper 88: Novel Control Scheme for Prosthetic Hands through Spatial Understanding

Abstract: A novel control scheme for prosthetic hands through spatial understanding is proposed. The proposed control scheme features an imaging sensor and an inertial measurement unit (IMU) sensor, which makes prosthetic hands capable of visual and motion sensing. The imaging sensor captures the scene where the user is willing to grasp the object. The control system recognizes the target object, extracts its surface features and estimates its pose from the captured images. Then the spatial relationship is constructed between the hand and the target object. With the help of IMU sensors, the relationship can be tracked and kept wherever the prosthetic hand moves even the object is out of the view range of the camera. To interact with the user, this process is visualized using augmented reality (AR) technology. A test platform based on the proposed control scheme is developed and a case study is performed with the platform.

Author 1: Yunan He

Author 2: Osamu Fukuda

Author 3: Nobuhiko Yamaguchi

Author 4: Hiroshi Okumura

Author 5: Kohei Arai

Keywords: Prosthetic hand; vision-inertial fusion; pose estimation; motion tracking; internal measurement unit; augmented reality; control scheme; spatial features

Paper 89: Deep Acoustic Embeddings for Identifying Parkinsonian Speech

Abstract: Parkinson’s disease is a serious neurological impair-ment which adversely affects the quality of life in individuals. While there currently does not exist any cure for this disease, it is well known that early diagnosis can be used to improve the quality of life of affected individuals through various types of therapy. Speech based screening of Parkinson’s disease is an active area of research intending to offer a non-invasive and passive tool for clinicians to monitor changes in voice that arise due to Parkinson’s disease. Whereas traditional methods for speech based identification rely on domain-knowledge based hand-crafted features, in this paper, we investigate the efficacy of and propose the deep acoustic embeddings for identification of Parkinsonian speech. To this end, we conduct several experiments to benchmark deep acoustic embeddings against handcrafted features for differentiating between speech from individuals with Parkinson’s disease and those who are healthy. We report that deep acoustic embeddings consistently perform better than domain-knowledge features. We also report on the usefulness of decision-level fusion for improving the classification performance of a model trained on these embeddings.

Author 1: Zafi Sherhan Syed

Author 2: Sajjad Ali Memon

Author 3: Abdul Latif Memon

Keywords: Affective computing; deep acoustic embeddings; Parkinson’s disease; social signal processing