IJACSA Volume 6 Issue 11 - thesai.org

IJACSA Volume 6 Issue 11

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: Distance Prediction for Commercial Serial Crime Cases Using Time Delay Neural Networks

Abstract: The prediction of the next serial criminal time is important in the field of criminology for preventing the recurring actions of serial criminals. In the associated dynamic systems, one of the main sources of instability and poor performances is the time delay, which is commonly predicted based on nonlinear methods. The aim of this study is to introduce a dynamic neural network model by using nonlinear autoregressive time series with exogenous (external) input (NARX) and Back Propagation Through Time (BPTT), which is verified intensively with MATLAB to predict and model the crime times for the next distance of serial cases. Recurrent neural networks have been extensively used for modeling of nonlinear dynamic systems. There are different types of recurrent neural networks such as Time Delay Neural Networks (TDNN), layer recurrent networks, NARX, and BPTT. The NARX model for the two cases of input- output modeling of dynamic systems and time series prediction draw more attention. In this study, a comparison of two models of NARX and BPTT used for the prediction of the next serial criminal time illustrates that the NARX model exhibits better performance for the prediction of serial cases than the BPTT model. Our future work aims to improve the NARX model by combining objective functions.

Author 1: Anahita Ghazvini

Author 2: Siti Norul Huda Sheikh Abdullah

Author 3: Mohammed Ariff Abdullah

Author 4: Md Nawawi Junoh

Author 5: Zainal Abidin bin Kasim

Keywords: Criminology and Computational Criminology; Neural Network; modeling; NARX; BPTT; Quantum GIS

Paper 2: Using the Sub-Game Perfect Nash Equilibrium to Deduce the Effect of Government Subsidy on Consumption Rates and Prices

Abstract: Governments are interested in inducing positive habits and behaviors in its citizens and discouraging ones that are harmful to the individual or to the society. Taxation and legislation are usually used to discourage negative behaviors. Subsidy seems the politically correct way to encourage positive behaviors. In this paper, the Subgame Perfect Nash Equilibrium is used to deduce the effect of the government subsidy on the user consumption, prices and producer and distributor profits.

Author 1: Dr. Magdi Amer

Author 2: Dr. Ahmed Kattan

Keywords: game-theory; subsidy

Paper 3: SmartOrBAC

Abstract: The emergence of the Internet of Things (IoT) paradigm, provides a huge scope for more streamlined living through an increase of smart services but this coincides with an increase in security and privacy concerns, therefore access control has been an important factor in the development of IoT. This work proposes an authorization access model called SmartOrBAC built around a set of security and performance requirements. This model enhances the existing OrBAC (Organization-based Access Control) model and adapts it to IoT environments. SmartOrBAC separates the problem into different functional layers and then distributes processing costs between constrained devices and less constrained ones and at the same time addresses the collaborative aspect with a specific solution. This paper also presents the application of SmartOrBAC on a real example of IoT and gives a complexity study demonstrating that even though this model is extensive, it does not add additional complexity regarding traditional access control models.

Author 1: Imane BOUIJ-PASQUIER

Author 2: Anas ABOU EL KALAM

Author 3: Abdellah AIT OUAHMAN

Keywords: internet of things; security; privacy; access control model; authorization process

Paper 4: Implementation of Central Dogma Based Cryptographic Algorithm in Data Warehouse Architecture for Performance Enhancement

Abstract: Data warehouse is a set of integrated databases deliberated to expand decision-making and problem solving, espousing exceedingly condensed data. Data warehouse happens to be progressively more accepted theme for contemporary researchers with respect to contemporary inclination towards industry and executive purview. The crucial tip of the proposed work is integrated on delivering an enhanced and an exclusive innovative model based on the intention of enhancing security measures, which at times have been found wanting and also ensuring improved accessibility using Hashing modus operandi. An unsullied algorithm was engendered using the concept of protein synthesis, prevalently studied in Genetics, that is, in the field of Biotechnology, wherein three steps are observed, namely; DNA Replication, Translation and Transcription. In the proposed algorithm, the two latter steps, that is, Translation and Transcription have been taken into account and the concept have been used for competent encryption and proficient decryption of data. Central Dogma Model is the name of the explicit model that accounts for and elucidates the course of action for Protein Synthesis using the Codons which compose the RNA and the DNA and are implicated in numerous bio–chemical processes in living organisms. It could be observed that subsequently a dual stratum of encryption and decryption mechanism has been employed for optimal security. The formulation of the immaculate Hashing modus operandi ensure that there would be considerable diminution of access time, keeping in mind the apt retrieval of all indispensable data from the data vaults. The pertinent appliance of the proposed model with enhanced security might be in its significant service in a variety of organizations where accrual of protected data is of extreme magnitude. The variety of organizations might include educational organizations, corporate houses, medical establishments, private establishments and so on and so forth.

Author 1: Rajdeep Chowdhury

Author 2: Soupayan Datta

Author 3: Saswata Dasgupta

Author 4: Mallika De

Keywords: Data Warehouse; Central Dogma; Replication; Translation; Transcription; Codon; Data Mart; Hashing

Paper 5: Embed Attitude from Student on Elearning Using Instructional Design with ADDIE Model

Abstract: Attitude is very important in an education, without a good attitude certainly education will not be able to run smoothly, even education can be said to fail if the output of the education did not have a good attitude in the community in the workplace. To determine the value of the attitude in elearning is not easy. In this study will try to create a method or means that can be used to determine the value of the attitude of a student in the learning system elearning. The method to be used is instructional design using ADDIE Model, where the latter begins by determining the parameters to be assessed from that attitude, the parameters used are each - each part of Affective Learning. After determining the parameters are then carried out the design and manufacture of questioner, before this questioner deployed then ever before will be testing the validity and reliability using SPSS. If questioner has valid and reliabl, then the next can be done questioner deployment and then be evaluated. Questioner from spreading to some of the students showed that students that the attitude of the students already Very Good with a total student getting very good value are 96 people with a percentage of 48%.

Author 1: Ni Putu Linda Santiari

Keywords: Instructional Design; eLearning; Affective Learning; Reliability; Validity

Paper 6: Enhancing Performance of GIS on Cloud Computing

Abstract: Cloud computing provides a way of determining dynamically scalable and virtualized resources as a service over the Internet. GIS is a technology, which could use Cloud Computing for distributed parallel processing of a large set of data, store and share the results with users around the world. GIS is beneficial and works well when it be available to everyone, everywhere, anytime and with downcast fee of minimal sized in terms of technology and outlay. Cloud Computing used to portray and help users to use GIS applications in an easy way. This paper will study some example of a data structure like a K-d tree and Quad trees of GIS application and compare between them when storing these data structures on Cloud computing, the paper also portrays the results of the study of data structure on cloud computing platforms to retrieve data from cloud computing. The paper provides an application for “finding neighborhood from existing data stored.

Author 1: Ahmed Teaima A. T

Author 2: Hesham Ahmed Hefny. H. F. H

Author 3: BahaaShabana B.S

Keywords: Cloud Computing; GIS; Kd-tree; Quadtree

Paper 7: A Survey of Quality Prediction of Product Reviews

Abstract: With the help of Web-2.0, the Internet offers a vast amount of reviews on many topics and in different domains. This has led to an explosive growth of product reviews and customer feedback, which presents the problem of how to handle the abundant volume of data. It is an expensive and time-consuming task to analyze this huge content of opinions. Therefore, the need for automated sentiment analysis systems is vital. However, these systems encounter many challenges; assessing the content quality of the posted opinions is an important area of study that is related to sentiment analysis. Currently, review helpfulness is assessed manually; however the task of automatically assessing it has gained more attention in recent years. This paper provides a survey of approaches to the challenge of identifying the content quality of product reviews.

Author 1: H.Almagrabi

Author 2: A. Malibari

Author 3: J. McNaught

Keywords: sentiment analysis; product reviews; content analysis; helpfulness detection

Paper 8: Medical Image De-Noising Schemes Using Different Wavelet Threshold Techniques

Abstract: In recent years most of researcher’s has done tremendous work in the field of medical image applications such as Magnetic Resonance Imaging (MRI), Ultra Sound, CT scan but still there are many research and experiments in medical imaging field and diagnosing of human health by Health Care Institutes. There is a growing interest for medical imaging de-noising as a hot area of research and also imaging equipment as a device. It is used for better image processing and highlighting the important features. These images are affected with random noise during acquisition, analyzing and Transmission process. This results in blurry image visible in low contrast. Wavelet transforms have effective method to separate the noise from the original medical image by using threshold techniques without affecting the important data of an image. Wavelet transform enables us to use the forward wavelet transform to represent sub-band of the original image in decomposition process then reconstructing this sub band coefficients to original image using inverse wavelet transform. In this work, the quality of medical image has been evaluated using filter assessment parameters like Variance, standard deviation, the squared difference error between original medical image & de-noised image (MSE) and the ratio between original image & noisy image. From numerical results, we can see that the algorithm is efficient de-noising of noisy medical image. When, investigating with Baye’s threshold techniques it achieved the Best value of peak signal to noise ratio (PSNR). For best medical image de-noising, the wavelet based de-noising algorithm has been investigated and results of Baye’s techniques and hard & soft threshold methods have been compared.

Author 1: Nadir Mustafa

Author 2: Saeed Ahmed Khan

Author 3: Jiang Ping Li

Author 4: Mohamed Tag Elsir

Keywords: Baye’s Wavelet threshold; Discrete Wavelet; Medical Image De-noising; Magnetic Resonance Imaging (MRI)

Paper 9: Handwriting Word Recognition Based on SVM Classifier

Abstract: this paper proposed a new architecture for handwriting word recognition system Based on Support Vector Machine SVM Classifier. The proposed work depends on the handwriting word level, and it does not need for character segmentation stage. An Arabic handwriting dataset AHDB, dataset used for train and test the proposed system. Besides, the system achieved the best recognition accuracy 96.317% based on several feature extraction methods and SVM classifier. Experimental results show that the polynomial kernel of SVM is convergent and more accurate for recognition than other SVM kernels.

Author 1: Mustafa S. Kadhm

Author 2: Asst. Prof. Dr. Alia Karim Abdul Hassan

Keywords: Arabic Text; Preprocessing; Feature Extraction; SVM

Paper 10: Creating a Knowledge Database for Lectures of Faculty Members, Proposed E-Module for Isra University

Abstract: Higher education in Jordan is currently expanding as new universities open and compete for offering the best learning experience. Many universities face accreditation challenges, hence, they attend to recruit lecturers who may not have a solid teaching experience. Experienced lecturers tend to have high turnover rate, which cause knowledge loss. To prevent such loss, this research presents a knowledge repository framework. This framework will serve as a reference and a vessel of knowledge that builds and develops the educational and teaching capacities of professors/lecturers. It can also be seen as part of the electronic learning system, which brings benefits to students and enables them to retrieve any lectures they need. The main question we aim to answer is whether a knowledge memory can be designed and created to contribute in supporting the educational and teaching capacities of university lecturers. In order to answer this question, this research creates an electronic knowledge database to store explicit knowledge taken from lectures (written, audio and visual). These lectures are prepared and circulated or presented by university professors/lecturers throughout all university colleges and departments. This knowledge database resembles a cognitive memory that grows and develops with time.

Author 1: Dr. Amaal Al-Amawi

Author 2: Dr. Salwa Alsmarai

Author 3: Dr. Manar Maraqa

Keywords: Knowledge; knowledge database; electronic knowledge database; Knowledge sharing

Paper 11: A Comparative Study of Relational and Non-Relational Database Models in a Web- Based Application

Abstract: The purpose of this paper is to present a comparative study between relational and non-relational database models in a web-based application, by executing various operations on both relational and on non-relational databases thus highlighting the results obtained during performance comparison tests. The study was based on the implementation of a web-based application for population records. For the non-relational database, we used MongoDB and for the relational database, we used MSSQL 2014. We will also present the advantages of using a non-relational database compared to a relational database integrated in a web-based application, which needs to manipulate a big amount of data.

Author 1: Cornelia Gyorödi

Author 2: Robert Gyorödi

Author 3: Roxana Sotoc

Keywords: MongoDB; MSSQL; NoSQL; non-relational database

Paper 12: Role of Secondary Attributes to Boost the Prediction Accuracy of Students’ Employability Via Data Mining

Abstract: Data Mining is best-known for its analytical and prediction capabilities. It is used in several areas such as fraud detection, predicting client behavior, money market behavior, bankruptcy prediction. It can also help in establishing an educational ecosystem, which discovers useful knowledge, and assist educators to take proactive decisions to boost student performance and employability. This paper presents an empirical study that compares varied classification algorithms on two datasets of MCA (Masters in Computer Applications) students collected from various affiliated colleges of a reputed state university in India. One dataset includes only primary attributes, whereas other dataset is feeded with secondary psychometric attributes in it. The results showcase that solely primary academic attributes don’t lead to smart prediction accuracy of students’ employability, once they square measure within the initial year of their education. The study analyzes and stresses the role of secondary psychometric attributes for better prediction accuracy and analysis of students’ performance. Timely prediction and analysis of students’ performance can help Management, Teachers and Students to work on their gray areas for better results and employment opportunities.

Author 1: Pooja Thakar

Author 2: Prof. Dr. Anil Mehta

Author 3: Dr. Manisha

Keywords: Data Mining; Education; Prediction; Psychometric; Educational Data Mining

Paper 13: Image Processing Based Customized Image Editor and Gesture Controlled Embedded Robot Coupled with Voice Control Features

Abstract: In modern sciences and technologies, images gain much broader scopes due to the ever growing importance of scientific visualization (of often large-scale complex scientific/experimental data) like microarray data in genetic research, or real-time multi-asset portfolio trading in finance etc. In this paper, a proposal has been presented to implement a Graphical User Interface (GUI) consisting of various MATLAB functions related to image processing and using the same to create a basic image processing editor having different features like, viewing the red, green and blue components of a color image separately, color detection and various other features like noise addition and removal, edge detection, cropping, resizing, rotation, histogram adjust, brightness control that is used in a basic image editor along with object detection and tracking. This has been further extended to provide reliable and a more natural technique for the user to navigate a robot in the natural environment using gestures based on color tracking. Additionally, Voice control technique has been employed to navigate the robot in various directions in the Cartesian plane employing normal Speech recognition techniques available in Microsoft Visual Basic.

Author 1: Somnath Kar

Author 2: Ankit Jana

Author 3: Debarshi Chatterjee

Author 4: Dipayan Mitra

Author 5: Soumit Banerjee

Author 6: Debasish Kundu

Author 7: Sudipta Ghosh

Author 8: Sauvik Das Gupta

Keywords: Image Processing; Image Editor; Gesture Control; Embedded Robot; Voice Control

Paper 14: Gesture Recognition Based on Human Grasping Activities Using PCA-BMU

Abstract: This research study presents the recognition of fingers grasps for various grasping styles of daily living. In general, the posture of the human hand determines the fingers that are used to create contact between an object at the same time while developing the touching contact. Human grasping can detect by studying the movement of fingers while bending during object holding. Ten right-handed subjects are participated in the experiment; each subject was fitted with a right-handed GloveMAP, which recorded all movement of the thumb, index, and middle of human fingers while grasping selected objects. GloveMAP is constructed using flexible bend sensors placed back of a glove. Based on the grasp human taxonomy by Cutkosky, the object grasping is distinguished by two dominant prehensile postures; that is, the power grip and the precision grip. The dataset signal is extracted using GloveMAP, and all the signals are filtered using Gaussian filtering method. The method is capable to improving the amplitude transmission characteristic with the minimal combination of time and amplitude response. The result was no overshoot in order to smoothen the grasping signal from unneeded signal (noise) that occurs on the input / original grasping data. Principal Component Analysis – Best Matching Unit (PCA-BMU) is a process of justifying the human grasping data involves several grasping groups and forming a component identified as nodes or neuron.

Author 1: Nazrul H. ADNAN

Author 2: Mahzan T.

Keywords: recognition; grasp; grasp taxonomy; human finger; dimensionality reduction

Paper 15: Robust Convolutional Neural Networks for Image Recognition

Abstract: Recently image recognition becomes vital task using several methods. One of the most interesting used methods is using Convolutional Neural Network (CNN). It is widely used for this purpose. However, since there are some tasks that have small features that are considered an essential part of a task, then classification using CNN is not efficient because most of those features diminish before reaching the final stage of classification. In this work, analyzing and exploring essential parameters that can influence model performance. Furthermore different elegant prior contemporary models are recruited to introduce new leveraging model. Finally, a new CNN architecture is proposed which achieves state-of-the-art classification results on the different challenge benchmarks. The experimented are conducted on MNIST, CIFAR-10, and CIFAR-100 datasets. Experimental results showed that the results outperform and achieve superior results comparing to the most contemporary approaches.

Author 1: Hayder M. Albeahdili

Author 2: Haider A. Alwzwazy

Author 3: Naz E. Islam

Keywords: Convolutional Neural Network; Image recognition; Multiscale input images

Paper 16: L-Bit to M-Bit Code Mapping

Abstract: We investigate codes that map L bits to m bits to achieve a set of codewords which contain no consecutive n “0”s. Such codes are desirable in the design of line codes which, in the absence of clock information in data, provide reasonable clock recovery due to sufficient state changes. Two problems are tackled- (i) we derive n_min for a fixed L and m and (ii) determine m_min for a fixed L and n. Results benefit telecommunication applications where clock synchronization of received data needs to be done with minimum overhead.

Author 1: Ruixing Li

Author 2: Shahram Latifi

Author 3: Yun Lun

Author 4: Ming Lun

Keywords: overhead; mapping; synchronization; consecutive “0”

Paper 17: A Structural Equation Model (SEM) of Governing Factors Influencing the Implementation of T-Government

Abstract: Governments around the world have invested significant sums of money on Information and Communication Technology (ICT) to improve the efficiency and effectiveness of services been provided to their citizens. However, they have not achieved the desired results because of the lack of interoperability between different government entities. Therefore, many governments have started shifting away from the original concept of e-Government towards a much more transformational approach that encompasses the entire relationship between different government departments and users of public services, which can be termed as transformational government (t- Government). In this paper, a model is proposed for governing factors that impact the implementation of t-Government such as strategy, leadership, stakeholders, citizen centricity and funding in the context of Saudi Arabia. Five constructs are hypothesised to be related to the implementation of t-Government. To clarify the relationships among these constructs, a structural equation model (SEM) is utilised to examine the model fit with the five hypotheses. The results show that there are positive and significant relationships among the constructs such as the relationships between strategy and t-Government; the relationships between stakeholders and t-Government; the relationships between leadership and t-Government. This study also showed an insignificant relationship between citizens’ centricity and t-Government and also an insignificant relationship between funding and t-Government. document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet.

Author 1: Sameer Alshetewi

Author 2: Dr Robert Goodwin

Author 3: Faten Karim

Author 4: Dr Denise de Vries

Keywords: t-Government; e-Government; Strategy; Stakeholders; Citizens’ Centricity; Funding

Paper 18: User Interface Design of E-Learning System for Functionally Illiterate People

Abstract: Among different type of illiterate people, the print illiterates suffer most from getting crucial information passed around the society. Many print illiterate people are found in the developing countries and in many cases they live in the remote areas working as farmers. These people are deprived of the knowledge generated from the latest scientific researches. This research makes some recommendations related to developing user interface especially suitable for the print illiterate people. In this regard, a user interface is developed based on the recommendations from the previous researchers. The authors find the recommendations insufficient and develop another user interface based on the improvements proposed by the authors. Later both the user interfaces are tested by two different groups of print illiterate people in a remote village in Bangladesh. The test data shows that the proposed improvement contributes significantly to make the user interface more usable to the target population. 13 out of 15 users could complete the assigned task successfully using improved user interface. Whereas only 8 out of 14 users could do the same with the other user interface. Among the successful users, the improved user interface took 26% less time than that of the other user interface. Finally some recommendations to develop user interface for the functionally illiterate people are made based on the results and observations of this research.

Author 1: Asifur Rahman

Author 2: Akira Fukuda

Keywords: User Interface; Illiterate people; e-Learning

Paper 19: Acoustic Emotion Recognition Using Linear and Nonlinear Cepstral Coefficients

Abstract: Recognizing human emotions through vocal channel has gained increased attention recently. In this paper, we study how used features, and classifiers impact recognition accuracy of emotions present in speech. Four emotional states are considered for classification of emotions from speech in this work. For this aim, features are extracted from audio characteristics of emotional speech using Linear Frequency Cepstral Coefficients (LFCC) and Mel-Frequency Cepstral Coefficients (MFCC). Further, these features are classified using Hidden Markov Model (HMM) and Support Vector Machine (SVM).

Author 1: Farah Chenchah

Author 2: Zied Lachiri

Keywords: Mel Frequency Cepstral Coefficients (MFCC); Linear Frequency Cepstral Coefficients (LFCC);Hidden Markov Model (HMM); Support Vector Machine (SVM); emotion recognition

Paper 20: Proactive Software Engineering Approach to Ensure Rapid Software Development and Scalable Production with Limited Resources

Abstract: Nowadays, the need for building scalable systems in narrow time window is needed. While the efforts and accuracy usually required for building high scale systems is not simple, the agile nature of system requirements spawn a need for enhancing some software engineering practices. These practices should be integrated together in order to help software (SW) development teams to build, and test scalable systems rapidly with a high confidence level in their scalability. This research explains the proposed Proactive Approach, which presents a set of software engineering practices that could help in producing scalable system while minimizing the wasted time within the production cycle. This set of practices have been validated, verified and tested through building 46 releases of one of the most important, mission critical and scalable systems. Applying these practices succeeded to enhance average response time of web pages by %1921.5, test code churn by more than % 5000, time to release by % 300, and succeeded to produce a system that could stand against 95375 users with % 99.921 scalability ratio.

Author 1: A. B. Farid

Keywords: Software Engineering; Load Testing; Test Analysis; ISO 29119; Continuous Integration; Static Analysis; Stress Analysis; Application Scalability; Building High Scalability System; Build Verification Test; Software Configuration Management; Version Control;

Paper 21: Protein Sequence Matching Using Parametric Spectral Estimate Scheme

Abstract: Putative protein sequences decoded from the messenger ribonucleic acid (mRNA) sequences are composed of twenty amino acids with different physical-chemical properties, such as hydrophobicity and hydrophilicity (uncharged, positively charged or negatively charged amino acids). In this paper, the power spectral estimate (PSE) technique for random processes is applied to the protein sequence matching framework. First, the twenty kinds of amino acids are classified based on their hydrophobicity and hydrophilicity. Then each amino acid in the protein sequence is mapped to a corresponding complex value. Consider the various Hidden Markov chain orders in the complex valued sequences. The PSE method can explore the implicit statistical relations among protein sequences. The mean squared error between the power spectra of two sequences is determined and then used to measure their similarity. The experimental results verify that the proposed PSE method provides the consistent similarity measurement with the well-known ClustalW and BLASTp schemes. Moreover, the proposed PSE can show better similarity relevance than ClustalW and BLASTp schemes.

Author 1: Hsuan-Ting Chang

Author 2: Hsiao-Wei Peng

Author 3: Ciing-He Li

Author 4: Neng-Wen Lo

Keywords: protein sequence; amino acids; digital signal processing; parametric spectral estimate; hydrophilicity; hydrophobicity; Markov chain

Paper 22: Testing the Use of the Integrated Model in Designing the Management Information Systems by Using the Mathematical Probability Theories

Abstract: The integrated model is a new model that is recently developed to decrease from the classical approach weaknesses and problems in building the management information systems (MIS’s) that are used to solve the management problems in the practical life. The use of this integrated model needs to be tested, to prove how efficiently and successfully the model works. To achieving this objective, this paper uses the mathematical probability theories to implement an internal test of the integrated model work before using it in the practical life. The paper uses the qualitative research method in its methodologies.

Author 1: Mohammad M M Abu Omar

Author 2: Khairul Anuar Abdullah

Keywords: Integrated Model; Management Information System; MIS; Classical Approach; Information System Life Cycle; ISLC; Simple Random Sampling; Probability Theory

Paper 23: An Embedded Modbus Compliant Interactive Operator Interface for a Variable Frequency Drive Using Rs 485

Abstract: The paper proposes the architecture and software design of a Modbus Compliant Operator Interface Panel (MCOIP) for a high speed Variable Frequency Drive (VFD) – a state of the art embedded design that offers several key advantages over the existing proprietary industrial models in use today. The use of serial Modbus RTU communication over RS485 allows an economically feasible, open source, vendor neutral, feature laden, robust and safe operating model. Through the use of an ARM based RISC microcontroller, the low response time of the design makes the human machine interface more real-time and interactive.

Author 1: Adnan Shaout

Author 2: Khurram Abbas

Keywords: Modbus RTU; Variable Frequency Drive Operator Panel; Modbus Master VFD

Paper 24: A Sleep Monitoring System with Sleep-Promoting Functions in Noise Detection and Sound Generation

Abstract: Recently, there has been a growing demand and interest in developing sleep-promoting systems for improving sleep condition. Because sleep environments are various, and sensitivity to noise differs individually, it is difficult for current sleep-promoting systems to provide an adoptable solution. This paper develops a non-invasive sleep monitoring system with adaptive sleep-promoting sound according to sleep environments and sleep habits. For people who fall asleep in a quiet environment, a constant sound playing probably affects their sleep. The proposal is designed to distinguish the noise disturbances, and a sleep-promoting sound is triggered automatically. A device with multiple sensors: an infrared depth sensor, a RGB camera, and a four-microphone array, is used to detect sleep disturbances. When a noise is detected, an ambient sound is playing to cover the noise automatically. Besides, it also applies to people who are used to sleep with sound by providing additional sound playing from the beginning of their sleep. Moreover, from the input of depth signals and color images, the scores are calculated from the sleep information, and are record for sleep quality evaluation. An overnight experiment was carried out, and the results show the efficiency of the proposed system in diverse sleep environments. The adaptable method is feasible for individuals, and it is also convenient and cost-effective to be used in home context.

Author 1: Lyn Chao-ling Chen

Author 2: Kuan-Wen Chen

Keywords: ambient sound; image sequence analysis; noise detection; non-invasive sleep monitoring; sleep promotion

Paper 25: Impact of Heterogeneous Deployment on Source Initiated Reactive Approach

Abstract: Selection of an optimal number of high energy level nodes and the most appropriate heterogeneity level is a prerequisite in the heterogeneous deployment of wireless sensor network, and it serves several purposes like enhanced network lifetime, finest energy consumption, and optimal sensing coverage. The paper presents the mathematical modeling of cost, energy and sensing range analysis of 2-level, 3-level, and n-level heterogeneous wireless sensor network. An experimental investigation has been carried out to investigate the effect of heterogeneity on a proposed Energy Efficient Source Initiated Reactive Algorithm. Studies on these aspects have been done to find the limitations of the algorithm for homogeneous networks and to find how it enriches sensing range and network lifetime. Based on the simulated experimental and numerical results, a mathematical model is presented to calculate the optimal number of high-level nodes which can simultaneously enhance network lifetime and achieve optimal sensing coverage. The results are compared with the homogeneous network to prove the effectiveness of the stated approach and proposed a model.

Author 1: Nonita Sharma

Author 2: Ajay K Sharma

Author 3: Kumar Shashvat

Keywords: Wireless sensor networks (WSNs); Cost Analysis Model; Energy Analysis Model; Sensing Range Model; Optimality

Paper 26: Case Based Reasoning: Case Representation Methodologies

Abstract: Case Based Reasoning (CBR) is an important technique in artificial intelligence, which has been applied to various kinds of problems in a wide range of domains. Selecting case representation formalism is critical for the proper operation of the overall CBR system. In this paper, we survey and evaluate all of the existing case representation methodologies. Moreover, the case retrieval and future challenges for effective CBR are explained. Case representation methods are grouped in to knowledge-intensive approaches and traditional approaches. The first group overweight the second one. The first methods depend on ontology and enhance all CBR processes including case representation, retrieval, storage, and adaptation. By using a proposed set of qualitative metrics, the existing methods based on ontology for case representation are studied and evaluated in details. All these systems have limitations. No approach exceeds 53% of the specified metrics. The results of the survey explain the current limitations of CBR systems. It shows that ontology usage in case representation needs improvements to achieve semantic representation and semantic retrieval in CBR system.

Author 1: Shaker H. El-Sappagh

Author 2: Mohammed Elmogy

Keywords: Case based reasoning; Ontological case representation; Case retrieval; Clinical decision support system; Knowledge management

Paper 27: Arabic Alphabet and Numbers Sign Language Recognition

Abstract: This paper introduces an Arabic Alphabet and Numbers Sign Language Recognition (ArANSLR). It facilitates the communication between the deaf and normal people by recognizing the alphabet and numbers signs of Arabic sign language to text or speech. To achieve this target, the system able to visually recognize gestures from hand image input. The proposed algorithm uses hand geometry and the different shape of a hand in each sign for classifying letters shape by using Hidden Markov Model (HMM). Experiments on real-world datasets showed that the proposed algorithm for Arabic alphabet and numbers sign language recognition is suitability and reliability compared with other competitive algorithms. The experiment results show that the increasing of the gesture recognition rate depends on the increasing of the number of zones by dividing the rectangle surrounding the hand.

Author 1: Mahmoud Zaki Abdo

Author 2: Alaa Mahmoud Hamdy

Author 3: Sameh Abd El-Rahman Salem

Author 4: Elsayed Mostafa Saad

Keywords: hand gestures; hand geometry; Sign language recognition; image analysis; and HMM

Paper 28: Comparative Study Between METEOR and BLEU Methods of MT: Arabic into English Translation as a Case Study

Abstract: The Internet provides its users with a variety of services, and these services include free online machine translators, which translate free of charge between many of the world's languages such as Arabic, English, Chinese, German, Spanish, French, Russian, etc. Machine translators facilitate the transfer of information between different languages, thus eliminating the language barrier, since the amount of information and knowledge available varies from one language to another, Arabic content on the internet, for example, accounts 1% of the total internet content, while Arabs constitute 5% of the population of the earth, which means that the intellectual productivity of the Arabs is low because within internet use Internet's Arabic content represents 20% of their natural proportion, which in turn encouraged some Arab parties to improve Arabic content within the internet. So, many of those interested specialists rely on machine translators to bridge the knowledge gap between the information available in the Arabic language and those in other living languages such as English. This empirical study aims to identify the best Arabic to English Machine translation system, in order to help the developers of these systems to enhance the effectiveness of these systems. Furthermore, such studies help the users to choose the best. This study involves the construction of a system for Automatic Machine Translation Evaluation System of the Arabic language into language. This study includes assessing the accuracy of the translation by the two known machine translators, Google Translate, and the second, which bears the name of Babylon machine translation from Arabic into English. BLEU and METEOR methods are used the MT quality, and to identify the closer method to human judgments. The authors conclude that BLEU is closer to human judgments METEOR method.

Author 1: Laith S. Hadla

Author 2: Taghreed M. Hailat

Author 3: Mohammed N. Al-Kabi

Keywords: component; Machine Translation; Arabic-English Corpus; Google Translator; Babylon Translator; METEOR; BLEU

Paper 29: Analysis of Medical Domain Using CMARM: Confabulation Mapreduce Association Rule Mining Algorithm for Frequent and Rare Itemsets

Abstract: In Human Life span, disease is a major cause of illness and death in the modern society. There are various factors that are responsible for diseases like work environment, living and working conditions, agriculture and food production, housing, unemployment, individual life style etc. The early diagnosis of any disease that frequently and rarely occurs with the growing age can be helpful in curing the disease completely or to some extent. The long-term prognosis of patient records might be useful to find out the causes that are responsible for particular diseases. Therefore, human being can take early preventive measures to minimize the risk of diseases that may supervene with the growing age and hence increase the life expectancy chances. In this paper, a new CMARM: Confabulation-MapReduce based association rule mining algorithm is proposed for the analysis of medical data repository for both rare and frequent itemsets using an iterative MapReduce based framework inspired by cogency. Cogency is the probability of the assumed facts being true if the conclusion is true, means it is based on pairwise item conditional probability, so the proposed algorithm mine association rules by only one pass through the file. The proposed algorithm is also valuable for dealing with infrequent items due to its cogency inspired approach.

Author 1: Dr. Jyoti Gautam

Author 2: Neha Srivastava

Keywords: association rule mining; cogency; confabulation theory; medical data mining

Paper 30: On Attack-Relevant Ranking of Network Features

Abstract: An Intrusion Detection System (IDS) is an important component of the defense-in-depth security mechanism in any computer network system. For assuring timely detection of intrusions from millions of connection records, it is important to reduce the number of connection features examined by the IDS, using feature selection or feature reduction techniques. In this scope, this paper presents the first application of a distinctive feature selection method based on neural networks to the problem of intrusion detection, in order to determine the most relevant network features, which is an important step towards constructing a lightweight anomaly-based intrusion detection system. The same procedure is used for feature selection and for attack detection, which gives more consistency to the method. We apply this method to a case study, on KDD dataset and show its advantages compared to some existing feature selection approaches. We then measure its dependence to the network architecture and the learning database.

Author 1: Adel Ammar

Author 2: Khaled Al-Shalfan

Keywords: Intrusion detection; network security; feature selection; KDD dataset; neural networks

Paper 31: Generating Representative Sets and Summaries for Large Collection of Images Using Image Cropping Techniques and Result Comparison

Abstract: The collection of photos hosted on photo archives and social networking sites has been increasing exponentially. It is really hard to get the summary of a large image set without browsing through the entire collection. In this paper two different techniques of image cropping (random windows technique and sequential windows technique) have been proposed to generate effective representative sets. A ranking mechanism has been also proposed for finding the best representative set.

Author 1: Abdullah Al-Mamun

Author 2: Dhaval Gandhi

Author 3: Sheak Rashed Haider Noori

Keywords: summarization; representative set; image collection; diversity; coverage

Paper 32: Smart City Architecture: Vision and Challenges

Abstract: The concept of smart city was born to provide improved quality of life to citizens. The key idea is to integrate information system services of each domain, such as health, education, transportation, power grid etc., of the city to provide public services to citizens efficiently and ubiquitously. These expectations induce massive challenges and requirements. This research is aimed to highlight key ICT (Information and Communication Technology) challenges related to adaptation of smart city. Realizing the significance of effective data collection, storage, retrieval, and efficient network resource provisioning, the research proposes a high level architecture for smart city. The proposed framework is based on a hierarchical model of data storage and defines how different stakeholders will be communicating and offering services to citizens. The architecture facilitates step by step implementation towards a smart city, integrating services, as they are developed in a timely manner.

Author 1: Narmeen Zakaria Bawany

Author 2: Jawwad A. Shamsi

Keywords: Smart city; Data management; urban technology; socio-technical systems; smart city architecture

Paper 33: Video Summarization: Survey on Event Detection and Summarization in Soccer Videos

Abstract: In today's world, the rapid development of digital video and editing technology has led to fast growing of video data, creating the need for effective and advanced techniques for analysis and video retrieval, as multimedia repositories have made browsing, delivery of contents (video) and video retrieval very slow. Hence, video summarization proposes various ways for faster browsing among a large amount of data and also for content indexing. Many people spend their free time to watch or play different sports like soccer, cricket, etc. but it is not possible to watch each and every game due to the longer timing of the game. In such cases, the users may just want to view the summary of the video that is just an abstract of the original video, instead of watching the whole video that provides more information about the occurrence of various incidents in the video. It is preferable to watch just highlights of the game or just review/trailer of a movie. Apparently, summarizing a video is an important process. In this paper, video summarization approaches are discussed, that can generate static or dynamic summaries. We present different techniques for each mode in literature. We have discussed some features used for generating video summaries. As soccer is the world’s most famous game played and watched, it is taken as a case study. Research done in this domain is discussed. We conclude that there is a broad perspective for further research in this field.

Author 1: Yasmin S. Khan

Author 2: Soudamini Pawar

Keywords: Summarization; Sports Summarization; Soccer

Paper 34: Approximation Algorithms for Scheduling with Rejection on Two Unrelated Parallel Machines

Abstract: In this paper, we study the scheduling problem with rejection on two unrelated parallel machines. We may choose to reject some jobs, thus incurring the corresponding penalty. The goal is to minimize the makespan plus the sum of the penalties of the rejected jobs. We first formulate this scheduling problem into an integer program, then relax it into a linear program. From the optimal solution to the linear program, we obtain the two algorithms using the technique of linear programming rounding. In conclusion, we present a deterministic 3-approximation algorithm and a randomized 3-approximation algorithm for this problem.

Author 1: Feng Lin

Author 2: Xianzhao Zhang

Author 3: Zengxia Cai

Keywords: Scheduling; Rejection; Approximation algorithm; Linear programming; Rounding

Paper 35: Wireless Sensor Networks for Road Traffic Monitoring

Abstract: Wireless Sensor Networks (WSNs) consist of large number of sensor nodes. Each node is empowered by a com-munication interface that is mainly characterized by low power, short transmission distance and minimal data rate such as the maximum data rate in ZigBee technology is 256 kbps, while approximately the physical transmission range between 10 to 20 meters. Currently, WSN Technology is being distributed over a large roadway of areas, in order to monitor traffic and environmental data. This approach allows several Intelligent Transport Systems (ITSs) applications to exploit the primary collected data in order to generate intelligent decisions based on earlier valuable selected information. Therefore, in this work we present a MAC protocol that is suitable for WSN where its nodes are assigned to a linear topology. The investigated protocol is realized by adapting an already existing Jennic MAC protocol. We demonstrate the validity of the MAC by building a complete end-to-end road traffic monitoring system using 4 Jennic nodes deployed in an indoor environment with the aim to prove the MAC potential in meeting the expectations of ITS applications. It is appropriate to mention that the proposed implementation just considers stationary WSN nodes.

Author 1: Kahtan Aziz

Author 2: Saed Tarapiah

Author 3: Mohanad Alsaedi

Author 4: Salah Haj Ismail

Author 5: Shadi Atalla

Keywords: Wireless Sensor Networks(WSN); Linear Topology; Road Monitoring; Jennic MAC

Paper 36: AL-S2m: Soft road traffic Signs map for vehicular systems

Abstract: In this paper, we describe AL-S2m, a roadmap with traffic signs to be used in vehicular systems. AL-S2m is part of a more general system of traffic signs (TSs) management, called AL-S2, which includes two sides: central map server and client vehicular system. The server allows establishing, maintaining and disseminating AL-S2m. The client localizes the vehicle in AL-S2m and detects TSs. In this paper, we focus on the AL-S2m establishment. AL-S2m can handle variable TSs and its update is easy, which keeps it coherent with the reality. Also, it improves the map-matching algorithm. We implemented AL-S2m easily using an Android device.

Author 1: Ammar LAHLOUHI

Keywords: Road Traffic Signs, Roadmap, Map-Matching, Driver Assistance Systems, Autonomous vehicles

Paper 37: An Overview of Surface Tracking and Representation in Fluid Simulation

Abstract: Realism in fluid animation can be achieve with physics based techniques and is the best among other approaches. Now, this area constitutes hot researches. There are number of mechanisms evolved with the advent of both hardware and soft-ware technologies. Most of the fluid simulation methods described with or without a clear surface representation. This paper focused on a quantitative survey of various fluid surface tracking and representation techniques. Suitable tracking schemes with the hybrid fluid simulation approach may give mind blowing visual effects for various applications.

Author 1: Listy Stephen

Author 2: Anoop Jose

Keywords: Fluid simulation; Physics based animation; Real-ism; Surface representation; Tracking

Paper 38: Runtime Analysis of GPU-Based Stereo Matching

Abstract: This paper elaborates on the possibility to leverage the highly parallel nature of GPUs to implement more efficient stereo matching algorithms. Different algorithms have been implemented and compared on the CPU and the GPU in order to show the speedup gained by moving the computation to the graphics card. The results were evaluated for accuracy using the test available on the Middlebury website for stereo vision. An assessment of the runtime performance was done by a script which examined the runtime behaviour of the individual steps of the stereo matching algorithm.

Author 1: Christian Zentner

Author 2: Yan Liu

Keywords: stereo matching; GPU computing; runtime analysis; computer vision; image processing

Paper 39: A Multiple-Objects Recognition Method Based on Region Similarity Measures: Application to Roof Extraction from Orthophotoplans

Abstract: In this paper, an efficient method for automatic and accurate detection of multiple objects from images using a region similarity measure is presented. This method involves the construction of two knowledge databases: The first one contains several distinctive textures of objects to be extracted. The second one is composed with textures representing background. Both databases are provided by some examples (training set) of images from which one wants to recognize objects. The proposed procedure starts by an initialization step during which the studied image is segmented into homogeneous regions. In order to separate the objects of interest from the image background, an evaluation of the similarity between the regions of the segmented image and those of the constructed knowledge databases is then performed. The proposed approach presents several advantages in terms of applicability, suitability and simplicity. Experimental results obtained from the method applied to extract building roofs from orthophotoplans prove its robustness and performance over popular methods like K Nearest Neighbours (KNN) and Support Vector Machine (SVM).

Author 1: Abdellatif El Idrissi

Author 2: Youssef El Merabet

Author 3: Yassine Ruichek

Author 4: Raja Touahni

Author 5: Abderrahmane Sbihi

Author 6: Cyril Meurie

Author 7: Ahmed Moussa

Keywords: Object recognition; Region Similarity Measure; Tex-ture; Feature extraction; Orthophotoplans