IJACSA Volume 9 Issue 7 - thesai.org

IJACSA Volume 9 Issue 7

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: Wavelet/PSO-Based Segmentation and Marker-Less Tracking of the Gallbladder in Monocular Calibration-free Laparoscopic Cholecystectomy

Abstract: This paper presents an automatic segmentation and monocular marker-less tracking method of the gallbladder in minimally invasive laparoscopic cholecystectomy intervention that can be used for the construction of an adaptive calibration-free medical augmented reality system. In particular, the pro-posed method consists of three steps, namely, a segmentation of 2D laparoscopic images using a combination of photomet-ric population-based statistical approach and edge detection techniques, a PSO-based detection of the targeted anatomical structure (the gallbladder) and, finally, the 3D model wavelet-based multi-resolution analysis and adaptive 2D/3D registration. The proposed population-based statistical segmentation approach of 2D laparoscopic images differs from classical approaches (his-togram thresholding), in that we consider anatomical structures and surgical instruments in terms of distributions of RGB color triples. This allows an efficient handling, superior robustness and to readily integrate current intervention information. The result of this step consists in a set of point clouds with a loosely gradient information that can cover various anatomical structures. In order to enhance both sensitivity and specificity, the detection of the targeted structure (the gallbladder) is based on a modified PSO (particles swarm optimization) scheme which maximizes both internal features density and the divergence with neighboring structures such as, the liver. Finally, a multi-particles based representation of the targeted structure is constructed, thanks to a proposed wavelet-based multi-resolution analysis of the 3D model of the targeted structure which is registered adaptively with the 2D particles generated during the previous step. Results are shown on both synthetic and real data.

Author 1: Haroun Djaghloul

Author 2: Mohamed Batouche

Author 3: Jean-Pierre Jessel

Author 4: Abdelhamid Benhocine

Keywords: Medical image segmentation; monocular laparo-scopic cholecystectomy; deformable structures tracking; gallbladder segmentation and tracking; markerless augmented reality; wavelets; particles swarm optimisation; minimally invasive surgery (MIS); computer aided surgery (CAS)

Paper 2: Fuzzy Data Mining for Autism Classification of Children

Abstract: Autism is a development condition linked with healthcare costs, therefore, early screening of autism symptoms can cut down on these costs. The autism screening process involves presenting a series of questions for parents, caregivers, and family members to answer on behalf of the child to determine the potential of autistic traits. Often existing autism screening tools, such as the Autism Quotient (AQ), involve many questions, in addition to careful design of the questions, which makes the autism screening process lengthy. One potential solution to improve the efficiency and accuracy of screening is the adaptation of fuzzy rule in data mining. Fuzzy rules can be extracted automatically from past controls and cases to form a screening classification system. This system can then be utilized to forecast whether individuals have any autistic traits instead of relying on the conventional domain expert rules. This paper evaluates fuzzy rule-based data mining for forecasting autistic symptoms of children to address the aforementioned problem. Empirical results demonstrate high performance of the fuzzy data mining model in regard to predictive accuracy and sensitivity rates and surprisingly lower than expected specificity rates when compared with other rule-based data mining models.

Author 1: Mofleh Al-diabat

Keywords: Autistic traits; data mining; fuzzy rules; statistical analysis

Paper 3: Training Difficulties in Deductive Methods of Verification and Synthesis of Program

Abstract: The article analyzes the difficulties which Bachelor Degree in Informatics and Computer Sciences students encounter in the process of being trained in applying deductive methods of verification and synthesis of procedural programs. Education in this field is an important step towards moving from classical software engineering to formal software engineering. The training in deductive methods is done in the introductory courses in programming in some Bulgarian universities. It includes: Floyd’s method for proving partial and total correctness of flowchart programs; Hoare’s method of verification of programs; and Djikstra’s method of transforming predicates for verification and synthesis of Algol−like programs. The difficulties which occurred during the defining of the specification of the program, which is subjected to verification or synthesis; choosing a loop invariant and loop termination function; finding the weakest precondition; proving the formulated verifying conditions, are discussed in the paper. Means of overcoming these difficulties is proposed. Conclusions are drawn in order to improve the training in the field. Special attention is dedicated to motivating the use of specific tools for software analysis, such as interactive theorem proving system HOL, the software analyzers Frama−C and its WP plug−in, as well as the formal language ACSL, which allows formal specification of properties of C/C++ programs.

Author 1: Magdalina Todorova

Author 2: Daniela Orozova

Keywords: Program verification; deductive verification methods; automated theorem provers; proof assistants; education

Paper 4: Load Forecasting using Autoregressive Integrated Moving Average and Artificial Neural Network

Abstract: Electric load forecasting is a challenging research problem due to the complicated nature of its dataset involving both linear and nonlinear properties. Various literatures attempted to develop forecasting models that utilized statistical in combination with machine learning approaches deal with the dataset’s linear and nonlinear components to obtain close to accurate predictions. In this paper, autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN) were implemented as forecasting models for a power utility’s dataset in order to predict day-ahead electric load. Electric load data preparation, models implementation and forecasting evaluation was conducted to assess if the prediction of the models met the acceptable error tolerance for day-ahead electric load forecasting. A Java-based system made use of R Statistical Software implemented ARIMA(8,1,2) while Encog Library was used to implement the ANN model composing of Resilient Propagation as the training algorithm and Hyperbolic Tangent as the activation function. The ANN+ARIMA hybrid model was found out to deliver a Mean Absolute Percentage Error (MAPE) of 4.09% which proves to be a viable technique in electric load forecasting while showing better forecasting results than solely using ARIMA and ANN. Through this research, both statistical and machine learning approaches were implemented as a forecasting model combination to solve the linear and non-linear properties of electric load data.

Author 1: Lemuel Clark P. Velasco

Author 2: Daisy Lou L. Polestico

Author 3: Gary Paolo O. Macasieb

Author 4: Michael Bryan V. Reyes

Author 5: Felicisimo B. Vasquez Jr

Keywords: Electric load forecasting; autoregressive integrated moving average; artificial neural network

Paper 5: Interactive Visual Decision Tree for Developing Detection Rules of Attacks on Web Applications

Abstract: Creating detection rules of attacks on web applications is not a trivial task, especially when the attacks are launched by experienced hackers. In such a situation, human expertise is essential to produce effective results. However, human users are easily overloaded by the huge input data, which is meant to be analyzed, learned from, and used to develop appropriate detection rules. To support human users in dealing with the information overload problem while developing detection rules of web application attacks, we propose a novel technique and tool called Interactive Visual Decision Tree (IVDT). IVDT is a variant of the popular decision tree learning technique introduced in research fields such as machine learning and data mining, with two additionally important features: visually supported data analysis and user-guided tree growing. Visually supported data analysis helps human users cope with high volume of training data while analyzing each node in the tree being built. On the other hand, user-guided tree growing allows human users to apply their own expertise and experience to create custom split condition for each tree node. A prototype implementation of IVDT is built and experimented to evaluate its effectiveness in terms of detection accuracy achieved by its users as well as ease of working with. The experiment results prove some advantages of IVDT over traditional decision tree learning method, but also point out its problems that should be handled in future improvements.

Author 1: Tran Tri Dang

Author 2: Tran Khanh Dang

Author 3: Truong-Giang Nguyen Le

Keywords: Interactive Analytics; Security Visualization; Visual Decision Tree; Web Application Security

Paper 6: Developing a Candidate Registration System for Zambia School Examinations using the Cloud Model

Abstract: Cloud computing has in the recent past gained a lot of ground in this digital age. The use of cloud technologies in business has broken barriers in sharing information making the world one big global village. Regardless of where one is, data or information can be received or sent instantly disregarding distance. In this research, we investigated the challenges in registering candidates for school examinations and availability of internet services in various parts of Zambia and then present a candidate registration process based on the cloud model which is aimed at resolving challenges of distances from examination centres to the examining body in order to register for examinations as well as improving the timelines and cutting down the back and forth movements in the whole process. The web based registration system was developed and tested and the testing ascertained connectivity, functionality and scalability of the system.

Author 1: Banji Milumbe

Author 2: Jackson Phiri

Author 3: Monica M Kalumbilo

Author 4: Mayumbo Nyirenda

Keywords: Cloud computing; candidate registration; online registration; Zambia; school examinations; bulk SMS; automation; information communication technology; ICT

Paper 7: Deep Learning Features Fusion with Classical Image Features for Image Access

Abstract: Depending on the society, the access to the adult content can create social problems. This paper thus proposes a fusion approach for image based adult content filtering. The proposed approach merges the Deep Learning (DL) architecture and classical hand crafted feature extraction approaches. From the DL, we fuse the rich feature extraction capabilities of the Convolutional Neural Networks (CNNs) with the Correlograms features. We optimize the classification by integrating and modifying the Correlograms into skin Correlograms. The results show an increased performance by combining the DL learnt features with the classical hand crafted features. From an evaluation, the proposed approach achieves an Accuracy of 0.93. This work thus motivates the usage of classical hand crafted features to be exploited in the DL architectures for segmentation and detection scenarios.

Author 1: Rehan Ullah Khan

Keywords: Deep learning; content based filtering; content analysis; machine learning; support vector machines

Paper 8: Probabilistic Neural Network and Word Embedding for Sentiment Analysis

Abstract: In the present days, Artificial Intelligence (AI) is an attractive area of research along with numerous practicable purposes and vigorous subject matters and tasks, such as, understand speech, natural language, diagnose medicine and support basic research. In this study deep learning (DL) techniques, i.e. Probabilistic Neural Network (PNN) and Word Embedding (WE) will be used for sentiment analysis. The entire proposed framework will be divided into three phases: (a) normalization, (b) word vectorization, and (c) execution of proposed model.

Author 1: Saqib Alam

Author 2: Nianmin Yao

Keywords: Deep learning; probabilistic neural network; word embedding; sentiment analysis

Paper 9: Ranking Attribution: A Novel Method for Stylometric Authorship Identification

Abstract: Stylometric Authorship attribution is one of the essential approaches in the text mining. The present research endorses a Stylometric method called Stylometric Authorship Ranking Attribution (SARA) overcomes the usual problems which are processing time and accurate prediction results, without any human opinion that relays on the domain expert. This new method also uses the most effective attributes used in the Stylometric authorship prediction frequent word bag counts, whether it was frequent single, pair or trio words attributes, which are the most successful attributes in Stylometric prediction, having more alibi for author artistic writing style for our authorship recognition and prediction proposed technique. The experiments show that the proposed method produces superior prediction accuracy and even provides a completely correct result at the final stage of our experimental tests regarding the dataset scope.

Author 1: Marwa Taha Jamil

Author 2: Dr. Tareef kamil Mustafa

Keywords: Data mining; text mining; Stylometric Authorship Attribution; SARA

Paper 10: Dynamic Data Aggregation Approach for Sensor-Based Big Data

Abstract: Sensors are being used in thousands of applications such as agriculture, health monitoring, air and water pollution monitoring, traffic monitoring and control. As these applications collect zettabytes of data everyday sensors play an integral role into big data. However, most of these data are redundant, and useless. Thus, efficient data aggregation and processing are significantly important in reducing redundant and useless data in sensor-based big data frameworks. Current studies on big data analytics do not focus on aggregating and filtering data at multiple layers of big data frameworks especially at the lower level at data collecting nodes (sensors) that reduce the processing overhead at the upper layer, i.e., big data server. Thus, this paper introduces a multi-tier data aggregation technique for sensor-based big data frameworks. While this work focuses more on data aggregation at sensor networks. To achieve energy efficiency it also demonstrates that efficient data processing at lower layers (sensor) significantly reduces overall energy consumption of the network and data transmission latency.

Author 1: Mohammed S. Al-kahtani

Author 2: Lutful Karim

Keywords: Data aggregation; big data; sensor networks; energy efficiency; clustering

Paper 11: Measuring the Effect of Use Web 2.0 Technology on Saudi Students’ Motivation to Learn in a Blended Learning Environment

Abstract: Students’ motivation to learn is the goal of the educational process around the world. There is a close link between learning outcomes and students’ motivation to learn. Thus, the success of blended learning in Saudi higher education depends on not only using different teaching methods and massive expenditures on technology but also on students’ motivation to learn. The main objective of this study is to measure the effect of using the Web 2.0 technology on students’ motivation to learn in a blended learning environment through their attention, relevance, confidence, satisfaction inside in this environment. This study used a randomized experimental research design to examine differences in student’s motivation based on their use of Web 2.0 tools in a blended environment in the Computer Science at Al-Imam Muhammad Ibn Saud Islamic University (IMSIU). This study adopted Keller’s ARCS model of motivation to develop a comprehensive framework of factors that affect the use of Web 2.0 tools in blended learning environment. A questionnaire was conducted to collect data from students. Throughout our investigation, we found that there was a statistically significant difference at the level of 0.05 in overall student motivation between the experimental and control groups resulting from the using Web 2.0 tools technology. Moreover, students using Web 2.0 tools were found to exhibit a statistically significant higher degree of motivation. The results of this study can help decision makers readjust the learning strategy by realizing the importance of using Web 2.0 tools as the main platform in Saudi higher education.

Author 1: Sarah M. Bin-jomman

Author 2: Mona Al-Khattabi

Keywords: Web2.0 tools; blended learning; motivation; ARCS model

Paper 12: The Impact of Motivator and Demotivator Factors on Agile Software Development

Abstract: Since the last decade, Agile software development has emerged as a widely utilized software development method keeping in view the developing countries of South Asia. The literature reports significant challenges and barriers for agile in software industry and thus the area still has significant problems when considered with this domain. This study reports an industrial survey in Pakistani software industry practices and practitioners to elicit the indigenous motivator and demotivators of agile paradigm in Pakistan. This study provides a concrete ranking of motivator and demotivator factors which influence the agile paradigm. A lack of proper training and other identified issues indicate that the adoption of agile is in preliminary phases and serious effort is required to set the direction right for success of agile paradigm and its adopting institutions. The survey is conducted in 23 companies practicing agile organizations and involves 90 agile practitioners. Reports of 67 practitioners were finally selected after careful selection against selection criteria for this study. The results indicate various alarming factors which are different from reported literature on the subject. Tolerance to work is the most important motivating factor among Pakistan agile practitioners, likewise lack of resources is the highest demotivating factor. A detailed ranking list of motivators and demotivators and comprehensive data analysis has been provided in this paper which influences strongly the agile software development issues in Pakistan.

Author 1: Shahbaz Ahmed Khan Ghayyur

Author 2: Salman Ahmed

Author 3: Saeed Ullah

Author 4: Waqar Ahmed

Keywords: Agile software development; motivators; demotivators; success factors; barriers; agile methods; software development life cycle

Paper 13: Social Success Factors Affecting Implementation of Agile Software Development Methodologies in Software Industry of Pakistan: An Empirical Study

Abstract: During the past few years it has been observed that the implementation of Agile software development methodologies have become a part and parcel in software development projects not only in large and developed organizations but also in small organizations despite the existence of misapprehension that Agile methodologies are only valid for large scale projects and established organizations. Keeping in view the potential of Agile software methodologies and with the aim of eliminating this misconception, a mixed method methodology was adopted to conduct a study for determining the social factors that contribute or have influence in the successful implementation of Agile software development methodologies. In this study, face-to-face interview sessions were conducted with 271 software professionals that include Portfolio/Program/Project Managers, Scrum Masters and Product Owners representing 28 software development companies operating in Pakistan to gauge the influence of social factors on the success of Agile software projects. The study concluded that the size of the project has nothing to do with the success of a project or otherwise but there exist certain other factors like visionary leadership, degree or level of Agile software practices, congruence value, etc. contribute significantly in success of a project.

Author 1: Muhammad Noman Riaz

Author 2: Athar Mahboob

Author 3: Attaullah Buriro

Keywords: Agile methodologies; social factors; congruence value; visionary leadership; software developers

Paper 14: A Multi-Criteria Decision Making to Rank Android based Mobile Applications for Mathematics

Abstract: Exponential growth in the amount of mobile applications for Mathematics has led users to confusion and difficulty in selecting proper application manually which suits to their needs. Therefore, there exists an imperative need for automated and efficient selection of mobile applications for Mathematics where users still heavily trust either application store ratings or the content rated by the application developer. In this study, fuzzy scale weights together with ELECTRE I (ELimination and Choice Expressing REality) were used to solve a typical multi-criteria decision making problem on ranking selected mobile applications for Mathematics with respect to given set of criteria. The alternatives are mobile applications for Mathematics and were chosen from Google Play Store through considering top five highest user ratings and high usage frequencies. Ten sets of criteria on technical and pedagogical aspects specific to mobile applications and five alternatives were used in the ranking process. Findings suggest that ELECTRE I with fuzzy scale weights are remarkably practical for outranking and selection processes. Particularly in the case of unclear and imprecise ratings, this method could offer substantial solution.

Author 1: Seren Basaran

Author 2: Oluwatobi John Aduradola

Keywords: ELECTRE; mobile applications for mathematics; multi-criteria decision making; pedagogical requirements; technical requirements

Paper 15: Time Series Analysis for Shortened Labor Mean Interval of Dairy Cattle with the Data of BCS, RFS, Weight, Amount of Milk and Outlook

Abstract: MTime series analysis for shortened labor mean interval of dairy cattle with the data of Body Condition Score (BCS), Rumen Fill Score (RFS), Weight, Amount of Milk and Outlook is conducted. Method for shortened the labor mean internal of Japanese dairy cattle based on time-series analysis with the data of visual index of BCS, RFS, Weight, Amount of Milk and Outlook is proposed. In order to shortened the labor mean interval of dairy cattle is the purpose of the research. Through the experiments with 17 Japanese dairy cattle of the 17 Japanese anestrus Holstein dairy cattle, it is found that the combination of weight, BCS and amount of milk is a good indicator for identification of productive cattle. Therefore, the cattle which need hormone treatments can be identified.

Author 1: Kohei Arai

Author 2: Osamu Fukuda

Author 3: Hiroshi Okumura

Author 4: Kenji Endo

Author 5: Kenichi Yamashita

Keywords: Body Condition Score (BCS); Rumen Fill Score (RFS); Dairy cattle; Time-Series Analysis; Cattle Productivity

Paper 16: Preference in using Agile Development with Larger Team Size

Abstract: Agile software development includes a group of software development methodologies based on iterative development, where requirements and solutions evolve through collaboration between cross-functional self-organizing teams. Different software houses were visited in a developing country to determine the experiences faced by people working on a real world projects using Agile software development methodology following different variants in different team sizes to determine the preference of using Agile software development methodology in larger team sizes. Several people were surveyed out of which few responded with an opinion of not to use agile development in a team sizes exceeding 25 members. According to the experience of people the ideal team size was 5 to maximum 10. Because according to the survey increase in the number of individuals create issues of communication as it is not possible to keep everyone on the same track with larger teams especially in case of scrum meetings which usually held on daily basis, taking responsibilities as everyone becomes reluctant in taking responsibilities believing someone else will take it, sub teams because the more the number of individuals the more will be the sub teams which indirectly increases the dependency among the teams by breaking the tasks into much smaller chunks. The findings also suggest that customer feedback would increase if the team size is less than 25 which in turn says that the Quality of Software is increased. As this study had only focused on the software companies of a developing country it is recommended that further studies should be carried out by surveying the people of other different developed countries.

Author 1: Ahmed Zia

Author 2: Waleed Arshad

Author 3: Waqas Mahmood

Keywords: Agile Development; Ideal Team Size; Larger Team Size Problems

Paper 17: Green Cloud Computing: Efficient Energy-Aware and Dynamic Resources Management in Data Centers

Abstract: The uses of Cloud computing over the last years are constantly increasing since it has become a very important technology in the computing landscape. It provides to client decentralized services and a pay-as-you-go model for consuming resources. The growing need for the cloud services oblige the providers to adopt an enlarged sized data center infrastructure which runs thousands of hosts and servers to store and process data. As result, these large servers engender a lot of heat with visual carbon emission in the air, as well as important energy consumption and higher operating cost. This is why researches in energy economics continue to progress including energy saving techniques in servers, in the network, cooling, and renewable energies, etc. In this paper, we tackled the existing energy efficient methods in the green cloud computing fields and we put forward our green cloud solution for data center dynamic resource management. Our proposed approach aims to reduce the infrastructure energy consumption and maintain the required performances.

Author 1: Sara DIOUANI

Author 2: Hicham MEDROMI

Keywords: Cloud Computing; Green Cloud; Data Center; Energy Consumption; Resource Management

Paper 18: ASSA: Adaptive E-Learning Smart Students Assessment Model

Abstract: Adaptive e-learning can be improved through measured e-assessments that can provide accurate feedback to instructors. E-assessments can not only provide the basis for evaluation of the different pedagogical methods used in teaching and learning but they also can be used to determine the most suitable delivered materials to students according to their skills, abilities, and prior knowledge’s. This paper presents the Adaptive Smart Student Assessment (ASSA) model. With ASSA instructors worldwide can define their tests, and their students can take these tests on-line. ASSA determines the students’ abilities, skills and preferable Learning Style (LS) with more accuracy and then generates the appropriate questions in an adaptive way then presents them in a preferable learning style of student. It facilitates the evaluation process and measures the students’ knowledge level with more accuracy and then store it in the student’s profile for later use in the learning process to adapt course material content appropriately according to individual student abilities.

Author 1: Dalal Abdullah Aljohany

Author 2: Reda Mohamed Salama

Author 3: Mostafa Saleh

Keywords: Adaptive e-learning; e-assessments; adaptive assessment; smart assessment; Learning Style (LS)

Paper 19: Implementation of NOGIE and NOWGIE for Human Skin Detection

Abstract: The Digital image processing is one of the most widely implemented fields worldwide. The most applied applications of digital image processing are facial recognition, finger print recognition, medical imaging, law enforcement, cyber-crime investigation, identification of various diseases and criminals, etc. The subject to be discussed in this article is skin detection. Skin detection has solved many serious problems related to digital image process. It is one of the main features in making an intelligent image processing system. The proposed methodology conducts an improved and well enhanced skin detection, the skin and non-skin parts are divided from an input image or video, noise is removed, HSV is applied which also acts as a color model that generates more better results in accordance to RGB or YCbCr for skin and face identification. The algorithms, NOGIE (Noise Object Global Image Enhancement) and NOWGIE (Noise Object with Global Image Enhancement) are applied separately on the input and the results can be compared for better perception and understanding of the applied skin detection techniques, the skin parts are highlighted as “White” while the Non-skin parts are highlighted as “Black”. The results are different NOWGIE gives better results than the NOGIE due to the image enhancement technique. This methodology is subjected to be implemented in special security drones for the identification of suspects, terrorists and spy’s the algorithms provides the ability to detect humans from a non-skin background making an autonomous and excellent security system.

Author 1: M. Omer Aftab

Author 2: Junaid Javed

Author 3: M. Bilal

Author 4: Arfa Hassan

Author 5: M. Adnan Khan

Keywords: Skin detection; Digital Image Processing (DIP); Noise Object Global Image Enhancement (NOGIE); Noise Object with Global Image Enhancement (NOWGIE); Hue Saturation and Value (HSV); RGB

Paper 20: Relationship Strength Based Privacy for the Online Social Networks

Abstract: The trend of communication is changing from mobile messages to the online social networks, for example, Face-book. The social networking applications and websites provide many of the characteristics, such as personal photo sharing. On the positive side by that many individuals form the social relationships. However, the online social networks may lead to the misuse of personal information and its disclosure. The social networks are static and assume equal values for the individuals who are directly connected. On the other hand, in real life the social relationships are dynamic and they are based on different attributes such as location, family background, neighborhood and many more. In order to be secure from the undesirable consequences due to personal information leakage, the effective mechanisms are required. In this paper, a model is proposed for the privacy in online social networks. The proposed model restricts the disclosure of personal information to the individuals. The information of one individual may be disclosed based on the relationship strength and the context. The implementation of this model on the social networks reduces the percentage of information disclosure to the less known individuals.

Author 1: Javed Ahmed

Author 2: Adnan Manzoor

Author 3: Nazar H. Phulpoto

Author 4: Imtiaz A. Halepoto

Author 5: Muhammad Sulleman Memon

Keywords: Online social networks; privacy; social relationships

Paper 21: Performance Evaluation of Polynomial Pool-Based Key Pre-Distribution Protocol for Wireless Sensor Network Applications

Abstract: In nowadays, wireless sensor network (WSN) has been established as a leading emerging technology in the field of remote area distributed sensing due to its diverse application areas. Key pre-distribution is an important task in WSN because after the deployment of sensor nodes, their neighbors become strange to each other. To secure the communication, neighbor nodes have to generate a secret shared key, or a key-path must exist between these nodes. In this paper, we have discussed and presented various key pre-distribution protocols, namely, the polynomial pool-based key pre-distribution which is a scheme for creating pairwise keys between sensors on the foundation of a polynomial-based key pre-distribution protocol, introducing two effective instantiations: a random subset assignment key pre-distribution scheme and a grid-based key pre-distribution scheme. Other studied key pre-distribution schemes (KPDS) are Peer Intermediaries Key for Establishment (PIKE) and Group-based key pre-distribution scheme. The performances of these schemes have been assessed through the simulation of different grids under the TinyOS environment.

Author 1: Malek Ben Amira

Author 2: Mayssa Bouraoui

Author 3: Noureddine Boulajfen

Keywords: Key management; wireless sensor network (WSN); key pre-distribution schemes; polynomial pool-based KPDS; PIKE; group-based KPDS

Paper 22: Applications of Data Envelopment Analysis in Development and Assessment of Sustainability Across Economic, Environmental and Social Dimensions

Abstract: Recently, senior managers are paying much more attention to the environmental aspects of decision-making units. Technically, global economy is inextricably connected to the environment, as it is heavily dependent on extraction and exploitation of natural resources. In this article, we try to propose a number of models for efficiency evaluation that combine the growing concepts in environmental areas along with social and economic subjects. Generally speaking, if economic growth is to be continuous and effective in the long term, it must be based on a combination of economic, environmental and social components. The existing literature on data envelopment analysis (DEA) is often based on economic efficiency. However, due to the environmental pollution at a global level, there have been recent studies in relation to sustainability efficiency with focus on environmental and social aspects; although, these studies were limited and left much room for further research. The present study evaluates the efficiency of decision-making units using social, economic and environmental indicators, and tries to minimize the flaws of DEA in the proposed models by making relative comparisons to previous models.

Author 1: Hamid Hosseini

Author 2: Abbas Ali Noura

Author 3: Sara Fanati Rashidi

Keywords: Data envelopment analysis; desirable and undesirable outputs; strong and weak disposability; sustainability efficiency

Paper 23: An Improved Bat Algorithm based on Novel Initialization Technique for Global Optimization Problem

Abstract: Bat algorithm (BA) is a nature-inspired metaheuristic algorithm which is widely used to solve the real world global optimization problem. BA is a population-based intelligent stochastic search technique that emerged from the echolocation features of bats and created from the mimics of bats foraging behavior. One of the major issue faced by the BA is frequently captured in local optima while handling the complex real-world problems. In this study, a new variant of BA named as improved bat algorithm (I-BAT) is proposed. Improved bat algorithm modifies the standard BA by enhancing its exploitation capabilities, and secondly for initialization of swarm, a quasi-random sequence Torus has been applied to overcome the issue of convergence and diversity. Population initialization is a vital factor in BA, which considerably influences the diversity and convergence of swarm. In order to improve the diversity and convergence, quasi-random sequences are more useful to initialize the population rather than the random distribution. The proposed strategy is applied to standard benchmark functions that are extensively used in the literature. The experimental results illustrate the superiority of the proposed technique. The simulation results verify the efficiency of proposed technique for swarm over the benchmark algorithm that is implemented for the function optimization.

Author 1: Waqas Haider Bangyal

Author 2: Jamil Ahmad

Author 3: Hafiz Tayyab Rauf

Author 4: Sobia Pervaiz

Keywords: Bat algorithm; local optima; exploration and exploitation; quasi-random sequence

Paper 24: Effects of Modulation Index on Harmonics of SP-PWM Inverter Supplying Universal Motor

Abstract: This manuscript presents the effects of changing modulation indices on current and voltage harmonics of universal motor when it is supplied by single phase PWM (SP-PWM) inverter, the effect has been analyzed with simulation and experimental setup. For variable speed applications universal motor can be controlled either by phase angle control drive or by SP-PWM inverter drive. SP-PWM inverter-fed drive is common technique that is used to adjust the voltage applied to motor, so that variable speed can be obtained. With the application of SP-PWM inverter-fed drive, harmonics are generated because of power electronic devices. According to the IEEE standard 519, the total harmonic distortion (THD) must be within 5%. In this paper, the effect of modulation index (MI) is used to analyze THD content, and its variation alters the harmonic content. However, the effects are also analyzed through experimental setup in order to validate the system performance. In future work, keeping modulation index constant, different PWM strategies can be employed in order to decrease harmonics.

Author 1: Asif A. Solangi

Author 2: Mehr Gul

Author 3: Rameez Shaikh

Author 4: Farhana Umer

Author 5: Noman Khan Pathan

Author 6: Zeeshan Anjum Memon

Keywords: Harmonics; modulation index; SP-PWM inverter; universal motor

Paper 25: Data-driven based Fault Diagnosis using Principal Component Analysis

Abstract: Modern industrial systems are growing day by day and unlikely their complexity is also increasing. On the other hand, the design and operations have become a key focus of the researchers in order to improve the production system. To cope up with these chellenges, the data-driven technique like principal component analysis (PCA) is famous to assist the working systems. A data in bulk quanitity from the sensor measurements are often available in such industrial systems. Considering the modern industrial systems and their economic benifits, the fault diagnostic techniqes have been deeply studied. For example, the techniques that consider the process data as the key element. In this paper, the faults have been detected with the data-driven approach using PCA. In particular, the faults have been detected by using T^2 and Q statistics. In this process, PCA projects large data into smaller dimensions. Additionally it also preserves all the important information of process. In order to understand the impact of the technique, Tennessee Eastman chemical plant is considerd for the performance evaluation.

Author 1: Shakir M. Shaikh

Author 2: Imtiaz A. Halepoto

Author 3: Nazar H. Phulpoto

Author 4: Muhammad S. Memon

Author 5: Ayaz Hussain

Author 6: Asif A. Laghari

Keywords: Fault Diagnosis; Principal Component Analysis; Multivariate Statistical Approach; Tennessee Eastman Chemical Plant Introduction

Paper 26: A Practical Approach for Evaluating and Prioritizing Situational Factors in Global Software Project Development

Abstract: There has been an enormous increase in globaliza-tion that has led to more cooperation and competition across boundaries. Software engineering, particularly distributed soft-ware development (DSD) and global software development (GSD), is evolving rapidly and presents several challenges, such as ge-ographical separations, temporal differences, cultural variations, and management strategies. As a result, a variety of situational factors (SFs) arise that causes challenging problems in software development. Both literature and real world software industry study revealed that the extent of the effect of SFs may vary subject to a certain software project. Project executives should need to concentrate on the right SFs for the successful development of a specific project. This work first examines the optimal and most well-balanced GSD-related SFs and then presents a mechanism for prioritizing the SFs to better understand the extent to which an SF generally affects the GSD. A set of 56 SFs in 11 categories is identified and analyzed in this research. A fuzzy set theory based, multi criteria decision making (MCDM) technique, fuzzy analytical hierarchy process (FAHP) was proposed to extract the SFs that have the strongest effects on GSD. The proposed technique is intelligent and automated and can be customized to suit specific conditions and environments. Thus, it can provide support for a much-needed variation that is the hallmark of such software development environments. A case study of a global company working in collaboration on a project JKL was selected to identify and prioritize the most challenging SFs. A sensitivity analysis is carried out to evaluate the extent of the impact for highly ranked SFs related to JKL project.

Author 1: Kanza Gulzar

Author 2: Jun Sang

Author 3: Adeel Akbar Memon

Author 4: Muhammad Ramzan

Author 5: Xiaofeng Xia

Author 6: Hong Xiang

Keywords: Global software development (GSD); Situational Factors (SFs); Fuzzy Analytic Hierarchy Process (FAHP); Multi criteria decision-making (MCDM); fuzzy set theory; sensitivity analysis

Paper 27: Evaluation and Analysis of Bio-Inspired Optimization Techniques for Bill Estimation in Fog Computing

Abstract: In light of constant developments in the realm of Information Communication and Technologies, large-scale busi-nesses and Internet service providers have realized the limitation of data storage capacity available to them. This led organizations to cloud computing, a concept of sharing of resources among different service providers by renting these resources through service level agreements. Fog computing is an extension to cloud computing architecture in which resources are brought closer to the consumers. Fog computing, being a distinct from cloud computing as it provides storage services along with computing resources. To use these services, the organizations have to pay according to their usage. In this paper, two nature-inspired algorithms, i.e. Pigeon Inspired Optimization (PIO) and Binary Bat Algorithm (BBA) are compared to regulate the effective management of resources so that the cost of resources can be curtailed and billing can be achieved by calculating utilized resources under the service level agreement. PIO and BBA are used to evaluate energy utilization by cloudlets or edge nodes that can be used subsequently for approximating the utilization and bill through a Time of Use pricing scheme. We appraise above-mentioned techniques to evaluate their performance concerning the bill estimation based on the usage of fog servers. With respect to the utilization of resources and reduction in the bill, simulation results have revealed that the BBA gives pointedly better results than PIO.

Author 1: Hafsa Arshad

Author 2: Hasan Ali Khattak

Author 3: Munam Ali Shah

Author 4: Assad Abbas

Author 5: Zoobia Ameer

Keywords: Cloud computing; fog computing; bio-inspired al-gorithms; pricing; cloudlets

Paper 28: Detection of Sentiment Polarity of Unstructured Multi-Language Text from Social Media

Abstract: In recent years, Twitter has caught the attention of many researchers because of the fact that it is growing very rapidly in terms of number of users and also all the data present as tweets on twitter is public in nature while other social media networks such as Facebook, data is not completely public as users can restrict their post to only users present in their friend list. In this research study, aspect based sentiment analysis (ABSA) was done on the data acquired from social media related to the major cellular network companies of Pakistan (Telenor Pakistan, Mobilink Jazz, Zong, Warid and Ufone). For this research, we have specifically selected all tweets which are not only in English and Roman Urdu but also mixture of above two languages. We have employed natural language processing (NLP) techniques for pre-processing the dataset and machine learning (ML) techniques to detect the sentiments present in the data. The results are interesting and informative specially for policy makers of cellular companies. These companies can utilize this information to increase the performance of their services. In comparison with the state of the art algorithms, the performance of bagging algorithm with this framework on the acquired dataset has produced F Score of 92.25, which is very encouraging outcome of this research work.

Author 1: Saad Ahmed

Author 2: Saman Hina

Author 3: Raheela Asif

Keywords: Social media; sentiment analysis; data mining; cellular networks

Paper 29: An Algorithm that Prevents SPAM Attacks using Blockchain

Abstract: There are many systems and methods for prevent-ing spam attacks. However, at present there is no specific tried-and-true method for preventing such attacks. In this paper, we propose an algorithm, “SAGA BC” to prevent spam attacks using a blockchain technique and demonstrate its effectiveness by a simulation experiment. A person who sends an email using the “SAGA BC” must pay the processing cost with cryptocurrency. If an e-mail sent using this algorithm is received normally at a destination e-mail account, this fee is refunded. However, a lot of spam e-mails are not received normally, because addresses of the spam e-mails are indiscriminate. If a spammer sends spam using the “SAGA BC ,” he/she will lose the cryptocurrency fee for each such message. Thus, if using the “SAGA BC” to send e-mail becomes a standard practice for the general public, receiving e-mail servers and/or mailers will be able to easily judge incom-ing messages without using the “SAGA BC,” because spammers cannot use the “SAGA BC” without losing their cryptocurrency.

Author 1: Koichi Nakayama

Author 2: Yutaka Moriyama

Author 3: Chika Oshima

Keywords: Cryptocurrency; wallet account; Mail Send Coin (MSC)

Paper 30: Enhanced Textual Password Scheme for Better Security and Memorability

Abstract: Traditional textual password scheme provides a large number of password combinations but users generally use a small portion of available password space. Complex textual passwords are difficult to remember, therefore most users choose passwords with small length and contain dictionary words. Due to the use of small password length and dictionary words, textual passwords become easy to crack through offline guessability attacks. Traditional textual passwords scheme is also weak against keystroke logger attacks because alphanumeric characters are directly inserted into the password field. In this paper, enhancements are proposed in the registration and login screen of the traditional textual password scheme for improving security against offline guessability attacks and keystroke logger attacks. The proposed registration screen also improve memorability of traditional textual passwords through visual cues or pattern-based approach. In the proposed login screen, passwords are indirectly inserted into the password field, to resist keystroke logger attacks. A comparative analysis between the passwords created in traditional and proposed pattern-based approach is presented. The testing results show that users create strong and high entropy passwords in the proposed pattern-based approach as compared to the traditional textual passwords approach.

Author 1: Hina Bhanbhro

Author 2: Shah Zaman Nizamani

Author 3: Syed Raheel Hassan

Author 4: Sheikh Tahir Bakhsh

Author 5: Madini O.Alassafi

Keywords: Security; usability; alphanumeric passwords; authentication

Paper 31: Automated Quantification of Non-Calcified Coronary Plaques in Cardiac CT Angiographic Imagery

Abstract: The high mortality rate associated with coronary heart disease (CHD) has driven intensive research in cardiac image analysis. The advent of computed tomography angiogra-phy (CTA) has turned non-invasive diagnosis of cardiovascular anomalies into reality as calcified coronary plaques can be easily identified due to high intensity values. However, detection and quantification of the non-calcified plaques in CTA is still a challenging problem because of their lower intensity values, which are often similar to the nearby blood and muscle tissues. In this work, we propose Bayesian posterior based model for precise quantification of the non-calcified plaques in CTA imagery. The only indicator of non-calcified plaques in CTA is relatively lower intensity. Hence, we exploited intensity variations to discriminate voxels into lumen and plaque classes. Based on the normal coronary segments, we computed the vessel-wall thickness in first step. In the subsequent step, we removed vessel wall from the seg-mented tree and employed Gaussian Mixture Model to compute optimal distribution parameters. In the final step, distribution parameters were employed in Bayesian posterior model to classify voxels into lumen or plaque. A total of 18 CTA volumes were analyzed in this work using two different approaches. According to the experimental results, mean Jaccard overlap is around 88% with respect to the manual expert. In terms of sensitivity, specificity and accuracy, the proposed method achieves 84.13% ,79.15% and 82.02% success, respectively. Conclusion: According to the experimental results, it is shown that the proposed plaque quantification method achieves accuracy equivalent to human experts.

Author 1: M Moazzam Jawaid

Author 2: Sanam Narejo

Author 3: Nasrullah Pirzada

Author 4: Junaid Baloch

Author 5: C.C. Reyes-Aldasoro

Author 6: Greg Slabaugh

Keywords: Coronary segmentation; non-calcified plaques; vas-cular quantification; coronary wall analysis

Paper 32: Wakes-Ship Removal on High-Resolution Optical Images based on Histograms in HSV Color Space

Abstract: Ship detection on optical remote sensing images is getting great attention; however, some images called wakes-ship have not been taken into account yet. Current works in ship detection are focusing on in-shore detection where ships are in calm; furthermore, their methods get high Intersection Over Union (IoU), above 70%, but when computing IoU using only wakes-ship images the ratio is 22%. In this paper, it is presented a new framework to improve ship segmentation on wakes-ship images. In order to achieve this goal, it was considered HSV color space and histograms. First, ship detection was done using state-of-the-art ship detection methods. Second, bin histograms in HSV color space was used to get the colors that rely on wakes. Finally, the removal of wakes from ships was done using some discriminative properties. In this way, the increase of the IoU performance at wake-ship segmentation goes from 22% to 63%, which is an improvement of 186%.

Author 1: Fidel Indalecio Mamani Maquera

Author 2: Eveling Gloria Castro Gutierrez

Keywords: Wakes-ship removal; optical remote sensing; ship detection; HSV color space; histograms; intersection over union

Paper 33: Automatic Cyberbullying Detection in Spanish-language Social Networks using Sentiment Analysis Techniques

Abstract: Cyberbullying is a growing problem in our society that can bring fatal consequences and can be presented in digital text for example at online social networks. Nowadays there is a wide variety of works focused on the detection of digital texts in the English language, however in the Spanish language there are few studies that address this issue. This paper aims to detect this cybernetic harassment in social networks, in Spanish language. Sentiment analysis techniques are used, such as bag of words, elimination of signs and numbers, tokenization and stemming, as well as a Bayesian classifier. The data used for the training of the Bayesian classifier were obtained from the Spanish Dictionary of Affect in Language (SDAL), which is a database formed by more than 2500 words manually evaluated in three affective dimensions: Pleasantness, activation and imagery, as well as same 595 words obtained following the same procedure of SDAL was used with the help of the members of the Research Center, Technology Transfer and Software Development. As a result, the software developed has 93% success in the validation tests carried out.

Author 1: Rolfy Nixon Montufar Mercado

Author 2: Hernan Faustino Chacca Chuctaya

Author 3: Eveling Gloria Castro Gutierrez

Keywords: Cyberbullying; social media analytics; sentiment analysis; tokenization; stemming; bag of words

Paper 34: A Resource Recommendation Approach based on Co-Working History

Abstract: Recommending the right resource to execute the next activity of a running process instance is of utmost importance for the overall performance of the business process, as well as the resource and for the whole organization. Several approaches have recommended a resource based on the task requirements and the resource capabilities. Moreover, the process execution history and the logs have been used to better recommend a resource based on different human-resource recommender criteria like frequency and speed of execution, etc. These approaches considered the recommendation based on the individual’s execution history of the task that will be allocated to the resource. In this paper, a novel approach based on the co-working history of resources has been proposed. This approach considers the resources that had executed the previous tasks in the current running process instances. Then, it recommends a resource that has the best harmony with the rest of the resources.

Author 1: Nada Mohammed Abdulhameed

Author 2: Iman M. A. Helal

Author 3: Ahmed Awad

Author 4: Ehab Ezat

Keywords: Business process; process instance; co-working his-tory; human-resource recommender criteria; harmony

Paper 35: Global Citation Impact rather than Citation Count

Abstract: The progressing bloom in the tome of scientific literature available today debars researchers from efficiently shrewd the relevant from irrelevant content. Researchers are persistently engrossed in impactful papers, authors, and venues in their respective fields. Impact of an article depends on the citation received but just a citation count can’t give readers in-depth information about the article. That is the reason some articles are quantified unfairly on the basis of a citation count. In this paper, Global Citation Impact (GCI) is proposed which addresses the issue of considering citations of papers equally. Intuitively, the papers citing a paper are not of the same worth. The proposed index not only considers the number of citations (popularity) like many existing methods did but also considers the worth of citations (prestige). Results and discussions show that researcher whose work is cited by other prestigious papers gets higher rank which is quite fair crediting for research impact.

Author 1: Gohar Rehman Chughtai

Author 2: Jia Lee

Author 3: Muhammad Mehran Arshad khan

Author 4: Rashid Abbasi

Author 5: Asif Kabir

Author 6: Muhammad Arshad Shehzad Hassan

Keywords: Citation weighting; popular; global citations; prestigious; Global Citation Impact (GCI); research impact

Paper 36: Scientific Articles Exploration System Model based in Immersive Virtual Reality and Natural Language Processing Techniques

Abstract: After having carried out a historical review and identifying the state of the art in relation to the interfaces for the exploration of scientific articles, the authors propose a model based in an immersive virtual environment, natural user interfaces and natural language processing, which provides an excellent experience for the user and allows for better use of some of its capabilities, for example, intuition and cognition in 3-dimensional environments. In this work, the Oculus Rift and Leap Motion Hardware devices are used. This work aims to contribute to the proposal of a tool which would facilitate and optimize the arduous task of reviewing literature in scientific databases. The case study is the exploration and information retrieval of scientific articles using ALICIA (Scientific database of Peru). Finally, conclusions and recommendations for future work are laid out and discussed.

Author 1: Luis Alfaro

Author 2: Ricardo Linares

Author 3: Jose Herrera

Keywords: Immersive virtual environment; human computer interaction; natural user interfaces; natural language processing; Oculus Rift

Paper 37: An Efficient Fault Tolerance Technique for Through-Silicon-Vias in 3-D ICs

Abstract: Three-dimensional integrated circuits (3D-ICs) based on Through-Silicon-Vias (TSVs) interconnection technology appeared as a viable solution to overcome problems of cost, reliability, yield and stacking area. In order to be commercially feasible, the 3D-IC yield must be as high as possible, which requires a tested and reparable TSVs. To overpass this challenge, an integration of interconnect built-in self-test (IBIST) methodology for 3D-IC is given in the aims to test the defectives TSVs. Once the interconnection has been tested, the result extracted from IBIST initiate the repairing defectives TSVs based on the built-in self-repair (BISR) structure. This paper superposed two fault tolerance techniques in particularly the redundant TSV and the time division multiplexing access (TDMA) in case of multi defectives TSV. This novel repair architecture increase the yield of 3D-ICs in a high failure rate. It leads to 100% reparability for 30% failure rate. A parallel processing approach of the proposed structure is presented to accelerate the test and repair operations. Achieved experimental results with the proposed methodology scheme show a good performance in terms of repair rate and yield.

Author 1: Mohamed BENABDELADHIM

Author 2: Wael DGHAIS

Author 3: Fakhreddine ZAYER

Author 4: Belgacem HAMDI

Keywords: Fault tolerance; 3D-IC; TSV; IBIST; BISR; TDMA

Paper 38: Intrusion Detection and Prevention Systems as a Service in Could-based Environment

Abstract: Intrusion Detection and Prevention Systems (IDPSs) are standalone complex hardware, expensive to purchase, change and manage. The emergence of Network Function Virtualization (NFV) and Software Defined Networking (SDN) mitigates these challenges and delivers middlebox functions as virtual instances. Moreover, cloud computing has become a very cost-effective model for sharing large-scale services in recent years. Features such as portability, isolation, live migration, and customizabil-ity of virtual machines for high-performance computing have attracted enterprise customers to move their in-house IT data center to the cloud. In this paper, we formulate the placement of Intrusion Detection and Prevention Systems (IDPS) and introduce a model called Incremental Mobile Facility Location Problem (IMFLP) to study the IDPP problem. Moreover, we propose a novel and efficient solution called Adaptive Facility Location (AFL) to efficiently solve the optimization problem introduced in the IMFLP model. The effectiveness of our solution is evaluated through realistic simulation studies compared with other popular online facility location algorithms.

Author 1: Khalid Alsubhi

Author 2: Hani Moaiteq AlJahdali

Keywords: Facility Location Problem; Intrusion detection and Prevention Systems; Cloud Computing

Paper 39: Insights on Car Relocation Operations in One-Way Carsharing Systems

Abstract: One-way carsharing system is a mobility service that offers short-time car rental service for its users in an urban area. This kind of service is attractive since users can pick up a car from a station and return it to any other station unlike round-trip carsharing systems where users have to return the car to the same station of departure. Nevertheless, uneven users’ demands for cars and for parking places throughout the day poses a challenge on the carsharing operator to rebalance the cars in stations to satisfy the maximum number of users’ requests. We refer to a rebalancing operation by car relocation. These operations increase the cost of operating the carsharing system. As a result, optimizing these operations is crucial in order to reduce the cost of the operator. In this paper, the problem is modeled as an Integer Linear Programming model (ILP). Then we present three different car relocation policies that we implement in a greedy search algorithm. The comparison between the three policies shows that car relocation operations that do not consider future demands do not effectively decrease rejected demands. On the contrary, they can generate more rejected demands. Results prove that solutions provided by our greedy algorithm when using a good policy, are competitive with CPLEX solutions. Furthermore, adding stochastic modification on the input data proves that the results of the two presented approaches are highly affected by the input demand even after adding threshold values constraints.

Author 1: Rabih Zakaria

Author 2: Mohammad Dib

Author 3: Laurent Moalic

Author 4: Alexandre Caminada

Keywords: Carsharing; car relocation; ILP; greedy algorithm; CPLEX; green city

Paper 40: Load Balancing in Cloud Complex Systems using Adaptive Fuzzy Neural Systems

Abstract: Load balancing, reliability, and traffic are among the service-oriented issues in software engineering, and cloud computing is no exception to this rule and has put many challenges ahead of experts in this field. Considering the importance of the load balancing process in cloud computing, the purpose of this paper is to provide an appropriate solution for load balancing load in complex cloud systems using an adaptive fuzzy neural system. This system consists of four layers, and a particular operation is performed on each layer. The results of the experiments show that the system has better performance in the criteria mentioned above (balancing, traffic and reliability).

Author 1: Mohammad Taghi Sadeghi

Keywords: Load balancing; cloud computing; adaptive fuzzy neural system

Paper 41: Context Aware SmartHealth Cloud Platform for Medical Diagnostics

Abstract: Healthcare has seen a great evolution in current era in terms of new computer technologies. Intensive medical data is generated that opens up research in healthcare analytics. Coping with this intensive data along with making it meaningful to deliver knowledge and be able to make decisions are the most important tasks. To deduce the authenticity of the data on basis of precision, correction, associations and true meaning is important to validate the understanding of correct semantics. In case of medical diagnosis to form accurate understanding of associations while removing ambiguity and forming a correct picture of the case is of utmost importance. To come up with the right metrics for the diagnostic solution we have explored the known criteria to validate healthcare analytics techniques involved in formation of diagnosis that results in betterment and safety of patients under observations and heading towards possible treatments. In this work, we have proposed a thematic taxonomy for the comparison of existing healthcare analytics techniques with emphasis on diabetes and its underlying diseases. This analysis lead us to propose a data model for hybrid distributed simulation model for future Context Aware SmartHealth cloud platform for diagnostics. This platform is designed to inherit smartness of unsupervised learning which in turn would keep updating itself under supervised learning by qualified experts. Finally, the accuracy would be determined using HUM approach with biomarkers or a better accuracy model than AUC. The recommended action plan is also presented.

Author 1: Sarah Shafqat

Author 2: Almas Abbasi

Author 3: Muhammad Naeem Ahmad Khan

Author 4: Muhammad Ahsan Qureshi

Author 5: Tehmina Amjad

Author 6: Hafiz Farooq Ahmad

Keywords: Healthcare analytics; medical diagnostics; HL7; cloud platform; SmartHealth; big data

Paper 42: Detection of Parkinson's Disease through Acoustic Parameters

Abstract: Parkinson’s disease is a neurological disorder. It is the second most common disease after Alzheimer’s. Incidence rates for this disease are increasing rapidly with increasing life expectancy. The search for measures to diagnose the disease and monitor symptoms is an important step, despite the fact that it presents a number of challenges. Among the symptoms related to this disease is the disturbances of the voice which particularly occur in a remarkable way called hypokinetic dysarthria which is presented by the poverty of the gesture in all the characteristics of the speech (phonatory, prosodic, articulatory and rhythm). Our goal is to do a study based on voice analysis at the level of the glottis to examine some early parameters measured using the LF model and clinical manifestations to help diagnosis of the disease.

Author 1: Imen Daly

Author 2: Zied Hajaiej

Author 3: Ali Gharsallah

Keywords: Parkinson’s disease; LF model