IJACSA Vol. 10 Issue 9 (2019)

1

Artificial Intelligence Chatbots are New Recruiters

Author 1: Nishad Nawaz Author 2: Anjali Mary Gomes

The purpose of the paper is to assess the artificial intelligence chatbots influence on recruitment process. The authors explore how chatbots offered service delivery to attract and candidates engagement in the recruitment process. The aim of the study is to identify chatbots impact across the recruitment process. The study is completely based on secondary sources like conceptual papers, peer reviewed articles, websites are used to present the current paper. The paper found that artificial intelligence chatbots are very productive tools in recruitment process and it will be helpful in preparing recruitment strategy for the Industry. Additionally, it focuses more on to resolve complex issues in the process of recruitment. Through the amalgamation of artificial intelligence recruitment process is increasing attention among the researchers still there is opportunity to explore in the field. The paper provided future research avenues in the field of chatbots and recruiters.

Artificial intelligence chatbots recruitment process candidates experiences employer branding tool recruitment industry

Abstract doi.org/10.14569/IJACSA.2019.0100901 PDF

2

FPGA Implementation of RISC-based Memory-centric Processor Architecture

Author 1: Danijela Efnusheva

The development of the microprocessor industry in terms of speed, area, and multi-processing has resulted with increased data traffic between the processor and the memory in a classical processor-centric Von Neumann computing system. In order to alleviate the processor-memory bottleneck, in this paper we are proposing a RISC-based memory-centric processor architecture that provides a stronger merge between the processor and the memory, by adjusting the standard memory hierarchy model. Indeed, we are developing a RISC-based processor that integrates the memory into the same chip die, and thus provides direct access to the on-chip memory, without the use of general-purpose registers (GPRs) and cache memory. The proposed RISC-based memory-centric processor is described in VHDL and then implemented in Virtex7 VC709 Field Programmable Gate Array (FPGA) board, by means of Xilinx VIVADO Design Suite. The simulation timing diagrams and FPGA synthesis (implementation) reports are discussed and analyzed in this paper.

FPGA memory-centric computing processor in memory RISC architecture VHDL

Abstract doi.org/10.14569/IJACSA.2019.0100902 PDF

3

Authentication and Authorization Design in Honeybee Computing

Author 1: Nur Husna Azizul Author 2: Abdullah Mohd Zin Author 3: Ravie Chandren Muniyandi Author 4: Zarina Shukur

Honeybee computing is a concept based on advanced ubiquitous computing technology to support Smart City Smart Village (SCSV) initiatives. Advanced ubiquitous computing is a computing environment that contains many devices. There are two types of communication within Honeybee computing: client server and peer-to-peer. One of the authorization techniques is the OAuth technique, where a user can access an application without creating an account and can be accessed from multiple devices. OAuth is suitable to control the limited access of resources to the server. The server use REST API as web service to publish data from resources. However since Honeybee computing also supports peer-to-peer communication, security problem can still be an issue. In this paper, we want to propose the design of a secure data transmission for Honeybee computing by adopting the authorization process of OAuth 2.0 and Elliptic Curve Diffie-Hellman (ECDH) with HMAC-Sha. This article will also discuss the communication flow after adopting OAuth 2.0 and ECDH to the computing environment.

HMAC-Sha REST API peer-to-peer web service honeybee computing

Abstract doi.org/10.14569/IJACSA.2019.0100903 PDF

4

Smartphone Image based Agricultural Product Quality and Harvest Amount Prediction Method

Author 1: Kohei Arai Author 2: Osamu Shigetomi Author 3: Yuko Miura Author 4: Satoshi Yatsuda

A method for agricultural product quality and harvest amount prediction by using smartphone camera image is proposed. It is desired to predict agricultural product quality and harvest amount as soon as possible after the sowing. In order for that, satellite imagery data, UAV camera based images, ground based camera images are used and tried These methods do cost significantly and these do not work so well due to some reasons, in particular, most of farmers cannot use these properly. The proposed method uses just smartphone camera acquired images. Therefore, it is totally easy to use. If the results of prediction of product quality and harvest amount are not satisfied, then farmers have to add some additional fertilizer at the appropriate time. The experimental results with soy plantations show some possibility of the proposed method.

Smartphone camera image agricultural product quality and harvest prediction fertilizer control soy plantation

Abstract doi.org/10.14569/IJACSA.2019.0100904 PDF

5

Implementing a Safe Travelling Technique to Avoid the Collision of Animals and Vehicles in Saudi Arabia

Author 1: Amr Mohsen Jadi

In this work, a safe travelling technique was proposed and implemented a LoRa based application to avoid the collision of animals with vehicles on the highways of Saudi Arabia. For the last few decades, it has been a great challenge for the authorities to secure the life of animals and human being on the roads due to the sudden passage of animals on the highways. In such situations, drivers are not aware of the animal movement, and serious damage is observed with the life of both humans and animals. A LoRaWAN based architecture with a variety of advantages towards low cost and high accuracy of finding the movement of animals is possible with the proposed method and could deliver good results as well. The accuracy of this method was improved to a maximum extent as compared to the existing system due to the usage of LoRa sensors implanted in the animal’s skin to trace with the nodes and base stations easily.

LoRa Sensor-based mobile applications runtime monitoring tracking global positioning system

Abstract doi.org/10.14569/IJACSA.2019.0100905 PDF

6

A Compact Broadband Antenna for Civil and Military Wireless Communication Applications

Author 1: Zaheer Ahmed Dayo Author 2: Qunsheng Cao Author 3: Yi Wang Author 4: Saeed Ur Rahman Author 5: Permanand Soothar

This paper presents a compact broadband antenna for civil and military wireless communication applications. Two prototypes of the antenna are designed and simulated. The proposed antenna is etched on low cost substrate material with compact electrical dimensions of 0.207λ×0.127λ×0.0094λmm3 at 2GHz frequency. The simple microstrip feeding technique and antenna dimensions are involved in the design to attain the proper impedance matching. An optimization of variables is carried out by multiple rigorous simulations. The designed antennas have achieved the broadband impedance bandwidth of 89.3% and 100% at 10dB return loss. The antennas exhibit omni directional radiation pattern at lower resonances and strong surface current distribution across the radiator. The peak realized gain of 5.2dBi at 10.9GHz resonant frequency is realized. Results reveal that the proposed broadband antenna is a better choice for WiMAX, UWB, land, naval and airborne radar applications.

Compact antenna broadband microstrip feeding civil and military peak realized gain and impedance bandwidth

Abstract doi.org/10.14569/IJACSA.2019.0100906 PDF

7

Crowd-Generated Data Mining for Continuous Requirements Elicitation

Author 1: Ayed Alwadain Author 2: Mishari Alshargi

In software development projects, the process of requirements engineering (RE) is one in which requirements are elicited, analyzed, documented, and managed. Requirements are traditionally collected using manual approaches, including interviews, surveys, and workshops. Employing traditional RE methods to engage a large base of users has always been a challenge, especially when the process involves users beyond the organization’s reach. Furthermore, emerging software paradigms, such as mobile computing, social networks, and cloud computing, require better automated or semi-automated approaches for requirements elicitation because of the growth in systems users, the accessibility to crowd-generated data, and the rapid change of users’ requirements. This research proposes a methodology to capture and analyze crowd-generated data (e.g., user feedback and comments) to find potential requirements for a software system in use. It semi-automates some requirements-elicitation tasks using data retrieval and natural language processing (NLP) techniques to extract potential requirements. It supports requirements engineers’ efforts to gather potential requirements from crowd-generated data on social networks (e.g., Twitter). It is an assistive approach that taps into unused knowledge and experiences emphasizing continuous requirements elicitation during systems use.

Requirements engineering RE crowd data mining NLP Twitter continuous requirements elicitation

Abstract doi.org/10.14569/IJACSA.2019.0100907 PDF

8

Augmented Reality App for Teaching OOP

Author 1: Sana Rizwan Author 2: Arslan Aslam Author 3: Sobia Usman Author 4: Muhammad Moeez Ghauri

Now a days, there is demanding needs of developing interactive mediums of study. As our conventional methods of learning are not very effective. Programming has become one of the core subjects of every field of study due to their vast use. However, introducing computer programming to those students who’s are not familiar with programming is a tough task. Use interactive learning through visual effects using AR (Augmented Reality) developed to provide a platform for new students to interact more in learning environment. As this learning environment becomes more effective it is easier for new comers to understand key concepts of programming more effective way.

Augmented reality object-oriented programming unity visualization human computer interaction Vuforia rendering compiler

Abstract doi.org/10.14569/IJACSA.2019.0100908 PDF

9

A Readiness Evaluation of Applying e-Government in the Society: Shall Citizens begin to Use it?

Author 1: Laith T Khrais Author 2: Yara M. Abdelwahed Author 3: Mohammad Awni Mahmoud

As people are in the era of the web, most of the society is using networks in their daily task, governments had found, it is crucial to build an electronic entity that was named e-government, to make transactions easier for citizens, and to make government nearer to society. The objective of this study is to assess the extent of e-government application on different countries, particular in Jordan; in addition, several experiences were displayed in this study. The examination was qualitative through interviewing governmental employees for extracting results based on their answers, focusing on the continuity of using e-government by users as a dependent variable. The conclusion was that the policies are trending to build their e-government entity, and to make it available for citizens to use. Further, this study recommends the government to concentrate on the path of building individuals’ trust as well as using social influence to reinforce the idea of e-government service and evolve its usage.

e-Government citizens governmental transactions Jordan

Abstract doi.org/10.14569/IJACSA.2019.0100909 PDF

10

Generating and Analyzing Chatbot Responses using Natural Language Processing

Author 1: Moneerh Aleedy Author 2: Hadil Shaiba Author 3: Marija Bezbradica

Customer support has become one of the most important communication tools used by companies to provide before and after-sale services to customers. This includes communicating through websites, phones, and social media platforms such as Twitter. The connection becomes much faster and easier with the support of today's technologies. In the field of customer service, companies use virtual agents (Chatbot) to provide customer assistance through desktop interfaces. In this research, the main focus will be on the automatic generation of conversation “Chat” between a computer and a human by developing an interactive artificial intelligent agent through the use of natural language processing and deep learning techniques such as Long Short-Term Memory, Gated Recurrent Units and Convolution Neural Network to predict a suitable and automatic response to customers’ queries. Based on the nature of this project, we need to apply sequence-to-sequence learning, which means mapping a sequence of words representing the query to another sequence of words representing the response. Moreover, computational techniques for learning, understanding, and producing human language content are needed. In order to achieve this goal, this paper discusses efforts towards data preparation. Then, explain the model design, generate responses, and apply evaluation metrics such as Bilingual Evaluation Understudy and cosine similarity. The experimental results on the three models are very promising, especially with Long Short-Term Memory and Gated Recurrent Units. They are useful in responses to emotional queries and can provide general, meaningful responses suitable for customer query. LSTM has been chosen to be the final model because it gets the best results in all evaluation metrics.

Chatbot deep learning natural language processing similarity

Abstract doi.org/10.14569/IJACSA.2019.0100910 PDF

11

The Criteria for Software Quality in Information System: Rasch Analysis

Author 1: Wan Yusran Naim Wan Zainal Abidin Author 2: Zulkefli Mansor

Most of the organization uses information system to manage the information and provide better decision making in order to deliver high quality services. Due to that the information system must be reliable and fulfill the quality aspect in order to accommodate organization’s need. However, some of the information system still facing problems such as slow response time, problem with accessibility and compatibility issues between hardware and software. These problems will affect the acceptance and usage of the information system especially for non-computing users. Therefore, this study was aimed to investigate the factors that significantly contribute to the quality of software for information system. A survey was carried out by distributing a set of questionnaires to 174 respondents who are involved in development of software for information system. The data was analyzed using Rasch Measurement Model since it provides reliability of respondents and instruments. The result indicates that 30 factors had significantly contributed to the quality of software for information system and of these, six factors are under functionality, five for reliability, ten for usability, five for efficiency, two for compatibility and two for security. It is hoped that by identifying these factors, system developers can seriously consider of enhancing the quality of software for information system projects. In future, these factors can be used to develop an evaluation tool or metrix for quality aspects of software for information system projects.

Information system quality of software Rasch measurement model evaluation factors

Abstract doi.org/10.14569/IJACSA.2019.0100911 PDF

12

Chemical Reaction Optimization Algorithm to Find Maximum Independent Set in a Graph

Author 1: Mohammad A Asmaran Author 2: Ahmad A. Sharieh Author 3: Basel A. Mahafzah

Finding maximum independent set (MIS) in a graph is considered one of the fundamental problems in the computer science field, where it can be used to provide solutions for various real life applications. For example, it can be used to provide solutions in scheduling and prioritization problems. Unfortunately, this problem is one of the NP-problems of computer science, which limit its usage in providing solution for such problems with large sizes. This leads the scientists to find a way to provide solutions of such problems using fast algorithms to provide some near optimal solutions. One of the techniques used to provide solutions is to use metaheuristic algorithms. In this paper, a metaheuristic algorithm based on Chemical Reaction Optimization (CRO) is applied with various techniques to find MIS for application represented by a graph. The suggested CRO algorithm achieves accuracy percentages that reach 100% in some cases. This variation depends on the overall structure of the graph along with the picked parameters and colliding molecule selection criteria during the reaction operations of the CRO algorithm.

Chemical reaction optimization graph maximum independent set metaheuristic algorithm modified Wilf algorithm optimization problems

Abstract doi.org/10.14569/IJACSA.2019.0100912 PDF

13

DLBS: Decentralize Load-Balance Scheduling Algorithm for Real-Time IoT Services in Mist Computing

Author 1: Hosam E Refaat Author 2: Mohamed A.Mead

Internet of Things (IoT) has been industrially investigated as Platforms as a Services (PaaS). The naive design of these types of services is to join the classic centralized Cloud computing infrastructure with IoT services. This joining is also called CoT (Cloud of Things). In spite of the increasing resource utilization of cloud computing, but it faces different challenges such as high latency, network failure, resource limitations, fault tolerance and security etc. In order to address these challenges, fog computing is used. Fog computing is an extension of the cloud system, which provides closer resources to IoT devices. It is worth mentioning that the scheduling mechanisms of IoT services work as a pivotal function in resource allocation for the cloud, or fog computing. The scheduling methods guarantee the high availability and maximize utilization of the system resources. Most of the previous scheduling methods are based on centralized scheduling node, which represents a bottleneck for the system. In this paper, we propose a new scheduling model for manage real time and soft service requests in Fog systems, which is called Decentralize Load-Balance Scheduling (DLBS). The proposed model provides decentralized load balancing control algorithm. This model distributes the load based on the type of the service requests and the load status of each fog node. Moreover, this model spreads the load between system nodes like wind flow, it migrates the tasks from the high load node to the closest low load node. Hence the load is expanded overall the system dynamically. Finally, The DLBS is simulated and evaluated on truthful fog environment.

Cloud computing fog computing mist computing IoT load balancing reliability

Abstract doi.org/10.14569/IJACSA.2019.0100913 PDF

14

Decision Making Systems for Managing Business Processes in Enterprises Groups

Author 1: Ali F Dalain

In the current economic realities, the forms of integration business entities through the creation of enterprise groups (EGs), reorganized from industry structures or created a new by acquiring existing companies, are becoming increasingly relevant. The economic activity of the enterprise is carried out in the conditions of economic instability and improvement of the system economic relations, which imposes fundamentally new requirements in the sphere of managing the interaction of enterprises. Under these conditions, the successful development of the enterprises and often their very existence depend both on the effective use of the management systems themselves and on the competence of the management decisions made. Consequently, for decision makers and managers of Group Policy (GP), the problem of evaluating the development of GP and promptly making sound management decisions in an unstable and rapidly changing economic environment is considered a particular relevance. One of the promising ways to solve this problem is the development of decision support systems (DSS), using scientifically based decision-making methods based on modern mathematical apparatus and computer equipment. At present, the approach to managing the development of the EGs is associated with the representation of the latter as a multi-agent system (MAS). The DSS does not replace, but complements the existing management systems in the EGs, interacting with them, and uses in its work information about the functioning of EGs units.

Management systems decision support systems multi-agent systems group policy enterprise groups

Abstract doi.org/10.14569/IJACSA.2019.0100914 PDF

15

An Extended Consistent Fuzzy Preference Relation to Evaluating Website Usability

Author 1: Tenia Wahyuningrum Author 2: Azhari Azhari Author 3: Suprapto

In the current era, website developers recognize usability evaluation as a significant factor in the quality and success of e-commerce websites. Fuzzy Analytical Hierarchy Process (FAHP) is one method to measure the usability of the website. Several researchers have applied Logarithmic Fuzzy Preference Programming (LFPP) approach to deriving crisp weight from fuzzy pairwise comparison matrix of FAHP approach. However, there is a lack of LFPP method in determining the consistency index of the decision-maker judgment. In some cases, LFPP method will produce a consistency value of 0 from consistent fuzzy comparison matrices. This value indicates there is a contradiction with what the previous researchers have said, that a constant matrix value should be more than 0. This research proposes the extended Consistent Fuzzy Preference Relation (ECFPR) to assist the regular judgment for specifying the weights in measuring e-commerce website usability. The CFPR method used to form a new pairwise comparison matrix. ECFPR was calculating the lower and upper values at the fuzzy triangular number from the only n-1 comparison, where n is the number of criteria. The numerical experiment showed that the consistency index obtained by extended CFPR method was more significantly better than LFPP method. It was revealed that the optimal value always more than 0. The consistency index of ECFPR method has a higher mean value than LFPP, so that the use of the ECFPR method can improve the amount of consistency comparison matrices. The ECFPR method was also successfully implemented with the experimental case on evaluating e-commerce website usability.

Usability e-commerce website quality logarithmic fuzzy preference programming consistent fuzzy preference relations

Abstract doi.org/10.14569/IJACSA.2019.0100915 PDF

16

Socialization of Information Technology Utilization and Knowledge of Information System Effectiveness at Hospital Nurses in Medan, North Sumatra

Author 1: Roymond H Simamora

Background of this research is the globalization and development of science, especially in the field of information and communication technology and communication that has influenced and has implications for changes and renewal of people's lives, including in the field of nursing. So that the role of information and communication in this aspect of life is very important, even the futurists, for the most part, have an agreement that one of the most important strengths as the source of future power is information. Purpose: identify the use of information technology in nursing to determine the effectiveness of the use of information systems in nursing, identify nurses 'knowledge about the effectiveness of nursing information systems, identify nurses' knowledge seen from the socialization of the effectiveness of nursing information systems. Method: Quantitative Research Type with a survey approach conducted on 220 nurses. Significant validity test is <0.05, Cronbach Alpha reliability test> 0.60. The data is then tested in a classic assumption test consisting of multicollinearity tests, autocorrelation tests, heteroscedasticity tests, normality tests, multiple linear regression, t-tests, F tests, coefficient of determination tests. Results: the use of information technology affects the effectiveness of nursing information systems. Nurse knowledge does not affect the effectiveness of nursing information systems. Nurse knowledge seen from socialization does not affect the effectiveness of nursing information systems. The use of information technology and nurse knowledge influences the effectiveness of nursing information systems. The results of the coefficient of determination that affect the use of information technology, knowledge of nurses, socialization as a control variable on the effectiveness of nursing information systems. Suggestion: Hospital managers must pay attention to the quality of nursing human resources, through training, certification, recognition of competencies, supervision, selection, and guidance aimed at improving safe, comfortable and satisfying services for patients, families, communities.

Information systems knowledge nursing socialization

Abstract doi.org/10.14569/IJACSA.2019.0100916 PDF

17

Line Area Monitoring using Structural Similarity Index

Author 1: Abderrahman AZI Author 2: Abderrahim SALHI Author 3: Mostafa JOURHMANE

Real-time motion detection in specific area is considered the most important task in every video surveillance system. In this paper, a novel real time motion detection algorithm introduced to process Line zones called Line Monitoring Algorithm (LMA). This algorithm integrates Bresenham’s Algorithm and Structural Similarity Index (SSI) in order to achieve the best performance. Bresenham’s Algorithm is used to collect line pixels from two given points. Then, the SSI is used for real-time calculation of similarity for line motion detection. The most attractive side of using the LMA is that the algorithm does not need to compare all pixels of the whole images or regions for line areas monitoring. This algorithm has high capability, treatment speed and efficiency for motion detection and also demands less compilation time for the hardware performance. The main objective of this paper is to use a video surveillance system implementing LMA to supervise the Car Reverse Test (CRT) for driving license exam in Morocco. The evaluation of the experiment results in implementing the proposed algorithm is reported in this paper.

Bresenham’s Algorithm Structural Similarity Index SSI motion detection Line Monitoring Algorithm LMA OpenCV surveillance camera video surveillance system

Abstract doi.org/10.14569/IJACSA.2019.0100917 PDF

18

Customers Churn Prediction using Artificial Neural Networks (ANN) in Telecom Industry

Author 1: Yasser Khan Author 2: Shahryar Shafiq Author 3: Abid Naeem Author 4: Sheeraz Ahmed Author 5: Nadeem Safwan Author 6: Sabir Hussain

To survive in the fierce competition of telecommunication industry and to retain the existing loyal customers, prediction of potential churn customer has become a crucial task for practitioners and academicians through predictive modeling techniques. The identification of loyal customers can be done through efficient predictive models. By allocation of dedicated resources to the retention of these customers would control the flow of dissatisfied consumers thinking to leave the company. This paper proposes artificial neural network approach for prediction of customers intending to switch over to other operators. This model works on multiple attributes like demographic data, billing information and usage patterns from telecom companies data set. In contrast with other prediction techniques, the results from Artificial Neural Networks (ANN) based approach can predict the telecom churn with accuracy of 79% in Pakistan. The results from artificial neural network are clearly indicating the churn factors, hence necessary steps can be taken to eliminate the reasons of churn.

Neural Network ANN prediction churn management

Abstract doi.org/10.14569/IJACSA.2019.0100918 PDF

19

Modified Seam Carving by Changing Resizing Depending on the Object Size in Time and Space Domains

Author 1: Kohei Arai

Modified seam carving by switching from the conventional method to resizing method depending on the object size is proposed. When the object size is dominant in the scene of interest, the conventional seam carving shows deformation of components in the object. To avoid the situation, resizing method is applied rather than the conventional seam carving in the proposed method. Also, the method for video data compression based on the seam carving not only in image space domain but also in time domain is proposed. It is specific feature that original quality of video picture can be displayed when it is replayed. Using frame to frame similarity defined with histograms distance between the neighboring frames, frames which have great similarity can be carved results in data is compressed in time domain. Moreover, such carved frame can be recorded in the frame header so that the carved frame can be recovered in reproducing the compressed video. Thus, video quality can be maintained, no degradation of video quality at all. Compression ratio is assessed with the several video data. It is obvious that data compression ratio of the proposed space and time domain seam carving is greater than that of the conventional space domain seam carving.

Seam carving data compression in time and space domains video data compression

Abstract doi.org/10.14569/IJACSA.2019.0100919 PDF

20

A Novel Approach to Rank Text-based Essays using Pagerank Method Towards Student’s Motivational Element

Author 1: M Zainal Arifin Author 2: Naim Che Pee Author 3: Nanna Suryana Herman

Learning outcomes is one of the important factors to measure student achievement during the learning process. Today’s learning is more focused on problem-solving and reasoning to existing problems than an ordinary problem. Most exams have been directed to analysis questions for students to think and synthesize. As such is troublesome for most students, they are not ready to answer the question, thus, their answers almost similar to their friends. This implies that the teacher has tried to guide students to work professionally and originally. However, the Teacher facing difficulty assessing student’s work, especially if the assignments are conducted online without face-to-face instructions/discussions. To bridge such a gap, the teacher needs a method or algorithm to measure their rank to encourage students making an original answer. This research provides a solution in calculating students' ranks based on the similarity score of the essay answers. Pagerank is a ranking algorithm used by Google, this algorithm utilizes a Markov matrix that contains the direction of similarity score for each student. These scores are computed by the power method until converging. Rank is displayed to the teacher to review the similarity level of students' answers. As such is presented a line chart in which the x-axis refers to the students and the y-axis depicts the level of similarity. Ranking computation in Matlab produces an Eigen vector which acts as the rank measure. The higher the rank, the more similar is their answers to others. Hence, students with high ranks to work their answers more seriously ensure their original thoughts. In conclusion, the similarity score matrix using the PageRank algorithm can contribute to the teacher in providing peer motivation and encouraging student’s internal motivation by presenting the ranked-answers presentation.

Pagerank learning outcomes similarity

Abstract doi.org/10.14569/IJACSA.2019.0100920 PDF

21

Optimal Control and Design of Electrical Machines

Author 1: Wissem BEKIR Author 2: Lilia EL AMRAOUI Author 3: Frédéric GILLON

This paper presents a global optimization approach aiming to improve the energy efficiency of electrical machines. The process is made on a hybrid stepper motor allowing to simultaneously optimize design and command. This approach is axed around Pontryagin's maximum principle, which is applied to a magnetodynamic model based on permeances network model. The originality of the proposed approach is to obtain in the same process, the minimization of the energy by optimal control and the minimization of the energy by optimal sizing.

Optimal control optimal sizing Pontryagin’s maximum principle permeances network hybrid stepper motor energetic efficiency

Abstract doi.org/10.14569/IJACSA.2019.0100921 PDF

22

The Model of Game-based Learning in Fire Safety for Preschool Children

Author 1: Nur Atiqah Zaini Author 2: Siti Fadzilah Mat Noor Author 3: Tengku Siti Meriam Tengku Wook

The Model of Game-based Learning in Fire Safety developed for preschool children to educate them in learning fire safety issues. Due to the lack of awareness towards fire hazard, there are few factors that have arisen regarding this issue such as children’s ages, experiences and knowledge. The main objective of this study is to identify the user requirements of preschool children in developing the Model of Game-Based Learning in Fire Safety. This study involved six preschool children of Tabika Kemas Kampung Berawan, Limbang Sarawak by using User-Centered Design method. The ability of cognitive, behavior and psychomotor skills are the main aspects to develop the model. Thus, to lower the risk of injuries during practical training in real situation, there is a need to educate them using the technology of tablet. Therefore, a prototype has been developed known as APi Game-Based Learning as a platform for children to learn about fire safety issues. Hence, this APi prototype developed to validate the Model of Game-Based Learning in Fire Safety development for preschool children. Thus, the finding of the study showed the engagement of children in learning fire safety through game improved their knowledge, behavior and psychomotor skills. Overall, this study makes an important contribution in determining the usability on the level of effectiveness towards preschool children through active learning.

Game-based learning fire safety user-centered design effectiveness

Abstract doi.org/10.14569/IJACSA.2019.0100922 PDF

23

A Defeasible Logic-based Framework for Contextualizing Deployed Applications

Author 1: Noor Sami Al-Anbaki Author 2: Nadim Obeid Author 3: Khair Eddin Sabri

In human to human communication, context increases the ability to convey ideas. However, in human to application and application to application communication, this property is difficult to attain. Context-awareness becomes an emergent need to achieve the goal of delivering more user-centric personalized services, especially in ubiquitous environments. However, there is no agreed-upon generic framework that can be reused by deployed applications to support context-awareness. In this paper, a defeasible logic-based framework for context-awareness is proposed that can enhance the functionality of any deployed application. The nonmonotonic nature of defeasible logic has the capability of attaining justifiable decisions in dynamic environments. Classical defeasible logic is extended by meta-rules to increase its expressiveness power, facilitate its representation of complex multi-context systems, and permit distributed reasoning. The framework is able to produce justified decisions depending on both the basic functionality of the system that is itself promoted by contextual knowledge and any cross-cutting concerns that might be added by different authorities or due to further improvements to the system. Active concerns that are triggered at certain contexts are encapsulated in separate defeasible theories. A proof theory is defined along with a study of its formal properties. The framework is applied to a motivating scenario to approve its feasibility and the conclusions are analyzed using argumentation as an approach of reasoning.

Context-awareness nonmonotonicity defeasible logic distributed reasoning argumentation

Abstract doi.org/10.14569/IJACSA.2019.0100923 PDF

24

Investigation of Pitch and Duration Range in Speech of Sindhi Adults for Prosody Generation Module

Author 1: Shahid Ali Mahar Author 2: Mumtaz Hussain Mahar Author 3: Shahid Hussain Danwar Author 4: Javed Ahmed Mahar

Prosody refers to structure of sound and rhythm and both are essential parts of speech processing applications. It comprises of tone, stress, intonation and rhythm. Pitch and duration are the core elements of acoustic and that information can make easy to design and development for application module. Through these two peculiarities, the prosody module can be validated. These two factors have been investigated using the sounds of Sindhi adults and presented in this paper. For the experiment and analysis, 245 male and female undergraduate students were selected as speakers belonging from five different districts of upper Sindh and categorized into groups according to their age. Particular sentences were given and recorded individually from the speakers. Afterward, these sentences segmented into words and stored in a database consisting of 1960 sounds. Thus, distance of the frequency in pitch was measured via Standard Deviation (SD). The lowest Mean SD accompanied 0.25Hz and 0.28Hz received from male and female group of district Sukkur. The highest Mean SD has measured with male and female group of district Ghotki along 0.42Hz and 0.49Hz. Generally, the pitch of female’s speakers was found high in contrast to male’s speaker by 0.072Hz variation.

Prosody generation speech analysis pitch duration Sindhi sounds

Abstract doi.org/10.14569/IJACSA.2019.0100924 PDF

25

Classification of C2C e-Commerce Product Images using Deep Learning Algorithm

Author 1: Herdian Author 2: Gede Putra Kusuma Author 3: Suharjito

C2C (consumer-to-consumer) is a business model where two individuals transact or conduct business with each other using a platform. A consumer act as a seller put their product in a platform later will be displayed to another consumer act as a buyer. This condition encourages platform to maintain high quality product information especially image that is provided by the seller. Product images need to be relevant to the product itself. It can be controlled automatically using image classification. In this paper, we carried out a research to find out the best deep learning model in image classification for e-commerce products. A dataset of 12,500 product images is collected from various web sources to be used in training and testing process. Five models are selected and fine-tuned using a uniform hyperparameter set-up. Those hyperparameters are found by using a manual process by trying a lot of hyperparameters. The testing result from every model is presented and evaluated. The result shows that NASNetLarge yield the best performance among all evaluated models with 84% testing accuracy.

Image classification e-commerce product images deep learning hyperparameter tuning

Abstract doi.org/10.14569/IJACSA.2019.0100925 PDF

26

Design and Learning Effectiveness Evaluation of Gamification in e-Learning Systems

Author 1: Mohammad T Alshammari

This paper proposes a gamification design model that can be used to design and develop gamified e-learning systems. Furthermore, a controlled and carefully designed experimental evaluation in terms of learning effectiveness of gamification is offered. The experiment was conducted with 44 participants randomly assigned to an experimental ‘gamified’ condition and a controlled ‘non-gamified’ condition. In both conditions the same learning material, to teach computer security, were used. The main difference between the two conditions was the integration of gamification in an e-learning system designed based on the proposed model. The results indicate that learning using the gamified version of the e-learning system produces better short-term and medium-term learning gain than learning using the non-gamified e-learning version. Future avenues of research are also provided.

Gamification e-learning systems interaction design experimental evaluation

Abstract doi.org/10.14569/IJACSA.2019.0100926 PDF

27

Establishing News Credibility using Sentiment Analysis on Twitter

Author 1: Zareen Sharf Author 2: Zakia Jalil Author 3: Wajiha Amir Author 4: Nudrat Siddiqui

The widespread use of Internet has resulted in a massive number of websites, blogs and forums. People can easily discuss with each other about different topics and products, and can leave reviews to help out others. This automatically leads to a necessity of having a system that may automatically extract opinions from those comments or reviews to perform a strong analysis. So, it may help out businesses to know the opinions of people about their products/services so they can make further improvements. Sentiment Analysis or Opinion Mining is the system that intelligently performs classification of sentiments by extracting those opinions or sentiments from the given text (or comments or reviews). This paper presents a thorough research work carried out on tweets’ sentiment analysis. An area-specific analysis is done to determine the polarity of extracted tweets for make an automatic classification that what recent news people have liked or disliked. The research is further extended to perform retweet analysis to describe the re-distribution of reactions on a specific twitter post (or tweet).

Sentiment analysis tweets opinion mining

Abstract doi.org/10.14569/IJACSA.2019.0100927 PDF

28

A Nested Genetic Algorithm for Mobile Ad-Hoc Network Optimization with Fuzzy Fitness

Author 1: NourElDin S Eissa Author 2: Ahmed Zakaria Talha Author 3: Ahmed F. Amin Author 4: Amr Badr

One of the major culprits that faces Mobile Ad-hoc networks (MANET) is broadcasting, which constitutes a very important part of the infrastructure of such networks. This paper presents a nested genetic algorithm (GA) technique with fuzzy logic-based fitness that optimizes the broadcasting capability of such networks. While normally the optimization of broadcasting is considered as a multi-objective problem with various output parameters that require tuning, the proposed system taps another approach that focuses on a single output parameter, which is the network reachability time. This is the time required for the data to reach a certain percentage of connected clients in the network. The time is optimized by tuning different decision parameters of the Delayed Flooding with Cumulative Neighborhood (DFCN) broadcasting protocol. The proposed system is developed and simulated with the help of the Madhoc network simulator and is applied on different realistic real-life scenarios. The results reveal that the reachability time responds well to the suggested system and shows that each scenario responds differently to the tuning of decision parameters.

Broadcasting DFCN fuzzy logic genetic algorithms Madhoc simulator MANET

Abstract doi.org/10.14569/IJACSA.2019.0100928 PDF

29

Identification of Issues and Challenges in Romanized Sindhi Text

Author 1: Irum Naz Sodhar Author 2: Akhtar Hussain Jalbani Author 3: Muhammad Ibrahim Channa Author 4: Dil Nawaz Hakro

Now-a-days Sindhi language is widely used in internet for the various purposes such as: newspapers, Sindhi literature, books, educational/official websites and social networks communications, teaching and learning processes. Having developed technology of computer system, users face difficulties and problems in writing Sindhi script. In this study, various issues and challenges come in the Romanized Sindhi text by using Roman transliteration (Sindhi text (ST) forms of Romanized Sindhi text) are identified. These acknowledged issues are known as noise, written script of Romanized and its style, space issues in Romanized script, some characters not suitable in Romanized Sindhi, as a paragraph, rows, character issues, punctuation, row break and font style. However, this study provides the summary of issues and challenges of Romanized Sindhi text. This research work provides detailed information of issues and challenges faced by people during chatting in Romanized Sindhi text.

Romanized Sindhi Text (RST) Sindhi language issues and challenges transliterator social networks communication

Abstract doi.org/10.14569/IJACSA.2019.0100929 PDF

30

Strategic Planning towards Automation of Fiber To The Home (FTTH) Considering Optic Access Network (OAN) Model

Author 1: Abid Naeem Author 2: Shahryar Shafique Author 3: Zahid Wadud Author 4: Sheeraz Ahmed Author 5: Nadeem Safwan Author 6: Zeeshan Najam

With the intention to meet the increasing demand of future higher bandwidth applications, fiber based Gigabit Passive Optical Network (GPON) access is considered best resolution to deliver triple play services (voice, data, video). Hence, it becomes obligatory to migrate from traditional copper-based network to fiber-based. Due to rapid technological evolution, tough competition and budget limitation the service providers are struggling to provide a cost effective solution to minimize their operational cost with extra ordinary customer satisfaction. One of the factors that increase the cost of overall Fiber To The Home (FTTH) network is the unplanned deployment resulting in utilization of extra components and resources. Hence, it is imperative to determine a suitable technique, which helps to reduce planning process, required time and deployment cost through optimization. Automation based planning is one of the possible ways to automate the network design at probable lowest cost. In this research, a planning technique for migration from copper to fiber access network with a manageable and optimized Passive Optic Network (PON –FTTx) infrastructure is presented identifying a cost-effective strategy for developing countries.

Fiber To The Home Passive Optical Networks GPON triple play cost effective customer satisfaction

Abstract doi.org/10.14569/IJACSA.2019.0100930 PDF

31

Performance Evaluation of Different Data Mining Techniques for Social Media News Credibility Assessment

Author 1: Sahar F Sabbeh

Social media has recently become a basic source for news consumption and sharing among millions of users. Social media platforms enable users to publish and share their own generated content with little or no restrictions. However, this gives an opportunity for the spread of inaccurate or misleading content, which can badly affect users’ beliefs and decisions. This is why credibility assessment of social media content has recently received tremendous attention. The majority of the studies in the literature focused on identifying features that provide a high predictive power when fed to data mining models and select the model with the highest predictive performance given those features. Results of these studies are conflicting regarding the best model. Additionally, they disregarded the fact that real-time credibility assessment is needed and thus time and resources consumption is crucial for model selection. This study tries to fill this gap by investigating the performance of different data mining techniques for credibility assessments in terms of both functional and operational characteristics for a balanced evaluation that considers both model performance and interoperability.

Data mining performance evaluation news credibility Twitter social media

Abstract doi.org/10.14569/IJACSA.2019.0100931 PDF

32

Rule-based Emotion AI in Arabic Customer Review

Author 1: Mohamed M Abbassy Author 2: Ayman Abo-Alnadr

The e-commerce emotion analysis is notable and the most pivotal advance since it catches the customer emotion in a product, and emotions with respect to product to decide if the customer attitude is negative, positive, or neutral. Posting on the customer's reviews have turned into an undeniably famous path for individuals to share with different customers their emotion and feelings toward a product. This review has a significant impact on sales in the future. The proposed system utilizes mixed word from an adjective (adj) and adverb to improve the emotion analysis process utilized a rule-based emotion analysis. The system extracts an Arabic customer review and computes the frequency of each word. At that point, it computes the emotion and score of each customer review. The system likewise computes the emotion and score of straightforward Arabic sentence.

Component rule-based emotion customer review Arabic

Abstract doi.org/10.14569/IJACSA.2019.0100932 PDF

33

Microcontroller-based Vessel Passenger Tracker using GSM System: An Aid for Search and Rescue Operations

Author 1: Joel I Miano Author 2: Ernesto E. Empig Author 3: Alexander R. Gaw Author 4: Ofelia S. Mendoza Author 5: Danilo C. Adlaon Author 6: Sheena B. Cañedo Author 7: Roan Duval A. Dangcal Author 8: Angelie S. Sumalpong

The Maritime Transport industry in the Philippines has been growing through the years and has been a catalyst in the industrial development of the country. Although the maritime transport sector is one of the largest industries in the country, the safety devices and technology used are sluggish phase to change. The natural hazards and human error are main cause of maritime incidents, resulting to multiple casualties and missing persons every year of which this study seek to address the problem of safety in the maritime transport industry. The study aims to design and develop a system that will locate an overboard1 passenger whenever a vessel is in distress. The Floating Overboard Accident Tracking System (FLOATS) was conceptualized by combining the Search Theory, Theory of Planned Behavior (TPB) and Disaster Preparedness, and the increasing availability of tracking device and monitoring technologies and the advancement of communication systems. The system consists of the Global Positioning System (GPS) for location data, Global System for Mobile (GSM) communications for the transmission and reception of emergency messages, Arduino-Nano microcontroller to handle the processing, the used of an inflatable life jacket with signal light and a rescue update display using an organic light emitting diode (OLED) for the search and rescue operations. Tests and surveys established the functionality, reliability, and acceptability of the system, which will greatly benefit maritime incident responders by securing vessel passengers from hazards and reducing the time allotted through speedy search and rescue operations.

Global Positioning System (GPS) Global System for Mobile communications (GSM) Organic Light Emitting Diode (OLED) Arduino-Nano microcontroller tracking system life jacket life jacket light

Abstract doi.org/10.14569/IJACSA.2019.0100933 PDF

34

Utilizing Feature Selection in Identifying Predicting Factors of Student Retention

Author 1: January D Febro

Student retention is an important issue faced by Philippine higher education institutions. It is a key concern that needs to be addressed for the reason that the knowledge they gain can contribute to the economic and community development of the country aside from financial stability and employability. University databases contain substantial information that can be queried for knowledge discovery that will aid the retention of students. This work aims to analyze factors associated with student’s success among first-year students through feature selection. This is a critical step prior to modelling in data mining, as a way to reduce computational process and improve prediction performance. In this work, filter methods are applied on datasets queried from university database. To demonstrate the applicability of this method as a pre-processing step prior to data modelling, predictive model is built using the selected dominant features. The accuracy result jumps to 92.09%. Also, through feature selection technique, it was revealed that post-admission variables are the dominant predictors. Recognizing these factors, the university could improve their intervention programs to help students retain and succeed. This only shows that doing feature selection is an important step that should be done prior to designing any predictive model.

Educational data mining feature selection data preprocessing knowledge discovery student retention

Abstract doi.org/10.14569/IJACSA.2019.0100934 PDF

35

An Enhanced Deep Learning Approach in Forecasting Banana Harvest Yields

Author 1: Mariannie A Rebortera Author 2: Arnel C Fajardo

This technical quest aspired to build deep multifaceted system proficient in forecasting banana harvest yields essential for extensive planning for a sustainable production in the agriculture sector. Recently, deep-learning (DL) approach has been used as a new alternative model in forecasting. In this paper, the enhanced DL approach incorporates multiple long short term memory (LSTM) layers employed with multiple neurons in each layer, fully trained and built a state for forecasting. The enhanced model used the banana harvest yield data from agrarian reform beneficiary (ARB) cooperative of Dapco in Davao del Norte, Philippines. The model parameters such as epoch, batch size and neurons underwent tuning to identify its optimal values to be used in the experiments. Additionally, the root-mean-squared error (RMSE) is used to evaluate the performance of the model. Using the same set of training and testing data, experiment exhibits that the enhanced model achieved the optimal result of 34.805 in terms of RMSE. This means that the enhanced model outperforms the single and multiple LSTM layer with 43.5 percent and 44.95 percent reduction in error rates, respectively. Since there is no proof that LSTM recurrent neutral network has been used with the same agricultural problem domain, therefore, there is no standard available with regards to the level of error reduction in the forecast. Moreover, investigating the performance of the model using diverse datasets specifically with multiple input features (multivariate) is suggested for exploration. Furthermore, extending and embedding this approach to a web-based along with a handy application is the future plan for the benefit of the medium scale banana growers of the region for efficient and effective decision making and advance planning.

Yield forecasting Deep Learning Long short-term memory Banana harvest yield forecasting

Abstract doi.org/10.14569/IJACSA.2019.0100935 PDF

36

Developing a Dengue Forecasting Model: A Case Study in Iligan City

Author 1: Ian Lindley G Olmoguez Author 2: Mia Amor C. Catindig Author 3: Minchie Fel Lou Amongos Author 4: Fatima G. Lazan

Dengue is a viral mosquito-borne infection that is endemic and has become a major public health concern in the Philippines. Cases of dengue in the country have been recorded to be increasing, however, it is reported that the country lacks predictive system that could aid in the formulation of an effective approach to combat the rise of dengue cases. Various studies have reported that climatic factors can influence the transmission rate of dengue. Thus, this study aimed to predict the probability of dengue incidence in Iligan City per barangay based on the relationship of climatic factors and dengue cases using different predictive models with data from 2008 to 2017. Multiple Linear Regression, Poisson Regression, and Random Forest are integrated in a mini-system to automate the display of the prediction result. Results indicate that Random Forest works better with 73.0% accuracy result and 33.58% error percentage, with time period and mean temperature as predictive variables.

Dengue predictive models Pearson’s correlation multiple linear regression Poisson regression random forest

Abstract doi.org/10.14569/IJACSA.2019.0100936 PDF

37

Performance Evaluation of Network Gateway Design for NoC based System on FPGA Platform

Author 1: Guruprasad S P Author 2: Chandrasekar B.S

Network on Chip (NoC) is an emerging interconnect solution with reliable and scalable features over the System on Chip (SoC) and helps to overcome the drawbacks of bus-based interconnection in SoC. The multiple cores or other networks have a boundary which is limited to communicate with devices, which are directly connected to it. To communicate with these multiple cores outside the boundary, the NOC requires the gateway functionality. In this manuscript, a cost-effective Network Gateway (NG) model is designed, and also the interconnection of a network gateway with multiple cores are connected to the NoC based system is prototyped on Artix-7 FPGA. The NG mainly consists of Serializer and deserializer for transmitting and receiving the data packets with proper synchronization, temporary register to hold the network data, electronic crossbar switch is connected with multiple cores which are controlled by switch controller. The NG with the Router and different sizes of NoC based system is designed using congestion-free adaptive-XY routing. The implementation results and performance evaluation are analyzed for NG based NoC in terms of average Latency and maximum Throughput for different Packet Injection Ratio (PIR). The proposed Network gateway achieves low latency and high throughput in NoC based systems for different PIR.

Network gateway network on chip FPGA routing network interface crossbar switch

Abstract doi.org/10.14569/IJACSA.2019.0100937 PDF

38

Wireless Multimedia Sensor Networks based Quality of Service Sentient Routing Protocols: A Survey

Author 1: Ronald Chiwariro Author 2: Thangadurai. N

Improvements in nanotechnology have introduced contemporary sensory devices that are capable of gathering multimedia data in form of images, audio and video. Wireless multimedia sensor networks are designed to handle such type of heterogeneous traffic. The ability to handle scalar and non-scalar data has led to the development of various real-time applications such as security surveillance, traffic monitoring and health systems. Since, these networks are an emergent of wireless sensor networks; they inherit constraints that exist in these traditional networks. Particularly, these networks suffer from quality of service and energy efficiency due to the nature of traffic. This paper presents the characteristics and requirements of wireless multimedia sensor networks and approaches to mitigate existing challenges. Furthermore, a review of recent research on multipath routing protocols and multi-channel media access protocols that have quality of service assurances and energy efficiency in handling multimedia data have included.

Quality of service multipath routing multi-channel media access control energy efficiency

Abstract doi.org/10.14569/IJACSA.2019.0100938 PDF

39

Embedded System Interfacing with GNSS user Receiver for Transport Applications

Author 1: Mohmad Umair Bagali Author 2: Thangadurai. N

The real time vehicle movement traces using waypoint display on the base-map with IRNSS/NavIC and GPS dataset in the GUI simultaneously. In this paper, a portable electronic device with application software has been designed and developed, which would be used to capture the real time positional information of a rover using IRNSS-UR. It stores the positional information into database and displays the real time vehicle positional information like date, time, latitude, longitude and altitude using both GPS and IRNSS/NavIC receiver simultaneously. The designed hardware device with an application software developed helps in mapping the real time vehicle / rover movement at the same time which also helps in identifying the region with data loss, varying positional information, comparing the distance travelled by rover and also aid in retrieving the past surveys and mapping the traces of both IRNSS and GPS simultaneously. The vehicle movement using both IRNSS/NavIC and GPS are tracked on the base map to find the similarity and differences between two. During this research work it can be conclude that that the rover position using GPS and IRNSS were accurate and continuous in our survey duration except in few places. In that few places the data loss is observed because of the satellite visibility variations. For Indian region the IRNSS/NavIC can be a better replacement for GPS.

GNSS GPS IRNSS embedded systems

Abstract doi.org/10.14569/IJACSA.2019.0100939 PDF

40

Empirical Performance Analysis of Decision Tree and Support Vector Machine based Classifiers on Biological Databases

Author 1: Muhammad Amjad Author 2: Zulfiqar Ali Author 3: Abid Rafiq Author 4: Nadeem Akhtar Author 5: Israr-Ur-Rehman Author 6: Ali Abbas

The classification and prediction of medical diseases is a cutting edge research problem in the medical field. The experts of machine learning are continuously proposing new classification methods for the prediction of diseases. The discovery of classification rules from medical databases for classification and prediction of diseases is a challenging and non-trivial task. It is very significant to investigate the more promising and efficient classification approaches for the discovery of classification rules from the medical databases. This paper focuses on the problem of selection of more efficient, promising and suitable classifier for the prediction of specific diseases by performing empirical studies on bunch mark medical databases. The research work under the focus concentrates on the benchmark medical data sets i.e. arrhythmia, breast-cancer, diabetes, hepatitis, mammography, lymph, liver-disorders, sick, cardiotocography, heart-statlog, breast-w, and lung-cancer. The medical data sets are obtained from the open-source UCI machine learning repository. The research work will be investigating the performance of Decision Tree (i.e. AdaBoost.NC, C45-C, CART, and ID3-C) and Support Vector Machines. For experimentation, Knowledge Extraction based on Evolutionary Learning (KEEL), a data mining tool will be used. This research work provides the empirical performance analysis of decision tree-based classifiers and SVM on a specific dataset. Moreover, this article provides a comparative performance analysis of classification approaches in terms of statistics.

Classification rules discovery support vector machine decision tree

Abstract doi.org/10.14569/IJACSA.2019.0100940 PDF

41

Computer-based Approach to Detect Wrinkles and Suggest Facial Fillers

Author 1: Amal Alrabiah Author 2: Mai Alduailij Author 3: Martin Crane

Modern medical practice has embraced facial filler injections as part of the innumerable cosmetic procedures that characterize the current age of medicine. This study proposed a novel methodological framework. The Inception model is the core of the framework. By carefully detecting the classification of wrinkles, the model can be built for different applications to aid in the detection of wrinkles that can objectively help in deciding if the forehead area needs to have filler injections. The model achieved an accuracy of 85.3%. To build the Inception model, a database has been prepared containing face forehead images, including both wrinkled and non-wrinkled face foreheads. The face image pre-processing is the first step of the proposed framework, which is important for reliable feature extraction. First, in order to detect the face and facial landmarks in the image, a Multi-task Cascaded Convolutional Networks model has been used. Before feeding the images into the deep learning Inception model for classifying whether the face foreheads have wrinkles or no wrinkles, an image cropping process is required. Given the bounding box and the facial landmarks, face foreheads can be cropped accurately. The last step of the proposed methodology is to retrain an Inception model for the new categories (Wrinkles, No Wrinkles) to predict whether a face forehead has wrinkles or not.

Deep learning classification facial fillers wrinkle detection

Abstract doi.org/10.14569/IJACSA.2019.0100941 PDF

42

On Some Methods for Dimensionality Reduction of ECG Signals

Author 1: Monica Fira Author 2: Liviu Goras

Dimensionality reduction with two methods, namely, Laplacian Eigenmap (LE) and Locality Preserving Projections (LPP) is studied for normal and pathological noisy and noiseless ECG patterns. Besides, the possibility of using compressed sensing (CS) as a method of dimensionality reduction is also analyzed. The classification rate for the initial domain as well as in manifolds of various dimensions for the three cases are presented and compared.

Dimensionality reduction compressed sensing electrocardiography (ECG)

Abstract doi.org/10.14569/IJACSA.2019.0100942 PDF

43

Fraud Detection using Machine Learning in e-Commerce

Author 1: Adi Saputra Author 2: Suharjito

The volume of internet users is increasingly causing transactions on e-commerce to increase as well. We observe the quantity of fraud on online transactions is increasing too. Fraud prevention in e-commerce shall be developed using machine learning, this work to analyze the suitable machine learning algorithm, the algorithm to be used is the Decision Tree, Naive Bayes, Random Forest, and Neural Network. Data to be used is still unbalance. Synthetic Minority Over-sampling Technique (SMOTE) process is to be used to create balance data. Result of evaluation using confusion matrix achieve the highest accuracy of the neural network by 96 percent, random forest is 95 percent, Naïve Bayes is 95 percent, and Decision tree is 91 percent. Synthetic Minority Over-sampling Technique (SMOTE) is able to increase the average of F1-Score from 67.9 percent to 94.5 percent and the average of G-Mean from 73.5 percent to 84.6 percent.

Machine learning random forest Naïve Bayes SMOTE neural network e-commerce confusion matrix G-Mean F1-score transaction fraud

Abstract doi.org/10.14569/IJACSA.2019.0100943 PDF

44

Enhancing Visualization of Multidimensional Data by Ordering Parallel Coordinates Axes

Author 1: Ayman Nabil Author 2: Karim M. Mohamed Author 3: Yasser M. Kamal

Every year business is overwhelmed by the quantity and variety of data. Visualization of Multi-dimensional data is counter-intuitive using conventional graphs. Parallel coordinates are proposed as an alternative to explore multivariate data more effectively. However, it is difficult to extract relevant information through the parallel coordinates when the data are Multi-dimensional with thousands of lines overlapping. The order of the axes determines the perception of information on parallel coordinates. This paper proposes three new techniques in order to arrange the axes in the most significant relation between the datasets. The datasets used in this paper, for Egyptian patients, with many external factors and medical tests. These factors were collected by a questionnaire sheet, made by medical researchers. The first Technique calculates the correlation between all features and the age of the patient when they get diabetes disease. The second technique is based on merging different features together and arranging the coordinates based on the correlations values. The Third Technique calculates the entropy value for each feature and then arrange the parallel coordinates in descending order based on the positive or negative values. Finally based on the result graphs, we conclude that the second method was more readable and valuable than the other two methods.

Parallel coordinates visualization correlation coefficient entropy function

Abstract doi.org/10.14569/IJACSA.2019.0100944 PDF

45

A Map-based Job Recommender Model

Author 1: Manal Alghieth Author 2: Amal A. Shargabi

Location is one of the most important factors to consider when looking for offering a new job. Currently, there exist many job recommender systems to help match the right candidate with the right job. A review of the existing recommender systems, included within this article, reveals that there is an absence of appropriate mapping support offering for job recommendation. This article aims to propose a general map-based job recommender model, which is implemented and applied within a system for job seekers in Saudi Arabia. The system adapts content-based technique to recommend jobs using the cosine similarity and will help Saudi job seekers finding their desired job in an efficient way using interactive maps. This ultimately will contribute to Saudi Arabia moving forward to the digital transformation which is one of the major objectives to fulfill the Saudi vision 2030.

Recommender systems content-based recommendation location-based search maps

Abstract doi.org/10.14569/IJACSA.2019.0100945 PDF

46

A Comparison Review based on Classifiers and Regression Models for the Investigation of Flash Floods

Author 1: Talha Ahmed Khan Author 2: Muhammad Alam Author 3: Kushsairy Kadir Author 4: Zeeshan Shahid Author 5: M.S Mazliham

Several regions of the world have been affected by one of the natural disasters named as flash floods. Many villagers who live near stream or dam, they suffer a lot in terms of property, cattle and human lives loss. Conventional early warning systems are not up to the mark for the early warning announcements. Diversified approaches have been carried out for the identification of flash floods with less false alarm rate. Forecasting approaches includes some errors and ambiguity due to the incompetent processing algorithms and measurement readings. Process variables like stream flow, water level, water color, precipitation velocity, wind speed, wave’s pattern and cloud to ground (CG) flashes have been measured for the robust identification of flash floods. A vibrant competent algorithm would be required for the investigation of flash floods with less false alarm rate. In this research paper classifiers have been applied on the collected data set so that any researcher could easily know that which classifier is competent and can be further enhanced by combining it with other algorithms. A novel comprehensive parametric comparison has been performed to investigate the classification accuracy for the robust classification of false alarms. For the better accuracy more than one process variables have been measured but still contained some false alarm. Appropriate combination of sensor was integrated to increase the accuracy in results as multi-modal sensing device has been designed to collect the data. Linear discriminant analysis, logistic regression, quadratic support vector machine, k-nearest neighbor and Ensemble bagged tree have been applied to the collected data set for the data classification. Results have been obtained in the MATLAB and discussed in detail in the research paper. The worst accuracy of the classification (62%) has been achieved by the coarse k-NN classifier that means coarse k-NN produced 38% false negative rate that is not acceptable in the case of forecasting. Ensemble bagged trees produced best classification results as it achieved 99 % accuracy and 1% error rate. Furthermore, according to the comprehensive parametric comparison of regression models Quadratic SVM found to be the worst with mean square error of 0.5551 and time elapsed 13.159 seconds. On the other hand, Exponential Gaussian process regression performed better than the other existing approaches with the minimum root mean squared error of 0.0002 and prediction speed of 35000 observations per second.

Flash floods classification SVM k-NN logistic regression quadratic SVN ensemble bagged trees exponential GPR

Abstract doi.org/10.14569/IJACSA.2019.0100946 PDF

47

Authentication Modeling with Five Generic Processes

Author 1: Sabah Al-Fedaghi Author 2: MennatAllah Bayoumi

Conceptual modeling is an essential tool in many fields of study, including security specification in information technology systems. As a model, it restricts access to resources and identifies possible threats to the system. We claim that current modeling languages (e.g., Unified Modeling Language, Business Process Model and Notation) lack the notion of genericity, which refers to a limited set of elementary processes. This paper proposes five generic processes for modeling the structural behavior of a system: creating, releasing, transferring, receiving, and processing. The paper demonstrates these processes within the context of public key infrastructure, biometric, and multifactor authentication. The results indicate that the proposed generic processes are sufficient to represent these authentication schemes.

Security authentication conceptual modeling diagrammatic representation generic processes

Abstract doi.org/10.14569/IJACSA.2019.0100947 PDF

48

Internal Threat Defense using Network Access Control and Intrusion Prevention System

Author 1: Andhika Surya Putra Author 2: Nico Surantha

This study aims to create a network security system that can mitigate attacks carried out by internal users and to reduce attacks from internal networks. Further, a network security system is expected to be able to overcome the difficulty of mitigating attacks carried out by internal users and to improve network security. The method used is to integrate the ability of Network Access Control (NAC) and the Intrusion Prevention System (IPS) that have been designed and implemented in this study, then an analysis is performed to compare the results of tests that have been carried out using only the NAC with the results using integration of NAC capabilities and IPS. The results obtained from the tests that have been carried out, namely, the security system by using the integration of NAC and IPS capabilities is better than using only the NAC.

Attack integration Intrusion Prevention System (IPS) mitigation Network Access Control (NAC) network security

Abstract doi.org/10.14569/IJACSA.2019.0100948 PDF

49

CBRm: Case based Reasoning Approach for Imputation of Medium Gaps

Author 1: Anibal Flores Author 2: Hugo Tito Author 3: Carlos Silva

This paper presents a new algorithm called CBRm for univariate time series imputation of medium-gaps inspired by the algorithm called Case Based Reasoning Imputation (CBRi) for short-gaps. The performance of the proposed algorithm is analyzed in meteorological time series corresponding to maximum temperatures; also it was compared with several similar techniques. Although the algorithm failed to overcome in some cases to other proposals regarding precision, the results achieved are encouraging considering that some weaknesses of other proposals with which it was compared were outperformed.

Case Based Reasoning CBR CBRm univariate time series imputation medium-gaps

Abstract doi.org/10.14569/IJACSA.2019.0100949 PDF

50

Performance Impact of Relay Selection in WiMAX IEEE 802.16j Multi-hop Relay Networks

Author 1: Noman Mazhar Author 2: Muhammad Zeeshan Author 3: Anjum Naveed

Worldwide Interoperability for Microwave Access network accepts the challenge of last mile wireless access for internet. IEEE 802.16 standard, commercially known as WiMAX provide wireless broadband experience to the end subscribers and challenges many wired solutions like Digital Subscriber Line (DSL) and cable internet. Wireless network has many inherent issues like coverage holes; capacity optimization and mobility are few of them. Adding relays to multi-hop WiMAX IEEE 802.16j network present an effective solution to address them to some extent but this amendment does not elaborate any algorithm regarding the relay selection and narrate no performance guarantees. In this work, we proposed linear model that fairly allocates wireless resources among subscribers in 802.16j network. A relay selection algorithm is also presented to optimally select nodes with higher signal-to-noise ratio as relay station for nodes with lower signal-to-noise ratio objectively maximize overall network capacity. This scheme further extends network coverage area and improves network availability. We also did extensive performance evaluation of the proposed linear model. Results show that optimal relays selection scheme do provide a substantial increase of up to 66% in overall network capacity in the fixed WiMAX network. This improvement is substantial at places where network condition is not optimal. Investigating the problem further leads to the conclusion that the relay selection criterion is the key to achieve maximum network capacity.

WiMAX multi-hop wireless broadband relay SNR

Abstract doi.org/10.14569/IJACSA.2019.0100950 PDF

51

Evaluating Factors for Predicting the Life Dissatisfaction of South Korean Elderly using Soft Margin Support Vector Machine based on Communication Frequency, Social Network Health Behavior and Depression

Author 1: Haewon Byeon Author 2: Seong-Tae Kim

Since health and the quality of life are caused not by a single factor but by the interaction of multiple factors, it is necessary to develop a model that can predict the quality of life using multiple risk factors rather than to identify individual risk factors. This study aimed to develop a model predicting the quality of life based on C-SVM using big data and provide baseline data for a successful old age. This study selected 2,420 elderly (1,110 men, 1,310 women) who were 65 years or older and completed the Seoul Statistics Survey. The quality of life satisfaction, a binary outcome variable (satisfied or dissatisfied), was evaluated based on a self-report questionnaire. This study performed a Gauss function among the SVM algorithms. To verify the predictive power of the developed model, this study compared the Gauss function with the linear algorithm, polynomial algorithm, and sigmoid algorithm. Additionally, C-SVM and Nu-SVM were applied to four kernel algorithm types to create eight types, and prediction accuracies of the eight SVM types were estimated and compared. Among 2,420 subjects, 483 elderly (19.9%) were not satisfied with their current lives. The final prediction accuracy of this SVM using 625 support vectors was 92.63%. The results showed that the difference between C-SVM and Nu-SVM was negligible in the models for predicting the satisfaction of life in old age while the Gaussian kernel had the highest accuracy and the sigmoid kernel had the lowest accuracy. Based on the prediction model of this study, it is required to manage local communities systematically to enhance the quality of life in old age.

C-SVM communication frequency life satisfaction social network quality of life

Abstract doi.org/10.14569/IJACSA.2019.0100951 PDF

52

How to Improve the IoT Security Implementing IDS/IPS Tool using Raspberry Pi 3B+

Author 1: Ruíz-Lagunas Juan Jesús Author 2: Antolino-Hernández Anastacio Author 3: Reyes-Gutiérrez Mauricio René Author 4: Ferreira-Medina Heberto Author 5: Torres-Millarez Cristhian Author 6: Paniagua-Villagómez Omar

This work shows a methodology of implementation and testing of the system is proposed and tested with a prototype; it is constructed with sensors and actuators that allow monitoring the behavior of the system in an environment under threats. We used an IDS / IPS as a protection tool for IoT systems, based on Raspberry Pi and Raspbian operating system. It is described in a block diagram the testing method used. We implemented the IDS/IPS Snort tool in an embedded platform Raspberry. It presents also the state of the art of cloud frameworks that have the same objective of protecting. The main contribution is the implemented testing method for Snort that can be used with security rules in other applications of embedded IoT devices.

Security IoT IDS/IPS software Pentesting tools smart cities prototype Raspberry

Abstract doi.org/10.14569/IJACSA.2019.0100952 PDF

53

Intrusion Detection System based on the SDN Network, Bloom Filter and Machine Learning

Author 1: Traore Issa Author 2: Kone Tiemoman

The scale and frequency of sophisticated attacks through denial of distributed service (DDoS) are still growing. The urgency is required because with the new emerging paradigms of the Internet of Things (IoT) and Cloud Computing, billions of unsecured connected objects will be available. This document deals with the detection, and correction of DDoS attacks based on real-time behavioral analysis of traffic. This method is based on Software Defined Network (SDN) technologies, Bloom filter and automatic behaviour learning. Indeed, distributed denial of service attacks (DDoS) are difficult to detect in real time. In particular, it concerns the distinction between legitimate and illegitimate packages. Our approach outlines a supervised classification method based on Machine Learning that identifies malicious and normal packets. Thus, we design and implement Defined (IDS) with a great precision. The results of the evaluation suggest that our proposal is timely and detects several abnormal DDoS-based cyber-attack behaviours.

Distributed denial of service intrusion detection software software defined network machine learning synchronize acknowledgment bloom filter

Abstract doi.org/10.14569/IJACSA.2019.0100953 PDF

54

Development of a Vehicle for Driving with Convolutional Neural Network

Author 1: Arbnor Pajaziti Author 2: Xhevahir Bajrami Author 3: Fatjon Beqa Author 4: Blendi Gashi

The aim of this paper is the design, simulation, construction and programming of the autonomous vehicle, capable of obstacle avoidance, object tracking also image and video processing. The vehicle will use a built-in camera for evaluating and navigating the terrain, a six-axis accelerometer and gyro for calculating angular velocities and accelerations, Arduino for interfacing with motors as well as with Raspberry Pi which is the main on-board computer. The design of the vehicle is performed in Autodesk Fusion 360. Most of the mechanical parts have been 3D printed.¬¬ In order to control the chassis of the vehicle through the microcontrollers, the development of the PCB was required. On top of this, a camera has been added to the vehicle, in order to achieve obstacle avoidance and perform object tracking. The video processing required to achieve these goals is done by using OpenCV and Convolutional Neural Network. Among other objectives of this paper is the detection of traffic signs. The application of the Convolutional Neural Network algorithm after some of the examinations made has shown greater precision in recognizing STOP traffic sign of different positions and occlusion ratios, and finding the path for the fastest time.

Image processing traffic sign object tracking autonomous vehicle convolutional neural network

Abstract doi.org/10.14569/IJACSA.2019.0100954 PDF

55

Hybrid Latin-Hyper-Cube-Hill-Climbing Method for Optimizing: Experimental Testing

Author 1: Calista Elysia Author 2: Michelle Hartanto Author 3: Ditdit Nugeraha Utama

A noticeable objective of this work is to experiment and test an optimization problem through comparing hill-climbing method with a hybrid method combining hill-climbing and Latin-hyper-cube. These two methods are going to be tested operating the same data-set in order to get the comparison result for both methods. The result shows that the hybrid model has a better performance than hill-climbing. Based on the number of global optimum value occurrence, the hybrid model outperformed 7.6% better than hill-climbing, and produced more stable average global optimum value. However, the model has a little longer running time due to a genuine characteristic of the model itself.

Hill-climbing Latin-hyper-cube optimization

Abstract doi.org/10.14569/IJACSA.2019.0100955 PDF

56

Evaluation of Usability Dimensions of Smartphone Applications

Author 1: Shabana Shareef Author 2: M.N.A. Khan

This study analyses different techniques used for evaluation of various usability dimensions of software applications (apps) being used on the smartphones. The scope of this study is to evaluate various aspects of the usability techniques employed in the domain of smartphone apps. Usability assessment methodologies are evaluated for different types of applications running on different operating systems like Android, Blackberry and iOS, etc. Usability evaluation techniques and methodologies with respect to usability heuristics like field experiments, laboratory experiments models and usability standards are discussed in detail. The issues for evaluation of usability of smartphone apps are identified by considering limitations and areas of improvement outlined in the contemporary literature. A conceptual framework for usability evaluation of smartphone apps is also designed which would be validated through experimentation in the thesis work. This study is particularly useful to comprehend usability issues and their likely remedies to produce high quality smartphone apps.

Usability Jakob-Nielson usability heuristics smartphone applications ease of use understandability learning curve

Abstract doi.org/10.14569/IJACSA.2019.0100956 PDF

57

A New Shoulder Surfing and Mobile Key-Logging Resistant Graphical Password Scheme for Smart-Held Devices

Author 1: Sundas Hanif Author 2: Fahad Sohail Author 3: Shehrbano Author 4: Aneeqa Tariq Author 5: Muhammad Imran Babar

In globalization of information, internet has played a vital role by providing an easy and fast access of information and systems to remote users. However, with ease for authentic users, it has made information resources accessible to unauthorized users too. To authorize legitimate user for the access of information and systems, authentication mechanisms are applied. Many users use their credentials or private information at public places to access their accounts that are protected by passwords. These passwords are usually text-based passwords and their security and effectiveness can be compromised. An attacker can steal text-based passwords using different techniques like shoulder surfing and various key logger software, that are freely available over internet. To improve the security, numerous sophisticated and secure authentication systems have been proposed that employ various biometric authentication systems, token-based authentication system etc. But these solutions providing such high-level security, require special modification in the design and hence, imply additional cost. Textual passwords that are easy to use but vulnerable to attacks like shoulder surfing, various image based, and textual graphical password schemes are proposed. However, none of the existing textual graphical passwords are resistant to shoulder surfing and more importantly to mobile key-logging. In this paper, an improved and robust textual graphical password scheme is proposed that uses sectors and colors and introducing randomization as the primary function for the character display and selection. This property makes the proposed scheme resistant to shoulder surfing and more importantly to mobile key-logging. It can be useful for authentication process of any smart held device application.

Authentication graphical password shoulder surfing mobile key-logging security

Abstract doi.org/10.14569/IJACSA.2019.0100957 PDF

58

Deep CNN-based Features for Hand-Drawn Sketch Recognition via Transfer Learning Approach

Author 1: Shaukat Hayat Author 2: Kun She Author 3: Muhammad Mateen Author 4: Yao Yu

Image-based object recognition is a well-studied topic in the field of computer vision. Features extraction for hand-drawn sketch recognition and retrieval become increasingly popular among the computer vision researchers. Increasing use of touchscreens and portable devices raised the challenge for computer vision community to access the sketches more efficiently and effectively. In this article, a novel deep convolutional neural network-based (DCNN) framework for hand-drawn sketch recognition, which is composed of three well-known pre-trained DCNN architectures in the context of transfer learning with global average pooling (GAP) strategy is proposed. First, an augmented-variants of natural images was generated and sum-up with TU-Berlin sketch images to all its corresponding 250 sketch object categories. Second, the features maps were extracted by three asymmetry DCNN architectures namely, Visual Geometric Group Network (VGGNet), Residual Networks (ResNet) and Inception-v3 from input images. Finally, the distinct features maps were concatenated and the features reductions were carried out under GAP layer. The resulting feature vector was fed into the softmax classifier for sketch classification results. The performance of proposed framework is comprehensively evaluated on augmented-variants TU-Berlin sketch dataset for sketch classification and retrieval task. Experimental outcomes reveal that the proposed framework brings substantial improvements over the state-of-the-art methods for sketch classification and retrieval.

Deep convolutional neural network sketch recognition transfer learning global average pooling

Abstract doi.org/10.14569/IJACSA.2019.0100958 PDF

59

A Distributed Approach based on Transition Graph for Resolving Multimodal Urban Transportation Problem

Author 1: Mohamed El Moufid Author 2: Younes Nadir Author 3: Khalid Boukhdir Author 4: Siham Benhadou Author 5: Hicham Medromi

All over the world, many research studies focus on developing and enhancing real-time communications between various transport stakeholders in urban environments. Such motivation can be justified by the growing importance of pollution caused by the transport sector in urban areas. In this work, we propose an approach of assistance for displacement in urban environment taking advantages of multimodal urban transportation means, where several modes of public transports are available. In addition, we also consider the possibility of using both private modes of transport and cities parking. The proposed distributed approach described in this paper is based on an abstraction of a city multimodal graph according to the available modes of public transport and road traffic and transition graph approach to move from a mode to the other mode. Numerical results are developed to justify the effectiveness of our approach.

Multimodal transport distributed approach transition graph

Abstract doi.org/10.14569/IJACSA.2019.0100959 PDF

60

An Intelligent Semi-Latin Square Construct for Measuring Human Capital Intelligence in Recruitment

Author 1: Emmanuel C Ukekwe Author 2: Francis S. Bakpo Author 3: Mathew C.Okoronkwo Author 4: Gregory E.Anichebe

Processing speed and memory recall ability are two major Human Capital Intelligence attributes required for recruitment. Matzel identified five domains of Intelligence. Unfortunately, there were no stated means for measuring them. This paper presents a framework for measuring Processing speed and Memory intelligence domains using Sternberg and Posner paradigms of short memory scanning test. A Semi-Latin square was constructed and used as a competitive platform for n= 20 student-applicant contestants. The Cumulative Grade Point Average rankings of 20 randomly selected final year student-applicants were used for the test. Results show that the CGPA performance ranking of the student-applicants differ from that of the HCI using the framework. A Wilcoxon Signed-Ranks Test was used to determine if the disparity in performance ranking was significant. Results show that there is indeed a significant difference in the performance ranking of the student-applicants using both approaches. The automated Construct was implemented using PHP and Mysql and deployed at (hcipredictor.eu3.org).

Memory recall ability processing speed Sternberg paradigm Posner paradigm human capital intelligence semi-latin square

Abstract doi.org/10.14569/IJACSA.2019.0100960 PDF

61

Human Gait Feature Extraction based-on Silhouette and Center of Mass

Author 1: Miftahul Jannah Author 2: Sarifuddin Madenda Author 3: Tubagus Maulana Kusuma Author 4: Hustinawaty

When someone walks, there is a repetitive movement or coordinated cycle that forms a gait. Gait is different, unique and difficult to imitate. This characteristic makes gait one of the biometrics to find out one's identity. Gait analysis is needed in the development of biometric technology, such as in the field of security surveillance and the health sector to monitor gait abnormalities. The center of mass is the unique point of every object that has a role in the study of humans walking. Each person has a different center of mass. In this research, through a series of processes in image processing such as video acquisition, segmentation, silhouette formation, and feature extraction, the center of mass of the human body can be identified using a webcam with the resolution of 640 x 480 pixels and the frame rate of 30 frames/second. The results obtained from this research were gait frames of 510 frames from 17 pedestrian videos. Segmentation process using background subtraction separates the pedestrian object image from the background. Silhouette gait was produced from a series of image enhancement processes to eliminate noise that interferes the image quality. Based on the silhouette, feature extraction provides the center of mass to distinguish each individual's gait. The sequence of center of mass can be further processed for characterizing human gait cycle for various purposes.

Human gait center of mass silhouette feature extraction gait cycle people identification

Abstract doi.org/10.14569/IJACSA.2019.0100961 PDF

62

Computer Simulation Study: An Impact of Roadside Illegal Parking at Signalised Intersection

Author 1: Noorazila Asman Author 2: Munzilah Md Rohani Author 3: Nursitihazlin Ahmad Termida Author 4: Noor Yasmin Zainun Author 5: Nur Fatin Lyana Rahimi

Traffic congestion could be a serious road traffic problem particularly at intersections because of its potential impact on the risk of accidents, vehicle delays and exhaust emissions. In addition, illegal parking by road users at intersections can give additional deterioration to the intersections that may create additional traffic flow interruptions. This paper presented assessment of the illegal parking impact on signalized intersection at Parit Raja, Malaysia using simulation approach using PTV VISSIM simulation software. The results showed that if illegal parkings at Parit Raja intersection were banned, traffic delay and travel time of vehicles will be improved and thus, improving the intersection Level of Service.

Traffic simulation traffic flow signalized intersection level of service illegal parking

Abstract doi.org/10.14569/IJACSA.2019.0100962 PDF

63

Assessment of IPv4 and IPv6 Networks with Different Modified Tunneling Techniques using OPNET

Author 1: Asif Khan Babar Author 2: Zulfiqar Ali Zardari Author 3: Nazish Nawaz Hussaini Author 4: Sirajuddin Qureshi Author 5: Song Han

Currently, all the devices are using Internet protocol version 4 (IPv4) to access the internet. IP addresses of the IPv4 are now depleted from IPv4 pool announced by IANA (Internet Assigned Number Authority) in February 2011. To solve this issue Internet protocol version 6 (IPv6) is launched. But the main problem is current devices can’t support directly IPv6 that causes various compatibility issues. Many researchers have proposed various techniques, but still, their efficiency and performance is a big challenge. This study examines several mechanisms of transition IPv6 the backbone of multiprotocol label switching (MPLS) to evaluate & compare their performances. It involves comparing different performance metrics and manual tunneling tunnel efficiency metrics. The main goal of this paper is to examine the dissimilar tunneling techniques and find out which tunneling method is better in all performance, which increases network performance. Experimental results show that ISATAP is better performance in all metrics.

ISATAP tunneling techniques IPv4 IPv6 network performance

Abstract doi.org/10.14569/IJACSA.2019.0100963 PDF

64

Security and Privacy Awareness: A Survey for Smartphone User

Author 1: Md Nawab Yousuf Ali Author 2: Md. Lizur Rahman Author 3: Ifrat Jahan

Smartphone becomes one of the most popular devices in last few years due to the integration of powerful technologies in it. Now-a-days a smartphone can provide different services as like as a computer provides. Smartphone holds our important personal information such as photos and videos, SMS, email, contact list, social media accounts etc. Therefore, the number of security and privacy related threats are also increasing relatively. Our research aims at evaluating how much the smartphone users are aware about their security and privacy. In this study, firstly we have taken a survey for smartphone users to access the level of smartphone security awareness displayed by the public. We also determine whether a general level of security complacency exists among smartphone users and measure the awareness of android users regarding their privacy. From survey result we have found that, most of the people are not aware about their smartphone security and privacy. Secondly, based on survey results, we have shown a method to measure the level of awareness (LOA) for the smartphone users. By using this method, a user can easily measure his/her smartphone security and privacy related level of awareness.

Smartphone Smartphone Problems Level of Awareness (LoA) Security and Privacy

Abstract doi.org/10.14569/IJACSA.2019.0100964 PDF

65

Support Vector Machine for Classification of Autism Spectrum Disorder based on Abnormal Structure of Corpus Callosum

Author 1: Jebapriya S Author 2: Shibin David Author 3: Jaspher W Kathrine Author 4: Naveen Sundar

Autism Spectrum Disorders (ASD) is quite difficult to diagnose using traditional methods. Early prediction of Autism Spectrum Disorders enhances the in general psychological well- being of the child. These days, the research on Autism Spectrum Disorder is performed at a very high pace than earlier days due to increased rate of ASD affected people. One possible way of diagnosing ASD is through behavioral changes of children at the early ages. Structural imaging ponders point to disturbances in various mind regions, yet the exact neuro-anatomical nature of these interruptions stays misty. Portrayal of cerebrum structural contrasts in children with ASD is basic for advancement of biomarkers that may in the long run be utilized to enhance analysis and screen reaction to treatment. In this examination we use machine figuring out how to decide a lot of conditions that together end up being prescient of Autism Spectrum Disorder. This will be of an extraordinary use to doctors, making a difference in identifying Autism Spectrum Disorder at a lot prior organize.

Autism Spectrum Disorder (ASD) ASD screening data ABIDE machine learning

Abstract doi.org/10.14569/IJACSA.2019.0100965 PDF

66

IoT based Temperature and Humidity Controlling using Arduino and Raspberry Pi

Author 1: Lalbihari Barik

Internet of Things (IoT) plays a pivotal part in our mundane daily life by controlling electronic devices using networks. The controlling is done by minutely observing the important parameters which generate vital pieces of information concerning the functioning of these electronic devices. Simultaneously, this information will transmit these vital statistics from the transmitting device as well as save the same on the cloud to access by the applications and supplementary procedures to use them. This scrutiny associates the outcomes of the environmental observances like the humidity and temperature measurements using sensors. The gathered information could be profitably used to produce actions like distantly dominant cooling, heating devices, or long term statistics, which will be useful to control the same. The detected data are uploaded to the cloud storage through network and associate using android application. The system employs Arduino UNO with Raspberry Pi, HTU 211D sensor device, and an ESP8266 Wi-Fi module. The experimental results show the live temperature and humidity of the surroundings and the soil moisture of any plant using Arduino UNO with Raspberry Pi. Raspberry Pi is mainly used here for checking the temperature and humidity through the HTU 211D sensor element. The sensors are used for measuring the temperatures from the surroundings, storing displayed information with different devices. Here, the ESP8266 Wi-Fi module has been used for data storing purpose.

IoT Raspberry Pi Arduino UNO data transmission sensors

Abstract doi.org/10.14569/IJACSA.2019.0100966 PDF

67

The Use of Geospatial Technology for Epidemiological Chagas Analysis in Bolivia

Author 1: Natalia I Vargas-Cuentas Author 2: Alicia Alva Mantari Author 3: Avid Roman-Gonzalez

Chagas disease is caused by the parasite Trypanosoma Cruzi and transmitted by the Vinchuca. Bolivia is the country with the highest prevalence in the South American region; for example, in 2015, there was a prevalence of 33.4%. This disease causes severe intestinal and cardiac problems in the long term, 30% of the cases register cardiac symptoms, and 10% have alterations in the esophagus or colon. This research aims to analyze the relationship between environmental factors and Chagas outbreaks in an area of Bolivia to identify the environmental conditions in which the disease is transmitted, using epidemiological, meteorological data and also environmental indexes extracted from Landsat 8 satellite images. Through a Principal Components Analysis (PCA) of the environmental indexes extracted from the satellite images and the meteorological information, has been found that the environmental conditions that have a correlation with the occurrence of cases are: temperature, relative humidity, visibility, Normalized Difference Soil Index (NDSI) and Modified Normalized Difference Water Index (MNDWI).

Trypanosoma Cruzi Vinchuca Landsat 8 PCA Normalized Difference Soil Index (NDSI) Modified Normalized Difference Water Index (MNDWI)

Abstract doi.org/10.14569/IJACSA.2019.0100967 PDF

68

A Novel Secure Fingerprint-based Authentication System for Student’s Examination System

Author 1: Abdullah Alshbtat Author 2: Nabeel Zanoon Author 3: Mohammad Alfraheed

In the fingerprint image processing, various methods have been suggested as using band pass filter, Fouries transform filter and Fuzzy systems. In this paper, we present a useful and an applicable fingerprint security system for student’s examination using image processing on such away and a well-organized algorithm is applied. As a university team work, we have recently tested this security procedure for different samples of students in our institution. The experimental results show a high level of accuracy is obtained. Due to the need to connect and manage the connection, we use the Ethernet card and the Arduino Uno card which they are combined together in such a way to do so. Moreover, the administrator runs a special website in the PC to assign ID to the scanned fingerprint. The calculation of the proposed system is carried out by uploading a suitable Adafruit fingerprint library to the used Audruino Uno card. Finally, the most important security point is that the PC has been used not only to send the developing software into the Uno card but also to disconnect the process electronically while the code is running.

Finger-print examination system image processing bio informatics

Abstract doi.org/10.14569/IJACSA.2019.0100968 PDF

69

Ensemble and Deep-Learning Methods for Two-Class and Multi-Attack Anomaly Intrusion Detection: An Empirical Study

Author 1: Adeyemo Victor Elijah Author 2: Azween Abdullah Author 3: NZ JhanJhi Author 4: Mahadevan Supramaniam Author 5: Balogun Abdullateef O

Cyber-security, as an emerging field of research, involves the development and management of techniques and technologies for protection of data, information and devices. Protection of network devices from attacks, threats and vulnerabilities both internally and externally had led to the development of ceaseless research into Network Intrusion Detection System (NIDS). Therefore, an empirical study was conducted on the effectiveness of deep learning and ensemble methods in NIDS, thereby contributing to knowledge by developing a NIDS through the implementation of machine and deep-learning algorithms in various forms on recent network datasets that contains more recent attacks types and attackers’ behaviours (UNSW-NB15 dataset). This research involves the implementation of a deep-learning algorithm–Long Short-Term Memory (LSTM)–and two ensemble methods (a homogeneous method–using optimised bagged Random-Forest algorithm, and a heterogeneous method–an Averaged Probability method of Voting ensemble). The heterogeneous ensemble was based on four (4) standard classifiers with different computational characteristics (Naïve Bayes, kNN, RIPPER and Decision Tree). The respective model implementations were applied on the UNSW_NB15 datasets in two forms: as a two-classed attack dataset and as a multi-attack dataset. LSTM achieved a detection accuracy rate of 80% on the two-classed attack dataset and 72% detection accuracy rate on the multi-attack dataset. The homogeneous method had an accuracy rate of 98% and 87.4% on the two-class attack dataset and the multi-attack dataset, respectively. Moreover, the heterogeneous model had 97% and 85.23% detection accuracy rate on the two-class attack dataset and the multi-attack dataset, respectively.

Cyber-security intrusion detection system deep learning ensemble methods network attacks

Abstract doi.org/10.14569/IJACSA.2019.0100969 PDF

70

Timed-Arc Petri-Nets based Agent Communication for Real-Time Multi-Agent Systems

Author 1: Awais Qasim Author 2: Sidra Kanwal Author 3: Adnan Khalid Author 4: Syed Asad Raza Kazmi Author 5: Jawad Hassan

This research focuses on Timed-Arc Petri-nets-based agent communication in real-time multi-agent systems. The Agent Communication Language is a standard language for the agents to communicate. The objective is to combine Timed-Arc Petri-nets and FIPA Performatives in real-time multi-agent systems. FIPA standards provide a richer framework for the interaction of agents and makes it easier to develop a well-defined system. It also ensures the management by precisely specifying the agent’s interaction. Though FIPA protocol has already been described with the help of Petri-nets but this specification lacks the timing aspect that is a dire need for real-time multi-agent systems. The main objective of this research is to provide a method of modeling existing FIPA performatives by combining Timed-Arc Petri-nets in real-time multi-agent systems. We have used properties, such as liveness, deadlock and reachability for the formal verification of the proposed modeling technique.

Formal verification FIPA multi-agent systems timed-arc petri nets real-time systems

Abstract doi.org/10.14569/IJACSA.2019.0100970 PDF

71

Scale and Resolution Invariant Spin Images for 3D Object Recognition

Author 1: Jihad H’roura Author 2: Aissam Bekkari Author 3: Driss Mammass Author 4: Ali Bouzit Author 5: Patrice M´eniel Author 6: Alamin Mansouri Author 7: Michael Roy

Until the last decades, researchers taught that teaching a computer how to recognize a bunny, for example, in a complex scene is almost impossible. Today, computer vision system do it with a high score of accuracy. To bring the real world to the computer vision system, real objects are represented as 3D models (point clouds, meshes), which adds extra constraints that should be processed to ensure a good recognition, for example the resolution of the mesh. In this work, based on the state of the art method called Spin Image, we introduce our contribution to recognize 3D objects. Our motivation is to ensure a good recognition under different conditions such as rotation, translation and mainly scaling, resolution changes, occlusions and clutters. To that end we have analyzed the spin image algorithm to propose an extended version robust to scale and resolution changes, knowing that spin images fails to recognize 3D objects in that case. The key idea is to approach the representation of spin images of the same object under different conditions by the mean of normalization, either these conditions result in linear or non-linear correlation between images. Our contribution, unlike spin image algorithm, allows to recognize objects with different resolutions and scale. Plus it shows a good robustness to occlusions up to 60% and clutters up to 50%, tested on two datasets: Stanford and ArcheoZoo3D.

3D object recognition spin image resolution scaling

Abstract doi.org/10.14569/IJACSA.2019.0100971 PDF

72

A Novel Approach for Ontology-Driven Information Retrieving Chatbot for Fashion Brands

Author 1: Aisha Nazir Author 2: Muhammad Yaseen Khan Author 3: Tafseer Ahmed Author 4: Syed Imran Jami Author 5: Shaukat Wasi

Chatbots or conversational agents are the most projecting and widely employed artificial assistants on online social media. These bots converse with the humans in audio, visual, or textual formats. It is quite intelligible that users are keen interested in the swift and relatedly correct information for their hunt in pursuit of desired product, such that their precious time is not wasted through surfing multiple websites and business portals. In this paper, we present a novel incremental approach for building a chatbot for fashion brands based on the semantic web. We organized a dataset of 5,000 question and answers of top- 10 brands in the fashion domain, which covers the information about new arrivals, sales, packages, discounts, exchange/return policies, etc. We have also developed a dialogue interface for querying the system. The results generated against the queries are thoroughly evaluated on the criteria of time, context, history, duration, turns, significance, relevance, and fall back questions.

Artifical intelligence semantic web chatbots fashion ontology

Abstract doi.org/10.14569/IJACSA.2019.0100972 PDF

73

Multi-Sessions Mechanism for Decentralized Cash on Delivery System

Author 1: Nghia Duong-Trung Author 2: Xuan Son Ha Author 3: Tan Tai Phan Author 4: Phuong Nam Trieu Author 5: Quoc Nghiep Nguyen Author 6: Duy Pham Author 7: Thai Tam Huynh Author 8: Hai Trieu Le

To date, cash on delivery (COD) is one of the most popular payment methods in developing countries thanks to the blossom of customer-to-customer e-commerce. With the widespread of a very small business model and the Internet, online shopping has become part of people’s daily activity. People browse for desirable products at the comfort of their homes and ask the online vendor that a shipper can deliver the merchandise at their doorstep. Then, COD allows customers to pay in cash when the product is delivered to their desired location. Since customers receive goods before making a payment, COD is, therefore, considered as a payment system. However, the crucial issue that previous research has not yet addressed is that their models only support single delivering session at a time. More precisely, if the current buyer is not available to receive the goods, the shipper has to wastefully wait for the complete payment and he/she cannot start shipping another merchandise. The tracking system seems to poorly handle this issue. In particular, we propose a multi-session mechanism, which consists of blockchain technology, smart contracts and hyperledger fabric platform to achieve distributed and transparent across delivering sessions in the decentralized markets. Our proposed mechanism ensure the efficiency of delivering process. The authors release our sources codes for further reproducibility and development. We conclude that the integration of multi-session mechanism and blockchain technology will cause significant efficiency across several disciplines.

Blockchain cash on delivery multi-sessions decentralized system

Abstract doi.org/10.14569/IJACSA.2019.0100973 PDF

74

Prediction of Academic Performance Applying NNs: A Focus on Statistical Feature-Shedding and Lifestyle

Author 1: Shithi Maitra Author 2: Sakib Eshrak Author 3: Md. Ahsanul Bari Author 4: Abdullah Al-Sakin Author 5: Rubana Hossain Munia Author 6: Nasrin Akter Author 7: Zabir Haque

Automation has made it possible to garner and preserve students’ data and the modern advent in data science enthusiastically mines this data to predict performance, to the interest of both tutors and tutees. Academic excellence is a phenomenon resulting from a complex set of criteria originating in psychology, habits and according to this study, lifestyle and preferences–justifying machine learning to be ideal in classifying academic soundness. In this paper, computer science majors’ data have been gleaned consensually by surveying at Ahsanullah University, situated in Bangladesh. Visually aided exploratory analysis revealed interesting propensities as features, whose significance was further substantiated by statistically inferential Chi-squared (Χ2) independence tests and independent samples t-tests for categorical and continuous variables respectively, on median/mode-imputed data. The initially relaxed p-value retained all exploratorily analyzed features, but gradual rigidification exposed the most powerful features by fitting neural networks of decreasing complexity i.e., having 24, 20 and finally 12 hidden neurons. Statistical inference uniquely helped shed off weak features prior to training, thus optimizing time and generally large computational power to train expensive predictive models. The k-fold cross-validated, hyper-parametrically tuned, robust models performed with average accuracies wavering between 90% to 96% and an average 89.21% F1-score on the optimal model, with the incremental improvement in models proven by statistical ANOVA.

Educational Data Mining (EDM) Exploratory Data Analysis (EDA) median and mode imputation inferential statistics t-test Chi-squared independence test ANOVA-test

Abstract doi.org/10.14569/IJACSA.2019.0100974 PDF

75

Extending Conditional Preference Networks to Handle Changes

Author 1: Eisa Alanazi

Conditional Preference Networks (CP-nets) are a compact and natural model to represent conditional qualitative preferences. In CP-nets, the set of variables is fixed in advance. That is, the set of alternatives available during the decision process are always the same no matter how long the process is. In many configuration and interactive problems, it is expected that some variables are subject to be included or excluded during the configuration process due to users showing interest or boredom on some aspects of the problem. Representing and reasoning with such changes is important to the success of the application and therefore, it is important to have a model capable of dynamically including or excluding variables. In this work, we introduce active CP-nets (aCP-nets) as an extension of CP-nets where variable participation is governed by a set of activation requirements. In particular, we introduce an activation status to the CP-net variables and analyze two possible semantics of aCP-nets along with their consistency requirements.

AI changes CP-nets preferences decision mak-ing product configuration

Abstract doi.org/10.14569/IJACSA.2019.0100975 PDF

76

Thai Agriculture Products Traceability System using Blockchain and Internet of Things

Author 1: Thattapon Surasak Author 2: Nungnit Wattanavichean Author 3: Chakkrit Preuksakarn Author 4: Scott C.-H. Huang

In this paper, we successfully designed and de-veloped Thai agriculture products traceability system using blockchain and Internet of Things. Blockchain, which is the distributed database, is used for our proposed traceability system to enhance the transparency and data integrity. OurSQL is added on another layer to easier query process of blockchain database, therefore the proposed system is a user-friendly system, which cannot be found in ordinary blockchain database. The website and android application have been developed to show the tracking information of the product. The blockchain database coupling with Internet of Things give a number of benefits for our traceability system because all of the collecting information is in real-time and kept in a very secured database. Our system could have a huge impact on food traceability and supply chain management become more reliable as well as rebuild public awareness in Thailand on food safety and quality control.

Blockchain internet of things supply chain man-agement product traceability distributed database data integrity ourSQL

Abstract doi.org/10.14569/IJACSA.2019.0100976 PDF

77

Mobile Agent Platform based Wallet for Preventing Double Spending in Offline e-Cash

Author 1: Irwan Author 2: Armein Z. R. Langi Author 3: Emir Husni

Electronic cash (or e-cash) research has been going on for more than three decades since it was first proposed. Various schemes and methods are proposed to improve privacy and secu-rity in e-cash, but there is one security issue that less discussed mainly in offline e-cash, namely, double-spending. Generally, the mechanism to deal with double-spending in offline e-cash is performing double-spending identification when depositing the coin. Even though the mechanism is successful in identifying double-spender, but it cannot prevent double-spending. This paper proposes the Mobile Agent Platform based Wallet (MAPW) to overcome the double-spending issue in offline e-cash. MAPW uses the autonomy and cooperation of agents to give protection against malicious agent, counterfeit coin and duplicate coin. This model has been verified using Colored Petri Nets (CPN) and has proven to be successful in preventing double-spending, and overcoming malicious agent, and counterfeit coins.

e-Cash double-spending MAPW CPN

Abstract doi.org/10.14569/IJACSA.2019.0100977 PDF

78

Towards A Proactive System for Predicting Service Quality Degradations in Next Generation of Networks based on Time Series

Author 1: Errais Mohammed Author 2: Rachdi Mohamed Author 3: Al Sarem Mohammed Author 4: Abdel Hamid Mohamed Emara

The architecture of Next Generation of networks (NGN) aims to diversify the offer of operators in added value services. To do this, NGN offers a heterogeneous architecture for the services deployment. This poses significant challenges in terms of end-to-end assurance of services. For this purpose, we propose in this work the establishment of a proactive autonomous system, capable of ensuring an acceptable quality level according to Service Level Agreement (SLA) requirements. A system that is able to predict any QoS degradation due to the prediction model based on time series adapted to NGN.

Next Generation of Network (NGN) network management enhanced Telecom Operation Management (eTOM) frameworks prediction time series Ip Multimedia Subsystem (IMS) Service Level Agreement (SLA) Quality Of Service (QoS)

Abstract doi.org/10.14569/IJACSA.2019.0100978 PDF

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A Distributed Memory Parallel Fourth-Order IADEMF Algorithm

Author 1: Noreliza Abu Mansor Author 2: Norma Alias Author 3: Kamal Zulkifle Author 4: Mohammad Khatim Hasan

The fourth-order finite difference Iterative Alternating Decomposition Explicit Method of Mitchell and Fairweather (IADEMF4) sequential algorithm has demonstrated its ability to perform with high accuracy and efficiency for the solution of a one-dimensional heat equation with Dirichlet boundary conditions. This paper develops the parallelization of the IADEMF4, by applying the Red-Black (RB) ordering technique. The proposed IADEMF4-RB is implemented on multiprocessor distributed memory architecture based on Parallel Virtual Machine (PVM) environment with Linux operating system. Numerical results show that the IADEMF4-RB accelerates the convergence rate and largely improves the serial time of the IADEMF4. In terms of parallel performance evaluations, the IADEMF4-RB significantly outperforms its counterpart of the second-order (IADEMF2-RB), as well as the benchmarked fourth-order classical iterative RB methods, namely, the Gauss-Seidel (GS4-RB) and the Successive Over-relaxation (SOR4-RB) methods.

Fourth-order method finite difference red-black ordering distributed memory architecture parallel performance evaluations

Abstract doi.org/10.14569/IJACSA.2019.0100979 PDF