bluuebunny/crossref_metadata_2025 huggingface dataset:
A Combined Weighting for the Feature-Based Method on Topological Parameters in Semantic Taxonomy Using Social Media
Authors: Muttaleb Hasan Ali , Mohd Noor Noorhuzaimi , Hussein Rassem Taha , Muttaleb Hasan Ahmed , Hammood Waleed A.
Published: June 2020

The textual analysis has become most important task due to the rapid increase of the number of texts that have been continuously generated in several forms such as posts and chats in social media, emails, articles, and news. The management of these texts requires efficient and effective methods, which can handle the linguistic issues that come from the complexity of natural languages. In recent years, the exploitation of semantic features from the lexical sources has been widely investigated by researchers to deal with the issues of “synonymy and ambiguity” in the tasks involved in the Social Media like document clustering. The main challenges of exploiting the lexical knowledge sources such as1WordNet 3.1 in these tasks are how to integrate the various types of semantic relations for capturing additional semantic evidence, and how to settle the high dimensionality of current semantic representing approaches. In this paper, the proposed weighting of features for a new semantic feature-based method as which combined four things as which is “Synonymy, Hypernym, non-taxonomy, and Glosses”. Therefore, this research proposes a new knowledge-based semantic representation approach for text mining, which can handle the linguistic issues as well as the high dimensionality issue. Thus, the proposed approach consists of two main components: a feature-based method for incorporating the relations in the lexical sources, and a topic-based reduction method to overcome the high dimensionality issue. The proposed method approach will evaluated using WordNet 3.1 in the text clustering and text classification.
Preserving the Topology of Self-Organizing Maps for Data Analysis: A Review
Authors: Yusob B , Mustaffa Z , Shamsuddin S M
Published: June 2020

In Kohonen’s Self-Organizing Maps (SOM) algorithm, preserving the map structure to represent the real input patterns appears to be a significant process. Misinterpretation of the training samples can lead to failure in identifying the important features that may affect the outcomes generated by the SOM model. This paper presents detail explanation on SOM learning algorithm and its applications. Some issues related to SOM’s architecture are also discussed, namely the formulation of training data from input samples, and the Best Matching Unit (BMU) identification for better visualization of large datasets, and improvement made to the SOM algorithm.
A Model Of Factors Influencing Users’ Adoption Of Internet Of Things Services: A Case Study Of Iraqi Educational Institutions
Authors: Salah Hashim Hayder , Amin Al-Sulami Zainab
Published: June 2020

In the revolution of information technology, the appearance of Internet of things (IoT) is considered a significant evolution that can enhance the efficiency and prosperity of daily activities. In this context, a few people are aware of the important benefits that can be gained from using IoT services. Generally, previous researchers have studied the technical aspects of IoT, such as architectural dimensions, models, wireless sensor networks and attribute-based signature. The aim of this study is to identify the factors that influence users’ adoption of IoT services at an educational institution in Iraq. The stratified sampling techniques were used to collect data from 221 users (students and lecturers) at university of Basra. Meanwhile, results corroborate that social influence is the most important factor in relation to the behavioral intention (BI) to use IoT services follow by effort expectancy, security and, performance expectancy. Facilitating conditions and BI have an effective influence on the use behavior of IoT services. Ultimately, contributions and recommendations were demonstrated.
IoT-Based Smart Environment Monitoring System for Air Pollutant Detection in Kuantan, Pahang, Malaysia
Authors: Saradatul Akmar Zulkifli Nor , Ridwan Satrial Mohammad , Zamri Osman Mohd , Syahidatul Nadiah Ismail Nor , Rusydi Muhammad Razif Muhammad
Published: June 2020

Air pollution is one of the pollutions that release harmful chemicals and if not controlled, it will affect the human respiratory system. Currently, there are two Air Pollution Monitoring system located in Kuantan district to record the pollution’s reading once it detects the air pollutant however, the origin of the pollution cannot be detected by the system. As a solution, a Smart Environment Monitoring System for Air Pollution Using Internet of Things (IoTs) is developed to overcome the weaknesses. This project involved both hardware and software developments, in which hardware development includes the Arduino Uno, Carbon Monoxide (MQ 7), smoke/gas (MQ 135) sensors, a buzzer and LCD display. This system is design so that the current reading can be viewed by admin in a real-time value. Besides, they can also view the data for every 8 hours. These data can be printed in PDF format. In addition, when the reading reached unhealthy value, it will warn the community via its alarm system embedded in this system. However, this system is subject to limitation especially on data delays from sensors to database. As only one existing monitoring system that located at Beserah, thus this new feature will add a sub-sensor that located at the designated area which potentially expose to the air pollution.
Unstructured Malay Text Analytics Model in Crime
Authors: Mohemad Rosmayati , Naziah Mohd Muhait Nazratul , Maizura Mohamad Noor Noor , Ali Othman Zulaiha
Published: June 2020

The concern in crime prevention to realize a safe environment for public’s welfare is one of the central agenda to government authorities. The increasing volume of crime reports with unstructured data that are heavily loaded with text presents the biggest challenge to human experts in analysing the data efficiently as it contains lots of hidden and valuable information such as modus operandi and common crime patterns. The capacity limitations in human information processing demand an effective data analytics model to uncover these hidden and potentially meaningful information from large-scale of data especially in Malay language for improving analysis and supporting decision-making in crime investigation. The objective of this paper is to propose Unstructured Malay Text Analytics Model to support serial crime investigation based on modus operandi patterns. The significance output of this paper is to assist experts especially Royal Malaysia Police in crime investigation based on modus operandi patterns analysis as one of the efforts in reducing rate of crime index.
A Model Of Biodiversity Taxonomy Database (iTaxo) For Managing And Analysing Kenyir Landscape Biodiversity Data
Authors: Mohamad Asmara Salwana , Man Mustafa , Tajuddin Abdullah Mohd
Published: June 2020

Biodiversity data management and analysis is a complex task that must be handled by biodiversity researchers. The large amount of data and complicated interaction of the data are the main problem in the biodiversity field. Data accessibility also becomes the main issue to create a data sharing environment among other biodiversity researchers. This is important element since the data is related to each other. A standardized form of data model for biodiversity data is not well implemented in real world. This study discussed about the database model for biodiversity taxonomy data (iTaxo) in order to manage the biodiversity data. We implement the Kenyir landscape which is located at the state of Terengganu in Malaysia as our case study data since the landscape is rich with biodiversity species and ecosystem. As a result, the model can be applied to manage, analyse and evaluate the biodiversity status at the landscape for conservation effort.
Predicting Graduate Employability based on Program Learning Outcomes
Authors: Nor Afiqah Wan Othman Wan , Abdullah Aziman , Romli Awanis
Published: June 2020

Many studies based on the literature and adopted approach by Ministry of Higher Education regarding graduate employability are using survey. This approach is lack with on-demand analytical capability for impactful decision making. There is a lack of study that predicts the duration of graduate to get employed based on quantitative analysis. Since all institutions of higher education are compulsory to adopt and implement outcomes-based education (OBE), this study aims to develop a predictive model on GE based on program learning outcomes (PLO) data. There are two data sources used in this study, institutional academic database and online feedback from graduate. This study used simple linear regression to measure the degree of relationship between the category of PLO with the duration of graduate to get employed. This study received 47 responses from 216 with a response rate of 22%. PLO1 and PLO6 which are ‘knowledge’ and ‘problem solving and scientific skills’ respectively show high significance values on the duration of graduate to get employed. The linear models developed based on PLO1 and PLO6 were validated with error rate analysis and evaluated with error rate frequency analysis. The results show the model has potential value to be used to predict graduate employability performance within the time frame (6 months) as determined by Ministry of Higher Education. With prediction capacity from the developed model, more intervention program can be strategically planned to assure graduate can be employed in time and in-field.
A Conceptual IR Chatbot Framework with Automated Keywords-based Vector Representation Generation
Authors: Lokman A S , Ameedeen M A , Ghani N A
Published: June 2020

This paper proposes a conceptual remodel of Information Retrieval (IR) chatbot framework designed to eliminate the need for large Question-Answer (QA) pair dataset in chatbot’s machine learning training and knowledge base development. Within ten proposed framework’s components, we describe Ans2Q: a Neural Network model for question type approximation, and HR6: an IR score ranking calculation based on Ans2Q output. Fundamentally, these two components are the variance in which the proposed framework differs from others. Together with process flow explanation, we also provide several related formulas that hopefully can be used to implement this framework. Our general aim with this framework is to provide a tool that can be used to develop close domain chatbot with small knowledge and no readily available QA pair datasets.
Template Matching Analysis using Neural Network for Mobile Iris Recognition System
Authors: Farihan Mat Raffei Anis , Zulaikha Dzulkifli Siti , Shamsiah Abdul Rahman Nur
Published: June 2020

Today, the use of mobile phone among individuals with an advanced technology, acquisition and computation resources is crucial in securing personal data and services as well as in identifying a person. Furthermore, there has been growing interest of researchers using mobile as an acquisition device to capture an eye image in a non-cooperative environment (static motion and at different distances) for iris recognition. However, the use of mobile as an acquisition device still facing some noise factors because the quality of image captured in this environmental condition is low compared to a camera device. Besides, low awareness of user in handling the mobile devices and lack technical experiences in capturing iris images with this environmental condition is generally uncontrolled contribute to producing unpredictable low quality of eye images. Due to these issues, it led to incorrect segmentation of iris boundaries and subsequently lowered the ability to match the iris features. Hence, this condition contributes to performance degradation of iris recognition system. Therefore, to improve the ability to match the iris features in mobile iris recognition system, a combination with neural network is proposed. The iris database used to test against these methods is CASIA-Iris-M1-S2. The proposed method, a combination of Hamming distance and neural network has achieved a better result where it increases the existing method in term of accuracy for mobile iris recognition under non-cooperative environment.
RETRACTED: The prediction of undergraduate student performance in chemistry course using multilayer perceptron
Authors: Akmal Che Yahaya Che , Yahaya Yaakub Che , Firdaus Zainal Abidin Ahmad , Faizal Ab Razak Mohd , Fatin Hasbullah Nuresa , Fadli Zolkipli Mohamad
Published: June 2020

Chemical industry are key elements for changing crude materials to our ordinary objective merchandise. This has achieved an immense move in how things work. The disappointment pace of an understudy in a science course is additionally expanding with regards to requesting the compound specialists. Understudies who enlist the science course regularly bomb in the first or consequent semesters. Moreover, understudies are likewise unfit to comprehend in the event that they can adjust and graduate effectively in this program. The objective of this exploration is to foresee the future utilization of improved science for understudies to fizzled or graduate by upgraded Multilayer Perceptron (MLP) arrangement with Adaboost. The exactness of the outcomes is 92.23% percent.
Detection of Proliferative Diabetic Retinopathy in Fundus Images Using Convolution Neural Network
Authors: Abu Hassan Hasliza , Yaakob Marzuqi , Ismail Sasni , Abd Rahman Juwairiyyah , Mat Rusni Izyani , Zabidi Azlee , Mohd Yassin Ihsan , Md. Tahir Nooritawati , Mohamad Shafie Suraiya
Published: June 2020

Convolution Neural Network (CNN) is one of the techniques under Artificial Neural Network (ANN) used to develop a Deep Learning Neural Network (DLNN) algorithm for detection of Proliferative Diabetic Retinopathy (PDR) on the fundus images. About 116 PDR and 150 Non-Proliferative Diabetic Retinopathy (NPDR) of fundus images retrieved from the publicly available MESSIDOR database applied in this research. This study consisted three objectives that included the execution of two pre-processing techniques on the data-set which were resizing and normalizing the fundus images, developed deep learning operational Artificial Intelligence (AI) network of feature extraction algorithm for detection of PDR on the fundus images and determined the output classification of the network encompassing the accuracy, sensitivity and specificity. There were five different parameters carried out along this research. Here, Parameter 5 showed the best performance among the five parameters based on the value of accuracy, sensitivity, and specificity that was 73.81%, 76%, and 69% respectively.
Texture-based Feature using Multi-blocks Gray Level Co-occurrence Matrix for Ethnicity Identification
Authors: Osman M Z , Maarof M A , Rohani M F
Published: June 2020

Ethnicity identification for demographic information has been studied for soft biometric analysis, and it is essential for human identification and verification. Ethnicity identification remains popular and receives attention in a recent year especially in automatic demographic information. Unfortunately, ethnicity identification in a multi-class which consist of several ethnic classes may degrade the accuracy of the ethnic identification. Thus, this paper purposely analyses the accuracy of the texture-based ethnicity identification model from facial components under four-class ethnics. The proposed model involved several phases such as face detection, feature selection, and classification. The detected face then exploited by three proposed face block which are 1×1, 1×2 and 2×2. In the feature extraction process, a Grey Level Co-occurrence Matrix (GLCM) under different face blocks were employed. Then, final stage was undergone with several classification algorithms such as Naïve Bayes, BayesNet, k-Nearest Neighbour (k-NN), Random Forest, and Multilayer Perceptron (MLP). From the experimental result, we achieved a better result 2×2 face block feature compared to 1×1 and 2×2 feature representation under Random Forest algorithm.
GbLN-PSO Algorithm for Indoor Localization in Wireless Sensor Network
Authors: Shahkhir Mozamir M. , Binti Abu Bakar Rohani , Isni Soffiah Wan Din Wan , Musa Zalili
Published: June 2020

Localization is one of the issue in Wireless Sensor Network (WSN). The problem is the estimation error in the localization of the node. In this paper, we propose an indoor localization in WSN based on Global best Local Neighbourhood Particle Swarm Optimization (GbLN-PSO) algorithm. Three anchors are used as the distance measurement for each unknown node to be localized. A comparison of the performances of PSO and GbLN-PSO in terms of localization error (accuracy) and computation time is presenting using simulation in Matlab. The result shows that GbLN-PSO improved the accuracy approximately by 0.01% and reduced the computational time by 86.02% compared to PSO technique.
Applying Graph-based SLAM Algorithm in a Simulated Environment
Authors: Azril Badioze Zaman Nazrul , Abdul-Rahman Shuzlina , Mutalib Sofianita , Razif Shamsuddin Mohd
Published: June 2020

This paper explores the capabilities of a graph optimization-based Simultaneous Localization and Mapping (SLAM) algorithm known as Cartographer in a simulated environment. SLAM refers to the problem in which an agent attempts to determine its location in the immediate environment as well as constructing the map(s) of its environment. SLAM is one of the most important aspects in the implementation of autonomous vehicle. In this paper, we explore the capabilites of the Cartographer algorithm which is based on the newer graph optimization approach in improving SLAM problems. A series of experiments were tested in order to discover its Cartographer capabilities in tackling SLAM problems. Then, we compare the results of Cartographer with Hector SLAM, another graph-based SLAM algorithm. We present the results from the experiments which show some promising findings based on the amount of computer resources used and the quality of the map(s) produced.
A survey on technique for solving web page classification problem
Authors: Hawa Apandi Siti , Sallim Jamaludin , Mohamed Rozlina
Published: June 2020

Nowadays, the number of web pages on the World Wide Web has been increasing due to the popularity of the Internet usage. The web page classification is needed in order to organize the increasing number of web pages. There are many web page classification techniques that have been proposed by the other researchers. However, there is no comprehensive survey on the performance of the techniques for the web page classification. In this paper, surveys of the different web page classification techniques with the result of the techniques achieved are presented. The existing works of web page classification are reviewed. Based on the survey, we found that the neural network technique namely Convolutional Neural Network (CNN) produce high F-measure value and meet the real-time requirement for classification compared to the other machine learning technique.
Backpropagation Network Optimization Using One Step Secant (OSS) Algorithm
Authors: Solikhun  , Wahyudi Mochamad , Safii M. , Zarlis Muhammad
Published: June 2020

Education is one of the main indicators in national development efforts. The government in its efforts to realize national goals to educate the nation’s life has made a policy for compulsory 9-year schools, namely elementary and junior high schools. To find out how many residents use the education facilities provided by the government can be seen through school enrollment rates (SPR). A high School Participation Rate (SPR) means showing greater opportunities to access education in general. This study aims to optimize artificial neural networks with the One Step Secant (OSS) algorithm. Artificial Neural Network (ANN) is part of the artificial intelligence system (Artificial Intelligence, AI) which is one of the artificial representations of the human brain that always tries to simulate the learning process in the human brain. The sample data used for optimization is SPR Indonesia data by province. Using 4 architectural models with 5 input variables, 1 shadow layer and 1 output. The best results obtained between architectures 5-4-1, 5-8-1, 5-16-1 and 5-32-1 are architectures 5-16-1. Obtained prediction accuracy comparisons using the One Step Secant (OSS) algorithm and standard algorithms namely 96.97% and 100%. The standard algorithm is superior in accuracy, the One Step Secant (OSS) algorithm is superior in terms of iterations.
Resilient Algorithm In Predicting Fertilizer Imports by Major Countries
Authors: Solikhun  , Wahyudi Mochamad , Safii M , Zarlis Muhammad
Published: June 2020

In the last five years (2013-2017) Indonesia’s fertilizer production experienced volatile growth, but overall tended to increase at a rate of 1.7% per year. The research aims to optimize artificial neural networks with a resilient backpropagation algorithm (Rprop), artificial neural networks are one of the artificial representations of the human brain that always tries to simulate the learning process in the human brain. Sample data used for optimization is fertilizer import data according to the main country of origin and uses 4 architectures, the best results are obtained between architectures 6-8-1, 6-12-1, 6-16-1, and 6-32-1 is architecture 6-32-1 with 100% accuracy.
Artificial Intelligence (AI): A Review of its Uses in Language Teaching and Learning
Authors: Ali Zuraina
Published: June 2020

The uses of Artificial Intelligence (AI) seems to be relevant in many fields nowadays due to its ability in providing a simulation of human intelligence processes that are handled by machines; in particular computer systems. This paper concerns with reviewing the uses of AI in language teaching and learning. In particular, it reviews the research on the uses of AI in its application in the learning and teaching of language. Qualitative research method; specifically content analysis, is employed as the technique to review the articles that are obtained from relevant databases. Findings from the study reveal that there are four (4) themes emerge in the uses of AI in relation to teaching and learning a language. The uses of AI for pedagogy, therefore, prove that its uses eases language teaching and learning.
A Hybrid Screen Size Independent Authentication Scheme for Smart Devices
Authors: Faizuwan Bin Welhendri Ahmad , Azad Saiful , Al Noman Ranak Md Sayfullah
Published: June 2020

Although, several authentication schemes have already been proposed for smart devices; however, most of these schemes does not consider the fact that smart devices come in different sizes. Hence, they are not screen size independent — which is the point of interest in this paper. Again, alongside screen size independence, a secure scheme also must defend the aforementioned attacks. Taking these concerns into account, in this paper, a hybrid screen size independent authentication scheme is proposed for smart devices that integrates Vibration Code or VC and Press Touch Code or PTC using a juggling-based approach. Here, VC ensures resilience against the shoulder surfing attack since it is a sense based technique and hybridization of both these schemes contribute to attaining resilience against the smudge and brute force attacks up to some extent. In addition, the proposed scheme does not require a space more than the placement of a finger on the screen; and thus, it is screen size independent. The proposed scheme is evaluated in terms of security and functionality; and compared with other similar schemes where it outperforms the others.
Stability Analysis of Relay Access Protocol (RAP) using Fuzzy Logic in Wireless Sensor Networks
Authors: Abdul Rahman Azamuddin , Nizam Mohd Kahar Mohd , Isni Sofiah Wan Din Wan
Published: June 2020

Cluster Head (CHs) determination is critical whereas the combination of input parameters is expected to play an important role. However, each WSN’s protocol uses a different combination of input parameters based on residual energy and several other input parameters, which produce different amount of energy consumption. Besides, most of the previous works on cluster heads selection do not take received signal strength indication (RSSI) into consideration. As a result, the sensor nodes with bad RSSI can be elected as CHs which lead to an increase in energy consumption. Moreover, the CHs use single-hop communication to forward their sensed data to the Base Station (BS). This results in high energy consumption and consequently, decrease the network lifetime. In this case, the inter-node distances may degrade the performance of the network due to long distance. Thus, this paper proposed a Relay Access Protocol (RAP) in order to reduce energy consumption in cluster-based wireless sensor networks (WSNs). The RAP protocol is a 2-tier protocol used for energy efficiency by selecting Cluster Heads (CHs) based on Fuzzy Logic (FL). The performance of the proposed protocol was evaluated under two different scenarios and compared with existing protocols through simulations. The experimental results show that the proposed scheme outperformed the existing protocols in terms of First Node Dead (FND) and Last Node Dead (LND) in all of the scenarios.
Performance analysis on round trip available bandwidth estimation tools for wireless mesh network
Authors: Kamarudin I E , Ameedeen M A , Azmi Z R M , Ong M I U , Lokman A S
Published: June 2020

In wireless mesh network environment, available bandwidth (ABW) estimation is important to provide feedback on the quality of network infrastructure to end user. Recent study shows the emerging of Round Trip (RT) based ABW tools where it eliminates the need of having a receiver end. However, these tools are not fully tested in various network environment and benchmark with existing widely used ABW tools. In this paper, we perform an extensive performance analysis study on RT based tools with other widely used ABW tools by looking at the accuracy and consistency. In term of accuracy and consistency, RT based tools excel in these conditions.
Analysis of Mamdani Method To Increase The Level Of The Advantages Of Infrastructure Development For Optimal Network
Authors: Zendrato Niskarto , Zarlis M
Published: June 2020

Network infrastructure has an important role towards national economic development. According to data from the Director General Post and Telecommunication cellular users experienced a significant increase from year to year. Efforts to improve telecommunications services have several problems because there is a tower which disturbs the comfort of the surrounding community, location who violates the rules and BTS tower that does not have permission. Location selection perspective from government and operators have differences, while the infrastructure must in a strategic location to get results optimal for improving service. Therefore, there is a need for research to formulate criteria for tower construction locations according to the level to the required optimization the method used is Mamdani to determine factor in determining the location of the tower. Then after found a factor weighting of factors was done by Mamdani’s analysis. The last analysis is the formulation of criteria using Content Analysis. From the analysis 5 factors is electrical energy, the number of residents served, spatial palnning, tax cost and licensing process were generated used as a determining factor for the location of tower construction BTS (Base Transmitter Station) effectiveness.
Investigating the Barriers of Health Information System implementation in Malaysian Public Hospitals
Authors: Abd Razak Norsyaliza , Ithnin Norafida , Zamri Osman Mohd
Published: June 2020

The advent of information and communication technology (ICT) has shaped the world today. Due to the potential increase in technology, the introduction into the healthcare industry has been progressively investigated. In 1997, Malaysia introduced a health initiative called telehealth. However, progress in IT implementation is progressively slow. Although the global healthcare sector is increasing, the implementation of the Health Information System (HIS) in Malaysia has been slow-informed due to various barriers. These barriers are due to a series of problems related to end users and challenges for IT departments and vendors. This article therefore focuses on the current situation of HIS in selected Malaysian public hospital. In addition, an interview conducted with the end users, IT department and the system providers. The data from the interview was then converted into a summary of the table. In this context, the obstacles that delay the implementation of HIS in Malaysia have been identified primarily in terms of suppliers, financial, organizational and human aspects.
Preventive Self-Care Serious Games for Diabetes: A Game Design
Authors: Ihsan S N , Kadir T A A , Benedict R , Abdullah A R
Published: June 2020

Diabetes is a significant factor that contributes to obesity. In recent years, there has been an increasing number of patients, including adults and children diagnosis of diabetes. Extensive awareness has been carried out for educating people to prevent from diagnosed with diabetes such as sharing the information through electronic media, social media, forum by medical experts, and others. However, less implementation of awareness is given through entertainment computing, such as serious games to attract people to learn in a fun way. In order to overcome the problem, a serious game to prevent diabetes through awareness was developed, namely as Grab2BeHealthy to educate people in choosing healthy food. The primary purpose of this study is to design the game architecture for this serious game using SGDA framework. There are a few literature studies addressed on game design framework and the implementation of a serious game in the healthcare industry, especially for awareness and diseases prevention. The game design architecture was developed using game design framework as a guideline to meet the objective of prevention. The prototype development has been tested by 50 players to proof the game design architecture.
Requirement Prioritization Based on Non-Functional Requirement Classification Using Hierarchy AHP
Authors: Win Thant Z. , Mohamed Rozlina , Sallim Jamaludin
Published: June 2020

Requirement prioritization is a process in requirement engineering, which is a part of software development life cycle (SDLC). Requirement is prioritized due to constraints such as budget, time and resource allocation. Requirements of software is often classified as functional requirements (FR), and non-functional requirement (NFR). In order to produce a high-quality software, both requirement must be considered during requirement prioritization process. Various prioritization techniques have been invented, and Analytical Hierarchical Prioritization (AHP) is the most popular technique that has been cited. However, AHP does not support the NFR and unscalable. Meanwhile, Hierarchy-AHP has been introduced unto increase the scalability of AHP by using hierarchical requirements as input. Nevertheless, hierarchy-AHP does not meant for NFR and experimental result for increasing the scalability is not received significant attention. Thus, we intend to use NFR with large dataset on hierarchy-AHP. Aim of this paper is an exploration of hierarchy-AHP experimenting on RALIC dataset. Our major findings are: (i) NFR can be used hierarchy-AHP with minor process amendment, and (ii) hierarchy-AHP able to reduce pairwise comparison which is up to 97.33% for 403 number of requirements, compared to original AHP.
Analysis of energy consumption in a wireless sensor network using fuzzy memberships function
Authors: Isni Sofiah Wan Din Wan , Ramalingam Mritha , Abdullah Nuramera
Published: June 2020

The use of sensors in every application as well as in today’s life has become a huge demand. Since that, the public sphere is starting to move to mobile applications that are easy to access. For example, child monitoring at school, health monitoring, tracking system, fire detection and so on. Thus, the Wireless Sensor Network (WSN) is the preferred choice to meet these needs. However, despite the enthusiasm of building various applications using sensors, the WSN itself is still hampered by limited battery usage. Due to some applications that require long battery life, various types of research have been conducted to address this problem. One of these is the involvement of artificial intelligent (AI) in extending the life of a battery on a sensor. Fuzzy Logic (FL) is one of the preferred AI that have been chosen by researchers to be implemented with WSN especially to protract the lifetime of WSN. In Fuzzy Logic, there are three membership functions that need to investigate the capability of it towards the WSN applications. The proposed approach is by combining different types of Fuzzy Logic membership functions which are Triangular with Gaussian, Gaussian with Trapezoidal and Trapezoidal with Triangular to get the best results for analysing the use of sensor batteries. The parameters involved for the cluster head selection are communication cost, centrality and residual energy as Fuzzy inputs. This approach will use an existing algorithm which is Multi-Tier Algorithm (MAP) and this is a part of the MAP enhancement towards the WSN lifetime. The results will compare, discuss and analyst the number of dead nodes and energy usage of the sensor node during data transmission. In conclusion, through this approach, it able to prolong the lifetime for the sensor network since the proposed technique can reduce the energy usage of the sensor nodes.
Coinhive’s Monero Drive-by Crypto-jacking
Authors: Binti Abdul Aziz Azizah , Bin Ngah Syahrulanuar , Ti Dun Yau , Fui Bee Tan
Published: June 2020

This paper provides study of behaviour on a drive-by crypto-jacking, specifically Coinhive’s Monero. Crypto-jacking is a form of cyber threat where a host machine’s processing power hijacked thru infected website to solve cryptographic puzzles as an unwitting participant. The sample study share host machine and network behaviours when host visited websites that have been embedded with Coinhive’s Monero related script. These data collection of behaviour can be utilize to identify and detect Coinhive’s Monero mining activities in networks.
Cybersecurity Risk Assessment of the University of Northern Philippines using PRISM Approach
Authors: Irvin Lamarca Bryan
Published: June 2020

The University of Northern Philippines (UNP) rely on critical infrastructure systems to promote innovation and efficiency in fulfilling its core mandate - deliver quality education. Like any environment, UNP’s cyber environment is vulnerable to security risks which dampen the privacy and safety of stakeholders, the security of assets, and the confidentiality of business proceedings. To proactively address these and other potential risks, this study reviewed existing risk management frameworks used across governments and selected one to be utilized for improving organizational cyber policies and risk mitigation procedures and practices – PRISM, a model to identify and implement cybersecurity risk management tailored towards the problems and needs of the university. Results showed that at least half of the risk areas have poor preparedness level, stemming from the lack of institutionalization of knowledge and solutions, assessing unusual behavior, and proactive and enterprise risk management. Most risk areas seemed to be prioritized, had allocated resources, and has reactive risk management in place. GAP Analysis was also conducted in conjunction with the results of PRISM assessment to better steer the university in the right direction. The study concluded that PRISM is a tool that aided the university in laying down a formalized groundwork for cybersecurity.
An open cloud-based platform for the creation and delivery of smart applications and services
Authors: Fadhil Heba M. , Younis Mohamed I. , Alsewari AbdulRahman A. , Abd-Alrazzaq Mohammed , Toran Mahdi Ameer , Alomoush Alaa A.
Published: June 2020

With the increasing integration of computers and smartphones into our daily lives, in addition to the numerous benefits it offers over traditional paper-based methods of conducting affairs, it has become necessary to incorporate one of the most essential facilities into this integration; namely: colleges. The traditional approach for conducting affairs in colleges is mostly paper-based, which only increases time and workload and is relatively decentralized. This project provides educational and management services for the university environment, targeting the staff, the student body, and the lecturers, on two of the most used platforms: smartphones and reliable web applications by cloud hosting. The services include project management, attendance marking, various notifications and alerts, files and resources, a grading system, and an assignment management system. This paper also aims to ultimately digitalize most of the information inside the college which is a great challenge. The proposed system defines five actors, namely: an admin; who is the person with the highest authority within the system, the head of the department, the department coordinator, the project coordinator, the lecturers, and the students. Each of those actors has its privileges and the interaction among participants via the cloud. The mobile platform targets the students exclusively and interacts with the web platform. Finally, this paper stresses the benefits of the proposed system and gives the avenues for future development.
Region of Interest-based Tamper Detection and Lossless Recovery Watermarking Scheme on MRI and X-ray Medical Images
Authors: Ooi Jessie , Khor Hui-Liang , Liew Siau-Chuin
Published: June 2020

The advancement of the technology has introduced many applications that will ease human daily process and improve the way people connect to each other. One of the applications that has been introduced to the user and vastly improve our life is the healthcare system. Health care system allows the medical practitioner to aware of the patient’s previous health condition and documents their current. It also allows the medical practitioner to communicate with each other or get some advice from their peers and colleague even if they are far from each other physically. To ensure the authenticity of an image when it is transfer through the health care system, watermarking has been implemented. Through the process of watermarking the images, the authenticity of the images can be ensured. In this study, a ROI-based tamper detection and recovery watermarking scheme has been introduced. The proposed scheme has been tested out on the MRI and XA images. The PSNR value for MRI is 49.09 for the sequential mode and 49.07 in parallel mode. On the other hand, PSNR for XA images is 52.68 in the sequential mode and 52.68 in the parallel mode.
Reversible Watermarking Technique for Fingerprint authentication based on DCT
Authors: Lebcir Mohamed , Awang Suryanti , Benziane Ali
Published: June 2020

In this paper, a new reversible and blind fingerprint image watermarking technique based on the differential method and discrete cosine transform (DCT) domains is presented. The focus is to increase the security of the fingerprint image in authentication systems. Two DCT-transformed sub-vectors are employed to embed the bits of the watermark sequence in a differential scheme. The original sub-vectors are acquired by the DCT transform on the host fingerprint image. In the extraction process, a minor variance between the sub-vectors that correspond to the watermarked fingerprint image directly allows access to the embedded watermark sequence; therefore, the extraction process doesn’t require an original fingerprint. The original fingerprint image is then recovered from the watermarked fingerprint image based on the reversible watermarking technique. The similarity between the reversible fingerprint image and the original is considered, and we could extract minutiae points from it without a problem. The proposed technique is evaluated using 80 fingerprint images from 10 persons for each from FVC2002 fingerprint database. Eight fingerprint images have been taken from each person to be used as the template then the process was followed by embedding the watermark into each fingerprint image. The experimental results validate the proposed method able to give promising results in preserving the fingerprint image security.
Types of anti-phishing solutions for phishing attack
Authors: Hawa Apandi Siti , Sallim Jamaludin , Mohd Sidek Roslina
Published: June 2020

Nowadays, many people use Internet to do online activity. This scenario exposed them to danger in Internet which is phishing attack. In order to solve phishing attack, the anti-phishing solutions are needed. Based on our review, there are still lacks of articles that review on the types of anti-phishing solutions in detail. In this paper, a general idea of phishing attack and anti-phishing solutions is presented. The phishing attack can be classified into two categories which are social engineering and malware-based phishing attack. The anti-phishing solutions can be differentiating into two types which are phishing prevention and phishing detection. Compared to phishing prevention, the phishing detection is more important to solve the phishing attack. In phishing detection, there are two categories which are user awareness and software detection. The software detection has two methods which are traditional and automatic. There are two types for automatic method of software detection which are public phishing detection toolbars and academic phishing detection / classification schemes. Based on the comparison of all types of phishing detection, the academic phishing detection / classification schemes are more useful for phishing detection. For future work, we can do more research on the academic phishing detection / classification schemes that utilize deep learning to see its potential of accuracy to detect phishing websites.
A Survey on Current Malicious JavaScript Behavior of infected Web Content in Detection of Malicious Web pages
Authors: Nurulsafawati Wan Manan Wan , Nizam Mohmad Kahar Mohd , Mohd Ali Noorlin
Published: June 2020

In recent years, the advance growth of cybercrime has become an urgent issue to the security authorities. With the improvement of web technologies enable attackers to launch the web-based attacks and other malicious code easily without having prior expert knowledge. Recently, JavaScript has become the most common attack construction language as it is the primary browser scripting language which allow developer to develop sophisticated client-side interfaces for web application. This lead to the growth of malicious websites and as main platform for distributing malware or malicious script to the user’s computer when the user access to these webpages. Initial act and detection on such threats early in a timely manner is vital in order to reduce the damages which have caused billions of dollars lost every year. A number of approaches have been proposed to detect malicious web pages. However, the efficient detection of malicious web pages previously has generated many false alarm by the use of sophisticated obfuscation techniques in benign JavaScript code in web pages. Therefore, in this paper, a thoroughly survey and detailed understanding of malicious JavaScript code features will be provided, which have been collected from the web content. We conduct a thorough analysis and studies on the usage of different JavaScript features and JavaScript detection technique systematically and present the most important features of malicious threats in web pages. Then the analysis will be presented along with different dimensions (features representation, detection techniques analysis, and sample of malicious script).
Analysis of some indicators of environmental safety of buses with diesel engines (on the example of Moscow)
Authors: Boldin A P
Published: June 2020

The results of studies performed personally by the author and under his leadership on the current level of environmental safety of city buses of Russian-made ecological class Euro-4 (LiAZ-5292-21) and Euro-5 (LiAZ-5256-13 and LiAZ-529-22) according to the smoke level of the exhaust gases provided by GOST R 52160-2003, and not required in actual operation in terms of the content of nitrogen oxides NOX
in the exhaust gases of a diesel engine when operating at idle modes. Previous studies on passenger cars and light commercial vehicles with diesel engines (using the operating method developed by us) in accordance with UNECE rules 83.03 (based on urban driving cycles with the results of environmental pollution in g/km) showed an excess of NOX
emissions from 7 to 15 times, on the basis of which a simplified technology was developed to control the environmental safety of these vehicles by indicators of the content of CO2
and NOX
at idle for a conventional engine with a working m volume of 1 liter. This technique is appropriate to apply for a large vehicle mass, for which NOX
emissions at idle is much higher. This will provide operational control of environmental safety by NOX
and increased requirements for manufacturers. In a first approximation, the maximum permissible value of the NOX
content is justified – no more than 530 ppm. (For comparison, this indicator for cars is significantly lower – no more than 200 ppm). It was additionally established that for estimating CO2
greenhouse gas emissions it is enough (with an error of no more than 10 % to decrease) to use the general indicator of fuel economy – actual fuel consumption: at the same time per 1 kg of diesel fuel, gasoline, liquefied and compressed natural gas, used in operation, 1,500 g of CO2
will be accounted for (conversion to liters is carried out according to the energy density indicators). The studies were carried out as part of comprehensive work to improve the quality of technical servicing of cars, trucks and buses, conducted at the department “Operation of automobile transport and car service” MSARTU (Head of the Department of Doctor of Technical Sciences, Professor A.A. Solntsev).
Research of the influence of alternative fuels on diesel engine noise level
Authors: Iakovenko A L , Dunin A Y , Dushkin P V , Savastenko E A , Shatrov M G
Published: June 2020

One of the advantages of using alternative fuels in internal combustion engines is the possibility of reducing noise. The article presents the methodology and some results of the research of structure-borne noise of internal combustion engines related to the working process. The main characteristics of alternative fuels allowing reduction of the noise level by modifying parameters of the heat release law and reducing the pressure growth rate are analyzed. The results of calculated and experimental research of the diesel engine structure-borne noise during its operation on gas and mixed fuels are presented. The research showed that the greatest effect is achieved when converting diesel engine to spark ignition gas engine. For gas-diesel engines, noise reduction of the working process is much lower because a high compression ratio is preserved. The use of mixed fuel revealed a need to correct the injection advance angle to reduce the noise level.