ABSTRACT

        Industry 4.0 & Beyond                    

Business Informatics & Industry 4.0

Software Systems & Emerging Technologies

Communication Networks & Industry 4.0

Comparison between Communication Technology used in Smart Building

Sera Syarmila Sameon; Salman Yussof; Bo N. Jorgensen

Smart Building and its applications have become one of the essential ways of delivering a better quality of life and environment nowadays. The smart construction of building use the technology to share information between application by automate various processes, such as lighting, heating, ventilation and air conditioning (HVAC), security and other systems. A smart building and supervisory control systems infrastructure uses wired, wireless, and smart sensor networks to collect data according to the needs and services of the business. This paper presents a review and a comparison between the various communications technologies commonly used in smart building. Choosing an appropriate technology is crucial in smart building in ensuring the automated process run well.

Predicting Thermal Comfort of HVAC Building Using 6 Thermal Factors

Faridah Hani M Salleh; Mulyana Saripuddin; Ridha Omar

Predicting thermal comfort requires a set of reliable thermal factors for an accurate prediction. The effectiveness of using thermal factors varies depending on the environmental conditions and occupants' characteristics. Identifying thermal comfort in a commercial building is important for better management of the building's facilities. The objective of this research is to compare the performance of the six established thermal factors with actual users' responses in predicting thermal comfort, focusing on buildings operating with HVAC system. This research applies six machine-learning models for prediction process; and, one general method widely use to generate thermal comfort known as the PMV method. The experimental results prove that subspace K-Nearest Neighbor (s-KNN) can reach up to 80.41% of accuracy, and then followed by Begged Trees (BT) model (76.30%), Classification Tree (CT) (66%), Classification Neural Network (CNN) (55.67%), Support Vector Machine (SVM) (50.51%) and Kernel Naïve Bayes (KNB) (43.30%). Whilst, PMV method achieves the lowest result, with 22.68% accuracy only.

Applying Neuro-Fuzzy Model in Indoor Comfort Microclimate Control

Azizah Suliman; Raissa Uskenbayeva; Aigerim Altayeva

This paper proposes a concept for managing climate control systems (HVAC systems) in large commercial buildings based on predictive models. The proposed control concept reduces the consumption of gas used for room heating while maintaining the acceptable range of the required temperature. The authors propose fuzzy and neuro-fuzzy models to ensure comfort temperature and humidity in the indoor environment, and thus minimize energy consumption. The management strategy is formed using predictive models. The developed management strategy is applied to the climate control system through a hardware and software complex consisting of a client connected to the system and a server that forecasts changes in indoor temperature, gas consumption and forms a management strategy. The tests that implemented the proposed concept were performed in a commercial building. The efficiency of the proposed concept is shown in comparison with the control algorithm built into the HVAC system.

Identifying the Klang Valley Rail Riders' Travel Pattern for Future Expansion using Social Network Analysis

Muhammad Nor Hafiz Yaacob; Daeng Ahmad Zuhri Zuhud; Pritheega Magalingam; Nurazean Maarop; Ganthan Narayana Samy; Mohana Shanmugam

As the population grows, the importance of public transportation increased rapidly as people can use them to travel within the city or town easily. Due to the immense complexity of any public rail, a rich set of information can be uncovered and mined using Social Network Analysis (SNA). In this research, SNA is used to analyse Klang Valley's rail network and its daily riders' pattern. Klang Valley's rail network serves the area of Klang
Valley and Greater Kuala Lumpur with the length of 555.7KM. SNA is a type of analysis that characterizes any phenomena within entities' relationships based on their source and destination. A directed graph was constructed based on a dataset consisting of riders' travel history containing 120 nodes and 5567 edges; the nodes represent stations and the edges are daily-commuters' travel routes between various stations. Temporal analysis is then
performed on the dataset to understand the travel trends of residential and central business district (CBD) stations during peak and non-peak hours. Another analysis was also done to identify the rails' network performance when handling attacks on high degree stations; its robustness is also studied. It is found that Klang Valley's rail network is highly robust with more than 500 triad connection detected, ensuring it to recover easily if either one station fails. This research helps to provide new insight into future transportation planning and rail network traffic scheduling.

Blockchain and The Personal Data Protection Act 2010 (PDPA) In Malaysia

Salman Yussof; Hasventhran Baskaran; Fiza Abdul Rahim; Asmidar Abu Bakar

Blockchain is a time stamped ledger that is used to keep immutable records. This technology has gained immense popularity due to the use of its decentralized architecture in cryptocurrency platforms. Blockchain has been increasingly adopted in other sectors due to its ability to ensure data integrity. The increasing use of blockchain by the public has made it become a subject to data privacy laws. Based on the study conducted by other researchers, there are features of blockchain that conflict with certain elements in data privacy laws. The European Union's General Data Protection Regulation (GDPR), which becomes a model for data privacy act of many other countries, has been identified to be incompatible with blockchain. One of the research works in the area of blockchain is to figure out how blockchain can be made to comply with such privacy laws. In Malaysia, blockchain is still relatively new and a study needs to be done to evaluate its compatibility with Personal Data Protection Act 2010 (PDPA). Hence, the aim of this paper is to identify the gaps between blockchain and PDPA in terms of their compatibility and to propose solutions to bridge the gaps. Based on the gaps identified, the paper proposed the use permissioned or private blockchain, off-chain storage and stealth address to enable a blockchain application to be compliant with PDPA.

Smart Grid Digital Forensics Investigation Framework

Haris Iskandar Mohd Abdullah; Muhammad Zulhusni Mustaffa; Fiza Abdul Rahim; Zul-Azri Ibrahim; Yunus Yusoff; Salman Yussof; Asmidar Abu Bakar; Roslan Ismail; Ramona Ramli

The advancement in the development of smart grids may be carrying the risk of increased security vulnerabilities of the grid and allowing attackers to easily access the power system to either manipulate internal operation or disrupt the entire grid operations. In other words, a smart grid environment will be an important source of evidence in the event of a threat. Therefore, a systematic forensics investigation must be carried out to ensure that important steps are taken accordingly. Most digital forensics frameworks focused on the computing environment, which resulted in the absence of a knowledge base and resources to facilitate such an investigation in a smart grid environment. This study sheds light on the development of the proposed Smart Grid Digital Forensics Investigation Framework to support digital forensics investigations by taking the example of Stuxnet attacks. Existing digital forensics frameworks and models are reviewed to identify the suitability of phases to be included in the proposed framework.

An Empirical Study to Evaluate the Accessibility of Arabic Websites by Low-Vision Users

Muhammad Akram; Rosnafisah Sulaiman

Empirical study to identify the web accessibility issues in the Arabic version of websites can play a vital role to improve the quality of the website. World Health Organization (WHO) reported that more than one billion people having a different kind of disability. United Nation (U.N.) assembly prepared and passed a treaty in 2006 to protect and provide the rights of people with disability. The article 9 of treaty enforce all countries to identify and overcome the difficulties which hurdle the disabled people from accessing their environment, transportation, public facilities, services and information and communication technologies (ICT). Web content accessibility guidelines (WCAG) exists since the last two decades but still disable users are not able to benefit from the services provided by the website adequately. The research team found some web accessibility evaluation studies which are conducted mostly in western countries by involving the disable users for task-based evaluation for the English version of websites. However, our knowledge is quite low about the problems faced by disabled users of Arabic websites. To the best of our knowledge, this is the first research study which applied on the Arabic version of web sites to get the empirical evidence by involving the disable users in accessibility audit process. In this study, five Saudi Ministry websites selected for accessibility evaluation based on their frequency of usage. Twenty-five low-vision participants participated in this study. Each participant was given a set of task to perform on each chosen website. Participants in this study were asked to complete the task and rate the overall level of difficulty to accomplish the task on five-level scales. Problems faced by participants recorded, and the difficulty level to overcome each problem rated on five-level scales. After completing the task, each participant rated the level of compliance of the website using five-level scales with the web content accessibility guidelines 2.0. The study concluded that selected Arabic websites are not designed fully by applying the existing WCAG 2.0. However, many accessibility problems faced by disable user are complex, which cannot be addressed only by the current checklist. The conclusion enforces the involvement of disable user is significant in design and evaluation to improve the accessibility. Moreover, the research team believes that empirical evidence generated by this research study is an addition to the current body of accessibility evidence.

Application of Extreme Learning Machine in Predicting Short-Term Wind Speed

Chai Phing Chen; Sieh Kiong Tiong; Johnny Koh; Ammar Alkahtani; Dallatu Abbas Umar; Albert Fong

At present, wind energy is the fastest growing power generation sector with its economic advantage of being a rich, clean and environmentally sustainable resources. However, wind does not generally blow consistently which prevents wind turbines from functioning at maximum capacity and capability. However, the solution that had been put forward in this paper to overcome the aforementioned problem is able to be used to make the deterministic predictions study for the wind speed, and of which its model in this paper possesses a significant prediction accuracy and strong stability, which could be useful in predicting the randomness of short term wind speed accurately. Based on the prediction output results, the amount of power required to be generated for load dispatch planning could be calculated and used to produce a scientific basis with the purpose of designing an optimal power grid dispatching design. In this paper, Extreme Learning Machine (ELM) is used for predicting short-term wind speed, and through use of the ELM the prediction accuracy of wind speed was observed to be at 0.93 followed by the root mean square error rate at 1.9. With reference to the prediction results, the developed model is tested to be able to predict wind speed accurately

Improved Electromagnetsim-Like Algorithm for Economic Emission Dispatch Problem

Albert Fong; Chai Phing Chen; Johnny Koh; Sieh Kiong Tiong

This paper proposed an improved electromagnetism-like (EML) algorithm to optimize simultaneously the economic and emission dispatch problem considering operational boundaries. A new local search named Dynamic Drift Shift local search (DDSLS) is developed. From the experimental testing, the performance presented by the EML with DDSLS implementation effectiveness shows better quality as the solution obtained outperform the original EML using with and without line local search. The current proposal improved EML with DDSLS manage to perform the searching to obtain the best optimize total generation cost and minimizing the GHG for the load demand 800MW within the plants constrained limits.

Improvement from Proof Of Concept into the production environment: cater for high-performance capability

Nor Izyani Daud; Galoh Rashidah Haron; Moesfa Soeheila Mohamad; Dahlia Din

We developed the Trust Engine as a component of an adaptive multi-factor authentication system. The Proof-of-concept was tested and found to meet all functional requirements, but it does not meet the scalability requirement. It is crucial to make the Trust Engine scalable because the authentication platform serves applications that have up to millions of users. We discover that the architecture and database design of the Trust Engine component is the root cause. Thus, we redesigned the database and modified the architecture while maintaining correctness of the functions and compatibility with the authentication platform. Here we present the techniques we use and show the success with performance test results.

Design and Development of Machine Learning Technique for Software Project Risk Assessment - A Review

Mohd Hazli Mohamed Zabil; Mohamed Najah Mahdi; Kok Cheng Lim; Muhammad Sufyian Mohd Azmi; Azlan Yusof

Accurate assessment of software project risk is amongst the key activities in software project development. It directly impacts the time and cost of software projects. This paper presents a literature review of developing & design machine learning techniques for software project risk assessment techniques. The results of the review have concluded prominent trends of machine learning approaches, size metrics, and study findings in the growth and advancement of machine learning project management. Also, our contribution provides a deeper insight and an important framework for future work in the project risk assessment. Also, we demonstrated that the estimation of project risk using machine-learning is more efficient in reducing the project's fault that increases the probability for the software project's prediction and response and provides a further way to reduce the probability chances effectively and to increase the software development performance ratio

Software Project Management Using Machine Learning Technique - A Review

Mohd Hazli Mohamed Zabil; Mohamed Najah Mahdi; Muhammad Sufyian Mohd Azmi; Kok Cheng Lim; Azlan Yusof

Project management planning assessment is of great significance in project performance activities. The creation of project management cannot be effectively handled without a practical and rational strategy. This paper offers a large-scale review analysis of articles based on machine learning and risk evaluation management for projects; There are review can be representing and classified. The first group covers project management analysis and survey articles. The second category contains works on the steps and experimental criteria that widely used in the management of machine learning projects. Also, our contribution provides a deeper insight and an important framework for future work in the project risk assessment. In conclusion, we demonstrated that the estimation of project risk using machine-learning is more efficient in reducing the project's fault that increases the probability for the software project's prediction and response and provides a further way to reduce the probability chances effectively and to increase the software development performance ratio.

A Software Engineering Approach in Netball Performance Analysis: Training and Activities Features for Automatic Players Position Selection

Abdul Hadi Mohamad; Ramona Ramli; Amira Farisa Ramli

Player performance analysis and player selection are two critical activities that should be carried out by coaches to form sports teams. In netball, testing, training, and activities will be performed by coaches to collect player performance for analysis and player selection. Albeit its importance, coaches are still recording this data on paper-based sheets. The time-consuming and tedious manual evaluation process will be then conducted based on predefined criteria which likely leading to improper team formation, lack of transparency, and favouritism. Recent research in literature has led to player classification and physical performance evaluation using match data. While this is highly depending on coach experience, problems may occur which coaches expressed the demands of having a more systematic and guided approach to assist in player selection. In giving some guidance for performance evaluation and player selection, this study highlighted computer-based system features represented in flowcharts, considering inputs from practitioners and researchers using a mixed approach of data gathering. While algorithms are proposed to evaluate time and distance achieved by each player for team selection, two key factors in measuring player performance and quality are determined to assist computer system in analysing and making decisions for automatic selection. The results show that on average, around 35 per cent of errors are found upon pilot path testing. A systematic fixing is performed to improve the processes, and optimum results are achieved consequently. Initial findings obtained in this study may lead to further investigation of the algorithms in actual sports settings and environment.

Energy Usage Prediction for Smart Home with Regression Based Ensemble Model

Mohammad Shamsul Hoque

Residential sectors using energy mainly though lighting and HVAC (Heating, Ventilation and Air Conditioning) have become a significant consumer of world energy and it is expected to grow especially with the trend of increasing smart homes. To provide an optimum, accurate and reliable electricity distribution, load prediction is a prerequisite policy and operational implementation. Smart homes with the use of various sensors create big data that gives a favorable opportunity for developing data-driven energy usage prediction models. In this paper, a novel regression-based ensemble prediction model with inbuilt automated optimization for parameters is proposed to predict the demand of electricity. The model explains the 0.998 correlation between the features and their label, and achieved RMSE and Normalized Absolute Error as low as 5.508 and 0.0508 respectively. We have also proposed a novel data-driven classification of the energy usage by unsupervised learning through clustering.

Analyzing Algorithms to Detect Disaster Events using Social Media

Faris Azni Azlan; Azhana Ahmad; Salman Yussof; Azimah Abdul Ghapar

Disasters are instabilities that occur on the interface between society and the environment. During disasters, people communicate to inform and request for support for themselves or their community. Social media is used as a medium for communication due to its wide reach and global audience. During disasters, people communicate via messages regarding similar or different types of emergencies in the same general location. Interpreting and validating these messages during the occurrence of a disaster costs a significant time and loss. Therefore, this study presents a comparison between three algorithms, K-Nearest Neighbor (KNN), Naive Bayes (NB), and Support Vector Machine (SVM), to cluster and sort messages so that the process of examining them can be simplified and accelerated. In order to simulate the examining process further, a fuzzy system is introduced to automatically rate the severity of a disaster as described in each message in disaster environment. The results are discussed and performances are compared for each of the algorithms.

Implementation of Token Parsing Technique for Regex Based Classification of Unstructured Data for Cyber Threat Analysis

Muhammad Hazim; Norziana Jamil; Mohd Ezanee Rusli

Cyber Threat Intelligence (CTI) is a concept for information about cyber threats which were analysed, structured, and refined. This information is used to help organizations to understand the current risk that have different levels that might bring harm to their enterprises. Besides, CTI can also help organizations to plan for defensive countermeasures and protect themselves from the attacks that can cause them damage. In this paper, we introduce a token parsing technique for regex based classification of unstructured data for cyber threat analytic (CTA) engine that does threat analysis based on data crawled from several public resources. Our engine crawls and fetch data from the public resource in time series, analyse the data and provide a meaningful information to the user with the timeline of the fetched parameter. The collected data which appears as non-structured are converted by the engine to appear as a structured data and then be inserted into the database. Subsequently, the engine then analyses the threat data by modelling it before useful information be returned to the user. The challenge is to have a structured data useful for analysis. This paper explains how our token parsing technique is useful in regex based classification to convert the unstructured data into useful structured data.

MySD: A Smart Social Distancing Monitoring System

Mohd Ezanee Rusli; Salman Yussof; Mohammad Ali; Ahmed Hassan

Due to COVID-19 pandemic, society need to embrace and adopt new norm that includes practising social distance to break the transmission. The smart social distance application or tracker can help people to be constantly monitored and reminded to adhere to this practice. Direct impact that can be seen from this application will be lower or minimum number of COVID-19 cases due to high level of social distance compliance. This paper will present an innovative solution called MySD which stand for "My Safe Distance" that help users or public to observe social distance advice closely. It leverages smart phone hardware features that typically has Bluetooth transceiver as well GPS to determine safe distance and required level compliance.

 

Surface roughness optimization based on hybrid Harmony Search and Artificial Bee Colony algorithm in Electric Discharge machining process

Ashanira Mat Deris; Badariah Solemon

This paper presents the hybrid approach for optimization process of surface roughness in die sinking electric discharge machining (EDM) process. The optimization process is based on hybrid Harmony Search (HS) and Artificial Bee Colony (ABC). The data was collected from the experimental machining conducted using EDM AG40L. Harmony search (HS) algorithm is applied to optimize the surface roughness (Ra) performance of EDM process. However, HS was not sufficient enough to give better results in performing local search for numerical application in terms of fine-turning characteristic and convergence rate. Thus, hybrid HS-ABC is proposed to improve the weakness of the HS algorithm to get accurate optimal solution for EDM process. It was found that the optimal solution of the proposed HS-ABC has outperformed the result of single HS algorithm.

Functions-focused Building Energy Management Systems: A Review

Hairoladenan Kasim; Abbas M. Al-Ghaili

Building energy management systems (BEMSs) perform essential tasks to enhance energy-use performance. BEMSs play an important role in energy savings because it affects energy management related functions needed by a building. An efficiently designed BEMS would affect building energy efficiency and energy-use. There are several key functions each building might perform one or more than a function to contribute to energy-use depending on the purpose of BEMS embedded in that building. Some BEMSs might perform that a function to come up with a semi-optimal level in energy efficiency and energy-use. This review paper presents several function-purposed BEMSs. Monitoring of energy-use, measurement, analysis, estimation and optimization are some examples of functions BEMS performs. This review paper aims to give readers suggestions on which more suitable and adequate design of BEMS is preferable for a type of buildings than other systems to achieve a better energy management. In this review paper several top-tier researches' publishers e.g., Elsevier, IEEE, and ACM have been used from which papers are collected. This paper has considered papers published since 1982 to cover an extensive trend of research studies used for BEMS.

Lighting Systems Designed for Energy Savings in Buildings (LSD-ESB): A Review

Hairoladenan Kasim; Abbas M. Al-Ghaili; Zainuddin Hassan; Bo N. Jorgensen

Recently, lighting systems (LSs) have received much attention due to their importance in energy savings in buildings. There are different types of lighting systems designed (LSD) to save energy in buildings. Those systems have produced different energy savings rate(s) depending on the design's accuracy, parameters and factors considered during design, and the real scenario (i.e., building) for which the system is designed. All these factors play important roles in enhancing energy saving performance. Therefore, this review paper aims to compose a group of akin types of systems (ToSs) achieving a high level of "energy savings" rate when applied to a certain type of building (ToB). This paper has considered different ToS and ToBs. Due to the huge number of LSs applied to different ToBs, therefore cited papers from literature review are determined by two conditions, which are: selected papers are within the period of last twenty years and published by five well known publishers which are: Elsevier, IEEE, MDPI, Emerald, and ACM.

Adoption of Enterprise Architecture by Public Sector Organisations

Hairoladenan Kasim; Nor Azizah Ahmad; Sulfeeza Mohd Drus

The survey of public sector organisations (N = 255) applied the Institutional Theory (IT) and Technology- Organisation-Environment (TOE) framework. Findings
indicate that clear communication, good governance, expected benefit, size, normative pressure, coercive pressure, and mimetic pressure have a significant impact on the intention to adopt Enterprise Architecture (EA). The study leads an
operational review in the context of EA at the organisational level. The results also provide valuable insights into the management of the EA process for decision-makers and EA initiators.

A Review of Factors Influencing Patient Readmission Based on Data Quality Dimension Model

Noor Hafizah Hassan; Norshakirah Aziz; Ganthan Narayana Samy; Nurazean Maarop; Nur Azaliah Abu Bakar

Ensuring the quality of data in healthcare is important as to determine the level of performance of health services offered by them. The key factor that helps boost their performance are the enhancement of timely diagnosis and management of diseases and patient, keeping personnel satisfaction and lessen the hospital costs in running them. Assessing the quality of data in adoption electronic health record (EHR) and preventing the rising of patient readmission rates in Malaysia is the main concern of the project. However, there is lack of study on patient readmission factors in Malaysia and the adoption of electronic health record (EHR) also causing poor quality of patients' data to be analyzed. Lastly, delivering the information to patients is difficult and confusing because of lacking in medical knowledge and terms. Therefore, this paper proposes consistency as additional dimension for data quality dimension to be used for validating patient readmission dataset. Theoretically, this project is seen to improve the quality of information by proposing improvement in used dimension to assess data quality of EHR. On the other hand, practically, this paper will help to visualize the results of medicinal information in the form of dashboard, which can be easily understood by both patients and health practitioners.

Modeling Time Series Data with Deep Learning: A Review, Analysis, Evaluation and Future Trend

John Syin Ang; Kok-Why Ng; Fang-Fang Chua

Time series modeling has long been a challenging problem. In the recent year, deep learning (DL) has attracted huge attention in many fields of research, including time series analysis and forecasting. While the methods of DL are very broad and wide, we aim to review the recent impactful deep learning papers and provide insights from some of the notable DL models and evaluation methods on time series problems. Our main objective is to review and analyse the advantages and disadvantages of different models, evaluation methods, future trends and techniques of solving time series problem with DL.

The Adoption of Enterprise Architecture: An Institutional Theory Perception

Nor Azizah Ahmad; Sulfeeza Mohd Drus; Hairoladenan Kasim

Numerous models are suggested in the extant literature to explain the adoption of technologies in which pressure considerations of the adoption are not discussed. Based on institutional theory, this study suggests a model for analyzing the three pressures of normative, coercive, and mimetic factors  within the scope of enterprise architecture (EA). The model is examined by 255 respondents using the survey questionnaires. The results indicate that normative, coercive, and mimetic pressures have a significant impact on the intention to adopt EA. The study leads an operational review in the context of EA at the organizational level. The results also provide valuable insights into the management of the EA process for decision- makers and EA initiators.

Improving Big Data Technologies with Visual Faceted Search

Mohammed Najah Mahdi; Abdul Rahim Ahmad; Roslan Ismail; Mohammed Subhi

Because of the quantity, complexity, and speed measurements of big data, access to the data needed for big data applications becomes ever more challenging for both end-users and IT-experts. access. This has however led to problems of overloading information. While progress has been made in search engines, finding the right information still is a struggle
due to the exponential increase in the everyday information provided. The need for Big Data Analytics now powers other aspects of modern society, because they can build new linkages and discoveries that help drive tomorrow's innovation. Data visualization of big data applications is an important tool to interpret and understand immense data, making the technology more enticing to use. Visualization of a search result with faceted search is increasingly popular in search engines. This seeks to allow users to easily and effectively find their way through large document collections. FS strategies presume that correct information is required so that the value, importance, and expense of achieving the requested information is optimized. In this work, we propose a new FS framework for visualizing browsing and refinements of search results to allow users to visually build complex search queries. The proposed FS can also solve the problem of lexical uncertainty in current search engines and give users more interest.

Towards an Infostructure Maturity Model for Disaster Management

Aliza Abdul Latif; Noor Habibah Arshad; Norjansalika Janom

Maturity models has been accepted as a widely accepted tool in guiding the development of improvement program to transform an organization to a better state. Due to the ad-hoc and dynamic state of disaster management, activities conducted in dealing with disaster need to be assessed so it meets the objectives. This paper highlights a preliminary investigation
of a suitable assessment tool, aimed at the development of an infostructure maturity model for the domain of disaster management. The development of this model is justified to the
extent that there is no existing maturity model to assess infostructure, which is a new term developed specifically for disaster management. The research also aims to serve as a
preliminary work in developing a tool to determine the maturity of the activities involved in disaster management, specifically for electricity company.

Data Governance and Data Stewardship: A Success Procedure

Doris Hooi-Ten Wong

Data governance and data stewardship have a close relationship with each other. In order to start the governance and stewardship program, a better understanding of the concept and key elements is essential. In this paper, the overall of data governance and stewardship is discussed including the definition, goal, elements, focus area, framework, role, and responsibilities, how to implement and also how to measure its maturity state. Thus, this will give an insight into how to establish data governance and data stewardship in an organization.

Methodology for a Large Scale Building Internet of Things Retrofit

Sumayyah Dzulkifly; Hazleen Aris; Bo N. Jorgensen; Athila Santos

With the presence of sensing technologies today, buildings can be built fitted with various sensors and systems to encourage energy efficiency and sustainability. However, legacy buildings are not originally built to support internet-of-things (IoT) technology or to promote sustainability. Thus, legacy buildings that are still being actively used today require retrofitting works to improve the efficiency of the buildings. Retrofitting the legacy buildings with IoT capabilities can be very challenging due to the active use of the buildings, dynamic building functions, undocumented indoor modifications and vulnerable physical structure, among others. Therefore, a comprehensive methodology is needed for systematic deployment of the sensors and associated systems into the building, covering a complete cycle from identifying the needs and requirements of the system until its full operation. This study aims to devise and advocate a methodology for systematic retrofitting of sensors in legacy buildings and hence enabling the collection of energy consumption data. To do that, a 20-year old six-storey academic building in one of the institutes of higher learning in Malaysia is used as a case study. This paper explains the first part of the progressive development of the proposed methodology, which is on the protocol for the placement of the sensors and devices. The outcome of this study details the operational planning that can be referred to for future buildings retrofitting work on a similar scale. Issues and challenges faced in the process of establishing the protocol are also discussed.

Exploration Study of Skillsets Needed in Cyber Security Field

Fatin Hamizah Sohime; Ramona Ramli; Fiza Abdul Rahim; Asmidar Abu Bakar

Demands for professionals in the field of cyber security is high. In Malaysia, there is a huge shortage of skilled and qualified cyber security professionals. Diverse skill sets are required to keep up with the rapidly changing threat landscape. In order to meet future industry, many higher learning institutions in Malaysia offer many undergraduate and postgraduate cyber security programs. However, literature has reported that the lack of attention given between academia and industry appears when investigating their views concerning the
skills gap. With growing numbers of cyber security attacks, it is crucial to develop a model that would guide both main stakeholders, industry, and academia to identify and prioritize
skill sets needed to prepare students for the cyber security job market. This study will identify the required cyber security skillsets in preparing students for cyber security job market and develop a hierarchy structure for cyber security competency. The job position that is being reviewed is Information Security Analyst because it is the top job position for cyber security according to the literature review. All of the skills for information security analyst career are compared, and there are two skills reviewed which is soft skills and technical skills, including the various type of professional security certifications. This study developed a structured model for cyber security competency using the analytical hierarchy process (AHP).

Comparative Analysis on Student's Interest in Cyber Security Among Secondary School Students Using CTF Platform

Ahmad Dahaqin Ibrahim; Ahmad Haziq Ashrofie Hanafi; Haikal Rokman; Md Nabil; Zul-Azri Ibrahim; Fiza Abdul Rahim

In recent years, we see more degradation in the quality of cyber security graduates, especially in terms of not meeting the employer's perspective. They lack a certain amount of skills to be competent enough for the industry. This study aims to inculcate interest in cyber security at a younger age, specifically from secondary-level education. After exploring various methods of approaching this problem, Capture the Flag (CTF) has been chosen to introduce secondary school students to the cyber security competitive scene in the hope that they find fun factors to be more interested in the field of area. A one-day event was conducted with the implementation of the CTF competition at the end. A pre-test and post-test questionnaire were constructed and given to the participants to answer prior and after the event to measure any changes made to their view and interest of cyber security.

Preliminary Study: Knowledge Sharing In Collaborative E-Commerce

Syarifah Bahiyah Rahayu

Recent technological advancement in internet speed has contributed to the growing number of collaborative e-commerce. Interaction between businesses cause intensive information sharing via knowledge transfer and collaboration. Thus, protection of information in e-commerce requires significant measures to adequately mitigate the impact to business, customer and society. This paper reviews the elements related to knowledge sharing in collaborative e-commerce. Intensive literature reviews are discussed and analyzed to discover issues and challenges face by e-commerce collaborators in sharing knowledge. The result shows trade secret is applicable in developing and maintaining trust partnership in collaborative e-commerce. Next future work is to measure knowledge sharing awareness among collaborators of e-commerce. There is an opportunity to investigate this paper further in the legal perspective

Designing Self-reflective and Comprehensible Visualisations in Self-care Applications

Archanaa Visvalingam; Jaspaljeet Singh Ranjit Singh

Self-care applications are generally aimed to address various health issues, and they are commonly featured with visuals or graphical representations. These visuals educate users to comprehend their health status in taking a proactive role in managing their healthcare independently. Self-care visuals have a greater role to play in self-care applications, and therefore, they need to be carefully designed to be self-reflective and comprehensive, in meeting the current expectations and needs of the users. Existing guidelines for visuals are too focused and skewed towards the usability aspects, do not stress on self-care applications, and fail to address the current expectations of healthcare consumers. We have developed a model to aid developers to design effective self-reflective visualisations. In this paper, we have explored the perspectives of healthcare consumers on visuals in self-care applications. We also investigated the influence of age, gender, and technology experience on the design principles in the model. A quantitative study was executed involving 415 healthcare consumers to evaluate these design principles. SPSS was employed to analyse the data and several statistical tests were administered to assess the model. In general, it was apparent that healthcare consumers have specific requirements towards the visuals they come across in self-care applications. Overall, the following principles are found to be significant for designing visuals in self-care applications: flow, customizability, keystroke level, personalisation, heuristic evaluation, alerts and proactive support, cognitive walkthrough, accessibility, suitable graphs, dashboards, granularity, focus (self-care) and scope (healthcare).

Occupation-related Information Seeking Behaviour Models: A Comparative Study

Ammuthavali Ramasamy

This study explores a qualitative analysis of information needs and occupational-related information seeking behaviour models. The methodology includes a study of various occupational-related information behaviour models, a review of the subject literature and the exploration of relevant qualitative research methods. The paper shows how different factors influence the information needs of professionals. Different viewers' opinions on various models have been analysed and at the same time, the testing groups of each model have also been identified. The study concludes that each model represents a different but also an overlapping or similar approach to occupational-related information-seeking behaviour research.

A Fuzzy Logic Approach in assessing the trustworthiness of Instagram Sellers: A Customer's Perspective

Salwa Mustafa Din; Asmidar Abu Bakar; Ramona Ramli

Instagram has become a chosen platform for people to buy and sell online. Nevertheless, some studies have reported that frauds happened from buying and selling through the platform such as product quantity and specification is different as claimed, receipt of a defect product, and other frauds involving Instagram sellers. Hence, trust is vital when customers are engaged in S-Commerce activities on Instagram and there is a need for a trust model to evaluate trustworthiness of sellers. This paper proposes a Fuzzy Logic method in assessing the trustworthiness of Instagram Sellers, from a Customers' perspective. The trustworthiness of three Instagram Sellers was evaluated using the Fuzzy Inference System and values indicated moderate to high trust with values of more than 0.500. The study is expected to provide customers with a guideline on which factors that are important in assessing an Instagram Seller and a model to evaluate and quantify the trustworthiness of Instagram Sellers

A Systematic Review of Machine Learning in Substance Addiction

Zaihisma Che Cob; Nurul Fatin Zulkifli; Aliza Abdul Latif; Sulfeeza Mohd Drus

Substance abuse affects millions of people each year and it is not a disease that can be cured and with the emergence of machine learning, it have open doors for healthcare industry to incorporate technology to help healthcare workforce to make better decision in treating patients. By using Machine Learning in understanding substance abuse patients, it can help doctors to determine how to treat their patient. For that purpose, a Systematic Review is used from which 11 studies are used to study the effectiveness of machine learning application in addiction studies.

Features Selection for Data Mining using Machine Learning Algorithms for Privacy Preservation

Asmidar Abu Bakar; Fiza Abdul Rahim; Salman Yussof; Ramona Ramli; Norsyahirah Khairul Anuar; Abdul Rahim Ahmad

Data is an advantage because useful information is concealed in these large quantities of data. Data analytics needs more in-depth insight and the identification of fine-grain patterns to make precise predictions that allow better decision-making. To make identification towards the data, the privacy of the data must be preserving. Hence, it will ensure there is no leakage information to other parties. From previous papers, researchers published their article regarding privacy preservation and their methods to secure the information. As for our research, we propose features selection, data mining, and machine learning algorithms to preserve data privacy. Features selection is a process of lessening the number of input variables while designing a predictive model. It also uses filtering irrelevant and redundant features from our dataset and keeping the original features subset. Machine learning algorithms also took place in this research, and we make a comparison of features selection in machine learning algorithms using some characteristics. By using this approach, we can preserve our privacy of data securely.

A Framework for Transferring Knowledge between Expatriates and Local Employees in IT-based Organisations

Adam Matthew; Jaspaljeet Singh Ranjit Singh

Hiring expatriates with different cultural backgrounds can help increase an organisation's cohesiveness. The dependencies on expatriates by companies grows higher every year. The impact of expatriates leaving an organisation can cause an adverse effect unless the firm implements a strategy to foster the transfer of knowledge to local employees. Hence, a knowledge transfer framework is critical to aid the transfer of knowledge from expatriates to local employees in an organisation. We propose and evaluate a knowledge transfer framework listing essential factors that influence the successful knowledge transfer process between expatriates and the local employees at IT-based organisations. In this paper, we present a quantitative study involving 307 expatriates and local employees in IT-based organisations to confirm the significance of the identified factors from the literature. Several statistical analysis tests, such as the descriptive analysis, inferential analysis and correlation analysis, were administered. Results indicate that employee behaviour, organisational culture, the use of ICT, individual capital, social capital, and learning practices are the key factors that influence knowledge transfer process between expatriates and local employees. Results also show that there is no significant difference between the nationality of employees and the factors identified in the knowledge transfer framework. The outcome of this research will be useful to strategise knowledge transfer amongst expatriates and local employees within IT-based organisations.

Description for Smart Grid: Towards the Ontological Approach

Alicia Tang Yee Chong; Moamin A Mahmoud

Smart grids facilitate the use of distributed and renewable resources on the supply side and providing consumers with a range of tailored services on the consumption side. The introduction of energy smart grid in Malaysia allows other stakeholders to reap the potential benefits it offers. However, for the stakeholders to share knowledge and collaborate, a common language that can act as intermediary that allows any application to communicate with each other is needed. Ontologies have been successful in integrating the knowledge required for solving complex problems such as energy management problems. Ontology has become a promising future direction towards devising a context-aware middleware platform for the smart grid. Hence, we propose to develop ontology for energy smart grid to support information integration for existing smart grid applications and to produce an efficient, semantically aware and operationally accurate software environment for managing flexibility in Malaysian energy distribution networks.

Academic Emotions Review: Types, Triggers, Reactions, and Computational Models

Latha Subramainan; Moamin A Mahmoud

Although the significant of academic emotions have been highly emphasized by the field of social sciences' researchers. Although researchers from across the field of social sciences have highlighted the importance of academic emotions, it has mostly been neglected as a crucial factor to promote active and meaningful communication in a classroom. To tackle this issue, this paper provides a clear vision to understand the current state of research in this area. To do so, a comprehensive review is conducted on academic emotions to map the studies in the literature to a coherent taxonomy. This paper examines the literature on academic emotions to provide a systematic presentation to the concept and subsequently provides a baseline and recommendations for further research on students' engagement based on academic emotions.

A review of Smart Grid Technology, Components, and Implementation

Alicia Tang Yee Chong; Moamin A Mahmoud

This paper presents a brief review on smart grids. Environmental destruction that is marked by high CO2 level or green-house gas emissions due to excessive use of fossil fuels is a serious challenge that must be minimized immediately. One of the most prominent impact is the destruction of natural ecosystems such as forest fires due to very high temperature, rising sea level, flash flood, melting of iceberg in the north and south poles and uncertain natural climates. From the energy sec-tor that contribute most to global warming is the power generation sector. Currently there are still many power plants that use fossil fuels such as petroleum and coal as the main source of turbine drive in generating electrical energy. Renewable energy is capable of generating electrical energy without generating and increasing greenhouse gases. Current renewable energy utilization trends continue to increase which contributes to the birth of the smart grid concept. Current electricity transmission and distribution networks can be categorized as conventional electricity networks because they have not been able to provide excellent service and present real-time data. This network has not been able to provide reliability, safety and efficiency in supplying electrical energy even not yet have the flexibility to be integrated with the generation of renewable energy or microgrid. So the introduction of smart grid technology is a necessity to reduce the impact of global warming while encouraging efficiency, reliability and effective governance in the supply of electrical energy.

 

A Systematic Literature Review of Machine Learning Methods for Short-term Electricity Forecasting

Nur Shakirah Md Salleh; Azizah Suliman; Bo N. Jorgensen

Research in energy prediction is widely explored as it is used in long term planning like development investment and resource planning to estimating tariffs and analyzing and scheduling of distribution network. One of the methods applied in performing the forecasting is machine learning. There are many machine learning algorithms, dataset features, and evaluation metrics used. This paper offers to review articles on energy prediction published from 2016 until 2019. The review is made based on Systematic Literature Review method. A total of 119 articles were gathered from various sources such as IEEE, Science Direct and ResearchGate. The search made based on keywords such as machine learning, electricity, energy demand, forecast, and prediction. Based on the articles gathered, 31 articles were selected based on thorough examination on the title and abstracts. Six full materials are chosen for the final review. The review focused on i) standard dataset features chosen, ii) the machine learning algorithms applied and iii) the result based on evaluation metrics. Similarities found between the papers include the forecasting type, features selected, using various methods in performing the machine learning and applying multiple metric evaluations for a single dataset. The findings however show, the chosen machine learning algorithm and metric evaluation are different among the researchers and dataset size may influence the accuracy of the model generated.

A Review on Software Deployment Environment for Privacy protection in an Organizations

Asmidar Abu Bakar; Ramona Ramli; Fiza Abdul Rahim; Salman Yussof

Privacy protection is the main issue in many organizations, especially those handling a large volume of personal data in their daily works such as banks, healthcare, and energy such as electricity. An organization needs to manage the personally identifiable information (PII) such as name, address, identification numbers, etc. as well as sensitive data such as financial data. Data may share inter departments. Thus, incorporating privacy in an existing system or new software deployment in an organization is essential since it will protect the sensitive data. Privacy is a concerning issue as software deployment is no longer on a standalone basis. The company deploys software on cloud, web-based, mobile, and the pervasive environment. We have studied literature and discussed privacy and organizations to understand how it relates to each other; we also review the various environment where software may be deployed with privacy in mind.

Systematic Literature Review on Global Software Development Risks In Agile Methodology

Zuriyaninatasa Podari; Adila Firdaus

Organization development software has adapted global software development (GSD) with distributed development sites. The term of GSD can be described as developing software with development teams spread across different geographical locations. The distributed GSD is a benefit to the organization for the long term. The benefits of GSD include cost reduction, improved time to market, modularization of tasks, access to the trained workforce, acquisition and innovation, and proximity to market. The problems arise when there is a difference in understanding, workflows or processes, policies, and others in the global setting. The risks start developing further when organizations and stakeholders from various nations are involved. Each project that is to be developed, being developed, or after development is still burdened by risk. New requirements come with new risks, and this makes risk mitigation becomes difficult in a global organization that fails to understand and manage its risks. The risk has an impact on the cost and duration of the system, software, people, and others. This paper aims to identify the risk categories in a global setting along with how the Agile methodology provides the risk mitigation approach to handle different types of risks. A total number of conference papers were listed as necessary along with journal papers from the Systematic Literature Review (SLR) and other papers published between 2009 until 2019. Most of the work proposing methods used in Agile methodology to minimize the risk in a global and distributed team.

A literature review on the usage of Technology Acceptance Model for analysing a virtual reality's cycling sport applications with enhanced realism fidelity

Imran Mahalil; Azmi Mohd Yusof; Nazrita Ibrahim

The usage of Virtual Reality (VR) technology in VR sport to train athletes is becoming more popular. Previous researches on VR sport shows promising results where the acceptance of VR technology among athletes are increasing. It is also found that VR sport has created a positive improvement to athletes training performance. In many research related to VR sport, it is discovered that the acceptance of VR technology in sport is often validated through the use of Technology Acceptance Model (TAM). TAM has evolved where a new model called Unified Theory of Acceptance and Use of Technology (UTAUT) is introduced. One of the elements currently being evaluated is visual realism. Visual realism has elements that can be divided into two groups: basic and advance. Basic elements are visual, audio and interaction. Meanwhile advance elements are interactive platform, altitude-effect, temperature level, and wind-effect. These elements influence the fidelity of realism when athletes undergoes their VR training exercise to enhance athlete's performance. The aim of this paper is to identify the usage of TAM for validating users' acceptance towards VR-base exercise. In addition this paper also focuses on identifying the effective elements that influence athletes' acceptance on VR technology targeted to sport cycling.

Feasibility Study of Beef Quality Assessment using Computer Vision and Deep Neural Network (DNN) Algorithm

Wei Keong Tan; Zulkifli Bin Husin; Muhammad Amir Hakim Ismail

The beef quality relies upon the colour score of muscle during the grading stage. Colour scoring to be used in beef grading would be very critical and the current way of identification and determination of the quality of beef is still being done manually and susceptible to human error. The ability to automate the prediction of the beef quality will assist the meat industry through the grading phase to establish the colour score. Therefore, computer vision and deep neural network (DNN) were used to predict the beef quality by determining colour scores of beef muscle. Four hundred of beef rib-eye steaks were chosen and acquired for each image, which is the colour score of beef were assigned by expertise according to the standard colour cards. The image was processed and went through DNN classifier for determining beef quality. The proposed DNN classifier achieved the best performance percentage of 90.0%, showing that the computer vision integrated with the DNN algorithm can deliver an efficient implementation for predicting beef quality using colour scores of beef muscle.

Automating Switchgear Asset Supply Chain Management with IoT and RFID Technology

Manjit Singh Sidhu; Sharulhizam Shah; Faisal Mansor; Nur Elisha; Tharik Hussain; Saifuddin Saif

Assets such as switch gears may travel thousands of miles, changing hands several times or more, before they reach their final destinations. In this complex situation, missteps or assets being stolen along the supply chain are unavoidable. No matter how solid the logistics network for a particular asset, at some point a transporter will get stuck in traffic, or a crate will be delayed at a warehouse, or an asset will go missing altogether. With traditional supply chain management solutions, logistics managers often do not find out about delayed or misrouted assets until those assets arrive hours late or not at all at their destinations. These hours translate into lost productivity, delayed production and damaged client relationships. However, with newer technological advancements such as Internet of Things (IoT) and Radio Frequency Integrated Devices (RFID), these problems could be avoided or minimized and significantly reduce losses whereby the location of assets could be monitored in real time, as they travel along worldwide transportation routes. Furthermore, the technology allows the industry to visualize and manage critical goods from anywhere, at any time, on a global scale. In this research, we aim to design a conceptual framework and a physical design of automating switch gear asset supply chain management system for a TNB subsidiary company and test its effectiveness as a futuristic system for asset tracking. However this paper reports on the conceptual design approach.

Gene Regulatory Network Construction of Ovarian Cancer Based on Passing Attributes between Network for Data Assimilation

Yeo Isaac; Kohbalan Moorthy; Logenthiran Machap; Mohd Saberi Mohamad; Jamaludin Sallim

In the field of cancer informatics, there are computational methods or approach exists to share the same goal, which is to unravel the interactions between genes through the effort of gene regulatory network (GRN) inference and construction. Even now, such a complex task has always been challenging and at the same time, this challenge becomes a motivation for new methods to be invented. Hence, the development of PYPANDA, which is a new method for applying the assimilation of several different datasets input for the construction of the gene regulatory network. Moreover, this integration model is capable of redeeming information that was lost when using other methods that only utilize a single dataset, thus having an innate capacity of predicting a more accurate interaction between genes. The proposed improvement of PYPANDA in this article has been able to filter and determine the most informative or significant genes for the construction of the GRN. With this, the differences between the prior network and the improved PYPANDA network can be specified. As such, two new relationships between the highly informative genes that have not been identified before were successfully identified.

A Preliminary Study on Student Learning Difficulties in Engineering Mechanics Dynamics

Chen Kang Lee; Manjit Singh Sidhu; Seng Poh Lim; Zaimah Hasan; Seng Chee Lim; Saw Seow Hui

This paper presents a preliminary study on the learning difficulties faced by the engineering students in engineering mechanics dynamics course. The motivation for conducting this pilot study is to suggest the improvements in learning mechanics dynamics through Information and Communication Technology (ICT) using blended learning approach. A questionnaire was distributed to the mechanical engineering students in Universiti Tenaga Nasional (UNITEN) with a sample size of 45. The data was collected and analyzed. The preliminary results revealed that students faced difficulties in visualization for engineering problem solving thus affected the students to comprehend the mechanics dynamics concepts. Besides that, the results also shown that the engineering students perceived positively towards the use of ICT in aiding their learning process. Therefore, technology assisted learning using blended learning approach is proposed to aid in the students learning process for engineering problem solving tasks in mechanics dynamics.

Extended Reality: How They Incorporated for ASD Intervention

Siti Azreena Mubin; Vinesh Thiruchelvam; Andrew Yew Weiwen

Autism Spectrum Disorder (ASD) can cause difficulties of sufferers in social interaction with rigid and repetitive behavior. Children on the lower end of the spectrum who are harder to identify still require intervention for their condition. Extended Reality (XR) refers to the new paradigm of human-computer interaction that is currently predominantly realized with Augmented Reality (AR) and Virtual Reality (VR) as the human-computer interface. In this paper, reviewed literatures on XR applications found an eagerness of the ASD children to learn through XR. Additionally, most of the reviewed studies found moderate evidence for extended reality to assist children in ASD. Thus, fundamental study on immersive learning experiences in ASD are needed for our future works on autism intervention and detection.

A Generic Log analyzer for automated troubleshooting in container orchestration system

Rajendar Kandan; Mohammad Fairus Khalid; Bukhary Ikhwan Ismail; Mohd Nizam Mohd Mydin; Hishamadie Ahmad; Hong H. Ong

Containerization becomes the proven technology for modernization of any application. Hence the adaptability of the container orchestration platform also increases widely across the industry. Many orchestration systems are available in the market, which could deploy and manage containerized application. Log analyzes in such environment become complex and challenging as a large number of components been involved. Troubleshooting is also a difficult task in such environment because of the dependency of each system or component. In this paper we propose a generic log analyzer which could analyses the log and automates the process of troubleshooting in an orchestration environment.

An Algorithm for Automatically Updating a Forsyth-Edwards Notation String Without an Array Board Representation

Azlan Iqbal

We present an algorithm that correctly updates the Forsyth-Edwards Notation (FEN) chessboard character string after any move is made without the need for an intermediary array representation of the board. In particular, this relates to software that have to do with chess, certain chess variants and possibly even similar board games with comparable position representation. Even when performance may be equal or inferior to using arrays, the algorithm still provides an accurate and viable alternative to accomplishing the same thing, or when there may be a need for additional or side processing in conjunction with arrays. Furthermore, the end result (i.e. an updated FEN string) is immediately ready for export to any other internal module or external program, unlike with an intermediary array which needs to be first converted into a FEN string for export purposes. The algorithm is especially useful when there are no existing array-based modules to represent a visual board as it can do without them entirely. We provide examples that demonstrate the correctness of the algorithm given a variety of positions involving castling, en passant and pawn promotion.

A literature review on 6 Dimensional virtual reality sport applications toward higher presense

Imran Mahalil; Azmi Mohd Yusof; Nazrita Ibrahim

Virtual Reality (VR) is widely applied in various domains such as medical, training, education, entertainment, and etc. In relation to sport training, VR-based system is used by many athletes for various exercise such as cycling and rowing. The objective of sport training can be divided into two: improving athletes' performance or their fitness. In sport training, performance is measured by using a timer, heart rate device, breathing mask and many more, whereby athlete's fitness is measured by their stamina, strength and endurance. In the previous researchers' assessment, questionnaire is commonly used for analyzing purposes. From those researches, it was suggested that presence plays a significant role towards enhancing athletes' performance and fitness while undergoing the sport training. This paper covers issues that are related to presence, 6D, its effects to VR applications, and the methods of assessment in detail. From the findings, it is anticipated that when higher dimensional VR system and human sense are used, a realistic sense of presence will be felt which creates a more realistic experience. In addition, this paper also discusses how extra elements such as altitude, temperature, wind-effect and many more can affect the feeling of presence while using VR application. These extra elements would create a higher dimensional VR application. The aim of this study is to identify an effective combination elements or dimensions that maximizes a high-fidelity presence in VR-based sport training.

Detection of impulsive sounds in stream of audio signals

Azizah Suliman; Batyrkhan Omarov

Video analysis has become a standard feature of many security cameras. However, built-in audio analytics continues to be quite rare despite the presence of both the audio channel itself in the devices and the available computing power for processing audio data. Audio analytics has some advantages over video analytics such as cheaper devices and maintenance costs. Furthermore, when the system is running in real-time, the audio data stream is significantly smaller in volume than the data stream from video cameras and makes it more loyal requirements for the bandwidth of the data channel. Audio analytics systems can be particularly useful for urban surveillance with the start of automated broadcasting live video to the police console from the scene of an explosion and shooting. Audio analytics technologies can also be used to study video recordings and determine events. This article proposes a method for automatic detection of pulse sounds that signifies critical situation in audio signals based on Support Vector Machine learning models. The models were able to classify sounds from events such as gunshot, broken glass, explosion, siren, cry and dog barking with accuracy ranges from 95% to 81%.

Ontology for Evacuation Center in Flood Management Domain

Hasniza Yahya; Rohaini Ramli

Flood Management System in Malaysia is used by several agencies that need to collaborate and share information whenever a flood occurs. Currently, Portal Bencana owned by Agensi Pengurusan Bencana Malaysia (NADMA) serves as a platform for the public to acquire knowledge on any disaster including flood. The information in this portal is received from related agencies either manually or through websites. However, issues such as information is not accurate or information is not available are some of the common problems faced by the public. In order to have an accurate information, a standardized information using ontology needs to be developed and shared by all related agencies. In this paper, we propose an evacuation ontology on flood victim profiles as part of the flood management domain ontology.

Active Interaction Design For Stress Therapy Virtual Environment

Farhah Amaliya Zaharuddin; Nazrita Ibrahim; Azmi Mohd Yusof; Eze Manzura Mohd Mahidin; Mohd Ezanee Rusli

Virtual reality technology has been used as an alternative approach for stress therapy. However, designing a virtual environment for the purpose of stress therapy is still an issue since not many articles on designing a suitable 3D environment for stress relief are available. Existing literature published only focused on identifying the effectiveness of virtual reality as a stress therapy tool. This situation has left the researchers with the problem of having very little guidelines to be used as reference when designing the virtual environment. In our previous publication, an initial framework for designing a virtual environment for stress therapy was proposed. In this paper, a concept on one of the design elements proposed in the framework, which is active interaction, is presented. A pilot study to evaluate the usefulness of the element was carried out. Both discussion and findings from the evaluation process could serve as a guideline in designing the active interaction element in a virtual environment developed for stress therapy purpose.

Smart Web Data Analytics Quiz and Tests System

Manjit Singh Sidhu; Chen Kang Lee; Zuraidah Ali

In recent years, the increase in the number of students in classrooms has demanded more efficient analytics software tools for educators such as to grade, track and store student's learning activities and providing quick feedbacks. Analytics also refers to the scientific process that examines data to formulate conclusions and to present paths to make decisions. In this paper we demonstrate an analytics quiz and tests system that could be used by educators to analyze and improve questionnaires based on students' performance and to introduce the current possibilities in incorporating AI and analytics. An analysis of the system using the Technology Acceptance Model (TAM) with participants' results revealed that the web data analytics system has helped the instructors to determine the point of modifications in improving the quiz and tests questions especially for individual student as opposed to the traditional method since it is more tedious to grade and analyze the results.

A Framework for Sustainable Non-Profit Crowdsourcing Applications

Hazleen Aris; Farahidayah Mahmud

Due to the nature of the crowdsourcing applications that relies on the crowd participation to be successful, sustaining the volume of participating crowd is imperative. This is of particular concern in non-profit crowdsourcing applications where monetary rewards are not usually offered. To ensure this, factors that influence crowd to participate in non-profit crowdsourcing application need to be identified and means to address these factors when developing the crowdsourcing applications need to be devised. While existing work on the influencing factors are seen, the means to address the factors when developing the crowdsourcing applications are still absent. This research therefore extends the existing work on factors that influence the crowd participation in non-profit crowdsourcing applications by coming out with the framework containing the components that address the identified factors. A total of 11 components were included in the framework as a result of the analysis made on the identified influencing factors, which are divided into process and entity components. Preliminary evaluation performed by identifying the presence of the components in two selected non-profit crowdsourcing applications known to be successful shows the relevance of the components in addressing the factors that influence crowd participation. The presence of the framework will be able to serve as a guide for the non-profit crowdsourcing application developers to develop applications that would be able to sustain crowd participation and hence, contributing to the survival of the applications.

Fault Detection of Bearing using Support Vector Machine-SVM

Abdoulhdi A. Borhana; Danial Danish Bin Mustaffa Kamal; Sarmad Dashti Latif; Ali Najah Ahmed Almahfoodh; Yasir Hassan Ali; Ahmed El-Shafie

Modern spinning machinery is a crucial component of rolling element. The principal aim of this project is to create a support vector machine model, which is one of the AI techniques to detect and diagnose bearing fault at early stage. The development of the model should be able to forecast the bearing fault diameters based on the collected input variables. In order to achieve this objective, a set of bearing raw vibration frequency signal is acquired. The raw vibration signals were extracted. The extracted features are used as the inputs containing different motor loads, different motor speeds and different locations. The support vector machine approach is being used to run the simulation. The selection of kernel functions and other parameters are very important in the development of a reliable model. Trial and error method are used to identify the best combination of parameters for SVM model by comparing the MSE and CC values. The best kernel functions and parameters are set and the model is ready to be used to run the real data since it can provide the best and most accurate precision in early detecting bearing failures. Recommendation was made to improve the architecture of SVM model.

Performance Analysis of Deep Neural Networks for Object Classification with Edge TPU

Azlan Iqbal

Deep learning becomes a more popular, widespread, and common tool in almost any task that requires information extraction from a large dataset. Hence, the data transmission speed between the data-gathering devices and processing units can be crucial in hardware selection depending on the machine learning application. Generally, the processing unit is usually centralized, and the data transferring time will increase when the data-gathering devices were installed further away from the processing unit. The work aims to provide the performance analysis on Google's new machine learning hardware called Edge TPU that was created specifically for edge devices. Furthermore, the work also reviewed the different types of deep neural network models as current benchmarks in deep learning were tested with different hardware used in edge applications. The review also discussed the comparison of the performance of the edge device using the deep neural networks in Tensorflow. From the results obtained, the performance of the edge device with the Edge TPU is faster than the device without it.

Mobile Application: Mobile Assistance for Visually Impaired People - Speech Interface System (SIS)

Abdul Majid Norkhalid; Azimah Abdul Ghapar; Masyura Ahmad Faudzi; Fiza Abdul Rahim

The evolution of technology has been made to bring the most benefits to human-being on communication and to facilitate their day-to-day activities. Technologies contribution, such as mobile application, has been proved to have a high capability to cater to the needs of a person with disabilities. The usage of smartphone is limited for visually impaired(VI) community as most of the currently available mobile application are not user-friendly for people with vision disability. Although smartphones nowadays are offering accessibility services such as TalkBack for Android and VoiceOver for iOS, these accessibilities provide fewer functions on navigating the VI community. A speech interface system(SIS) in mobile application combined with object and distance detection could help the VI community to navigate to their destination only with the use of a smartphone. This Systematic Literature Review (SLR) provide our findings on the present study of speech recognition projects and the requirement issues of speech recognition in mobile application platform throughout supporting VI people. Three journal databases (Google Scholar, IEEE Explore and Science Direct) has been searching throughout this SLR with articles ranging from the year of 2013-2019. A result of 136 article titles, abstracts of 73 articles were examined, 35 full-text articles were selected for final review. A total of 19 articles are analyzed. The purpose of the SLR is to collect the technique, evaluation methods and the validation of the projects.

Social Distancing Detection with Deep Learning Model

Yew Cheong Hou; Mohd Zafri Baharuddin; Salman Yussof; Sumayyah Dzulkifly

The paper presents a methodology for social distancing detection using deep learning to evaluate the distance between people to mitigate the impact of this coronavirus pandemic. The detection tool was developed to alert people to maintain a safe distance with each other by evaluating a video feed. The video frame from the camera was used as input, and the open-source object detection pre-trained model based on the YOLOv3 algorithm was employed for pedestrian detection. Later, the video frame was transformed into top-down view for distance measurement from the 2D plane. The distance between people can be estimated and any noncompliant pair of people in the display will be indicated with a red frame and red line. The
proposed method was validated on a pre-recorded video of pedestrians walking on the street. The result shows that the proposed method is able to determine the social distancing measures between multiple people in the video. The developed technique can be further developed as a detection tool in real-time application.

CheckMyCode: Assignment Submission System with Cloud-Based Java Compiler

Afiqah Azahari; Arniyati Ahmad; Syarifah Bahiyah Rahayu; Hazali Halip

Learning programming language of Java is a basic part of the Computer Science and Engineering curriculum. Specific Java compiler is a requirement for writing and convert the writing code to executable format. However, some local installed Java compiler are suffered from compatibility, portability and storage space issues. These issues sometimes affect student-learning interest and slow down the learning process. This paper is directed toward the solution for such problems, which offers a new programming assignment submission system with cloud-based Java compiler or also known as CheckMyCode. Leveraging cloud-computing technology in terms of its availability, prevalence and affordability, CheckMyCode implements Java cloud-based programming compiler as a part of the assignment management system. CheckMyCode system is a web-based system that allows both main users, which are a lecturer and student to access the system via a browser on PC or smart devices. Modules of submission assignment system with cloud compiler allow lecturer and student to manage Java programming task in one platform. A framework, system module, main user and feature of CheckMyCode are presented. Also, taking into account are the future study/direction and new enhancement of CheckMyCode.

Evaluating Smartphone Addiction Disorder among University Students

Suthashini Subramaniam; Jaspaljeet Singh Ranjit Singh; Alan Cheah; Mohana Shanmugam; Saraswathy Shamini Gunasekaran

Smartphones have become essentials in our life in this era, especially in the life of university students. It has become a one-stop-centre for many things. Students set schedules and meetings on their phone, set up online meetings via skype, make conference video calls to communicate with their friends and family, review their course notes on the phone as well as participate in online classes with a smartphone. The usage of smartphones has become endless. Together with that, addiction towards smartphones has become inevitable. Smartphone addiction has caused many issues, such as emotional distress, neck pain and sleep disorder. In this paper, we report the results of a quantitative study that was conducted to evaluate smartphone addiction disorder among 135 university students. A survey consisting of the 26 items of Smartphone Addiction Inventory (SPAI) was administered to determine the impact of smartphone usage on university students and to study the factors related to the addiction. Exploratory Factor Analysis (EFA) was carried out to study the underlying connection within the 26 items and the students. Results indicate that smartphone addiction among the university students is in the average, as the total SPAI score for 73 participants focuses in the middle range. The EFA outcome suggested a two-factor solution: Dependency and Well-beingness. The results of our study indicate that students' well-being is getting affected by the usage of smartphones.

 

Critical Information Infrastructure Protection Framework Development: Preliminary Findings from the Malaysian Public Sector

Saiful Bahari Mohd Sabtu; Kamaruddin Malik Mohamad

This paper reports preliminary findings on the Malaysian Public Sector Critical Information Infrastructure Protection (MPS CIIP) Framework development. Malaysia Public Sector (MPS) requires sectoral references in the form of directive and guidelines to facilitate its organization's management and operations. MPS organizations also require a framework to maintain CII operational security and resilience as stated in the National Cyber Security Policy (NCSP). As sectoral references pertaining to CIIP is currently lacking, this research aims to fill in the void and provide feasible CIIP Framework based on the Secure and Resilience attribute mentioned in the NCSP. This paper outlined ongoing work to develop a secure and resilient CIIP using the Grounded Theory method. As part of an observational descriptive study, we conducted a pilot interview among CII personnel in the MPS to acquire preliminary input and to test our interview questions. The outcome of this phase yielded a better understanding of the MPS CIIP landscape and provided future direction in terms of revised interview questions and better respondent selection criteria. The pilot interview process garnered up to date opinion on current public sector CIIP issues and challenges. Respondents provided valuable up to date insights such as top-to-bottom directive operating model, governance, finance and resource constraints, information sharing, dependency on focal point agency, CIIP monitoring and preparedness level. MPS CIIP Framework will be developed in line with NCSP vision to create secure, resilient, and self-reliant CIIs. The framework would assist related MPS agencies to deliver its initiatives such as preparedness and capacity building programs to increase its security and resilience.

Analysis of the Paradigm on Tor Attack Studies

Mohamad Amar Irsyad Mohd Aminuddin; Zarul Fitri Zaaba; Azman Samsudin; Nor Badrul Anuar; Sazali Sukardi

The massive popularity of Tor as an anonymity platform is undeniable as researchers have studied multiple attacks on the platform. With various attack studies that had been published over 15 years, this paper intends to survey, investigate and classify these studies' paradigm in the perspective of study type, attack mode, target type and mitigation suggestion. Knowledge of these studies' paradigm will allow for an in-depth understanding of the practice, direction and trend of Tor attack studies over the years. In addition, this will present a better comprehension on the diverse techniques used in Tor attack studies. At the end of this study, specific trends of Tor attack studies could be highlighted. Out of 85 reviewed Tor attack studies, only five is non-experimental studies. The most focus attack mode is an active attack and followed by a passive attack. For the target type, the majority of the studies is concentrating on the user as their attack target. Also, out of 85 reviewed Tor attack studies, only 32 studies had discussed a mitigation technique to their proposed attack.

Phishing Attack Simulation: Measuring Susceptibility among Undergraduate Students

Tuan Nur Hanis Tuan Kob; Raja Feninferina Raja Azman; Fiza Abdul Rahim

The Internet Users Survey 2018 statistics compiled by Malaysian Communications and Multimedia Commission (MCMC) has shown significant increase in the total of internet users. Out of 87.4% internet users, 30.0% of them are in their 20's, with the average of 8.0 hours daily internet usage. Thus, the study focuses on undergraduate students and aims to identify and measure their susceptibility to falsely trust a bogus source through phishing attack simulation, as there is a high susceptibility of them being drawn to such attacks due to high rate of internet usage.

Passive and Active Reconnaissance: A Social Engineering Case Study

Md Nabil; Fiza Abdul Rahim; Siti Nurhidayah Zulkiffli

Love scams, online scams and how absurd as it may seemed, still causes people to lose their money. This issue happens because some people are still susceptible to social engineering. Our investigation attempts to find out how people could for this type of social engineering act on social media. Social engineering could be categorized into passive and active reconnaissance. Our experiment involves individuals such as UNITEN students and Instafamous to find out if they would be deceived by such attempts. Methodology proposed is the attack cycle that consists of four steps: information gathering, establishing relationship and rapport, exploitation, and execution. This case study will also explore the possible uses of valuable information of target that can be gathered based on passive and active reconnaissance.

Intrusion Detection Systems for SCADA Networks - A Short Review

Qais Qassim; Norziana Jamil; Mohammed Najah Mahdi

Securing and protecting Supervisory Control and Data Acquisition (SCADA) systems have been an active topic of research for the past decades due to the catastrophic and disastrous consequences when these systems are breached or compromised. Therefore, possible cyberattacks and malicious behaviours must be addressed instantly to prevent catastrophic and disastrous consequences on the national critical infrastructures. To this end, intrusion detection systems are considered as an essential security defence mechanism for SCADA networks. It can effectively detect potential cyberattacks and malicious activities and prevent catastrophic consequences. However, zero-day, deception and stealth attacks require a special type of detection methods in which it should be able to identify anomalies and detect possible malicious activities. There are a handful number of studies that have been carried out previously in this regard. However, this area of research is still immature and emerging. Therefore, this research is intended to investigate the merits, limitations and drawbacks of the existing detection methods, investigates the cyberattacks on SCADA systems, identify key features that can be used to discover irregular activities and to put forward the requirements and recommendations for detect cyberattacks and malicious activities in the SCADA networks.

Cyber Threat Extenuation for Substation Data Communication: Evaluation of Encrypt-Authenticator Prototype for IEC 60870-5 Data

Razali Jidin; Norziana Jamil; Azril Azam Abdul Rahim

Data communicated at many existing secondary electrical substations is currently not encrypted. In order to improve their data security level, a low-cost cryptography prototype is proposed. Two lightweight and conventional crypto-algorithm types are chosen to encrypt, authenticate and encrypt-authenticate data packets of IEC 60870-5, the data protocol being used at most substations. Implementations of selected algorithms are on a programmable logic arrays (FPGA) to meet communication time sensitivity while employing an eight-bit processor. The FPGA implementations are optimized for speed rather than footprint. The processor is mainly to handle processing of data packets either coming from or going into substation devices. The performance in term of latency for both the conventional and lightweight cryptography is evaluated to meet different data transmission speeds. The lightweight encryption has lower latency compared to the conventional one, while both types of algorithms yield similar latency for the authentication as expected.

The Vision of 5G and Cell-Free Communication Networks in Malaysia

Mohammed Najah Mahdi; Khalid Sheikhidris Mohamed; Abdul Rahim Ahmad; Adamu Muhammad Buhari; Mohammed Subhi

5G wireless networks are expected to handle the ever-growing data avalanche, classical deployment/optimization approaches such as the hyper-dense deployment of base stations or having more bandwidth are cost-inefficient and are therefore seen as stopgaps. 5G networks will be fast and able to support many more devices simultaneously than the networks that came before - this is particularly important considering that by 2025 there will be approximately 41.6 billion connected Internet of Things devices in use, according to analyst IDC. And many of them, such as autonomous vehicle systems and industrial sensors, will require reliable access to cloud services in order to function safely. While 4G networks (that use Internet Protocol, or IP, broadband connectivity and are reaction-based) have dominated the connectivity landscape, AI and its various sub-categories, including machine learning and deep learning, have been evolving, ready to provide individuals with 5G networks that address the poor efficiency of the spectrums we saw with 4G. In this work, we propose a location optimization technique that selects optimal locations for the antennas in the service area to ensure maximum coverage and employ machine learning algorithms in order to dynamically allocate resources to mitigate interference for users and save energy for service providers.

A Quick Review of Security Issues in Telemedicine

Rina Azlin Razali; Norziana Jamil

Telemedicine has been in demand due to the flexibility that it offers with the aid of advanced technologies. Most of the developed countries implement telemedicine services to allow both patients and medical doctors to access medical data and medical services quickly and efficiently. However, as the data and services are made accessible online, the data security becomes the concern. Without proper security mechanism in place, the vulnerabilities of telemedicine systems can be exploited which will give negative impact to the patients and medical services as a whole such as wrong treatment being given and confidential data leakage. This paper analyze the security issues in telemedicine as an IOT system and identify related security countermeasures from related works to manage the security issues in telemedicine.

A Systematic Literature Review of the Mobile Application for Object Recognition for Visually Impaired People

Zulfadhlina Amira Nor Hisham; Masyura Ahmad Faudzi; Azimah Abdul Ghapar; Fiza Abdul Rahim

Nowadays, mobile applications or also known as mobile apps are one of the most important sources of communication and gives a lot of benefits to a person. Mobile technology advancements have brought an excessive change in people's daily lifestyles which has led to high demand for developing software that can give benefit to a person that runs on a mobile device. Mobile apps are represented visually on a smartphone, which it is not user-friendly for visually impaired people. The number of visually impaired people are growing in each of the countries in the world. Most technologies are being developed to help people with visual impairment because they still have difficulties in using assistive technologies. This systematic literature review attempts to provide findings on the project for visually impaired people to recognize objects using their smartphones. Systematic Literature Review (SLR) method was used to collect and review the current state of analysis concerning object recognition for visually impaired people on mobile platforms. Overall, this research covered articles published between 2013 until 2019 available in three-wide databases (ScienceDirect, IEEE Xplore and Google Scholar) using the methodology for systematic review proposed by Kitchenham. Using this methodology, keyword search and exclusion criteria to select articles about the topic were used. After viewing the total of 371 titles of articles based on keywords, 323 abstracts were examined thoroughly. Then, 21 full-text articles were chosen for the final review. The findings show the techniques, tools used and methods on how the system was implemented to recognize object.

The Study of Interactivity in First Aid Treatment Medical Facilities for Mobile Application

Nur Syahela Hussien; Masyarah Zulhaida Masmuzidin; Adib Ismail Khafidz; Mahfuzah Mohaidin; Alia Amira Abd Rahman

As accident or emergency can happen in anytime and anywhere in our daily life, not many people that can act spontaneously and if such situation happens right in front of them because lack of knowledge or skill to perform the first aid properly without worsening the condition. By taking the benefit of smartphone being carried by most people in this research study the interactivity development a mobile application that provide the symptom of illness and list of treatment that act as a guideline for the user to properly perform the first aid treatment. The two Dimensional (2D) animation was created to help visualize the step and to enhance the confidence level of the user by having the basic knowledge first aid treatment. The mobile application also able to predict the location of the medical facilities as it is important to estimate the distance and also the time taken for medical unit to apply the first aid treatment.

Exploratory Review on Decision Support System for Disaster Management

Sean Goh Ka Hong; Azhana Ahmad; Raja Feninferina Raja Azman; Masyura Ahmad Faudzi

Disaster is a natural catastrophe that causes a huge amount of property damage and loss of life and leads to the collapse of societal order and economy despair. Therefore, disaster management techniques are used to prevent catastrophic events from causing more harm to the economy and infrastructure. Governments from around the world also employ decision support system to assist in disaster management efforts. The employment of disaster management decision support system improves the efforts of search and rescue teams by allocating the right resources on the right time. However, in the study of task complexity, a task doer needs more than an adequate amount of resources to complete all incoming tasks. Therefore, this paper reviews studies of disaster management, decision support system and disaster management decision support system. The findings will be use to find a new direction in designing a task management technique to help search and rescue teams increase their efficiency in carry out their daily duties.

 

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