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2024 | Book

Advances in Visual Informatics

8th International Visual Informatics Conference, IVIC 2023, Selangor, Malaysia, November 15–17, 2023, Proceedings

Editors: Halimah Badioze Zaman, Peter Robinson, Alan F. Smeaton, Renato Lima De Oliveira, Bo Nørregaard Jørgensen, Timothy K. Shih, Rabiah Abdul Kadir, Ummul Hanan Mohamad, Mohammad Nazir Ahmad

Publisher: Springer Nature Singapore

Book Series : Lecture Notes in Computer Science


About this book

This book constitutes the refereed proceedings of the 8th International Conference on Advances in Visual Informatics, IVIC 2023, held in Selangor, Malaysia in November 2023.

The 51 full papers presented were carefully reviewed and selected from 101 submissions.

The conference focused on 6 tracks: Modeling and Simulation, Mixed Reality and HCI, Systems Integration and IoT, Cybersecurity, Energy Informatics and Intelligent Data Analytics.

Table of Contents



Managing Personal Information

There is an increasing awareness of the potential that our own self-gathered personal information has for our wellness and our health. This is partly because of our increasing awareness of what others – the major internet companies mainly – have been able to do with the personal information that they gather about us. The biggest hurdle to us using and usefully exploiting our own self-gathered personal data are the applications to support that. In this paper we highlight both the potential and the challenges associated with more widespread use of our own personal data by ourselves and we point at ways in which we believe this might happen. We use the work done in lifelogging and the annual Lifelog Search Challenge as an indicator of what we can do with our own data. We review the small number of existing systems which do allow aggregation of our own personal information and show how the use of large language models could make the management of our personal data more straightforward.

Alan F. Smeaton

Modeling and Simulation

A Visual Real-Time Mobile E-Logbook System to Capture Design Activities, Decisions, and Ideas for Engineers

Logbook is one of the important tools to engineers, which is used to capture engineers’ design activities, decisions, and ideas. It is useful to engineers in accomplishing project designs, organising, tracking, and providing post-design evaluation. However, the lack of digitalization of logbooks had made the engineers’ design activities heavily dependent on human intervention. Therefore, this research work explores the potential of digitalization of logbooks by redesigning and developing the paper-based logbook into a visual real-time mobile application. The focus of the developed visual e-logbook is to move the old-fashioned way of capturing design activities and decisions into a more systematic and effective way which allows engineers to capture and access logs at any point of time. The visual e-logbook application uses Swift programming language, SwiftUI framework and XCode as the main Integrated Development Environment (IDE). The implemented visual e-logbook is then tested with case studies and results show that the visual e-logbook has good potential for engineering design projects by replacing the paper logbook. Feedback is thus collected from the case studies for future implementation such as search and filtering function, enable collaboration, and reminder function.

Kok Weng Ng, Yun Ching Tan, JianBang Liu, Mei Choo Ang
A Virtual Reality Development Methodology: A Review

This paper presents several appropriate methodological approaches to the development of virtual reality (VR) in several fields to meet the needs of today’s industry. It is based on the proposed several selected methodologies, including the important phases such as design, creation, implementation and evaluation of individual courses implemented in a VR environment. Many VR applications have been developed and recommended to the public. However, there is little research that specifically examines the role of a development methodology in the field of VR. The progress of VR development methodology is still slow; the proposed methodologies are mostly immature and used on an ad-hoc basis; and therefore, more issues need to be explored. This paper attempts to provide the latest updates in VR development methodology using a selected narrative review method on related articles between 2000 until 2023. Our study shows that VR development methodologies still need more improvement, and most methodologies are defined according to the development purpose and its context. There is no consensus among researchers about the similarities that should exist in VR development methodologies.

Mohd Amran Md Ali, Mohammad Nazir Ahmad, Wan Salwina Wan Ismail, Nur Saadah Mohamad Aun, Mohd Akif Farhan Ahmad Basri, Shima Dyana Mohd Fazree, Nor Hidayati Zakaria
Deep Learning and Sentiment Analysis-Based Cryptocurrency Price Prediction

The rapid growth of Cryptocurrency dramatically influences the social and economic climate that has developed the trends for investors to seek opportunities for generating income from cryptocurrency investment trading. Cryptocurrency is volatile in nature due to the interdependence of cryptocurrency, market noise and many dependent factors. This has gained the attention of investors to rely on prediction models to forecast prices. Researchers proposed and implemented prediction models that utilized machine learning, deep learning algorithms and sentiment-based algorithm hybrid models. Researchers deduced that deep learning algorithms can capture the dependency features of cryptocurrency to increase accuracy in price prediction. In this paper, we proposed a system framework namely, DLCFS (Deep Learning Cryptocurrency Forecasting considering Sentiment), for cryptocurrency price prediction that considers the market features, trading volume, and interdependency between cryptocurrency and market sentiments. We conduct price forecasting for Bitcoin, Ethereum and Litecoin using their price history, and Reddit Submissions of cryptocurrency. Additionally, we have inferred the results for the performance of prediction models comparing DLCFS against machine learning. Results show that DLCFS outperformed the regression machine learning in predicting the price of Bitcoin, Litecoin, and Ethereum, considering market sentiment, with Correlation Coefficient (R) being 99.18%, 96.82% and 99.05% respectively.

Jia Ming Low, Zi Jian Tan, Tiong Yew Tang, Narishah Mohamed Salleh
A Comparative Study of Univariate and Multivariate Time Series Forecasting for CPO Prices Using Machine Learning Techniques

The Malaysian palm oil sector has significantly contributed to developing the domestic economy and the global palm oil market. However, the fluctuation in Crude Palm Oil (CPO) prices poses a significant risk to farmers, producers, traders, consumers, and others involved in CPO production and marketing. An accurate CPO price forecasting technique is required to aid decision-making in risky and unpredictable scenarios. Hence, this project aims to compare the performances of four-time series forecasting models, including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and CNN-LSTM, in the context of univariate and multivariate analysis for CPO prices in Malaysia. This research methodology is based on five phases: research understanding, data understanding, data preparation, modeling, and evaluation. Monthly CPO prices, the production and export volume of CPO, selected vegetable oil prices, crude oil prices, and monthly exchange rate data from January 2009 to December 2022 were utilized. The metrics evaluation of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were then performed to compare and evaluate the performance of the models. Experimental analysis indicates that the CNN model trained on a multivariate dataset with carefully selected significant independent variables outperformed other models. With a configuration of 500 epochs and early stopping, it achieved remarkable results compared to models trained using a univariate approach, boasting an RMSE of 245.611 and a MAPE of 7.13.

Juz Nur Fatiha Deena Mohd Fuad, Zaidah Ibrahim, Noor Latiffah Adam, Norizan Mat Diah
Improving Autonomous Robot Gripper Position on Lifting Trash Objects based on Object Geometry Parameters and Centroid Modification

Waste management in the modern urban era requires a more advanced and efficient approach. Autonomous robots have emerged as an innovative solution to address this challenge. The robot’s success in collecting trash cannot be separated from the ability of the robot’s gripper to pick up the trash. This study aims to improve the positioning accuracy of the gripper robot in lifting trash objects. If the geometrical parameters of the objects are different, there is a possibility that the centroid points are in the 2D area so that the gripper robot cannot grip and lift the trash objects. Likewise, if there is a difference in weight, the robot gripper will have difficulty lifting the object because the object is one-sided. For this reason, removing trash objects needs to be improved by considering several parameters, not only the centroid parameter. A method for lifting trash objects is proposed based on several parameters: geometry, centroid and trash object type. The proposed method is Object Geometry Parameters and Centroid Modification (OGP-CM). The test results show that the OGP-CM method can set the centroid position based on the geometric parameters and the type of trash. On the same object geometry, the improvement in accuracy is relatively low, ranging from 0.46% to 1.72%. A relatively great improvement in accuracy occurs for different object geometries, ranging from 11.54% to 13.09%. Thus, improving the position of the autonomous robot gripper in lifting objects using OGP-CM has been successfully carried out.

Emil Naf’an, Riza Sulaiman, Nazlena Mohamad Ali
A Web Application to Recommend Songs Based on Human Facial Expressions and Emotions

Facial expressions are a common non-verbal way of how humans show and express their emotions to others. Emotions can be categorized as positive and negative emotions, derived from facial expressions, in which negative emotions can affect a person’s behavior and thinking. Music is a common remedy for people to cope with both positive and negative emotions. The use of deep learning to identify emotions based on human expression can be an effective and efficient way to provide solutions for humans because it can mimic the way humans think while requiring less time and effort. By creating a solution that encompasses recommending songs from emotion detected using deep learning, it can benefit society health and entertainment-wise. This paper presents a project that focuses on developing such a solution and testing its performance and effectiveness to users, in improving their emotions via songs. The method used for this web application is OpenCV and DeepFace as face detector and emotion recognition system, respectively; while the song recommendations are pulled via Spotify API, where all these elements are deployed in a web application using Streamlit. DeepFace has been stated to have an accuracy of around 97% for its facial recognition functionality, along with their facial attribute analysis, which can be considered reliable enough to recognize emotions. For future work, other factors that can help to identify emotions are to be put more focus on, as it is envisaged to improve the emotion recognition system in this web application.

Qhairun Nisa’ Mohd Hanafi, Suziah Sulaiman, Saipunidzam Mahamad
Understanding Text Messages for Anxiety Therapy Through Topic Modeling

Digital health platforms such as text messaging and mobile therapy are being increasingly embraced by patients as a valuable source of anxiety treatment. This mobile therapy-generated treatment comes in the form of text messages, and is normally known as short text and sparse. Since many real-world text-based data need semantic interpretation to reveal meaningful and relevant latent topics, research in Short Text Topic Modelling (STTM) was conducted. The current study examines the topics included in anxiety mobile therapy using STTM, particularly from the text messages sent by mental health professionals. Prior to the actual experiment, initial study was conducted using four topic modelling techniques with 28 text messages from anxiety therapy datasets and different hyperparameter settings. The performance evaluation includes classification accuracy, purity, normalized mutual information, and topic coherence. Based on the performance, Latent Feature Dirichlet Multinomial Mixture (LFDMM) with α = 0.1, β = 0.01, and K = 8 is found to be the most suitable hyperparameter setting for the anxiety text messages dataset and is used further in the actual experiment with 53 sample text. The findings from the actual experiment show that the anxiety text messages dataset comprises 8 interpretable topics that are classified under the domain of energy recharge, locus of control, mutual respect, activity scheduling, handling uncertainty, medium of communication, managing thoughts and health, and hope and readiness.

Teh Faradilla Abdul Rahman, Norshita Mat Nayan

Mixed Reality and Human-Computer Interaction

Theoretical Analysis of Research Methodology to Study Emotions Using Emotion AI Among Malaysian E-Learning Tertiary Students for Prototype of Adaptive Interface

The e-learning method has been in use for more than a decade now. The e-learning method was intensely used during covid-19 pandemic giving an option for learning experiences remotely in various locations. Currently, e-learning optionally facilitates for students from various locations to study even in remote areas. Sustaining the e-learning facilities with improvements becomes essential since the world is moving towards IR4.0 where learning can happen from any part of the world using a diversified technology. This analysis is looking at theoretical concepts to analyze the viability to use emotions to study the requirements analysis for Adaptive Interface Design. This analysis is conducted to identify the emotion and to find appropriateness of using emotions AI tool to study the emotions. It is targeted for Malaysian students’ environment. It will be based on current widely used LMS used to identify the methods of analyzing the emotions among Malaysian students of entrepreneurship subject. It is concluded that emotions are an essential feature of User Interface study for further study of influences. Emotions AI can be used with mixed mode method for accuracy. The research will pave the way for Adaptive user interface in e-learning systems. The purpose of Adaptive user interface is to enhance essentially subject-wise and program-wise creativity skills in meeting essential skills of Education 5.0.

Vasaki Seenivasagam, Zainab Abu Bakar, Norshuhani Zamin, Yazrina Yahya
Human Gesture Recognition for Elderly People Using User Training Interaction Data

Research on human-computer interaction (HCI) has been widely developed for older people. However, there needs to be more research studies on the deep learning model implementation of human gesture image data to monitor the activities of older people. There are four main stages of research, including data preparation, feature extraction using pre-trained models VGG16 and VGG19, training without and with fine-tuning, and comparing the performance of the deep learning model. This study used the dataset of Ralf Leistad Gesture with data classes as backward, forward, left, right, still, and stop. Then, the data is implemented in the data augmentation method using rotation, brightness, width shift, height shift, horizontal flip, and vertical flip. As a result of the experiment, VGG16 achieved an accuracy of 96.88%, and VGG19 reached an accuracy of 96.88%.

Nur Ani, Nazlena Mohamad Ali, Vina Ayumi
Virtual Reality for Social-Emotional Learning: A Review

Virtual reality (VR) is an immersive technology that can simulate different environments and experiences. Social-emotional learning (SEL) is a process through which individuals develop the skills, knowledge, and attitudes to understand and manage their emotions, establish positive relationships, and make responsible decisions. SEL promotes healthy emotional regulation in adolescents. However, VR interventions for adolescent emotion regulation have received less attention. The aim of this research is to identify a VR element that includes knowledge in relation to SEL since 2017 through systematic literature reviews (SLRs). A broad review of the current literature was conducted in three databases, namely Scopus, IEEE, and WOS. Data were extracted, including age ranges, year published, and medical procedures, using a search term. The result suggests a requirement list to design a virtual reality for social-emotional learning that promotes a positive impact on emotion regulation for Malaysian adolescents.

Irna Hamzah, Ely Salwana, Mark Billinghurst, Nilufar Baghaei, Mohammad Nazir Ahmad, Fadhilah Rosdi, Azhar Arsad
Embodied Narrative: Data Storytelling of Online Artwork Experiences

This paper describes a pilot ethnomethodology study of visiting a 3D virtual gallery that exhibits 3D models of physical artworks and of viewing photographs of artworks in a 2D online artist portfolio. Artwork, with its intrinsic information and layers of meaning, is a form of data which narrates a compelling and evocative story to engage audience. The digital landscape is a compelling medium to deliver this storytelling. However, such approach demands a delicate balance between technology incorporation and artistic vision. In an era of converging data and art, this paper explores the aesthetics of data storytelling and immersive online art experiences. Amid CoVID-19 lockdowns, physical art exhibitions migrated online, prompting a study of 3D virtual gallery visits and 2D online artist portfolios. Participants engaged in think-aloud protocols while navigating both formats, revealing insights into rationalizing actions and interpreting artworks online. Challenges emerged, including disrupted responses due to the inability to discern texture in 3D models or artwork photos. Navigational control, viewing distance, detailed artwork descriptions, and stable internet connections further influenced the online viewing experience. This prompts a further exploration on means to curate online exhibitions thoughtfully, where data aesthetics and artistic storytelling is harmonized for realistic interpretation within the digital expanse.

Hanif Baharin, Afdallyna Fathiyah Harun, Noris Mohd Norowi
Participatory Design Workshop to Create a Virtual Reality Musical Instrument Based on Tumbuk Kalang

This paper describes a participatory design workshop to design a virtual reality musical instrument based on Tumbuk Kalang. Originating as a performance for rice harvesting festivals, Tumbuk Kalang as a traditional musical instrument consists of a mortar and pestles. Several players, each holding a pestle will coordinate their movements to pound on the same mortar to create rhythmic sounds whilst singing. These days, Tumbuk Kalang is no longer associated with rice harvest but has been innovated in cultural performances that include songs, dances, and other musical instruments. As machines take over rice production, pestles and mortars are out of use and the tradition of Tumbuk Kalang may be lost. Thus, this research proposes to create a Virtual Reality (VR) Tumbuk Kalang so that it can be preserved and played by future musicians. However, instead of recreating Tumbuk Kalang in VR, we suggest that innovation should be introduced in the design of the new VR Tumbuk Kalang. Hence, we have conducted a participatory design workshop with 12 musicians to design the new VR Tumbuk Kalang. The findings from the workshop produces six new designs of VR musical instruments based on Tumbuk Kalang. The contributions of this paper include the new designs of VR Tumbuk Kalang and the participatory design method used which may be replicated by other researchers who are aiming to preserve traditional musical instruments in the form of VR.

Hanif Baharin, Noris Norowi, Khatriza Ahmad Saffian, Yap Eng Sim
Evaluating the Effectiveness of E-Learning Website Using Electroencephalogram

Although e-learning technology provides numerous benefits for educators, enticing students to use e-learning services is a challenge, particularly for the e-learning websites of higher education institutions in Malaysia. E-learning websites of Malaysian higher education institutions have few guidelines for user interface design that foster emotional engagement. Due to this problem, there is a significant percentage of student disengagement on e-learning platforms. A visualization pattern as a guideline has been proposed for designing an e-learning GUI website for HEIs in Malaysia. The proposed guideline has been implemented on the prototype website’s graphical user interface (GUI). The effectiveness of the GUI was evaluated using an electroencephalogram (EEG) device and resulted in 65% of the participants being in the ‘Most Effective’ category, with the highest average of most effectiveness standing at 32.6%. The research has demonstrated that the developed guideline increased student engagement with the GUI of the e-learning website prototype. The guideline is intended to assist higher education institutions (HEIs) and website developers and designers in creating e-learning websites that can sustain students’ interest in e-learning over time.

Alberto Aning, Aslina Baharum, Nur Faraha Mohd Naim, Nurhafizah Moziyana Mohd Yusop, Dian Darina Indah Darius, Noorsidi Aizuddin Mat Noor, Farhana Diana Deris
Compustory: A Virtual Museum Game for Modeling the History of Computer Evolution

Museums have an educational function that can be used as a means of education, especially in learning history at school. As we know, the COVID-19 pandemic has changed the face-to-face learning system to virtual. So, teachers cannot invite students to study history in museums. Virtual museum games can be an alternative solution to this problem by presenting remote facilities that can make it easier for students to explore knowledge about history without any restrictions. This study aims to develop a virtual museum game to model the history of computers evolution. This study uses the DDD-E model in developing this game. The reason for using this model is that every development and addition of new features to the game will be evaluated. Development evaluation is carried out by conducting expert validation using the LORI instrument. The virtual museum game “Compustory” was developed using a first-person perspective with 3D models to visualize the evolutionary history of computers. The results of the validation show that the average percentage score obtained is 99% with the eligibility qualification of “Very Eligible” used as an educational medium to teach the history of computer development in schools and is considered capable of making learning more enjoyable and recreation.

Warda Azzahra, Munir, Wahyudin
The Use of Augmented Reality, Virtual Reality, and Mixed Reality in Communication Children’s with ASD: Systematic Literature Review

Autism spectrum disorder (ASD) is a neurobiological developmental disorder that affects communication and social interaction. Autistic children often have difficulty communicating effectively, both verbally and nonverbally. Good verbal and nonverbal communication skills help children with autism participate in social interactions. In this paper, we present a systematic literature review focused on analyzing AR/VR/Mixed reality technologies used to improve communication skills in children with ASD based on research published over the last ten years and available in relevant scientific databases. We reviewed 22 studies showing target area communication, how AR/VR/Mixed reality is used in the context of communication skills in ASD children, user experience and accessibility, technology limitations, and findings. This systematic review of the literature shows that the majority of the communication areas targeted are verbal and nonverbal simultaneously. Overall, the use of AR/VR/Mixed reality can be an alternative to improve children’s communication. Technologies that can facilitate the different abilities and emotions of children with ASD are considered in developing AR/VR/Mixed reality in the future.

Azizah Nurul Khoirunnisa, Munir, Laksmi Dewi, Rasim, Nissa Nur Azizah, Zsalzsa Puspa Alivia
Hybrid on a Budget: An Autoethnographic Study

This study is an autoethnography which looked at the experience of hybrid teaching of a member of the academic society. It offers insights into the thoughts and actions of a private university lecturer in identifying suitable equipment that is within the limited budget of the said lecturer. The needs, wants and constraints were the main considerations in designing an environment for hybrid teaching on a budget. The findings also highlight the physical and mental demands of conducting a hybrid class. The study produced the S2C2 Model for the setting up of a hybrid environment which in actuality can be used as a basic guide for designing hybrid classrooms for those who are on a budget as well as for those with a limitless budget.

Shariffah Bahyah Binti Syed Ahmad, Syed Nasir Alsagoff Bin Syed Zakaria
Adoption Barriers of Assistive Ambient Technology: A Systematic Literature Review

As the global populace is aging, many countries are preparing for a better environment to age in place. Ambient assistive technology refers to devices and systems that help to improve the quality of life by promoting independence, safety, and comfort in their living environment. Despite the abundance of studies focusing on elderly technology adoption, resistance to utilizing such technology persists among this demographic. Moreover, there remains a lack of understanding regarding the barriers to technology adoption within the aging population. The elderly are more comfortable aging in place, and there is evidence that technology can play a vital role in supporting their daily life. To investigate the barriers faced by the elderly in adopting assistive ambient technology, a systematic review was conducted. This study includes articles selected from Scopus and Web of Science databases. Three groups of keywords were combined: those relating to adoption barriers, assistive technology, and the elderly. Using thematic analysis, data from 43 articles were analyzed, yielding six main themes: 1) cost; 2) environmental; 3) personal; 4) psychological; 5) social; and 6) technological. The results of this study are expected to aid in the development of technology catering to the preferences of the elderly.

Nik Izyan Fatini Musri, Rozianawaty Osman, Nurzeatul Hamimah Abdul Hamid, Fariza Hanis Abdul Razak
The Affordances and Usability Evaluation for HeartM 3.0: A Mobile Heart Monitoring Application

The use of health applications on mobile phones is gaining traction especially when the COVID-19 pandemic hit the world. Such applications enable patients to keep track of their health without the need of physically going to the hospitals to meet their doctors. In the context of mobile heart applications for heart disease patients, the need for a highly usable application is even more vital as the heart patients are considered most vulnerable if they infected by the COVID-19 virus. HeartM 3.0 is the application developed to enable the patients to carry out self-monitoring of their heart conditions. This study aims to discuss the affordances and usability evaluations of HeartM 3.0 from the perspective of the heart patients. HeartM 3.0 were evaluated based on its affordances which consist of medical, social and technological affordances, and usability elements which consist of learnability, effectiveness, memorability, error, and satisfaction. The questionnaire was adapted from the PSSUQ (Post Study System Usability Questionnaire). The study recruited thirty five heart patients in Charitas Hospital Palembang, Indonesia. From the participants’ assessments, the study found that the heart patients feel satisfied with what is provided by the HeartM 3.0 application and stated that HeartM 3.0 is comfortable to use, patients are willing to use it continuously, and in overall, they are satisfied with HeartM 3.0. This paper theoretically contributes to provide recommendations to application developers and mobile health designers on the importance of meeting the affordances and usability elements desired by the users, especially for mobile health applications for chronic illnesses.

Muhammad Sobri, Mohamad Taha Ijab, Norshita Mat Nayan, Alexander Edo Tondas
Game-Based Mobile Application for Tarannum Learning

The melodious recitation of Quran verses, known as Tarannum, impacts the reciters’ and audiences’ understanding and perception. Several experts on Tarannum melodies often train their apprentices in continuous lessons covering techniques on voice activation and correct recitations. The availability of an interactive application can help promote constant training and effectively complement the lessons. An interactive approach to Tarannum lessons is not widely available and is often developed for recognising the Arabic alphabet rather than practising melodies and recitations. A mobile application is one of the most widely implemented learning and training applications, as it offers interactive feedback to motivate users to continue the lessons repetitively. The lack of mobile applications to promote Tarannum learning reduces the interest in becoming competent reciters. This is because traditional melody training requires one-on-one instruction from an instructor to perfect the melody. The lack of an interactive Tarannum application affects Muslims who cannot practice the Tarannum skills and foundation through conventional learning sessions. Therefore, this project proposes a mobile learning application for Tarannum. The application can assist Muslims in learning Tarannum by improving their melodic recitation techniques and, simultaneously, can give the user an enjoyable experience by proposing a game-based learning approach. This was done in response to the need for an application to assist Muslims in learning Tarannum and evaluating their Tarannum techniques. For the extended project, the application can be enhanced by implementing an algorithm for Tarannum melody detection that will be more helpful for people who want to learn Tarannum.

Muhammad Irfan Mohd Nadziman, Haslizatul Fairuz Mohamed Hanum, Nur Aina Khadijah Adnan, Norizan Mat Diah, Zainab Abu Bakar
Review of User Satisfaction Models in the Context of Digital Libraries Setting

Student satisfaction is of critical importance in online education, but relatively few research has investigated the factors that contribute to it in developing nations. Libraries located in public universities have, over the course of many years, shelled out a substantial amount of money in order to subscribe to a wide variety of digital resources. These resources include online publications from respectable publishers, journals, online databases, books, monographs, and other resources for the storing of information. Previous research has shown, however, that despite online libraries’ success in locating relevant material, digital library use remains low. The literature review consists largely of studies that evaluate digital libraries based on how satisfied its users are. Nevertheless, the evaluation models for user satisfaction lack the constructs, and factors that contribute to user satisfaction. The primary goals of this study are to (1) identify the most relevant models used to assess user satisfaction in digital library settings, and (2) establish the prevalence users of digital libraries. This review includes, among other things, various publications that were published between the years 2019 and 2023. It emphasized the need for study user satisfaction in the context of digital libraries since this factor could impact the useability and innovation of such education platform.

Hend Ali Musbah Jebril, Mohammad Nazir Ahmad

Systems Integration and IoT, Cybersecurity, Energy Informatics

Design and Development of an Automated Filament Changing System for Fused Deposition Modelling (FDM) 3D Printer Using Axiomatic Design and TRIZ

One of the most widely used 3D printers is the Fused Deposition Modelling (FDM) printer. However, it has a very long printing process which may encounter printing failure and cause wastage of resources. Therefore, in this study, an attempt to design and develop a home-made and simple automatic filament changing system that is compatible with FDM printers using Bowden tube extrusion system. The designed system is expected to refill filaments particular for overnight 3D printing process. Axiomatic Design and TRIZ are used in this study to systematically design and solve problems faced throughout this research project. Conceptual designs were derived, and the final conceptual design consists of Central Control Sub-System, Filament Guide Sub-System, Merger Sub-System, and Spool Rollers Sub-System. The most critical sub-systems are the Filament Guide Sub-System and Merger Sub-System, which allows filament to refill without human interaction and allow two filament inputs to pass through a merging component to enable one single output. For the Central Control Sub-System, Arduino programming language was used with a MAKER UNO board, an Arduino compatible low-cost microcontroller to control the refilling process. Three limit switches which act as 2 input sensors and 1 output sensor respectively working in tandem with and 2 stepper motors were used to actuate the refilling process. The design of the automatic filament changing system was successful and the testing of the system was successful on the sub-systems level.

Kok Weng Ng, Jia Wei Wong, JianBang Liu, Mei Choo Ang
Blockchain-Based Traceability Method - A Review

Effective output optimization in various industries requires precise monitoring of all processes and data involved. To facilitate further optimization, information must be easily accessible and traceable. While data traceability can be achieved using various technologies, some methods may have limitations, such as a lack of real-time monitoring or difficulty integrating with existing systems. In recent years, the emergence of blockchain technology has enabled new possibilities for data traceability. Blockchains are composed of blocks of hashed data that are linked to earlier blocks, providing a reliable means of tracing data. This paper explores the fundamental concepts of blockchain technology and its features, highlighting the limitations of traditional traceability systems and the advantages offered by blockchain. Then, this paper reviews existing literatures to provides a comprehensive overview of blockchain-based traceability methods, offering insights into their applications, benefits, challenges, and future prospects.

David Wong You King, Muhammad Arif Riza, Liew Kok Leong, Ummul Hanan Mohamad, Rabiah Abdul Kadir, Mohammad Fairus Zulkifli, Mohammad Nazir Ahmad
Responding to Regional Revitalisation and Socio-economic Challenges in Japan: Government Approaches and Use of Advanced Technologies

Japan has one of the world's fastest ageing populations, leading to a shrinking workforce and labour shortages in various industries, with consequent impacts on various aspects of Japanese life and society. Economic stagnation and deflation, energy security and environmental concerns, agricultural decline and its socio-economic effects, advances in technology and innovation are now key issues facing Japan, and government policies and initiatives have been designed to address these challenges and concerns. The objective of this paper is to explore the approaches and policies of the Japanese government and the status of the promotion and adoption of advanced technologies to address the socio-economic challenges facing Japan. The research paper examines the policies and approaches of the government, companies and farmers, and the status of the use of advanced technologies in agriculture to address socio-economic challenges, in line with the achievement of globally promoted goals. This research paper highlighted the policies and approaches applied by the government, companies and farmers, and the current status of the use of advanced technologies in agriculture to address socio-economic challenges and global strategic goals. The study shows that the Ministry of Agriculture, Forestry and Fisheries is leading the Japanese government's efforts to promote the use of advanced technologies to increase agricultural production and address socio-economic issues such as an ageing population, rural depopulation and economic stagnation in rural areas, as young people migrate to cities. Meanwhile, the Japanese government continues its initiatives and commitment to the global goals of developing sustainable energy and food supplies.

Yasuki Shima, Ali Fathelalem Hija
Evaluation of Smart Community Engagement in Riyadh, Saudi Arabia

COVID19 has created a global effect on economies, people and governments around the world. This includes the Kingdom of Saudi Arabia (KSA). Although the KSA’s peak of reported COVID cases was from March to July 2020 the pattern shows significant reduction from August 2020 onwards. Nonetheless, many people are still losing their jobs and businesses are shutting down. Due to the prolonged lockdown period announced by the KSA government from 9 March 2020, there has been an increased, 70%, demand for digital services during the lockdown, as compared to the previous year (according to the government figures). This is due to the high restriction of movements for everyone. People started to use online services to purchase items and food; meanwhile, lessons in schools and universities remained online to the date this proposal was written. Many people are also working from home. Online systems such as Tabaud, Mawid, Tawakkalna and Tetamman Many apps were developed for use during the pandemic. For example, the Tabaud, Mawid, Tawakkalna and Tetamman apps for smartphones are among the latest by KSA government efforts to combat and contain the virus. The KSA introduced Vision 2030 to empower their people to diversify the economy of the country. In Vision 2030, the KSA has prioritized the rapid growth of ICT as a building foundation of digital development. This study focuses on evaluating the smart community engagement in Riyadh focusing on the level of acceptance and execution from the perspective of the public and relevant agencies. This research seeks to understand the level of community engagement in Riyadh towards the use of ICT in improving quality of life. This could help move forward the development of the smart community concept involving the engagement of relevant authorities.

Norshuhani Zamin, Mervin Esckalin Mary, Abdul Wahab Muzaffar, Ku Ruhana Ku-Mahamud, Mohd Azhar Ibrahim Residi
Cloud Service Provider Cost for Online University: Amazon Web Services versus Oracle Cloud Infrastructure

The number of Cloud Service Providers (CSPs) that provide their services differs from each other in terms of cost. This has made it difficult for companies and organizations to choose the best and least expensive cloud service providers. In light of the current crises that the world is going through and with the many financial problems faced by many companies, including Al-Madinah International University, it was necessary to take some measures that would mitigate the impact of financial problems, and therefore this study came to present a comparative study between each of cloud service providers Amazon Web Services (AWS) and Oracle Cloud Infrastructure (OCI) which provide Virtual Private Server (VPS) services in terms of cost to help Al-Madinah International University in making the appropriate decision in choosing the cloud service provider that suits its financial requirements.

Yazeed Al Moaiad, Zainab Abu Bakar, Ahamd Mhd Emad Diab, Norshuhani Zamin, Yazrina Yahya
Elevating Database Performance: Current Caching and Prefetching Strategies for Online Databases in Nigeria

This study investigated caching and prefetching techniques to improve data access performance in online databases, considering factors like data popularity, access patterns, and cache coherence. The research design adopted for this study was the descriptive survey. The population comprised of 1387 undergraduates computer science students in public tertiary institutions in Ekiti State. Simple random sampling technique was adopted to select 150 computer science students from three public tertiary institutions in the study area. The instrument used for data collection was a structured 4 Likert type questionnaire. The questionnaire was distributed to the respondents to find out the effectiveness of caching and prefetching techniques on online database. The instrument was both face and content validated by two experts from department of computer science in Bamidele Olumilua University of Education Science and Technology, Ikere-Ekiti, Ekiti State. The reliability of the instrument was ensured using Pearson Product Moment Correlation formula which yielded a coefficient of 0.97. The data collected were analyzed using descriptive statistics such as mean and standard deviation. The result showed that the current caching and prefetching techniques employed in online databases are highly effective; the different access patterns have effect on the effectiveness of caching and prefetching techniques in online databases and there are impacts of cache coherence mechanisms on the efficiency of caching and prefetching techniques in online databases. It was therefore recommended that the inclusion of caching and prefetching in curriculum is important across all educational level in Nigeria. In addition, caching and perfecting has come under fire for focusing mostly on computer science.

Olatunji Austine Kehinde, Zahidah Zulkifli, Ely Salwana Mat Surin, Nur Leyni Nilam Putri Junurham, Murni Mahmud
Exploring Data Wiping Practices in the Royal Malaysian Air Force (RMAF) HQ

Data wiping is a very important part of cybersecurity. It is the act of deleting data so that it cannot be recovered. The Royal Malaysian Air Force (RMAF) as part of the Malaysian Armed Forces (MAF) has also recognized the importance of data wiping in safeguarding national security. Senior staff officers at the RMAF headquarters, Ministry of Defence (MINDEF), are given laptops for work-related purposes. As such, the laptops might contain secret and top-secret work-related documents. When the officer is posted out from MINDEF, laptops will have to be returned. Malaysia is experiencing rapid economic growth and digital transformation, but it faces cybersecurity challenges that threaten its stability. Cyber threats come from various sources, leading to data breaches and other incidents. Data wiping, the secure erasure of data from storage devices, is crucial for protecting sensitive information. The Personal Data Protection Act (PDPA) requires organizations to implement data erasure measures. This paper aims to explore the current data wiping practices in the RMAF.

Syed Nasir Alsagoff Bin Syed Zakaria, Kuan Fook Chao, Zuraini Zainol
Affordances-Based Behavior Change for Energy Efficiency Among Malaysians: A Conceptual Model

Climate change is a pressing global issue that affects countries worldwide. To mitigate its impacts, reducing carbon emissions is crucial. One approach is minimizing energy consumption, including among domestic consumers. Understanding the driving factors influencing consumer behavior change is essential, and Information Systems (IS) have been proposed as intervention tools. However, research on how IS’s functional affordances influence behavior changes in residential energy consumers is lacking, along with comprehensive models for explaining behavior change. The need to address climate change has prompted the search for strategies to reduce carbon emissions. Energy consumption reduction, particularly in households, is pivotal. This demands an understanding of factors driving consumer behavior change, wherein Information Systems (IS) can play a role. However, research gaps exist concerning the influence of IS functional affordances on residential energy consumers’ behavior change. The research aims to achieve several objectives. First, it seeks to study the factors that shape behavior change in energy efficiency, specifically those enabled by IS. Second, the goal is to formulate an affordance-based model that elucidates behavior change towards energy efficiency. Lastly, the research aims to evaluate and validate this proposed model. To address the objectives, a multi-faceted approach leveraging key theories is adopted. The Affordance Theory, Sensemaking Theory, Belief-Action-Outcome Framework, and Energy Informatics Framework will provide valuable insights. By integrating these theories, a preliminary Affordance-based Behavior Change Model for Energy Efficiency is conceptualized. This research holds significant potential outcomes. It can shed light on the factors influencing behavior change in the context of energy efficiency, facilitated by IS. The development of an affordance-based model could offer a comprehensive framework for understanding and predicting consumer behavior changes.

Mohamad Taha Ijab, Hamizah Mohamad Hariri, Norshita Mat Nayan, Mohd Azul Mohamad Salleh, Suraya Hamid

Intelligent Data Analytics

The Role of Mass Media as a Communications Distributor for Tourism Villages in Indonesia

The management of tourist villages is not far from the touch of stakeholders in a tourism area regarding the pentahelix component i.e.: academia, business, community, government, and media. Developments that continue to increase in today’s modern era make tourism very close to the mass media. The mass media has undergone many changes, which have a high potential to support the information dissemination system as a tourist communicator closest to tourists. This paper presents the role of mass media as a communications distributor for tourism villages in Indonesia. We use library research approach aimed to explore the role of the mass media as a communicator for tourism villages in general and in a concentrated manner. We collect data from 100 scientific articles relevant to the topic of in-depth discussion to find a portrait of the situation or an in-depth exploration of the mass media as a communicator in a tourist village as a whole, general and focused so that a specific role can be found as a core communicator in a tourist village. The results found the form of mass media which is divided into three forms: print media, electronic media, and online media; where it was found that mass media has a principal core role as a communication distributor in disseminating information on tourism villages, with the main role of developing theories regarding the processes and effects of mass communication. In developing the theory in the form of: Composing the message; Presenting the message; and Channeling the message. Our study contributes to a better understanding of the current research landscape and identifies areas for future research development related to the role that the mass media in the tourism industry.

Prasiwi Citra Resmi, John J. O. I. Ihalauw, Dwiyono Rudi Susanto, Damiasih Damiasih, Suhendroyono Suhendroyono, Tutut Herawan
Creating Values for Big Data Analytics through Business and Technology Alignment

Meaningful insights are the most important outcome of a big data analytics project (BDA). As the BDA project has been widely used to facilitate business decisions, many organizations focus on gaining valuable insights into their business performance, especially as one of the determining factors for organizations to outrun their competitors in the industry. The research from the current literature found that there needs to be more technical consideration for the valuable measure of business performance based only on the business perspective rather than considering the advanced technological perspective: big data analytics. On the other hand, the technical point of view is focused more on data than realizing real business needs. Hence, this research aims to introduce, identify, or develop know-how mapping between business and technical points of view for valuable insights. So then, the BDA can work on understanding what the business would want and aligning it with what the data could provide. With that, the first step is to identify elements and define the definitions of the valuable insights. Second, this research will serve as a how-knowledge guideline for business analytics in achieving valuable insights. This research is intended to shed light and clarify valuable insights from business and technical points of view while developing any BDA project in the business organization.

Luen Mun Chong, Suraya Yaacob, Wan Farahwani Wan Fakhruddin, Nur Azaliah Abu Bakar
Web-Based Mental Health Predicting System Using K-Nearest Neighbors and XGBoost Algorithms

Problems with mental health are common presently and have been a worry for a long time. Mental health problems, like anxiety, depression, and panic attacks, can be caused by numerous things. Therefore, recognising the start of mental disease is becoming increasingly crucial to maintaining a good life balance. This study uses machine learning to identify any possible mental health disorders in an individual to attain this goal. The investigation employed supervised machine learning to predict mental health status, namely K-Nearest Neighbors (KNN) and XGBoost, with performance evaluation criteria including accuracy, precision, recall, and F1 score. When these two algorithms were compared, it was discovered that XGBoost produced a more effective prediction model, which was then employed to develop a web-based mental health prediction system. The web-based method creates a questionnaire for mental health issues. Based on the user’s responses to the questions, the system will predict his or her mental health status as normal, depression, anxiety, stress, loneliness, or regularity. Every component of the system, including buttons and forms, has been successfully tested using functionality tests. Moreover, the system’s advantages, weaknesses, and future study directions are identified.

Nurul Farhanaa Zulkefli, Norizan Mat Diah, Azlan Ismail, Haslizatul Fairuz Mohamed Hanum, Zaidah Ibrahim, Yunifa Miftachul Arif
Genre Classification in Music using Convolutional Neural Networks

With the advancement of technology and computational power, crafting a chart-topping song has become more effortless than before, achievable from the convenience of our residences with just a computer at hand. This has led to the emergence of vast arrays of catalogs of music, containing a variety of genres and styles from different music makers with different ethnicities and backgrounds, resulting in a large database that clogs most music streaming platforms with little automated categorization. Based on the GTZAN audio dataset, this paper revisits the use of Convolution Neural Networks (CNN) for classifying different types of music genres. Using Mel-frequency cepstral coefficients (MFCC) features, the CNN model achieved an accuracy of 85%. As a result of the careful design of the CNN model, it is on par with many latest and greatest CNN frameworks.

Andrew Bawitlung, Sandeep Kumar Dash
Harnessing Technology for Efficient Coagulation Profile E-Reporting: A Design Thinking Approach

The coagulation profile report contains international normalized ratio (INR) blood test, activated partial thromboplastin clotting time (APTT), platelets, and fibrinogen. The report is for identifying abnormal blood clotting tendencies by examining key factors associated with bleeding issues. Traditional paper-based reporting methods frequently encounter difficulties such as manual data entry errors, delayed result dissemination, and restricted accessibility. The incorporation of technology in coagulation profile e-reporting offers a promising solution for addressing these deficiencies and revolutionizing the documentation and communication of laboratory results. This exploratory study seeks to evaluate the practicability and prospective advantages of integrating technology into coagulation profile reporting processes. Utilizing a design-thinking methodology, this study investigates the stakeholder and users using persona, empathy, journey mapping, ideation and prototyping processes in order to determine the requirements for a proposed mobile application. The paper presents a solution to enhance existing operations by describing its components, prototype design, and validation procedure. The solution is driven by the imperative to provide timely notifications to healthcare providers regarding the availability of laboratory results in the electronic medical record (EMR) system.

Puteri N. E. Nohuddin, Prasis Ja Singh, Kelvin Ch’ng, Phan Nop So Phon, Nora Azima Noordin, Zahidah Abd Kadir, Zuraini Zainol
The Impact of Preprocessing Techniques Towards Word Embedding

In this study, we analyze the performance of various pre-processing methods and classification algorithms on health-related tweet data on Twitter. The data set consists of a number of different pre-processing methods, such as Z-score Scaling, Min-max Scaling, Decimal Scaling, Log Transformation, Percentage Scaling, and Log2 Scaling, as well as two main classification algorithms: Naive Bayes and Logistic Regression. The results of the analysis show that the pre-processing method has a significant effect on the performance of the classification algorithm. Z-score Scaling emerges as a stable option and provides good accuracy for both algorithms. However, Min-max Scaling is more suitable for Logistic Regression than Naive Bayes. In addition, Logistic Regression tends to provide higher accuracy in some pre-processing methods. We also suggest further exploration to understand how this pre-processing method might apply to different types of data and its impact on other classification algorithms. In addition, the selection of alternative models, hyperparameter optimization, and data enrichment are areas that can be improved to obtain better classification results. This study underscores the importance of careful pre-processing and selection of appropriate pre-processing methods in applying classification algorithms to text data. The results and recommendations in this study can be a guide for researchers and practitioners in making better decisions in the classification analysis of Twitter data about health.

Mustazzihim Suhaidi, Rabiah Abdul Kadir, Sabrina Tiun
Predict Traffic State Based on PCA-KMeans Clustering of neighbouring roads

During the past few years, time series models and neural network models have been widely used to predict traffic conditions based on historical data, speeds, weather, accidents, and special holidays. However, in previous studies, these models were commonly used for predicting traffic flow, rather than predicting traffic flow propagation. Research in traffic flow propagation is relevant because it may guide people in avoiding neighbouring roads which are affected by congestion. We proposed the similarity of Principal Component Analysis (PCA) to investigate the relationship between roads by clustering similarity values between target roads and neighbouring roads. The results were then visualized on a map for further observation. Furthermore, the high relationship roads obtained from the cluster were then used for predicting traffic state using a naïve Bayes method. Based on the visualization of results on maps, and by observing the prediction results using naïve Bayes, obtained that utilizing PCA with K-Means improves the outcomes in obtaining high relationship roads compared with k-means only.

Bagus Priambodo, Bambang Jokonowo, Samidi, Azlina Ahmad, Rabiah Abdul Kadir
Unleashing Trustworthy Cloud Storage: Harnessing Blockchain for Cloud Data Integrity Verification

This position paper explores the transformative potential of blockchain technology in ensuring data integrity within cloud storage systems. The increasing adoption of cloud storage services has raised concerns about the security and integrity of stored data. In this paper, we argue that integrating blockchain technology into cloud data integrity verification schemes offers a robust and decentralized solution. Through a comprehensive review of relevant literature, we examine the benefits and challenges associated with this approach. Our analysis reveals that blockchain-based data integrity verification schemes provide tamper-proof and transparent mechanisms, enhancing trust and security in cloud storage environments. We further discuss the potential impact of this integration on data privacy, scalability, and performance. While acknowledging the current limitations and ongoing research efforts, we propose that embracing blockchain for cloud data integrity verification can foster a new era of trustworthy and reliable cloud storage systems. This position paper aims to stimulate further discussion and research on the transformative role of blockchain in ensuring data integrity within the cloud.

Zhenxiang Li, Mohammad Nazir Ahmad, Yuanrong Jin, Wang Haipei, Liang Zhantu
A Novel Approach of Adpative Window 2 Technique and Kalman Filter- “KalADWIN2” for Detection of Concept Drift

A recommendation engine (RE) is a machine learning technique that provides personalized recommendations and anticipates a user's future preference for a collection of goods or services. In Online Supervised Learning (OSL) settings like various REs, where data vary over time, Concept Drift (CD) issue usually occurs. There are many CD Detectors in the literature work but the most preferred choice for the non-stationary, dynamic and streaming data is the supervised technique- Adaptive Window (ADWIN) approach. The paper aims towards the limitations of the ADWIN approach, where ADWIN2 approach is more time &memory efficient than ADWIN. The paper also focusses on novel proposed technique of the combination of Kalman Filter and ADWIN2 approach, named-“KalADWIN2”, as it’s the best estimator for detection even in noisy environment. It ultimately helps in fast CD detection in REs.

Anagha Chaudhari, Hitham Seddig A.A., Roshani Raut, Aliza Sarlan
Unleashing the Power of Visuals: A Captivating Exploration of Scientific Data Visualization Methods and Techniques

Scientific visualizations are the building blocks for conveying results and ideas to a wide range of audiences. It is one of the steps in the data science process, and it should be done before the data presentation phase. Because some data is not easy to read for humans, visualization can help transform the data into visual content such as graphs or plots, as the requirement for scientific visualization techniques and methods is increasing greatly. Reviewing various visualization methods is necessary to increase knowledge about it. In this paper, an overview of scientific data visualization methods and techniques has been reviewed. To conduct the experiment, a total of five peer-reviewed articles relating to scientific visualization techniques were chosen. The problem with this study is that people still do not have a clear idea of how to use or what is the importance of scientific visualization. The findings of this paper show that the occurrence of visualization techniques improves the effectiveness of reading data and helps people to understand data in a more vivid way. A systematic literature review has been adopted in this study to assist the author in doing a study without bias. Finally, this paper can help readers increase their knowledge of the scientific visualization technique.

Aslina Baharum, Rozita Ismail, Ismassabah Ismail, Noorsidi Aizuddin Mat Noor, Farhana Diana Deris, Suhaida Halamy
Blockchain Technology for Traceability Monitoring in Food Supply Chain

Food supply is the network of companies, individual activities and resources involved in the creation and delivery of food product to customers. The food supply include all the stages of the food process, from raw material acquisition to final delivery of the finished food product. In Malaysia, food supply in a critical issue because the country is heavily reliant on food imports to meet its domestic demand. The factors influence the food supply in Malaysia are dependency on imports food, climate change, infrastructure and logistics, food safety and security and consumer demand. Based on this issues, fluctuate prices of poultry product are happened in Malaysia. In this study, blockchain technology is propose to solve the situation. Blockchain technology has the potential to change the food supply by providing a transparent, traceability, and security to the system for tracking and verifying the information and detail of the poultry product from the farm to the consumer table. Feature like traceability and transparent in blockchain can provide end-to-end traceability of poultry product, allowing consumers to track the entire journey information of the product from the farm to the retail store. Based on this feature, it can help prevent fraudulent document and manipulation price by identifying the original price state by government each stages in food supply.

Mohammad Fairus Zulkifli, Rabiah Abdul Kadir, Mohammad Nazir Ahmad, David Wong You King, Muhammad Badrun Al-Muhaimin Baharon
Data Mining in Establishing the Indirect Reference Intervals of Biochemical and Haematological Assays in the Paediatric Population: A Review

Reference intervals (RIs) are fundamental values accompanying medical laboratory results that allow interpretation by medical practitioners, thus influencing patient management. Traditionally, RIs are established by recruiting 120 healthy reference individuals and applying statistical analysis to the results. This method is challenging due to the technical and ethical issues involved. Therefore, many laboratories either adapt RIs provided by the manufacturers of their analytical platforms or the results of RI studies done in other countries. The advent of data mining technology has allowed an alternative method, the indirect RIs (IRIs) approach, which applies appropriate statistical techniques to patient data stored in the laboratory electronic medical records to establish the IRIs. This review briefly highlights the historical aspect of IRI determination, provides a general outline of the steps involved and reviews publications that have used data mining to establish the paediatric IRI over the past ten years.

Dian N. Nasuruddin, Ely Salwana, Mahidur R. Sarker, Adli Ali, Tze Ping Loh
A Visual-Based Energy Efficient Digital Agro (EE i-Agro) Project for Design & Technology Subject, Based on Computational Thinking Skills Across STEM

Computational Thinking (CT) is a concept used as a systemtic way of thinking and in problem-solving process, not just for computer science but other domains of knowledge. Various research conducted at high school and tertiary levels of education has shown that CT integrated into lessons across STEM have shown positive results. This study conducted at elementary school integrates CT with the Design and Technology (RBT) subject particularly in the Visual-Based Project Production Package Model (VB-P3) across STEM, using computational thinking skills, in the Energy Efficient i-Agro Reservoir Crop System (EE i-Agro) project production process for the Design and Technology (RBT) subject in primary schools. This package is aimed at improving students’ problem-solving and thinking skills by making them more creative and innovative. The VB-P3 also includes the development of RBT Energy Efficient i-Agro learning model and modules (EEi-Agro LMM). The VB-P3 model was based on the ‘prototyping’ iterative model comprising of five (5) main phases; namely analysis, design, development, implementation, and evaluation with appropriate iteration. This model is also based on the COMEL learning model with attributes such as interactivity, fun learning, engaging and motivating, which helps students’ learning, with additional components and elements. Hence, this paper highlights the evaluation of the Visual-Based Project Production Package Development (VB-P3) model to develop the Energy Efficient i-Agro Reservoir Crop System (EE i-Agro) Project which can improve thinking and problem-solving skills, to prepare students for the new learning environment and instill sustainability practices in students facing energy transition, climate change and global warming experienced globally.

Halimah Badioze Zaman, Rahimah Ismail, Nazrita Ibrahim, Ummul Hanan Mohamad
Multilingual Speech Emotion Recognition Using Deep Learning Approach

Human emotion is an inherent part of human beings, and it is used to express their feelings to the listeners. While emotions are mostly conveyed via facial expressions, spoken words also contain emotions to reflect a speaker’s emotional state. This project focused on researching and evaluating the deep neural network performance on multi-lingual speech emotion recognition on RAVDESS, EMO-DB and combination of both emotional speech databases. Methodology used in the project was divided into five steps: data collection and speech signal extraction, signal conversion, image recognition using transfer learning, result validation and implementation of trained network in graphical user interface (GUI). The research on AlexNet and SqueezeNet in transfer learning was carried out by training the networks using different number of maximum epochs, learning rate and image augmentations. The research showed that AlexNet provided the higher validation accuracy than SqueezeNet at 66.20% during training the combined RAVDESS and EMO-DB databases. As for the testing data, the trained model obtained an F1-score of 0.6253 on testing 264 sample data.

Chu Sheng Liau, Kai Sze Hong
Covid-19 Detection Using Coughing Sounds with Mel-frequency Cepstral Coefficients and Long Short-Term Memory

As there are a lot of limitations on current existing approach in screening of COVID-19 infection, an efficient approach must be introduced to the healthcare application as soon as possible in order to inhibit the spreading chain of COVID-19 around the world. Human can listen to audio file, but could not interpret the audio signal precisely. However, computers with deep learning algorithm could do so while handling huge amount of data. Therefore, the main focus of this research project is to develop a deep learning model in detecting COVID-19 infection through the analysis of coughing sound, Long Short-Term Memory (LSTM) is used as the deep learning neural network in this research project. It is an improved version of recurrent neural network (RNN) and it is specialized in processing time-series data which is also known as audio signals. As a result, the aim of this research project is to build a LSTM model with Mel-Frequency Cepstral Coefficients (MFCCs) feature as a diagnostic tool for COVID-19 infection. In order to achieve this, Coswara database is utilised as the source of coughing dataset, the coughing dataset is then go through the pre-processing process and hence employed for the model learning and training. Lastly, the trained model has achieved an accuracy of about 58% and its feasibility was evaluated with an unseen test dataset based on the classification report metrics.

Jia Chong Lim, Kai Sze Hong
Enhancing Diabetes Prediction and Classification Using the Bidirectional Neighbor Graph Algorithm

The global prevalence of diabetes, a chronic health condition with diverse implications, necessitates improved prediction and classification methods. In this research, we propose a novel framework employing the bidirectional neighbor graph (BNG) algorithm to enhance diabetes prediction. By leveraging graph-based semi-supervised learning, we compare BNG with existing systems, thereby improving data structure modeling. The BNG algorithm addresses missing data and aims to optimize predictions for individuals with diabetes. This innovative approach holds promise for advancing diabetes research and creating more accurate prediction models for this condition. The methodology establishes a network connecting nodes to their nearest neighbors in both forward and backward directions. The evaluation of the model performance reveals an AUC (Area Under the Curve) score of approximately 0.86, demonstrating its efficacy in distinguishing true and false positive values across diverse classification thresholds. Moreover, BNG models effectively capture comprehensive and distinct features from the input data, resulting in improved classification performance. Additionally, the BNG method showcases computational efficiency, making it highly suitable for large-scale applications.

Bashar Hamad Aubaidan, Rabiah Abdul Kadir, Mohamad Taha Ijab
Feature Selection Techniques on Breast Cancer Classification Using Fine Needle Aspiration Features: A Comparative Study

Breast cancer remains a prevalent invasive cancer in women as it ranks as the second leading cause of cancer-related death among women. It poses a significant global medical challenge due to its substantial increase in cases over the last decade. Early detection of breast cancer is vital; hence the development of computer-aided diagnosis (CAD) systems is crucial in assisting pathologists to accurately interpret and diagnose the tumor. Feature selection plays a significant role in CAD as it involves choosing the most relevant and informative features from the original dataset to improve the performance of the system. Thus, this study focuses on evaluating various feature selection methods on fine needle aspiration (FNA) features which are adapted from Wisconsin Diagnostic Breast Cancer (WDBC) dataset from UCI Repository. The analysis involved five feature selection techniques; Information Gain (InfoGain), Correlation Feature Selection (CFS), Fast-Correlation Based Filter (FCBF), Consistency and Relief-F with three different machine learning classifiers including Logistic Regression (LR), Support Vector Machine (SVM) and Random Forest (RF) with 10-fold cross-validations. Based on the experimental outcomes, it was observed that FCBF with LR classifier surpassed other FS techniques (ACC = 0.9718 and AUC = 0.993) with 7 features. On the other hand, Relief-F outshined other FS with both classifiers of SVM (ACC = 0.9772 and AUC = 0.971) and RF (ACC = 0.9684 and AUC = 0.991). This study validated that the Relief-F technique exhibited supremacy over other FS techniques. However, the task of identifying important features from high-dimensional data remains a significant obstacle in intelligent diagnosis. Henceforth, it is essential to dedicate further efforts to the development of CAD systems using efficient feature selection techniques to maximize the performance and effectiveness of diagnostic models.

Shahiratul A. Karim, Ummul Hanan Mohamad, Puteri N. E. Nohuddin
Machine Learning Trends in Mushroom Agriculture: A Systematic Review Methodology

The optimization of sustainable growth and management of mushrooms requires the utilization of machine learning models and appropriate evaluation techniques. Prior to implementing machine learning model in agricultural settings, preliminary trials are often conducted to mitigate potential risks. During the experimental phase, sample data sets are obtained from various agriculture sources or existing data repositories. In this paper a systematic review methodology is employed to analyze the machine learning models used in mushroom farming. The review encompasses 71 articles analyzed from 2014 to 2023, derived from published sources such as PubMed, Willey Online Library, IEEE, and Google Scholar. The purpose is to address several research questions, including the identification of trends in the use of machine learning models for mushroom farming, comprehension of the evaluation techniques utilized, selection of data sources, and knowledge of current methodologies and learning strategies in machine learning as they pertain to agriculture. Overall, this review provides valuable insight into the everyday practices of machine learning in the context of mushroom farming. Researchers and practitioners can utilize the findings to develop effective models, evaluation techniques, and learning strategies in this field.

Bayu Priyatna, Zainab Abu Bakar, Norshuhani Zamin, Yazrina Yahya
Is ChatGPT not Appropriate for Religious Use?

Five days after the launching of ChatGPT the number of users hit one million and the investment collected hit more than twenty billion US dollars in a couple of months. Nevertheless, the public reaction to the surprise launching of ChatGPT met with mixed feelings. Some paraded the release with positive reactions and considered it as the breakthrough of the year. Some consider it as faux science and an incompetent linguistic system. Thousands of public figures signed an open petition for immediate pause of AI experiments like ChatGPT. It is likened to the Manhattan Project in the sudden invention of the atomic bomb without much thought given on its impact. From the perspective of Islamic world, the Malaysian former minister of religious affairs has announced that ChatGPT is not appropriate to be used as reference for religious matters. Many have written and commented on the shortcomings of ChatGPT, such as, suffering from the hallucination and lack of traceable classical reasoning mechanism. It is not able to track and explain the sources of knowledge that are used to derive and support the conclusions made. This paper is to present the results obtained from ChatGPT on some WH questions and make assessment on their factual accuracy. A data set, obtained from previous research on Al-Quran knowledge base system, is used to evaluate the performance of ChatGPT. These experiments will provide some concrete technological reasons to support or reject the view made by the former Malaysian religious minister whether ChatGPT is not appropriate for use in religious matters.

Tengku M. T. Sembok, Sharyar Wani
A Visual-Based Energy Efficient Chatbot: Relationship between Sentiment Analysis and Customer Satisfaction

The evolution of Chatbots today has been seen to be popular in various service sectors such as education, business, as well as banking. The assistance provided by the system has been able to ease the tasks that otherwise need to be done by human agents. This paper highlights the development of the visual-based energy efficient Chatbot (VBE2Chatbot), testing, and explores the changes of sentiment projected by the end user during the pre-survey stage and post-survey stage. The visual-based and energy efficient Chatbot system refers to one that is designed using visual elements (such as images, videos, or graphical interfaces) to enhance user experience whilst being optimized for energy efficient (optimising code, server-side processing, smart wake-up and sleep modes and caching). This paper also highlights the findings of sentiment analysis based on customer satisfaction. On the whole, findings of the study showed positive results on its satisfaction based on the design and development of a visual-based energy efficient Chatbot (VBE2Chatbot). More work needs to be conducted to integrate AI, visual elements, and energy efficient elements into the VBE2Chatbot.

Nurul Muizzah Johari, Halimah Badioze Zaman, Hanif Baharin, Puteri N. E. Nohuddin
Abstractive Summarization Evaluation for Prompt Engineering

The task of summarizing large documents for easier and faster readability is widely acknowledged and a standard task in the field of Natural Language Processing. The metrics that are used to measure the working of this task are based on statistical measures such as n-grams and Longest Common Subsequences. Abstractive summarization is a type of automatic text summarization which refers to creating the summary from the main document without entirely copying words from the original document. With the advent of Deep learning architectures, abstract summarization has increased in popularity. The Large language models which are used for abstractive text summarization need proper prompts to generate summary. Engineering proper prompts is important as the quality of the summary generated depends on the prompt. In this paper, an abstractive measure of similarity is proposed where the textual similarity is measured by using Euclidean distance to compare a Principal Component based transformed BERT Embedding vector of the document and the summary. The metric is used to create prompts for Generative Pretrained Transformer and Text to Text Transfer Transformer models which are standard state-of-the-art language models. The summary generated shows significant improvement and the prompts generated by using the abstractive comparison metric are seen to have the perplexity almost the same as the document thus promising better summarization results.

Shayak Chakraborty, Partha Pakray
Fuzzy Soft Set Based Classification for Rock Dataset

One of the main tasks in geological studies is rock classification. To examine rock samples in this classification usually requires a human expert. Thus, the igneous rocks’ classification task will become challenging because of igneous rocks’ diverse composition. One data mining technique based on Fuzzy soft set can be used for classification. Several similarity measures have been proposed on the fuzzy soft set. In this paper, we conduct an experiment to explore the fuzzy soft set classifier applying several measurement to calculate the similarity, i.e., generalized fuzzy soft sets, similarity based on matching function, similarity based on set theoretic approach, similarity measure based on distance. The classification of igneous rocks is carried out in this experiment based on their chemical composition and compared it in terms of accuracy, precision, and recall. According to our simulation results, the Euclidean distance still outperforms to another measure in terms of classification accuracy, precision, and recall.

Rahmat Hidayat, Azizul Azhar Ramli, Mohd Farhan Md. Fudzee, Iwan Tri Riyadi Yanto
A Diabetes Prediction Model with Visualized Explainable Artificial Intelligence (XAI) Technology

Diabetes is a group of non-communicable diseases (NCD) that cannot be cured by current medical technologies and can lead to various serious complications. Significantly reducing the severity of diabetes and its associated risk factors relies on accurate early prediction. Some machine learning algorithms have been developed to assist in predicting diabetes, but their predictions are not always accurate and often lack interpretability. Therefore, further efforts are required to improve these algorithms to achieve the level of clinical application. The aim of this paper is to find a high-performance and interpretable diabetes prediction model. Firstly, the dataset is subjected to necessary preprocessing, including missing value imputation using K-nearest neighbors (KNN) and data balancing using adaptive synthetic sampling (ADASYN). Then, with 10-fold cross validation, the predictive performance of six machine learning algorithms is compared in terms of accuracy, precision, recall, and F1 score. Finally, the prediction results are globally and locally explained using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). The experimental results demonstrate that the eXtreme Gradient Boosting (XGBoost) algorithm provides the best predictive performance. The visualized eXplainable Artificial Intelligence (XAI) techniques offer valuable explanatory information, helping healthcare professionals and patients better understand the risk and prediction results of diabetes.

Yanfeng Zhao, Jun Kit Chaw, Mei Choo Ang, Marizuana Mat Daud, Lin Liu
Advances in Visual Informatics
Halimah Badioze Zaman
Peter Robinson
Alan F. Smeaton
Renato Lima De Oliveira
Bo Nørregaard Jørgensen
Timothy K. Shih
Rabiah Abdul Kadir
Ummul Hanan Mohamad
Mohammad Nazir Ahmad
Copyright Year
Springer Nature Singapore
Electronic ISBN
Print ISBN

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