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3rd International Conference on Creative Communication and Innovative technology, ICCIT 2024

作者： Zakaria, Noor Azura Syazwana Tajuddin, Nur Sufea Mohd Supian, Nur Faraayuni Dhiya'ul Hafidh Fatah Yasin, Mohammad Lutfiani, Ninda Ahmad, Nurul Shuhada International Islamic University Malaysia Department of Computer Science Kulliyyah of Information and Communication Technology Kuala Lumpur Malaysia Studies Kolej Universiti Islam Perlis Faculty of Islamic Department of Usuluddin Perlis Malaysia University of Raharja Faculty of Economics and Business Tangerang Indonesia Senze for Mine Therapy Centre Perlis Malaysia

ISBN: (纸本)9798350367492

People with ASD typically have difficulty with social communication and interaction, learning disabilities and difficulty paying attention. Technologies currently being developed for Autism Spectrum Disorder (ASD) focus more on children's behavioral development. Parents and caregivers of children with autism face greater challenges than typically developing children. In addition, parents with ASD children experienced serious psychological problems such as anxiety and depression, especially during Covid-19, as their children were limited to attend therapy sessions that usually take place in the therapy center. It is also a challenge for parents to cope with their children daily. Hence, supporting them to manage this issue through a telehealth platform is crucial to improve their psychological well-being. Therefore, the objective of this research is to identify the features related to telehealth for ASD children that were used in the previous study. The literature search was conducted using the online database which is IEEE Xplore. The number of 35 studies was reduced to 9 after conducting the screening processes. The final 9 studies were selected that highlighted the features of telehealth for ASD children. Therefore, the expected outcome from this research is to classify all the identified features and modules into their specific categories. It is hoped that further studies may lead to an effective digital intervention platform for better development of ASD children, which could assist parents and caregivers manage their ASD children in behavioral, communication, educational, and other aspects. © 2024 IEEE.

关键词： Mapping

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A Fine-Tuned Large Language Model for Domain-Specific with Reinforcement Learning

A Fine-Tuned Large Language Model for Domain-Specific with R...

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Creative Communication and Innovative technology (ICCIT), IEEE

作者： Amelia Ritahani Ismail Amira Shazleen Aminuddin Afiqa Nurul Noor Azura Zakaria Wafa Haussain Nasser Fadaaq Department of Computer Science Kulliyyah of Information and Communication Technology International Islamic University Malaysia Kuala Lumpur Malaysia Faculty of Computer Studies Arab Open University Riyadh Saudi Arabia

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

Large Language Models (LLMs) like GPT-3 and BERT have significantly shown advancement in natural language processing by providing robust tools for understanding and generating human languages. However, their broad but shallow knowledge across many domains often leads to less effective performance in domain-specific tasks, where detailed and special-ized knowledge is needed. To address this limitation, this paper investigates the effectiveness of fine-tuning LLMs for specific domains. The approach incorporates reinforcement learning to integrate user feedback, allowing the model to dynamically adjust and refine its responses. This will ensure the model adapts iteratively, improving communication and interaction with users. The fine-tuned model’s performance is evaluated using two domain-specific datasets—medical and dental. Evaluation metrics such as Levenshtein distance and cosine similarity are used to assess the textual accuracy and semantic relevance of the fine-tuned model. The results from the dental the medical datasets indicate a low level of textual differences and strong semantic alignment, respectively. These suggest that the fine-tuned model effectively processes and preserves the integrity of domain-specific content with the potential of fine-tuning LLMs to enhance their applicability in specific domains.

关键词： Performance evaluation Adaptation models Accuracy Large language models Semantics Refining Reinforcement learning Natural language processing Dentistry

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Applied Gamification to Enhance Customer Loyalty for Fintech Industry 8

Applied Gamification to Enhance Customer Loyalty for Fintech...

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8th IEEE International Conference on Engineering Technologies and Applied sciences, ICETAS 2023

作者： Qureshi, Muhammad Ali Ghulamani, Sumbul Abdallah, Ssekamanya Siraje Thartori, Voltisa Khowaja, Kamran Mahdi, Ghulam Shah, Asadullah Szabist University Department of Computer Science Hyderabad Pakistan Department of Educational Psychology and Counseling Kulliyyah of Education Malaysia Unitar International University Petaling Jaya Malaysia Kimberley Lark Corporation RoswellGA United States Kulliyah of Information and Communication Technology International Islamic Malaysia

ISBN: (纸本)9798350327090

In this study, a mobile application prototype for fintech industry is proposed which aims to addresses users' problems and engage them based on incentivized mechanism through applied gamification used for the users. Users will perform transactions and in return get some points as rewards, which they can use for cashback rewards or send these points to someone they want. For this research, Figma is used as a tool for designing wireframes and prototypes which later included in UseBerry to perform usability tests from users to collect their responses. Around 50 participants have been invited to collect their responses based on using the prototype and answering a few questions based on that. Results have shown an enormously positive response from users who really want to get incentives through their daily transactions and can get whatever benefits they are provided. However, this research is limited to a few features and more features and testing can be done based on the current response from the users which will directly impact user's daily life and improve conversions for the banking and fintech industry. © 2023 IEEE.

关键词： Software prototyping

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Integration and Validation of a Robust Process for Cloud Applications

Integration and Validation of a Robust Process for Cloud App...

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IEEE International Conference on Engineering Technologies and Applied sciences (ICETAS)

作者： Sheeba Shahid Sven Eselgrimm Asadullah Shah Ghulam Mahdi Kulliyyah of Computer Science and Information Technology International Islamic University Malaysia ORBIT Gesellschaft für Applikations-und Informationssysteme mbH Germany Kulliyyah of information and Communication Technology International Islamic University Malaysia Kimberly Clark Corporation Roswell GA USA

The development and administration of software applications have undergone a transformation because of the introduction of cloud computing. Flexible, scalable, and cost-effective Software-as-a-Service (SaaS) applications are hosted on scalable cloud infrastructures. However, there are additional difficulties in integrating and evaluating cloud apps, especially given the various control system features of private, public, and hybrid cloud models. The integration and validation process for cloud apps is examined in this research article along with methods for resolving related problems. The paper outlines the identification, connection, data mapping, and testing processes of integration, which is the merging of systems. It makes a distinction between integration between private and public clouds. Cloud-based apps are validated to make sure they adhere to performance, security, and reliability standards. It includes a discussion of testing techniques, including load and stress testing. Lack of control over the underlying infrastructure, changing environments, and shared infrastructure are difficulties in validation. A thorough validation strategy that includes establishing requirements, developing test plans, choosing suitable validation tools, and iterating the validation process is recommended to overcome these challenges. Organizations can profit from cloud computing by adopting a systematic and effective validation approach to ensure their applications match the essential requirements, regardless of the difficulties in integrating and validating cloud apps.

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Enhanced EEGNet Optimization Using Lightweight Deep Neural Network for Detecting Human Stress Levels from Raw EEG Signals Dataset

Enhanced EEGNet Optimization Using Lightweight Deep Neural N...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Silabdi Hana Raini Hassan Ahmed Rimaz Faizabadi Abdullah Gubbi Mohammed Zakir Bellary V Manjula Department of Computer Science Kulliyyah of Information & Communication Technology International Islamic University Malaysia Kuala Lumpur Malaysia Department of Computer Science Kulliyyah of Information & Communication Technology International Islamic University Kuala Lumpur Malaysia Centre for Unmanned Technologies International Islamic University Malaysia Kuala Lumpur Malaysia Department of Electronics and Communication Engineering Bearys Institute of Technolgy (VTU) Mangalore India Department of Artificial Intelligence and Machine learning P.A.College of Engineering(VTU) Mangalore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Accurate stress detection is essential in neuroscience and healthcare since human stress levels have a substantial impact on general well-being. Although EEG-based stress detection is a popular non-invasive method, real-time applications are limited by the high processing costs of classic feature extraction techniques. This work removes the requirement for intricate feature extraction by presenting an efficient EEGNet framework for real-time stress prediction utilizing raw EEG signals. The suggested approach improves accuracy and efficiency by utilizing Z-score normalization and adjusting batch size. Compared to traditional CNNs, the lightweight EEGNet architecture's depth-wise and separable convolutions ensure computational efficiency by reducing trainable parameters by a factor of ten. The proposed method outperformed current EEGNet-based techniques in stress identification by 5% to 18%. Furthermore, the improved system efficiently handles raw EEG signals, which is suitable for real-time, resource-constrained scenarios. The results shows the effectiveness of model-centric optimization in deep learning and open the door to the creation of more effective stress detection techniques. This work provides a workable solution for real-time non-invasive stress monitoring, highlighting the significance of lightweight frameworks customized for particular applications.

关键词： Deep learning Accuracy Feature extraction Brain modeling Electroencephalography Real-time systems Data models Stress Optimization Monitoring

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IoT-based Heartbeats Monitoring System

IoT-based Heartbeats Monitoring System

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Mohamad Asyraf Noorza Bin Mohd Tamron Zainab S. Attarbashi Mustafa Ali Abuzaraida Nesrine Atitallah Saman Iftikhar Dini Oktarina Dwi Handayani Atikah Balqis Binti Basri Kulliyyah of Information & Communication Technology International Islamic University Malaysia Selangor Malaysia Department of Computer Science Faculty of Information & Communication Technology International Islamic University Malaysia Selangor Malaysia Computer Science Department Faculty of Information Technology Misurata University Libya Faculty of Computer Studies Arab Open University Saudi Arabia Department of Information Systems Faculty of Information and Communication Technology International Islamic University Malaysia Selangor Malaysia

The advent of the Internet of Things (loT) has opened up a realm of possibilities in developing globally accessible systems and devices. With the current state of internet speeds, these systems offer real-time control, making them more powerful than ever. Simultaneously, the healthcare sector has been witnessing notable advancements, but it also grapples with an increasing number of health issues, with heart problems standing out as a prominent concern in many countries. These problems manifest in various symptoms, some of which can lead to more serious conditions, such as irregular heartbeats. In response to these challenges and leveraging cutting-edge technology, researchers have embarked on endeavors to enhance healthcare devices and render them more readily available to medical professionals. This paper explores the merging of loT technology with healthcare, specifically focusing on the development of a system designed to monitor and record a patient’s heartbeat. This is achieved through the deployment of a Heartbeat/SpO2 Sensor, and the data is seamlessly transmitted to a mobile application accessible remotely. The outcomes of this study demonstrate that the heartbeat data captured by Arduino is effectively transmitted to the cloud, meeting the system’s predefined objectives. This represents a significant stride in harnessing loT’s potential to enhance healthcare practices and patient monitoring.

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Shaping the Digital Future of Civil Service: An Assessment of Digital Transformation and Data science Competencies 8

Shaping the Digital Future of Civil Service: An Assessment o...

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8th IEEE International Conference on Software Engineering and computer Systems, ICSECS 2023

作者： Junaini, Syahrul Nizam Bujang, Sopian Basmawi, Nadri Aetis Fauzaman, Jusmawati Universiti Malaysia Sarawak Faculty of Computer Science and Information Technology Kota Samarahan Sarawak94300 Malaysia Universiti Malaysia Sarawak Faculty of Cognitive Science and Human Development Kota Samarahan Sarawak94300 Malaysia Leadership Institute of Sarawak Civil Service Kuching Sarawak93250 Malaysia Abdulhamid Abusulayman Kulliyyah of IRKHS International Islamic University Malaysia Kuala Lumpur50728 Malaysia

ISBN: (纸本)9798350310931

In the era of digital transformation and data proliferation, the need for effective digital competency assessment is increasingly critical. However, existing frameworks often lack comprehensive integration of key digital transformation and data science competencies necessary for roles within the civil service sector. This study introduces a robust instrument to profile competency domains critical to digital transformation and data science roles in the civil service. Leveraging a four-phase mixed-method methodology, including brainstorming, external validation, and a pilot study, the instrument was developed, validated, and tested among 30 state government servants. The reliability of the domains-Data Analytics, Data science Management, Data and Digital Architecture, and Digital Transformation-was confirmed by excellent Cronbach's Alpha values ( 0.9). Content validity was evaluated using Lawshe's Content Validity Ratio (CVR) and Index (CVI), indicating strong validity for Digital Transformation and Data and Digital Architecture domains, while suggesting refinement for Data Analytics and Data science Management. The proposed instrument, validated through self-evaluation scores, illustrates potential for career and organizational development within the civil service, emphasizing its practical value and feasibility. © 2023 IEEE.

关键词： Data Analytics

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Cloud-Based IoT System for Real-Time Harmful Algal Bloom Monitoring: Seamless ThingsBoard Integration via MQTT and REST API

Cloud-Based IoT System for Real-Time Harmful Algal Bloom Mon...

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Student Conference on Research and Development (SCOReD)

作者： Ammar Haziq Annas Ahmad Anwar Zainuddin Amir ‘Aatief Amir Hussin Nik Nor Muhammad Saifudin Nik Mohd Kamal Normawaty Mohammad Noor Roziawati Mohd Razali Dept. of Computer Science Kulliyyah of Information and Communication Technology International Islamic UniversityMalaysia Kuala Lumpur Malaysia Kulliyyah of Science IIUM Jln Sultan Hj Ahmad Shah Bandar Indera Mahkota Institute of Oceanography & Maritime Studies Kuantan Pahang Malaysia Institut Penyelidikan Perikanan (FRI) Malaysia Bayan Lepas Malaysia

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

Harmful Algal Blooms (HABs) pose a severe threat to aquatic ecosystems, drinking water supplies, and public health. Existing water quality monitoring methods are often costly and complex thus limiting their accessibility and scalability for continuous monitoring. This work proposes a scalable, cloud-based IoT solution for real-time water quality monitoring, specifically targeting HAB detection using cost-effective sensors. The system architecture utilizes Microsoft Azure hosted ThingsBoard platform for data management, integrating telemetry data from sensors via MQTT and REST API protocols to enable reliable, low-latency data transmission and storage. Through a combination of numeric and graph-based dashboards, end users can monitor both real-time and historical data, supporting early anomaly detection and rapid response. This work uses a cloud-based system to manage IoT devices, ensures secure data transmission, and displays the data on ThingsBoard without needing extra cloud services. The proposed system is cost-effective, flexible, and easily extendable to different monitoring environments. In addition, it could be contributing to proactive water resource management and public health protection against HABs threats.

关键词： Data visualization Water quality Real-time systems Sensor systems Sensors Internet of Things Telemetry Public healthcare Monitoring Water resources

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Machine Learning Classification Model for Identifying Internet Addiction Among University Students

Machine Learning Classification Model for Identifying Intern...

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computer Technologies (ICCTech), International Conference on

作者： Tan Hui Theng Mun Hou Kit Dini Handayani Department of Mechatronic and Biomedical Engineering Faculty of Engineering and Science Universiti Tunku Abdul Rahman Selangor Malaysia Computer Science Department Kulliyyah of Information and Communication Technology International Islamic University Malaysia Selangor Malaysia

In this era of globalization, Internet addiction is a concerning issue, especially among university students as they are required to use the internet for academic purposes. However, things might go wrong when they are addicted to the Internet as the Internet does not only provide knowledge but also entertainment such as music, videos, games, social media, etc. Internet addiction was exposed to the public when Young introduced Internet addiction in her study as well as an assessment for Internet addiction known as Young's Internet addiction test (IAT) which is a questionnaire. Nonetheless, there are some issues associated with the questionnaire regarding the integrity and literacy of the participants as well as the experience of the specialist which might introduce inconsistencies in the assessment of one's Internet addiction level. Hence, the machine learning algorithm is introduced to replace the conventional assessment method for Internet addiction. In this study, three machine learning models are developed and compared. The three models include convolutional neural network (CNN), K-nearest neighbours (KNN), and logistic regression (LR). The low Alpha power band of the EEG data is transformed into spectrograms and utilized as the input for the machine learning models. The spectrograms are presented as images and fed into the CNN model. On the other hand, as KNN and LR could not take in images as the input data, the magnitude of each frequency in every time segment of each spectrogram is computed and fed into the KNN and LR. The results show that CNN gives the best performance in terms of overall accuracy, precision, recall, and F1-score, while KNN gives the most consistent performance.

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Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions

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Complex and Intelligent Systems 2023年第4期9卷 4705-4731页

作者： Alamoodi, A.H. Zaidan, B.B. Albahri, O.S. Garfan, Salem Ahmaro, Ibraheem Y. Y. Mohammed, R.T. Zaidan, A.A. Ismail, Amelia Ritahani Albahri, A.S. Momani, Fayiz Al-Samarraay, Mohammed S. Jasim, Ali Najm R.Q.Malik Faculty of Computing and Meta-Technology (FKMT) Universiti Pendidikan Sultan Idris (UPSI) Perak Malaysia Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliu 64002 Taiwan Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Department of Computing Faculty of Arts Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim Malaysia Computer Science Department College of Information Technology Hebron University Hebron Palestine Department of Computing Science Komar University of Science and Technology (KUST) Sulaymaniyah Iraq SP Jain School of Global Management Sydney Australia Department of Computer Science Kulliyyah of Information and Communication Technology International Islamic University Malaysia Kuala Lumpur Malaysia Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq E-Business and Commerce Department Faculty of Administrative and Financial Sciences University of Petra Amman 961343 Jordan Foundation of Alshuhda Baghdad Iraq Medical Intrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq

When COVID-19 spread in China in December 2019, thousands of studies have focused on this pandemic. Each presents a unique perspective that reflects the pandemic’s main scientific disciplines. For example, social scientists are concerned with reducing the psychological impact on the human mental state especially during lockdown periods. computer scientists focus on establishing fast and accurate computerized tools to assist in diagnosing, preventing, and recovering from the disease. Medical scientists and doctors, or the frontliners, are the main heroes who received, treated, and worked with the millions of cases at the expense of their own health. Some of them have continued to work even at the expense of their lives. All these studies enforce the multidisciplinary work where scientists from different academic disciplines (social, environmental, technological, etc.) join forces to produce research for beneficial outcomes during the crisis. One of the many branches is computer science along with its various technologies, including artificial intelligence, Internet of Things, big data, decision support systems (DSS), and many more. Among the most notable DSS utilization is those related to multicriterion decision making (MCDM), which is applied in various applications and across many contexts, including business, social, technological and medical. Owing to its importance in developing proper decision regimens and prevention strategies with precise judgment, it is deemed a noteworthy topic of extensive exploration, especially in the context of COVID-19-related medical applications. The present study is a comprehensive review of COVID-19-related medical case studies with MCDM using a systematic review protocol. PRISMA methodology is utilized to obtain a final set of (n = 35) articles from four major scientific databases (scienceDirect, IEEE Xplore, Scopus, and Web of science). The final set of articles is categorized into taxonomy comprising five groups: (1) diagnosis

关键词： COVID-19 Data privacy Federated learning Monoclonal antibodies Multi-criterion decision making Treatment

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