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Assistive technology for Children with Learning Disabilities: A Systematic Literature Review 17

Assistive Technology for Children with Learning Disabilities...

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17th International Conference on Ubiquitous information Management and Communication, IMCOM 2023

作者： Abd Khalid, Ahmad Haiqal Mohkhlas, Nur Nazihah Zakaria, Noor Azura Rejab, Mazidah Mat Karim, Ruwinah Abdul Suharsiwi, Suharsiwi International Islamic University Malaysia Department of Computer Science Kulliyyah of Information and Communication Technology Gombak Malaysia Universiti Tun Hussien Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Penawar Special Learning Centre Kuala Lumpur Malaysia Universitas Muhammadiyah Jakarta Department of Elementary Education Jakarta Indonesia

ISBN: (纸本)9781665453486

The enhancement of technology is well-developed and assistive technology is one of the foremost that has been used significantly. Individuals with disabilities use assistive technology to perform the desired activity that would otherwise be difficult or impossible. Mobility devices such as walkers and wheelchairs, as well as hardware, software, and peripherals that assist people with disabilities in accessing computers or other information technologies, are examples of assistive technology. Learning disabilities are caused by genetic and neurobiological factors that affect brain functioning, influencing one or more cognitive processes related to learning. This paper discusses a comprehensive systematic literature review (SLR) of the assistive technology (AT) for children with learning disabilities with combination of Kitchenham and PRISMA approach. This paper discusses on the assistive technologies developed for children with learning disabilities according to its type, purposes, techniques used, and platform or delivery system used to host the developed assistive technologies for children with learning disabilities. © 2023 IEEE.

关键词： Assistive technology

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Machine Learning Classification Model for Identifying Internet Addiction among University Students 2

Machine Learning Classification Model for Identifying Intern...

引用

2nd International Conference on computer Technologies, ICCTech 2023

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

ISBN: (纸本)9781665455824

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. © 2023 IEEE.

关键词： Convolutional neural networks

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Performance Evaluation of Medical Image Denoising using Convolutional Autoencoders

Performance Evaluation of Medical Image Denoising using Conv...

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Creative Communication and Innovative technology (ICCIT), IEEE

作者： Amelia Ritahani Ismail Muhammad Zulhazmi Rafiqi Azhary Nor Aiman Zaharin Noor Azwan Ahsiah Ismail Nisrin Amer Alsaiari Department of Computer Science Kulliyyah of Information Communication Technology International Islamic University Malaysia Kuala Lumpur Malaysia Department of Computer Science Kulliyyah of Information and Communication Technology International Islamic University Malaysia Kuala Lumpur Malaysia Faculty of Computer Studies Arab Open University Saudi Arabia

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

Convolutional Autoencoders (CAEs) are neural network architectures specifically designed for image-processing tasks. CAEs also show a great performance in image denoising as it has the capability to efficiently eliminate noise from images while retaining crucial features and structural integrity. However, the effectiveness of CAEs in denoising tasks can be limited by the specific characteristics and complexities present in the images; for example, different intensities and distribution of noise will lead to challenges in achieving consistent and highquality denoising results across diverse datasets like medical images. This paper, therefore, explores the capabilities of CAEs in effectively denoising images corrupted by varying levels of Gaussian noise. The CAE’s performance is evaluated under different levels of noise severity using two datasets: PathMNIST and Brain MRI Scan images. Findings indicate that at a low noise level, CAEs achieve impressive results with PathMNIST images, demonstrating high image fidelity. However, Brain MRI Scan images show moderate denoising suggesting opportunities for improvement. As noise severity increases, both datasets experience reduced denoising performance, highlighting the need for enhanced CAE configurations to maintain image quality in critical applications like medical diagnosis.

关键词： Image quality PSNR Accuracy Convolution Magnetic resonance imaging Noise Noise reduction Medical diagnostic imaging Noise level Image denoising

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Sustainable IoT-Based Data Logger System for Marine Applications Using Renewable Energy 8

Sustainable IoT-Based Data Logger System for Marine Applicat...

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

作者： Faizabadi, Ahmed Rimaz Adnan, Khandakar Ameem Norazaruddin, Muhammad Aiman Mohd Zaki, Hasan Firdaus Haziq Bin Abang Akral, Abang Mohd Mohammed Shweesh, Osamah Ebrahim Kulliyyah of Engineering International Islamic University Malaysia Department of Mechatronics Engineering Kuala Lumpur Malaysia Kulliyyah of Information and Communication Technology International Islamic University Malaysia Department of Computer Science Malaysia Kulliyyah of Engineering International Islamic University Malaysia Department of Mechatronics Engineering Malaysia Hidrokinetik Technologies Sdn Bhd Autonomous Marine Systems Kuala Lumpur Malaysia

ISBN: (纸本)9798350327090

Navigational data is vital for various fields of applications. Satellite communications are costly while Automatic Identification System (AIS) data has considerable interval which can make crucial incidents during the voyage unrecoverable. The storage attached to the existing loggers are limited and the GSM900 communication link is slow. This paper presents the design and implementation of an Internet of Things (IoT) based data logging system tailored for marine applications that improves cost, power consumption and durability while providing efficient and reliable data logging capabilities in the harsh marine environment. A programmable multithreaded low-power UR32 industrial router from Milesight replaces the simple Microcontroller Unit (MCU), providing a dual-SIM 4G connectivity failsafe. The proposed design minimizes the required components and enables seamless data acquisition and remote monitoring. Using photovoltaic (PV) solar panels, our logger can run entirely on renewable energy giving an endurance of almost 24 hours on a single charge. It can log data from various sensors over serial communication ports with uninterrupted log intervals. The communication link is improved from 2kbps to more than 7 mbps while storage is improved by 32 times. The complete system is packaged into an IP68 rating enclosure. The proposed solution contributes to cost-effective and energy-efficient IoT-based data logging systems for marine applications, enabling better understanding and managing marine resources and ecosystems using renewable energy. © 2023 IEEE.

关键词： Internet of things

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Interpretation of Machine Learning Model Using Medical Record Visual Analytics 8th

Interpretation of Machine Learning Model Using Medical Recor...

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8th International Conference on Computational science and technology, ICCST 2021

作者： Khalid, Nur Hidayah Mohd Ismail, Amelia Ritahani Aziz, Normaziah A. Department of Computer Science Kulliyyah of Information and Communication Technology International Islamic University Malaysia PO. Box 10 Kuala Lumpur50728 Malaysia

ISBN: (纸本)9789811685149

The state of the art of medical application that being implemented are mostly based on common machine learning model. Nevertheless, one of the drawbacks of the practice of medical diagnosis is the lack of explanation on the proposed solution, which is also known as a black box, without knowing the internal decision process between the input and output. It will lead to untrustworthiness and difficult to understand by the medical expert. They are questioning how the complexity of machine learning methods decide on the output without clear and understandable explanations. Moreover, in machine learning field the characteristic of a black box model may lead to biased data analysis and incorrect output decisions. There is work that uses visual analytics techniques to interpret the machine learning output to ease the understanding of medical experts. However, the functionality of existed and combined visual analytics techniques is not sufficient to visualized and interpreted the output of machine learning operation. Other visual analytic techniques faced the same problem, unreliability to produce strong reason on the output when working with complex machine learning models. This paper analyzed several visual analytics approaches instantiated in machine learning algorithm for medical record analytics. The motivation of this paper is to allow medical experts to understand the interpretation of a black box machine learning model in predicting medical outcome. This paper studied on the effectiveness of visual analytics techniques to identify the appropriate technique to be instantiated to the machine learning algorithm to further elaborate the results obtained by demonstrating transparency, interpretability and explainability of the machine learning algorithm. The visual analytics that are been studied are Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP). Based on the comparison of LIME and SHAP methods, this paper found that SHAP

关键词： Machine learning

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Hybrid Agile Model, the Synergy of Waterfall Model and Scrum in a Software Project

Hybrid Agile Model, the Synergy of Waterfall Model and Scrum...

引用

information technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Norzariyah Yahya Abdul Muizz Abdul Salam Siti Sarah Maidin Normi Sham Awang Abu Bakar Department of Computer Science Kulliyyah of Information and Communication Technology International Islamic University Malaysia Kuala Lumpur Malaysia Faculty of Data Science and Information Technology (FDSIT) INTI International University Malaysia

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

Hybrid agile is one of the recent software engineering process model used by a software engineering team in a software development project. A hybrid agile model is used by software engineering team to meet the requirements of a software project. However, what does the hybrid agile model used by a software engineering team look like? A qualitative study was conducted to answer this question, and a series of interviews were chosen as a data collection instrument. The interview participants are practitioners involved in software projects using hybrid agile. The recorded interviews are transcribed and analysed using thematic analysis. The finding shows that the software engineering team uses the Waterfall model in the planning, design, deployment and maintenance phases, while scrum in the development and testing phases. The results have led to the hybrid agile model presented in this study, which leverages the strengths of each model to ensure the completion of a software project. Future iterations of this study will assess a project's software quality, comparing the proposed model with plan-driven and scrum.

关键词： Instruments Software quality Planning Maintenance Scrum (Software development) Interviews information technology Software development management Software engineering Testing

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Enhanced EEGNet Optimization Using Lightweight Deep Neural Network for Detecting Human Stress Levels from Raw EEG Signals Dataset 3

Enhanced EEGNet Optimization Using Lightweight Deep Neural N...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

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

ISBN: (纸本)9798331508456

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. © 2025 IEEE.

关键词： Deep neural networks

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Evaluating Usability of Learning Disability Assessment System 8

Evaluating Usability of Learning Disability Assessment Syste...

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8th IEEE International Conference on Computing, Engineering and Design, ICCED 2022

作者： Fikriah Hamum Junaidi, Putri Sakinah Amira Imi, Nur Zakaria, Noor Azura Abdul Karim, Ruwinah Lubis, Muharman Rejab, Mazidah Mat International Islamic University Malaysia Kulliyyah of Information and Communication Technology Department of Computer Science Kuala Lumpur Malaysia Penawar Special Learning Centre Kuala Lumpur Malaysia Telkom University Information System Department Bandung Indonesia Universiti Tun Hussien Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia

ISBN: (纸本)9781665453899

The Learning Disability Assessment System (LeDAS) entails crucial decision making to produce a diagnosis referring to the information inserted by the therapists and parents. In this manner, accuracy of the information system is a pivot to generate an effective system. Research evaluation of LeDAS is necessary due to the fact that it is a recently developed system with over 3,000 patients currently using it thus it is imperative to evaluate the system to measure its usability. The purpose of this research is to evaluate the usability of the developed LeDAS web-based system in order to ensure the system can fulfill the user's satisfaction and be usable. The conducted survey targets therapists and parents to collect data. The data produced from this study will be used to provide insight into the need of furthering proficiency in generating a satisfactory system. This evaluation analyzed using the descriptive statistic, exploratory factor analysis (EFA), reliability testing and hypothesis testing. The result showed that 5 hypotheses were supported. © 2022 IEEE.

关键词： Factor analysis

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Deep Radiomics: A Picture’s Worth a Thousand Words - A Review

Deep Radiomics: A Picture’s Worth a Thousand Words - A Revi...

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Humanitarian technology Conference (KHI-HTC), IEEE Karachi Section

作者： Syeda Meraj Asadullah Shah Ahsiah Ismail Tengku Mt Sembokb Syed Shadab Syed Aftab Syeda Nageena Nusratullah Khan Kajal Nusratullah Department of Information Systems Kulliyyah of Information and communications Technology Selangor Malaysia Department of Computer Science Kulliyyah of Information and communications Technology Selangor Malaysia Department of Information Technology Al Musanna College of Technology City Oman Department of Aeronautical Engineering Technology Higher Colleges of Technology Al-Ain UAE Department of Health and Family welfare Karnataka Koppal India Computer Science and engineering department Yanbu Industrial College Royal Commission Yanbu KSA

In recent decades, the prevalence of diseases has witnessed a notable surge. The increasing health challenges have led to a greater use of advanced diagnostic machines for detecting and understanding these disease conditions. The diagnostic modalities, such as X-rays, CT scans, MRI scans, and nuclear medicine imaging, generate a wealth of interpretative data through medical images. Deep Radiomics aims to improve the efficiency and accuracy of image interpretation by automating the process of feature extraction, allowing for more comprehensive insights for advancing personalized medicine and improving outcomes in medical imaging. Enhance disease detection, diagnosis, and subsequent treatment strategies.

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Utilizing Different Edge Detection and Preprocessing Techniques to Improve the Accuracy of Durian Cultivar Detection using Convolutional Neural Networks

Utilizing Different Edge Detection and Preprocessing Techniq...

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

作者： Amir ‘Aalieff Bin Amir Hussin Harith Syazwan Bin Haril Muzammil Muhammad Afiq A. Bin Shaharuddin Amelia Ritahani Binti Ismail Ahmad Anwar Bin Zainuddin Department of Computer Science Kulliyyah of Information and Communication Technology International Islamic University Malaysia (IIUM) Gombak Malaysia

Using machine learning to recognize images of fruits or plants is a very practical and focused application. When using machine learning principles to classify specific fruit varieties, as opposed to traditional approaches, a higher level of accuracy is promised but also requiring more training data. Particularly in situations where different cultivars of the same fruit family are valued differently, the capacity to recognize and classify diverse cultivars of a fruit family is crucial. The objective of this study is to develop a practical method for classifying the many cultivars of Durio zibethinus, also known as Durian, based on the visual traits of the crop using Convolution Neural Networks (CNNs) to improve the accuracy and speed of cultivar recognition. Utilizing a database of images with tagged Durian cultivars. Images from two high-value classes of Durian cultivars are utilized to train the neural network. The first phases in the research project involve pre-processing and converting the images using Canny, Laplacian, Sobel on X-axis, and image binarization. Next up are model training, model validation, and network architecture development. Finally, they were combined as an ensemble machine learning method using majority voting to examine the potential for enhancing prediction performance. Laplacian edge detection was found to yield the best results of the four studied approaches, while ensemble methods gave more accurate results than a single method.

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