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

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

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.

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Smart Seed Classification System based on MobileNetV2 Architecture 2

Smart Seed Classification System based on MobileNetV2 Archit...

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2nd International Conference on Computing and information technology, ICCIT 2022

作者： Hamid, Yasir Wani, Sharyar Soomro, Arjumand Bano Alwan, Ali A. Gulzar, Yonis Abu Dhabi Polytechnic Information Security and Engineering Technology Abudhabi United Arab Emirates International Islamic University Malaysia Kulliyyah of Information and Communication Technology Department of Computer Science Gombak Malaysia College of Business Administration King Faisal University Department of Management Information Systems Al-Ahsa Saudi Arabia School of Theoretical and Applied Science Ramapo College of New Jersey MahwahNJ United States College of Business Administration King Faisal University Department of Management Information Systems Al-Ahsa Saudi Arabia

ISBN: (纸本)9781665436052

The agricultural transformation in the last decade using artificial intelligence has led to significant gains in productivity and profitability. The traditional machine learning approaches present inherent limitations in extracting features and information from image data. Deep learning techniques, particularly CNN's, help to overcome these limitations due to their multi-level architecture. Various deep learning applications in agriculture include crop disease identification, fruit classification, and germination rate monitoring. Seed image analysis is considered a significant task for the preservation of biodiversity and sustainability. This research uses MobileNetV2, a deep learning convolutional neural network (DCNNs) for seed classification. This model has been preferred due to its simple architecture and memory-efficient characteristics. A total of 14 different classes of seeds were used for the experimentation. The results indicate accuracies of 98% and 95% on training and test sets, respectively. © 2022 IEEE.

关键词： Seed

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Risk Aware k-Min Search Algorithm 6

Risk Aware k-Min Search Algorithm

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6th IEEE International Conference on Engineering, Technologies and Applied sciences, ICETAS 2019

作者： Iqbal, Javeria Ahmad, Iftikhar Shah, Asadullah Asadullah, A.B.M. Kulliyyah of Information and Communication Technology International Islamic University Malaysia University of Engineering and Technology Department of Computer Science and Information Technology Peshawar Pakistan

ISBN: (纸本)9781728140827

Online search and trading algorithms are designed with the prime objective of maximizing the overall profit of investors in a financial market. Investors in any financial market tend to manage their risk levels for better overall returns by the end of investment horizon. Thus the real world applicability of such algorithms lacks the aspect of risk management policy. We consider the problem of online k-min search, where an online player (investor) wants to to acquire (buy) k units of an asset by using the minimum possible wealth. In this work, we investigate the k-min search problem and introduce the risk aware k-min search algorithm under the risk reward framework. Further, we show that the presented algorithm meets the bounds presented by AI-Binali (Algorithmica, 1999). © 2019 IEEE.

关键词： Financial markets

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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

Evaluating Usability of Learning Disability Assessment Syste...

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

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

ISBN: (纸本)9781665453905

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.

关键词： Systematics System performance Decision making Reliability Usability Testing information systems

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Medical Image Analysis using Deep Learning: A Review 7

Medical Image Analysis using Deep Learning: A Review

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7th IEEE International Conference on Engineering Technologies and Applied sciences, ICETAS 2020

作者： Nisa, Syed Qamrun Ismail, Amelia Ritahani Ali, M. A. B. Md Khan, Mohammad Shadab International Islamic University Malaysia Kulliyyah of Information and Communication Technology Department of Computer Science Kuala Lumpur50728 Malaysia

ISBN: (纸本)9780738105048

Over the recent past, deep learning is one of the core research directions which has gained a great deal of attention due to its outstanding performance in the area of medical image analysis. This paper aims to present a review of deep learning concepts related to medical imaging. We examine the use of deep learning for medical image analysis including segmentation, object detection and classification. Deep learning techniques including convolutional neural networks (CNNs), recurrent neural network (RNNs) and auto- encoder (AE) are also discussed in this paper. © 2020 IEEE.

关键词： Medical imaging

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Systematic Mapping of Telehealth Features for Autism Spectrum Disorder Children: A Preliminary Results

Systematic Mapping of Telehealth Features for Autism Spectru...

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Creative Communication and Innovative technology (ICCIT), IEEE

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

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

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.

关键词： Autism Waste materials Systematics Databases Navigation Telemedicine Psychology Medical treatment Process control Object recognition

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New design of NTRU key using QKD variants

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AIP Conference Proceedings 2024年第1期3009卷

作者： Saba Alaa Abdulwahhab Qasim Mohammed Hussien Imad Fakhri Taha Computer Science Tikrit University Tikrit-Iraq Kulliyyah of Information and Communication Technology Kuala Lumpur-Malaysia

As life developed, cryptography became crucial. As its utility is not limited to changing the structure of information and making it difficult to interpret (even if it reaches unauthorized individuals), it proves that the information is real from its source and has not been modified during the transmitting or saving process. In this research modifying the NTRU (Number Theory Research Unit) cryptosystem by employing QKD (quantum key distribution) to change the mathematical structure with a physical structure for the public as well as text encryption and decryption. As a result, to obtain higher security to detect EAV and automatically change the key if EAV detects and intercepts the data when the transmutation between ALICE and BOB is quantum-mechanically approved, the laws of quantum mechanics can be used to make cryptographic protocols that are always safe.

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3D Hologram in Education to Enhance the Learning Power of Students

3D Hologram in Education to Enhance the Learning Power of St...

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

作者： Azra Qazi Sumbul Ghulamani Voltisa Thartori Ssekamanya Siraje Abdallah Asadullah Shah Kamran Khowaja Department of Computer Science SZABIST University Hyderabad Pakistan Unitar International University Petaling Jaya Malaysia Department of Educational Psychology and Counseling Kulliyyah of Education International Islamic University Malaysia (IIUM) Malaysia Kulliyah of Information and Communication Technology International Islamic University Malaysia (IIUM) Malaysia

Integration and implementation of technological tools in educational system are useful for teaching and learning. After the global pandemic it has become necessary for the educational system to go for secure way to cater the need of learning. No doubt traditional ways are the foundation for the educational industry, but ancient ways are hindering the paths for developing deeper understanding. This paper has proposed the hologram 3D technology for making learning effective and improving long term understanding. As with the advancement in technology, several methods have served this purpose, but they always lack something. As a result, students are always left with confusion and cannot keep track of the topic. A survey was conducted from 50 students in the architecture department of Mehran University of engineering, Sindh, Pakistan. The results showed that students were able to understand more with the help of hologram technology, as compared to the traditional means of educating. They recommended it for their institute to use it. Results show that about 73% of the audience have agreed to the concept of integration of 3D Hologram with education.

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

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

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International Conference on Software Engineering and computer Systems (ICSECS)

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

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 & 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 & 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.

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