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1st IEEE Karachi Section Humanitarian technology Conference, Khi-HTC 2024

作者： Zulkifli, Zahidah Halim, Muhammad Darwish Bin Abdul Shaffei, Arief Iskandar Bin Alsaiari, Nisrin International Islamic University Malaysia Kulliyyah of Information and Communication Technology Department of Information System Malaysia Arab Open University Faculty of Computer Studies Saudi Arabia

ISBN: (纸本)9798350373042

This project focuses on the development of a website system designed to facilitate knowledge acquisition for students enrolled in the Kulliyyah of Information Communication and technology (KICT) at the International Islamic University Malaysia (IIUM). The aim is to alleviate the information overload experienced by new intake students during the Taaruf week (orientation week). Despite its establishment in 2010, KICT faces a persistent challenge where new students remain unaware of crucial system details, including rules, regulations, and academic matters. To address this issue, the project endeavors to provide a comprehensive platform enabling new students to access pertinent information and knowledge about KICT. An analysis of seven similar existing systems has been conducted, highlighting their main functions, advantages, and disadvantages. The system Development Life Cycle methodology has been selected for its practicality in guiding the project through the phases of Final Year Project (FYP) 1 and FYP2. Data collection involved surveying 30 KICT students to identify challenges faced by new students in obtaining the necessary information. Results underscore the urgent need for a technically guided application to enhance student understanding of KICT. The logical design of the app encompasses a Use Case Diagram, Use Case Narrative, Activity Diagram, Interface Flow, Low-Fidelity Design, and Interface Flow. The 'i-know' system has been developed using PHP, HTML, CSS, and Javascript, with connectivity to MySQL through phpMyAdmin for user authentication. The User Acceptance Test (UAT) was conducted during implementation, following beta testing. The paper concludes with insights into the system's effectiveness and its potential to address the identified challenges faced by new KICT students. © 2024 IEEE.

关键词： Knowledge management

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Security Assessment Algorithms for Transmission and Services in SDN-Based Mobile Networks 9

Security Assessment Algorithms for Transmission and Services...

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9th International Conference on computer and Communications, ICCC 2023

作者： Zhu, Wei Bao, Chunhui Chen, Tianxiang Zhuang, Yi Jiangsu Automation Research Institute Embedded Computer System Architecture Lianyungang China Nanjing University of Aeronautics and Astronautics Faculty of Computer Science and Technology Nanjing China

ISBN: (纸本)9798350317251

In response to the challenge of comprehensively security assessment and analysis in existing mobile networks from both upper-layer services and network transmission levels, A SDN-based security assessment model for transmission and service security in mobile networks (SAMTS) is proposed. This model introduces security assessment algorithms, in which static configuration information and dynamic abnormal information are considered comprehensively to judge the trend and the risk degree of attack when evaluating the security of network nodes. Meanwhile, the node importance of service orientation and transmission is integrated for security evaluation. The effectiveness of the proposed method is verified by simulation and comparison experiments, demonstrating its higher evaluation accuracy compared with similar algorithms. © 2023 IEEE.

关键词： Wireless networks

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Preliminary Exploration on Node-To-Node Fault Tolerance Coordination in Distributed system

Preliminary Exploration on Node-To-Node Fault Tolerance Coor...

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2023 IEEE International Conference on Computing, ICOCO 2023

作者： Saadoon, Muntadher Hamid, Siti Hafizah Ab. Sofian, Hazrina Altarturi, Hamza Nasuha, Nur Azizul, Zati Hakim Khalid, Haqi Universiti Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur50603 Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Artificial Intelligence Kuala Lumpur50603 Malaysia Universiti Putra Malaysia Faculty of Engineering Department of Computer and Communication Selangor 43400 Malaysia

ISBN: (纸本)9798350302684

Internet of Things, edge computing devices, the widespread use of artificial intelligence and machine learning applications, and the extensive adoption of cloud computing pose significant challenges to maintaining fault tolerance in distributed systems. As the volume and complexity of systems continue to grow, the probability of system failures also grows, leading to downtime and poor performance. Fault tolerance is pivotal in modern computing systems by preventing downtime and service disruptions, even during failures. However, adopting fault tolerance comes with additional resource consumption, such as memory, CPU, disk, and network bandwidth usage, impacting system performance. Balancing fault tolerance with system performance is crucial to optimize resource utilization while minimizing the costs associated with detecting and recovering from faults. This paper introduces a node-to-node fault tolerance coordination mechanism for distributed systems to facilitate the tradeoff between fault tolerance and the cost of performance penalties and resource consumption. The proposed mechanism is preliminarily implemented on HDFS cluster. Our proposal enables opportunities to minimize the cost of the fault tolerance impact on performance and resource usage compared to the existing fault tolerance coordination mechanism. © 2023 IEEE.

关键词： Fault tolerance

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Smart Contract Evaluation By Multi-Criteria Analysis: Selection Challenges And Open Issues, a Review 3

Smart Contract Evaluation By Multi-Criteria Analysis: Select...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Alshahrani, Norah M. Kiah, M.L. Mat Zaidan, B.B. University of Malaya Faculty of Computer Science & It Department of Computer Systems & Technology Kuala Lumpur Malaysia King Khalid University Department of Computer Science Abha Saudi Arabia University of Malaya Faculty of Computer Science & It Department of Computer System & Technology Kuala Lumpur Malaysia Future Technology Research Center National Yunlin University of Science and Technology Douliou Yunlin Taiwan

ISBN: (纸本)9798350305333

Smart contracts have received increasing attention in recent years for their potential to enable decentralized and automated transactions in various fields, including finance, supply chain management, and real estate. However, selecting and evaluating the appropriate smart contract for a specific application remains challenging due to the large number of available options and various criteria that need to be considered. This paper discusses the challenges and open issues in selecting and evaluating appropriate smart contracts for various applications. It proposes multi-criteria analysis (MCA) to evaluate and compare different options based on multiple criteria. The study aims to provide a comprehensive review of the current state-of-the-art in the field by considering various criteria used in the selection process, such as security, scalability, and performance. The review covers the challenges associated with critical criteria for smart contract selection, including security, privacy, efficiency, scalability, and regulatory concerns. Additionally, the paper presents various criteria used in different domains/groups to carry out various blockchain- related tasks, such as operational function, asset management, mitigation, analysis, communications, planning, and others. The criteria vary from one person to another and from application to application, and the importance level of the criteria is subjective. The paper highlights the need for more research on smart contract platforms' long-term performance and scalability and more comprehensive and objective evaluation methods for MCA of smart contract selection. © 2023 IEEE.

关键词： Smart contract

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Developing Engagement in the Learning Management system Supported by Learning Analytics

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computer systems Science & Engineering 2022年第7期42卷 335-350页

作者： Suraya Hamid Shahrul Nizam Ismail Muzaffar Hamzah Asad W.Malik Department of Information System Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Faculty of Computing and Informatics Universiti Malaysia SabahSabahMalaysia Department of Computing School of Electrical Engineering and Computer Science(SEECS)National University of Sciences and Technology(NUST)IslamabadPakistan

Learning analytics is an emerging technique of analysing student par-ticipation and *** recent COVID-19 pandemic has signiﬁcantly increased the role of learning management systems(LMSs).LMSs previously only complemented face-to-face teaching,something which has not been possible between 2019 to *** date,the existing body of literature on LMSs has not analysed learning in the context of the pandemic,where an LMS serves as the only interface between students and ***,productive results will remain elusive if the key factors that contribute towards engaging students in learning are notﬁrst identiﬁ***,this study aimed to perform an exten-sive literature review with which to design and develop a student engagement model for holistic involvement in an *** required data was collected from an LMS that is currently utilised by a local Malaysian *** model was validated by a panel of experts as well as discussions with *** is our hope that the result of this study will help other institutions of higher learning determine factors of low engagement in their respective LMSs.

关键词： Engagement analysis learning analytics learning management system student engagement

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Handwritten Digit Classification based on Ensemble Majority Voting Classifier 11

Handwritten Digit Classification based on Ensemble Majority ...

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11th IEEE International Conference on systems and Control, ICSC 2023

作者： Alsharif, Mariam Abdo, Amina Bozed, Kenz A University of Benghazi Faculty of Information Technology Benghazi Libya Collage of Computer Technology-Benghazi Departemnt of Artificial Intelligence Benghazi Libya University of Benghazi Department of Computer System Design Benghazi Libya

ISBN: (纸本)9798350304886

Handwritten digit recognition is a significant challenge in the field of machine learning, particularly for pattern recognition and computer vision applications. It has found applications in various areas, such as identifying digits on utility maps, postal mail zip codes, bank check amounts, and more. Offline handwritten digits possess distinct characteristics, including size, orientation, position, and thickness. Each person's handwriting is unique, making the classification process more difficult. Additionally, the high similarity between certain digits and the over fitting problem with high-dimensional data can increase computational time and cost. Consequently, numerous researchers have developed and implemented different machine learning algorithms to effectively address the issue of recognizing handwritten digits. This paper introduces a novel approach to improve the classification performance and accuracy of handwritten digit recognition. The proposed method utilizes an ensemble majority vote classifier, which combines three classifiers in each experiment by utilizing a range of different algorithms;Naïve Bayes (NB), Adaptive Boosting Algorithm (AdaBoost), K-Nearest Neighbors (K-NN), Multi-layer Perceptron Classifier (MLP), eXtreme Gradient Boosting algorithm (XGBoost), and Decision Tree Algorithm (DT) to form a single ensemble classifier. To validate the approach, we used the MINST free public handwritten dataset, to ascertain the classifier with the highest rate of accuracy in this study. The results obtained from the experiments demonstrated that the full performance of Ensemble model classifiers MLP, XGBOOST, and DT is superior to AdaBoost, K-NN, and NB. It achieved the highest accuracy level, reaching 98% in the experiment. The experimental results of this research showed that the recognition accuracy was improved compared to previous works. © 2023 IEEE.

关键词： Machine learning

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A 180-Degree Phase Difference Output of Wilkinson Power Divider Using Microstrip Parallel Coupled Lines 6

A 180-Degree Phase Difference Output of Wilkinson Power Divi...

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6th International Conference on Power, Energy and Innovations, ICPEI 2024

作者： Phanomupatum, Pratya Phromloungsri, Ravee Tama, Rachata Pongthavornkamol, Tiwat Wongpatsa, Decha Sonasang, Somchat Nakhon Phanom University Faculty Industrial Technology Department of Electronics Nakhon Phanom Thailand Udon Thani Rajabhat University Computer and Communication Engineering Faculty of Technology Udon Thani Thailand National Electronic and Computer Technology Center National Science and Technology Development Agency Location and Automatic Identification System Research Team Communication and Network Research Group Pathumthani Thailand Faculty Industrial Technology Nakhon Phanom Thailand

ISBN: (纸本)9798350356779

The design of a 180° output phase difference Wilkinson power divider for L-band applications is detailed in this article. The Wilkinson power is generated through the utilization of the parallel coupler's phase shifter. To showcase the potential of the suggested power divider, an FR4 microwave substrate is fabricated with a narrow band of 1.6 GHz to accommodate radio astronomy systems. The insertion loss, return loss, and isolation loss for the power divider's difference phase are accurately simulated at the operational frequency. © 2024 IEEE.

关键词： Radio astronomy

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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within computer Science and Engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

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Clustering Mechanism in Particle Swarm Optimization Algorithm for Data Aggregation 13

Clustering Mechanism in Particle Swarm Optimization Algorith...

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13th IEEE Symposium on computer Applications and Industrial Electronics, ISCAIE 2023

作者： Sharmin, Sharmin Ahmedy, Ismail Noor, Rafidah Md Ismail, Habibah Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Univesrsiti Teknologi MARA Computer Science Unit Centre of Foundation Studies Selangor Dengkil43800 Malaysia

ISBN: (纸本)9798350347319

Wireless Sensor Networks (WSNs) consist of numerous sensor nodes with distinct resources working together to achieve a common objective efficiently. A critical issue is extending the network lifetime of WSNs, especially for data transfer, as sensor nodes depend on their internal batteries to carry out essential functions and communicate with one another. Efficient energy use per sensor is essential for ensuring the overall energy efficiency of the network. Clustering has been the most energy-efficient approach but lacking a method for selecting the most effective cluster head results in decreased data aggregation efficiency, leading to the increased power consumption of sensor nodes. The project aims to enhance energy efficiency and extend network service life by using Particle Swarm optimization (PSO) for clustering in WSNs. PSO is used to select the cluster head based on member node distance from the base station and remaining energy. The proposed PSO technique was tested against Low Energy Adaptive Clustering Hierarchy (LEACH), Stable Election Protocol (SEP), Zonal Stable Election Protocol Z-SEP, and Extended Stable Election Protocol (EZ-SEP) and found to be more effective in reducing energy usage and prolonging the network's lifespan. The recommended technique can extend the service life of WSNs and reduce energy consumption, ultimately leading to more cost-effective and eco-friendly solutions for various applications such as environmental monitoring, surveillance, and healthcare. © 2023 IEEE.

关键词： Energy utilization

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A Mathematical Model for COVID-19 Image Enhancement based on Mittag-Leffler-Chebyshev Shift

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computers, Materials & Continua 2022年第10期73卷 1307-1316页

作者： Ibtisam Aldawish Hamid A.Jalab Department of Mathematics and Statistics College of ScienceImam Mohammad Ibn Saud Islamic UniversityRiyadh11432Saudi Arabia Department of Computer System&Technology Faculty of Computer Scienceand Information TechnologyUniversiti Malaya50603Kuala LumpurMalaysia

The lungs CT scan is used to visualize the spread of the disease across the lungs to obtain better knowledge of the state of the COVID-19 *** diagnosing of COVID-19 disease is a complex challenge that medical system face during the pandemic *** address this problem,this paper proposes a COVID-19 image enhancement based on Mittag-Leffler-Chebyshev polynomial as pre-processing step for COVID-19 detection and *** proposed approach comprises the MittagLeffler sum convoluted with Chebyshev *** idea for using the proposed image enhancement model is that it improves images with low graylevel changes by estimating the probability of each *** proposed image enhancement technique is tested on a variety of lungs computed tomography(CT)scan dataset of varying quality to demonstrate that it is robust and can resist significant quality *** blind/referenceless image spatial quality evaluator(BRISQUE),and the natural image quality evaluator(NIQE)measures for CT scans were 38.78,and 7.43 *** to the findings,the proposed image enhancement model produces the best image quality ***,this model considerably enhances the details of the given datasets,and it may be able to assist medical professionals in the diagnosing process.

关键词： CT scans COVID-19 Mittag-Leffler Chebyshev polynomial fractional calculus

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