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9th International Conference on information Management, ICIM 2023

作者： Nasir, Wan Mohd Haffiz Mohd Jusoh, Yusmadi Yah Binti Abdullah, Rusli Bin Abdullah, Salfarina Binti Faculty of Computer Science and Information Technology Selangor Malaysia

ISBN: (纸本)9798350336382

Big Data Analytics implementation in healthcare can provide an end-to-end solution with better information value insights. This paper will highlight expert opinion in verifying the quality factors in big data analytics (BDA) that affect the performance of healthcare organizations and present the findings in developing the BDA quality model. This expert verification study was carried out through hybrid approach sessions either face-to-face or by virtual online meetings with two academicians, two specialists, one data scientist, two subject matter experts, and also as BDA expert, one industry expert, one senior ICT Director involve in healthcare, and one content validity expert. All the experts have a vast experience and implementation of BDA in healthcare. The results of these exercises have confirmed and verified ten BDA quality factors which consist of reliability, accuracy, completeness, timeliness, format, accessibility, usability, maintainability, and portability were commensurate with the research model. The analysis of this expert verification study has been done through descriptive analysis such as frequency, mean, and standard deviation. The pilot study will be commenced once the verification process and analysis are completed and the BDA quality model will be validated later through actual study. © 2023 IEEE.

关键词： Data Analytics

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Systematic Literature Review for Optimization System with Advection-Diffusion-Reaction Non-Linear Equation in Water Quality

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2541-2554页

作者： Suryana, Ino Chaerani, Diah Abdullah, Atje Setiawan Prabuwono, Anton Satria Muslihin, Khoirunnisa Rohadatul Aisy Irmansyah, Athaya Zahrani Department of Computer Sciences Faculty of Mathematics and Natural Science Universitas Padjadjaran Sumedang45363 Indonesia Department of Mathematics Faculty of Mathematics and Natural Science Universitas Padjadjaran Sumedang45363 Indonesia Department of Computer Sciences Faculty of Mathematics and Natural Science Universitas Padjadjaran Sumedang45363 Indonesia Department of Information Technology Faculty of Computing and Information Technology in Rabigh King Abdul Aziz University Saudi Arabia Faculty of Mathematics and Natural Science Universitas Padjadjaran Indonesia

Water quality is an essential component of environmental health and sustainability, including various parameters such as chemical, physical, and biological attributes. These parameters determine the suitability of water for consumption, recreation, and ecosystem health. Clean and safe water availability is a crucial foundation for human well-being, ecological balance, and sustainable development, indicating the need to monitor and control water quality to meet human demands. Based on the importance of water quality, this research aimed to provide a comprehensive overview of previous investigations related to the optimization system of the advection-diffusionreaction (ADR) non-linear equation in water quality. Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines were conducted to ensure a structured article selection, data extraction, and analysis approach. This was followed by presenting a bibliometric analysis of selected data to support the study of research trends. The results showed that using ADR non-linear equation systems in modeling water quality optimization was a potential area requiring further exploration. Moreover, this research provided valuable information that could be used as a reference for further investigation of water quality optimization. A future research design is also presented. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Advection-diffusion-reaction Optimization PRISMA Sustainable Development Goals (SDGs) Systematic literature review Water quality

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Visualizing Adidas and Nike Customer Segments Using K-Means Clustering 8

Visualizing Adidas and Nike Customer Segments Using K-Means ...

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8th International Conference on Electrical, Telecommunication and computer Engineering, ELTICOM 2024

作者： Br Ginting, Dewi Sartika Maulana Manru, Muhammad Azi Sachio, Wilbert Neilson Tarigan, Abiyan Agitia Marianto, Ivanny Putri Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9798331531249

This study explores the application of the K-Means Clustering algorithm to categorize Adidas and Nike customers based on their behavior and preferences. The analysis was conducted on a dataset of 3,268 products from Adidas and Nike, focusing on grouping customer behavior into actionable segments. The K-Means algorithm identified key features, with Listing Price and Sale Price as significant variables. The Elbow Method determined the optimal number of clusters as three (k=3), and the Silhouette Score confirmed the model's effectiveness. The results show that the K-Means algorithm effectively distinguishes customer segments within the Adidas vs. Nike shoe dataset, represented through scatter plots and silhouette diagrams that reveal well-separated clusters. These findings are valuable for refining marketing and pricing strategies, enabling Adidas and Nike to better target specific customer groups. Future research could enhance these findings by experimenting with additional features, exploring alternative clustering algorithms like hierarchical clustering or DBSCAN, and fine-tuning parameters such as the number of clusters or preprocessing techniques to further optimize results. © 2024 IEEE.

关键词： Costs

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MoBShield:A Novel XML Approach for Securing Mobile Banking

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computers, Materials & Continua 2024年第5期79卷 2123-2149页

作者： Saeed Seraj Ali Safaa Sadiq OmprakashKaiwartya Mohammad Aljaidi Alexandros Konios Mohammed Ali Mohammed Abazeed Department of Computer Science Nottingham Trent UniversityClifton LaneNottinghamNG118NSUK Department of Computer Science Faculty of Information TechnologyZarqa UniversityZarqa13110Jordan Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Mobile banking security has witnessed significant R&D attention from both financial institutions and *** is due to the growing number of mobile baking applications and their reachability and usefulness to ***,these applications are also attractive prey for cybercriminals,who use a variety of malware to steal personal banking *** literature in mobile banking security requiresmany permissions that are not necessary for the application’s intended security *** this context,this paper presents a novel efficient permission identification approach for securing mobile banking(MoBShield)to detect and prevent malware.A permission-based dataset is generated for mobile banking malware detection that consists large number of malicious adware apps and benign apps to use as training *** dataset is generated from 1650 malicious banking apps of the Canadian Institute of Cybersecurity,University of New Brunswick and benign apps from Google Play.A machine learning algorithm is used to determine whether amobile banking application ismalicious based on its permission ***,an eXplainable machine learning(XML)approach is developed to improve trust by explaining the reasoning behind the algorithm’s *** evaluation tests that the approach can effectively and practically identify mobile banking malware with high precision and reduced false ***,the adapted artificial neural networks(ANN),convolutional neural networks(CNN)and XML approaches achieve a higher accuracy of 99.7%and the adapted deep neural networks(DNN)approach achieves 99.6%accuracy in comparison with the state-of-the-art *** promising results position the proposed approach as a potential tool for real-world scenarios,offering a robustmeans of identifying and thwarting malware inmobile-based banking ***,MoBShield has the potential to significantly enhance the security and trustworthiness of

关键词： Security malware detection deep learning convolutional neural networks deep neural networks

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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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Mathematical Problem Solving in Arabic: Assessing Large Language Models 6

Mathematical Problem Solving in Arabic: Assessing Large Lang...

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6th International Conference on AI in Computational Linguistics, ACLing 2024

作者： Mahgoub, Abeer Khoriba, Ghada Elsabry, Elhassan Anas NLP Research Group Center for Informatics Science School of Information Technology and Computer Science Nile University Giza Egypt

This paper comprehensively evaluates the efficacy of different large language models (LLMs) in addressing mathematical challenges expressed in natural languages, mainly focusing on low-resource languages like Arabic. The main challenge of this problem is that despite the considerable size and impressive problem-solving capabilities of these models, they still require enhancements to achieve satisfactory performance for mathematical problem-solving. An LLM must be trained or fine-tuned extensively in understanding and solving mathematical problems articulated in natural language contexts. This is further complicated by the scarcity of high-quality, annotated datasets in low-resource languages. To overcome this problem, we introduce a method for generating synthetic data to address the challenge of data scarcity in Arabic. This approach includes using LLMs to create high-quality, synthetic mathematical problem datasets that can be used to augment the training process. We propose the Synthetic Data Augmentation (SDA) framework, an advanced variant of traditional data augmentation techniques explicitly tailored for mathematical problem-solving in low-resource languages. Experimental studies reveal that using synthetic data significantly improves the performance of LLMs in solving mathematical problems in Arabic. This study contributes to the field of natural language processing and provides insights into the potential of synthetic data generation to enhance LLM performance in low-resource languages. © 2024 Elsevier B.V.. All rights reserved.

关键词： Data assimilation

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Developing a method to detect driver drowsiness based on a single EEG channel and discriminated features

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International Journal of Intelligent Systems Technologies and Applications 2024年第1期22卷 29-40页

作者： Hussein, Raed Mohammed George, Loay E. Miften, Firas Sabar Iraqi Commission for Computers and Informatics Informatics Institute of Postgraduate Studies Baghdad Iraq Computer Sciences Department University of Information Technology and Communication Baghdad Iraq Computer Sciences Department College of Education for Pure Science University of Thi-Qar Thi-Qar Iraq

Driver drowsiness is one of the leading causes of road deaths and transportation industry dangers. Due to its direct evaluation of neurophysiological brain activity, electroencephalography (EEG) has been regarded as one of the most reliable physiological indicators for identifying driver drowsiness. This study proposes a straightforward, cost-effective method for detecting driver drowsiness using a single channel. The contribution of this research is the discovery of drowsiness using discriminated features [moments features (M1, M2, M3, M4), roughness features (R1, R2, R3, R4), zero crossing rate (ZCR), sample entropy (SE) and median absolute deviation (MAD)] from publicly available datasets. A novel model was introduced in this study, which involved the fusion of wavelet transform Daubechies order 4 (WTDB4) and residue decomposition (RD) techniques for feature extraction. Various classification algorithms, including the least-square support vector machine (LSSVM) and ensemble models were compared in terms of their performance metrics. The algorithm that exhibited superior accuracy with reduced computational time was chosen to classify the driver's status into two groups: awake and drowsy. Notably, the proposed model achieved an impressive accuracy of 97.95%. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Electroencephalography

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Dynamic UAV Deployment in Multi-UAV Wireless Networks: A Multi-Modal Feature-Based Deep Reinforcement Learning Approach

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IEEE Internet of Things Journal 2025年第12期12卷 18765-18778页

作者： Bai, Yu Xie, Boxuan Liu, Ying Chang, Zheng Jäntti, Riku Aalto University Department of Information and Communications Engineering Espoo02150 Finland University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China University of Jyväskylä Faculty of Information Technology Jyväskylä40014 Finland

The use of Unmanned Aerial Vehicles (UAVs) as aerial base stations has attracted increasing research interest in recent years. A key challenge in this field is determining how to deploy multiple UAVs in dynamic environments, particularly where mobile user demands fluctuate. To address this challenge, this paper presents an adaptive UAV deployment scheme in a dynamic multi-UAV wireless network, considering the mobility of UAVs and users, state variability, and adjustable UAV transmission power. By jointly optimizing the UAVs’ operational modes, transmission power levels, and movement strategies, our objective is to achieve a trade-off between minimizing power consumption and maximizing ground user coverage. A Deep Reinforcement Learning (DRL) approach is proposed to address these challenges. To capture the dynamic variations of users and UAVs in the environment, a multi-modal feature state space is designed, consisting of both a multi-channel image and vectors. The image component integrates real-time data on user distribution and the UAV coverage area, while the vectors represent UAV operational modes, position data, and system temporal information. These multi-modal features are processed using a combination of Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs) for advanced feature extraction. To enhance training stability and efficiency, the proposed approach updates parameters using the Proximal Policy Optimization (PPO) method. Simulation results demonstrate the effectiveness of the proposed scheme in balancing power consumption and coverage while effectively managing system dynamics. © 2014 IEEE.

关键词： Deep reinforcement learning

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Efficient Power Control for UAV Based on Trajectory and Game Theory

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computers, Materials & Continua 2023年第3期74卷 5589-5606页

作者： Fadhil Mukhlif Ashraf Osman Ibrahim Norafida Ithnin Roobaea Alroobaea Majed Alsafyani Information Assurance and Security Research Group(IASRG) School of ComputingFaculty of EngineeringUniversiti Teknologi MalaysiaJohorMalaysia Faculty of Computing and Informatics University Malaysia SabahKota KinabaluMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

Due to the fact that network space is becoming more limited,the implementation of ultra-dense networks(UDNs)has the potential to enhance not only network coverage but also network *** Aerial Vehicle(UAV)communications have recently garnered a lot of attention due to the fact that they are extremely versatile and may be applied to a wide variety of contexts and purposes.A cognitive UAV is proposed as a solution for the Internet of Things ground terminal’s wireless nodes in this *** the IoT system,the UAV is utilised not only to determine how the resources should be distributed but also to provide power to the wireless *** quality of service(QoS)offered by the cognitive node was interpreted as a price-based utility function,which was demonstrated in the form of a non-cooperative game theory in order to maximise customers’net utility *** energyefficient non-cooperative game theory power allocation with pricing strategy abbreviated as(EE-NGPAP)is implemented in this study with two trajectories Spiral and Sigmoidal in order to facilitate effective power management in Internet of Things(IoT)wireless *** has also been demonstrated,theoretically and by the use of simulations,that the Nash equilibrium does exist and that it is one of a *** proposed energy harvesting approach was shown,through simulations,to significantly reduce the typical amount of power thatwas *** is taken into consideration to agree with the objective of 5G *** order to converge to Nash Equilibrium(NE),the method that is advised only needs roughly 4 iterations,which makes it easier to utilise in the real world,where things aren’t always the same.

关键词： UAV spiral&sigmoid trajectory drones IoT game theory energy efficiency 6G

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Improved Dynamic Johnson Sequencing Algorithm(DJS)in Cloud Computing Environment for Efficient Resource Scheduling for Distributed Overloading

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Journal of Systems science and Systems Engineering 2024年第4期33卷 391-424页

作者： Anurag Sinha Pallab Banerjee Sharmistha Roy Nitasha Rathore Narendra Pratap Singh Mueen Uddin Maha Abdelhaq Raed Alsaqour Department of Computer Science Engineering ICFAI Tech SchoolICFAI UniversityJharkhandRanchi834004India Department of Computer Science Engineering Amity UniversityJharkhandRanchi835303India Department of Computing and Information Technology Usha Martin UniversityRanchi835103India Department of Information Technology Bharti Vidyapeeth’s College of EngineeringPaschim ViharNew DelhiIndia Department of CSE Delhi UniversityNew DelhiIndia College of Computing and Information Technology University of Doha for Science and TechnologyDoha24449Qatar Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdurrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computing and InformaticsSaudi Electronic University93499RiyadhSaudi Arabia

This study introduces an innovative approach to optimize cloud computing job distribution using the Improved Dynamic Johnson Sequencing Algorithm(DJS).Emphasizing on-demand resource sharing,typical to Cloud Service Providers(CSPs),the research focuses on minimizing job completion delays through efficient task *** Johnson’s rule from operations research,the study addresses the challenge of resource availability post-task *** advocates for queuing models with multiple servers and finite capacity to improve job scheduling models,subsequently reducing wait times and queue *** Dynamic Johnson Sequencing Algorithm and the M/M/c/K queuing model are applied to optimize task sequences,showcasing their efficacy through comparative *** research evaluates the impact of makespan calculation on data file transfer times and assesses vital performance indicators,ultimately positioning the proposed technique as superior to existing approaches,offering a robust framework for enhanced task scheduling and resource allocation in cloud computing.

关键词： First come first served(FCFS)scheduling round robin task scheduling queuing model Johnson scheduling algorithm

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