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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Mawere, Talent Rajalakshmi, Selvaraj Kuthadi, Venu Madhav Dinakenyane, Otlhapile Botswana International University of Science and Technology Department of Computing and Informatics Palapye Botswana

ISBN: (纸本)9798331518981

Forecasting Stock market prices is ongoing research with researchers aspiring to develop models that supersede previous accuracy rates. Historically, statistical models like the GARCH and ARIMA models have been used for predictive analytics tasks, however, the volatility and complex nature of stock market data make it difficult to complete prediction tasks with such models. Various machine learning models like the Random Forest algorithm and Support Vector Machine have also been used for stock market prediction, at times achieving significant prediction accuracy in some tasks. However, this may not be sufficient. Of late researchers are experimenting with deep learning algorithms for stock market prediction with these remarkably improving accuracy rates. In this study, we conduct a comparative analysis of prominent stock price forecasting models to identify the best-performing model. By using a systematic review of the literature, we evaluate the top-performing models in terms of prediction accuracy, adaptability, and robustness. Results show that variations of the ensemble LSTM model outperform other models. Additionally, we observe that models with knowledge retention generally outperform traditional machine learning approaches in stock market prediction tasks. Our findings have significant implications for developing effective stock market prediction systems and contributing to the growth of AI-driven financial analytics. © 2024 IEEE.

关键词： Prediction models

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Addressing Imbalance in Health Datasets: A New Method NR-Clustering SMOTE and Distance Metric Modification

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Computers, Materials & Continua 2025年第2期82卷 2931-2949页

作者： Hairani Hairani Triyanna Widiyaningtyas Didik Dwi Prasetya Afrig Aminuddin Department of Electrical Engineering and Informatics Faculty of EngineeringUniversitas Negeri MalangMalang65145Indonesia Department of Computer Science Universitas BumigoraMataram83127Indonesia Department of Computer Graphic and Multimedia Faculty of ComputingCollege of Computing and Applied SciencesUniversiti Malaysia Pahang Al-Sultan AbdullahPekan26600Malaysia

An imbalanced dataset often challenges machine learning, particularly classification methods. Underrepresented minority classes can result in biased and inaccurate models. The Synthetic Minority Over-Sampling Technique (SMOTE) was developed to address the problem of imbalanced data. Over time, several weaknesses of the SMOTE method have been identified in generating synthetic minority class data, such as overlapping, noise, and small disjuncts. However, these studies generally focus on only one of SMOTE’s weaknesses: noise or overlapping. Therefore, this study addresses both issues simultaneously by tackling noise and overlapping in SMOTE-generated data. This study proposes a combined approach of filtering, clustering, and distance modification to reduce noise and overlapping produced by SMOTE. Filtering removes minority class data (noise) located in majority class regions, with the k-nn method applied for filtering. The use of Noise Reduction (NR), which removes data that is considered noise before applying SMOTE, has a positive impact in overcoming data imbalance. Clustering establishes decision boundaries by partitioning data into clusters, allowing SMOTE with modified distance metrics to generate minority class data within each cluster. This SMOTE clustering and distance modification approach aims to minimize overlap in synthetic minority data that could introduce noise. The proposed method is called “NR-Clustering SMOTE,” which has several stages in balancing data: (1) filtering by removing minority classes close to majority classes (data noise) using the k-nn method;(2) clustering data using K-means aims to establish decision boundaries by partitioning data into several clusters;(3) applying SMOTE oversampling with Manhattan distance within each cluster. Test results indicate that the proposed NR-Clustering SMOTE method achieves the best performance across all evaluation metrics for classification methods such as Random Forest, SVM, and Naїve Bayes, compared t

关键词： SMOTE modification Clustering-SMOTE manhattan distance

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Shear Let Transform Residual Learning Approach for Single-Image Super-Resolution

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Computers, Materials & Continua 2024年第5期79卷 3193-3209页

作者： Israa Ismail Ghada Eltaweel Mohamed Meselhy Eltoukhy Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia51422Egypt Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia

Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution ***-resolution is of paramount importance in the context of remote sensing,satellite,aerial,security and surveillance ***-resolution remote sensing imagery is essential for surveillance and security purposes,enabling authorities to monitor remote or sensitive areas with greater *** study introduces a single-image super-resolution approach for remote sensing images,utilizing deep shearlet residual learning in the shearlet transform domain,and incorporating the Enhanced Deep Super-Resolution network(EDSR).Unlike conventional approaches that estimate residuals between high and low-resolution images,the proposed approach calculates the shearlet coefficients for the desired high-resolution image using the provided low-resolution image instead of estimating a residual image between the high-and low-resolution *** shearlet transform is chosen for its excellent sparse approximation ***,remote sensing images are transformed into the shearlet domain,which divides the input image into low and high *** shearlet coefficients are fed into the EDSR *** high-resolution image is subsequently reconstructed using the inverse shearlet *** incorporation of the EDSR network enhances training stability,leading to improved generated *** experimental results from the Deep Shearlet Residual Learning approach demonstrate its superior performance in remote sensing image recovery,effectively restoring both global topology and local edge detail information,thereby enhancing image *** to other networks,our proposed approach outperforms the state-of-the-art in terms of image quality,achieving an average peak signal-to-noise ratio of 35 and a structural similarity index measure of approximately 0.9.

关键词： Super-resolution shearlet transform shearlet coefficients enhanced deep super-resolution network

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E-CommerceShield: A Framework for Enhanced Security in E-Commerce with Awareness, DNS Matching, and Blockchain Integration 30

E-CommerceShield: A Framework for Enhanced Security in E-Com...

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30th IEEE International Conference on Telecommunications, ICT 2024

作者： Belghith, Aymen College of Informatics and Computing Saudi Electronic University Computer Science Department Saudi Arabia

ISBN: (纸本)9798350356694

Security attacks on e-commerce platforms are increasingly prevalent, posing significant risks to both users and organizations. These attacks aim to cause financial harm or disrupt e-commerce services, highlighting the critical need for robust security measures. This study investigates key security attacks targeting e-commerce platforms and evaluates the efficacy of various attack detection methods. Through a comprehensive literature review and descriptive analysis, the study identifies financial frauds, phishing, and Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks as the major threats to e-commerce systems. Furthermore, the study examines different detection methods, revealing that blockchain-based security systems emerge as the most effective solution against a range of identified security attacks. Building upon these findings, this paper proposes a novel framework, dubbed e-CommerceShield, which integrates user awareness, DNS matching, and blockchain technology to enhance security in e-commerce environments. The framework prioritizes proactive security measures, empowers users with awareness and education, and leverages advanced technologies to detect and prevent security threats effectively. By adopting the e-CommerceShield framework, e-commerce platforms can fortify their security posture and safeguard the integrity of online transactions, thereby fostering trust and confidence among users in the digital marketplace. © 2024 IEEE.

关键词： Blockchain

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A Taxonomy of Factors that Influence the Multiple-Cloud computing Utilization 13

A Taxonomy of Factors that Influence the Multiple-Cloud Comp...

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13th International Conference on Computer Technologies and Development, TechDev 2024

作者： Al-Qahtani, Hassan Saad Saudi Electronic University College of Computing and Informatics Department of Information Technology Saudi Arabia

ISBN: (纸本)9798331539771

There are various factors that associated with the architecture of Multiple-Cloud computing, which have negative impact that reduce the utilization of MCC. Our study is proposing a comprehensive taxonomy of these factors. The proposed classification will help to apply efficient solutions/approaches that prevent, avoid, or reduce the negative impact of these factors;and enhance dealing with these factors. This study identifies and categorize the factors into five primary dimensions: operational factors, data privacy and integrity factors, performance factors, and law and legislations factors. © 2024 IEEE.

关键词： Cloud computing architecture

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Enhanced Blockchain Scalability for IoT-based Smart Devices -A Generic Model Development 13

Enhanced Blockchain Scalability for IoT-based Smart Devices ...

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

作者： Gurunathan, Mathuri Mahmoud, Moamin A. Faisal, Faisal Hadi Universiti Tenaga Nasional College of Graduate Studies Kajang Malaysia College of Computing and Informatics Universiti Tenaga Nasional Institute of Informatics and Computing in Energy Department of Computing Kajang Malaysia

ISBN: (纸本)9798350347319

Blockchain become well known as there is third-party contribution while doing the exchanges or transactions. Blockchain is the innovation for utilizing digital forms of money. It draws in the consideration of specialists and academicians, along with various highlights of Blockchain it is having the significant issue of versatility and scalability which can be classified into throughput, cost, limit, and systems networking. Scalability influences due to some different components like block size and block interval time which likewise may lessen the security. The system may get powerless against various attacks on the off chance that we indiscriminately change the scalability. To address these challenges, this paper con-ducts an intensive analysis of blockchain scalability challenges in the IoT context and subsequently proposes a generic model to enhance scalability. The investigation covers restrictions on different blockchain stages and how system parameters such as throughput, latency, block size, and network influence a peer-topper system's effectiveness, security, and energy consumption. © 2023 IEEE.

关键词： Energy efficiency

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Use of gamification and game-based learning in educating Generation Alpha: A systematic literature review

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Educational Technology and Society 2024年第2期27卷 114-132页

作者： Fernando, Pumudu A. Premadasa, H. K. Salinda Department of Computing Informatics Institute of Technology Sri Lanka Centre for Computer Studies Sabaragamuwa University Sri Lanka

Children born after 2010 are labelled as members of Generation Alpha, who currently pursue their primary education. Gamification and game-based learning methodologies have gained popularity in the global education sector in the recent past. The goal of this study is to investigate the present state of adopting gamification and game-based learning for primary education student cohorts, with the aid of recent peer-reviewed research publications. By employing a systematic mapping design, reviewed paper results are categorized and further evaluated in relation to the attributes such as type of gamification, game mechanics and elements used, evaluation context, type of experimental outcome, gamified academic subject area, and type of application. With the aid of review results, this study proposes a mapping of gamification learning mechanics, suited to address Generation Alpha traits. It also recommends future research directions, which include investigating the realistic learning preferences of Generation Alpha, the need for designing novel educational game elements and adaptive gamification learning strategies, and the importance of long-term studies with diverse learner samples. The findings of this study will support prospective stakeholders, such as researchers, educators, parents, and educational policymakers, to utilize gamification approaches effectively in educating Generation Alpha. © (2023), (International Forum of Educational Technology and Society). All Rights Reserved.

关键词： Adaptive learning Game-based learning Gamification Generation Alpha Primary education

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Distributed intelligence for IoT-based smart cities: a survey

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Neural computing and Applications 2024年第27期36卷 16621-16656页

作者： Hashem, Ibrahim Abaker Siddiqa, Aisha Alaba, Fadele Ayotunde Bilal, Muhammad Alhashmi, Saadat Mehmood Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates School of Computing College of Arts and Sciences Universiti Utara Malaysia Kedah Sintok06010 Malaysia Department of Computer Science Federal College of Education Zaria Kaduna Tudun Wada810282 Nigeria Department of Pharmaceutical Outcomes and Policy University of Florida GainesvilleFL32610 United States Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Petaling Jaya47500 Malaysia Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates

The remarkable miniaturization of Internet of Things (IoT)-based systems and the rise of distributed intelligence are promising research paradigms in the design of smart cities. IoT and distributed intelligence are conjoined. On the one hand, IoT provides a digital connection to everyday physical devices, and on the other hand, distributed intelligence allows these connected devices to work together in a distributed environment to solve complex problems through learning and reasoning. In this article, we investigate and report the recent premier advances in distributed intelligence for IoT-based smart cities. We use bibliometric techniques which provide essential insights into many studies to evaluate the literature including publication year, type, and citation status on distributed intelligence and its variations published between 2013 and 2023. We categorize and classify literature by devising a taxonomy based on essential parameters, technologies, approaches, applications, and implications with respect to distributed intelligence. Moreover, we provide recent ongoing advancements in distributed intelligence and IoT such as sustainability, blockchain, 5G technologies, AI, edge computing expansion and IoT expansion. We also highlight some of the use cases which include traffic management, energy management, waste management, public safety, and healthcare. We provide core layers and services for implementing distributed intelligence in IoT-based smart cities and present several research challenges and issues. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Smart city

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Analysis of Indonesian Opinion on the Arrival of COVID-19 Vaccine Using Machine Learning 11

Analysis of Indonesian Opinion on the Arrival of COVID-19 Va...

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11th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2024

作者： Alfat, Lathifah Uddin, Nur Nasucha, Mohammad Mohamad, Masurah Masrom, Suraya Universitas Pembangunan Jaya Department of Informatics South Tangerang Indonesia Universiti Teknologi Mara College of Computing Informatics and Mathematics Perak Malaysia

ISBN: (纸本)9798350389289

The coronavirus had developed into a global concern by the end of 2019. This virus spread rapidly to all countries after founded in China. WHO has determined it as a pandemic called COVID-19. The first COVID-19 case in Indonesia occurred on March 2, 2020. Following this, the number of COVID-19 cases in Indonesia rose daily. The Indonesian government had resolved this health disaster in many ways. One of them is distributing COVID-19 vaccines. However, some vaccine brands are available in Indonesia to support the end of the pandemic. Sinovac, AstraZeneca, Moderna, Pfizer, and Sinopharm are among them. This study intends to evaluate Indonesian sentiment based on the arrival of news about each vaccination brand. Preprocess data uses NLTK to perform tasks such as folding, tokenizing, calculating the frequency distribution, filtering stop words, normalization, and stemming. Textblob calculates and classifies tweet data as positive, negative, or neutral. Then, evaluate Naive Bayes, Decision Tree, and KNearest Neighbors to look for the best model. Based on the findings, Indonesians have a neutral to positive opinion with the best response to the Moderna brand. The best machine learning model is the Decision Tree algorithm in data training and testing, with ratio of 90:10. © 2024 IEEE.

关键词： COVID-19

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Enhancing intrusion detection in IIoT: optimized CNN model with multi-class SMOTE balancing

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Neural computing and Applications 2024年第24期36卷 14643-14659页

作者： Eid, Abdulrahman Mahmoud Soudan, Bassel Nassif, Ali Bou Injadat, MohammadNoor Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan

This work introduces an intrusion detection system (IDS) tailored for industrial internet of things (IIoT) environments based on an optimized convolutional neural network (CNN) model. The model is trained on a dataset that was balanced using a novel multi-class implementation of synthetic minority over-sampling technique (SMOTE) that ensures equal representation of all classes. Additionally, systematic optimization will be used to fine tune the hyperparameters of the CNN model and mitigate the effects of the increased size of the training dataset. Evaluation results will demonstrate substantial improvement in performance when the optimized CNN model is trained on the balanced dataset. The proposed IDS will be evaluated using the IIoT-specific WUSTL-IIOT-2021 dataset, and then its generalization capability will be verified using the non-domain specific UNSW_NB15 dataset. The model’s performance will be evaluated using accuracy, precision, recall, and F1-score metrics. The results will demonstrate that the proposed IDS is highly effective with performance exceeding 99.9% on all performance metrics. The IDS is also highly effective in detecting intrusion for generic IT networks achieving improvements in excess of 30% compared to the default baseline model. The results emphasize the versatility and effectiveness of the proposed IDS model, making it a reliable and adaptable solution for enhancing network security across diverse network environments. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Neural network models

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