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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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Design and Development of Culture-Based Learning Management System for City college of Calamba: A Qualitative Approach 24

Design and Development of Culture-Based Learning Management ...

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8th International Conference on Education and Multimedia Technology, ICEMT 2024

作者： Almonte, Regina G. Montiano, Alvin M. Salili, Paul Brian C. Garcia, Leonard M. Hipolito, Ramil R. Department of Computing and Informatics City College of Calamba Philippines Management Information Systems Department City College of Calamba Philippines

ISBN: (纸本)9798400717611

This paper explores the design and development of e-Guro 1.0, a culture-based learning management system (CLMS) for the City college of Calamba. Using a qualitative approach, research begins with the exploratory analysis that leverages observations and interviews to understand the current state of teaching and learning in CCC as well as its requirements based on the perspective of the management and instructors. The waterfall methodology with focus group discussion. to provide a methodical and sequential foundation for e-Guro development. Integrating the cultural elements into e-Guro 1.0, was refined through the iterative feedback loops that involve 23 instructors from diverse departments of the institution through the use of Focus Group Discussion. Their comments and suggestions provide an improvement to the e-Guro 1.0's overall efficacy, usefulness, and sensitivity to cultural differences. Findings revealed that e-GURO 1.0 developed were recommended for utilization for instructions. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Integrating Deep Learning and Counterfactual Methods for Causal Inference in Genomics

Integrating Deep Learning and Counterfactual Methods for Cau...

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2024 International Conference on Electrical and Computer Engineering Researches, ICECER 2024

作者： Gobonamang, Tshepo Kitso Mpoeleng, Dimane School of Computing and Information System Botswana Accountancy College Gaborone Botswana School of Pure and Applied Sciences BIUST Department of Computing and Informatics Palapye Botswana

ISBN: (纸本)9798331539733

In the dynamic field of genomics, understanding the causal connections between biological variables is pivotal for advancing personalized healthcare. This research presents an innovative method for causal inference in human genomics, combining deep learning, counterfactual reasoning, and a hybrid suffix tree algorithm. Utilizing the principles of causal calculus, particularly do-calculus, the study tackles the challenges associated with identifying causal relationships from observational datasets. The methodology focuses on disease prediction by analyzing genomic information, such as DNA sequences, mRNA transcription profiles, and mutation patterns, sourced from the Critical Assessment of Functional Annotation (CAFA) dataset. The study introduces a robust AI-based framework for causal inference, structured around Pearl's three-tier hierarchy: association, intervention, and counterfactual reasoning. This frame-work addresses misconceptions around correlation and causation, illustrates the effects of variable manipulation, and constructs hypothetical scenarios to assess the impact of interventions. Using this approach on the CAFA dataset uncovered causal links between genomic features and disease outcomes, offering deeper insights into disease mechanisms and the efficacy of potential interventions. This work highlights the necessity of embedding causal inference into machine learning models for genomic research, ensuring more precise and fair AI-driven solutions. By merging deep learning, causal inference, and genomics, this study lays the groundwork for future innovations in personalized medicine and beyond. © 2024 IEEE.

关键词： Deep learning

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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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Real-time Web Server Log Processing with Big Data Technologies

Real-time Web Server Log Processing with Big Data Technologi...

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2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024

作者： Alhammadi, Omar Abul, Osman Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798350379433

The continuous monitoring and analysis of system logs are essential for ensuring the stability, security, and performance of modern digital systems. However, the sheer volume and complexity of log data pose significant challenges for timely and efficient analysis. In this project, we propose a distributed system for live streaming and analysis of system logs, leveraging the strengths of Kafka, Apache Spark, and Hadoop. Our solution aims to bridge the gap between traditional batch processing approaches and the need for real-time detection and resolution of system issues. By integrating Kafka for reliable log data ingestion, Spark for real-time processing, and Hadoop for distributed storage, we create a scalable pipeline capable of handling large volumes of log data with precision and speed. Through experiments, we demonstrate the scalability and efficiency of our solution, showcasing significant reductions in processing time with increasing numbers of worker nodes and CPU counts. This project lays the groundwork for future endeavors in advanced log analysis and anomaly detection, ultimately enhancing the operational resilience and security posture of organizations in today's digital landscape. © 2024 IEEE.

关键词： Big data

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Risk of Cardiovascular Heart Disease Using Data Mining Approach 5

Risk of Cardiovascular Heart Disease Using Data Mining Appro...

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5th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2024

作者： Shafri, Nur Alya Ahmad Nizam, Nur Afiqah Aliya Mohd Hussin, Siti Aida Sheikh Zahid, Zalina College of Computing Informatics & Mathematics Universiti Teknologi Mara Department of Mathematical Sciences Selangor Malaysia

ISBN: (纸本)9798331528553

Cardiovascular heart disease (CVD) stands as the primary global cause of death, with its prevalence increasing notably with age. This study utilised a data mining model to identify crucial risk variables for CVD, selecting the most effective model among decision trees, logistic regression, and artificial neural networks based on performance. Risk factors encompassed variables such as sex, age, education, smoking habits, medical history, and physiological measures. Following dataset analysis and cleaning, model validation involved assessing confusion matrices and ROC curves. SAS Enterprise Miner determined the variable importance, revealing that age significantly impacts CVD risk in decision tree datasets. In logistic regression, age emerged as the most crucial variable with the lowest p-value (p = 0.0036). The artificial neural network (ANN) highlighted seven variables with high R-squared values, indicating their contribution to CVD risk. The results, indicate that ANN achieved the highest evaluation in terms of sensitivity and accuracy, while Decision Tree has the highest value in specificity. In conclusion, the comparative analysis underscores the ANN as the optimal model for identifying CVD risk factors in the given dataset. © 2024 IEEE.

关键词： Logistic regression

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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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Machine Learning-Based Big Data Analytics in Smart Cities: A Survey of Current Trends and Future Research Directions 11

Machine Learning-Based Big Data Analytics in Smart Cities: A...

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11th IEEE/ACM International Conference on Big Data computing, Applications and Technologies, BDCAT 2024

作者： Althabahi, Mariam Hassan Ahmad Jan, Mian Brik, Bouziane Foufou, Sebti University of Sharjah College of Computing and Informatics Department of Computer Science Sharjah27272 United Arab Emirates

ISBN: (纸本)9798350367300

Efficient utilization of Big data in smart cities is crucial for smooth operation of urban environments. Machine learning-enabled big data analytics is essential for optimizing city operations, improving resource management, and enhancing the quality of urban life. By leveraging machine learning (ML) algorithms to process and analyze the vast amounts of data generated in smart cities, authorities can gain insights and make real-time data-driven decisions. This article provides a comprehensive survey of how ML techniques are applied to analyze the large volumes of data generated by smart cities to improve urban living. Various ML algorithms, such as supervised, unsupervised, and reinforcement learning (RL) are discussed by highlighting their roles in numerous applications. Moreover, their distinguishing features are examined, enabling the selection of the most suitable algorithms for various applications in smart cities. Finally, the survey explores various challenges and suggests numerous research directions. © 2024 IEEE.

关键词： Adversarial machine learning

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User Experience on a Culture-Based Learning Management System: Students' Perspective 12

User Experience on a Culture-Based Learning Management Syste...

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12th International Conference on Information and Education Technology, ICIET 2024

作者： Almonte, Regina Garcia Montiano, Alvin Monteron Cerequillo Salili, Paul Brian Garcia, Leonard Marudo Hipolito, Ramil Roxas Fernando, Arlou Hemedez City College Of Calamba Department Of Computing And Informatics Laguna Philippines City College Of Calamba Management Information Systems Department Laguna Philippines

ISBN: (纸本)9798350371772

User Experience (UX) refers to the individual experience of users when using software. An institution must analyze the user experience, especially for custom-built software. The goal of this study is to assess the UX to verify that e-Guro, the City college of Calamba's culture-based Learning Management System, satisfies student expectations. To achieve this purpose, an adapted culture-based questionnaire was used to assess UX. With an aggregate mean of 3.62, which was evaluated as acceptable, e-Guro satisfied the users' expectations in terms of goal content, usability objectives, accessibility goals, and engagement goals. The findings provide important insight into how to develop a culture-based learning management system to help achieve teaching and learning objectives more effectively. © 2024 IEEE.

关键词： Learning systems

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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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