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IEEE International Conference on Multi Topic

作者： Shoaib Hassan Qianmu Li Affan Yasin Muhammad Zubair School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing Jiangsu P.R.China School of Software Northwestern Polytechnical University Xian Shaanxi P.R.China Faculty of Information Technology & Computer Science University of Central Punjab Lahore Pakistan

Requirements change throughout the software development lifecycle, from requirement elicitation and analysis to software operation. software requirements can be traced back to their source and shown to depend on one another. According to studies, the tools and techniques for the present traceability methodologies need to be revised to prevent the practical application of traceability. We analyzed evaluation techniques and requirements traceability tools in this article to determine whether evaluation techniques are truly being used and supported by software tools. As a result, we noted no connection between the approaches and tools examined and that some criteria, such as stakeholder communication, difficulty of tools, and requirement stability, needed to be better considered in the evaluated tools.

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Application of Nonlinear Gaussian Process Regression Models for CO2 Emissions Prediction

Application of Nonlinear Gaussian Process Regression Models ...

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International Conference on Innovations in Information Technology

作者： Ahmed Abdelhadi Ahmed Yousif Badri Asaad Shamseldin Department of Computer Science & Software Engineering College of Information Technology (CIT) United Arab Emirates University Al Ain UAE Department of Mechanical and Mechatronics Engineering Faculty of Engineering University of Auckland Auckland New Zealand Department of Civil and Environmental Engineering Faculty of Engineering University of Auckland Auckland New Zealand

The growing concern over climate change led to a massive corpus of literature addressing the impact of various factors in terms of carbon dioxide (CO2) emissions levels. However, the majority of existing literature employs linear models to a singular factor, neglecting the relation of these factors and their impact in conjunction on CO2 emissions levels. To answer this question, this study conducted an intensive literature review to identify proper factors to deduce parameters that influence CO2 emissions levels. These parameters, derived from data spanning from 1965 to 2021 in the United Arab Emirates, underwent thorough analysis using Dominance Analysis to establish their significance in relation to CO2 emissions levels. Subsequently, we employed nonparametric nonlinear Gaussian Process Regression (GPR) models with diverse kernels to assess their performance against traditional parametric linear models. The results showed that nonparametric nonlinear GPR models provide the most accurate predictions for CO2 emissions, with the GPR-Matern kernel outperforming all other models, achieving an R-squared value of 98.1%. Our findings highlight the importance of adopting a nonlinear approach for predicting and forecasting CO2 emissions levels when considering the interplay of driving factors.

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Geometrically Inspired Kernel Machines for Collaborative Learning Beyond Gradient Descent

arXiv

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arXiv 2024年

作者： Kumar, Mohit Valentinitsch, Alexander Fuchs, Magdalena Brucker, Mathias Bowles, Juliana Husakovic, Adnan Abbas, Ali Moser, Bernhard A. Faculty of Computer Science and Electrical Engineering University of Rostock Germany Software Competence Center Hagenberg GmbH HagenbergA-4232 Austria School of Computer Science University of St Andrews United Kingdom Primetals Technologies Austria GmbH LinzA-4031 Austria Institute of Signal Processing Johannes Kepler University Linz Austria

This paper develops a novel mathematical framework for collaborative learning by means of geometrically inspired kernel machines which includes statements on the bounds of generalisation and approximation errors, and sample complexity. For classification problems, this approach allows us to learn bounded geometric structures around given data points and hence solve the global model learning problem in an efficient way by exploiting convexity properties of the related optimisation problem in a Reproducing Kernel Hilbert Space (RKHS). In this way, we can reduce classification problems to determining the closest bounded geometric structure from a given data point. Further advantages that come with our solution is that our approach does not require clients to perform multiple epochs of local optimisation using stochastic gradient descent, nor require rounds of communication between client/server for optimising the global model. We highlight that numerous experiments have shown that the proposed method is a competitive alternative to the state-of-the-art. © 2024, CC BY.

关键词： Stochastic systems

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Blockchain Solution For SMEs cybersecurity Threats In E-Commerce

Blockchain Solution For SMEs Cybersecurity Threats In E-Comm...

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computer science and Emerging Technologies (CSET), International Conference on

作者： Firas H. Zawaideh Waheeb Abu-Ulbeh Salameh A. Mjlae Yousef A. Baker El-Ebiary Yazeed Al Moaiad Sunanda Das Department of Cyber Security Faculty of Science and Informaiton Technology Irbid National University Irbid Jordan Cybersecurity Department Faculty of Administrative sciences and Informatics Al-Istiqlal University Palestine CS Department Prince Abdullah bin Ghazi Faculty of Information Technology Al-Balqa Applied University (BAU) Jordan Faculty of Informatics and Computing UniSZA Malaysia Faculty of Computer and Information Technology MEDIU Malaysia School of Computer Science and Engineering Jain University Bangalore India

The rapid growth of electronic commerce (e-commerce) has revolutionized the way Small and Medium-sized Enterprises (SMEs) conduct business, enabling them to reach global markets and unlock new growth opportunities. However, this digital transformation has also exposed SMEs to an escalating array of cybersecurity threats that could compromise their data, financial integrity, and reputation. Blockchain technology, known for its decentralized and immutable nature, has emerged as a potential solution to enhance the security and resilience of e-commerce operations for SMEs. This research paper aims to investigate the current state of cybersecurity preparedness among SMEs engaged in e-commerce and identify the prevalent threats they face in the digital landscape. To achieve this goal, the study incorporates systematic literature review (SLR) method to examine the best technological solution to address the cybersecurity issues faced by e-commerce organizations in this paper of SMEs operating in e-commerce to gather data on their security practices, incident history, and perceptions of cybersecurity risks. In the context of this research, blockchain technology will be explored as a potential mechanism to enhance the security of e-commerce operations for SMEs. The inherent features of blockchain, such as decentralization, transparency, and immutability, could help protect sensitive data and thwart cyberattacks. By exploring the integration of blockchain into e-commerce systems, the research aims to shed light on the potential benefits and challenges associated with its adoption by SMEs. The research findings will not only shed light on the prevailing cybersecurity practices within SMEs and highlight their vulnerabilities but will also explore how blockchain technology can be leveraged to address these challenges. By understanding these patterns and exploring the feasibility of blockchain integration, the research aims to provide comprehensive recommendations for enhancing cy

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Enhancing Early Childhood Learning using Image Processing for Sinhala Medium Education

Enhancing Early Childhood Learning using Image Processing fo...

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Advancements in Computing (ICAC), International Conference on

作者： R.G.V Saranga A.H.N. Jayasinghe U.A.S. Divyantha S.W.L.P de Silva N.H.P. Ravi Supunya Swarnakantha P. K. Suriya Kumari Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

The study aims to develop a mobile application for young children to learn Sinhala letters, shapes, colors, and storytelling incorporating machine learning models to evaluate and enhance educational activities. With the rise of online education during the COVID-19 pandemic, the familiarity of children with mobile devices provides an opportunity to create an engaging and educational experience. The application will teach Sinhala letters using object images, allowing children to upload their own images for feedback. It also includes a feature for children to practice writing letters and analyze their progress. Also, the application introduces colors and shapes in Sinhala, encouraging children to draw and track their improvement. Additionally, the application aims to generate stories in Sinhala to improve children's creativity and thinking knowledge. This research addresses a critical gap in existing Sinhala learning applications by integrating machine learning for activity assessment, promising to significantly impact and improve early language education for children.

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Detection of Thyroid Disease Through Machine Learning Technique

Detection of Thyroid Disease Through Machine Learning Techni...

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Information Technology (ACIT)

作者： Mutaz AlMomani Ahmad Al-Qerem Shadi Nashwan Issam Jebreen Mohammed Rajab Mohammad Alauthman Mohammad Jeadi Ali Elrashidi Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Cybersecurity Department Faculty of Information Technology Middle East University Amman Jordan University Headquarter University of Anbar Ramadi Iraq Department of Information Security University of Petra Amman Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Thyroid disease, one of the most common endocrine disorders, requires prompt diagnosis to effectively treat it. To improve diagnostic precision and expedite medical procedures, this paper explores the use of Machine Learning (ML) approaches for the early identification and categorization of thyroid diseasees. Sophisticated machine learning methods, including support vector machines, neural networks, and decision trees, are used to leverage a variety of datasets. These models outperform traditional diagnostic techniques in their capacity to identify complex patterns and minute variations in thyroid function parameters. The application of machine learning to the diagnosis of thyroid disease not only increases diagnostic accuracy but also makes preventive healthcare approaches possible. Personalized risk assessments are made possible by ML models, which analyze a wide range of patient-specific characteristics and help to improve the targeting of therapies. Furthermore, decision-making procedures are accelerated by the real-time processing powers of machine learning systems, which may shorten the interval between diagnosis and the start of therapy. this paper emphasizes how machine learning has revolutionized the diagnosis of thyroid disease and how it has the potential to completely change clinical procedures. The integration of sophisticated algorithms and extensive datasets presents opportunities for enhanced precision, effectiveness, and customization of diagnostic methods, which might eventually lead to better patient outcomes and more efficient use of healthcare resources for thyroid condition treatment. In this paper, the results were as follows: Two classifiers performed the best: LMT and LogitBoost. The Trees and Meta-learning strategy performed the best learning strategy, while the CorrelationAttributeEval and InfoGainAttributeEval methods performed the best feature selection.

关键词： Support vector machines Metalearning Neural networks Medical treatment Machine learning Real-time systems Risk management Regression tree analysis Thyroid Diseases

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Distributed optimization for IoT attack detection using federated learning and Siberian Tiger optimizer

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ICT Express 2025年第3期11卷 542-546页

作者： Gupta, Brij B. Gaurav, Akshat Alhalabi, Wadee Arya, Varsha Alharbi, Eman Chui, Kwok Tai Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India School of Cybersecurity Korea University Seoul South Korea Kyung Hee University 26 Kyungheedae-ro Dongdaemun-gu Seoul South Korea Ronin Institute Montclair NJ United States International Center for AI and Cyber Security Research and Innovations (CCRI) Asia University Taichung 413 Taiwan Department of Computer Science Immersive Virtual Reality Research Group King Abdulaziz University Jeddah Saudi Arabia Hong Kong Metropolitan University Hong Kong SAR China Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

The rapid growth of IoT devices has heightened the risk of botnet attacks, calling for scalable and distributed detection solutions. In this context, this study proposes a distributed optimization system for IoT attack detection using CNN model utilizing federated learning. After optimizing the hyperparameters of the model at the server, the Siberian Tiger Optimization (STO) method distributes these values to clients for dispersed training. Our model achieves accuracy, recall, and precision of 0.89978, 0.94355, and 0.94455, respectively, using the N-BaIoT dataset. These findings show, in spite of latency issues, the efficiency of federated learning in distributed IoT security systems. © 2025

关键词： CNN model Distributed optimization Federated learning IoT attack detection Siberian Tiger Optimization (STO)

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Hierarchical Continual Learning for Domain-Knowledge Retention in Healthcare Federated Learning

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IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Choudhry, Imran Arshad Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia University of Central Punjab Faculty of Information Technology and Computer Science Department of Software Engineering Lahore Pakistan

Internet of Medical Things (IoMT) applications encounter issues with data protection, continual adaptation, and domain-specific knowledge retention, especially in consumer-centric IoMT scenarios. We overcome these obstacles and facilitate effective knowledge retention and task adaptation in IoMT applications. This study attempts to create a unique privacy-preserving federated learning framework that combines a hierarchical learning structure with Continual Learning (CL). Despite the advancements in Federated Learning (FL), current models have trouble integrating changing datasets in real-time while protecting privacy, as well as catastrophic forgetting, which occurs when previously learned knowledge is lost when adjusting to new tasks. We present a hierarchical learning framework that makes use of three levels of models -Junior Model (JM), Consultant Model (CM), and Senior Consultant Model (SCM) -to overcome these drawbacks. Each level of the model aids in archived retention and domain-knowledge adaptation. To guarantee that the model maintains valuable information over time and adapts to new tasks with ease, our method blends domain adaptation strategies with ongoing learning approaches like knowledge distillation and elastic weight consolidation (EWC). We compare the suggested methodology with current state-of-the-art (SOTA) models on healthcare datasets for tasks like illness diagnosis and medical image categorization. According to our findings, the hierarchical continual learning model performs better than SOTA techniques in terms of accuracy, task adaptability, and privacy protection. In the healthcare industry, our study sets a new standard for privacy-preserving, continuously adaptable federated learning systems, allowing for real-time, scalable IoMT applications that can adapt dynamically to a variety of changing datasets. © 1975-2011 IEEE.

关键词： Patient treatment

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Information Theoretic-based Feature Selection (IFS) for Machine Learning 1

Information Theoretic-based Feature Selection (IFS) for Mach...

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1st International Conference on Multidisciplinary engineering and Applied science, ICMEAS 2021

作者： Suleiman, Muhammad A. Labadin, Jane Nile University of Nigeria Faculty of Natural and Applied Sciences Dept. of Software Engineering and Information Technology Abuja Nigeria University Malaysia Sarawak Faculty of Computer Science and Information Technology Kota Samarahan Malaysia

ISBN: (纸本)9781665434935

Mutual Information (MI) has been a popular choice for the selection criterion function of the feature selection techniques. However, only a handful of methods of formulating MI estimator for regression tasks exist, despite the existence of numerous methods of MI estimation for classification tasks in the literature. This work proposed a feature selection method based on mutual information for both classification and regression tasks. Unlike the existing MI estimator for regression tasks which are widely non-parametric, the proposed method provides a suitable way to estimate MI based on conventional kernel density estimation numerically. Two different experiments were conducted to evaluate the proposed IFS method. The first test is designed to assess the correctness of the formulated MI estimator by comparing it with various existing benchmarks. The second experiment evaluates the proposed method using natural domain data. Overall results obtained from these experiments show that the proposed method was successfully able to select relevant features. © 2021 IEEE.

关键词： Feature Selection

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A Novel Attention-based Explainable Deep Learning Framework Towards Medical Image Classification

A Novel Attention-based Explainable Deep Learning Framework ...

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Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Gladys W. Muoka Ding Yi Chiagoziem Chima Ukwuoma Moindi Duke Martin Ahmet Arif Aydin Mugahed A. Al-Antari School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu China Department of Computer Science Unicaf University Lilongwe Lilongwe Malawi Department of Computer Engineering Faculty of Engineering İnönü University Turkey Department of Artificial Intelligence College of Software & Convergence Technology Daeyang AI Center Sejong University Seoul Korea

Deep learning applications for medical image classification have shown remarkable promise, particularly incorporating attention-based neural networks. This is particularly relevant in medical imaging, where the integration of Artificial Intelligence assists with various imaging tasks, including classification, segmentation, and detection. Deep learning is revolutionizing medical research and playing a significant role in advancing personalized clinical treatment. However, the lack of interpretability in these models presents a significant obstacle to their adoption in clinical practice. Therefore, there is a growing need for a comprehensive understanding of artificial intelligence systems and their internal mechanisms, capabilities, and limitations, which is the focus of the field of explainable AI. This study proposes a novel attention-based explainable deep learning framework for medical image classification tasks, including Covid-19, breast cancer (BreakHis), lung cancer (LC2500), and Retinal optical coherence tomography (OCT). The proposed framework recorded overall accuracies of 98% (Covid-19 Radiography), 95% (BreakHis), 99.8% (LC2500), and 95%(OCT). For visual analysis of the outcomes, we employ and use the LIME, SHAP, and ELI-5 to analyze the achieved results. The study’s primary goal is to bridge the gap between the high performance achieved by attention-based models and the necessity for transparency and interpretability in medical image diagnostics.

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