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8th International Conference on Data Science and Machine Learning Applications, CDMA 2025

作者： Rabbani, Zeeshan Ebrahim Hosseini, Seyed Pervez Chattha, Shahbaz Alandjani, Gasim Abosaq, Nasser Abdul Majid, Mazlina School of Information Technology Whitecliffe College Auckland New Zealand School of Information Technology Whitecliffe College Christchurch New Zealand School of Information Technology Whitecliffe College Wellington New Zealand Yanbu Industrial College Computer Science and Engineering Yanbu Saudi Arabia Universi Malaysia Pahang Al-Sultan Faculty of Computing Abdullah Pekan Malaysia

ISBN: (纸本)9798331539696

The increasing density of mobile networks has made the identification of optimal new coverage sites a critical challenge for mobile operators. Traditional methods relying on manual inspections and council plans are labour-intensive and insufficient. This study addresses this issue by leveraging data science and machine learning techniques to predict the suitability of new mobile cell sites. Utilizing data from Radio Spectrum Management New Zealand and Stats New Zealand, the study employs Random Forest as Machine learning model. Key factors considered include existing operator information and population density. The methodology integrates data cleaning, feature engineering, and model training, with validation performed using Power BI. Results demonstrate an accuracy of 84.62% for the Random Forest model, highlighting the importance of geographical location and population density in site suitability predictions. This approach will streamline the site selection process and enhance service quality, positioning operators as leaders in underserved areas. © 2025 IEEE.

关键词： Random forests

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AI-driven intrusion prevention system: leveraging mathematical foundations for advanced threats detection

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International Journal of information technology (Singapore) 2025年 1-6页

作者： Vashistha, Avneesh Alghamdi, Tami Abdulrahman Almalki, Sultan Ahmed Ead, Waleed M. Kaur, Gaganpreet Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-NCR Campus Modinagar Ghaziabad India Computer Science Department Faculty of Computing and Information Al-Baha University Al-Baha Saudi Arabia Computer Department Applied College Najran University Najran Saudi Arabia Faculty of Computing and Information Al-Baha University Al-Baha Saudi Arabia Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef Egypt Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

The given paper introduces a multi-staged AI-driven intrusion prevention system (IPS) that utilizes mathematical foundations like statistical analysis, probabilistic models, and optimization techniques to design strategic algorithms that recognize patterns as well as detect anomalies & potential attack vectors, which usually get missed out on by traditional security tools. The study focuses on the hybrid behavior that combines supervised and reinforcement learning (RL) methods to provide useful analysis of network traffic, which improves early detection of threats, whether they are known or unknown. This research demonstrates how the proposed AI-driven model can achieve a substantial decrease in the rate of false positives and improve threat detection accuracy. Thus, the given framework acts as a proactive component against today’s evolving cybersecurity threats and hence allows the creation of an efficient and responsive enterprise security framework. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Feedback loop Intrusion prevention Network traffic Reinforcement learning Threat detection

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Unveiling Effective Heuristic Strategies: A Review of Cross-Domain Heuristic Search Challenge Algorithms

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Computer Modeling in Engineering & Sciences 2025年第2期142卷 1233-1288页

作者： Mohamad Khairulamirin Md Razali MasriAyob Abdul Hadi Abd Rahman Razman Jarmin Chian Yong Liu Muhammad Maaya Azarinah Izaham Graham Kendall Faculty of Information Science and Technology Universiti Kebangsaan MalaysiaBangi43600SelangorMalaysia Center for Artificial Intelligence Technology Universiti Kebangsaan MalaysiaBangi43600SelangorMalaysia Faculty of Medicine Universiti Kebangsaan Malaysia Medical CentreCheras56000Kuala LumpurMalaysia School of Engineering and Computing MILA UniversityNilai71800Negeri SembilanMalaysia School of Computer Science University of Nottingham-Malaysia CampusSemenyih43500SelangorMalaysia

The Cross-domain Heuristic Search Challenge(CHeSC)is a competition focused on creating efficient search algorithms adaptable to diverse problem *** hyper-heuristics are a class of algorithms that dynamically choose heuristics during the search *** selection hyper-heuristics have different imple-mentation ***,comparisons between them are lacking in the literature,and previous works have not highlighted the beneficial and detrimental implementation methods of different *** question is how to effectively employ them to produce an efficient search ***,the algorithms that competed in the inaugural CHeSC have not been collectively *** work conducts a review analysis of the top twenty competitors from this competition to identify effective and ineffective strategies influencing algorithmic performance.A summary of the main characteristics and classification of the algorithms is *** analysis underlines efficient and inefficient methods in eight key components,including search points,search phases,heuristic selection,move acceptance,feedback,Tabu mechanism,restart mechanism,and low-level heuristic parameter *** review analyzes the components referencing the competition’s final leaderboard and discusses future research directions for these *** effective approaches,identified as having the highest quality index,are mixed search point,iterated search phases,relay hybridization selection,threshold acceptance,mixed learning,Tabu heuristics,stochastic restart,and dynamic *** are also compared with recent trends in *** work enhances the understanding of selection hyper-heuristics,offering valuable insights for researchers and practitioners aiming to develop effective search algorithms for diverse problem domains.

关键词： Hyper-heuristics search algorithms optimization heuristic selection move acceptance learning diversification parameter control

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Enhancing single-lead electrocardiogram arrhythmia detection with empirical mode decomposition

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Neural computing and Applications 2025年 1-23页

作者： Issa, Mohamed F. Yousry, Ahmed Tuboly, Gergely Wang, Zeyu Zoltan, Juhasz Selim, Mazen M. AbuEl-Atta, Ahmed H. Department of Scientific Computing Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Electrical Engineering and Information Systems University of Pannonia Veszprem8200 Hungary Department of Mechatronics Delta University for Science and Technology Gamasa35712 Egypt

Cardiac arrhythmias pose a significant challenge to health care, requiring accurate and reliable detection methods to enable early diagnosis and treatment. However, traditional ECG beat classification methods often lack robustness and fail to generalize effectively to diverse clinical scenarios, particularly when using single-lead-I recordings, which are less studied compared to lead II. To address this gap, this paper proposes a novel method that combines deep learning and empirical mode decomposition (EMD) for arrhythmia detection using single-lead-I ECG recordings obtained with electrodes on each hand. The hybrid model comprises 149 layers of one-dimensional convolution, max pooling, and residual blocks. By leveraging EMD, it classifies ECG beats into normal beats, premature ventricular contractions, and atrial premature beats with high sensitivity (99.86%, 99.72%, and 99.84%, respectively) using the first four intrinsic mode functions (IMFs). The proposed method is applicable in clinical and out-of-hospital monitoring scenarios, and an accompanying web application provides real-time analysis and diagnostic results. This approach addresses a critical gap in lead-I ECG analysis, enhancing diagnostic precision and expanding the scope of ECG applications in health care. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Empirical mode decomposition

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Perception-Centric Explainable AI: Bridging Cognitive Theories and HCI Design for Enhanced User Experience

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Procedia Computer Science 2025年 257卷 297-304页

作者： Sara Alhasan Reem Alnanih Computer Science Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia

Artificial Intelligence (AI) systems increasingly influence high-stakes decision-making, necessitating explainability tailored to non-technical users. This research investigates integrating cognitive theories, Human-Computer Interaction (HCI) principles, and Explainable AI (XAI) techniques to enhance user perception, a core cognitive function. A perception-focused framework was developed and tested using a medical symptom checker tool. The study involved 20 participants divided into control and experimental groups. The experimental group used an enhanced tool with features like visual hierarchy and consistency, while the control group used a basic version with minimal explainability. Participants completed pre-test, testing, and post-test phases, including surveys and objective tasks measuring recognition and recall. Results showed that 90% of the experimental group achieved ”Good Performance” in recognition tasks compared to 20% in the control group. Subjective feedback indicated significantly higher un-derstandability, with an average rating of 4.8 out of 5 for the enhanced tool versus 3.1 for the control. These findings underscore the effectiveness of combining HCI principles and cognitive theories, such as Gestalt and Feature Integration, in reducing cognitive load and improving clarity. This multidisciplinary approach advances the design of intuitive, user-centered AI systems, fostering trust and accessibility.

关键词： Cognitive Functions Cognitive Theories Explainable Artificial Intelligence (XAI) Human-Centered XAI Human-Computer Interaction (HCI) Medical Diagnosis Tool

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AutoQ 2.0: From Verification of Quantum Circuits to Verification of Quantum Programs 31th

AutoQ 2.0: From Verification of Quantum Circuits to Verific...

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31st International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2025, which was held as part of the International Joint Conferences on Theory and Practice of Software, ETAPS 2025

作者： Chen, Yu-Fang Chung, Kai-Min Hsieh, Min-Hsiu Huang, Wei-Jia Lengál, Ondřej Lin, Jyun-Ao Tsai, Wei-Lun Institute of Information Science Academia Sinica Taipei Taiwan Hon Hai Quantum Computing Research Center Taipei Taiwan Faculty of Information Technology Brno University of Technology Brno Czech Republic Innovation Frontier Institute of Research for Science and Technology & Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9783031906596

We present a verifier of quantum programs called AutoQ 2.0. Quantum programs extend quantum circuits (the domain of AutoQ 1.0) by classical control flow constructs, which enable users to describe advanced quantum algorithms in a formal and precise manner. The extension is highly non-trivial, as we needed to tackle both theoretical challenges (such as the treatment of measurement, the normalization problem, and lifting techniques for verification of classical programs with loops to the quantum world), and engineering issues (such as extending the input format with a support for specifying loop invariants). We have successfully used AutoQ 2.0 to verify two types of advanced quantum programs that cannot be expressed using only quantum circuits: the repeat-until-success (RUS) algorithm and the weak-measurement-based version of Grover’s search algorithm. AutoQ 2.0 can efficiently verify all our benchmarks: all RUS algorithms were verified instantly and, for the weak-measurement-based version of Grover’s search, we were able to handle the case of 100 qubits in ∼20 minutes. © The Author(s) 2025.

关键词： Qubits

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Android Malware Detection via Hybrid Lion-Bee Optimization with Bi-Directional Recurrent Neural Network 3

Android Malware Detection via Hybrid Lion-Bee Optimization w...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Rajeyyagari, Sivaram Elfaki, Mohamed Ahmed Rashid, Khan Asif Bala, B. Kiran Reyad, Omar College of Computing and Information Technology Shaqra University Department of Computer Science Shaqra Saudi Arabia K.Ramakrishnan College of Engineering Department of Ai and Ds Trichy India College of Computing and Information Technology Shaqra University Shaqra Saudi Arabia Faculty of Computers and Artificial Intelligence Sohag University Sohag Egypt

ISBN: (纸本)9798331508456

The number of malicious programs that target the Android OS has significantly expanded with the rise in mobile device usage. It is crucial to identify, prevent, and defeat Android malware assaults since they have long presented a severe threat to Android apps. The emergence of secure Android app ecosystem depends on recognizing and classifying harmful applications into groups are similar to another. Malware families can be categorized in order to recognize harmful activity and to systematically spot dangerous-patterns. Thus, the study suggests a hybrid strategy that combines the attributes discovered from doing static and dynamic malware assessment for enhanced identification and categorization of Android malware. This method more effectively addresses the issue of studying, identifying Android malware. The feature extracted from image sections utilizing the hybrid lion and artificial bee colony (HL-ABC) optimization. RNN were used to classify the retrieved features. Each segment of a malware image file served as a test case for the categorization performance. This is contrasted with other current ways to show the effectiveness of the suggested strategy. Comparing the suggested model to other approaches, the study's findings demonstrate its effectiveness in identifying and classifying Android malware. © 2025 IEEE.

关键词： Android malware

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TOWARDS A ROBUST QUALITY ASSURANCE FRAMEWORK FOR CLOUD computing ENVIRONMENTS

arXiv

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arXiv 2025年

作者： Alharbi, Mohammed Ahmad Qureshi, Rizwan Department of Information Technology Faculty of Computing and Information Technology King Abdul-Aziz University Jeddah80213 Saudi Arabia

Trends such as cloud computing raise issues regarding stable and uniform quality assurance (QA) and validation of software requirements. Current QA frameworks are poorly defined, often not automated, and lack the flexibility needed for on-demand, cloud-based environments. These gaps lead to inconsistencies in service delivery, challenges in scaling organizational capacity, and internal and external inefficiencies that affect the reliability and effectiveness of cloud services. This paper presents a detailed framework for QA in cloud computing systems and advocates for standardized, automated, and adaptable systems to address these challenges. It aims to establish generic QA policies, incorporate intelligent techniques to enhance extendibility, and create adaptive solutions to manage the inherent attributes of cloud computing environments. The proposed framework is evaluated through survey questionnaires from industry practitioners, and descriptive statistics summarize the results. The study demonstrates the promise, effectiveness, and potential applicability of integrating a single QA framework to enhance the software’s functionality, dependability, and future adaptability in cloud computing systems. © 2025, CC BY.

关键词： Cloud platforms

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Ransomware Defense Strategies Using Cyber Threat Intelligence: A Case Study of a Saudi Organization 43rd

Ransomware Defense Strategies Using Cyber Threat Intelligenc...

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43rd IBIMA Conference on Artificial intelligence and Machine Learning, IBIMA-AI 2024

作者： Wali, Arwa Alamri, Sharifah Alsharif, Sumayah Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9783031790850

Despite businesses taking severe measures to maintain the highest security levels in their cyberspace, the possibility of a cyberattack still exists. Currently, ransomware attacks represent the most common type of cybercrime. Hackers can quickly overcome an organization's cyber defenses, compromise its cyberspace, and profit significantly in the process. Ransomware attacks expose businesses to major technical, operational, and financial disruptions, resulting in revenue losses and potentially leading to customer attrition and loss of future business opportunities due to damage to the company’s image. The least expensive solution in ransomware attacks is to pay the ransom to regain control over the compromised cyberspace;however, this approach does not guarantee the restoration of data. Cyber threat intelligence (CTI) is an essential and effective tool for defending against ransomware attacks targeting organizations and businesses. Although CTI applications are used worldwide, Saudi organizations still lack methods for detecting ransomware and cyberattacks. Therefore, this study defines a cyber defense strategy for ransomware attacks using CTI tools, based on information collected from a Saudi organization, Key Car Rental. It evaluates the effectiveness of these tools in detecting ransomware attacks and how they enhance situational awareness by proactively integrating threat information. The results demonstrate the effectiveness of CTI tools in detecting ransomware attacks and providing improvements to an organization's overall security by identifying potential threats before they become actual attacks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cyber attacks

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Analyzing Sentiments towards Performance of New Academic Programs in A Ghanaian University: A Case of Ghana Communication technology University

Research Square

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Research Square 2025年

作者： Susuassey, Wonder Kwaku Adjei, Daniel Agbeli, Deborah Esenam Asante, Emmanuel Frimpong Mensah, Dorinda Nyarko Faculty of Computing and Information Systems Ghana Communication Technology University Accra Ghana

Introduction of new programs in educational institutions in Ghana requires the introduction of a new curriculum. However, the performance of the new programs after the admission of students into the programs becomes questionable as the least performance is achieved. As Ghanaian universities continue to expand their curriculum to meet the evolving demands of the digital economy, understanding the perceptions and experiences of the academic community becomes essential for gauging the success and potential areas for improvement of these programs. The study performs a supervised sentiment analysis on reviews gathered from students enrolled in new academic programs towards the recommendation of the programs to people. The primary data for this study were drawn from a sample of 226 students, using both open-ended and closed-ended structured questionnaires. In the study, four machine learning methods, including Multinomial Naïve Bayes, Support Vector Machine, Logistic Regression, and Random Forest were applied. The methods are compared, and the empirical findings reveal that the Support Vector Machine and Random Forest approaches outperformed the Multinomial Naïve Bayes and Logistic Regression approaches, resulting in generally positive sentiment towards the performance of the new academic programs. By analyzing the sentiments of students, the study recommends actionable insights that can guide the university in enhancing its academic offerings and aligning them with the expectations of its community. © 2025, CC BY.

关键词： Adversarial machine learning

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