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

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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Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

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Computers, Materials & Continua 2025年第1期82卷 1151-1169页

作者： Khalil Ullah Song Jian Muhammad Naeem Ul Hassan Suliman Khan Mohammad Babar Arshad Ahmad Shafiq Ahmad Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Department of Computer Science Abbottabad University of Science and TechnologyHavelianAbbottabad22500Pakistan Department of Computing and Electronics Engineering Middle East CollegeMuscat124Oman Department of CS&IT Pak-Austria Fachhochschule Institute of Applied Sciences and Technology MangHaripur22621Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile *** networks are sufficiently scaled to interconnect billions of users and *** in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous *** continuously improves its network functionality to support these *** input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in *** article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial *** technique aims to create long-lasting and secure NextG networks using this extended *** viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this ***,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

关键词： Next generation networks massive mimo communication network artificial intelligence 5G adversarial attacks channel estimation information security

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YOLOCSP-PEST for Crops Pest Localization and Classification

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Computers, Materials & Continua 2025年第2期82卷 2373-2388页

作者： Farooq Ali Huma Qayyum Kashif Saleem Iftikhar Ahmad Muhammad Javed Iqbal Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Science&Engineering College of Applied Studies&Community ServiceKing Saud UniversityRiyadh11362Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan

Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.

关键词： Deep learning classification of pests YOLOCSP-PEST pest detection

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Deploying Agrivoltaics in Sub-Saharan Africa: A Sustainable Pathway Towards Energy-Food Security-Challenges and Opportunities: A Review

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IEEE Access 2025年 13卷 87810-87833页

作者： Abubakar, Suleiman Ibrahim See, Chan Hwang Sukki, Firdaus Muhammad Mahendiran, Ramasamy Sundaram, Senthilarasu Abidin, Mohamad Izani Zainal Kamarudin, Mohd Khairul Amri Edinburgh Napier University Merchiston Campus School of Computing Engineering and the Built Environment EdinburghEH10 5DT United Kingdom University Sultan Zainal Abidin Faculty of Applied Social Science Gong Badak Campus Terengganu Kuala Nerus21300 Malaysia Tamil Nadu Agricultural University Department of Renewable Energy Engineering AEC&RI Coimbatore India Teesside University Tees Valley School of Computing Engineering and Digital Technologies MiddlesbroughTS1 3BX United Kingdom Higher Colleges of Technology Faculty of Applied Media Abu Dhabi United Arab Emirates

Agrivoltaics (APV) offer an innovative solution to the pressing energy and food security challenges in Sub-Saharan Africa (SSA). Over 600 million people in this region lack access to energy, and food insecurity remains pervasive. By combining photovoltaic (PV) systems with agricultural production, APV optimizes land-use efficiency, enabling concurrent renewable energy generation and enhancing agricultural productivity. This review critically evaluates the potential of APV systems to address these dual challenges, focusing on SSA’s unique socioeconomic and environmental contexts. Key findings highlight the APV’s ability to mitigate agricultural constraints, such as water scarcity and climate variability, while providing decentralized energy solutions to rural communities. The analysis emphasizes strategic APV design considerations, including panel height, spacing, and crop compatibility, which are essential for optimizing energy and crop yields. However, challenges such as high initial costs, limited technical capacity, and sociocultural acceptance pose significant barriers to its widespread adoption. This review discusses policy recommendations for addressing these barriers, including financial incentives, technology transfer frameworks, and stakeholder engagement strategies. The novelty of this work lies in its tailored approach to SSA, integrating evidence from global APV case studies and proposing localized implementation strategies that align with the region’s development priorities. This study advances the understanding of APV as a pathway to achieving Sustainable Development Goals (SDGs) in SSA by offering a dual-purpose model for energy access and food security. Overcoming adoption barriers through innovative governance, research, and community engagement will be pivotal to unlocking the transformative potential of APV in SSA’s energy-food landscape. © 2013 IEEE.

关键词： Food security

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Cognitive Load Optimization in Digital (ESL) Learning: A Hybrid BERT and FNN Approach for Adaptive Content Personalization

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International Journal of Advanced Computer science and Applications 2025年第4期16卷 564-576页

作者： Ramesh, Komminni Thomas, Christine Ann Osei-Asiamah, Joel Pagidipati, Bhuvaneswari Muniyandy, Elangovan Reddy, B. V. Suresh El-Ebiary, Yousef A. Baker BoS Anurag Engineering College Suryapet District Telangana Kodad India Department of English and Cultural Studies Christ University Bengaluru India Gauteng Province Pretoria South Africa West Godavari Dt Andhra Pradesh Bhimavaram534204 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of CSE Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Faculty of Informatics and Computing UniSZA University Malaysia

Traditional English as a Secondary Language (ESL) learning platform rely on static content delivery, often failing to adapt to individual learners’ cognitive capacities, leading to inefficient comprehension and increased cognitive load. A novel hybrid Feedforward Neural Network and Bidirectional Encoder Representation Transformer (FNN-BERT) framework stands as our solution because it performs dynamic content personalization through predictions of real-time cognitive load. The proposed approach incorporates Feedforward Neural Networks (FNN) alongside Bidirectional Encoder Representations from Transformers (BERT) to process behavioral analytics for optimized content complexity adjustment and adaptive and scalable learning delivery. Real-time adaptability, scalability and high computational needs of current models reduce their effectiveness in personalized learning environments. Through the application of Test of English for International Communication (TOEIC), International English Language Testing System (IELTS) and Test of English as a Foreign Language (TOEFL) datasets, our methodology uses Feedforward Neural Network (FNN) to forecast cognitive load based on student engagement behaviors and application errors then Bidirectional Encoders Representations from Transformer (BERT) processes content difficulty adjustments automatically. The proposed model delivers a 95.3% accuracy rate, 96.22% precision level, 96.1% recall capability and 97.2% F1-score which surpasses conventional Artificial Intelligence-based English as a Secondary Language (ESL) learning systems. The system makes use of Python for its implementation to improve understanding as well as student focus and mental processing speed. Personalized content presentation methods lead to lower cognitive strain which simultaneously advances student achievement numbers. The research adds value to smart educational frameworks through its introduction of a scalable framework that allows adaptable learning systems for E

关键词： Feedforward neural networks

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Pose Estimation of Spacecraft Using Dual Transformers and Efficient Bayesian Hyperparameter Optimization

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International Journal of Advanced Computer science and Applications 2025年第4期16卷 629-644页

作者： Raja, N. Kannaiya Ramesh, Janjhyam Venkata Naga El-Ebiary, Yousef A. Baker Muniyandy, Elangovan Reddy, N. Konda Kumar, Vanipenta Ravi Devarasetty, Prasad School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Engineering Mathematics K L University Greenfields Guntur Dist Andhra Pradesh Vaddeswaram522302 India Department of Mathematics Annamacharya University New Boyanapalli Rajampet India Department of Computer Science and Engineering DVR & Dr HS MIC College of Technology Andhra Pradesh Kanchikacherla India

Spacecraft pose estimation is an essential contribution to facilitating central space mission activities like autonomous navigation, rendezvous, docking, and on-orbit servicing. Nonetheless, methods like Convolutional Neural Networks (CNNs), Simultaneous Localization and Mapping (SLAM), and Particle Filtering suffer significant drawbacks when implemented in space. Such techniques tend to have high computational complexity, low domain generalization capacity for varied or unknown conditions (domain generalization problem), and accuracy loss with noise from the space environment causes such as fluctuating lighting, sensor limitations, and background interference. In order to overcome these challenges, this study suggests a new solution through the combination of a Dual-Channel Transformer Network with Bayesian Optimization methods. The innovation is at the center with the utilization of EfficientNet, augmented with squeeze-and-excitation attention modules, to extract feature-rich representations without sacrificing computational efficiency. The dual-channel architecture dissects satellite pose estimation into two dedicated streams—translational data prediction and orientation estimation via quaternion-based activation functions for rotational precision. Activation maps are transformed into transformer-compatible sequences via 1×1 convolutions, allowing successful learning in the transformer's encoder-decoder system. To maximize model performance, Bayesian Optimization with Gaussian Process Regression and the Upper Confidence Bound (UCB) acquisition function makes the optimal hyperparameter selection with fewer queries, conserving time and resources. This entire framework, used here in Python and verified with the SLAB Satellite Pose Estimation Challenge dataset, had an outstanding Mean IOU of 0.9610, reflecting higher accuracy compared to standard models. In total, this research sets a new standard for spacecraft pose estimation, by marrying the versatility of deep le

关键词： Convolutional neural networks

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Adaptive AI-Based Personalized Learning for Accelerated Vocabulary and Syntax Mastery in Young English Learners

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International Journal of Advanced Computer science and Applications 2025年第4期16卷 678-687页

作者： Aravind, Angalakuduru Durairaj, M. Chitkara, Preeti Baker El-Ebiary, Yousef A. Muniyandy, Elangovan Ushasree, Linginedi Ammar, Mohamed Ben Kodad Telangana 508206 India Dept.of English Panimalar Engineering College Poonamallee Chennai600123 India Head PR & International Relations Department of Applied Sciences KIET Group of Institutions Delhi-NCR Ghaziabad India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur Dist. 522302 India Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia

Language acquisition is an integral part of early schooling, but young English language learners struggle to learn vocabulary and syntax since they are not provided with specialized instruction. Conventional teaching may vary according to different learning speeds and it leads to unbalanced levels of proficiency among students and possibly leading to disengagement among slow learners. The present computer-assisted learning aids provide practice interactively but without real-time adaptation and personalized feedback, limiting their capacity to address learners’ unique problems. To overcome these constraints, this study suggests an Artificial Intelligence based personalized learning system that supports vocabulary and syntax learning via adaptive learning models, NLP-based chatbots and gamified interactive lessons. The system dynamically adapts content according to students’ most recent performance in real time to enable a personalized learning experience, which results in efficient Learning. The research has experimental study design, and two groups are considered, an AI-supported learning group and a traditional learning group. Pre-test and post-test design measures the effects of the system on vocabulary recall and syntax correctness. Other learner engagement rates like survey results and qualitative feedback inform learner experience and learning efficacy. Initial results indicate that learners working with the Artificial Intelligence powered learning system gained 25percent in recalling vocabulary and 30percent in syntax accuracy over the control group. Further, learner engagement rates are elevated because of real-time feedback and gamification components. These results emphasize the promise of AI-based personalized learning to boost language acquisition and lay the basis for further effective innovations in adaptive education technologies. © (2025), (science and Information Organization). All rights reserved.

关键词： Students

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Securing Autonomous Vehicles with Smart AI Security Agents

Securing Autonomous Vehicles with Smart AI Security Agents

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International Conference on New Trends in computing sciences (ICTCS)

作者： Haidar Jabbar Samir Al-Janabi Francis Syms Faculty of Applied Science and Technology Humber Polytechnic Toronto Canada Department of Computing and Software McMaster University Hamilton Canada

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

As Autonomous Vehicles (AVs) become increasingly integrated into modern transportation systems, they face rising exposure to complex cybersecurity threats. Existing security frameworks, based on static rules, signature detection, and conventional Artificial Intelligence (AI), are insufficient in addressing the dynamic and adaptive nature of cyberattacks in AVs environment. This study presents a multi-layered defense approach powered by autonomous AI agents capable of independently detecting, interpreting, and responding to a wide range of cyber threats in real time. These agents employ Machine Learning (ML), Deep Learning (DL), anomaly detection, and adversarial resilience techniques to counter sophisticated attacks such as sensor manipulation, AI corruption, GPS spoofing, and Vehicle-to-Everything (V2X) based intrusions. The proposed architecture contributes to the advancement of intelligent, self-adjusting security mechanisms tailored for the evolving cybersecurity landscape of autonomous systems.

关键词： Prevention and mitigation Transportation Market research Threat assessment Real-time systems Artificial intelligence Vehicle dynamics Autonomous vehicles Vehicle-to-everything Resilience

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Polyp image segmentation based on improved planet optimization algorithm using reptile search algorithm

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Neural computing and Applications 2025年第8期37卷 6327-6349页

作者： Abd Elaziz, Mohamed Al-qaness, Mohammed A. A. Al-Betar, Mohammed Azmi Ewees, Ahmed A. Department of Mathematics Faculty of Science Zagazig University Zagazig Egypt College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China Faculty of Computer Science and Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates College of Computing and Information Technology University of Bisha Bisha Saudi Arabia Department of Computer Damietta University Damietta Egypt Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan

To recognize the potential for colon polyps to develop into cancer over time, early diagnosis is crucial for preventative healthcare. Timely identification significantly improves the prognosis and treatment outcomes for colorectal cancer patients. Image segmentation is crucial in medical image analysis for accurate diagnosis and treatment planning. Therefore, in this study, we present an alternative multilevel thresholding polyp segmentation method (MPOA) to enhance the segmentation of polyp images. The proposed method is based on enhancing the planet optimization algorithm (POA) by integrating operators from the reptile search algorithm (RSA). The evaluation of the developed MPOA is tested with different polyp images and compared with other image segmentation approaches. The results highlight the superior capability of MPOA, as evidenced by various performance measures in effectively segmenting polyp images. Furthermore, metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and fitness values demonstrate that MPOA outperforms the basic version of POA and other methods. The evaluation outcomes underscore the significant impact of RSA in enhancing the performance of POA for the segmentation of polyp images. © The Author(s) 2025.

关键词： Diseases

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Electrohysterogram Data Augmentation Using Generative Adversarial Network for Pregnancy Outcome Prediction

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IEEE Access 2025年 13卷 92468-92482页

作者： Cheema, Muhammad Omar Din, Zia Mohy Ud Imran, Azhar Khan, Fawad Salam Al-Rawi, Mahmood Basil A. El-Meligy, Mohammed Gul, Jahan Zeb Air University Department of Biomedical Engineering Islamabad44000 Pakistan Air University Faculty of Computing & AI Department of Creative Technologies Islamabad44000 Pakistan King Saud University College of Applied Medical Sciences Department of Optometry Riyadh Saudi Arabia Amman Arab University College of Computer Science and Informatics Amman Jordan Maynooth University Department of Electronic Engineering Ireland

There are many difficulties in managing and detecting preterm pregnancies, especially in the early stages. Analyzing electrohysterogram data, which show the electrical activity of uterine muscles, is a promising non-invasive method for classifying term and preterm pregnancies. However, the effectiveness of machine learning classifiers is hampered by the inherent class imbalance in datasets. In order to overcome this constraint, this study uses a Generative Adversarial Network to produce high-quality synthetic EHG data while maintaining the statistical properties of actual signals. By successfully reducing class imbalance, the enhanced dataset makes it possible for machine learning models to be trained with resilience. Real and synthetic features did not differ significantly, according to statistical validation using a t-test (p > 0.05). With this method, the Random Forest classifier obtained a ROC-AUC of 0.99 and a 10-fold cross-validation accuracy of 98%. The suggested GAN-based method’s simplicity and efficacy demonstrate its promise for resolving class imbalance and developing reliable classification systems for diagnostic applications. © 2013 IEEE.

关键词： Generative adversarial networks

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