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Designing a hybrid neural network framework for real-time anomaly detection in cybersecurity applications

International Journal of information Technology (Singapore) 2025年 1-7页

作者： Jain, Aaditya Ead, Waleed M. Alshahrani, Mohammed Altamimi, Jawaher Suliman Aklan, Fawaz Rihan, Shaza Dawood College of Computing Sciences and Information Technology Teerthanker Mahaveer University Uttar Pradesh Moradabad India Faculty of Computing and Information Al-Baha University Al-Baha Saudi Arabia Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef Egypt Computers Department Applied College Najran University Najran Saudi Arabia Applied College Najran University Najran Saudi Arabia Department of Computer Science Faculty of Applied Science Taiz University Taiz P.O. Box 6803 Taiz Yemen

As cybersecurity is becoming a prominent domain, it is an imperative to provide real-time anomaly detection for early threat detection and to keep networks safe. In this paper, an optimized hybrid neural network-framework is proposed to detect anomalies at several attack vectors with high efficacy. The framework comprises convolutional neural network equipped with long-short term memory (CNN-LSTM) for temporal and spatial representation of data. As the system accommodates new data and keeps on learning, it adjusts itself becoming a significant mean in counteracting against emerging threats with less latency in detection. In order to verify the efficiency and robustness of the proposed framework, a series of real-time simulation is designed using key performance metrics such as detection rate, false alarm rate and processing time. The results show higher improvement in accuracy and speed of anomaly detection, which makes the framework a useful instrument to reinforce cybersecurity defense during real-time scenarios. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Anomaly detection Convolutional neural network (CNN) Cybersecurity Hybrid approach Long-short term memory (LSTM)

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Multilingual Hate Speech Detection: Innovations in Optimized Deep Learning for English and Arabic Hate Speech Detection

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SN computer Science 2025年第3期6卷 1-16页

作者： AL-Sukhani, Hassan Bsoul, Qusay Elhawary, Abdelrahman H. Nasr, Ziad M. Mansour, Ahmed E. Batyha, Radwan M. Alqadi, Basma S. Alqurni, Jehad Saad Alfagham, Hayat Madbouly, Magda M. Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman Jordan Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt Faculty of Information Technology Applied Science Private University Amman 11931 Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia Department of Educational Technologies College of Education Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Department of MIS College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Institute of Graduate Studies & Research Alexandria University Alexandria Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt

This paper presents the development of a multilingual hate speech detection model that effectively processes and classifies content in both Arabic and English. The study leverages both traditional machine learning models, such as K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM), as well as advanced deep learning models, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) networks. A key challenge addressed is the classification of mixed-language content, which is common on social media platforms in the MENA region. To enhance detection performance, preprocessing techniques were applied to the text data, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the dataset. The results show that the Bi-LSTM model outperformed traditional machine learning approaches, particularly in identifying hate speech across multiple languages. The proposed model demonstrates superior accuracy and robustness in handling mixed-language input, providing a more effective solution for real-world hate speech detection tasks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalance Deep learning Hate speech detection Machine learning Mixed Arabic and English training Multilingual NLP Natural language processing (NLP) Sentiment analysis

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A system for detecting scratches on the surface of small-sized metal shafts after the grinding process using optical techniques combined with computer vision.

A system for detecting scratches on the surface of small-siz...

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International Symposium on applied Science 2024, ISAS 2024

作者： Dau, Sy Hieu An, Khang Le Nguyen Thi, Phuc Dang Department of Applied Physics Applied Sciences Faculty Ho Chi Minh city University of Technology Vietnam National University HCMC Ho Chi Minh City Viet Nam Department of Computer Science Faculty of Information Technology Industrial University of Ho Chi Minh City Ho Chi Minh City Viet Nam

During the grinding process of metal machine parts requiring a high degree of smoothness, the use of diamond-surfaced grinding tools, specialized grinding stones, or high-hardness metals are common choices. However, during grinding, surface tool damage or misalignment between grinding surfaces can cause defects such as cracks, scratches, or complete surface destruction. Detecting small scratches is one of the biggest challenges due to their size and distribution, making it difficult to quickly identify them using the human eye. Automated systems, on the other hand, struggle to highlight scratches because automatic optical lighting systems face strong reflections from the metal surface, especially cylindrical surfaces after being polished. As a result, the images obtained lack controlled reflection positions and contrast between normal and scratched areas, leading to inefficiency in detecting these scratches. In this study, we propose an optical system combined with lighting techniques to highlight scratches on the surface of a cylindrical machine part after polishing, thereby detecting and classifying these defects to make pass-fail decisions in practical production line applications. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Grinding (machining)

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Ensuring efficient power system operation in partial phase modes

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Energy Reports 2025年 13卷 5187-5195页

作者： Kuchanskyy, Vladislav Zaitsev, Ievgen Korotkov, Serhii Marchenko, Vitalii Department of power-supply systems optimization Institute of Electrodynamics National Academy of Sciences of Ukraine Beresteyskiy 56 Kyiv03680 Ukraine Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment Institute of Electrodynamics National Academy of Sciences of Ukraine Beresteyskiy 56 Kyiv03680 Ukraine Department of Applied Information Systems Faculty of Information Technology Taras Shevchenko Nation University of Kiev Volodymyrska Str. 64/13 Kyiv01601 Ukraine Center for Information-Analytical and Technical Support of Nuclear Power Facilities Monitoring National Academy of Sciences of Ukraine Akademika Palladina Avenue 34-A Kyiv Ukraine Department of Сomputer Engineering State University of Information and Communication Technologies st. Solom'yanska 7 Kyiv03110 Ukraine Department of Cybersecurity Systems and Technologies State University of Information and Communication Technologies st. Solom'yanska 7 Kyiv03110 Ukraine

The article proposes a method for assessing the static stability of a power system in incomplete-phase modes. An analysis of permissible power system modes in the event of incomplete-phase operating modes is performed. A general approach to solving the problem of finding reserves of static aperiodic stability is formulated, with the help of which generalized equations of limit modes are obtained. The influence of a controlled shunt reactor on the throughput of intersystem power transmission lines is considered. A method for determining the limits of aperiodic static stability is developed, taking into account the asymmetry caused by the incomplete phase mode of operation. Experimental studies have been conducted that have shown the effectiveness of applying the developed method for finding limit states in the direction of weighting to solve practical problems of estimating reserves of static aperiodic stability. The work comprehensively considers the problem of asymmetric modes in main electric networks using the example of a positional model of the electric power system. Special attention is paid to introducing into the region of permissible modes according to the criterion of static stability according to the criteria of the power transmission node and, accordingly, the transmitted power along the power transmission lines. To introduce into the region of permissible modes, controlled shunt reactors are used with the corresponding developed voltage regulation law, which ensures that the maximum permissible voltage values along the line route and in the nodes of the positional model are not exceeded. The work also evaluates the influence of the incomplete phase mode on the voltage values in the nodes under conditions of different numbers of phases of shunt reactor groups. © 2025

关键词： Electric power transmission networks

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Dynamic Reconfigurable Photovoltaic Array Architectures a Review 4th

Dynamic Reconfigurable Photovoltaic Array Architectures a Re...

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4th International Conference on Electronic Engineering and Renewable Energy systems, ICEERE 2024

作者： Bouselham, Loubna Hajji, Bekkay Rabhi, Abdelhamid Mellit, Adel Benhaddou, Mohammed Energy Embedded System and Information Processing Laboratory National School of Applied Sciences Mohamed First University Oujda Morocco National School of Architecture Oujda Morocco Modeling Information and Systems Laboratory University of Picardie Jules Verne 3 Amiens France Renewable Energy Laboratory Faculty of Sciences and Technology Jijel University Jijel Algeria

ISBN: (纸本)9789819799749

Mismatch losses significantly reduce the output power of photovoltaic (PV) arrays. Among available mismatch mitigate techniques, the dynamic reconfigurable photovoltaic array (DRPVA) architectures can be considered as a promising technique to compensate the power losses. In contrast to fixed photovoltaic architectures, the DRPVA architectures modify dynamically the interconnections between the PV modules of the same PV array (PVA) according to the operating conditions. This paper presents the different approaches of DRPVA architectures proposed in the literature. These approaches are classified according to the reconfiguration strategy adopted and compared in terms of power enhancement, topology used, and additional material required. Through this study, the main constraints limiting the DRPVA efficiency are determined and discussed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Dynamic reconfigurable PV array Mismatch losses Partial shading Power enhancement Total-cross-tied

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MOCBOA:Multi-Objective Chef-Based Optimization Algorithm Using Hybrid Dominance Relations for Solving Engineering Design Problems

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computer Modeling in Engineering & sciences 2025年第4期143卷 967-1008页

作者： Nour Elhouda Chalabi Abdelouahab Attia Abdulaziz S.Almazyad Ali Wagdy Mohamed Frank Werner Pradeep Jangir Mohammad Shokouhifar Computer Science Department University of M’silaIchebiliaM’sila28000Algeria Computer ScienceDepartment Mohamed El Bachir El IbrahimiUniversity of Bordj BouArreridjBordj BouArreridj34000Algeria Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Faculty of Mathematics Otto-von-Guericke UniversityMagdeburg39016Germany University Centre for Research and Development Chandigarh UniversityMohali140413India Department of Electrical and Computer Engineering Shahid Beheshti UniversityTehran14399Iran DTU AI and Data Science Hub(DAIDASH) Duy Tan UniversityDa Nang550000Vietnam

Multi-objective optimization is critical for problem-solving in engineering,economics,and *** study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct *** approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto *** comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume *** paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.

关键词： Multi-objective optimization chef-based optimization algorithm(CBOA) pareto dominance epsilon dominance cone-epsilon dominance strengthened dominance

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan 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 Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (Science and information Organization). All rights reserved.

关键词： Quantum cryptography

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Automatic detection of gastrointestinal system abnormalities using deep learning-based segmentation and classification methods

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Health information Science and systems 2025年第1期13卷 1-25页

作者： Şener, Abdullah Ergen, Burhan Management Information Systems Faculty of Economics and Administrative Sciences Fırat University Elazığ Turkey Computer Engineering Faculty of Engineering Fırat University Elazığ Turkey

Early diagnosis and precise treatment of gastrointestinal (GI) diseases are crucial for reducing mortality and improving quality of life. In this context, the detection and classification of abnormalities in endoscopic images is an important support for specialists during the diagnostic process. In this study, an innovative deep learning approach for the segmentation and classification of pathological regions in the GI system is presented. In the first phase of the study, a novel segmentation network called GISegNet was developed. GISegNet is a deep learning-based architecture tailored for accurate detection of anomalies in the GI system. Experiments conducted on the Kvasir dataset showed that GISegNet achieved excellent results on performance metrics such as Jaccard and Dice coefficients and outperformed other segmentation models with a higher accuracy rate (93.16%). In the second phase, a hybrid deep learning method was proposed for classifying anomalies in the GI system. The features extracted from the transformer-based models were fused and optimized using the Minimum Redundancy Maximum Relevance (mRMR) algorithm. The classification process was performed using Support Vector Machines (SVM). As a result of feature fusion and selection, the second model, which combined features from DeiT and ViT models, achieved the best performance with an accuracy rate of 95.2%. By selecting a subset of 300 features optimized by the mRMR algorithm, the accuracy (95.3%) was maintained while optimizing the computational cost. These results show that the proposed deep learning approaches can serve as reliable tools for the detection and classification of diseases of the GI system.

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The Effect of AI Gamification on Students’ Engagement and Academic Achievement in Malaysia: SEM Analysis Perspectives

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IEEE Access 2025年 13卷 70791-70810页

作者： Ahmed Dahri, Nisar Yahaya, Noraffandy Mugahed Al-Rahmi, Waleed Almuqren, Latifah Almgren, Abeer S. Alshimai, Abdulrahman Samed Al-Adwan, Ahmad Johor Bahru81310 Malaysia Sohar University Faculty of Language Studies Sohar311 Oman Dar Al Uloom University College of Business Administration Department of Management Information System Riyadh13314 Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia King Saud University College of Arts Department of Visual Arts Riyadh11451 Saudi Arabia Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Business School Department of Business Technology Amman19328 Jordan

Gamification and Artificial Intelligence (AI) have transformed the educational environment, offering innovative approaches to enhance student engagement and learning outcomes. By using the power of gamification and AI, educators can create immersive, personalized, and interactive learning experiences that increase motivation, participation, and academic achievement. This study investigates the relationships between gamification, artificial intelligence, student engagement, and achievement in educational contexts. A quantitative approach was employed, combining online surveys and structural equation modeling (SEM) analysis with 290 students from Malaysia. SEM analysis was performed using SmartPLS 4 software. The findings reveal significant positive relationships between gamification and student achievement, gamification and student engagement, gamification and artificial intelligence, and AI with both student engagement and achievement. Furthermore, student engagement was found to influence student achievement positively. The study’s implications suggest that educators and instructional designers should adopt these technologies to create intelligent, interactive, and personalized learning environments. Future research directions include exploring long-term effects and contextual factors influencing student outcomes to promote student success in the digital age. However, the study is limited to a single country context (Malaysia), and relies on self-reported survey data, which may influence generalizability and introduce potential biases. © 2013 IEEE.

关键词： Federated learning

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Machine Learning Approaches in Cybersecurity to Enhance Security in Future Network Technologies

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SN computer Science 2025年第4期6卷 1-18页

作者： Ahmad, Sultan Haque, Md. Alimul Abdeljaber, Hikmat A. M. Eljialy, A.E.M. Nazeer, Jabeen Mishra, B.K. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Mohali Punjab Gharuan 140413 India Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Information Systems College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia Department of Physics Veer Kunwar Singh University Ara 802301 India

As technology continues to evolve rapidly, cybersecurity has become a critical global concern. The increasing sophistication of cyber threats poses significant risks to individuals, businesses, and governments. To combat these threats, cybersecurity tools play a crucial role in monitoring, detecting, and mitigating security risks in digital environments. These tools ensure data protection, prevent unauthorized access, and safeguard sensitive information. However, traditional cybersecurity approaches are struggling to keep pace with emerging cyber-attacks. In response, machine learning (ML) has emerged as a powerful solution for enhancing cybersecurity strategies. ML algorithms enable organizations to analyze large datasets, identify anomalies, and predict potential threats with greater accuracy. Cybersecurity tools, integrated with ML, act as the final line of defense against attacks such as data breaches, identity theft, and system intrusions. This research explores the application of ML in selecting and optimizing cybersecurity models for enterprise ICT systems. It also emphasizes the growing demand for skilled professionals who can develop and implement ML-based security solutions. By examining current trends and future possibilities, this study provides valuable insights into the role of ML in strengthening cybersecurity measures and enhancing overall digital protection for organizations worldwide. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber security Cybercrime Data analysis ML algorithms Network security Threats

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