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YOLO-RAW: Advancing UAV Detection With Robustness to Adverse Weather Conditions

IEEE Transactions on Intelligent Transportation Systems 2025年第6期26卷 7857-7873页

作者： Munir, Adnan Siddiqui, Abdul Jabbar Hossain, M. Shamim El-Maleh, Aiman Computer Engineering Department Dhahran31261 Saudi Arabia KFUPM SDAIA-KFUPM Joint Research Center for Artificial Intelligence IRC for Intelligent Secure Systems and Computer Engineering Department Dhahran31261 Saudi Arabia King Saud University College of Computer and Information Sciences Research Chair of Pervasive and Mobile Computing Department of Software Engineering Riyadh12372 Saudi Arabia KFUPM Computer Engineering Department Information and Computer Science Department IRC for Intelligent Secure Systems Dhahran31261 Saudi Arabia

With the widespread adoption of unmanned aerial vehicles (UAVs) in various applications (e.g., aerial transportation, traffic monitoring), there have been apprehensions regarding the associated risks of employing UAVs in both civilian and military contexts, including concerns about privacy infringement, safety issues, and security threats. Although several methods have been proposed to detect UAVs, a pressing open challenge is posed by varying adverse weather conditions that could degrade the performance of many existing methods. To address these limitations, this work proposes a YOLO-based (You Only Look Once) novel model, YOLO-RAW that exhibits improved performance in adverse weather conditions and considers different scales of UAVs. Furthermore, to facilitate a more comprehensive evaluation of the proposed model’s effectiveness in UAV detection, we have curated a complex background dataset and introduced three distinct test sets affected by adverse weather conditions. These three test sets comprise the Rainy Test Set (RTS), the AWGN (Additive White Gaussian Noise) Test Set (ATS), and the Motion Blurred Test Set (MBTS). The comprehensive experiments demonstrate the effectiveness of the proposed YOLO-RAW model over its counterparts in detecting UAVs under adverse conditions. © 2000-2011 IEEE.

关键词： Drones

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Predicting land suitability for wheat and barley crops using machine learning techniques

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Scientific Reports 2025年第1期15卷 1-19页

作者： Ganati, Bikila Abebe Sitote, Tilahun Melak Department of Computer Science Faculty of Computing and Informatics Jimma Institute of Technology Jimma University P.O. Box 378 Jimma Ethiopia Department of Software Engineering College of Engineering Addis Ababa Science and Technology University P.O. Box 16417 Addis Ababa Ethiopia

Ensuring food security to meet the demands of a growing population remains a key challenge, especially for developing countries like Ethiopia. There are various policies and strategies designed by the government and stakeholders to confront the challenge. One of the strategies is using technology solutions to increase crop productivity. Precision agriculture using advanced technology has been utilized to increase crop yield. Identifying suitable land for a crop is one of the important factors that will affect the crop’s yield. The existing approach to land suitability identification for a crop is time-consuming, expensive, and inaccurate. In this study, land suitability has been predicted for the two widely grown cereal crops in Ethiopia—wheat and barley—using machine learning techniques. The dataset was obtained from the engineering Corporation of Oromia (ECO). To make it suitable for modelling, we have pre-processed it. Features have been selected with univariate feature selection (UFS), recursive feature elimination with cross validation (RFECV), and sequential forward selection (SFS). Then, random forest (RF), gradient boosting (GB), and K-nearest neighbour (KNN) were used to predict the land suitability of the two selected crops. To optimize the performance of the models, hyperparameters were tuned with cross-validated randomized searches. The performance of the models has been evaluated using stratified tenfold cross-validation with performance metrics such as accuracy, precision, recall, and F1-score. GB with the SFS has better performance than the other models, with accuracy of 99.41%, precision of 99.37%, recall of 99.34%, and an F1-score of 99.35%. We believe that predicting land suitability accurately using machine learning techniques for the two commonly cultivated cereal crops in Ethiopia will be helpful in increasing the crops’ productivity. The developed model is very accurate. It can be used to develop a decision support system to identify the land s

关键词： Barley Gradient boosting Land suitability Machine learning Sequential forward selection Wheat

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A Blockchain-Based Distributed Collaborative Sensing and Spectrum Access Approach for Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Peng, Yuhuai Li, Yuan Guo, Yu Zhang, Dawei Khan, Fazlullah Alturki, Ryan Alshawi, Bandar Northeastern University College of Computer Science and Engineering Shenyang110819 China Zhiyuan Research Institute Xihu District Hangzhou China Zhengzhou University School of Computer and Artificial Intelligence Henan Zhengzhou450001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China KKR and KSR Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh Guntur India Division of Research and Development Lovely Professional University Phagwara India University Centre of Research and Development Chandigarh University Mohali India Umm Al-Qura University Department of Software Engineering College of Computing Makkah Saudi Arabia Umm Al-Qura University College of Computing Department of Computer and Network Engineering Makkah Saudi Arabia

Cognitive radio (CR) provides key technical support for the seamless communication of consumer electronic (CE) devices through distributed cooperative sensing and spectrum access. However, its openness and dynamic nature introduce potential security risks and challenges. Due to the openness of wireless communication, spectrum sensing and access are vulnerable to malicious attacks, and resource-constrained devices cannot effectively implement artificial intelligence (AI)-driven encryption and access control strategies. To address these limitations, this study proposes a blockchain-based distributed collaborative sensing method for CE devices. In the proposed method, after sensing, the smart contracts verify node legitimacy using the ring signature method. The malicious users are rejected by the outlier detection method, which improves the collaborative anti-interference ability. In addition, by using a centralized training and distributed execution architecture, a secure spectrum sensing strategy based on multi-agent reinforcement learning is designed. The proposed strategy ensures that each node makes independent decisions, which enhances the adaptability of the CE ecosystem. The proposed method is verified by extensive simulations, and the results show that it can maintain spectrum awareness of more than 95.72% and an access success rate of 91.19% under malicious attacks. Moreover, communication overhead is significantly reduced by approximately 93.53%. © 1975-2011 IEEE.

关键词： Blockchain

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Detection of ARP Spoofing Attack in Healthcare Networks Using a LSTM Model

Detection of ARP Spoofing Attack in Healthcare Networks Usin...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Salam Al-E'mari Yousef Sanjalawe Ghader Kurdi Budoor Allehyani Information Security Department Faculty of Information Technology University of Petra (UoP) Amman Jordan Information Technology Department King Abdullah II School for Information Technology The University of Jordan (UJ) Amman Jordan Department of Data Science College of Computing Umm Al-Qura University (UQU) Makkah Saudi Arabia Department of Software Engineering College of Computing Umm Al-Qura University (UQU) Makkah Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Healthcare networks face significant cybersecurity threats due to their extensive interconnectivity, with Address Resolution Protocol (ARP) spoofing posing a major risk to the confidentiality and integrity of medical data. ARP spoofing is a Man-in-the-Middle (MITM) attack where an attacker sends falsified ARP messages to associate their MAC address with the IP address of a legitimate device, enabling unauthorized interception or manipulation of network traffic. In this paper, we propose an LSTM-based detection model designed to effectively identify ARP spoofing attacks in healthcare environments. Unlike traditional methods that rely on static markers at fixed time intervals, the LSTM model leverages temporal dependencies in network traffic, enhancing detection accuracy and adaptability to evolving attack patterns. Experimental results based on the CICIoMT Dataset 2024 demonstrate that the proposed model achieves a detection accuracy of 98.5%, significantly outperforming conventional approaches. These findings highlight the effectiveness of LSTM networks in mitigating ARP spoofing threats, offering a scalable solution for securing healthcare networks.

关键词： Adaptation models Protocols Accuracy Computational modeling Intrusion detection Telecommunication traffic Internet of Medical Things IP networks Long short term memory Faces

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LFQAP: A Lightweight and Flexible Quantum Artificial Intelligence Application Platform

Quantum Engineering

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Quantum engineering 2025年第1期2025卷

作者： Zhang, Xin Li, Xiaoyu Hou, Yuexian College of Intelligence and Computing Tianjin University No.135 Ya Guan Road Tianjin300350 China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China Institute of Electronics and Information Industry Technology of Kash Xinjiang Kash844000 China

Quantum artificial intelligence (AI) is one of the critical research domains in the field of quantum computing and holds significant potential for practical applications in the near future. A quantum AI software platform serves as a fundamental infrastructure for advancing research and facilitating applications in this area. Such a platform supports essential tasks including quantum AI model training, inference, and the deployment of diverse applications. The current quantum AI software platforms prioritize comprehensive functionality;however, they often lack scalability, making it challenging to integrate new features flexibly. Given the broad and evolving research landscape of quantum AI algorithms, it is crucial to develop a software framework that is both user-friendly and capable of autonomous functional extension. In this paper, we present a lightweight, scalable, and open-source quantum AI platform designed to support the training and inference of variational quantum algorithms. This platform employs a hierarchical and structured architecture, enhancing the overall manageability and modularity of the software. Notably, it exhibits improved scalability, incorporating a compiler module for the first time. This module enables support for user-defined quantum devices, including both real physical quantum computers and quantum circuit simulators, as well as custom-defined optimizers. The platform integrates both tensor network simulator and full-amplitude simulator, providing powerful ability for quantum AI research. Utilizing these simulators, we conducted training experiments on three publicly available datasets and compared the results with TensorFlow Quantum. The experimental results validate the reliability and effectiveness of our platform, demonstrating its potential as a powerful tool for quantum AI applications. Copyright © 2025 Xin Zhang et al. Quantum engineering published by John Wiley & Sons Ltd.

关键词： Qubits

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RsDiff: Rational score based knowledge graph diffusion for recommendation

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Information Sciences 2025年 717卷

作者： Cui, Mengmeng Wu, Siyu Chen, Hao Zhang, Xiangnan School of Software Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Jiangsu Nanjing China National Research Facility For Phenotypic and Genotypic Analysis of Model Animals China Agricultural University Beijing China

The Knowledge graph (KG) provides auxiliary information to improve the recommendation system performance. However, the knowledge graph includes a large number of triplets that have nothing to do with the recommendation task, leading to suboptimal results. To address this challenge, we propose a knowledge graph diffusion model based on rationality score for recommendation, called RsDiff. Firstly, we design a rational scoring mechanism for the knowledge graph triplets. Then, we propose a knowledge graph diffusion model based on rational scores to mitigate the impact of noise. Finally, we employ cross-view contrastive learning to align collaborative signals across different graphs. Experiments show that our proposed RsDiff outperforms the most advanced recommendation models in terms of NDCG@20 and Recall@20 indicators in the Last-FM, Alibaba-iFashion, and MIND datasets. © 2025

关键词： Graph neural networks

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PA-Rank: A GAN and Reinforcement Learning Powered Framework for Multi-Metric Anomaly Detection and Causal Diagnosis

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IEEE Internet of Things Journal 2025年

作者： Wang, Zhaowen Zhou, Yong Zhang, YangSiyu Cong, Fengyu Zhou, Dongdong An, Zhijian School of Software Engineering Dalian University of Technology Dalian116024 China Dalian University of Technology School of Software Dalian116024 China Dalian University of Technology School of Biomedical Engineering Faculty of Medicine Dalian116024 China Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology China University of Jyväskylä Faculty of Information Technology Jyväskylä40014 Finland Dalian University of Technology School of Computer Science and Technology Dalian116024 China Faculty of Information Technology University of Jyvaskyla Jyvaskyla40014 Finland

The increasing scale and complexity of modern IT systems necessitate advanced solutions for monitoring and managing performance anomalies. Artificial intelligence for IT operations (AIOps) has emerged as a promising approach to enhance the efficiency and effectiveness of IT operations. However, existing methods struggle with effectively detecting anomalies in multi-dimensional performance data and accurately identifying their root causes in complex interdependent systems. This paper proposes a novel framework, PA-Rank, that combines generative adversarial networks (GANs), reinforcement learning, and graph-based methods to address these challenges comprehensively. For anomaly detection, an unsupervised GAN-based model is developed to identify anomalous time periods and assign weighted scores to metrics, facilitating precise anomaly identification. For root cause localization, a Causal Graph Construction Model (CGCM) has been developed, utilizing a reinforcement learning-based causal discovery method that is integrated with graph attention networks (GAT) to construct a causal graph representing the relationships between metrics. A random walk algorithm further ranks metric importance during anomalies, enabling effective root cause localization. Extensive experiments on real-world datasets, including SMD, ASD, and DAMADICS, demonstrate the superiority of PA-Rank over traditional statistical and state-of-the-art machine learning methods. On the SMD dataset, the proposed framework achieved an F1 score of 0.9542 for anomaly detection and consistently identified root causes among top-ranked candidates on the Pymicro and RMS datasets with the highest PR@Avg scores. These results underscore PA-Rank’s efficacy in diagnosing performance anomalies and supporting efficient system maintenance. © 2014 IEEE.

关键词： Reinforcement learning

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Tip the Scales: Achieving Balance in Adversarial Examples Across Modalities

Tip the Scales: Achieving Balance in Adversarial Examples Ac...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhenbo Shi Zhidong Yu Yuxuan Zhang Shuchang Wang Xiaoman Liu Wei Yang Liusheng Huang School of Computer Science and Technology University of Science and Technology of China Hefei China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Laboratory for Advanced Computing and Intelligence Engineering Wuxi China School of Software Engineering University of Science and Technology of China Suzhou China Hefei National Laboratory University of Science and Technology of China Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the field of multimodal learning, controlling the training of unimodal encoders from different perspectives is a primary approach to addressing Training Imbalance. However, the inherent capacity limitations of the modality affect the model’s capability. Therefore, generating adversarial examples that can achieve balanced transferability remains a challenging and perplexing problem. In this paper, we propose the InterModality Balanced Attack (MOBA) to address this problem. MOBA leverages Aggregated Modality Perturbation (AMP), which exploits the unbalanced effects of text and image perturbations to maximize the impact on the victim model. AMP capitalizes on the intrinsic feature connections between modalities during the optimization process, adjusting perturbations through Cross-Modality Discrepancy Loss to enhance attack success rates. Additionally, we devise the Transferability-Enhanced Evolution (TEE) to overcome the issue of diminished attack transferability due to model capacity limitations. TEE employs Transfer-Driven Optimization Loss to alleviate overfitting in single models, thereby enhancing the generalization ability.

关键词： Training Adaptation models Perturbation methods Architecture Buildings Signal processing Acoustics Speech processing Optimization Overfitting

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Digital Human Techniques for Education Reform 24

Digital Human Techniques for Education Reform

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7th International Conference on Educational Technology Management, ICETM 2024

作者： Guo, Peirong Zhang, Qi Tian, Chunwei Xue, Wanli Feng, Xiaocheng School of Software Northwestern Polytechnical University Shaanxi Xi’an China School of Economics and Management Harbin Institute of Technology at Weihai Shandong Weihai China Shenzhen Research Institute of Northwestern Polytechnical University Northwestern Polytechnical University Guangdong Shenzhen China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology Heilongjiang Harbin China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400717468

The rapid evolution of artificial intelligence, big data, and generative AI models has ushered in significant transformations across various sectors, including education. Digital Human Technique, an innovative technology grounded in advanced computer science and artificial intelligence, is reshaping educational paradigms by enabling virtual humans to simulate human behavior, express emotions, and interact with users. This paper explores the application of Digital Human Technique in education reform, focusing on creating immersive, intelligent classroom experiences that foster meaningful interactions between teachers and students. We define Digital Human Technique and delve into its key technical components such as character modeling and rendering, natural language processing, computer vision, and augmented reality technologies. Our methodology involves analyzing the role of educational digital humans created through these technologies, assessing their impact on educational processes, and examining various application scenarios in educational reform. Results indicate that Digital Human Technique significantly enhances the learning experience by enabling personalized teaching, increasing engagement, and fostering emotional connections. Educational digital humans serve as virtual teachers, interactive learning aids, and facilitators of emotional interaction, effectively addressing the challenges of traditional educational methods. They also promote a deeper understanding of complex concepts through simulated environments and interactive digital content. © 2024 Copyright held by the owner/author(s).

关键词： Digital elevation model

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Chebyshev Polynomial Based Emergency Conditions with Authentication Scheme for 5G-Assisted Vehicular Fog computing

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Al-Shareeda, Mahmood A. Gaber, Tarek Alqarni, Mohammed A. Alkinani, Monagi H. Almazroey, Alaa Atallah Almazroi, Abdulwahab Ali Southern Technical University Department of Electronic Technologies Basra Technical Institute Basra61001 Iraq Engineering and Environment University Salford School of Science Manchester United Kingdom University of Jeddah College of Computer Science and Engineering of Software Engineering Jeddah Saudi Arabia University of Jeddah College of Computer Science and Engineering Department of Computer Science and Artificial Intelligence Jeddah Saudi Arabia Modern Aljazeera Company Jeddah Saudi Arabia University of Jeddah College of Computing and Information Technology at Khulais Department of Information Technology Jeddah Saudi Arabia

Supporting vehicular emergency applications requires fast access to infrastructure so vehicles can call for help. Because of their environment's poor wireless qualities, vehicle-infrastructure communication paths lack security. Modern authentication systems used to close security vulnerabilities require a lot of computing and storage power from the vehicle's OBU. Thus, a 5G automobile network emergency situations Chebyshev polynomial using fog computing and authentication is proposed. The Chebyshev chaotic mapping algorithm uses a chaotic map, rotation, and XOR to generate a one-way hash and eliminate modular multiplication index or scalar multiplication on the elliptic curve. The proposed scheme has four stages: installation system, initialization, enrollment, mutual authentication, and emergency request. Critical to emergency services, the suggested protocol works better in resource-limited settings like car systems. The security analysis reveals that the suggested approach guarantees message authenticity and integrity, non-repudiation, traceability, unlinkability, identity privacy, certificate independence, and emergency response. The proposed method cannot execute forgery, impersonation, or replay attacks. Proposed method has lower computational, communication, and storage overhead than earlier efforts. © 2004-2012 IEEE.

关键词： Fog computing

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