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Informatica (Slovenia) 2025年第11期49卷 165-176页

作者： Ilias, Shaik Mohammad Sharmila, V. Ceronmani Durga, V. Sathya Dept. of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India Department of Information Technology Hindustan Institute of Technology and Science Chennai India Department of Computer Science&Engineering Hindustan Institute of Technology and Science Chennai India

If adversaries were to obtain quantum computers in the future, their massive computing power would likely break existing security schemes. Since security is a continuous process, more substantial security schemes must be developed. Current PQC schemes primarily focus on data security or key exchange, and further improvement towards enhanced PQC primitives is required. Our proposal in this research is an innovative paradigm for PQC-focused cloud data security. The proposed HEDT approach achieves encryption and decryption with significantly lower latency (20% improvement) and higher reliability than AES, DES, and RSA, as demonstrated through experimental results. Furthermore, ECSIDH, a hybrid key exchange mechanism combining SIDH and ECDH, improves security strength by 50% while maintaining computational costs within 1.13x of SIDH. Compared to individual key exchange schemes like SIDH, ECSIDH offers superior security as a PQC candidate. These results confirm the robustness and efficiency of the proposed framework in ensuring secure data outsourcing and key exchange in cloud environments. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Quantum computers

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YOLO-LFD: A Lightweight and Fast Model for Forest Fire Detection

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computers, Materials & Continua 2025年第2期82卷 3399-3417页

作者： Honglin Wang Yangyang Zhang Cheng Zhu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Electrical&Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA

Forest fires pose a serious threat to ecological balance, air quality, and the safety of both humans and wildlife. This paper presents an improved model based on You Only Look Once version 5 (YOLOv5), named YOLO Lightweight Fire Detector (YOLO-LFD), to address the limitations of traditional sensor-based fire detection methods in terms of real-time performance and accuracy. The proposed model is designed to enhance inference speed while maintaining high detection accuracy on resource-constrained devices such as drones and embedded systems. Firstly, we introduce Depthwise Separable Convolutions (DSConv) to reduce the complexity of the feature extraction network. Secondly, we design and implement the Lightweight Faster Implementation of Cross Stage Partial (CSP) Bottleneck with 2 Convolutions (C2f-Light) and the CSP Structure with 3 Compact Inverted Blocks (C3CIB) modules to replace the traditional C3 modules. This optimization enhances deep feature extraction and semantic information processing, thereby significantly increasing inference speed. To enhance the detection capability for small fires, the model employs a Normalized Wasserstein Distance (NWD) loss function, which effectively reduces the missed detection rate and improves the accuracy of detecting small fire sources. Experimental results demonstrate that compared to the baseline YOLOv5s model, the YOLO-LFD model not only increases inference speed by 19.3% but also significantly improves the detection accuracy for small fire targets, with only a 1.6% reduction in overall mean average precision (mAP)@0.5. Through these innovative improvements to YOLOv5s, the YOLO-LFD model achieves a balance between speed and accuracy, making it particularly suitable for real-time detection tasks on mobile and embedded devices.

关键词： Forest fire detection YOLOv5 lightweight small object detection

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A social diagnosis mechanism for healthcare knowledge sharing

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Journal of information science 2025年第3期51卷 720-743页

作者： Lin, Lien-Fa Li, Yung-Ming Lin, Yen-Chen Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Taiwan Institute of Information Management National Yang Ming Chiao Tung University Taiwan

In recent years, social networks have grown rapidly, and their applications in the healthcare domain are increasingly proposed. Using the crowd wisdom generated from social networks, we can find similar and reliable people sharing helpful experiences. The existing dedicated social networking services for health mainly focus on sharing, but not categorising and extracting. In this research, we construct an environment for social knowledge sharing and expert referring. Analysing queries from online public health databases and the factors of health similarity, social reliability and social intimacy, we extract health knowledge to recommend relevant social knowledge (also called threads) and helpful experts providing consulting. Specifically, the proposed social diagnosis mechanism helps the health seeker to identify relevant threads and recommends enthusiastic experts for healthcare support. Experimental results reveal that the proposed mechanism can effectively improve healthcare knowledge sharing and realise diagnosis support from the crowd. © The Author(s) 2023.

关键词： Knowledge management

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Effective Adversarial Examples Detection Improves Screw Detection in Dust Pollution Within Industry System

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1963-1971页

作者： Tseng, Fan-Hsun Zeng, Jiang-Yi Chen, Chi-Yuan Cho, Hsin-Hung National Cheng Kung University Department of Computer Science and Information Engineering Tainan City70101 Taiwan National Ilan University Department of Computer Science and Information Engineering Yilan26047 Taiwan

Screws play a critical role as essential components across various industries. To meet market demands and standards, screw manufacturers are embracing digital transformation and leveraging artificial intelligence (AI) techniques. AI models have also been proposed for identifying defective products. However, most studies are limited to controlled environments, and the presence of dust particles during screw manufacturing poses challenges for visual-based AI applications. To address this, we propose a solution involving the use of adversarial examples (AE). These examples are employed to simulate dust particles on camera lenses. We introduce a robust AE detection method designed to enhance the accuracy of screw recognition. This approach aims to improve the overall efficiency of the screw manufacturing industry. Experimental results show that our proposed mechanism can effectively improve screw identification accuracy in dust-polluted environments. © 1975-2011 IEEE.

关键词： Screws

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A Novel Music Recommendation System Using Filtering Techniques

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第4期48卷 595-610页

作者： Vashishtha, Srishti Varshney, Deepika Sarin, Eva Kaur, Simran Computer Science and Engineering Dept. Bharati Vidyapeeth’s College of Engineering GGSIPU New Delhi India Computer Science and Engineering Dept. Jaypee Institute of Information Technology Noida India

With the growth of the World Wide Web, a large amount of music data is available on the Internet. A large number of people listen to music online rather than downloading and listening offline. But only some sites provide personalized and accurately recommended songs while they listen to an auto-playing playlist. Hence the need for recommendation systems arises. Two approaches can be applied to the recommendation system: content based filtering and collaborative filtering. While in content-based filtering approach, analysis on the songs’ content which has been preferred by the user in history is done and the songs with relative similarity are recommended. While latter suggests songs that certain users of similar listening pattern have preferred. But collaborative filtering-based recommendation systems not only requires time to attain stability but also might recommend unsuitable music because it is not personalized to each user’s preference. Also, the latter requires the songs to be listened by a few users already to recommend it any other user. Hence for overall analysis, answers to few questions need to be implemented in recommendation systems: the very first one is how the properties should be analyzed, next one is how the analysis should be done and last is how the songs related to the user’s preference should be chosen. So, for suggesting the better system which solves these three questions as well as provides better and personalized recommendations, In this paper we build a collaborative filtering as well as content based filtering recommendation system, where various factors determine the essence of the songs, i.e. liveliness, keys used, loudness, pitch, valence, etc., are analyzed for comparison. From the experimental analysis in has been identified that content-based filtering technique performed best on KNN machine learning classifier with accuracy of 85%. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Collaborative filtering

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LesionAid: vision transformers-based skin lesion generation and classification – A practical review

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Multimedia Tools and Applications 2025年 1-22页

作者： K, Mallikharjuna Rao Krishna, Ghanta Sai Supriya, Kundrapu Sorgile, Meetiksha Data Science and Artificial Intelligence International Institute of Information Technology Naya Raipur India Computer Science and Engineering International Institute of Information Technology Naya Raipur India

Skin cancer is one of the most prevalent forms of human cancer. It is recognized mainly visually, beginning with clinical screening and continuing with the dermoscopic examination, histological assessment, and specimen collection. Deep convolutional neural networks (CNNs) perform highly segregated and potentially universal tasks against a classified fine-grained object. This study suggests a novel multi-class prediction framework that uses ViT and ViTGAN to categories skin lesions. To address the class disparity, GANs (Generative Adversarial Networks) based on vision transformers are used. The framework comprises four main phases: ViTGANs, Image processing, and explainable AI. Phase 1 consists of generating synthetic images using ViTGAN to balance all the classes in the dataset. To enhance the amount of the data, the second phase involves using various morphological processes and data augmentation techniques. In phases three and four, a ViT model for edge computing systems that can recognize patterns and classify skin lesions from the user's skin that is visible in the picture is developed. In phase 3, after classifying the lesions into the desired class with ViT, we will use explainable AI (XAI) that leads to more explainable results (using activation maps, etc.) while ensuring high predictive accuracy. The results demonstrate that the model used for generating synthetic images has achieved an FID score of 13.32 and the ViT model has achieved 99.2% as its training accuracy and 97.4% as its validation accuracy. The whole framework is compared with the existing frameworks for skin lesion detection. And explainable AI (XAI) is used in the proposed framework in order to increase model transparency and boost trust among users by illustrating the main factors affecting predictions. This interpretability helps physicians in making more informed decisions by providing clear insights into the model's reasoning. © The Author(s), under exclusive licence to Springer science+Bu

关键词： Generative adversarial networks

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A Privacy-Preserving Data Aggregation Protocol for Internet of Vehicles with Federated Learning

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Xu, Zisang Zhang, Ruirui Liang, Wei Li, Kuan-Ching Gu, Ke Li, Xiong Huang, Jialun Computer and Communication Engineer Institute Changsha University of Science and Technology Changsha China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Department of Computer Science and Information Engineering Providence University Taichung Taiwan Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

Federated learning (FL) is widely used in various fields because it can guarantee the privacy of the original data source. However, in data-sensitive fields such as Internet of Vehicles (IoV), insecure communication channels, semi-trusted RoadSide Unit (RSU), and collusion between vehicles and the RSU may lead to leakage of model parameters. Moreover, when aggregating data, since different vehicles usually have different computing resources, vehicles with relatively insufficient computing resources will affect the data aggregation efficiency. Therefore, in order to solve the privacy leakage problem and improve the data aggregation efficiency, this paper proposes a privacy-preserving data aggregation protocol for IoV with FL. Firstly, the protocol is designed based on methods such as shamir secret sharing scheme, pallier homomorphic encryption scheme and blinding factor protection, which can guarantee the privacy of model parameters. Secondly, the protocol improves the data aggregation efficiency by setting dynamic training time windows. Thirdly, the protocol reduces the frequent participations of Trusted Authority (TA) by optimizing the fault-tolerance mechanism. Finally, the security analysis proves that the proposed protocol is secure, and the performance analysis results also show that the proposed protocol has high computation and communication efficiency. IEEE

关键词： Vehicles

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Detection of Stuttered Speech with Repetition Using Wavelet Packet Transformation and Neuro Fuzzy Classifier

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SN computer science 2025年第5期6卷 1-9页

作者： Drakshayini, K.B. Anusuya, M.A. Vani, H.Y. Chayadevi, M.L. Department of Computer Science and Engineering VTU Belagaum India Department of Computer Science and Engineering JSSSTU Mysuru India Department of Information Science and Engineering JSSSTU Mysuru India Department of Computer Science and Engineering BNMIT Mysuru India

Repetition is the most prominent type of dysfluency in stuttered speech. Spectral energy, entropy, ZCR, and centroid are extracted using wavelet packet transformation, and similarity between these features for adjacent frames is measured linguistic hedges on these factors are fed as input to fuzzy ANN. Fuzzy ANN is used to classify stuttering speech signals with repetition and fluent speech signals. Correcting repetition in stuttered speech is handled by fuzzy ANN based on fuzzy rules passed concerning similarity measures of spectral parameters like spectral energy, ZCR, entropy, and centroid. The same process was also examined for fuzzy entropy alone for analysis purposes for different kinds of repetition in stuttered speech. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adaptive neuro fuzzy classifier Centroid Energy Fuzzy entropy Linguistic hedges Wavelet packet transformation ZCR

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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

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Neural Computing and Applications 2025年第17期37卷 11459-11477页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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Research Progress in Solar Flare Prediction Methods

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Research in Astronomy and Astrophysics 2025年第3期25卷 280-309页

作者： Ke Han Zhen Liu Xian-Yi Zhao Yi-Fei Li De-Quan Zheng Jie Wan School of Computer and Information Engineering Harbin University of Commerce Faculty of Computing Harbin Institute of Technology School of Energy Science and Engineering Harbin Institute of Technology

Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar flares in order to ensure the safety of human ***,the research focuses on two directions:first,identifying predictors with more physical information and higher prediction accuracy,and second,building flare prediction models that can effectively handle complex observational *** terms of flare observability and predictability,this paper analyses multiple dimensions of solar flare observability and evaluates the potential of observational parameters in *** flare prediction models,the paper focuses on data-driven models and physical models,with an emphasis on the advantages of deep learning techniques in dealing with complex and high-dimensional *** reviewing existing traditional machine learning,deep learning,and fusion methods,the key roles of these techniques in improving prediction accuracy and efficiency are *** prevailing challenges,this study discusses the main challenges currently faced in solar flare prediction,such as the complexity of flare samples,the multimodality of observational data,and the interpretability of *** conclusion summarizes these findings and proposes future research directions and potential technology advancement.

关键词： Sun: activity Sun: flares (Sun:) sunspots Sun: magnetic fields magnetohydrodynamics (MHD)

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