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15th International Conference on Intelligent Computing and Networking, IC-ICN 2024

作者： Kumar, Pramod Quraishi, Aadam Rusho, Maher Ali Raparthi, Mohan Degadwala, Sheshang Computer Science and Technology Ganga Institute of Technology and Management Haryana Jhajjar Kablana124104 India Intervention Treatment Institute HoustonTX United States Degree Program University of Colorado Boulder CO United States Software Engineer Alphabet Life Science DallasTX75063 United States Department of Computer Engineering Sigma University Gujarat Vadodara India

ISBN: (纸本)9789819786305

Effectively harnessing feature correlations is crucial for optimal performance in video action recognition tasks, whether in spatial or temporal dimensions. Convolutional operations excel at capturing local features through correlations among nearby points, while self-attention mechanisms capture global information by enabling interactions among all feature points. However, the limitations of a single convolutional layer in comprehending holistic feature correlations and the tendency of self-attention layers to overlook local positional characteristics necessitate an innovative approach. We introduce the Spatial–Temporal Convolutional Attention Network, comprising the Spatial Convolutional Attention Network and the Temporal Convolutional Attention Network. This approach strategically combines the strengths of self-attention for global context and convolution for local features, mitigating the limitations of individual layers. Key innovations include [explicitly state innovations]. To reinforce the uniqueness of our approach, we provide specific comparisons with existing methods, highlighting its superiority. In rigorous experiments, our proposed network demonstrates enhanced recognition performance, quantified through metrics such as accuracy and F1 score improvements over baseline models. By effectively capturing feature correlations, our approach elevates the neural network's capacity for spatiotemporal modeling, marking a significant advancement in video action recognition. Elevating the neural network's capacity for spatiotemporal modeling. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Elastic Spectrum Sensing: An Adaptive Sensing Method for Non-Terrestrial Communication Under Highly Dynamic Channels

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IEEE Transactions on Wireless Communications 2025年第5期24卷 3868-3881页

作者： Xu, Tianheng Xu, Yinjun Zhou, Ting Zhang, Haijun Hu, Honglin Leung, Victor C. M. Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai201210 China Shanghai Frontier Innovation Research Institute Shanghai201100 China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100049 China Shanghai University School of Microelectronics Shanghai200444 China Institute of Artificial Intelligence Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing100083 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T1Z4 Canada

With the rapid development of space technology, the role of satellite communications has become progressively significant. Non-terrestrial communication is deemed a critical scenario in the sixth generation (6G) communication systems, which showcases seamless connectivity, minimal geographic constraints and substantial communication capacity. Simultaneously, satellites and terminals spring up, spatial density increases, which further emphasizes the scarcity of spectrum resources. Consequently, to improve spectrum utilization for non-terrestrial communication is a significant concern. Spectrum sensing, which allows dynamic resource reuse, plays an important role in 6G. However, high mobility in non-terrestrial scenarios poses great challenges, such as fast time-varying channels, Doppler effect, etc., which seriously affect sensing accuracy and cannot well support optimal spectrum utilization. Motivated by such circumstances, this paper proposes an elastic sensing method for the downlink non-terrestrial communication scenario. Firstly, we design the system architecture and sensing workflow. To overcome the negative effects raised by high mobility, we propose the elastic sensing criterion and multi-area dividing scheme for the sensing zone. Thresholds affected by the elastic sensing are derived for different areas. Finally, the numerical results show that from -10 dB to -5 dB, the proposed method can improve the total performance by an average of 28.3% while stabilizing the false alarm probability around 0.1 typical level and demonstrating higher constancy compared with traditional technologies from −10 dB to −5 dB. © 2002-2012 IEEE.

关键词： Satellite communication systems

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Enhancing malware detection performance: leveraging K-Nearest Neighbors with Firefly Optimization Algorithm

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Multimedia Tools and Applications 2025年第12期84卷 10071-10094页

作者： Al Saaidah, Adeeb Abualhaj, Mosleh M. Shambour, Qusai Y. Abu-Shareha, Ahmad Adel Abualigah, Laith Al-Khatib, Sumaya N. Alraba’nah, Yousef H Department of Networks and Cybersecurity Al-Ahliyya Amman University Amman Jordan Department of Software Engineering Al-Ahliyya Amman University Amman Jordan Department of Data Science and Artificial Intelligence Al-Ahliyya Amman University Amman Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon College of Engineering Yuan Ze University Taoyuan32003 Taiwan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

Malware detection plays a crucial role in ensuring robust cybersecurity amidst the ever-evolving cyber threats. This research paper delves into the realm of machine learning (ML) algorithms for malware detection, with a specific emphasis on the K-Nearest Neighbors (KNN) algorithm, utilizing tailored parameter settings and the Firefly Optimization Algorithm (FOA). The study leverages the MalMem-2022 dataset to assess the efficacy of KNN and KNN with FOA in malware detection. The impact of parameter tuning and feature selection on classification is elucidated by comparing the performance of both approaches. Encouragingly, the results reveal promising advancements in one of the multiclass classification scenarios when employing KNN with FOA, yielding noteworthy enhancements in Accuracy, Recall, Precision, Matthews Correlation Coefficient, TNR, and F1-score by 2.65%, 2.65%, 2.47%, 3.59%, 0.17%, and 2.61%, respectively. These findings accentuate the significance of optimizing KNN’s parameters and implementing FOA for feature selection, culminating in heightened accuracy in malware detection. The symbiotic fusion of KNN and FOA exhibits remarkable effectiveness in augmenting the classification model’s performance, thus offering valuable insights for bolstering cybersecurity measures. Adopting ML algorithms, particularly KNN with specific parameter settings and FOA, presents a promising avenue for enhancing malware detection capabilities. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Nearest neighbor search

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Crop protection and disease detection using artificial intelligence and computer vision: a comprehensive review

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Multimedia Tools and Applications 2025年第7期84卷 3723-3743页

作者： Shah, Kanish Sushra, Rajat Shah, Manan Shah, Dhairya Shah, Haard Raval, Megh Prajapati, Mitul Software Engineer Walmart SunnyvaleCA United States Atmiya Vidya Mandir Gujarat Surat India Department of Chemical Engineering School of Energy Technology Pandit Deendayal Energy University Gujarat Gandhinagar India Robotics ECE Concentration Northeastern University BostonMA United States Department of Computer Science Concentration in AI Hofstra University HempsteadNY United States Department of Chemical Engineering S. S Agrawal Institute of Engineering & Technology Gujarat Navsari India R &D Engineer Span Inspection Systems Pvt. Ltd Gujarat Gandhinagar India

The technological advancements in the field of agriculture have increased to a great extent in recent years, and many techniques have evolved from other techniques. Some methods are improved or upgraded from the previous versions by implementing a new model or using better hardware devices. This has been helpful for the farmers in increasing crop productivity, and the life expectancy of crops has also increased as the diseases inside or outside the crops can be detected much earlier, and learning at an early stage helps prevent other crops. In this paper, we have presented a study where many varieties of fruits and vegetables have been taken to determine which method was used for a particular crop. By analyzing the various works carried out by the authors, it was inferred that most of the works revolved around image processing and hyperspectral imaging. Due to this, we had also included most of the papers, particularly as the models and hardware components used were much better than other works. Then, a comparative study was done where different fruits and vegetables highlighted the two main areas: the method used and the accuracy obtained. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Crops

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Unlocking the potential of RNN and CNN models for accurate rehabilitation exercise classification on multi-datasets

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Multimedia Tools and Applications 2025年第3期84卷 1261-1301页

作者： Zaher, Moamen Ghoneim, Amr S. Abdelhamid, Laila Atia, Ayman Software Engineering Program Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Computer Science Department Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Information System Department Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Cairo Ain Helwan11795 Egypt Giza October12451 Egypt

Physical rehabilitation is crucial in healthcare, facilitating recovery from injuries or illnesses and improving overall health. However, a notable global challenge stems from the shortage of professional physiotherapists, particularly acute in some developing countries, where the ratio can be as low as one physiotherapist per 100,000 individuals. To address these challenges and elevate patient care, the field of physical rehabilitation is progressively integrating computer Vision and Human Activity Recognition (HAR) techniques. Numerous research efforts aim to explore methodologies that assist in rehabilitation exercises and evaluate patient movements, which is crucial as incorrect exercises can potentially worsen conditions. This study investigates applying various deep-learning models for classifying exercises using the benchmark KIMORE and UI-PRMD datasets. Employing Bi-LSTM, LSTM, CNN, and CNN-LSTM, alongside a Random Search for architectural design and Hyper-parameter tuning, our investigation reveals the (CNN) model as the top performer. After applying cross-validation, the technique achieves remarkable mean testing accuracy rates of 93.08% on the KIMORE dataset and 99.7% on the UI-PRMD dataset. This marks a slight improvement of 0.75% and 0.1%, respectively, compared to previous techniques. In addition, expanding beyond exercise classification, this study explores the KIMORE dataset’s utility for disease identification, where the (CNN) model consistently demonstrates an outstanding accuracy of 89.87%, indicating its promising role in both exercises and disease identification within the context of physical rehabilitation. © The Author(s) 2024.

关键词： Classification (of information)

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Class-Aware Prompting for Federated Few-Shot Class-Incremental Learning

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Liang, Fang-Yi Zhan, Yu-Wei Liu, Jiale Zhang, Chong-Yu Chen, Zhen-Duo Luo, Xin Xu, Xin-Shun Shandong University School of Software Jinan250101 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Yunnan Key Laboratory of Software Engineering Kunming650504 China

Few-Shot Class-Incremental Learning (FSCIL) aims to continuously learn new classes from limited samples while preventing catastrophic forgetting. With the increasing distribution of learning data across different clients and privacy concerns, FSCIL faces a more realistic scenario where few learning samples are distributed across different clients, thereby necessitating a Federated Few-Shot Class-Incremental Learning (FedFSCIL) scenario. However, this integration faces challenges from non-IID problem, which affects model generalization and training efficiency. The communication overhead in federated settings also presents a significant challenge. To address these issues, we propose Class-Aware Prompting for Federated Few-Shot Class-Incremental Learning (FedCAP). Our framework leverages pre-trained models enhanced by a class-wise prompt pool, where shared class-wise keys enable clients to utilize global class information during training. This unifies the understanding of base class features across clients and enhances model consistency. We further incorporate a class-level information fusion module to improve class representation and model generalization. Our approach requires very few parameter transmission during model aggregation, ensuring communication efficiency. To our knowledge, this is the first study to explore the scenario of FedFSCIL. Consequently, we designed comprehensive experimental setups and made the code publicly available. © 2025 IEEE. All rights reserved.

关键词： Federated learning

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Variational quantum classifier-based early identification and classification of chronic kidney disease using sparse autoencoder and LASSO shrinkage

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PeerJ computer science 2025年 11卷

作者： Parthasarathi, P. Alshahrani, Haya Mesfer Venkatachalam, K. Cho, Jaehyuk Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode India Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Saudi Arabia Department of Software Engineering Jeonbuk National University Jeonju Korea Republic of Department of Software Engineering Division of Electronics Information Engineering Jeonbuk National University Jeonju Korea Republic of

The two leading causes of chronic kidney disease (CKD) are excessive blood pressure and diabetes. Researchers worldwide utilize the rate of globular filtration and kidney inflammation biomarkers to identify chronic kidney disease that gradually reduces renal function. The mortality rate for CKD is high, and thus, a person with this illness is more likely to pass away at a younger age. Healthcare professionals must diagnose the various illnesses connected to this deadly disease as promptly as possible to lighten the impact of CKD. A quantum machine learning (QML) based technique is presented in this research to help with the early diagnosis and prognosis of CKD. The proposed research comprises four phases: data pre-processing, data augmentation, feature selection, and classification. In the first phase, Kalman filter and data normalization techniques are applied to handle the missing and noisy data. In the second phase, data augmentation uses sparse autoencoders to balance the data for smaller classes. In the third phase, LASSO shrinkage is used to select the significant features in the dataset. Variational Quantum classifiers, a supervised QML technique, are employed in the classification phase to classify chronic kidney diseases. The proposed system has been evaluated on the UCI dataset, which comprises 400 CKD patients in the early stages with 25 attributes. The suggested system was assessed using F1-score, precision, recall, and accuracy as evaluation metrics. With a 99.2% classification accuracy, it was found that this model performed better than the other traditional classifiers used for chronic kidney disease classification. Copyright 2025 Parthasarathi et al. Distributed under Creative Commons CC-BY 4.0

关键词： Blood pressure

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A Transfer Learning-Based Framework for Enhanced Classification of Perceived Mental Stress Using EEG Spectrograms

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IEEE Access 2025年 13卷 89266-89286页

作者： Sheharyar Khan Sadam Hussain Noorani Jaroslav Frnda Usman Rauf Aamir Arsalan Sanay Muhammad Umar Saeed Department of Computer Engineering University of Engineering and Technology Taxila Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and Communications University of Žilina Žilina Slovakia Department of Software Engineering Fatima Jinnah Women University Rawalpindi Pakistan

Stress is a significant health concern, impacting both physical and mental well-being. Prolonged exposure to stress can lead to numerous physical health issues, including cardiovascular diseases, and a weakened immune system. This study presents a novel methodology for classifying perceived mental stress using electroencephalography (EEG) signals. By utilizing the publicly available Leipzig Study for Mind-Body-Emotion Interactions dataset, we analyze EEG data collected from 53 participants over a 7-minute resting-state duration. Our approach involves transforming EEG signals into spectrograms using the Short-Time Fourier Transform (STFT), resulting in a time-frequency representation of the input signals. We employ transfer learning to fine-tune three pre-trained deep neural networks i.e., ResNet50, EfficientNetB0, and DenseNet121 for classifying stress into two and three levels. Our findings demonstrate that the ResNet50 model achieves superior classification accuracies of 95.80% and 86.02% for two and three-level stress classification, respectively, outperforming existing state-of-the-art methods. This study is the first to utilize STFT-generated spectrograms and transfer learning for perceived stress classification, highlighting the efficacy of deep learning techniques in quantifying perceived mental stress through non-invasive EEG recordings. Our results indicate that the proposed method can significantly enhance the accuracy of stress classification frameworks, offering potential improvements in mental health assessment and intervention strategies.

关键词： Human factors Electroencephalography Accuracy Stress measurement Spectrogram Physiology Transfer learning Feature extraction Brain modeling Electrodes

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A novel facial expression recognition framework using deep learning based dynamic cross-domain dual attention network

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PeerJ computer science 2025年 11卷 e2866-e2866页

作者： Alzahrani, Ahmed Omar Alghamdi, Ahmed Mohammed Ashraf, M. Usman Ilyas, Iqra Sarwar, Nadeem Alzahrani, Abdulrahman Alarood, Alaa Abdul Salam Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Makkah Jeddah Saudi Arabia Department of Software Engineering College of Computer Science and Engineering University of Jeddah Makkah Jeddah Saudi Arabia Department of Computer Science Government College Women University Sialkot Punjab Sialkot Pakistan Department of Computer Science Bahria University Punjab Lahore Pakistan

Variations in domain targets have recently posed significant challenges for facial expression recognition tasks, primarily due to domain shifts. Current methods focus largely on global feature adoption to achieve domain-invariant learning;however, transferring local features across diverse domains remains an ongoing challenge. Additionally, during training on target datasets, these methods often suffer from reduced feature representation in the target domain due to insufficient discriminative supervision. To tackle these challenges, we propose a dynamic cross-domain dual attention network for facial expression recognition. Our model is specifically designed to learn domain-invariant features through separate modules for global and local adversarial learning. We also introduce a semantic-aware module to generate pseudo-labels, which computes semantic labels from both global and local features. We assess our model’s effectiveness through extensive experiments on the Real-world Affective Faces Database (RAF-DB), FER-PLUS, AffectNet, Expression in the Wild (ExpW), SFEW 2.0, and Japanese Female Facial Expression (JAFFE) datasets. The results demonstrate that our scheme outperforms the existing state-of-the-art methods by attaining recognition accuracies 93.18, 92.35, 82.13, 78.37, 72.47, 70.68 respectively. Copyright 2025 Alzahrani et al. Distributed under Creative Commons CC-BY 4.0

关键词： Deep learning

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The design of continuous surgical robotic arms with variable stiffness material and magnetic guidance

The design of continuous surgical robotic arms with variable...

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5th International Conference on Signal Processing and Machine Learning (CONF SPML 2025)

作者： Yixin Zhang Department of Computer Science and Software Engineering Hebei University of Technology Langfang Hebei 065000 People's Republic of China

ISBN: (数字)9781837243143

In recent years, with the aging population and the increasing incidence of natural cavity diseases and cancer tumors, the demand for minimally invasive surgery has risen. This, in turn, requires robotic arms with greater flexibility and precision. However, traditional rigid surgical robotic arms make it difficult to meet these needs. This paper includes an illustration of the invention related to a continuous surgical robotic arm which is combined with magnetic guidance and a variable stiffness material. This paper aims to explore the advantages of continuum robots based on Phase Change Materials (PCMs) in surgical applications, especially through the stiffness adjustability of the material and the combination of magnetic materials to improve positioning accuracy. In this study, low-melting-point alloys were chosen as the variable stiffness material. This paper analyzed a flexible and precise surgical robotic arm by summarizing and combining previous experiments on the properties of low-melting alloys under different conditions and simulating magnetic navigation in complex environments. Through this design, the robots can enhance the safety, versatility and collaboration ability among the continuous robots, opening up new ways for endocavitary robotic surgery.

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