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IEEE Transactions on Machine Learning in communications and Networking 2025年 3卷 381-394页

作者： Wu, Meng-Hsun Chen, Hong-Yunn Yang, Ta-Wei Hsu, Chih-Chuan Huang, Chih-Wei Chou, Cheng-Fu National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan National Pingtung University Department of Computer Science and Information Engineering Pingtung91201 Taiwan Graduate Institute of Networking and Multimedia National Taiwan University Taipei10617 Taiwan Texas A&M University Department of Computer Science and Engineering College StationTX77840 United States National Central University Department of Communication Engineering Taoyuan320 Taiwan

In massive MIMO systems, achieving optimal end-to-end transmission encompasses various aspects such as power control, modulation schemes, path selection, and accurate channel estimation. Nonetheless, optimizing resource allocation remains a significant challenge. In path selection, the direct link is a straightforward link between the transmitter and the receiver. On the other hand, the indirect link involves reflections, diffraction, or scattering, often due to interactions with objects or obstacles. Relying exclusively on one type of link can lead to suboptimal and limited performance. Link management (LM) is emerging as a viable solution, and accurate channel estimation provides essential information to make informed decisions about transmission parameters. In this paper, we study LM and channel estimation that flexibly adjust the transmission ratio of direct and indirect links to improve generalization, using a denoising variational autoencoder with attention modules (DVAE-ATT) to enhance sum rate. Our experiments show significant improvements in IRS-assisted millimeter-wave MIMO systems. Incorporating LM increased the sum rate and reduced MSE by approximately 9%. Variational autoencoders (VAE) outperformed traditional autoencoders in the spatial domain, as confirmed by heatmap analysis. Additionally, our investigation of DVAE-ATT reveals notable differences in the temporal domain with and without attention mechanisms. Finally, we analyze performance across varying numbers of users and ranges. Across various distances—5m, 15m, 25m, and 35m—performance improvements averaged 6%, 11%, 16%, and 22%, respectively. © 2023 CCBY.

关键词： Resource allocation

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Deep Learning Models for Acoustic Monitoring of the Himalayan Monal and Other Elusive Bird 4

Deep Learning Models for Acoustic Monitoring of the Himalaya...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Nikshya, J. Ebens Raj, J. Relin Francis Krishnan, R. Santhana Rajkumar, G.Vinoth Settu, Imran Javeed Kavitha, S. Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India J. P. College of Engineering Department of Electronics and Communication Engineering Tenkasi India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798331523923

Acoustic monitoring of rare and elusive bird species presents significant challenges due to the complexity of environmental noise and the variability in bird vocalizations. This study proposes a novel deep learning framework, BioAudio VisionNet (BAVNet), specifically designed for the acoustic identification and monitoring of the Himalayan Monal and other rare bird species. The performance of the proposed algorithm was evaluated against three established algorithms: Support Vector Machines with Acoustic Features (SVM-AF), Audio Spectrogram Transformer (ASF), and Deep Belief Networks (DBN). Simulation experiments were conducted using metrics such as accuracy versus the number of epochs, processing time versus accuracy, and spectrogram classification accuracy versus spectrogram resolution. The results demonstrated that BAVNet achieved a notable improvement in classification accuracy, computational efficiency, and adaptability to varying spectrogram resolutions when compared to existing methods. These findings highlight the potential of BAVNet for real-time biodiversity monitoring in challenging environments. Future work can explore the integration of additional contextual data, such as environmental factors, to further enhance system performance. © 2025 IEEE.

关键词： Birds

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IoT-Assisted AI Framework for Preserving Biodiversity: Monitoring Rare Trees in Dynamic Forest Ecosystems

IoT-Assisted AI Framework for Preserving Biodiversity: Monit...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Rajan, K. Palanivel Raj, J. Relin Francis Krishnan, R. Santhana Balamuralikrishnan, G. Vidyasree, G. Jothi, J. Nirmal Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Sri Sai Ranganathan Engineering College Department of Computer Science and Engineering Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi India

ISBN: (纸本)9798331509828

The preservation of biodiversity, particularly the monitoring of rare tree species in dynamic forest ecosystems, presents significant challenges due to the complexities in detecting and analyzing forest changes. This research introduces a novel IoT -assisted AI framework, termed FoLiA-Net (Forest Lightweight Adaptive Network), designed to improve the efficiency and accuracy of monitoring rare trees. The proposed algorithm combines IoT sensor data with machine learning techniques to adaptively detect, classify, and track rare tree species across large and dynamically changing forest areas. The framework utilizes a lightweight neural network structure to ensure real-time processing while minimizing computational resources, making it suitable for deployment in resource-constrained environments. Simulation experiments were conducted to evaluate the performance of FoLiA-Net in comparison with existing algorithms, including Faster R-CNN (F-R-CNN), Random Forest Classifier (RFC), and Gradient Boosting Machines (GBM). The evaluation was based on several key performance metrics such as accuracy, latency and detection efficiency. The results revealed that FoLiA-Net outperformed the existing algorithms in terms of accuracy and computational efficiency, demonstrating a notable improvement in the monitoring of rare tree species in diverse forest ecosystems. The proposed framework is particularly effective in large-scale forest monitoring, providing a valuable tool for biodiversity conservation efforts © 2025 IEEE.

关键词： Biodiversity Forest Ecosystems Machine Learning Rare Trees Simulation Sustainability

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Hybrid Deep Learning for International Trading: Integrating LSTM and Transformer Models 4

Hybrid Deep Learning for International Trading: Integrating ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Akhila, T.S. Relin Francis Raj, J. Kani Jesintha, D. Santhana Krishnan, R. Vinoth Kumar, V. Sundaravadivel, P. Dhanalakshmi College of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331523923

This research proposes a hybrid deep learning model combining Long Short-Term Memory (LSTM) networks and Transformers to enhance decision-making in international trading. The model leverages advanced techniques such as RoBERTa for text tokenization, FinBERT for sentiment analysis, and XLM-Roberta for multilingual language processing. LSTM is employed to analyze sequential numerical data like historical prices and trading volumes, providing accurate price and volatility forecasts. The Transformer component processes textual data to extract sentiment classifications and event-driven market impacts, facilitating a comprehensive understanding of market conditions. A fully connected fusion layer integrates insights from both components, enabling the model to classify market conditions holistically. The optimization process utilizes the Adam optimizer with learning rate scheduling to ensure efficient and effective training. Loss functions like Mean Squared Error (MSE) and Cross-Entropy Loss are employed to optimize numerical predictions and categorical outcomes, respectively. By combining temporal analysis with sentiment-driven insights, this hybrid model offers improved price forecasting, advanced multilingual sentiment analysis, and event-driven market condition predictions. The proposed approach aims to provide traders with actionable insights, enabling more informed decision-making in the highly dynamic and multilingual environment of international trading. © 2025 IEEE.

关键词： Costs

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Med-tech device security through advanced server cryptography

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International Journal of Information Technology (Singapore) 2025年第2期17卷 1015-1022页

作者： Sharan, Preeta Yasmeen, M.S. Nagesh, N. Parimi, Pranav Chetan, V. Roy, Sandip Kumar Department of Electronics and Communication (R & D) The Oxford College of Engineering Bangalore India Department of Electronics and Communication Engineering The Oxford College of Engineering Bangalore India Department of Computer Science Engineering Amrita Vishwa Vidyapeetham Bangalore India Research and Development The Oxford College of Engineering Bangalore India S P Jain School of Global Management Dubai United Arab Emirates

To protect secrecy without compromising data, the study outlines a strong security framework for communications between mobile devices and servers and recommends encryption as the best option. Traditional encryption techniques are not immediately transferable due to the limitations of e-health data, including its size, redundancy, and bandwidth especially when patient data is moving across public networks. Patient privacy may be compromised as a result of this security flaw. Strong encryption measures must be put in place in the healthcare industry to combat this and ensure that confidential patient data may only be retrieved securely and by authorised personnel, protecting data integrity and privacy. To strengthen communication in the healthcare industry, the study presents a new, simplified encryption and decryption technique. We carefully examine, evaluate, and then benchmark this technique against accepted encryption standards. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Data Security Healthcare device security RSA algorithm Secured communication

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Implementation of Secure and Verifiable Access Control Procedures using the NTRU Cryptosystem to Store Big Data in the Cloud Environment

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Recent Patents on engineering 2025年第2期19卷 57-70页

作者： Babu B, Ravindra Saxena, Swati Widjaja, Gunawan Vinodha D, Vedha Omarov, Batyrkhan Ramaiah, Gurumurthy B. Vats, Prashant Adama Ethiopia Department of Computer Science KCG College of Technology Chennai India Universitas Krisnadwipayana Jawa Barat 13077 Indonesia Department of Electronics and Communication Engineering JCT College Of Engineering and Technology Pichanur Coimbatore India International University of Tourism and Hospitality Turkistan Kazakhstan Technical and Vocational Training Institute Addis Ababa Ethiopia Department of Computer Science and Engineering Faculty of Engineering and Technology SGT University Haryana Gurugram India

Background: Due to their complexity and size, deploying ciphertexts for clouds is considered the most useful approach to accessing large data stores. Methods: However, access to a user's access legitimacy and improving a decrypted text on the cloud depending on an improved access policy (AP) specified by the data owner are the key challenges for making large data storage realistic and effective in clouds. The traditional ways either totally remove the problem of AP development or offer renewal to arbiter power, but in real-time, enhancing the AP is essential to maximising security and handling agility. Results: In this patent paper, a safe and verifiable access control program characterised by the NTRU cryptographic system for large storage of data in the clouds is proposed. Primarily, an improved NTRU decryption protocol to deal with the decryption failures of the prime NTRU is established, and in addition, the program is analysed for its security strength and computational performance. When a new AP is specified by the data owner, the cloud server allows the program to improve ciphertext and allows the owner to verify the upgrade to oppose the cloud's fraudulent behaviour. Conclusion: It enables (i) checking the user's legitimacy to access the data owner and qualified users, and (ii) allowing the user to verify the data provided by other users for the recovery of the right user. Strong analysis can prevent and block delinquency from various attacks, namely the collusion attack that could potentially target fraud users. © 2025 Bentham Science Publishers.

关键词： Cloud security

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Dermo classify: A dermatologist skin disease detection and classification using DCNN

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Research on Biomedical engineering 2025年第1期41卷 1-18页

作者： Sudar, K. Muthamil Nagaraj, P. Muneeswaran, V. Panda, Baidyanath Bhoi, Akash Kumar Department of Computer Science and Engineering Mepco Schlenk Engineering College Tamilnadu Sivakasi India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar Krishnankoil India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Virudhunagar Krishnankoil India LTIMindtree 1 American Row 3 Floor HartfordCT06103 United States Directorate of Research Sikkim Manipal University Gangtok Sikkim737102 India

Purpose: In current circumstances, investigators who are doing exploration in various regions remote from their old neighborhood are experiencing different skin issues like dermatitis, dry skin, and psoriasis because of environment or weather condition changes, food sensitivities, and water changes. This results in itching and dry patching on their skin. An affected individual should investigate and analyze the skin sickness, take appropriate precautions to keep the illness from spreading and seek therapy from a skin expert as quickly as time permits. Since the investigators come from different parts of the country, they are unfamiliar with hospitals and places, so they avoid such skin diseases without realizing the severity of the sickness. To address this, we propose a computer-aided system that detects and classifies skin diseases early, helping individuals to identify their condition and seek appropriate treatment quickly. Methods: To detect skin disease at an early stage, this work epitomizes a strategy using a computer-aided model according to sophisticated deep learning neural network techniques such as convolutional neural network (CNN), Residual neural networks, and Xception neural network to detect skin disease. We use the HAM-10000 data set, which contains data on seven different types of skin diseases. Results: The CNN model produced the most significant performance accuracy of 98.3718% in the experiments. Conclusion: The paper presents a promising approach for skin disease detection and classification using DCNNs. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2024.

关键词： Convolutional neural networks

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Machine Learning-Based Crop Recommendation System for Sustainable Agriculture 4

Machine Learning-Based Crop Recommendation System for Sustai...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Sriharsha, Kantheti Chandan Joshi, Kotapuri Rahul Reddy, Vummadi Ravi Sankar Chowdary, Vadlapudi Kalyan Hanuman Hajarathaiah, Koduru Rajak, Shaik School of Computer Science and Engineering VIT-AP University Amaravati India Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Amaravati India

ISBN: (纸本)9798331523923

This research paper addresses the critical challenges in agricultural crop recommendation systems through machine learning approaches. Current systems face significant limitations including limited integration of real-time environmental data, inconsistent prediction accuracy across diverse geographical regions, difficulty in handling multiple conflicting parameters simultaneously, and lack of interpretability in recommendation decisions. Recent techniques like Random Forest, Support Vector Machines, and Deep Neural Networks have attempted to address these issues but with varying degrees of success. This paper proposes an enhanced crop recommendation system that leverages multiple machine learning algorithms to overcome these limitations. The system implements real-time soil and weather parameter integration, while providing interpretable recommendations to farmers. Our approach achieves 99.54% accuracy in crop recommendations, significantly improving upon existing systems. The proposed model serves as a comprehensive solution for sustainable agriculture, contributing to improved crop yield and farmer decision-making processes. The system's effectiveness is validated across different agricultural zones, demonstrating its practical applicability and scalability in diverse farming conditions. © 2025 IEEE.

关键词： Adversarial machine learning

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Hybrid Power-Frequency Multiple Access With SIC-Free for Holographic Reconfigurable Intelligent Surface-Based Systems

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IEEE Wireless communications Letters 2025年第5期14卷 1566-1570页

作者： Le, Anh-Tu Vu, Thai-Hoc Da Costa, Daniel Benevides Voznak, Miroslav VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Ostrava708 00 Czech Republic University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan44610 Korea Republic of King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia

This paper proposes a hybrid power-frequency multiple access (HPMA) strategy to enhance the reliability, energy, and capacity of wireless communication systems. HPMA, a technique that reconciles all mutual benefits of orthogonal multiple access (OMA) and non-OMA (NOMA), removes the need for successive interference cancellation (SIC) and bypasses the user pairing inherent to NOMA. Focusing on holographic reconfigurable intelligent surfaces-aided systems servicing two users under imperfect channel estimation conditions, we establish theoretical frameworks for outage probability (OP) and ergodic capacity (EC), where both metrics are quantified with exact closed-form expressions and asymptotic analyses for scenarios with high signal-to-noise ratio (SNR) as well as for low-rate region cases. The former scenario provides insights into system diversity, modulation, and coding gains, and the ergodic slope;whereas the latter scenario elucidates trade-offs between energy and reliability and the method for maximizing energy-spectral efficiency. Numerical results demonstrate that HPMA outperforms conventional OMA and NOMA, improving OP in most SNR ranges in addition to enhancing EC and energy efficiency at low to moderate SNRs. © 2012 IEEE.

关键词： Hybrid power

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Ultra Energy Efficient Reliable and Robust DLJLFET-Based-PUF for IoT Devices 3rd

Ultra Energy Efficient Reliable and Robust DLJLFET-Based-PUF...

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Rajan, Chithraja Kumar, Rakesh Panchore, Meena Singh, Jawar School of Computer Science and Engineering Ramdeobaba University Maharashtra Nagpur440013 India Department of Electronics and Communication Engineering National Institute of Technology Patna Bihar800005 India Department of Electronics and Communication Engineering Indian Institute of Technology Patna Patna India

ISBN: (纸本)9789819743582

Physically Unclonable Function (PUF) emerged as a prominent hardware security solution for Internet of Things (IoTs) devices. PUFs are also used for generating secret keys for cryptography and for low-cost authentication in the high security of integrated circuits (ICs). Some natural variations occur during the manufacture of ICs. These variations are inevitable and uncertain. The PUF generates a different and unique random number by using this variation as the output key for the same input, even though this variation in the output is very small. In this article, a detailed comparative performance analysis of junctionless field effect transistor (JLFET)-based PUF and dopingless JLFET (DLJLFET)-based PUF is presented. A PUF circuit is designed using a five-stage ring oscillator (RO). It is observed that the delay in DLJLFET-based RO is 61% lower and consumes 47% less energy than JFLET-based RO at a supply voltage of 0.5 V because JLFET shows a higher leakage current due to high doping in the channel. Further, we did Monte Carlo simulation for 100 runs and observed that DLJLFET-PUF gives more unique PUF as mean HD is close to 50% and frequency of oscillation of DLJLFET is about 2 times higher than JLFET-PUF so DLJLFET-PUF will take less time during encryption and decryption for incoming and outgoing data, thus the output key generation will be faster. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hardware security

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