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IEEE Open Journal of the Communications Society 2025年 6卷 4311-4327页

作者： Sadia, Haleema Hassan, Ahmad Kamal Abbas, Ziaul Haq Abbas, Ghulam Baker, Thar Saeed, Nasir United Arab Emirates University College of Engineering Department of Electrical and Communication Engineering Al Ain United Arab Emirates Ghulam Ishaq Khan Institute of Engineering and Technology Faculty of Electrical Engineering Pakistan Ghulam Ishaq Khan Institute of Engineering and Technology Faculty of Computer Science and Engineering Pakistan University of Brighton School of Architecture Technology and Engineering BrightonBN2 4GJ United Kingdom

Power splitting based simultaneous wireless information and power transfer (PS-SWIPT) appears to be a promising solution to support future self-sustainable Internet of Things (SS-IoT) networks. However, the performance of these networks is constrained by radio frequency signal strength and channel impairments. To overcome this challenge, intelligent reflecting surfaces (IRSs) have recently been introduced in IoT networks to improve network efficiency by controlling signal reflections. In this article, we propose a novel IRS-enabled phase cooperative framework that aims to maximize the energy efficiency (EE) of the PS-SWIPT based SS-IoT network. Our framework leverages the idea of phase cooperation between two distinct networks without requiring additional hardware resources. We employed transmit beamforming (BF) at access points (APs) and phase shift optimization at the IRS with effective phase cooperation between APs. The EE maximization problem turns out to be NP-hard, so first, an alternating optimization (AO) is solved for the user’s network using heuristic BF approaches and phase optimization via semidefinite relaxation (SDR) approaches. To combat the computational complexity of AO, we also propose a low-complexity alternative solution by exploiting heuristic BF schemes and an iterative algorithm, i.e., the element-wise block-coordinate descent method for phase shifts optimization. The proposed solutions’ analysis shows low computational complexity and fast convergence, achieving near-optimal EE performance for different network settings. We conducted extensive simulations to evaluate our proposed framework’s performance and unveiled significant EE performance achieved by the IoT network consistent with the numerical findings. This work demonstrates the potential of IRS phase cooperation for enhancing the EE of different networks without constraining hardware resources. © 2020 IEEE.

关键词： Energy transfer

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Revolutionizing Healthcare: The Role of AI and BigData Analytics in Disease Prevention and Management

Revolutionizing Healthcare: The Role of AI and BigData Analy...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Vaishnavi Gatkine Saksham N. Khode Palash Gourshettiwar Computer Science & Medical Engineering Faculty of Engineering And Technology DMIHER (DU) Wardha Maharashtra

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Artificial Intelligence (AI) and Big Data Analytics are greatly transforming the healthcare scenario by facilitating the early detection of diseases, personalized medicine, and improved public health interventions. AI, by employing machine learning and deep learning, processes big and complex datasets, whereas Big Data detects patterns and predicts risks, enhancing resource utilization and readiness for health emergencies. Yet, the technologies are faced with a range of challenges that include data security concerns, matters of integrating many systems, and the requirement of real-time processing in ever-changing healthcare environments. Although recent technological advancements such as sophisticated deep learning algorithms and decentralized data processing have been promising solutions to some of these challenges, issues of easy integration and privacy remain. This paper suggests an objective-oriented framework to enhance the application of AI and Big Data solutions in healthcare to overcome current limitations and establish a more efficient, personalized, and predictive healthcare system.

关键词： Deep learning Technological innovation Data privacy Sentiment analysis Scalability Medical services Big Data Resource management Artificial intelligence Diseases

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Crop Disease Recognition and Classification: A Deep Dive into Machine Learning Techniques - A Survey 1st

Crop Disease Recognition and Classification: A Deep Dive int...

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1st International Research Conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Sangar, G. Rajasekar, V. Department of Computer Science and Engineering Faculty of Engineering and Technology SRMIST Vadapalani Chennai India

ISBN: (纸本)9783031823886

In the changing farming business, crop diseases and other challenges highlight the need for early detection and effective disease control to ensure global food sustainability. Deep Learning (DL) and Machine Learning (ML) are vital to plant leaf sickness identification, as this survey shows. Deep learning models like CNNs can automatically detect complex patterns in plant leaf photos, enabling reliable disease diagnosis. Mean- while, machine learning models like Support Vector Machines extract important elements from image data, providing valuable insights. Transfer learning with pre-trained models like VGG and ResNet and data augmentation improve model generalization. Deep learning (DL) models’ accuracy, precision, recall, and F1 score measure their ability to identify healthy and unhealthy plants. Deep learning (DL) and machine learning (ML) methods are used to diagnose plant leaf diseases in this survey. This study aims to synthesize knowledge, identify trends and tendencies, and suggest precision agriculture research directions. This survey seeks to advance sustainable crop management by understanding early and accurate disease detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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A Resource-Efficient Federated Learning Framework for Intrusion Detection in IoMT Networks

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IEEE Transactions on Consumer Electronics 2025年

作者： Fahim-Ul-Islam, Md. Chakrabarty, Amitabha Alam, Md. Golam Rabiul Maidin, Siti Sarah Brac University Department of Computer Science and Engineering Bangladesh INTI International University Faculty of Data Science and Information Technology Nilai Malaysia

The emergence of medical sensors in Smart Healthcare Systems (SHS) has enabled the development of sophisticated Internet of Medical Things (IoMT) networks which is crucial for tracking vital physiological parameters in consumer electronics. These networks are progressively incorporated into consumer devices allowing users to monitor their health parameters effortlessly. Nonetheless, they encounter considerable security and privacy issues stemming from weaknesses in data transfer. Despite the potential of Intrusion Detection Systems (IDS), there is a critical need for a real-time, highly precise attack detection system optimized for the edge-centric Internet of Medical Things (IoMT) environment in consumer sensor devices. Therefore, we introduce the federated kolmogorov-arnold network (FedIoMT), an advanced federated learning framework that utilizes meta-learning along with an advanced clustering method to achieve robust model aggregation. Our FedIoMT acts as both an adaptive personalized updating mechanism and a shared global classifier. In addition, we propose a novel intrusion detection model that uses the kolmogorov-arnold convolutional network (KANConvNet) as its local classifier, which improves scalability and interpretability in the FL framework updates. Our FedIoMT model demonstrates superior performance compared to other existing FL-based methods, achieving validation accuracies of 99.38%, 99.23%, 99.26%, and 99.2% across four benchmark IoMT datasets under the IID setting. FedIoMT utilizes significantly more floating point operations per second (FLOP) counts up to 2.8 times more than other models and also excels in memory efficiency, consuming up to 93% less peak memory across devices compared to the alternatives. We thoroughly evaluated FedIoMT on embedded consumer devices, demonstrating its resource-efficient characteristics. © 2025 IEEE.

关键词： Network intrusion

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Developing a Test Strategy for Mathematics of Arrays Computation Cores

Developing a Test Strategy for Mathematics of Arrays Computa...

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International Electrical engineering Congress (iEECON)

作者： Ian Grout Lenore Mullin Department of Electronic and Computer Engineering Faculty of Science and Engineering University of Limerick Limerick Ireland Department of Computer Science College of Nanotechnology Science and Engineering University at Albany SUNY Albany U.S.A.

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

Small size, hardware-only, custom data processing cores connected to a host processor are increasingly considered in digital systems design to provide the ability for accelerating data processing operations through hardware acceleration and parallel processing. These can be realized in different forms such as a custom core connected to a host processor on the same die as the processor, as a separate die (e.g., a chiplet) within the same package as the processor or as a separate Integrated Circuit. These require advanced design flows, low-geometry fabrication processes, and suitable test circuit insertion to implement a suitable test strategy. In this paper, the testability of custom hardware cores is considered through test circuit insertion based on the IJTAG standard. The core is to perform data processing operations based on the Mathematics of Arrays and serial communications is based on the Serial Peripheral Interface. This interface is used for both normal operating mode and the IJTAG based test mode. The system design is considered at the pre-synthesis design development stage and identifies architectural considerations for such system designs.

关键词： Fabrication Electrical engineering Memory management Parallel processing Data processing Mathematics Arrays System analysis and design Standards Hardware acceleration

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Interpretable Arabic Hate Speech Detection using Large Language Model

Interpretable Arabic Hate Speech Detection using Large Langu...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Majed Alwateer Ibrahim Gad Mahmoud Elmarhomy Ghada Elmarhomy Hasan Hashim Malik Almaliki El-Sayed Atlam Department of Computer Science College of Computer Science and Engineering Taibah University Saudi Arabia Computer Science Department Faculty of Science Tanta University Tanta Egypt Computer Science and Information College of Applied Taibah University Saudi Arabia Department of Information Systems College of Computer Science and Engineering Taibah University Saudi Arabia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

Social media platforms serve as significant spaces for users to have conversations, discussions and express their opinions. However, anonymity provided to users on these platforms allows the spread of hate speech and other offensive material. Due to the wide-ranging nature of these platforms, there is a critical need to automatically detect and report occurrences of hate speech. There are various detection methods, but many of them operate as black boxes, lacking interpretability and explainability by design. To address the lack of interpretability, this study explores the development of an interpretable framework to detect hate speech in Arabic using large language models (LLMs). The proposed approach combines advanced natural language processing techniques with interpretable machine learning methods to enhance understanding of model decisions. The experimental results demonstrate that the model achieves high accuracy while maintaining interpretability, enabling users to understand the reasoning behind the detections. The proposed method achieves an accuracy of 0.846%, with a precision of 0.843% and a recall of 0.846%, outperforming existing Arabic hate speech detection models. These results show the effectiveness of combining LLM with interpretability for this critical task, providing a reliable and transparent solution for automated moderation of harmful content.

关键词： Technological innovation Accuracy Social networking (online) Smart cities Large language models Hate speech Text categorization Writing Reliability Stakeholders

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Enhancing Healthcare Data Security: Computational Efficiency of Blockchain-Based Digital Signatures 5th

Enhancing Healthcare Data Security: Computational Efficiency...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Xu, Li Goyal, S.B. Rajawat, Anand Singh Niranjala, T. H. A. Shyama Heshini City Graduate School City University Petaling Jaya46100 Malaysia Nanchang Institute of Science and Technology Nanchang China Faculty of Information Technology City University Petaling Jaya46100 Malaysia School of Computer Science and Engineering Sandip University Nashik India

ISBN: (纸本)9789819771776

This study aims to examine the computational efficiency of blockchain-based digital signature technologies with respect to healthcare data security. When thinking about the revolutionary opportunities and risks posed by healthcare data digitization, concerns regarding data privacy and security have come to the forefront. Blockchain technology, which is both decentralised and immutable, offers a promising alternative. Two of its primary uses, data integrity and identity authentication, are made possible by digital signature systems. We conducted a comprehensive analysis to find out how efficient different protocols are in healthcare computing. Among these protocols were EdDSA, ECDSA, and Schnorr signatures. Our research indicates that efficiency is significantly affected by protocol selection. Despite the strong security it provides, data validation delays may occur due to the increased processing expense generated by ECDSA. Schnorr signatures and EdDSA are viable solutions that offer a reasonable compromise between security and efficiency. By delving into optimization methodologies and associated difficulties, this research paved the road for the ethical application of blockchain technology in healthcare. The healthcare industry, policymakers, and IT developers all benefit from our study when it comes to selecting and improving methods for secure and efficient data storage. This study uniquely examines the computational efficiency of blockchain-based digital signature protocols in healthcare data security, addressing optimization methodologies and highlighting the suitability of Schnorr signatures and EdDSA. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Opposition-based multi-objective ant colony optimization framework for sustainable retrofitting: time–cost–energy–risk trade-offs

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Asian Journal of Civil engineering 2025年第5期26卷 2223-2239页

作者： Pokkuluri, Kiran Sree Chauhan, Trupti Ravindra Macedo, Víctor Daniel Jiménez Manjunath, T.C. Bharadwaj, Manish Sethi, Krushna Chandra Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Design Manipal School of Architecture and Planning Manipal Academic of Higher Education Manipal Karnataka Udupi India Mechanical Engineering Faculty Michoacan University of Saint Nicholas of Hidalgo Morelia Mexico Department of Computer Science and Engineering Rajarajeswari College of Engineering Bengaluru India Department of Management Studies Atal Bihari Vajpayee Indian Institute of Information Technology and Management Gwalior India Civil Engineering Department Centurion University of Technology and Management Gajapati India

The increasing emphasis on sustainable retrofitting of buildings has brought attention to the need for optimizing trade-offs among critical objectives, such as time, cost, energy consumption, and risk. This study proposes an opposition-based multi-objective ant colony optimization (OB-MOACO) framework to address these challenges effectively. The framework incorporates opposition-based learning to enhance solution exploration and convergence in multi-objective optimization problems, specifically tailored for sustainable retrofitting projects. The study develops a time–cost–energy–risk trade-off (TCERT) model with four objectives: minimizing retrofitting time, cost, energy consumption, and risks. The model integrates constraints related to budget, project timelines, and energy efficiency to ensure feasibility and sustainability. A detailed case study of retrofitting a mixed-use building is presented, encompassing 11 aspects such as structural reinforcement, energy efficiency enhancement, and accessibility improvements. Results demonstrate the superiority of OB-MOACO over conventional methods like NSGA-III, MODE, and MOPSO in achieving Pareto-optimal solutions. Key performance metrics, including hypervolume (HV) and inverted generational distance (IGD), highlight the model's efficiency in balancing competing objectives. The study contributes to advancing sustainable retrofitting practices by providing an innovative, cost-effective, and energy-efficient decision-making framework. Implications for practitioners and policymakers are discussed, alongside recommendations for future research. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Multi-objective optimization Opposition-based ant colony optimization Pareto optimization Sustainable retrofitting Time–cost–energy–risk trade-off

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Deep Learning Utility for Gas/Odor Sensor Signature Analysis

Deep Learning Utility for Gas/Odor Sensor Signature Analysis

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Yashasvi Raut Shiv Nath Chaudhri Department of Computer Science and Engineering Faculty of Engineering and Technology DMIHER (DU) Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Gas/odor sensors are integral components of sensor systems used in diverse applications such as food quality control, environmental monitoring, medical diagnostics, odor profiling, industrial safety, agriculture, explosives detection, wine tasting, pharmaceutical quality control, and toxic gas detection. Traditional approaches in this domain of gas/odor sensing technologies face challenges related to accuracy, cross-sensitivity, and drifted real-time performance. The integration of deep learning techniques with gas sensors has shown promise in overcoming these limitations. Moreover, this amalgamation of gas sensing and deep learning strategies enables accurate identification of gases/odors, handling complex patterns, and leading optimal consumption of resources. In this paper, an overview of advancements in deep learning-enabled gas sensing has been presented that focused on methodologies, sensor types, and applications. This review also highlights the state-of-the-art techniques and identifies gaps for future directions for research.

关键词： Deep learning Accuracy Hydrogen Quality control Real-time systems Sensor systems Sensors Protection Gas detectors Petroleum

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A Multi-Scale Deep Learning Framework Combining MobileViT-ECA and LSTM for Accurate ECG Analysis

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IEEE Access 2025年 13卷 85473-85492页

作者： Ba Mahel, Abduljabbar S. Al-Gaashani, Mehdhar S. A. M. Alkanhel, Reem Ibrahim Hassan, Dina S. M. Muthanna, Mohammed Saleh Ali Muthanna, Ammar Aziz, Ahmed University of Electronic Science and Technology of China School of Life Science and Technology Chengdu610054 China University of Electronic Science and Technology of China School of Resources and Environment Chengdu610056 China Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Technology P.O. Box 84428 Riyadh11671 Saudi Arabia Tashkent State University of Economics Department of International Business Management Tashkent Uzbekistan RUDN University Department of Applied Probability and Informatics Peoples’Friendship University of Russia 6 Miklukho-Maklaya St Moscow117198 Russia Faculty of Computer and Artificial Intelligence Benha University Department of Computer Science Egypt Central Asian University Engineering school Tashkent Uzbekistan

Electrocardiogram (ECG) analysis is crucial for diagnosing cardiovascular diseases (CVD), especially atrial fibrillation (AF), a prevalent cardiac rhythm abnormality. However, the variability and complexity of ECG signals make AF classification challenging, highlighting the need for more accurate and reliable methods. This paper introduces a novel deep learning (DL) architecture designed to enhance the processing, feature extraction, and analysis of ECG signals. The proposed model utilizes a hybrid framework that combines standard and dilated convolutional networks, advanced attention mechanisms, and temporal sequence learning to address the complexities of ECG data. A parallel architecture integrates a MobileViT block for efficient spatial feature extraction and an Efficient Channel Attention (ECA) module to refine feature representations. Simultaneously, a Long Short-Term Memory (LSTM) network captures temporal dependencies in the data. The concatenation of the outputs from both the MobileViT-ECA block and the LSTM network allows for the extraction of both local and global features. Fully connected layers, along with dropout regularization, ensure robust performance and effective classification. We analyzed ECG signals of three different lengths (1, 2, and 3 seconds) to investigate how segment length affects model performance. Additionally, we conducted various classification scenarios and an ablation study to simulate real-world conditions and identify key components of the architecture. Our experimental findings show that the model attains high accuracy in ECG classification and AF identification, with average metrics including accuracy of 87.80%, recall of 87.80%, F1-score of 87.45%, and specificity of 95.66%. The proposed model outperforms existing methods on the same AF dataset, demonstrating its superior performance. This method accurately classifies ECG signals and detects AF, highlighting its potential for clinical application. The results help move forwar

关键词： Electrocardiography

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