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Early Diabetic Retinopathy Cyber-Physical Detection System Using Attention-Guided Deep CNN Fusion

IEEE Transactions on Network Science and engineering 2025年第3期12卷 1898-1910页

作者： Hossain, M. Shamim Shorfuzzaman, Mohammad King Saud University Research Chair of Pervasive and Mobile Computing Department of Software Engineering College of Computer and Information Sciences Riyadh12372 Saudi Arabia Taif University Department of Computer Science College of Computers and Information Technology Taif21944 Saudi Arabia

Diabetic retinopathy is the most common and severe eye complication of diabetes, and it can cause vision loss or even blindness due to retina damage. Automatic and faster detection of various DR stages is crucial and can benefit both patients and ophthalmologists. With the ubiquity of measurement devices and growing processing power, cyber-physical characterization is becoming an enabler technology in many disciplines. This paper proposes a cyber-physical system (CPS) framework that will aid clinicians in establishing an early diagnosis of DR. Particularly;we present a component-based CPS architecture to use a deep learning-based predictive model deployed on the cloud for effective DR diagnosis and incorporate medical devices. To this end, a deep learning-based explainable CNN fusion model has been introduced in the proposed framework for automatic screening and interpretation of DR stages using digital fundus images. We extract salient features using various fine-tuned CNN models in conjunction with an attention network, and we use a locally connected layer to calculate the weighted contribution of these networks. We measure the performance of our approach using a public dataset comprising fundus images of five different categories. Experimental results demonstrate the proposed approach's effectiveness for faster and more accurate detection of various DR stages. © 2013 IEEE.

关键词： Biomedical equipment

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Long-term solar radiation forecasting in India using EMD, EEMD, and advanced machine learning algorithms

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Environmental Monitoring and Assessment 2025年第3期197卷 1-36页

作者： RajasundrapandiyanLeebanon, T. Murugan, N. S. Sakthivel Kumaresan, K. Jeyabose, Andrew Department of Electrical and Electronics Engineering TamilNadu College of Engineering Tamil Nadu Coimbatore India Department of Mechanical Engineering Park College of Engineering & Technology TamilNadu Coimbatore India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Solar radiation plays a critical role in the carbon sequestration processes of terrestrial ecosystems, making it a key factor in environmental sustainability among various renewable energy sources. This study integrates two advanced signal processing techniques—empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD)—with machine learning (ML) algorithms, including multilayer perceptron (MLP), random forest regression (RFR), support vector regression (SVR), and ridge regression, to forecast long-term solar radiation. Meteorological data spanning 13 years (2000–2012) from seven locations across India (Bhubaneswar, Chennai, Delhi, Hyderabad, Nagpur, Patna, and Trivandrum) were used for training and testing. The optimal model was identified based on performance metrics, including the highest linear correlation coefficient (R), and the lowest mean absolute error (MAE) and root mean square error (RMSE). The results indicate that EEMD integrated with ML algorithms consistently outperformed EMD-based approaches. Among the ML models evaluated, EEMD integrated with MLP achieved the best performance across all locations, with RMSE = 0.332, MAE = 0.26, and R2 = 0.99. Furthermore, a comparative analysis with previous studies demonstrated that the proposed approach provides superior accuracy, underscoring its efficacy in solar radiation forecasting. © The Author(s) 2025.

关键词： Empirical mode decomposition

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Intelligent Guitar Chord Recognition Using Spectrogram-Based Feature Extraction and AlexNet Architecture for Categorization

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 758-767页

作者： Korade, Nilesh B. Salunke, Mahendra B. Bhosle, Amol A. Sangve, Sunil M. Joshi, Dhanashri M. Asalkar, Gayatri G. Kadu, Sujata R. Sarwade, Jayesh M. Department of Computer Engineering JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India Department of Computer Engineering PCET’s Pimpri Chinchwad College of Engineering and Research Ravet Maharashtra Pune412101 India Department of Computer Science and Engineering School of Computing MIT Art Design and Technology University Loni Kalbhor Maharashtra Pune412201 India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Bibwewadi Pune411037 India Department of Computer Engineering Terna Engineering College Maharashtra Mumbai India Department of Information Technology JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India

Chord prediction plays a key role in the advancement of musical technological innovations, such as automatic music transcription, real-time music tutoring, and intelligent composition tools. Accurate chord prediction can assist musicians, educators, and developers in constructing tools that help in learning, playing, and composing music. Background noise and audio distortions may have an impact on chord prediction accuracy, particularly in real-world situations. Chords can have distinct voicings or finger positions on the guitar, resulting in slight variations in audio representation. This study focuses on the classification of guitar chords using techniques of deep learning. There are eight major and minor guitar chords in the dataset. They have been turned into spectrograms, chromagrams, and Mel Frequency Cepstral Coefficients (MFCC) so that features can be extracted. Various deep learning architectures, including CNN, ResNet50, AlexNet, and VGG, were employed to classify the chords. Experimental results demonstrated that the spectrogram-based AlexNet model outperforms others, achieving good accuracy and robustness in chord classification. The proposed study demonstrates the efficiency of spectrograms and advanced deep learning models for audio signal processing in music applications. By automating chord detection, this study provides beneficial resources for music learners as well as educators, enabling more efficient learning and real-time feedback during practice sessions. © (2025), (Science and Information Organization). All Rights Reserved.

关键词： Deep learning

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A Risk Identification Method for Power Operation Scenarios Using Image Caption and Semantic Text Similarity Analysis

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IEEE Transactions on Industrial Informatics 2025年第6期21卷 4488-4498页

作者： Li, Wei Ma, Fuqi Jia, Rong Wang, Bo Wang, Hongxia Alharbi, Abdullah M. Xi'an University of Technology School of Electrical Engineering Xi'an710048 China Wuhan University School of Electrical Engineering and Automation Wuhan430072 China University of Denver Department of Electrical and Computer Engineering DenverCO80208 United States Prince Sattam bin Abdulaziz University Department of Electrical Engineering College of Engineering Wadi ad-Dawasir11942 Saudi Arabia

Power production is a complex process that involves multiple interactions, which require rich semantic knowledge to categorize and evaluate. Utilizing high-level image understanding to accurately identify risks is significant in ensuring staff safety and grid stability. In this work, we propose image captioning and semantic text similarity analysis (IC-STSA), a risk identification method for complex power operation scenarios the employs image captioning and semantic text similarity analysis. First, image caption is used to transform an image at the site into the text that best describes the visual data. Next, a safe power production (SPP) knowledge base is established, after which semantic text similarity analysis is introduced to transform the risk identification into a text classification problem. By comparing the image caption information and the SPP knowledge base and identifying similarities, we achieve refined descriptions of operation scenarios and thereby risk identification. In the final step, actual images are used to verify the findings of the IC-STSA. Results show the proposed method performs well, generating accurate, targeted descriptions, and risk identification. © 2025 IEEE. All rights reserved.

关键词： Semantics Decoding Visualization Safety Production Feature extraction Electronic mail Vectors Long short term memory Knowledge based systems

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Machine Learning-Driven MPPT Techniques for Optimal Solar Energy Utilization

Machine Learning-Driven MPPT Techniques for Optimal Solar En...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Khanam, Jobeda J. Issapour, Marjaneh Foo, Simon Y Castillo, Paulo Farmingdale State College Department of Electrical and Computer Engineering Technology NY United States FAMU-FSU College of Engineering Department of Electrical and Computer Engineering FL United States

ISBN: (纸本)9798331504847

In this paper, we utilized machine learning (ML) algorithms to optimize Maximum Power Point Tracking (MPPT) in photovoltaic systems. Predicting the optimal voltage is important as, at that voltage, the system gains maximum energy output and efficiency. We predicted optimal voltage from irradiance and temperature datasets by applying ML algorithm. We evaluated several ML models, finding Linear Regression to perform best with an MSE (mean square error) of 0.0024 and RMSE (root mean square error) of 0.0489. The proposed solution integrates an ML (linear regression)-driven voltage predictor, a PID controller, and a DC-DC buck-boost converter in a MATLAB simulation environment. In MATLAB Simulink, we observed that the buck-boost converter produced the same voltage as the predicted value across the load. © 2025 IEEE.

关键词： MATLAB

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xCViT:Improved Vision Transformer Network with Fusion of CNN and Xception for Skin Disease Recognition with Explainable AI

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computers, Materials & Continua 2025年第4期83卷 1367-1398页

作者： Armughan Ali Hooria Shahbaz Robertas Damaševicius Department of Electrical Engineering Wah Engineering CollegeUniversity of WahWah Cantt47040Pakistan Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Applied Informatics Vytautas Magnus UniversityKaunas44309Lithuania

Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)*** identification of skin cancer enhances the likelihood of effective treatment,as delays may lead to severe tumor *** study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis,with an architecture that integrates a Vision Transformer,a bespoke convolutional neural network(CNN),and an Xception *** were evaluated using two benchmark datasets,HAM10000 and Skin Cancer *** the HAM10000,the model achieves a precision of 95.46%,an accuracy of 96.74%,a recall of 96.27%,specificity of 96.00%and an F1-Score of 95.86%.It obtains an accuracy of 93.19%,a precision of 93.25%,a recall of 92.80%,a specificity of 92.89%and an F1-Score of 93.19%on the Skin Cancer ISIC *** findings demonstrate that the model that was proposed is robust and trustworthy when it comes to the classification of skin *** addition,the utilization of Explainable AI techniques,such as Grad-CAM visualizations,assists in highlighting the most significant lesion areas that have an impact on the decisions that are made by the model.

关键词： Skin lesions vision transformer CNN Xception deep learning network fusion explainable AI Grad-CAM skin cancer detection

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Reliable Task Offloading for 6G-Based IoT Applications

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computers, Materials & Continua 2025年第2期82卷 2255-2274页

作者： Usman Mahmood Malik Muhammad Awais Javed Ahmad Naseem Alvi Mohammed Alkhathami Department of Computer Software Engineering National University of Science and Technology(NUST)Islamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadIslamabad45550Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and *** this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task ***,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource ***,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities *** paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH ***,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH *** performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed *** simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.

关键词： 6G IoT task offloading fog computing

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A Systematic Three-Stage Safety Enhancement Approach for Motor Drive and Gimbal Systems in Unmanned Aerial Vehicles

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IEEE Transactions on Power Electronics 2025年第7期40卷 9329-9342页

作者： Jie, Huamin Zhao, Zhenyu Li, Hong Gan, Theng Huat See, Kye Yak Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore Zhejiang University College of Electrical Engineering Hangzhou310058 China National University of Singapore Temasek Laboratories 117411 Singapore

Electromagnetic compatibility (EMC) is critical for ensuring the reliability and safety of power electronics-related assets, such as unmanned aerial vehicles (UAVs). EMC encompasses two key aspects: electromagnetic interference (EMI) and electromagnetic susceptibility (EMS). While EMI has been widely studied, EMS in power electronics systems, particularly with sensitive control and sensing modules, is gaining increasing attention due to rising threats from intentional or unintentional electromagnetic (EM) noises. Therefore, enhancing the EM safety of these systems is essential. This article proposes a systematic three-stage safety enhancement approach for power electronics-related systems in UAVs. In stage 1, a quantitative risk assessment strategy based on the EMS test results is introduced, identifying the motor drive and gimbal systems of the UAVs as the most vulnerable components. Stage 2 analyzes their failure mechanisms, offering valuable insights for further hardening. Stage 3 develops a comprehensive lightweight strategy to enhance the UAV safety. Using a DJI UAV as a case study, the proposed approach demonstrates the capability of the proposed EM hardening solutions against external EM noises with the electric field strength up to 200 V/m from 2 MHz to 18 GHz. The total weight increases by only 1.2% with a negligible addition to volume. © 2025 IEEE.

关键词： Antennas

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A Novel Mixed-Signal Flash-based Finite Impulse Response (FFIR) Filter for IoT Applications 25

A Novel Mixed-Signal Flash-based Finite Impulse Response (FF...

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30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Lee, Cheng-Yen Khatri, Sunil P. Ghrayeb, Ali Department of Electrical and Computer Engineering Texas A and M University College StationTX United States Department of Electrical and Computer Engineering Texas A and M University at Qatar Doha Qatar

ISBN: (纸本)9798400706356

In this paper, we present a novel mixed-signal flash-based Finite Impulse Response (FFIR) filter architecture for IoT applications. The FFIR filter is scalable in that it can implement any filter with up to a provisioned maximum number of taps. Our FFIR filter utilizes flash transistors, a type of non-volatile memory (NVM) device, to perform analog computations in the current domain, achieving low power, energy, and area requirements. This design is well-suited for the Internet of Things (IoT) applications and other scenarios where resources are highly constrained. Our FFIR filter consists of several Flash-based Coefficient Multipliers (FCMs). The FIR coefficients of each FCM are stored in its constituent flash transistors, with the threshold voltage (Vt) of the flash transistors serving as a proxy for the filter coefficients. Furthermore, the impact of process or voltage variations is mitigated by precisely tuning the Vt of the flash transistors. The tuning of the Vt of the flash transistors can be performed either in the factory by the manufacturer (to negate process variations), or by the user in the field (to negate voltage variations or aging effects). We evaluate the tolerance of FFIR filters to manufacturing variations through Monte Carlo analysis, demonstrating robustness to process and VDD variations. Our FFIR design achieves a significant improvement over previous approaches. Compared to Digital FIR (DFIR) filters operating at the fastest frequency, we reduce the average of power, energy, and area by 4.05×, 1.95×, and 6.06×, while achieving an average peak signal-to-noise ratio (PSNR) of 38.04 dB and an average effective number of bits (ENOB) of 8.87 bits. In addition, we compare our FFIR filter with state-of-the-art Analog FIR (AFIR) filters as well. Our designs demonstrate significantly improved performance of at least 1.3×, 5.3×, and 18.5× in terms of energy per tap, area, and latency, respectively, when compared with the best among 4 recently published

关键词： Signal to noise ratio

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Classification of human-written and AI-generated sentences using a hybrid CNN-GRU model optimized by the spotted hyena algorithm

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Alexandria engineering Journal 2025年 126卷 116-130页

作者： Ragab, Mahmoud Ashary, Ehab Bahaudien Kateb, Faris Hakeem, Abeer Mosli, Rayan Albogami, Nasser N. Nooh, Sameer Information Technology Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Electrical and Computer Engineering Department Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Information Systems Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

The rapid advancement of artificial intelligence (AI) in generating human-like text poses significant challenges in distinguishing between human-written and AI-generated content. Recent advancements in natural language generation have significantly enhanced the quality and variety of AI-generated text, making it almost indistinguishable from human-written content. ChatGPT, a popular AI model, belongs to the generative pre-trained transformer family. While human content is created with a clear intent to convey meaning, AI-generated text aims to replicate human-like language. Classifying human-written and AI-generated sentences is crucial for addressing issues like fake news, plagiarism, and spamming. AI text often follows repetitive patterns, while human writing is more creative and original, making detection significant for combating misinformation. Therefore, this study proposes to classify human-written and AI-generated sentences using a hybrid CNN-GRU model optimized by the Spotted Hyena Algorithm (CHWAIG-DLSHO) approach. The approach involves preprocessing text data through tokenization, lemmatization, and data splitting, followed by word embedding using Latent Dirichlet Allocation (LDA). A hybrid convolutional neural network (CNN) and gated recurrent unit (GRU) model is employed for sentence classification. The spotted hyena optimizer (SHO) model is utilized to fine-tune the hyperparameters of the CNN-GRU model, enhancing its performance. The analysis of the CHWAIG-DLSHO method takes place utilizing AI vs. human text dataset. The performance validation of the CHWAIG-DLSHO method portrayed a superior accuracy value of 99.17 % over existing techniques. © 2025 The Authors

关键词： Convolutional neural networks

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