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7th International Conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Vasuki Rohinidevi, V. Deeban Chakravarthy, V. Deepak, S. Prem Kumar SRM Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India B V Raju Institute of Technology Department of Computer Science and Engineering Narsapur India

ISBN: (纸本)9798350372816

Deep learning, a branch of artificial intelligence, has drawn interest from the academic and corporate realms, especially in areas like speech and image analysis, video processing, and natural language processing. Its use in agricultural plant protection has expanded to include the diagnosis and evaluation of pests and plant diseases. In this situation, there are several benefits to using deep learning. It removes the biases related to manually chosen pathogen properties, improves objectivity when determining disease characteristics, and hastens the advancement of contemporary technology. In order to diagnose agricultural leaf illnesses, this study explores the most recent developments in deep learning methods, including CNN, DENSNET, InceptionV3, VGG16, Resnet50, and MobileNet, in addition to a variety of sensors. This project aims to aid scientists in the field by tackling challenges in merging deep learning and cutting-edge imaging tools to enhance illness identification. It addresses current trends and barriers, shedding light on unresolved issues and paving the way for further research. By clarifying persistent problems and posing new questions, it fosters progress and innovation in the field of medical imaging and diagnosis. © 2024 IEEE.

关键词： Contrastive Learning

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Improved MPPT algorithm: Artificial neural network trained by an enhanced Gauss-Newton method

AIMS Electronics and Electrical Engineering

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AIMS Electronics and electrical engineering 2023年第4期7卷 380-405页

作者： Dkhichi, Fayrouz Laboratory of Networks Computer Science Telecommunications & Multimedia Electrical Engineering Department Superior School of Technology Hassan II University of Casablanca Morocco

A novel approach defined by the artificial neural network (ANN) model trained by the improved Gauss-Newton in conjunction with a simulated annealing technique is used to control a step-up converter. To elucidate the superiority of this innovative method and to show its high precision and speed in achieving the right value of the Maximum Power Point (MPP), a set of three comparative Maximum Power Point Tracker (MPPT) methods (Perturbation and observation, ANN and ANN associated with perturbation and observation) are exanimated judiciously. The behavior of these methods is observed and tested for a fixed temperature and irradiance. As a result, the proposed approach quickly tracks the right MPP = 18.59 W in just 0.04382 s. On the other hand, the outstanding ability of the suggested method is demonstrated by varying the irradiance values (200 W/m , 300 W/m , 700 W/m , 1000 W/m , 800 W/m and 400 W/m ) and by varying the temperature values (15°C, 35°C, 45°C and 5°C). Therefore, the ANN trained by Gauss-Newton in conjunction with simulated annealing shows a high robustness and achieves the correct value of MPP for each value of irradiance with an efficiency 99.54% and for each value of temperature with an efficiency 99.98%;the three other methods sometimes struggle to achieve the right MPP for certain irradiance values and often remains stuck in its surroundings. © 2023 The Author(s).

关键词： Maximum power point trackers

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KiAtt Fusion: Remote Sensing-Based Water Bodies Segmentation for Environmental Monitoring 4

KiAtt Fusion: Remote Sensing-Based Water Bodies Segmentation...

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4th International Conference on Digital Futures and Transformative Technologies, ICoDT2 2024

作者： Sikander, Saad Rehman, Hamood Ur Naqvi, Syed Fakhr Abbas Aslam, Waqar Perwaiz, Nazia Sir Syed CASE Institute of Technology Department of Electrical Engineering Islamabad Pakistan Sir Syed CASE Institute of Technology Department of Electrical Engineering and Computer Science Islamabad Pakistan National University of Sciences and Technology NUST School of Electrical Engineering and Computer Science Islamabad Pakistan

ISBN: (纸本)9798350391862

Monitoring the environment and managing water bodies are crucial for preserving ecosystems and ensuring sustainable resource utilization. This study aims to propose a robust approach for segmenting water bodies by combining various complementary attributes extracted from deep learning techniques. By leveraging deep contextual features learned from attention regions and enhanced edge detection, our method significantly improves the accuracy of detecting water bodies in low-resolution satellite imagery. Utilizing S1 and S2 Sentinel imagery data and exploring multi-band features, we enhance key attributes through advanced UNet architectures - specifically, Ki-UNet and Attention UNet - and integrate complementary features for downstream tasks. Additionally, we assembled a local dataset from the Khyber Pakhtunkhwa (KPK) region of Pakistan, facilitating precise water body segmentation from satellite or aerial views. Our ensemble model achieved remarkable accuracy and Intersection over Union (IoU) scores, reaching up to 99.01% and 96.2%, respectively, surpassing state-of-the-art models. This research provides automated, accurate segmentation techniques essential for environmental management and resource assessment, offering a promising solution for delineating water bodies. github link : (https://***/SaadBaloch96/Dataset/blob/main/***) © 2024 IEEE.

关键词： Environmental monitoring

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Finite-Time Synchronization of Complex Networks With Intermittent Couplings and Neutral-Type Delays

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IEEE/CAA Journal of Automatica Sinica 2023年第10期10卷 2026-2028页

作者： Engang Tian Yi Zou Hongtian Chen School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Dear Editor, This letter focuses on the finite-time synchronization(FTS) of neutral-type complex networks with intermittent couplings. Different from most of the existing references concerning neutral-type systems,a delay-independent dynamical event-triggering controller is considered, operating the same way as the intermittent coupling and excluding the Zeno behavior naturally.

关键词： neutral letter concerning

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A Physical-Based Artificial Neural Networks Compact Modeling Framework for Emerging FETs

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IEEE Transactions on Electron Devices 2024年第1期71卷 223-230页

作者： Yang, Ya-Shu Li, Yiming Kola, Sekhar Reddy Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory The Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan

We report a compact modeling framework based on the Grove-Frohman (GF) model and artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The framework consists of two ANNs;the first ANN constructed with the drain current model not only can capture the main trend of device I - V characteristics but also can predict its variation even when the amount of training data for the ANN is insufficient or outside the range of applied biases. The second one is then designed to improve the model accuracy by further minimizing the errors between the target and the model outputs. We implement the proposed framework to accurately model emerging GAA nanosheet (NS) MOSFETs and complementary FETs (CFETs) without suffering from divergent issues in circuit simulation. In addition, nonphysical behaviors, such as nonzero current at zero bias, do not occur in the modeling framework. Compared to recently reported machine-learning (ML) models, our approach can achieve a similar level of model accuracy with merely 20% amount of the training data. © 1963-2012 IEEE.

关键词： Neural networks

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ECG Signal Classification and Anomaly Detection using LSTM and HMM Models 11

ECG Signal Classification and Anomaly Detection using LSTM a...

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11th IEEE International Conference on Advances in computing and Communications, ICACC 2024

作者： Karthik, Garikipati Muppala, Tejaswi Vinitha Chowdary, A. Jayan, Sarada Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Mathematics Bengaluru India

ISBN: (纸本)9798350379136

Accurate detection and classification of electrocardiogram (ECG) signals is required for cardiac condition diagnosis and timely medical intervention. However, capturing the complex temporal dependencies in ECG data is difficult and hence may provide less effective performance comparatively. The project proposes an innovative approach to detecting and classifying the anomalies in ECG using LSTM and HMM. Preprocessing and normalization of the ECG data are done. After that, the sequence is prepared for input in LSTM, which builds a strong LSTM model with reinforced regularization techniques designed for feature learning from the ECG data. The features extracted by LSTM were concatenated with the original features and applied to train the HMM for anomaly detection based on estimating the likelihood of the log probabilities. Thresholding the log probabilities will locate anomalies. The next step is the implementation of HMM to classify ECG signals. The proposed method shows the framework comprehensively for effective detection of anomaly and classification, combining the strong points of both LSTM and HMM. The results obtained are employed to illustrate and prove the efficiency of the proposed combined approach and emphasise the importance of the combination of deep learning and statistical models for further analysis of ECG signals. © 2024 IEEE.

关键词： Markov processes

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Data-Driven Power Flow Estimation for MVDC Distribution Systems Based on Physics-Embedded FCN

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IEEE Transactions on Smart Grid 2025年第4期16卷 2816-2829页

作者： Sun, Pingyang Wu, Rongcheng Shen, Zhiwei Wang, Hongyi Li, Gen Khalid, Muhammad Konstantinou, Georgios School of Electrical Engineering and Telecommunications UNSW Sydney Australia University of Technology Sydney Data Science Institute Australia Aalborg University Department of AAU energy Denmark Energy Technology and Computer Science Section Department of Engineering Technology Ballerup2750 Denmark King Fahd University of Petroleum and Minerals Electrical Engineering Department Interdisciplinary Research Center for Sustainable Energy Systems Dhahran31261 Saudi Arabia

Maintaining high prediction accuracy with varying grid topologies poses a significant challenge to adopting neural network (NN)-based approaches for power flow (PF) estimation in medium-voltage direct current (MVDC) distribution systems. This paper proposes a physics-embedded fully convolutional network (PEFCN) to improve the accuracy. Physics-embedded techniques are incorporated in the proposed PEFCN through 1) architecture reconstruction, and 2) loss function reformulation. Architecture is reconstructed by input channel conversion calculation in a new physics operation layer. This process offers physical connections among the three input matrix channels (voltage, current, and line conductance). Three new physics loss terms are included in the loss function to restrict the outliers violating the limits of converter power ratings and terminal MVDC voltages. The two operations enable the FCN to achieve improved prediction accuracy and strong generalization capabilities. Five MVDC distribution systems, characterized by different dc voltage levels and topology configurations, serve to validate the superiority of the proposed PEFCN over other NNs in the PF estimation for scenarios involving both fixed and varying system structures. Moreover, a modified IEEE 69-bus distribution system is further used to demonstrate the applicability of the proposed PEFCN for larger systems. © 2010-2012 IEEE.

关键词： DC distribution systems

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Dynamic Traffic Signal Optimization Utilizing Real-Time Lane Analysis with YOLO Framework 4

Dynamic Traffic Signal Optimization Utilizing Real-Time Lane...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Chauhan, Naman Kumar, Aman Ravindran, Sindhu Nabi, Fizza Ghulam Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India University of the Punjab Department of Industrial Engineering and Management Lahore Pakistan

ISBN: (纸本)9798350387490

For applications in autonomous driving and surveillance, efficient vehicle detection is of significant interest. The YOLO framework marked a paradigm shift in this landscape by becoming synonymous with real-time object detection. In this work, we propose the integration of real-time vehicle detection within a stream of optimizations to the YOLO architecture, which focus on fast yet accurate identification. It's that far because the YOLOv8 comes in as prowess, from empirical validation with an impressive 98.9% on mAP50 and 91.7% on mAP50- 95, surpassing v7 and v9. This achievement not only speaks of the model's accuracy but also reinforces the capability for real- time processing. Implications are brought forth in transportation safety and urban planning promising transformative potential. This experiment will demonstrate the potential of YOLO in the intelligent aspect of object detection for revolutionizing and enhancing several various fields. The outcome of the YOLO model is then employed to modify the traffic signal intervals based on the vehicle density of each lane in a simulation environment that has been designed using Pygame. © 2024 IEEE.

关键词： Traffic signals

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Playing Maze using Voice Commands 14

Playing Maze using Voice Commands

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Sainadh, K. Vishnu Satwik, K. Ashrith, V. Vekkot, Susmitha Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Department of Electronics & Communication Amrita School of Computing Bengaluru India

ISBN: (纸本)9798350335095

In recent years, speech recognition has become increasingly well-known as a result of the numerous applications it has in virtually every industry. These applications include wake-word, sentiment, command, and interactive game play. Interest in voice in games has increased recently. Voice-controlled interaction expanded video game accessibility. However, in order to prevent an unwelcome delay, using one's voice as a controller for a game calls for real-time processing. This research paper puts forward the idea of controlling the well-known maze game with speech commands recognized by a trained convolutional neural network (CNN). A dataset of the speech commands "Left", "Right", "Up", "Down", "Go", and "Stop"was prepared for training, validation, and testing. In order to recognize all six speech commands, an optimal bark spectrogram and the CNN-based spoken command recognition model is used. The results from this work indicated that, the proposed algorithm was able to recognize all six commands with a high recognition accuracy of 94.57% and recall, precision, F1-Score of 94.8%, 94.43%, 94.53% respectively. The proposed algorithm is incorporated into a maze game in the final step. © 2023 IEEE.

关键词： Statistical tests

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Advancing Web Browser Forensics: Critical Evaluation of Emerging Tools and Techniques

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SN computer science 2025年第4期6卷 1-28页

作者： Chand, Rishal Ravikesh Sharma, Neeraj Anand Kabir, Muhammad Ashad Department of Computer Science and Mathematics The University of Fiji Lautoka Fiji School of Computing Mathematics and Engineering Charles Sturt University Bathurst 2795 NSW Australia

As the use of web browsers continues to grow, the potential for cybercrime and web-related criminal activities also increases. Digital forensic investigators must understand how different browsers function and the critical areas to consider during web forensic analysis. Web forensics, a subfield of digital forensics, involves collecting and analyzing browser artifacts, such as browser history, search keywords, and downloads, which serve as potential evidence. While existing research has provided valuable insights, many studies focus on individual browsing modes or limited forensic scenarios, leaving gaps in understanding the full scope of data retention and recovery across different modes and browsers. This paper addresses these gaps by defining four browsing scenarios and critically analyzing browser artifacts across normal, private, and portable modes using various forensic tools. We define four browsing scenarios to perform a comprehensive evaluation of popular browsers—Google Chrome, Mozilla Firefox, Brave, Tor, and Microsoft Edge—by monitoring changes in key data storage areas such as cache files, cookies, browsing history, and local storage across different browsing modes. Overall, this paper contributes to a deeper understanding of browser forensic analysis and identifies key areas for enhancing privacy protection and forensic methodologies. © The Author(s) 2025.

关键词： Browser artifacts Browser forensics Cybercrime Digital forensics Forensics tool Web browser

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