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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Sathishkumar, S. Santhoshkumar, S.P. Kumar, Satyam Banupriya, N. Hariharasudhan, S. Gajalakshmi, S. Hindustan Institute of Technology Department of Computer Science and Engineering Coimbatore India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi India School of Cs and It Mahatma Gandhi Central University Motihari India Sri Eshwar College of Engineering Department of Computer and Communication Engineering Coimbatore India Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore India

ISBN: (纸本)9798331508456

Masses of people around the world are affected by Parkinson's disease (PD), a progressive neurological disorder. Timely and precise diagnosis is critical for effective management. Yet conventional diagnostic methods often lack compassion and specificity. Advances in machine learning and cerebral imaging offer promising approaches to improve PD diagnosis. This investigation investigates an ensemble machine learning move toward to enhance the identification of Parkinson's disease using cerebral imaging data. By integrating models such as decision trees (DT), support vector machines (SVM), and neural networks, the ensemble method combines their strengths to create a reliable diagnostic system. Feature extraction and preprocessing further enhance interpretability and performance. Compared to individual models, the ensemble approach significantly improves diagnostic accuracy, sensitivity, and specificity. Brain imaging aids differential diagnosis in Parkinsonism, but no Cerebral imaging methods is currently recommended for routine clinical practice despite their proven utility. Larger trial are needed to validate the medical value and long-standing cost-effectiveness of Cerebral imaging for managing patients within healthcare systems like the NHS. © 2025 IEEE.

关键词： Neurodegenerative diseases

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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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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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Ocular Eye Disease Prediction Using Machine Learning 15th

Ocular Eye Disease Prediction Using Machine Learning

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15th International Conference on Soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Kongala, Lavanya Bikku, Thulasi Preethi, B. Zaveriya Ruman, S.K. Susmitha, P. Sowjanya, B. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Amrita School of Computing Amaravati Amrita Vishwa Vidyapeetham Andhra Pradesh Vengal522503 India Department of Computer Science and Engineering Vignan’s Nirula Institute of Technology and Science for Women Guntur India

ISBN: (纸本)9783031810794

The human eye, often considered the most crucial sense organ for perceiving and understanding the world, plays a fundamental role in our ability to see and interpret our surroundings. However, vision can be significantly impacted by various ocular eye disorders, which pose significant challenges to our visual capabilities. Among these disorders, one of the most prevalent conditions is cataract, a condition characterized by cloudiness in the lens of the eye, resulting in impaired vision and a blurred perception of objects. Typically, cataracts are more commonly found in individuals of advanced age due to natural aging processes. Identifying and diagnosing ocular eye illnesses can be a complex and challenging task, often requiring sophisticated techniques and tools to aid in the process. In recent research endeavors, automated learning algorithms have emerged as valuable tools for assisting in the detection of ocular eye disorders. These algorithms, such as Convolutional Neural Networks (CNNs), are employed alongside image pre-processing techniques to analyze and interpret ocular data. By leveraging the power of machine learning, researchers have been able to develop and refine predictive models for ocular diseases. To evaluate the accuracy of disease prediction models, various design pattern graphs can be utilized. These graphs serve as visual representations, showcasing the performance and reliability of the algorithms in predicting ocular eye disorders. By employing these advanced technological approaches, researchers and medical professionals can enhance their ability to identify and diagnose ocular conditions, ultimately leading to more effective treatments and interventions for individuals affected by these diseases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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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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Native Nest: An E-Commerce Platform for Promoting Tribal Products and Culture 8

Native Nest: An E-Commerce Platform for Promoting Tribal Pro...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Rajendrakumar, S. Chowta, Yashasvi Tadikonda, G Harshavardhan Reddy, Sathi Lakshmi Priyatha Karthik, Mukkanti Venkata Sai Shanmugam, Selvanayaki Kolandapalayam Thangavel, Senthil Kumar Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Chemical Engineering and Materials Science Coimbatore641112 India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore641112 India Ashland University Department of Mathematics and Computer Science OH United States

ISBN: (纸本)9798350376425

This research study proposes a new online platform, Native Nest, to address the challenges faced by the commerce industry in Ooty District. This initiative provides a sustainable platform for registered tribal artisans to connect with buyers, promoting their cultural heritage and products. The platform features a user-friendly interface, adjustable layout, and reliable infrastructure, ensuring a seamless experience for both clients and administrators. QR codes strategically placed throughout Ooty facilitate easy access to the platform, expanding its reach to tourists and locals. Native Nest offers detailed product information and enables online purchasing, eliminating the need for middlemen and providing a convenient shopping experience. By empowering tribal communities and promoting their cultural heritage, Native Nest contributes to the socio-economic development of the region. This initiative enables cultural exchange and expands the market share of tribal arts and expressions, creating a more comprehensive and sustainable marketplace. © 2024 IEEE.

关键词： Marketplaces

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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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Fine-Tuned T5 for Auto-Grading of Quadratic Equation Problems 2

Fine-Tuned T5 for Auto-Grading of Quadratic Equation Problem...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Balakrishnan, Roshni M. Pati, Peeta Basa Singh, Rimjhim Padam Santhanalakshmi, S. Kumar, Priyanka Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidhyapeetham Bengaluru India Department of Computer Science University of Texas Saint Antonio United States

Assessments constitute a fundamental and inevitable component of any educational journey. Manual effort required for the evaluation of these assessments is very high. Automation of the evaluation process and grading helps in making the review process more efficient, objective, and scalable, thereby reducing the workload of human reviewers. Automating the grading process for multiple-choice and short-answer assessments is relatively straightforward, but it poses significant challenges when applied to the evaluation of formal languages, particularly in the context of mathematical assessments. In this paper a model that automatically evaluates and grades the Quadratic Equation problems is presented. The study is conducted using a manually curated dataset comprising 1200 solutions to various quadratic equation problems. Embeddings of the quadratic solutions are generated using Google's T5 Model. These embeddings are then used to train different traditional and ensembled machine learning models along with complex Deep learning models like LSTM and Bi LSTM. An in-depth analysis of the fine-tuned T5 model's performance, evaluating its effectiveness in comparison with the pretrained T5 model in automatic grading of quadratic equation problems has been explored. Fine-tuning significantly contributes to the reduction of error by 70% and a noticeable increase in the R2 value to 97%. © 2024 Elsevier B.V.. All rights reserved.

关键词： Grading

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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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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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