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3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Manna, Tanajit Mohankumar, N. Banerjee, Dhrubojyoti Mukherjee, Shibabrata Bhattacharjee, Bidrohi Maity, Heranmoy Pailan College of Management & Technology Electronics & Communication Engineering Kolkata India Computer Science & Engineering Puna India Pailan College of Management & Technology Electrical & Electronics Engineering Kolkata India

ISBN: (纸本)9798350374643

The study offers a workable, low-cost solution to the near-far delinquent in CDMA systems by enhancing communication in Direct Sequence Spread Spectrum (DSSS) multiuser systems as opposed to single users. The existing telecommunications system's hear ability problem, which requires improvement, has several security applications. This study uses the Multi Operator Finding Decorelator (MUD-Decorrelator) & MUD-Decision Feedback approach to offer a comparison analysis of Bit Error Rate (BER) vs. Signal to Noise Ratio in near-far situations. A MUD-Decision Feedback system is used to alter the modulated signal's BER rather than relying on a single user and a MUD-Decorelator. The communicated and established Power Spectral Densities (PSD) of mutually systems show no change in the signal characteristics. © 2024 IEEE.

关键词： Bit error rate

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Lag Synchronization and Lag H∞ Synchronization for Multiweighted Coupled Reaction-Diffusion Neural Networks Suffering Topology Attacks

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IEEE Transactions on Control of Network Systems 2025年第2期12卷 1488-1499页

作者： Wang, Jin-Liang Zhao, Xin-Yu Ren, Shun-Yan Huang, Tingwen Tiangong University Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems School of Computer Science and Technology Tianjin300387 China Tiangong University School of Electronics & Information Engineering Tianjin300387 China University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Chengdu611731 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

This paper mainly discusses two kinds of coupled reaction-diffusion neural networks (CRNN) under topology attacks, that is, the cases with multistate couplings and with multiple spatial-diffusion couplings. On one hand, by adopting state feedback control scheme, the problem of the lag synchronization for the multistate coupled CRNN is addressed. Moreover, the influences of external disturbances for the lag synchronization of the CRNN with multistate couplings are also taken into consideration. On the other hand, several lag and lag H∞ synchronization conditions for CRNN with multiple spatial-diffusion couplings are derived under the help of the Kronecker product and the state feedback control method. Finally, two numerical examples are provided to verify the correctness of the derived criteria. © 2014 IEEE.

关键词： State feedback

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DeepFakeGuard: Safeguarding Digital Platforms Against Fake Profiles Using AI 4

DeepFakeGuard: Safeguarding Digital Platforms Against Fake P...

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

作者： Swarna Sudha, M. Manjula, S. Bharathi, Indira Krishnasamy, Valarmathi Vijayalakshmi, K. Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India School of Computer Science and Engineering Vellore Institute of Technology Chennai India PSR Engineering College Department of Electronics and Communication Engineering Sivakasi India

ISBN: (纸本)9798331523923

This research introduces DeepFakeGuard, a hybrid deep learning framework designed to detect fake profiles on social media platforms, addressing the growing threat of fraudulent accounts online. DeepFakeGuard integrates multiple data modalities, including textual, visual, behavioral, and network features, to accurately classify profiles as real or fake. Leveraging advanced deep learning models such as BERT for textual analysis, ResNet for image processing, and LSTM for modeling behavioral patterns, DeepFakeGuard provides a comprehensive and robust solution for identifying fake profiles across diverse platforms. The framework processes a rich set of profile characteristics, including account information, posting behavior, post sentiment, and network relationships, ensuring a multi-dimensional analysis of social media accounts. The hybrid architecture of DeepFakeGuard merges outputs from individual sub-models, which are then processed through dense layers for final classification using a sigmoid activation function. This multi-faceted approach significantly enhances detection accuracy, precision, and recall compared to traditional methods. The framework is highly scalable and adaptable to various social media platforms, offering a versatile tool for strengthening online security. With strong performance metrics, including high classification accuracy, DeepFakeGuard demonstrates its potential for real-world applications in mitigating the risks associated with fake profiles. By fostering secure and trustworthy social media environments, this research contributes to the ongoing fight against fraudulent accounts, paving the way for safer online interactions. © 2025 IEEE.

关键词： Contrastive Learning

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Colon Cancer Detection Using A Lightweight-CNN With Grad-CAM++ Visualization 3

Colon Cancer Detection Using A Lightweight-CNN With Grad-CAM...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Tasmim Bakchy, Sagor Chandro Peyal, Hasibul Islam Hoshen, Md. Rifat Siddique, Md. Abu Ismail Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Electronics & Communication Engineering Hajee Mohammad Danesh Science & Technology University Dinajpur Bangladesh Electronics & Telecommunication Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (纸本)9798350388282

Colon cancer is caused by cells that proliferate in the colon, a part of the large intestine. Early detection of cancer may make treatment easier and save many lives each year. The goal of this study is to identify improved approaches to diagnosing colon cancer rapidly and correctly. It intends to accomplish this through the use of a computer program known as a lightweight and efficient Convolutional Neural Network (CNN), which operates quickly and requires little computational power. The method may aid doctors in detecting colon cancer more successfully than earlier. The proposed model outperforms prior efforts with an amazing validation accuracy of 99.48\%. In our study, we employed 10,000 images from two separate classes to distinguish colon cancer cells from normal colon cells. The suggested model performs admirably, as indicated by its remarkable precision, recall, and F1-score, all of which are greater than 99\%, as well as its perfect Area Under Curve (AUC) score of 100\%. The parameters in the suggested model are lower than those in the current transfer learning models. We also compared our novel CNN model to the industry-recognized MobileNetv2 transfer learning standard to demonstrate our method's remarkable effectiveness. To illustrate the model's decision-making process, Gradient-weighted class activation mapping, or Grad Cam++, has been used. © 2024 IEEE.

关键词： Convolutional neural networks

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Query-Based Text Summarization: A Comparative Investigation and TextRank Implementation 5th

Query-Based Text Summarization: A Comparative Investigation ...

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5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Unhale, Varad Udchan, Suyash Shah, Mohak Kadam, Bhakti D. Department of Electronics and Telecommunication Engineering SCTR’s Pune Institute of Computer Technology Pune India

ISBN: (纸本)9789819788606

Owing to the proliferation of internet and multimedia applications, there has been a huge increase in digital content in the form of text, images, audio and video worldwide. The various applications of text summarization has attracted the computer vision researchers to generate the optimal text summaries. Several methodologies have been reported in literature to generate text summaries. However, the key challenge is incorporating user’s preference as text summarization is a subjective task. User generated queries act as a guiding beacon in the summarization process. Instead of producing generic text summaries, query-based text summarization techniques offer user preferred responses. This paper begins by discussing the fundamental concepts and objectives of text summarization, emphasizing on the role of user queries in the summarization process. It proceeds to categorize existing query-based summarization techniques into distinct paradigms, including extractive, abstractive, and hybrid approaches, highlighting the advantages and limitations of each. This paper presents the evolution of query-based text summarization with developed techniques, available datasets, evaluation metrics, and performance comparison. After conducting a survey on query-based text summarization, a text summarization using the TextRank algorithm is implemented and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) scores are calculated for the generated summaries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Structured Query Language

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Survey and Fundamental Implementation of Denoising Techniques for Radar Signal Processing 8th

Survey and Fundamental Implementation of Denoising Technique...

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8th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Chande, Dhaval Manale, Abhijeet Jangale, Dnyaneshwar Yerram, Ravinder Electronics and Telecommunication Engineering SCTR’s Pune Institute of Computer Technology Pune India

ISBN: (纸本)9789819766802

The research survey paper provides a comprehensive overview of radar signal processing and denoising techniques, including a fundamental implementation of basic signal denoising. It emphasizes the significance of extracting meaningful information from raw radar signals and the challenges posed by noise and interference. The paper focuses on the application of wavelet-based denoising methods, particularly the Multi-Band Wavelet Transform with Improved Thresholding and Complex Wavelets techniques. It aims to categorize and evaluate these methods based on their effectiveness in noise reduction, signal-to-noise ratio enhancement, and preservation of crucial information for analysis. The ultimate goal of the paper is to serve as a valuable resource for advancing radar signal processing through the exploration and evaluation of various denoising techniques, providing insights and guidance for future research in this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Noise abatement

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Leveraging U-Net Variants for High-Precision Flood Mapping: A Performance Evaluation on the ETCI Dataset 2

Leveraging U-Net Variants for High-Precision Flood Mapping: ...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Thirugnanasammandamoorthi, Puviyarasi Ghosh, Debabrata Dewangan, Ram Kishan Thapar Institute of Engineering and Technology Department of Electronics and Communication Engineering Punjab India Bennett University School of Computer Science Engineering and Technology India

ISBN: (纸本)9798331534950

Flood mapping is a crucial task in mitigating the adverse impacts of flooding by providing accurate spatial information about inundated areas. This study explores flood mapping using the ETCI dataset, leveraging the U-Net model and its enhanced variants. Initially, a basic U-Net architecture is employed to generate flood maps, followed by progressively improved versions incorporating batch normalization, dropout, and attention mechanisms. These modifications aim to enhance model generalization, robustness, and feature extraction capabilities. For the purpose of evaluating the model's performance, metrics like accuracy, F1-score, and Intersection over Union (IoU) are utilised. The generated flood maps demonstrate the potential of advanced U-Net architectures for precise and efficient flood delineation, providing a valuable tool for disaster management and planning. © 2024 IEEE.

关键词： Disaster prevention

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A novel approach to detect, analyze and block adversarial web pages

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International Journal of Information technology (Singapore) 2024年第6期16卷 3617-3622页

作者： Kumar, M. Ranjith Naik, Darshana A. Kapila, Neha Mohan, Chinnem Rama Prasad, Ch Raghava Shelke, Chetan Rao, C. V. Guru School of Computer Science and Engineering Vellore Institute of Technology Department of Computer Science and Engineering M S Ramaiah Institute of Technology Department of Electronics and Electrical Engineering Lovely Professional University Department of Computer Science and Engineering Narayana Engineering College Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Alliance College of Engineering and Design Alliance University School of CS & amp AI SR Universtity

The phenomenon of distraction is very common, and its adverse effects are seen among people. The major cause underlying this issue is the ease with which adversarial web sites and web pages can be accessed. It is of utmost importance to locate, evaluate, and actively block such web pages in an effort to comprehensively and globally solve this societal issue. Thus, the given paper proposes an extension or plug-in to detect, analyze, and block websites smartly. The proposed approach takes keywords entered by the user as input into consideration, which eventually leads to the generation of web pages and web links. The filtering of web links is done by the proposed extension, followed by the extraction of features, the utilization of support vector machines (SVM) for binary classification, and the summarization of textual data using natural language processing (NLP). Lastly, the precise results corresponding to relevant web links are presented to the users, which will increase their productivity, thereby reducing their distraction levels under all working circumstances. The performance of the proposed approach is validated against existing recent studies based on evaluation metrics such as adversarial website detection time (ms) and accuracy (%). © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adversarial web pages Distraction Natural language processing (NLP) Support vector machine (SVM) Text summarization

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Smart Farming System Using IoT for Efficient Crop Growth 3

Smart Farming System Using IoT for Efficient Crop Growth

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Ganesh, Jetti Venkata Bisht, Saksham Reddy, Shashidhar Babu, A. Mohan SRM Institute of Science and Technology Ramapuram Chennai Department of Electronics and Computer Engineering India

ISBN: (纸本)9798350354171

The Shrewd Cultivating Framework utilizes a combination of sensors and IoT innovation to make a robotized checking and control framework for agrarian purposes. The utilization of sensors like ultrasonic, soil dampness, DHT11, and LDR, interfaces with a microcontroller, empowers real-time information collection on plant development, soil dampness substance, temperature, and light escalated. The system's integration with IoT encourages farther get to screen and oversee these parameters. In case of unfavorable conditions such as moo soil dampness, the framework triggers caution through a buzzer, guaranteeing incite consideration and activity to keep up ideal edit development conditions. This framework presents a arrangement to improve edit surrender by empowering productive and opportune intercession based on exact natural data. © 2024 IEEE.

关键词： Microcontrollers

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Sign Language Recognition Using ML and DL 8th

Sign Language Recognition Using ML and DL

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8th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Nawadkar, Manish Bhalerao, Niket Utekar, Vedang SCTR’s Pune Institute of Computer Technology Electronics and Telecommunication Engineering Pune India

ISBN: (纸本)9789819766833

Dynamic Sign Language Recognition (DSLR) is a vital area of research with applications in human–computer interaction, accessibility, and communication. This study explores the utilization of Machine Learning (ML) and Deep Learning (DL) techniques for DSLR, bridging the gap between computer vision and natural language processing. We present an overview of the motivation and context for applying ML and DL to DSLR, encompassing recent advancement from 2010 to 2023. The reviewed works are categorized based on their ML and DL strategies, highlighting their contributions to the field. Additionally, we address critical prerequisites for deploying ML and DL-based DSLR systems in real-world settings, offering a qualitative assessment of how well the surveyed works fulfill these criteria. We conclude by discussing unresolved challenges and future directions in DSLR research. Furthermore, our research will involve tracking emotions, hand gestures, and lip tracing within the context of DSLR. Enhancing the comprehensiveness of sign language interpretation. We conclude by discussing unresolved challenges and future directions in DSLR research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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