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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Kartheesan, l. Sam Kumar, G.V. Krishnan, Sundara Rajulu Navaneetha Tamilvizhi, T. Surendran, R. Vel Tech Rangarajan Dr.Sangunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Dhanalakshmi Srinivasan University Department of Computer Science and Engineering Trichy Samayapuram Campus Tiruchirappalli India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529635

The task of anomaly detection has become critical in many applications, including network security, fraud detection, and fault diagnosis. Traditionally, most of the existing methods fail with high-dimensional data, making it difficult and challenging to get accurate anomaly detection with low false positives. The main techniques proposed so far may also face problems in being computationally costly and not adaptive to the variability of the data characteristics. Therefore, there is an urgent need to design a well-built, efficient, and adaptive framework that properly identifies anomalies within large volumes of complex datasets while optimizing the selection of features. To solve these issues, this paper proposes a novel advanced preprocessing, feature extraction, feature selection, and classification methods integrated into the anomaly detection framework. Initially, the dataset undergoes data cleaning, normalization, and standardization to ensure uniformity in scale and distribution. Then it will extract statistical features that encapsulate critical insights from the raw data. For the detection stage, we propose deep Bilstm, a hybrid deep learning architecture that leverages the strengths of Bi-lstm by using lightweight densely connected layers for efficient anomaly classification. The hyperparameter of the deep Bi-lstm model is fine-tuned using the Self-Adaptive Pufferfish Optimization Algorithm (SAPFOA). The entire framework is implemented using the Python tool. Evaluate the effectiveness of our approach over the Ton-IoT dataset, which is the most frequently used benchmark of network intrusion and anomaly detection. © 2024 IEEE.

关键词： Network security

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Enhancing Speech Recognition for Hearing-Impaired Individuals Using deep learning Models 7

Enhancing Speech Recognition for Hearing-Impaired Individual...

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

作者： Kalaiarasi, K. Medidhi, Winson Niresh Kumar, S. Thenappan, S. Sathiyaseelan, R. Mohana Sundaram, K. Saveetha Institute of Medical and Technical Sciences Department of ECE Saveetha School of Engineering Chennai India Vijayawada Andhra Pradesh Kanuru India St. Joseph's College of Engineering OMR Department of CSE Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Arunai Engineering College Department of Computer Science Engineering Tamil Nadu Thiruvannamalai India R.M.D. Engineering College Department of Computer Science and Engineering Thiruvallur Kavaraipettai India

ISBN: (纸本)9798350372816

This study examines the constraints of current methodologies, specifically in diverse acoustic environments, to surmount the obstacles associated with speech recognition for individuals with hearing impairments. Conventional processes frequently experience reduced accuracy and utility as a result of their incapability to dynamically adjust to evolving circumstances. This study presents an innovative approach that surpasses these constraints through the utilization of a transformer-based model. Through the implementation of sophisticated data augmentation techniques, the proposed model demonstrates exceptional performance across various acoustic environments, notwithstanding obstacles such as speaker adjustments and background noise. One notable characteristic of this methodology is its transformer architecture, which aptly captures subtle speech inflections by employing self-attention processes. Additionally, through independent learning, the model improves its ability to address the unique challenges associated with hearing-impaired speech. Integrating these components, the model achieves superior accuracy and adaptability compared to current technologies. © 2024 IEEE.

关键词： Self-supervised learning

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Video Enhancement Algorithm using Pre-and Post-Processing for Compressed Videos 2

Video Enhancement Algorithm using Pre-and Post-Processing fo...

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2nd International Conference for Advancement in Technology, ICONAT 2023

作者： Nandyal, Suvarna Patil, Vijayalaxmi S. P.D.A College of Engineeing Department of Computer Science Kalburgi585102 India Basaveshwar Engineering College Department of Information Science and Engineering Bagalkot587102 India

ISBN: (纸本)9781665475174

The preferred technique for sending picture sequences has quickly evolved to include standards-based video compression methods. The ITU family of standards and H265 are two notable examples. Since these standards govern how the decoder should operate, and necessary to understand that they are not bit-exact. This is a crucial point to keep in mind. The method for controlling the processing rate, as well as the pre-and post-processing phases, are entirely under the creator of the system's command. They could cause observable differences between two realizations that comply with the same norm if they provide the desired effect. This work examines various pre-processing and post-processing methods for video compression. Then, go through our ongoing research on compression-improvement techniques. Although they are explained about H265, these methods may be used with any compression standard. We used Edge Preserving Filter and Edge Protecting Filter as Post-and Pre-processing filters. The experimental results show good video qualiry improvement by increasing around 8 dB of PSNR. © 2023 IEEE.

关键词： Image compression

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leveraging Grey Wolf Optimizer for Reliable data Transmission in Underwater Wireless Sensor Networks

Leveraging Grey Wolf Optimizer for Reliable Data Transmissio...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Umamahesh, G. Senthurya, S. Khatarkar, Poonam Alsalami, Zaid Kaur, Jaspal Ramesh, P.S. Aditya Engineering College Department of Computer Science & Engineering India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Ies College of Technology Department of Electrical & Electronics Engineering M.P. Bhopal India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India

ISBN: (纸本)9798350390759

This research investigates the application of the Grey Wolf Optimizer (GWO) to enhance reliable data transmission in Underwater Wireless Sensor Networks (UWSNs). UWSNs are critical for various applications, including environmental monitoring and underwater exploration, but face challenges such as high propagation delays, limited bandwidth, and energy constraints. The GWO algorithm, inspired by the social hierarchy and hunting behavior of grey wolves, is employed to optimize routing paths, minimize energy consumption, and improve packet delivery ratio. The methodology includes simulating UWSNs with different network configurations using synthetic and real-world datasets, and implementing the GWO algorithm in Python within the NS-3 simulation framework. Comparative analysis with traditional routing protocols, AOdV and dSR, demonstrates that the GWO-based routing algorithm significantly reduces energy consumption, increases packet delivery ratio, lowers latency, and extends network lifetime. These results underscore the effectiveness of GWO in enhancing UWSN performance. The study concludes that GWO offers a robust and adaptable solution for reliable data transmission in UWSNs, with potential for further enhancements through hybrid optimization techniques and real-world validations. This research provides a comprehensive understanding of GWO's application in UWSNs, contributing to advancements in underwater communication technologies. © 2024 IEEE.

关键词： Wireless sensor networks

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Enhanced Investment decision Making with a Reinforcement learning-Based Multi-Agent Portfolio Management System 2

Enhanced Investment Decision Making with a Reinforcement Lea...

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2nd IEEE International Conference on data Science and Network Security, ICdSNS 2024

作者： Ram, Kottooru Saaketh Raja Selvaganapathy, M. Nandhini, I. Tamilselvi, M. Kalaivani, M. Natarajan, B. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India C. K. College of Engineering and Technology Department of Electroncis and Communication Engineering Cuddalore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electron Cis and Communication Engineering Chennai Avadi India Roever Engineering College Department of Computer Science and Engineering Tamilnadu Perambalur India K. Ramakrishnan College of Engineering Department of Electron Cis and Communication Engineering Trichy India School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Tamilnadu India

ISBN: (纸本)9798350373110

The portfolio management system using traditional machine learning approaches struggles with data quality and interpretability of models, while needing to adapt to constantly evolving market dynamics. The Reinforcement learning (Rl) algorithms have proven to be a promising approach in handling diverse datasets with unpredictable performance. The proposed approach utilizes multiple Rl alogrithms such as deep deterministic Policy Gradient (ddPG), Proximal Policy Optimisation (PPO), and Advantage Actor-Critic (A2C) to improve investment decision-making, historical data from the Yahoo Finance dataset. This system incorporates three well-established reinforcement learning algorithms to dynamically adjust portfolio allocations. The A2C algorithm combines actor-critic techniques to optimize long-term portfolio performance, while the PPO algorithm maintains a balance between exploration and exploitation, enhancing returns stability. The proposed ddPG algorithm utilizes a deep neural network to determine the optimal action-value function, enabling informed investment decisions. Tested across various assets in the Yahoo Finance dataset, it consistently outperforms traditional portfolio management methods. This flexible system can be tailored to suit different investment objectives and risk tolerances. © 2024 IEEE.

关键词： Adversarial machine learning

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Cloud-Enabled Fingerprint Attendance Management System Using Convolutional Neural Networks in Educational Institutions

Cloud-Enabled Fingerprint Attendance Management System Using...

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2024 International Conference on Advances in data engineering and Intelligent Computing Systems, AdICS 2024

作者： Antony, N. Vini Sugapriya, K. Gowriswari, S. Prabakaran, T. Mouleeswaran, S.K. Sujatha, S. R.M.D. Engineering College Department Of Electronics And Communication Engineering Tamil Nadu Chennai India The Kavery Engineering College Department Of Science And Humanities Tamil Nadu Salem India Sri Sairam Engineering College Department Of Instrumentation And Communication Engineering Tamil Nadu Chennai India Joginpally B.R. Engineering College Department Of Computer Science And Engineering Telangana Hyderabad India Dayananda Sagar University Department Of Computer Science And Engineering Karnataka Bangalore India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350364828

Challenges with accuracy, efficiency, and safety concern traditional techniques of managing attendance in educational institutions. A fingerprint attendance management system can be accessed over the cloud and incorporates convolutional neural networks (CNNs) for improved biometric authentication. With cloud computing, the system can store and analyze data centrally, making attendance records and analyses easily accessible. Using CNNs improves the speed and accuracy of fingerprint matching, and the suggested system uses fingerprint recognition as a reliable biometric modality. The system can adapt to different fingerprint patterns and environmental circumstances thanks to the convolutional neural network layers trained on a vast collection of fingerprint photos. Students' and teachers' attendance may be reliably and safely verified in this way. The link with the cloud also allows for real-time data synchronization, meaning authorized people may obtain attendance information instantaneously. The system uses encryption to further guarantee the security and confidentiality of sensitive biometric data in the cloud. © 2024 IEEE.

关键词： Network layers

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Paddy Plant disease detection using Hybrid deep learning Algorithms 8

Paddy Plant Disease Detection using Hybrid Deep Learning Alg...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Suguna, R. deepa, S. Kanimozhi, T. Chandru, M. Arun, G. Harshini, M. Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam Tamilnadu Erode638401 India Kongu Engineering College Perundurai Department of Computer Technology - Pg Tamilnadu Erode638060 India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore641202 India College Department of Computer Science Erode India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Kongu Engineering College Tamilnadu Perundurai Erode638060 India

ISBN: (纸本)9798350367904

Agriculture is the backbone of India's economy, supporting a large portion of the population and establishing the country as a key global player in food production. India ranks as the second-largest producer of both paddy and wheat. However, agricultural productivity is highly susceptible to various plant diseases, which can significantly reduce crop yields and quality. Paddy, one of the most important staple crops, is particularly vulnerable to diseases caused mainly by viral and bacterial related diseases. The climate conditions are also play a vital role in paddy plant diseases. This research work proposes a deep learning-based approach for early detection and classification of paddy diseases. The proposed research devises a hybrid model that combines the VGG19 and ResNet50 algorithms, the system analyses images of both healthy and disease-affected paddy leaves, sourced from the Kaggle repository. This research focuses on predicting four major paddy diseases that can cause yield losses of up to 80%, and in severe cases, can destroy an entire crop: (i) Paddy Blast, (ii) Bacterial leaf Blight, (iii) Sheath Blight, and (iv) Ufra disease. The proposed model achieves a high accuracy rate of 98% in predicting paddy diseases and offers a rapid classification process, showcasing the potential of advanced image processing and deep learning techniques to mitigate the impact of paddy plant diseases on agricultural productivity. © 2024 IEEE.

关键词： Crops

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design and Analysis of Printed Conformal Antenna System for Inter and Intra Vehicular (V2V) Communication Utilizations

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Progress In Electromagnetics Research C 2024年 142卷 143-150页

作者： Raju, Padmanabha Rao, Bathula Sadasiva Jackson, Beulah Islam, Tanvir Madhav, Boddapati Taraka Phani das, Sudipta devi, Yalavarthi Usha Department of ECE Sri Vishnu Engineering College for Women AP Vishnupur Bhimavaram India Department of ECE Swarnandhra College of Engineering and Technology Seetarampuram AP Narsapur India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States Antennas and Liquid Crystal Research Center Department of ECE Koneru Lakshmaiah Education Foundation AP India Department of Electronics and Communication Engineering IMPS College of Engineering and Technology West Bengal Malda732103 India

A multiple antenna placement system analysis for the improvement of efficiency and capacity for inter and intra vehicular communication is proposed in this article. Four antennas are placed in the four locations of the vehicular body which includes roof, side mirror, rear screen, and dashboard. The constituted antenna occupying the dimension of 40×38.5×0.2 mm3 on flexible substrate material of photo paper and the bending analysis of the model as per the conformal nature on vehicular body is also analyzed and presented in this work. The received power from each receiving antenna response with respect to the transmitter has been analyzed. The channel capacity with respect to the antenna position for V2V communication is analyzed in different areas and in different environmental conditions. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Receiving antennas

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Network Intrusion detection System for Feature Extraction Based on Machine learning Techniques 5

Network Intrusion Detection System for Feature Extraction Ba...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Nalini, N. Chaudhary, Ankur Surendran, S. Muthuraja, M. Ahmed, Ibrar Nandhini, T.J. Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Noida Institute of Engineering and Technology Department of Computer Science Engineering Delhi India Tagore Engineering College Department of Computer Science Engineering Chennai India Kongu Engineering College Department of Computer Science Engineering Erode India Chennai India

ISBN: (纸本)9798350321425

Network Intrusion detection (NId) examines data from the network in search of malicious activity to identify illegal access. This investigation will center on Network Intrusion detection (NId) technology, its progression, and the strategic value it provides. The most common types of criminal activity involving computers are illegal access, theft, and denial-of-service assaults. In recent years, experts in computer security have developed many cutting-edge solutions to protect hosts and networks from these types of threats. Both the military and commercial sectors have a requirement for intrusion detection technology. This is because intrusion detection is the most essential study field for the future of network information security. The idea of pre-processing data before training algorithms is presented in this study. The data that has been preprocessed reveals that the present Random Forest model performs better than alternative ANN models. The NSl-Kdd dataset model has an accuracy of 99.12% after data pre-processing and feature extraction have been performed. © 2023 IEEE.

关键词： Feature extraction

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Sentimental Insight 2.0: Exploring the Frontiers of Opinion Mining with deep learning 2

Sentimental Insight 2.0: Exploring the Frontiers of Opinion ...

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2nd International Conference on distributed Computing and Optimization Techniques, ICdCOT 2024

作者： Murugan, S.Karthick Jayachandran, S. Mageshwari, G. latha, J. Naveen, G. R. M. D. Engineering College Kavaraipettai Department of Computer Science and Engineering Tamilnadu Thiruvallur India Srm Institute of Science and Technology Department of Computer Science and Application Chennai Ramapuram India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamilnadu Tiruvallur India R. M. K. Engineering College Department of Computer Science and Engineering Tiruvallur Tamilnadu Kavaraipettai India Madha Institute of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350382952

'Sentimental Insight 2.0' emerges as revolutionary research using deep learning techniques in response to the increasing demand for more complex sentiment analysis in the age of information abundance. Traditional sentiment analysis methods sometimes fail to accurately capture the particulars of emotions, sarcasm, and context, demanding a more advanced technique. The existing system's limitations include its inability to detect minor sentiment variations and its vulnerability to noisy and distorted data. The proposed system integrates cutting-edge deep learning architectures such as recurrent neural networks (RNNs), extended short-term memory networks (lSTMs), and transformer models into an extensive structure. The result showcases the advanced sentiment analysis, outperforming existing systems with 94% accuracy, 93% precision, 91% recall, and 93% F1 score, demonstrating superior computational efficiency and emotion classification across diverse sentiments. In the information-centric world, the study's results significantly expand sentiment analysis skills, providing a vital resource for applications in social media analytics, consumer feedback, and market sentiment prediction. © 2024 IEEE.

关键词： Sentiment analysis

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