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Gaussian PI Controller Network Classifier for Grid-Connected Renewable Energy System

Intelligent Automation & Soft Computing 2023年第1期35卷 983-995页

作者： Ravi Samikannu K.Vinoth Narasimha Rao dasari Senthil Kumar Subburaj Department of Electrical Computer and Telecommunications Engineering Botswana International University of Science and TechnologyBotswana Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennaiTamil NaduIndia Department of Electrical Power Engineering College of EngineeringDefence UniversityEthiopia Department Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and TechnologySembakkamTamil NaduIndia

Multi-port converters are considered as exceeding earlier period decade owing to function in a combination of different energy sources in a single processing *** energy sources are playing a significant role in the modern energy system with rapid *** renewable sources like fuel combustion and solar energy,the generated voltages change due to their environmental *** develop energy resources,electric power generation involved huge *** power and output voltages are plays important role in our work but it not considered in the existing *** considering the power and voltage,Gaussian PI Controller-Maxpooling deep Convolutional Neural Network Classifier(GPIC-MdCNNC)Model is introduced for the grid-connected renewable energy *** input information is collected from two input *** that,input layer transfer information to hidden layer 1 fuzzy PI is employed for controlling voltage in GPIC-MdCNNC *** layer 1 is transferred to hidden layer *** activation is employed for determining the output voltage with help of the *** last,the output layer offers the last value in GPIC-MdCNNC *** designed method was confirmed using one and multiple sources by stable and unpredictable input ***-MdCNNC Model increases the performance of grid-connected renewable energy systems by enhanced voltage value compared with state-of-the-art *** control technique using GPIC-MdCNNC Model increases the dynamics of hybrid energy systems connected to the grid.

关键词： Multi-port converters renewable sources fuzzy PI controller gaussian activation function fuel cell

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Stress detection Using SVM and Virtual Assistant

Stress Detection Using SVM and Virtual Assistant

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2025 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Amale, Pranav Vibhute, Anup Mathpati, Mahesh S. Jatti, Ashwini V Thigale, Somnath B. Dr. D. Y. Patil Institute of Technology Electronics and Telecommunications Department Pune India Dr. D. Y. Patil Institute of Technology Instrumentation Engineering Department Pune India SVERIs College of Engineering Electronics and Telecommunications Department Pandharpur India Fabtech College of Engineering Computer Science and Engineering Department Sangola India

ISBN: (纸本)9798331529833

This paper presents a chatbot model designed to detect stress in users through simple yes-or-no questions. Utilizing a Support Vector Machine (SVM) algorithm, the model achieves high accuracy in identifying stress levels and provides users with actionable advice or medical recommendations. The chatbot's user-friendly interface is built using Anaconda Navigator and Python, ensuring ease of use for non-technical users. The model achieves a 100% accuracy rate on the given dataset, making it a promising tool for stress detection and early intervention. © 2025 IEEE.

关键词： Python

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detection of dysrhythmias from Electrocardiogram Signals Using Noise Removal and deep learning Techniques

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SN computer Science 2024年第7期5卷 882页

作者： Rajesh, P. Balasubramaniyan, M. Thiyagu, T.M. Azhagiri, M. Shanmuganathan, C. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R & amp D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Sri Venkateswara College of Engineering and Technology (Autonomous) Andhra Pradesh Chittoor 517127 India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai 600089 India

Heart related diseases have created a major loss in human lives. Healthy heart ensures good standard of living from infant to aged people for a long period. Predicting heart related diseases prior to serious cause would mostly reduce death and save human life. One such heart related issue is dysrhythmias which is an unstable rhythm occurs in the normal heart beat of the humans due to improper electrical dispositions that synchronizes the heartbeat. This endangered disease would definitely lead to the end of life irrespective of the age group. This unstable rhythm would be diagnosed mostly using Electrocardiogram (ECG) which is an electrical waveform that depicts the heartbeat. This research aimed to discover a novel method that makes use of the ECG waveforms as a training and testing dataset with the most incipient deep learning (dl) algorithms’ to predict dysrhythmia earlier. The dl models like Convolutional Neural Network (CNN) and Bi directional long Short Term Memory (BlSTM) are combined to experiment the input data by segmenting the signals. A noise removal technique was used to remove the noise in the waveforms of the ECG data and built a two dimensional image which is the input to the proposed method. The proposed method produced remarkable results as 99.36 percent in accuracy, 0.99 as F1 score. The objective of the investigation is to save human lives from serious and severe dysrhythmias by predicting it as early as possible by using the proposed methodology. © The Author(s), under exclusive licence to Springer Nature Singapore Pte ltd. 2024.

关键词： BlSTM CNN dysrhythmias ECG Noise removal

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Your Interest, Your Summaries: Query-Focused long Video Summarization

arXiv

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arXiv 2024年

作者： Patel, Nirav Prajapati, Payal Shah, Maitrik The Department of Computer Engineering L. D. College of Engineering Gujarat Ahmedabad380015 India

Generating a concise and informative video summary from a long video is important, yet subjective due to varying scene importance. Users’ ability to specify scene importance through text queries enhances the relevance of such summaries. This paper introduces an approach for query-focused video summarization, aiming to align video summaries closely with user queries. To this end, we propose the Fully Convolutional Sequence Network with Attention (FCSNA-QFVS), a novel approach designed for this task. leveraging temporal convolutional and attention mechanisms, our model effectively extracts and highlights relevant content based on user-specified queries. Experimental validation on a benchmark dataset for query-focused video summarization demonstrates the effectiveness of our approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Structured Query language

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Exploring Quantum Machine learning Algorithms for Enhanced data Classification and Clustering in Complex Systems - Integration with Gradient Boosting and K-means for Improved Performance

Exploring Quantum Machine Learning Algorithms for Enhanced D...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Babu, M. Arun, M. Pushparani, S. Muthukrishnan, A. Poongavanam, N. Rajalakshmi Engineering Çollege Department of Information Technology Thandalam Chennai India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331542344

This research focuses on the implementation of quantum machine learning with the classical models such as Gradient Boosting and K-means clustering for efficient classification and clustering of most complex datasets in advanced systems. Taking advantage of the large datasets that are handled by the qc, this work examines Quantum Support Vector Machines (QSVM), Quantum Boosting, and Quantum K-means. To compare the efficiency of these hybrid models three datasets - the healthcare, the financial, and the cyber-security datasets - are used. Our findings indicate that quantum optimization algorithms achieve better accuracy in clustering in comparison to the classical counterparts and are computationally faster. This work shows how quantum machine learning can lead to the enhancement of difficult data processing tasks, a major issue in the quantum computing domain, with specific reference to resources and high-dimensional data. The opportunity to integrate quantum and classical models successfully is the best basis for further developments in machine learning. © 2024 IEEE.

关键词： Adversarial machine learning

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Identification of Indian Traffic Signs based on different Models and Conditions: A Review 6

Identification of Indian Traffic Signs based on Different Mo...

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6th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2022

作者： Parmar, Pratik Bharatsinh Oza, Bhavesh A. L D College of Engineering Software Engineering Department Gujarat Ahmedabad India

ISBN: (纸本)9781665482714

As a crucial part of the larger Intelligent Transportation System, the Traffic Sign Recognition system is essential. That's because drivers may be safer and more proficient on the road thanks to the information provided by traffic signs. The major drawback of these approaches is that systems based on color and shape are very susceptible to variations in light, which may frequently occur under Indian weather conditions. To effectively recognize and identify Indian traffic signs, this research aims to compare all models with their pros and cons. We have examined a deep learning-based detection and identification system as a potential solution to this problem. We also considered very challenging classes of Indian traffic signs that have not appeared in existing research. Images of Indian roads serve as the dataset for the proposed model's training and testing phases. This Review explains the many characteristics, detectors, benefits, and limitations, as well as future research directions, for Indian traffic sign detection in various environments. © 2022 IEEE.

关键词： Traffic signs

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Performance Analysis of Various 5G Mobile Architectures

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第8期16卷 94-106页

作者： Belani, Jainam d. Thakore, Yugma B. Kotak, Nirali A. Borisagar, Komal R. Sedani, Bhavin S. Electronics and Communication Department L.D. College of Engineering Ahmedabad India Graduate School of Engineering and Technology Gujarat Technological University Ahmedabad India

The field of Telecommunication has progressed and emerged to be a successful field of technology. From communicating via pigeons and cannonballs to video conferencing for a meeting, the communication has improved surpassing every user’s expectation. With the introduction of wireless communication, the field of communication took a different turn. Just when we thought we reached the zenith of wireless communication, an esoteric concept of Standalone (S.A.) and Non-Standalone (N.S.A.) opened the gates to more efficient communication, expanding the horizons of technology. The 5G network has started developing over some regions in the world but the real question remains;which is, whether an efficient communication is worth the economy spent on developing it or certain advantages of this networks should be given up to protect the country’s economy. 3rd Generation Partnership Project (3GPP) introduces mainly two network architectures for the deployment of 5G cellular systems over existing lTE networks: Non-Standalone (NSA) and Standalone (SA). NSA permits a quick 5G advantage course of action with the least speculation by leveraging the lTE frameworks which has already been established by cellular Radio Access Technology (RAT), i.e. it follows full 5G protocols and works within the 5G New Radio (NR). This analytical paper elaborates on the performance of these two architectures for the implementation of 5G cellular technology, where the first one would be a high technology-centric approach with less cost efficiency and the other one would be the economy savvy aspect with a little bit of negotiation with technology upgrades © 2022. International Journal of Interactive Mobile *** Rights Reserved.

关键词： Beamforming

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Sustainable AI Systems for Monitoring and Predicting Wildfires in Vulnerable Forest Regions

Sustainable AI Systems for Monitoring and Predicting Wildfir...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Raj, T. Selvin Retna Raj, J. Relin Francis Krishnan, R. Santhana Balamuralikrishnan, G. Vikkiramapandian, d. Jothi, J. Nirmal Dmi College of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Psna College of Engineering and Technology Department of Information Technology Dindigul India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India

ISBN: (纸本)9798331509828

Wildfires pose significant threats to ecological systems, human safety, and economic stability, especially in vulnerable forest regions. To address this critical issue, a sustainable AI-driven framework, WiSEFire (Wildfire Surveillance and Estimation), is proposed for real-time monitoring and prediction of wildfires. The framework integrates a hybrid approach, utilizing Random Forest for feature analysis and Gated Recurrent Units (GRU) for temporal fire prediction. The system processes multi-source data, including satellite imagery, IoT sensors, and meteorological information, to enhance detection accuracy and prediction reliability. Simulation experiments were conducted using benchmark datasets to evaluate WiSEFire against state-of-the-art algorithms, including Convolutional Neural Networks (CNN) and long Short-Term Memory (lSTM) networks. Performance metrics such as accuracy, latency, and detection efficiency were analyzed. The results demonstrated that WiSE Fire outperformed existing methods, achieving superior detection precision and reduced latency while maintaining energy efficiency. This research highlights the potential of sustainable AI frameworks for mitigating wildfire risks and improving emergency response capabilities © 2025 IEEE.

关键词： Convolutional neural networks

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Overview of the Third Shared Task on Speech Recognition for Vulnerable Individuals in Tamil 4

Overview of the Third Shared Task on Speech Recognition for ...

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4th Workshop on language Technology for Equality, diversity, Inclusion, lT-EdI 2024

作者： Bharathi, B. Chakravarthi, Bharathi Raja Sripriya, N. Natarajan, Rajeswari Suhasini, S. Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu India School of Computer Science University of Galway Ireland SASTRA University India R.M.D. Engineering College Tamil Nadu India

ISBN: (纸本)9798891760813

The overview of the shared task on speech recognition for vulnerable individuals in Tamil (lT-EdI-2024) is described in this paper. The work comes with a Tamil dataset that was gathered from elderly individuals who identify as male, female, or transgender. The audio samples were taken in public places such as marketplaces, vegetable shops, hospitals, etc. The training phase and the testing phase are when the dataset is made available. The task required of the participants was to handle audio signals using various models and techniques, and then turn in their results as transcriptions of the provided test samples. The participant’s results were assessed using WER (Word Error Rate). The transformer-based approach was employed by the participants to achieve automatic voice recognition. This overview paper discusses the findings and various pre-trained transformer-based models that the participants employed. © 2024 Association for Computational linguistics.

关键词： Speech recognition

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A Novel deep learning Based Approach for detecting and localization of Pulmonary Cancer 14

A Novel Deep Learning Based Approach for Detecting and Local...

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14th International Conference on Cloud Computing, data Science and engineering, Confluence 2024

作者： Mishra, Amit Kumar Sharma, Rahul Singh, Jagendra Singh, Prabhishek Manoj, diwakar Tiwari, Mohit Graphic Era Hill University Department of Computer Science & Engineering Dehradun India D. Y. Patil International University Pune India School of Computer Science Engineering and Technology Bennett University Greater Noida India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Bharati Vidyapeeth's College of Engineering Dept. of Computer Science & Engineering Delhi India

ISBN: (纸本)9798350344837

In the ever-evolving field of scientific diagnostics, the early diagnosis of pulmonary most cancers continues a key undertaking. This observation proposed a unique deep learning-primarily based approach, in particular using Generative Adversarial Networks (GANs), intending to modernise the identification and localization of pulmonary malignancies through scientific imaging. Our models, trained using a varied dataset, demonstrated a promising accuracy fee of 70% within the sample set, suggesting its ability to adeptly differentiate between malignant and non-malignant instances in scientific images. While the conclusions suggest a significant growth in lung cancer detection, also they highlight locations demanding in addition refinement. The balance of technological prowess and scientific significance, as reflected through criteria like sensitivity and specificity, remains a focus topic for future projects. The outcomes of this research are substantial. Beyond the on the spot discoveries, the take a look at emphasizes the transformational possibility of incorporating sophisticated AI approaches into healthcare. As the scientific network grapples with the difficulties of early cancer identification, gear like the one displayed in this study ought to usher in a new age in diagnostics-marked by accuracy, efficiency, and patient-centricity. In conclusion, this have a look at now not simplest adds a fresh diagnostic tool to the sector but moreover sets the way for future innovations within the confluence of AI and healthcare. © 2024 IEEE.

关键词： Generative adversarial networks

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