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2nd International Conference on Cyber Physical Systems, Power Electronics and Electric Vehicles, ICPEEV 2024

作者： Priya, r.A. Prakash, Vinoth John rajakumar, P. Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350388664

This paper aims to produce energy at a very low wind speed. This fact, as well as the growing interest in the field of block chain technology, makes it possible to use this type of wind turbine in private household and domestic use as part of a distributed energy system instead of solar panels. To avoid a von Karman vortex street effect that occur when the cylinders rotate in the air flow, as well as to ensure maximum power, the optimal ratio between the wind speed and the cylinder rotation speed is determined using finite element analysis. © 2024 IEEE.

关键词： Wind turbines

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G-bot: Illuminating Government Programs with Intelligent Assistance 5

G-bot: Illuminating Government Programs with Intelligent Ass...

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5th International Conference on data Intelligence and Cognitive Informatics, ICdICI 2024

作者： Ponkumar, d. david Neels Ganesh, d. Sai Siva Teja, d. dharma rohith, Ayyapu Venkata Sai Ashok, Chitrada Tejesh, Obili Setty Sai Naga Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350389609

Citizens have struggled to get complete state government initiative information for years. G-bot uses a novel approach to link people to state-level laws and activities. G-bot uses advanced Natural Language Processing (NLP) to enable easy access to government initiative information. The G-bot NLP module, its core, has been carefully trained to provide accurate results. This training gives users accurate and current information about government efforts to help them make informed choices about their welfare and development. G-bot links citizens to government services to improve understanding, transparency, and civic involvement. The user-friendly design makes the enormous database of state-level programs and laws easy to traverse. G-bot in a Flask app makes it easier for users to traverse a large amount of information. G-bot advances user-centric applications beyond information retrieval. The seamless integration of AI-driven language processing represents a new era in arming people with the information to actively shape their communities. G-bot uses suggested Named Entity recognition (NEr) technology to accurately identify and categorize important entities in government documents and communications. Users may easily find information about government programs, policies, agencies, and persons with this application. G-bot improves search results using Named Entity recognition, helping users make educated decisions. © 2024 IEEE.

关键词： Bot (Internet)

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Machine Learning based Early detection and Monitoring of Parkinson disease 9

Machine Learning based Early Detection and Monitoring of Par...

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Aanandha Saravanan, K. Aloy Anuja Mary, G. Sathyasri, B. Muthukumaran, d. Prabha, M. Ambika Bhuvaneswari, C. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798350384208

An extended period of time is necessary to develop Parkinson's disease, a neurodegenerative disorder. The Pd can also be identified using human motion analysts, who analyze and identify body movement. According to modern research, changes in speech and vocal sounds are early signs of Parkinson's syndrome. This paper recommends an early detection mechanism created on the examination of speech signals for vocal Parkinson's disease. Treatments are available to ease the symptoms of Pd, but the disease cannot be cured. Illness and pain relief treatments available. In mandate to diminish the threat of decease, we are discovering this disease as early as possible. The stenographic and voice recording are both being used to detect Pd. IoT cloud is used to diagnose the detected information. The optimal model was developed after combining various algorithms and methods of feature selection. Synthetic Minority Oversampling technique (SMOTE) can be used to address the extremely unbalanced data-set. © 2023 IEEE.

关键词： Internet of things

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An Intelligent Emotion recognition System based on Speech Terminologies using Artificial Intelligence Assisted Learning Scheme 9

An Intelligent Emotion Recognition System based on Speech Te...

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9th International Conference on science, technology, Engineering and Mathematics, ICONSTEM 2024

作者： Kapileswar, N. Simon, Judy devi, K. Kavitha Polasi, Phani Kumar Vinod, dasari Naga Harish, Chappagadda Srm Institute of Science And Technology Department of Ece Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science And Technology Department of Ece Chennai Avadi India

ISBN: (纸本)9798350365092

From HCI to mental health monitoring, emotion identification from voice signals is an important and vital task. We provide a novel method for reliable emotion identification from audio data called Artificial Intelligence Assisted Learning Scheme (AIALS). AIALS combines the architectures of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). The CrEMA-d dataset, which stands for Crowd-sourced Emotional Multimodal Actors dataset, is used in our work to train and assess the suggested system. The dataset is rich in authentic emotional speech examples. Feature extraction and emotion classification are the two primary components of the AIALS framework that has been suggested. during the feature extraction stage, pertinent acoustic information is retrieved from raw speech signals by computing Mel-frequency Cepstral coefficients (MFCCs). Subsequently, spectrograms are generated to extract spatial features using the CNN architecture. In the emotion classification stage, the extracted features are fed into the hybrid LSTM-CNN model, which effectively learns temporal dependencies and spatial patterns from the input data. Our experimental results demonstrate the effectiveness of the proposed AIALS framework in accurately recognizing emotions from speech signals. By leveraging the complementary strengths of LSTM and CNN architectures, our model achieves an impressive accuracy of 98% on the CrEMA-d dataset, outperforming existing state-of-the-art approaches. Furthermore, the proposed system exhibits robustness across different emotional categories, showcasing its generalization capability. © 2024 IEEE.

关键词： Long short-term memory

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Octa multibit upset correction in SrAM memories using interleaved MPC code

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International Journal of Information technology (Singapore) 2025年 1-7页

作者： Sindhu, T.V. devi, P. Kalpana Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India

In space applications, cosmic radiation-induced multiple cells upset (MCU) poses a major stability issue for static random-access memory (SrAM). during the past decade, radiation-induced MCUs have been responsible for a significant increase in the proportion of mistakes affecting multiple memory cells. This research proposes an Interleave Multibit Parity Code (IMPC) for Octa multibit upset correction in SrAM, achieving low latency and error rates. The IMPC technique predicts MCUs before decoding and corrects adjacent 8-bit errors effectively, utilizing a parity-check matrix with minimal redundancy. Additionally, the encoder is reused to reduce overhead without interfering with the encoding and decoding process by reducing the area overhead of additional circuits. With the 45-nm library, encoders and decoders can be constructed with a minimal amount of area and delay overhead. Experimental results shows that the proposed code is more area-efficient, faster, and more power-efficient than current ECC codes. © Bharati Vidyapeeth's institute of Computer Applications and Management 2025.

关键词： Error correction codes Multiple cell upset Parity code SrAM memory

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Analysis of deep Learning based Optimization techniques for Oral Cancer detection 4

Analysis of Deep Learning based Optimization Techniques for ...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Subha, K.J. Bennet, Anto M. Pranay, Gaddam Bharadwaj, Ketham reddy, Polu Vikram Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798350300093

The main aim is to examine the application of deep learning technique in detecting oral cancer at an early stage. The focus is on evaluating the performance of various optimization techniques to deep learning technique through analyzing images of the mouth and throat for oral cancer detection. The goal is to determine the most effective method for identifying oral cancer and gain insights into potential improvements. The importance of early detection of oral cancer cannot be overstated, as it plays a crucial role in improving patient outcomes through earlier treatment and higher survival rates. The findings have the potential to contribute to the development of more accurate and efficient methods for oral cancer diagnosis and make a positive impact in the field. © 2023 IEEE.

关键词： Convolutional neural networks

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Forest Wild Fire detection using deep Learning Approach 9

Forest Wild Fire Detection using Deep Learning Approach

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Aanandha Saravanan, K. MuthuKumaran, d. Aloy Anuja Mary, G. Sathyasri, B. Prabha, M. Ambika Bhuvaneswari, C. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798350384208

disasters are happening inrecent times around the world. One of the major disasters which have drawn the most attention is the large area fires, especially forest areas. Forest fires can occur naturally and also sometimes by humans. Forest Fires just does not harm the plants and trees of the forest, but also the animalsliving in the forest. It also affects the nearby places where humans live since smoke, carbon dioxide and carbon mono-oxide concentration increase inthe atmosphere which can lead to several health problems such as respiratory problems. We can detect the fire by deploying various wireless sensor nodes in the forest areas by which they sense the fire and can inform us, so that we can control the damage to be done by forest fire. We can increase the efficiency of discovery exhausting different approaches on the information collected by various wireless sensor nodes. In this report we will compare some of the machine learning techniques used by researchers with wireless sensor nodes in predicting forest fire. © 2023 IEEE.

关键词： Fires

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Energy-Efficient I-rAW: An Intelligent-routing Solution for Wireless Body Area Networks 1

Energy-Efficient I-RAW: An Intelligent-Routing Solution for ...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Bhuvaneswari, Govindappagari Gomathi, N. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Avadi Dept. of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350352931

recent advancements in sensing technology and the integration of electronic devices have given rise to a promising innovation called Wireless Body Area Networks (WBANs). This technology holds great potential to revolutionize healthcare by enabling remote health monitoring for patients not close to healthcare facilities. However, the sensor nodes placed on or beneath the skin to capture various physiological signals are limited by their battery life. This paper introduces the Intelligent-routing Algorithm for WBANs (I-rAW) to address this challenge, designed to prolong these sensor nodes' operational lifespan. The system uses two sinks on the patient's front and rear body to gather data from sensor nodes arranged into clusters and relay information through a cluster head (CH). The Tunicate Swarm Algorithm (TSA) facilitates the cluster head selection, considering residual energy, average network energy, node-to-sink distance, path loss, and energy consumption rate. Using dual sinks helps mitigate network hot-spot issues by reducing the need for multi-hop communication. According to simulation data, I-rAW outperforms the dual Sink technique, employing the dSCB protocol regarding stability and overall network lifespan. Furthermore, I-rAW outperforms other contemporary protocols across various performance metrics. © 2024 IEEE.

关键词： Sensor nodes

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Sparrow Search Algorithm-based resource Management in Internet of Things (IoT)

EAI Endorsed Transactions on Energy Web

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EAI Endorsed Transactions on Energy Web 2022年第37期9卷 1-11页

作者： Karthick, S. Gomathi, N. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology ChennaiTamilnadu India

INTrOdUCTION: The principle of Internet of Things (IoT) completely concentrates on integrating heterogeneous entities for establishing seamless cooperation among virtual and physical entities. IoT has facilitated a paradigm shift of the Internet from collaborating networks to physical world interaction. The IoT devices are potent in sensing, processing, communicating and data storing that are derived from the physical world. The majority of the IoT applications are considered to be pervasive in characteristics and face huge challenges due to unattended IoT devices and constrained ***: The proposed Sparrow Search Algorithm-based resource Management (SSArM) targets on the potential assignment of resources to gateways in an IoT environment with better establishment in maintaining the tradeoff between intensification and diversification. It concentrates on the reduction of total data transmission cost in the IoT ***: In this paper, Sparrow Search Algorithm-based resource Management (SSArM) is proposed based on the inspired by the foraging, group wisdom and anti-predation characteristics of sparrow for potential assignment of multiple resources to gateways in IoT. This SSArM balances the degree of exploitation and exploration in the optimization search space to an acceptable ***: The simulation results of this proposed SSArM confirmed better throughput of 23.82%, reduced delay of 18.21% and minimized energy consumption of 20.28% when compared to the existing ***: This SSArM offers better accuracy in stability, convergence rate, precision in searching and preventing the value of local point of optimality © 2021 S. Karthick et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited

关键词： resource allocation

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Efficiency Improvement in Solar Cells: Comparing Single Junction and Tandem Photovoltaic's by Varying Anti-reflective Coating Thickness 5

Efficiency Improvement in Solar Cells: Comparing Single Junc...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： rajendiran, P. Bhuvaneswari, P. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai Tamilnadu Avadi India

ISBN: (纸本)9798350379945

This study explores the efficiency comparison between a single-junction photovoltaic cell and a tandem photovoltaic cell by varying the thickness of the anti-reflective coating using the nano-Hub simulator. In this research, we have collected 40 samples from single-junction and tandem photovoltaic solar cells. The study separates the solar cells into two groups: Group 1, consisting of single junction solar cells with 20 samples, and Group 2, consisting of tandem solar cells with 20 samples. To find the better solar cells, we have used statistical tool. Based on the concept of statistics the computations use G-power 0.8, with alpha set at 0.05 and beta at 0.2, ensuring a 95% confidence interval. From the study, we found the efficiency of the tandem cell is greater (i.e., 62.89%) than that of the single junction cell (i.e., 55.38%). The simulation study shows a p-value of 0.02, indicating statistical significance at a level less than 0.005. The research findings show that adjusting the thickness of the anti-reflective coating leads to superior performance in tandem solar cells compared to single-junction solar cells. © 2024 IEEE.

关键词： Antireflection coatings

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