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6th International Conference on Devices, Circuits and Systems, ICDCS 2022

作者： Bommi, R.M. Jacintha, V. Subramanian, Suresh Sundarambal, B. Karthikeyini, Chokiyan Institute of Ece Saveetha School of Engineering Chennai India Kings Engineering College Dept of Ece Chennai India Sri Indu Inst. of Engg & Technology Dept of Ece Chennai India Chennai Institute of Technology Dept of Cse Chennai India Excel Engineering College Dept of Ece Chennai India

ISBN: (纸本)9781665480949

Nowadays Air pollution is an emerging issue so we have to check the quality of the air and control air pollution for our future and healthy life. In this paper, we have proposed a framework which is used to see the air quality and check the live air status by the IoT devices (Internet Of Things), when the pollution rate exceeds the threshold value actuation process will be initiated. This method helps to check air pollution in multiple places by authorities and the higher authorities will be indicated by Short Message Service (SMS). Explicitly we should check and control the live air from air pollution, so convert the toxic gases from catalytic converters and poisons in fumes gas to less poisonous toxins by redox response mechanism. With the help of filters, carbon dioxide gas is separated, and zapping with laser on CO2 leads to the separation of carbon and oxygen. The catalytic converters and the laser are triggered by the threshold value respectively. In this manner, the result of the usage will be unadulterated oxygen and the isolated carbon is assimilated utilizing carbon sponges. For model execution, we have used Arduino UNO, sensor gadgets, Embedded C and MATLAB programming for graphical examination. © 2022 IEEE.

关键词： Toxicology Carbon dioxide Programming Air pollution Laser modes Software Internet of Things

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NeuroSpectrum Deep Learning Architecture for Automated Spectrum Analysis Resource Optimization

NeuroSpectrum Deep Learning Architecture for Automated Spect...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： K. Tamilselvan T. Senthil B. Buvaneswari Santhiya S Mukesh Soni Department of Information Technology AVS Engineering College Salem India Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies chittoor India Department of Information Technology Panimalar Engineering College Chennai India Department of Computer science and Engineering Excel Engineering College Namakkal India Department of Division of Research and Development Lovely Professional University Phagwara India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Reducing the availability of spectrum, as well as the nature of allocating channels in the present wireless telecommunication networks, is one of the critical issues facing modern wireless technology. Such spectrum sensing methods may not be highly flexible or efficient in environments where the radio spectrum undergoes frequent changes. This work proposes an improved deep learning spectrum sensing model based on CNN with reinforcement learning. The proposed system design calls for a pioneering dual mechanism, incorporating both spectrum analysis and a resource allocation learning model. The framework achieved remarkable performance metrics: 96 The spectrum sensing method achieved a 3% occurrence rate, a 94.8% precision rate for resource allocation, and a 92.7% efficiency in dynamic spectrum use. In the present investigation, we have employed it for different topologies and got 45% improvement in spectrum efficiency and 87% less interference than the conventional techniques. The work challenges deep learning-based spectrum sensing as well as management and indicates that they are prepared for the following generation of the wireless communication network.

关键词： Deep learning Wireless sensor networks Wireless networks Transfer learning Training data Sensors Resource management Optimization Spectral analysis Radio spectrum management

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NeuroSpectrum Deep Learning Architecture for Automated Spectrum Analysis Resource Optimization 3

NeuroSpectrum Deep Learning Architecture for Automated Spect...

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

作者： Tamilselvan, K. Senthil, T. Buvaneswari, B. Santhiya, S. Soni, Mukesh Avs Engineering College Department of Information Technology Salem India Sreenivasa Institute of Technology and Management Studies Department of Computer Science and Engineering chittoor India Panimalar Engineering College Department of Information Technology Chennai India Excel Engineering College Department of Computer Science and Engineering Namakkal India Lovely Professional University Department of Division of Research and Development Phagwara India

ISBN: (纸本)9798331508456

关键词： Cognitive radio

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Investigation on SAR in Hexagonal Shape Monopole Ultra-Wideband Antenna to Identify Female Breast Cancer 6

Investigation on SAR in Hexagonal Shape Monopole Ultra-Wideb...

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6th International Conference on Devices, Circuits and Systems, ICDCS 2022

作者： Subramanian, Suresh Sundarambal, B. Karthikeyini, Chokiyan Bommi, R.M. Jacintha, V. Sri Indu Institute of Engineering and Technology Dept. of Ece Hyderabad India Chennai Institute of Technology Dept. of Cse Chennai India Excel Engineering College Dept. of Ece Namakkal India Saveetha School of Engineering Simats Department of Electronics and Communication Engineering Chennai India Kings Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9781665480949

Specific Absorption Rate (SAR) is utilized as a standout amongst the applicable parameters to recognize the malignant tumor/cancer in the female breast through the electrical properties like permittivity and conductivity. The structure of Hexagonal Shape Monopole Ultra-Wideband Antenna (HSMUA) designed using Roger RT duroid 5880 materials with size of 57x49x1.6 mm3 is used to differentiate the malignant tumor/cancer in the human female breast. A 60 mm radius of 3- dimensional hemispherical breast model is designed. The tumor of various sizes is incorporated in glandular tissue and its SAR1g values are tabulated. © 2022 IEEE.

关键词： Solid modeling Shape Malignant tumors Breast Ultra wideband antennas Specific absorption rate Skin

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Classification for Crop Pest on U-SegNet 7

Classification for Crop Pest on U-SegNet

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Anitha Rani, A. Shaikhul Ashraf, Mohd Abul Ala Walid, Md Lakshmi Prasanna, K. Dey, Amar Kumar Saikanth, D.R.K. Department of ECE Excel Engineering College Tamilnadu Namakkal India India Foursteps Training Solutions Tamil Nadu Chennai India School of Agriculture SR University Andhrapradesh Warangal India Electronics and Telecommunication Engineering Department Bhilai Institute of Technology Chhattisgarh Durg India Department of Agricultural Extension College of Agriculture PJTSAU Telangana Hyderabad India

ISBN: (纸本)9781665464086

As a long-standing issue, pests and illnesses in agriculture consistently cause large annual yield decreases. It's no secret that deep learning (DL) models are quite effective in facial recognition, therefore many people in the agricultural industry are curious about them. S ince the algorithms used in existing methods of pest identification are sophisticated and there is a shortage of relevant data, the methods' accuracy in recognising and categorizing pests is low. When insects are misidentified as another type of pest, the wrong pesticides may be used, negatively affecting both crop productivity and the environment. Pre-trained deep learning architectures like Unet and ResNet were used to compare the proposed model to the suggested U-SegNet for insect categorization. This study also enhances the data to stop the network from becoming overly specialized. The accuracy of the proposed model has been analyzed by examining the impact of hyperparameters. The highest possible accurate classification rate has been accomplished at 93.54%. © 2023 IEEE.

关键词： Crops

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Design and Implementation of an Astute Infant Monitoring System Based on YOLO v8 Algorithm

Design and Implementation of an Astute Infant Monitoring Sys...

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International Conference on Computing and Networking technology (ICCNT)

作者： T. Arunkumar S Maheswaran P. Dineshkumar K. Geetha A. Sureshkumar S. Praveenkumar Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamilnadu India Department of Electronics and Communication Engineering Kongu Engineering College Perundurai Tamilnadu India Department of Information Technology K.S.Rangasamy college of Technology Tiruchengode Tamilnadu India Department of Computer Science and Engineering Excel Engineering College Namakkal Tamilnadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Parents today have a greater workload because they are working hard at their careers. As a result, there is less opportunity of giving babies better care. Children’s health may suffer greatly as a result of this. Even if the infants are appropriately and consistently monitored, there is a risk of losing life. For this, we use cameras to monitor the external conditions. The system is designed to monitor and alert the baby emotions to the parents. The monitoring of baby’s motion, emotions and surrounding is done by the camera that is fixed in an angle that it can cover the baby to the maximum extend. The camera that are fixed turn according to the baby’s movement, so that it continuously monitors the baby. The system will alert the user if there in some negative emotion given by the baby. The system is trained in a way that it monitors the surroundings around the baby carefully. (eg: fire). This is done using YOLO v8. YOLO v8 is an anchor-free model.

关键词： YOLO Pediatrics engineering profession Computational modeling Cameras Monitoring

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Optimizing Combustion Efficiency and Emission Control in Premixed Charge Compression Ignition Engine Using Ethanol Vapour and Alexandrian Laurel Methyl Ester

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SSRN 2024年

作者： Masilamani, Gowtham Prakash, R. Department of Aeronautical Engineering Excel Engineering College Tamil Nadu Komarapalayam637 303 India School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore632 014 India

The automotive sector faces challenges due to high petroleum demand, fuel shortages, and emission standards. Scientists are researching advanced combustion to replace diesel fuels, exploring alternatives like ethanol with properties similar to diesel. In the present work, Alexandrian Laurel raw is extracted oil and converted into Alexandrian laurel methyl ester, this blend AL30 is used as the main fuel, injected at 23obTDC. The ethanol fuel is used as an intermediate fuel to act as a fuel oxidizer. The ethanol fuel is injected into the premixture device, and the fumigator converts liquid ethanol into vapour form. That ethanol vapour is passed into the inlet manifold section in different percentiles 10%, 20% and 30% along with inlet air during suction stroke. The combustion, performance and emission characteristics are calculated in the premixed charge compression ignition engine with two different fuels used (Alexandrian laurel methyl ester 30% with ethanol vapour 10, 20 and 30% by vol basis) and contrasted than diesel fuel. The experimental values reveal 3.15% of maximum brake thermal efficiency for F-E30, 5.2% 37% lower emissions for carbon monoxide, and hydrocarbons at F-E30, F-E20. 11.2%, 18.9% higher cylinder pressure, HRR at F-E20. The BSFC, NOx emissions are escalated. © 2024, The Authors. All rights reserved.

关键词： Ethanol

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Comparison of Machine Learning and Deep Learning models for Cervical Cancer Prediction 6

Comparison of Machine Learning and Deep Learning models for ...

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6th International Conference on Devices, Circuits and Systems, ICDCS 2022

作者： Sundarambal, B. Karthikeyini, Chokiyan Bommi, R.M. Subramanian, Suresh Jacintha, V. Chennai Institute of Technology Dept. of Cse Chennai India Excel Engineering College Dept. of Ece Namakkal India Saveetha School of Engineering SIMATS Dept. of Ece Chennai India Sri Indu Institute of Engineering and Technology Dept. of Ece Hyderabad India Kings Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9781665480949

Traditional cervical cancer type categorization relies heavily on the pathologist's experience, which is inefficient and inaccurate. Colposcopy is an important part of preventing cervical cancer. Over the last 50 years, colposcopy, in combination with precancer screening and therapy, has played a critical role in lowering the incidence and death of cervical cancer. It justifies the importance of predicting cervical cancer at an early stage. For prediction, the proposed approach makes use of the UCI data repository and machine learning classifiers. The data is preprocessed, and the repository is updated with feature extraction and validations. After that, the pre-processed data is then applied to various machine learning classifiers namely Logistic Regression, Random Forest, Gradient Boosting, Support vector machine (SVM), and all its ensemble classifier. The ensemble classifier has not surpassed the results of individual classifier and thus preferred deep learning model using artificial neural network. The comparison is done in terms of accuracy and fmeasure along with recall and precision. The results revealed that the neural network model is the best with 97.67% of accuracy and 98.78% of F-score. © 2022 IEEE.

关键词： Deep learning Support vector machines Measurement Medical treatment Artificial neural networks Predictive models Feature extraction

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Application of Deep Learning Algorithm for Prediction of Flood Severity

Application of Deep Learning Algorithm for Prediction of Flo...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： C Jamunadevi N Naveen M Manimaran M Janani Department of Computer Technology PG Kongu Engineering College Erode Department of IT Excel Engineering College Namakkal School of Computing Science and Engineering VIT Bhopal University Bhopal Madhya Pradesh India M.Sc (Software Systems) Kongu Engineering College Erode

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Natural disasters like floods have caused immense harm to lives and resources, underscoring the need for accurate prediction methods to mitigate their impact. Meteorological flood indices such as the standardized precipitation index help identify flood occurrences and severity levels, enabling proactive measures. While past research primarily used statistical methods like Least Square-Support Vector Machine (LS-SVM), these often lack accuracy for long-term forecasts. Advancements in machine learning, particularly artificial neural networks and deep neural networks, offer more accurate predictions over extended periods. Long Short-Term Memory (LSTM) models and Convolutional Neural Networks (CNN) are used to predict flood indices, handling real-time nonlinear data effectively. Benchmarking trials have shown that an LSTM and CNN configuration produced the best predictions, with an accuracy, prediction, MSE, and RMSE of 97.93,0.89, 0.01, and 0.10 respectively.

关键词： Accuracy Weather forecasting Predictive models Surfaces Data models Vectors Floods

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Demand Response Strategies in Microgrids with Electric Vehicle Loads

Demand Response Strategies in Microgrids with Electric Vehic...

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IEEE Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Sarathkumar D B. K. Parrthipan Murugesan Manivel T. Jayakumar J Sivadasan P. Maheswari Department of Electrical and Electronics Engineering Kongu Engineering College Tamil Nadu India Department of Mechanical Engineering Kamaraj College of Engineering and Technology Tamil Nadu India Department of Electrical and Electronics Engineering Karpagam Institute of Technology Coimbatore Tamilnadu India Department of Electrical and Electronics Engineering Nandha Engineering College Tamilnadu India Department of Electronics and Communication Engineering PSR Engineering College Sivakasi Tamil Nadu India Department of Electrical and Electronics Engineering Excel Engineering College Tamilnadu India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The integration of electric vehicles (EVs) into microgrids has introduced new challenges and opportunities in energy management. As the number of EVs increases, their charging demand can significantly impact the stability and efficiency of microgrids. This paper investigates various demand response (DR) strategies tailored to manage EV loads within microgrids effectively. The study explores both centralized and decentralized DR mechanisms, assessing their impact on load balancing, peak demand reduction, and energy cost minimization. The performance of these strategies is evaluated through simulations in diverse operating scenarios, considering factors such as renewable energy variability, grid constraints, and user preferences. The results highlight the potential of advanced DR techniques, such as price-based and incentive-based schemes, to optimize energy distribution and enhance the resilience of microgrids. The paper also discusses the role of real-time communication and control technologies in facilitating effective EV integration. By addressing the dynamic nature of EV loads, this research provides insights into designing efficient, reliable, and sustainable microgrids capable of accommodating growing electrification demands.

关键词： Renewable energy sources Vehicle-to-grid Microgrids Power system stability Load management Demand response Stability analysis Real-time systems Optimization Resilience

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