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Influence of copper anodization on structural and heat transfer characteristic of aluminum piston

Materials Today: Proceedings 2022年 59卷 1407-1413页

作者： Selvam, M. Amala Justus Arunkumar, S. Balambica, V. Padmanabhan, S. Kumar, T. Vinod Shai Sundaram, V.S. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Chennai India Bharath Institute of Science and Technology Chennai India

The piston transmits force from the combustion gas expansion to the crankshaft through the connecting rod in an engine. The piston head is exposed to tremendous pressure by expanding combustion gases at a greater magnitude and at a high temperature. The piston should withstand these thermal shocks. Today, there are many different technical and industrial techniques available to alter the characteristics of a material to meet the needs of a certain application. Anodization is a surface treatment process that is used exclusively for aluminum and its alloys to increase durability and corrosion resistance. With the capacity to control the factors involved in the anodizing process, it is possible to change some of the characteristics of the finished anodized goods. This article examines the anodizing of aluminum pistons with and without a copper coatings. The purpose of this study is to evaluate if the piston is capable of withstanding high equivalent stress and low heat flux. Static structural analysis and steady state thermal analysis are performed using ANSYS software. The results of this research show that aluminum anodized with copper improves the structural integrity of the aluminum piston while also enhancing its structural and heat transfer properties. © 2021

关键词： Heat transfer

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Comparative Performance of Machine Learning Models in Predicting Plant Growth using IoT data 5

Comparative Performance of Machine Learning Models in Predic...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： rajathi, K. Alex david, S. ruth Naveena, N. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of computer science and engineering Chennai India Hindustan Institute of Technology & Science Department of Mathematics Chennai India

ISBN: (纸本)9798331504403

The use of greenhouses in agriculture is greatly facilitated by the deployment of Internet of Things (IoT) technologies, where researchers test novel configurations for monitoring and controlling plant development. In this study, a dataset of conventional and IoT greenhouses in Tikrit University is utilized to predict plant growth variables using machine learning methods. The collection has 30,000 entries in all with a total of 14 characteristics. data pre-processing included label encoding, feature scaling, and treatment for missing values. In this work, performance is compared with four machine learning modules: XGBoost Classifier - random Forest Classifier- dNA Support Vector Machine (SVM)- Logistic regression. The results showed that XGBoost performed better than other models. These results show how complex machine learning techniques could be employed to minutely and efficiently monitor plant growth attributes which in turn can augment the crop yield. © 2024 IEEE.

关键词： Adversarial machine learning

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Analysis of different CNN Algorithms to detect dermatological Issues 10

Analysis of Different CNN Algorithms to Detect Dermatologica...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Preethi, G. Kanimozhi, J. Vaitheeshwari, S. Aishwarya, S. Abi Malleswari, M. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Computer Science and Engineering Tamilnadu India Information Technology Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Tamilnadu India

ISBN: (纸本)9798350384369

One of the most prevalent ailments nowadays is dermatological disease. They can also be brought on by direct contact with someone who is infected or by a variety of other causes, including inheritance, skin-dwelling organisms, parasites, external stimuli, and pathogens confined in pore spaces in the skin. Such dermatological conditions can also be lifelong in nature. The good news is that most of these illnesses are quickly treatable when detected, diagnosed, and taken care of at the appropriate time. Similar to this, skin cancer affects a great number of individuals. Melanoma and non-melanoma skin cancers are the two categories into which skin cancer falls. Currently, 1, 32, 000 cases of skin cancer are reported annually throughout the world. The data obtained from the models created by deep learning is extracted using a variety of feature extraction techniques. The likelihood that persons with these conditions would not survive has decreased as a result of improvements in computational methods for image processing and the emergence of several deep Learning approaches. Numerous algorithms have been developed so far, including Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN) and Fully Convolutional Networks (FCN). The paper discusses many deep learning methods and approaches used to forecast skin malignancies and skin disease, analyzes the most effective method, and concludes that Convolutional neural networks (CNN) is the most effective algorithm producing the greatest results in terms of evaluation, precision as well as accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Gender Based Analysis of Insulin Like Growth Factor-1 (IFG-1) and Prevalence of Osteoporosis in South Indian Population 3

Gender Based Analysis of Insulin Like Growth Factor-1 (IFG-1...

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3rd IEEE International Conference on Intelligent techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Sridevi, S. Hemalatha, K. Gowthami, S. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Computer Science and Engineering Chennai India Bannari Amman Institute of Technology Biomedical Engineering Sathyamangalam India

ISBN: (纸本)9798350361186

IGF-1 plays a pivotal role in promoting cell proliferation and growth, making it a key factor in growth-related diseases such as osteoporosis and diabetes. Serum IGF-1 levels can contribute to abnormal growth patterns, underscoring its significance in the pathophysiology of these conditions. Gender-based IGF-1 analysis is crucial for understanding and addressing variations in insulin-like growth factor-1 levels between men and women, providing valuable insights into potential gender-specific health implications and optimizing personalized healthcare interventions. The present study focuses gender based analysis in south Indian population because race is also one of the important factor in IGF 1 level. In this study, 195 blood samples have been collected from male and female volunteers and their IGF 1 value were quantified using systematic biochemical procedure with the help of ELIZA reader. Age-related analysis showed that IGF levels decrease with older age in both men and women. Women have significantly lower IGF 1 levels than men (mathbfp © 2024 IEEE.

关键词： Cell proliferation

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Automatic segmentation based on optimization U-Net neural network (OU-NetNN) for fetal cardiac ultrasound images

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International Journal of Information technology (Singapore) 2025年第4期17卷 2477-2484页

作者： Sekar, Satish rufus, Herald Anatha Department of Electronics and Communication Engineering Vel Tech Ranganathan Dr. Sagunthala R &D Institute of Science and Technology Chennai India

The primary cause of significant birth defects is congenital heart disease (CHd), which is a significant worldwide health issue. The method remains a hard challenge because of the broad variances ascribed to several circumstances, including maternal obesity, amniotic fluid volume, and great vessel links. This work suggests deep learning-based computer-aided fetal heart echocardiogram exams with an instance segmentation strategy, which naturally segments the four standard heart images. In this study, a real-time fetal cardiac identification is performed utilizing ultrasound (US) pictures with the Modified You Only Look Once (MYOLO) method for accurate and timely detection. Firstly, the speckle noise of the US pictures is reduced using the denoising Autoencoder Network (dAN). Secondly, localization is achieved in a fuzzy attention U-Net (FAU-Net). Next, MYOLO is optimized to function at its best, utilizing few resources. Finally, Beetle Swarm Optimization (BSO) is embedded in the U-Net Neural Network (OU-NetNN) to determine which neural network layout is most suitable for segmentation. The average fitness values in various dimensions were compared to the associated optimal values to validate the effectiveness of the recommended optimization technique. There are three assessment techniques, such as intersection over union (IoU), pixel accuracy (PA), and dice coefficient (dice), to measure segmentation accuracy. The suggested approach works much better than current techniques in the area of segmenting fetal cardiac ultrasound pictures by ventricular septal defect (VSd), atrioventricular septal defect (AVSd), and normal, according to quantitative and visual assessments. © Bharati Vidyapeeth's institute of Computer Applications and Management 2024.

关键词： Beetle swarm optimization (BSO) Congenital heart disease (CHd) Image segmentation Modified you only look once v5 (MYOLO5) Object localization

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Water Quality Monitoring with Satellite Image Using Convolutional Neural Network 14

Water Quality Monitoring with Satellite Image Using Convolut...

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14th International Conference on Computing Communication and Networking technologies, ICCCNT 2023

作者： Thylashri, S. Manikanta, Allika Poojitha, Indla Varshitha, Kakarla Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350335095

Monitoring the quality of water becomes vital in maintaining the security as well as the viability of water supplies. The manual sampling and analysis in laboratories used in conventional monitoring techniques can be time-consuming and expensive. during this era, imagery from satellites became a useful tool for assessing water quality across wide geographic areas. This paper is to assess and monitor water quality parameters using Convolutional Neural Network with 98% accuracy. It employs data acquired from satellites to detect and quantify various factors impacting water quality, such as pH, salinity, turbidity, chlorophyll and temperature. Spatial analysis differentiates water quality characteristics whereas Temporal analysis compares satellite data over time to detect changes in water quality. The proposed system offers significant advantages over traditional monitoring methods. It can be further improved by incorporating more advanced machine learning algorithms and satellite imagery. © 2023 IEEE.

关键词： Water quality

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Channel Estimation Algorithm Based on Factor Graph for GFdM-UWA System

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Journal of Engineering science and technology review 2023年第3期16卷 61-65页

作者： Jada, Lingaiah Shiyamala, S. School of Electrical and Communication Engineering | ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamilnadu AvadiChennai600 062 India

According to the characteristics of the wireless channel, channel estimation algorithms based on the factor graph are proposed for Generalized Frequency division Multiplexing over Underwater Acoustics (GFdM-UWA) system to improve the precision of channel estimation and bandwidth efficiency, which include two-dimension joint channel estimation and one-dimensional channel estimation algorithm. Time-varying frequency-selective fading channels can be modelled as 1st ordered autoregressive processes and approximating messages as Gaussian distribution, a novel channel estimation and symbol detection method for GFdM-UWA system is deduced by applying sum-product algorithm on the factor graph. Simulation results show that the algorithm can achieve good performance with low computational complexity. © 2023 School of science, IHU. All rights reserved.

关键词： Channel estimation

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Waste to Energy Treatment for India Using CrITIC-WASPAS Method under Interval-Valued Pythagorean Neutrosophic Fuzzy Set

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Neutrosophic Sets and Systems 2023年 61卷 493-506页

作者： devi, r. Narmada Sowmiya, S. Sundareswaran, r. Broumi, Said Department of Mathematics Vel Tech Rangarajan Dr.Sanguthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Mathematics Vel Tech Rangarajan Dr.Sanguthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Mathematics Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai India Laboratory of Information Processing Faculty of Science Ben MaAZSik University of Hassan II Casablanca Morocco

Municipal solid waste management (MSWM) has emerged as a major issue in India due to the massive amounts of waste generated on a daily basis. Governments are focusing on establishing a proper waste management system, including a timeline for the installation of waste processing and disposal facilities to reduce waste. For this problem, waste to energy (WtE) technologies have been identified because of their ability to convert waste into green energy while minimizing associated issues. This research investigates the different kinds of WtE treatments based on various factors, namely eco-friendly, budget friendly, technical, and social aspects. The findings of this study revealed which WtE treatments are best suited for waste management systems that convert green energy. In this paper, we propose the best WtE treatment for India using the interval-valued pythagorean neutrosophic fuzzy set (PNFS). We employ the WASPAS-CrITIC method in the interval-valued pythagorean neutrosophic fuzzy environment for this WtE treatment problem. Finally, a numerical example and a comparison are provided to illustrate the reliability and efficiency of the proposed technique. © (2023), (University of New Mexico). All rights reserved.

关键词： Fuzzy sets

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Surplus data Prediction and Classification of Textual-data Using Machine and deep Learning Comparative Analysis 2

Surplus Data Prediction and Classification of Textual-Data U...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： rajalakshmi, N.r. Saravanan, S. Singha, Anupam Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350369960

In recent years, several classification techniques have become available to categorize texts efficiently. In the field of machine learning, the construction of classifiers involves analyzing and learning from well-labeled datasets with specified categories. Likewise, deep learning's performance improvement in the text categorization sector comes from its ability to achieve high precision with a less complex setup and processing. This work emphasizes machine learning, and deep learning techniques are used to classify textual data. On account of the fact that textual data often carries a significant amount of unnecessary data, pre-processing is a crucial step, e.g.,. fill in the blanks and remove duplicate rows, then clean up the data. Following this, deep learning algorithms such as bidirectional long shortterm memory (BiLSTM), recurrent weighted average (rWA), and conditional random field (CrF) are used for classification, along with traditional machine learning algorithms like naive Bayes, gradient boosting, and support vector machines (SVMs). This work reveals that BiLSTM turned out to be the most accurate, achieving a classification accuracy of 98.5 %, outperforming both other models and baseline investigations. © 2023 IEEE.

关键词： bi-directional long short-term memory deep learning machine learning text classification textual data

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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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