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detection of Brain Tumour By Segmenting the Magnetic resonance Image Using K-Nearest Neighbour Algorithm and Compare the Sensitivity and Accuracy With Convolutional Neural Network Algorithm

Detection of Brain Tumour By Segmenting the Magnetic Resonan...

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2023 Intelligent Computing and Control for Engineering and Business Systems, ICCEBS 2023

作者： Sheela, M.Sahaya Nalini, N. Uganya, G. Sathesh, M. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350394580

The main objective of this research is to use the 'K-Nearest Neighbour' ('KNN') algorithm to detect brain tumours in MrI images and to compare its sensitivity and accuracy to that of the 'Convolutional Neural Network' ('CNN')algorithm. Brain tumours were identified in MrI scans using the 'KNN' and 'CNN' algorithm, and the sensitivity and accuracy of the identified images were compared. Group 1 with 20 samples using 'KNN' and Group 2 with 20 samples using 'CNN' were analysed by compiling the entire dataset of 40 samples with 80% of pretest power. Pursuant to the simulation, the 'KNN' algorithm has a 95% sensitivity to detect brain tumours, whereas the 'CNN' approach has a 90% sensitivity to do so. While 'CNN' algorithm only diagnoses brain tumours with 84.10% accuracy, 'KNN' algorithm does so with an accuracy of 98.5%. © 2023 IEEE.

关键词： accuracy convolutional neural network algorithm Image segmentation K-Nearest Neighbour algorithm machine learning sensitivity

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Automatic Trash Monitoring System 8

Automatic Trash Monitoring System

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Kalpana devi, P. Gadde, Mukesh Narayana Oli, Prativa Somineni, Meghana Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dept of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350396638

The research over the past years concludes that nearly "62 million Tones "of waste is being produced in "INdIA"yearly. This is because it is becoming increasingly difficult for national and local governments to assure the effective sustainable management of trash due to the fast rise in the volume of solid and hazardous garbage caused by ongoing economic expansion, urbanization, and industrialization. Though, the municipalities have implemented a system called "Swachh Bharat Mission (Grameen) Phase-II "to segregate liquid and solid waste for the improvement of cleanliness in rural areas throughout the country. But this process fails to ensure the efficient management of waste segregation. Manually no one in the world separates the waste Since this issue has been in the spotlight for recent years, few people have designed some systems for waste segregation using conveyor belts but that requires lots of space and is time-consuming. Now, keeping that in mind, this study has proposed and designed a smart automation system called "Automatic Trash Monitoring Bin (ATM BIN)", which aims to improve effective waste segregation. It comprises several sensors and servo motors that work together and produces effective outcomes. This system offers a long-term solution for determining the volume of waste and timely garbage collection and increases operating effectiveness, while also decreasing management expenses, resource consumption, and roadside emissions. This system also prevents the problem of trash being thrown in landfills. © 2023 IEEE.

关键词： Inductive sensors

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revolutionizing Visual Artistry: Exploring the Transformative Power of Computational Photography 8

Revolutionizing Visual Artistry: Exploring the Transformativ...

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

作者： Annie Silviya, S.H. Sriman, B. Sathish Kumar, P. Adithyaa, A. Satyanarayanaa, H. Priyadharshini, K. Rajalakshmi Institute of Technology Computer Science Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Computer Science Engineering Chennai India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Computer Science Engineering Chennai India Rajalakshmi Institute of Technology Computer and Communication Engineering Chennai India

ISBN: (纸本)9798350367904

In the past decade, there has been a significant shift in the way we consume and utilize visual media. The swift evolution in smartphone cameras has made photography and videography available to a large audience and the rise of social media has greatly boosted the influence of smartphone photography, making it an essential tool for amateurs and professionals alike. Computational photography - capture, enhancement and manipulation of images by digital computation, a field drawn from computer vision and image processing, lies at the core of this paradigm shift. This raises questions about whether smartphone cameras can truly rival traditional photography equipment. This paper reviews the field of Computational Photography, specifically its application in low-light imaging, dynamic range optimization, and bokeh effects, while drawing a comparison between photos captured by smartphones and dSLr. © 2024 IEEE.

关键词： Smartphones

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Iris Flower Species detection using Machine Learning technique 3

Iris Flower Species Detection using Machine Learning Techniq...

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3rd IEEE International Conference on Intelligent technologies, CONIT 2023

作者： Hemalatha, d. reddy, Yaramala Tarunkumar Lakshmikanth, K. davusaheb Saajid, Shaik Chennuru Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Tamil Nadu Chennai India

ISBN: (纸本)9798350338607

Iris flower species classification and prediction is a popular task in machine learning, where the goal is to predict the species of an iris flower based on its physical characteristics. This task is typically performed using the Iris dataset, which contains measurements for sepal length, sepal width, and petal length for 150 iris flowers. This paper presents machine learning techniques to classify iris flower species using decision Trees, Gaussian Naive Bayes, Support Vector Machines, and k-nearest neighbor models. We discuss the data preprocessing steps, model selection, hyperparameter tuning, and evaluation metrics used to optimize the performance of each model. We compare each model's accuracy, precision, recall, and F1 score and demonstrate how the selected model can be used to make predictions on new data. Our results show that the Gaussian Naive Bayes model performs the best, achieving an accuracy of 1.0 on the Iris dataset. We have found that the Gaussian Naive Bayes classifier gives the best accuracy compared to KNN, SVM, and decision Tree models. Overall, this paper provides a comprehensive overview of iris flower species classification and prediction using machine learning. © 2023 IEEE.

关键词： Classification (of information)

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Coin detection by an Improved Convolutional Neural Network Approach 5

Coin Detection by an Improved Convolutional Neural Network A...

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

作者： Bennet, M. Anto remya, r. Valarmathy, K. dhivyasri, G. Manikandan, M. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology India The Oxford College of Engineering United Kingdom Sai Vidhya Institute of Technology India Presidency University India

ISBN: (纸本)9798350379945

The coin detection in vending machines is a crucial task for ensuring accurate and efficient transactions. In this work, an improved Convolutional Neural Network (CNN) approach for coin detection is introduced. The CNN model has been trained with the images of various types and sizes to recognize and classify these coins accurately. CNN architecture leverages its ability to extract relevant features from coin images, enabling it to detect coins regardless of orientation and lighting conditions. A promising result has been attained in terms of coin detection accuracy and speed, making this approach for real-time applications in vending machines. This research contributes to improving the reliability and efficiency of vending machine transactions, reducing errors, and enhancing user experiences. The CNN-based coin detection approach holds significant potential for various vending machine applications, from beverage dispensers to ticket kiosks, and can be adapted to accommodate different coin denominations and currencies. © 2024 IEEE.

关键词： Convolutional neural networks

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detection of Bone Fracture using Prewitt Edge Algorithm and Comparing with Laplacian Algorithm to Increase Accuracy and Sensitivity. 4

Detection of Bone Fracture using Prewitt Edge Algorithm and ...

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

作者： Nalini, N. Uganya, G. Sathesh, M. Sheela, M.Sahaya Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350300093

The purpose of the research was to is to compare accuracy and specificity in the bone fracture detection using novel modified Prewitt Edge detection (PEd) with Laplacian Edge detection (LEd). Two groups are compared, novel modified Prewitt Edge detection (PEd) (N=10) and Laplacian edge detection (LEd) (N=10) The overall sample size was calculated using the G Power software with an alpha of 0.05, enrollment ratio of 0.1, confidence interval of 5%, and power of 80%. Using the SPSS statistical package, an independent sample t-test was used to compare the accuracy and specificity rate. Novel modified Prewitt edge detection (PEd) algorithm found to be statistically significant when compared with the Laplacian edge detection (LEd) classifier which gives accuracy p= 0.026, and specificity p=0.001(p © 2023 IEEE.

关键词： Machine learning

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Surface Modification by Abrasive Water Jet Milling of Ti-6Al-4V Alloy for Biomedical Application 1st

Surface Modification by Abrasive Water Jet Milling of Ti-6Al...

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Proceedings of the 1st International Conference on Sustainable technologies and Advances in Automation, Aerospace and robotics, STAAAr 2022

作者： Naresh raj, K.L. Pavithra, E. Department of Mechanical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India

ISBN: (纸本)9789819923489

Titanium alloys are a type of biomaterial commonly seen in orthopaedic implants. Surface properties such as wettability and roughness are crucial for cell adherence, spreading, and proliferation at the implant/tissue interface, as they affect the biological response. Several surface texturing technologies have been developed to attain these properties for biomedical applications. These methods have constraints that limit their effectiveness, despite their potential. Abrasive water jet texturing could be a possible choice in this scenario. This study uses a controlled depth milling technique to use abrasive water jet milling to modify the Ti-6Al-4V alloy surface. Nozzle speed and jet pressure were taken as variable parameters, and the modified surface topography and contact angle were measured with a digital microscope, and the surface roughness was measured using a contact type tester. The obtained surface was hydrophilic, and the corrugated surface produced by the #80 abrasive particles used in the milling operation is appropriate for implant surfaces in biomedical applications. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Textures

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Outlier detection in Indoor Localization using 'random Forest' and 'Support Vector Machine'

Outlier Detection in Indoor Localization using 'Random Fores...

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2023 Intelligent Computing and Control for Engineering and Business Systems, ICCEBS 2023

作者： Uganya, G. Sathesh, M. Sheela, M. Sahaya Nalini, N. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350394580

The objective of this work is to detect outliers in indoor localization using machine learning such as 'random Forest' ('rF') and 'Support Vector Machine' ('SVM'). These two algorithms are considered as two groups. Group1 is 'rF' by taking 20 samples and group2 is 'SVM' by taking 20 samples that are analyzed with 80% of pretest power and 0.05 alpha value. random Forest Algorithm achieves an accuracy of 83.9%, precision rate of 83.23% and recall rate of 83.77% in a balanced dataset. In an unbalanced dataset, it achieves an accuracy of 64.9%, precision rate of 64.03% and recall rate of 65.48%. Support Vector Machine achieves an accuracy of 82.9%, precision rate of 82.08% and recall rate of 82.41% in a balanced dataset. In an unbalanced dataset, it achieves an accuracy of 59.76%, precision rate of 60.36% and recall rate of 60.72%. The p value of 0.02 for accuracy, 0.03 for precision and 0.01 for recall rate was obtained ('p © 2023 IEEE.

关键词： Innovative Indoor localization Machine learning Outlier detection random Forest Support Vector Machine

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deep learning based predicting urban traffic congestion with rGB-coded images using GrU-CNN and LSTM

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Multimedia Tools and Applications 2024年第38期83卷 86261-86280页

作者： P, rajesh Azhagiri, M. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Campus Tamil Nadu Chennai600 089 India

road traffic management requires the ability to foresee geographical congestion conditions in an urban road traffic network. The proposed investigation is aimed to envisage the presence of blockage in a specific region of geographical location using gated recurrent unit (GrU) combined with the convolutional neural network (CNN) and long short-term memory (LSTM). The data related to road traffic is extracted from the time—frequency domain of a specific urban road. The collected data is then compressed into a rGB coded image which is the input to the GrU which determines the parameters of traffic blockage in a specific region of the road. The road traffic network features are extracted from those parameters and identified using a CNN's feature extraction technology. We used LSTM model to assess the time series temporal images of the road traffic blockage and to utilize the time division to pool the flow of traffic. For model validation, CTT (Chicago Traffic Tracker) dataset network is chosen. The parameters assessed had ensured highest prediction accuracy of 98.87%, F1-score of 0.98, 0.971 of specificity, 0.987 of recall and 0.978 of precision to forecast the presence road traffic. The proposed model demonstrates a significant advancement in road traffic detection with an accuracy of 98.87%, surpassing existing models that achieve the highest accuracy of 94.3%. This represents a notable improvement of approximately 4.77% over the highest accuracy achieved by the previous models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Feasibility study to implement the composite material plating in Floaters

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Materials Today: Proceedings 2022年 54卷 839-844页

作者： Balaji, V. Anand, P. rajesh, d. Prakash, d. Surrya Karthick, M. Department of Mechanical Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India

The Floaters find wide application in the offshore oil and gas industry for drilling, extraction, storage, and processing of crude oil and gas. The more predominant concern about floaters is their weight, and where the floor plating contributes more weight. Each additional tonnage changes the floaters' stability, capital expenditures, and revenues. A decade ago, in the offshore industry, composite materials were chosen as alternate materials and they are being used as utility piping, risers & guards, production tubing, walkway grating, etc., This feasibility paper will analyse deeply why the composite material is still in an ideal state for plating application, issues to be faced during engineering, construction, installation and operation phases, along with solutions and economic and technical benefits of its implementation. This paper is fully focused on implementing this technique in floating products in offloading and storage. This paper definitely brings out the valuable guidelines for the industry to implement this idea widely in all the forthcoming floaters. © 2021

关键词： Floors

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