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Optimization of machining parameters in EDM using Taguchi based grey relational analysis

Materials Today: Proceedings 2022年 82卷 43-46页

作者： Ganapathy S. Palanivendhan M. Susitra K. Balasubramanian P. Department of Mechanical Engineering Sri Sai Ram Engineering College Chennai 600044 India Centre for Automotive Materials Assistant Professor Department of Automobile Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu 603203 India Department of Computer Science and Engineering Sri Sai Ram Engineering College Chennai 600044 India Department of Mechanical Engineering A.V.C College of Engineering Mayiladuthurai 609305 India

The present investigation optimizes process parameters such tool size, peak current, pulse on time, and dielectric pressure in the EDM process. The material removal rate affects the machining operation's quality. The goal of the present work is to maximize material removal rate using Taguchi technique-based optimization of machining parameters. On an EDM, the experimental study for machining operations was carried out. For EN-31, machining operations are carried out using an orthogonal array with L9. The ANOVA technique was used to evaluate the experimental results. Using a technique called as grey relational analysis, the optimum conditions for material removal rate are determined. The optimum approach for a set of linked parameters is evaluated using the grey relation grade. © 2023 Elsevier Ltd. All rights reserved.

关键词： Anova EDM EN-31 GRA MRR

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Message Communication Challenges in Heterogeneous Architecture of Internet of Vehicles: A Review

Message Communication Challenges in Heterogeneous Architectu...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Jitendra Kumar Y.D.S. Arya Akash Sanghi Inverters University Bareilly & Assistant Professor Teerthanker Mahaveer University Moradabad UP India Invertis University Bareilly UP India Department of Computer Science & Engineering Invertis University Bareilly UP India

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

Inclusion of Internet of vehicles (IoV) an emerging technology in smart city for vehicular communication is covering the Internet of things (IoT) technology into vehicular networks for benefiting from omnipresent Internet connectivity. Smart city as a demand of new way of living of current and coming generation by gathering the information through the various electronic devices. The one of the key point in smart city is making traffic planning more secure and accurate by using the better message communication between vehicles. A huge number of researchers have been worked on vehicular networks for message communication but researchers are still having to do work on to achieve the higher data rate, faultless network connectivity, message scalability, message security and improved quality of services for message communication for making IoV more effective. IoV is more capable for comprehensively information processing that is collected from the vehicles, roads and surroundings and helps the drivers for driving the vehicles in safer zone. The Internet of Vehicles (IoV) model integrates vehicular intelligence in addition to leveraging ubiquitous vehicle connection for a multitude of applications. This research technology used in network communication including the smart city requirements, traffic control for increasing the life security.

关键词： Wireless communication Accuracy Smart cities Scalability Roads Traffic control Security Standards Internet of Vehicles Systematic literature review

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Developing a New MapReduce Framework with SVM-based Software Solution for Lyme Lesion Impact on Alzheimer's Disease Prevention

Developing a New MapReduce Framework with SVM-based Software...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Saravanan. M.S G. Sreehitha Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences and Assistant Professor Sri Padmavati Mahila Visvavidyalayam Tirupathi Andhra Pradesh India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Alzheimer's disease has many symptoms to occur, one of the latest research studies identified that Lyme disease, caused by “Borrelia burgdorferi” also one of the reasons indirectly affect the neuroinflammation. The dataset used for this research study is supplied by many web sources, that are involved in feature extraction to apply on machine learning techniques for extracting meaningful knowledge. This work proposing a new MapReduce framework-based Support Vector Machine (SVM) model for better prediction of impact of Lyme on Alzheimer's disease. The MapReduce based framework using SVM model creates more impact on prediction of Alzheimer's disease at the earliest. The clinical data with images are collected from distributed environments and applied on preprocessing and feature extraction stages then applied to proposed MapReduce based SVM model for better accuracy and optimal classification for complex patters on Lyme image dataset. From these results the Alzheimer's risk factors are predicted for early treatment process. The results obtained by this study will give early decision making for highly available sources of treatment procedures and gives more insight on preventive care on each and every patient involved in diagnosis of Lyme disease prediction, lead to identify the Alzheimer's disease prediction.

关键词： Support vector machines Accuracy Machine learning algorithms Software algorithms Machine learning Predictive models Feature extraction Software Classification algorithms Alzheimer's disease

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IoT based Power and Water Theft Detection in Urban Cities

IoT based Power and Water Theft Detection in Urban Cities

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Praful V. Nandankar G.N.R. Prasad M. Lakshminarayana Mr. Doddabasappa N G. Satish Ramya Maranan Assistant Professor Department of Electrical Engineering Government College of Nagpur Maharashtra India Sr. Asst. Professor Department of Master of Computer Application Chaitanya Bharathi Institute of Technology (A) Hyderabad India Assistant Professor Department of Medical Electronics Engineering M S Ramaiah Institute of Technology Karnataka India Assistant Professor School of Electrical and Electronic Engineering REVA University Bengaluru Karnataka India Associate Professor Department of Electrical and Electronics Engineering Sree Dattha Institute of Engineering and Science Sheriguda Ibrahimpatnam Hyderabad Telangana India Assistant Professor Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai India

Many people in metropolitan areas have experienced problems with energy and water shortages. Some people used modern technologies and advancements to theft water and power illegally. Because the planet absolutely needs both water and power. Currently, the Internet of Things (IoT), smart meters, smart pipelines, and smart homes are being heavily integrated to replace traditional systems for detecting theft activities. This can be digitizing both the meter readings and the data that has been gathered. Wireless technology may be used to collect and analyze the data, which helps to reduce the need for physical labor. In this project, we suggested creating an IoT-based system for water and power theft detection. The proposed project aims to design a system to monitor the amount of power and water used by the people and indicates if water and electricity theft activities happen. This research study offers a user interface system to help people to monitor whether anyone is stealing their water or power. This study employs a hardware like voltage sensors, current sensors, flow sensors, and additionally, the Arduino IoT cloud to enable remote control and monitor the water and power usage.

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A Robust Statistical CNNCTC-Based AI Model for Tracking and Monitoring COVID-19 1st

A Robust Statistical CNNCTC-Based AI Model for Tracking and ...

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1st International Virtual Conference on Industry 4.0, IVCI4.0 2020

作者： Chelliah, Balika J. Arunkumar, S. Prabavathi, R. Aarthi, B. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Campus Chennai India Assistant Professor Sri Sairam Institute of Technology West Tambaram Chennai India

ISBN: (纸本)9789811612435

The world today battles the new coronavirus (COVID-19) spread that can lead to a major endemic or pandemic outbreak with devastating outcomes which has overturned all our usual calculus seemingly. Because of the unavailability of a vaccine and the serious extent of infectiousness, social distancing is perhaps the only way left to fight the infection. The tracking system is still a challenge for monitoring the COVID-19 affected person and the community spread. This leads to an epidemic situation, where the number of secondary infections keeps going higher and higher which intern leads to the difficulty of tracking and treating each infected person. An AI-based mathematical modelling is proposed for tracking and monitoring the pandemic outbreak. A GPS tracking system, geolocation using Fit bit and Wi-Fi tracking modules are developed to locate the secondary infections and the carriers. Calculation of real-time coordinates and the conditional probabilities with respect to time is done followed by the classification based on risk using CNN connectionist temporal classification (CTC) model, a type of neural network which is used to train the recurrent process. Monitoring the affected person using sensors like thermal sensors and a tracking chip inbuilt in a wristband for real-time updates. A comparative study is done on various classification models. Therefore, the system provides a robust model with 96.5% accuracy to combat this pandemic situation. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Knowledge Extraction Using Auto Regression Method -A Tourist Information Extraction and Analytics

EAI Endorsed Transactions on Energy Web

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EAI Endorsed Transactions on Energy Web 2021年第35期8卷 1-7页

作者： Arun, Manicka Raja M Sumitha, T. Maria, Michael Visuwasam L Rejin, Paul N R Assistant Professor Department of Computer Science and Engineering R.M.K College of Engineering and Technology Chennai India Assistant Professor Department of Computer Science and Engineering R.M.K Engineering College Chennai India Assistant Professor Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai India

Data analytics is played a vital role in Information Technology and Information Technology essential services ITeS for making effective decisions. The demand for tourism and prediction of tourist arrivals are important for tourism organisation. In this paper, we analyse tourist and extract information using data analytics process. The web data are processed using travellers' details and applying an aggregate function to calculate the searching index. Here, we use the autoregression analytics method for accurate prediction. The tourist information is recorded and creates a system log for processing and extracting information. The interaction between each record and their logs are used for data processing and analytics model. This paper uses a recommendation system for the data analytics process and compares the results with existing models. Our proposed method provides good and accurate results for tourism organisation.___________________________________________ © 2021. Arun Manicka Raja M et al.,. All Rights Reserved.

关键词： Data Analytics

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Secure Localization Based Authentication (SLA) Strategy for Data Integrity in WNS

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computers, Materials & Continua 2021年第6期67卷 4005-4018页

作者： V.Manikandan M.Sivaram Amin Salih Mohammed V.Porkodi K.Shankar Assistant Professor Research Research CenterLebanese French UniversityErbil44001Iraq Vice President Lebanese French UniversityErbil44001Iraq Department of Information Technology College of Engineering and Computer ScienceLebanese French UniversityErbil44001Iraq Department of Computer Applications Alagappa UniversityKaraikudi630003India

Wireless Sensor Networks(WSN)has been extensively utilized as a communication model in Internet of Things(IoT).As well,to offer service,numerous IoT based applications need effective transmission over unstable *** ensure reliability,prevailing investigations exploit multiple candidate forwarders over geographic opportunistic routing in ***,these models are affected by crucial denial of service(DoS)attacks,where huge amount of invalid data are delivered intentionally to the receivers to disturb the functionality of ***,secure localization based authentication(SLA)is presented to fight against DoS attack,and to fulfil the need of reliability and *** examining state information,SLA projects a trust model to enhance efficacy of data ***,of the prevailing opportunistic protocols,SLA guarantees data integrity by modelling a trust based authentication,providing protection against DoS attackers and diminishing computational ***,this model acts as a verification strategy to accelerate?attackers and to handle *** strategy helps SLA in eliminating duplicate transmission and by continuous verification that results from conventional opportunistic *** is performed in a MATLAB environment that offers authentic and reliable delivery by consuming approximately 50%of the cost in contrast to other *** anticipated model shows better trade off in comparison to the prevailing ones.

关键词： Wireless sensor networks opportunistic routing secure localization based authentication denial of service computational cost

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Seamless Interaction through Gesture Recognition: Integrating Virtual Canvas, Keyboard, Calculator And Mouse with Voice Assistance on a Unified Platform

Seamless Interaction through Gesture Recognition: Integratin...

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Trends in Electrical, Electronics, computer engineering Conference (TEECCON)

作者： Jimsha K Mathew D Yashas M Shivani Kashyap K Jyothsna K Vinay Prasad Pratibha Prakash Machakanur Sr. Assistant Professor Dept of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bengaluru Dept of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bengaluru Dept of Information Science and Engineering New Horizon College of Engineering Bengaluru Dept of Computer Science and Engineering New Horizon College of Engineering Bengaluru

ISBN: (数字)9798331505714

ISBN: (纸本)9798331505721

This paper discusses the project ‘Seamless Interaction through Gesture Recognition,’ which is a system designed for the human-computer interaction without touching any tool, it simulates it virtually through three gadgets: a gesture-controlled mouse with voice recognition facilities, an air-based keyboard, calculator and a drawing canvas. In order to interpret the hand movements, gestures, and voice commands that are captured by the webcam and microphone, the system applies computer vision, machine learning, and speech recognition technologies. Hand motions made by the virtual mouse communicate to the computer what actions are to be done and where to locate the cursor. Additionally, the computer recognizes the voice commands which are an aide to such usage. The virtual keyboard, on its part, suggests finger positions through the air as keystrokes, virtual calculator processes a calculation based on hand gestures, and the virtual drawing tool is the one that notices the hand actions that it changes into digital artwork. All of these were developed by the OpenCV and MediaPipe frameworks, the programs that allow correct real-time hand tracking and gesture identification. The findings are a valuable contribution to computer interaction formation, which can lead to a decrease in the use of physical interaction devices and a shift to more natural interaction methods.

关键词： Hands Human computer interaction computer vision Calculators Webcams Keyboards Speech recognition Machine learning Gesture recognition Real-time systems

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Early Localized Lyme Disease prediction using Image Processing and Machine Learning Techniques

Early Localized Lyme Disease prediction using Image Processi...

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Integration of Emerging Technologies for the Digital World (ICIETDW), International Conference on

作者： Saravanan M.S G. Sreehitha Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences and Assistant Professor Sri Padmavati Mahila Visvavidyalayam Tirupathi Andhra Pradesh India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

The purpose of this study is to predict the early Lyme disease with the set of images and applying few machine learning algorithms. For this dataset taken from the public medical image repositories such as DermNet NZ, HAPiD, etc., using this two Convolutional Neural Network (CNN) machine learning algorithms such as VGGNet and GoogLeNet. The dataset size predicted using gpower tool with eighty percent of sample setting. With the sample size prediction the CNN type of VGGNet and GoogLeNet both are iterated and the results are recorded using training and testing data. The results were such as accuracy, precision, recall, F1-score, and ROC-AUC are determined to predict early localized Lyme disease. It was found that the novel CNN VGGNet model achieved an accuracy of 96.84% and other performance metrics compared to the CNN GoogLeNet algorithm’s 91.34% for the Lyme disease dataset. Hence this research study can help the healthcare practitioners or experts to identify the early growth of Lyme also this study result accepted the statistical significant of p value of 0.001, which is less than 0.05 in the independent t-test on both the machine learning algorithms. Therefore, the results indicate that the CNN VGGNet machine learning algorithm proves useful for early Lyme disease prediction and is effective at recognizing skin lesions.

关键词： Measurement Training Machine learning algorithms Accuracy Image processing Machine learning Skin Convolutional neural networks Lesions Diseases

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A Review on Forensic Investigation Analysis in Cloud Computing Environments

A Review on Forensic Investigation Analysis in Cloud Computi...

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International Conference on Computational science and Technology (ICCST)

作者： R.Sharmikha Sree K. Raja Department of Computer Science and Engineering Sri Sairam Engineering College SRM University Assistant Professor Department of CSE SRM Institute of Science and Technology Ramapuram Chennai

ISBN: (纸本)9781665476560

In cloud forensics, scientific concepts, methods, and approaches are used to find, collect, retain, evaluate, and report digital data in order to reorder events. For everyone involved, including cloud service providers (CSPs), cloud consumers, researchers, and forensic practitioners, cloud forensics is getting increasingly complex. If a criminal want to conceal their dirty laundry, they can use cloud storage to conceal sexual photographs of children, documents related to terrorist groups, etc. To investigate crimes involving the cloud, forensic investigations must be undertaken in the cloud. While attempting to collect evidence, forensic investigators face significant obstacles posed by the cloud's dynamic and dispersed nature. In this paper, Cloud-based forensics professionals are only able to conduct investigations if they have access to the proper tools and techniques for retrieving relevant digital evidence from a device is discussed. It further presents the challenges associated with cloud based forensics and applications to move forward the research in current area.

关键词： Cloud computing Scientific computing Forensics Terrorism Taxonomy Faces Logistics

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