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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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role of dynamic Communication for IoT devices 2

Role of Dynamic Communication for IoT Devices

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2nd International Conference on Automation, Computing and renewable Systems, ICACrS 2023

作者： Gaikwad, Nitesh Shiyamala, S. Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi-Vel Tech Road Vel Nagar Avadi Tamil Nadu Chennai600062 India

ISBN: (纸本)9798350340235

The use of microarchitecture has become increasingly widespread in various digital applications, resulting in significant improvements in device performance. In the realm of Internet of Things (IoT), a personalized processor can play a crucial role in enhancing communication performance. It's worth noting that IoT microarchitecture is typically designed for dynamic environments where it must effectively manage a multitude of concurrent tasks. However, a malfunctioning processor can lead to problems, such as increased energy consumption. Therefore, it's essential to have a comprehensive understanding of the advantages and disadvantages associated with microarchitecture when selecting processors. This research has laid the groundwork for a clear demonstration and exploration of the executive framework's pivotal role in IoT organizations. © 2023 IEEE.

关键词： adaptive connectivity dynamic communication enhanced security event triggering Internet of Things real-time data exchange remote control scalability

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Elevating Medical Training: A Synergistic Fusion of AI and Vr for Immersive Anatomy Learning and Practical Procedure Mastery

Elevating Medical Training: A Synergistic Fusion of AI and V...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Harinee, S. Vimal raja, r. Mugila, E. Govindharaj, I. Sanjaykumar, V. ragavendhiran, T. Computer Science and Engineering C.K. College of Engineering and Technology Cuddalore India Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

Virtual reality with its 3d visualization have brought an overwhelming change in the face of medical education, especially for courses like human anatomy. The proposed virtual reality system to bring massive improvements in the education received by a medical student studying for their degree courses. The project puts forward the text-to-speech and speech-to-text aligned system that simplifies the usage of a chatbot empowered by OpenAI GPT-4 and allows pupils to vocally speak with Avatar, the set virtual assistant. Contrary to the current methodologies, the setup of virtual reality is powered by avatars and thus covers an enhanced virtual assistant environment. Avatars offer students the set of repeated practicing of medical procedures on it, and the real uniqueness in the proposed product. The developed virtual reality environment is enhanced over other current training techniques where a student should interact and immerse in three-dimensional human organs for visualization in three dimensions and hence get better knowledge of the subjects in greater depth. A virtual assistant guides the whole process, giving insights and support to help the student bridge the gap from theory to practice. Then, the system is essentially Knowledge based and Analysis based approach. The combination of generative AI along with embodied virtual agents has great potential when it comes to customized virtual conversation assistant for much wider range of applications. The study brings out the value of acquiring hands-on skills through simulated medical procedures and opens new frontiers of research and development in AI, Vr, and medical education. In addition to assessing the effectiveness of such novel functionalities, the study also explores user experience related dimensions such as usability, task loading, and the sense of presence in proposed virtual medical environment. © 2024 IEEE.

关键词： Virtual environments

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Enhancing Efficiency of Crop recommendation using Incremental rank-based Feature Selection Method 2

Enhancing Efficiency of Crop Recommendation using Incrementa...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Umamaheswari, r. Kannan, E. Vel Tech Rangarajan Dr.Sagunthala R & D Institute of science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350368413

Traditional crop cultivation depends on the farmer's choice, but nowadays, for suggesting suitable crops, a crop recommendation system is required for facilitating optimal crop yield predictions. This study investigates the effectiveness of an innovative approach, named as Incremental rank-based feature selection method, which can improvise the accuracy and efficiency of recommendation systems. Existing feature selection methods often stuck with the high-dimensional dataset as it is more complex. In contrast, proposed method systematically ranks and selects features based on their relevance to crop yield. Through rigorous experimentation and diverse agricultural datasets, it is demonstrated that the incremental approach consistently outperform than traditional methods in terms of predictive accuracy metrics such as Mean absolute error (MAE), root mean squared error (rMSE), and r-squared (r2).Findings reveal that the incremental rank-based feature selection technique not only enhances predictive accuracy (MAE=0.245, rMSE=0.313, r2=0.878) but also significantly reduces computational complexity. The proposed method exhibits a runtime efficiency of 8.0 seconds, making it suitable for real-time decision-making. By iteratively selecting the most informative features, the method improves model interpretability and generalization capabilities. © 2024 IEEE.

关键词： computational efficiency Crop recommendation system feature selection incremental rank-based method predictive accuracy

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Experimental investigation of nano reinforced aluminium based metal matrix composites

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Materials Today: Proceedings 2022年 54卷 852-857页

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

The objectives of this work is to create an aluminum-based hybrid composite using the stir casting technique. Aluminium alloys are frequently utilised in the marine, concrete, transportation, and aircraft sectors due to their excellent strength-to-weight ratio. Stir casting was used to create particles strengthened metal matrix composites. The samples were characterised using optical microscopy and hardness testing. The results showed that the stir casting technique can advance the mechanical properties of samples by ensuring a uniform distribution of CNT/ Alumina elements in the A6061 matrix. As a result, simulating the mechanical characteristics of a newly generated raw sample is critical for assessing the structure's durability. © 2021

关键词： Tensile strength

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A Parallel Approach for Sentiment Analysis on Social Networks Using Spark

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Intelligent Automation & Soft Computing 2023年第2期35卷 1831-1842页

作者： M.Mohamed Iqbal K.Latha Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai600062India University College of Engineering Anna University(B.I.T Campus)Tiruchirappalli620024India

The public is increasingly using social media platforms such as Twitter and Facebook to express their views on a variety of *** a result,social media has emerged as the most effective and largest open source for obtaining public *** node computational methods are inefﬁcient for sentiment analysis on such large *** or parallel or distributed proces-sing are two options for dealing with such large amounts of *** parallel programming frameworks,such as MPI(Message Processing Interface),are dif-ﬁcult to use and scale in environments where supercomputers are *** the Apache Spark Parallel Model,this proposed work presents a scalable system for sentiment analysis on Twitter.A Spark-based Naive Bayes training technique is suggested for this purpose;unlike prior research,this algorithm does not need any disk *** of tweets have been classiﬁed using the trained *** with various-sized clusters reveal that the suggested strategy is extremely scalable and cost-effective for larger data *** is nearly 12 times quicker than the Map reduce-based model and nearly 21 times faster than the Naive Bayes Classiﬁer in Apache *** evaluate the framework’s scalabil-ity,we gathered a large training corpus from *** accuracy of the classi-ﬁer trained with this new dataset was more than 80%.

关键词： Social networks sentiment analysis big data spark tweets classiﬁcation

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Segmentation Of Intracerebral Haemorrhage CT-Scan Images Using N-Net Architecture 3

Segmentation Of Intracerebral Haemorrhage CT-Scan Images Usi...

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3rd International Conference on Electrical, Electronics, Information and Communication technologies, ICEEICT 2024

作者： Santhi, P. Maheswari, S. deepa, K. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tiruchirapalli Avadi India

ISBN: (纸本)9798350369083

In recent years, image segmentation has emerged as a critical task in computer vision and various applications such as medical image processing, autonomous driving, and satellite imagery analysis. deep learning techniques, particularly convolutional neural networks (CNNs), have played a pivotal role in revolutionizing image segmentation by offering unprecedented accuracy and efficiency. Many algorithms have used for the segmentation such as U-Net, V-Mesh, U-Net++ and etc. But, U-Net having the challenges in parameter transfer, model size and in speed. To overcome these issues, this paper explores the evolution of image segmentation techniques into ICH (Intracerebral Haemorrhage) for better diagnosis. This model is tested using ICH dataset available in Kaggle. In which, N-Net have produced the better performance in terms of precision, dice, recall and F1 Score. © 2024 IEEE.

关键词： Mesh generation

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A Novel Metaheuristic Algorithm Optimized deep CNN-LSTM Model for Amla disease Identification 2

A Novel Metaheuristic Algorithm Optimized Deep CNN-LSTM Mode...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Prema, K. Suganya, V. Visumathi, J. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350315905

The quality evaluation of agricultural goods are vital components in improving their vendibility and managing wastes. Fruit diseases present a persistent risk to producers and consumers. Precision agriculture can assist in making decisions on the management of fruit diseases by using computer vision-based approaches. Manual disease detection is highly challenging and it takes an excessive amount of effort, knowledge, and processing time. deep learning is therefore used to better accurately diagnose diseases. In this work, it is aimed to present deep learning framework approach for quickly identifying the apparent defects in amla fruit. In the proposed algorithm, deep convolutional neural network model with marine predator optimization algorithm is used for optimizing the accuracy of disease detection in amla fruit. The structure of the deep convolutional neural network comprises components for feature extraction and optimum classification. The hyperparameters for training convolutional neural network layers are tuned by marine predator optimization algorithm. The efficiency of proposed model is evaluated and compared to other optimization strategies. The optimized deep learning model outperforms by providing greater classification accuracy when compared to the current approaches, according to numerical analysis, demonstrating its potential for accurate disease detection and classification. © 2023 IEEE.

关键词： Fruits

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