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International Conference on Computing and Networking technology (ICCNT)

作者： John T Mesia dhas Utpal Saikia K K Arun C.Vimala Josphine M.Theodore Kingslin N Padmavathi Department of Computer Science and Engineering School of Computing Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology (deemed to be university) Chennai Tamilnadu India Department of Mathematics Silapathar College Silapathar Dhemaji Assam India Department of Mechanical Engineering Kumaraguru College of Technology Coimbatore Tamilnadu India Artificial Intelligence and Data Science R.M.K Engineering College Kavaraipettai Department of Electronics & Communication Engineering R.M.K College of Engineering & Technology Puduvoyal Tamilnadu India Department of Electronics and Instrumentation Engineering R.M.D. Engineering College Tamilnadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The optimization of welding techniques for oil rig components is crucial to upholding the structural integrity and safety of offshore drilling activities. In this study, we utilize a residual VGGNet architecture, which is a modified version of the well-known VGGNet, for the purpose of extracting crucial information from images of welding processes. The methodology employed in this study involves the collection of a complete dataset comprising welding process images obtained from diverse oil rig welding activities. The provided images are accompanied by annotations that provide the associated welding parameters, including current, voltage, and welding speed. The residual VGGNet is subsequently trained to acquire knowledge about the complex associations between picture features and welding parameters. In order to develop a reliable mathematical model, regression techniques are utilized to precisely establish the relationship between the extracted features and welding parameters. The performance of the model is evaluated using cross-validation and compared to traditional regression models to emphasize its greater ability to capture intricate patterns within the data.

关键词： Accuracy Welding Oils Voltage Feature extraction Mathematical models data models Safety reliability Optimization

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A Novel Intelligent AI-Based Security to Enhance the data Communication: An Empirical review

A Novel Intelligent AI-Based Security to Enhance the Data Co...

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International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2022

作者： Shaik, Amjan Hung, Bui Thanh Chakrabarti, Prasun Siva Shankar, S. Parveen, Nikhat Department of Computer Science and Engineering Dean R&D St. Peter’s Engineering College Maisammaguda Telangana Hyderabad India Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh Ho Chi Minh Viet Nam Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur India Adjunct Distinguished Professor ITM SLS Baroda University Gujarat Vadodara India CSE K G Reddy College of Engineering and Technology Moinabad India CSE Koneru Lakshmaiah Education Foundation AP Guntur Vaddeswaram India

ISBN: (纸本)9789819947164

The transmission of digital data between two devices is termed data communication. As the world nowadays relies on digital communication, the transferring of data among various devices is vitally important, and hence, the security of the data is a concern. Several works are carried out to secure the data transmission between the devices in various applications like mobile communication, wireless communication, vehicular communication, and more. Incorporated with security, the data classification is also performed to ignore the unwanted or hackers’ data. In this work, we have discussed several secure data communication-related papers from various applications. Works including machine learning-based approaches such as decision tree, linear regression, logistic regression, KNN, K-means, and deep learning-based approaches such as a convolutional neural network, recurrent neural network, and long short-term memory approaches were discussed and listed with their limitations along with merits. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： decision trees

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Enhancing Biomedical Image Interpretation Through a Hybrid Machine Learning Algorithm 7

Enhancing Biomedical Image Interpretation Through a Hybrid M...

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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： rajkumar, r. Vempaty, Lakshmi Namratha Thiyagarajan, r. Celine Kavida, A. Hemane, Harshal Vijayakumar, S. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India New York University Lead Data Scientist Avis Budget Group - Decision Technology and Business Intelligence NY United States Shreenivasa Engineering College Department of Biomedical Engineering Dharmapuri India Vel Tech Multi Tech Dr Rangarajan Dr. Sakunthala Engineering College Department of Physics Chennai India College of Engineering Pune India Paavai Engineering College Department of Electronics and Communication Engineering Tamil Nadu Namakkal India

ISBN: (纸本)9798350340600

In contemporary medicine, biomedical image interpretation is essential for disease diagnosis and the selection of appropriate treatments. However, manually scrutinizing these images is time-consuming and may lead to erroneous conclusions. The proposed work offers a novel approach to resolving these issues by utilizing a mixed machine-learning technique to improve the interpretation of biological images. The proposed system accurately evaluates physical images using machine learning techniques, including convolutional neural networks and decision trees. The algorithm aims to integrate the most beneficial aspects of multiple image analysis methods to enhance their overall performance. Using a carefully selected dataset, demonstrate the algorithm's precision and robustness compared to other approaches. The findings imply that the algorithm could substantially alter the interpretation of biological images in clinical and academic settings. This discovery has far-reaching implications, paving the way for improved diagnostic precision and further study of the human body. This hybrid method is a promising step toward automating image processing and paves the way for new research and implementation opportunities in healthcare technology. © 2023 IEEE.

关键词： decision trees

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Optical parameters extraction of soil and its microplastics contamination using terahertz spectroscopy

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Heliyon 2024年第19期10卷 e38326页

作者： Meenaakumari, M. Shoba, S. Manikandan, E. Karthigeyan, K.A. Nithyapriyan, S.A. Meikeerthivasan, V. Hemeshwar, S. Centre for Advanced Materials and Innovative Technologies Vellore Institute of Technology Campus Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai Campus India Centre for Advanced Data Sciences Vellore Institute of Technology Chennai Campus India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

The primary focus of this study investigates the potential of continuous-wave terahertz frequency-domain spectroscopy(CW-THz-FdS) as a rapid, non-destructive technique for detecting microplastic contamination in soil. Focusing on low-density polyethylene (LdPE) as a model microplastic, we prepared soil mixtures with varying LdPE concentrations (1 %,5 %,10 %,15 % by weight). We used a THz-FdS system operating between 0.1 and 0.7 THz to analyze the optical properties (transmission coefficient, attenuation and refractive index) of these mixtures and LdPE samples separately. Notably both soil and LdPE particles were of micrometer scale. Our results revealed distinct, concentration-dependent variations in these properties. The refractive index for the higher concentration (15 %) ranged from 2.2 to 2.3, while the attenuation coefficient varies between 10 and 14 cm⁻¹. Crucially, we identified polylactic acid [PLA] as a suitable sample holder material due to its high transmission in the THz range. Consequently, this study suggests that terahertz spectroscopy can be a promising technique for detecting microplastic contamination in soil, offering a non-destructive and sensitive method for environmental monitoring. © 2024 The Authors

关键词： Electro-optic sampling Micro plastic Non-invasive Terahertz spectroscopy

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Artificial Intelligence based Facial Emotion recognition with deep Neural GAN Augmentation

Artificial Intelligence based Facial Emotion Recognition wit...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： d. Menaka d. Karthika Priya C. reeda Lenus A Ashwini P. Josephin Shermila Banu Priya Prathaban Department of Electronics and Instrumentation Engineering Noorul Islam Centre for Higher Education Kumaracoil India Department of Artificial Intelligence and Data Science Easwari Engineering College Ramapuram Chennai India Department of Physics S.A.Engineering College Chennai India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Data Science R.M.K.College of Engineering and Technology Chennai India Department of Networking and Communication School of computing SRM institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Facial recognition system in computer vision is widely used in human-computer interface and clinical diagnostics. Nonetheless, achieving high accuracy in this task still poses a challenge due to issues of inadequate amount and imbalance of data to train the models as well as issues of generalization and robustness. The findings of this research propose the use of Generative Adversarial Network (GAN) data augmentation for face emotion classification specifically for this approach. The proposed strategy is based on the generation of artificial ‘emotional’ images on the face using deep Neural Self Attention GANs (dNSAGANs). This way, to capture all the variations of facial expressions the GAN takes a large number of images and thus makes the augmented data richer and more realistic. This larger data set is then used to train a deep learning architecture that performs the face emotion classification thereby enabling the network to learn enhanced discriminative features. To collect face photos the FEr2013 dataset is used. The training process, entrained the generator and discriminator to minimize the mean squared error while the discriminator was simultaneously trained to robustly distinguish generated images from real ones. Altogether, the proposed Adam optimizer was more accurate than Adagrad and Adadelta for all epochs. The percentage of accuracy is about $88.64 \%$ for 100 epochs using the proposed optimizer techniques.

关键词： Training Emotion recognition Adaptation models Accuracy Face recognition Computational modeling Generative adversarial networks data models robustness Time complexity

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Hybrid Blockchain and IoT driven Framework for Supply Chain Optimization by Enhancing Transparency, Efficiency and Cost reduction

Hybrid Blockchain and IoT Driven Framework for Supply Chain ...

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Electronics and renewable Systems (ICEArS), International Conference on

作者： Pradeep Kumar S CH Hussaian Basha C. Arun Prasath G. Amirthayogam Syed Ismail Abdul Lathif r. Leelavathi Computer Science Design Department Rajalakshmi Engineering College Thandalam Chennai Department of Electrical and Electronics Engineering SR University Warangal Department of ECE Mahendra Engineering College Namakkal Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai Department of Biomedical Engineering Velalar College of Engineering and Technology Erode

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Blockchain technology represents a modern supply chain management revolution which fixes operational challenges while providing enhanced transparency at reduced costs. The research develops HB-SCOF (Hybrid Blockchain-Enabled Supply Chain Optimization Framework) which combines blockchain self-verification databases with automatic smart contracts to maintain continuous supply chain observation and evaluate data credibility. The framework integrates tamper-proof transaction tracking mechanisms with unified supply chain network connectivity for securing data transmission as a trust delivery method through unbreakable shared databases. The retail-industry case study demonstrated a 25% decrease in expenses stemming from agency elimination alongside enhanced flow management and better stock distribution. The system demonstrates its effectiveness through assessments of key performance indicators including transaction speed and data accuracy together with business cost efficiency. This part looks at implementation methods through training stakeholders in addition to transforming organizational policies which produce successful outcomes. Blockchain technology labs verify its capability to enhance supply chain management efficiency while showing realistic deployment methods.

关键词： Training Costs Supply chain management Supply chains Smart contracts Key performance indicator Blockchains Stakeholders Internet of Things Optimization

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Initial Study on Applicability of Thermal Imaging for Microplastics Identification in Aquatic Medium

SSRN

引用

SSrN 2023年

作者： Manikandan, E. Pravin, Sheena Christabel Kiruthika, V. Gopikashree, S. Chandan, Sai Gokul H.T. Karthigeyan, K.A. Shoba, S. Centre for Innovation and Product Development Vellore Institute of Technology Chennai Campus Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai Campus Chennai India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Centre for Advanced Data Science Vellore Institute of Technology Chennai Campus Chennai India

Owing to plastic's durability, plastic waste management is challenging. The current issue is that millions of tonnes of plastic end up in the ocean, possibly resulting in microplastic pollution and becoming the most prevalent underwater contaminant. Efficient identification of microplastic pollution is a scientific challenge because as the size of the particles decreases, it becomes more difficult to recognize and detect them. In this work, we demonstrate a deep learning-based approach for microplastics identification. The dataset is acquired using the thermal imaging technique. data collection is followed by a data pre-processing step and finally, a classification step. The reported best accuracy, 97.42%, is achieved using the CNN model. The proposed approach allows for the deployment of increasingly diversified models as deep-learning models are still progressing. © 2023, The Authors. All rights reserved.

关键词： data handling

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient digital Identity Management with Token-based Selective disclosure

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

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Electronics and renewable Systems (ICEArS), International Conference on

作者： Velmurugan M Shinzeer C K G. Maya r. ramya r Kanimozhi Syed Ismail Abdul Lathif Department Of Computer Science & Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science And Technology Chennai Department of School of Computer Applications Lovely Professional University Phagwara Punjab Faculty of Management SRM Institute of Science and Technology Kattankulathur Department of Electronics and Communication Engineering K.Ramakrishnan College of Engineering Samayapuram Computer Science and Engineering Muthayammal Engineering College Rasipuram Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions.

关键词： resistance Privacy Fault tolerance Smart contracts Fault tolerant systems Throughput Energy efficiency Blockchains Identity management systems Interoperability

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Falcon Optimized CNN for the detection of Alzheimer's disease in MrI Image

Falcon Optimized CNN for the Detection of Alzheimer's Diseas...

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Smart Systems for applications in Electrical Sciences (ICSSES), International Conference on

作者： S. Febeena Ezhil Jothi L. Mahalakshmi P. Elamurugan Naveenkumar Kaliyan J. Swapna r. rajeshwari Department of Artificial Intelligence and Data Science Adhi College of Engineering And Technology Kanchipuram Department of Biomedical Engineering Kongunadu College of Engineering and Technology Trichy Department of Instrumentation and Control Engineering Sri Manakula Vinayagar Engineering College Puducherry Department of Computer science and Engineering Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Electrical and Electronics Engineering J.P. College of Engineering Ayikudi Tenkasi

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

degenerative illnesses that lead to the eventual death of brain neurons afflict a large number of people. Compared to the traditional Machine Learning (ML) approach, the recent success in the medical community of classifying Alzheimer's disease (Ad) utilized a deep Learning (dL) technique eliminates the necessity for manual feature extraction techniques. Although dL methods are widely used, there is currently a dearth of trustworthy and comprehensive research on the quantitative analysis of brain Magnetic resonance Image (MrI) data. In this paper, an effective Ad classification approach is proposed that use MrI to classify brain structure and diagnose Ad. Gaussian filter is used to eliminate noises at different degrees of complexity, noisy backgrounds, low contrast, and partial volume effect in the original input image. Next, K-Means clustering segmentation is useful for dividing image into different regions based on pixel intensity values. Key features for categorization are extracted using the Gray Level Co-Occurrence Matrix (GLCM) method. A Convolutional Neural Network (CNN), enhanced by Falcon Optimization Algorithm (FOA), is employed for Ad categorization. The proposed system, implemented on the Python platform using the best Alzheimer's MrI dataset, demonstrates an enhanced accuracy of 93.21 %. Additionally, this model-based approach provides a capable avenue for advancing research in the early detection of Ad and holds substantial potential in differentiating Ad patients from healthy individuals.

关键词： Training Accuracy Magnetic resonance imaging Neurons Magnetic resonance Feature extraction Convolutional neural networks Alzheimer's disease Optimization Testing

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detection of Malfunctioning Modules in Photovoltaic Power Plants using Unsupervised Feature Clustering Segmentation Algorithm

arXiv

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arXiv 2022年

作者： dwivedi, divyanshi Yemula, Pradeep Kumar Pal, Mayukha Data Science Research Intern ABB Ability Innovation Center Hyderabad500084 India Department of Electrical Engineering Indian Institute of Technology Hyderabad502205 India Global R&D Leader Data Science at ABB Ability Innovation Center Hyderabad500084 India

The energy transition towards photovoltaic solar energy has evolved to be a viable and sustainable source for the generation of electricity. It has effectively emerged as an alternative to the conventional mode of electricity generation for developing countries to meet their energy requirement. Thus, many solar power plants have been set up across the globe. However, in these large-scale or remote solar power plants, monitoring and maintenance persist as challenging tasks, mainly identifying faulty or malfunctioning cells in photovoltaic (PV) panels. In this paper, we use an unsupervised deep-learning image segmentation model for the detection of internal faults such as hot spots and snail trails in PV panels. Generally, training or ground truth labels are not available for large solar power plants, thus the proposed model is highly recommended as it does not require any prior learning or training. It extracts the features from the input image and segments out the faults in the image. Here we use infrared thermal images of the PV panel as input, passed to a convolutional neural network which assigns cluster labels to the pixels. Further, optimize the pixel labels, features and model parameters using backpropagation based on iterative stochastic gradient descent. Then, we compute similarity loss and spatial continuity loss to assign the same label to the pixel with similar features and spatial continuity to reduce noises in the image segmentation process. The effectiveness of the proposed approach was examined on an online available dataset for the recognition of snail trails and hot spot failures in monocrystalline solar panels. © 2022, CC BY-NC-Nd.

关键词： Solar energy

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