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Systematic analysis of an unreliable batch service retrial queueing system with two sort of vacation

International Journal of Applied Systemic Studies 2023年第3期10卷 243-264页

作者： Niranjan, S.P. Indhira, K. Chandrasekaran, V.M. Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Mathematics School of Advanced Sciences VIT Vellore India

This paper concentrates on bulk arrival and batch service retrial queueing system with server failure, two types of vacation and constant retrial rate. If an arrival of customers finds a server is free then batch service will be provided with minimum of one customers and maximum of ‘b’ customers. On the contrary, if the arrival of customers finds the server is busy then entire customers will join in the orbit to explore service again. After service completion, if there are no customers in the orbit then the server leaves for working vacation. during working vacation, the server provides service in lower service rate compared to regular service rate. In the working vacation completion, if the orbit size is zero then the server leaves for non-working vacation. The server may get failure in working period. If the server got failure with probability (φ) then the renewal of service station will be considered. On the other hand, if there is no breakdown and orbit is empty then the server leaves for working vacation with probability (1 – φ). For this designed queueing model probability generating function of the orbit size will be obtained. Various performance measures are also be derived with suitable numerical illustrations. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Sales

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IOT Enabled Cloud Based risk Monitoring System in Food Supply Chain 1

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7th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Sathyasri, B. Mary, G. Aloy Anuja Bhuvaneswari, C. Ambika Aanandha Saravanan, K. Kumar, S. Vishnu Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi600062 India

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

The world of supply chains is a complex and ever-changing landscape, driven by the demanding needs of customers who want top-notch quality, lightning-fast delivery times, and a commitment to sustainability. Within this intricate web of logistics, the handling of perishable goods is a particularly intimidating challenge, with strict regulations in place to ensure safety and quality. But fear not, for a revolutionary new solution is at hand, this essential model proposes an IoT-based risk monitoring system (IoTrMS) that uses a path following robot equipped with a suite of cutting-edge devices like Ir, Ultrasonic and MQ4 sensors to manage and mitigate risks related to product quality and occupational safety in cold storage facilities. With real-time monitoring and risk assessment throughout the supply chain, this prototype promises to reduce health risks for the dedicated human workers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cold storage

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Early Prediction of Heart disease using Modified Optuna-CNN Model: Performance Analysis

IAENG International Journal of Computer Science

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IAENG International Journal of Computer science 2025年第2期52卷 464-473页

作者： r, Jeen retna Kumar J, Shajeena S, Saumiya P, Bini Palas M, Anushia r Stanley, Berakhah F Department of ECE Vel Tech Rangarajan Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE SRM Institute of Science and Technology Tiruchirapalli India Department of ECE Chennai Institute of Technology Chennai India Department of ECE SRM Easwari Engineering College Chennai India Department of CSE DMI College of Engineering Chennai India

Heart disease is currently a major cause of death in the world. Primitive detection of heart disease is important for improving patient results and reducing the burden of cardiovascular disease on society. In recent years, deep CNN (convolutional neural networks) have shown promising results for detecting heart disease using patient parameters such as age, sex, blood pressure, cholesterol levels, BMI, and family history of heart disease. Also, choosing an appropriate hyperparameter is crucial for the success of model performance and obtaining optimum results. Optuna is a hyperparameter optimization algorithm that helps in finding the optimal set of hyperparameters. This paper presents a modified optuna-CNN model for early detection of heart disease by patient parameters. The procedure includes gathering information, preparing the data, choosing a suitable model, training the model, assessing its performance, and implementing it in a healthcare environment. The paper also discusses the potential benefits of using deep CNN for early detection of heart disease, including improved accuracy, sensitivity, and specificity, which can aid in early diagnosis and timely intervention. Overall, the application of deep convolutional neural networks (CNNs) to identify heart disease in its early stages based on patient characteristics is a hopeful field of study that could enhance patient results and minimize the consequences of heart disease on people and the community. © (2025), (International Association of Engineers). All rights reserved.

关键词： Convolutional neural networks

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Secure Access with Audio Signature Authentication Using Audio Point Positioning technique 2nd

Secure Access with Audio Signature Authentication Using Audi...

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2nd International Conference on deep sciences for Computing and Communications, IcondeepCom 2023

作者： Pandithurai, O. Sriman, B. Saipriya, S. Shifi, S.J. Computer Science and Engineering Rajalakshmi Institute of Technology Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9783031689079

despite the fact that audio authentication has been around for some time, usage has lately increased as a result of advancements in speech recognition technology and rising security concerns. They have reached their height in recent years as a result of their versatility, increased security, and simplicity of use. They offer a quick and secure way to authenticate users and protect sensitive information, and as speech recognition technology advances, their use will undoubtedly grow. Since it is simple to remember and use, image authentication has grown in popularity recently. However, studies show that it may be susceptible to guessing-based attacks, especially if the image is instantly recognizable or publicly accessible, and it may not be suitable for all users, particularly those who have trouble using touch screens. Image authentication has lately been substituted by audio validation since it may be more accessible to people with visual impairments. The audio point positioning algorithm is used in this audio authentication to incorporate the sound positions from the audio that has been provided for validation. The search issue is overcome in this work by conducting the user’s search through analysis of the search engine logs that contain click through data. A framework model search engine based on user desire with this helps to ease this using click-through data from feedback sessions, users’ preferences can be mined. recently, audio validation has taken the role of image authentication since it may be more accessible to persons who are blind and is also more secure. Using the audio point positioning algorithm and the least significant bit coding approach, sound positions in the audio that is provided for validation are incorporated into this audio authentication. Because it incorporates unique security issues and permissions, this type of authentication is typically difficult to guess and access. The audio that has been verified at particular points is played an

关键词： Authentication

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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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Experimental investigation on mechanical properties of Basalt/Jute/SiC reinforced hybrid polymer composites

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Materials Today: Proceedings 2022年 59卷 1636-1642页

作者： Ganesh, r. Anand, P. Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

recent years, researchers focuses on hybridization of composite with natural fiber reinforcement and adding fillers for improving thermal and mechanical thermal behaviour of composites. These natural fiber reinforced bio polymers acts as partial eco-friendly materials. It makes the need for creating design database for hybrid composite materials. In the present work, hybrid composite laminates were fabricated with Basalt and Jute fiber reinforcements using the hand layup technique with 5% and 10% Silicon carbide and also absence of it. There are six hybrid composite laminates were fabricated along with five stacks alternative fiber layers and Five stacks of basalt and jute fiber reinforced polymer composite laminate with 5% and 10% SiC filler. All such composite laminates are compared with five stacks of five layered composite laminates made of individual Basalt and Jute fiber to analyse mechanical behaviours like tensile, flexural and wear properties using ASTM standards. results were analysed and fractured surfaces were examined using scanning electron microscope. © 2022

关键词： Hybrid composites

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Progressive Growing of Generative Adversarial Network for Multi-Scale Masked Fusion Network in Precise 3d Medical Image Segmentation 5

Progressive Growing of Generative Adversarial Network for Mu...

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

作者： Swapna, J. Kiruba, r. roselin Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600062 India

ISBN: (纸本)9798350379945

Image segmentation is a vital computer vision technique, simplifying image representation and aiding object recognition. In medical imaging, generating 3d volumetric images involves a complex process integrating image acquisition, segmentation, and volumetric reconstruction. This work proposes a novel and comprehensive methodology, addressing challenges associated with spatial and temporal dependencies in 3d medical image synthesis. The proposed method unfolds in three stages. Firstly, a multi-label segmentation strategy is employed to meticulously delineate anatomical or pathological structures within 3d medical images, resulting in detailed 3d masks. These masks serve as foundational elements for subsequent image synthesis. Secondly, a conditional medical image generation model utilizes these masks as input conditions to produce synthetic images aligning precisely with specified labels. This ensures the portrayal of anatomical and pathological features consistent with the input segmentation. The crux of the proposed methodology lies in the third stage, involving the sequential and autoregressive generation of 3d mask sequences. This intricate process captures spatial dependencies and intricate patterns within the 3d volumetric space. Evaluation against state-of-the-art methods, including Vid2Vid, 3d-a-WGAN, and ddPM, uses metrics such as Learned Perceptual Image Patch Similarity (LPIPS) and Frechet Inception distance (FId). results indicate the proposed method's superiority, particularly in terms of anatomical fidelity and perceptual quality. The work contributes significantly to advancing medical image synthesis, with potential applications in pathology detection, treatment planning, and quantitative analysis. © 2024 IEEE.

关键词： Image segmentation

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An intelligent speech enhancement model using enhanced heuristic-based residual convolutional neural network with encoder-decoder architecture

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International Journal of Speech technology 2024年第3期27卷 637-656页

作者： Balasubrahmanyam, M. Valarmathi, r.S. Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India

As the listening capacity exists in humans, they are facing the critical issues of understanding the speeches even in the presence of some background or other noises in the world. To diminish the noises, Speech Enhancement (SE) is the process of improving the quality of the speech signal by applying some techniques without degrading any such information. Hence, it is used for hearing-aid people, speech recognition, etc. recent researchers have developed some works to increase speech intelligibility. Substantial success has been achieved by executing the supervised learning methods. Nevertheless, the existing process incurs such shortcomings as attaining maximum error, computation burden, and so on. To overcome that, certain deep learning methodologies are immensely involved in SE for determining the spectrogram magnitude when reconstructing the signal source by removing the noise. Thus, it also becomes a more challenging task to acquire a clean speech signal. Though these methods aim to present the speech as more clear and intelligible, it may arise such intricacies that degrade the quality and efficiency. As it contains beneficial structure and resources, still it is in the scope of developing the novel SE model. To conquer these complexities, a successful SE task is offered utilizing deep learning in this paper. This recommended work performs the SE which employs deep learning to denoise a noisy speech to generate a quality speech. At first, the speech signal that contains noises such as cooler noise or fan noise, restaurant noise, railway station noise, factory noise, traffic in the journey, bus-stand noise, cinema theater noise, and clouding areas noise are gathered from the standard online sources. After that, this noisy speech signal is forwarded to Adaptive residual Convolutional Neural Networks with Encoder-decoder (ArCNNetEd) architecture, where the parameters involved in this framework are optimized with the support of random revised drawer Algorithm (rrdA

关键词： Speech enhancement

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Empowering and Enhancing User Experience in Web Application for Public Schemes 8

Empowering and Enhancing User Experience in Web Application ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： dilip Charaan, r.M. Jenefa, Leena Sumanth, B.r. Bhagiradhi, A. Lakshmi Harini, Yerramsetty Maheshwaran, M. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India Hindustan Institute of Technology and Science School of Management Chennai Padur India

ISBN: (纸本)9798350376425

This research study intends to create a user-centric website to address issues related to rural development. technological advancement for enhancing rural inhabitants' access to government services. To create a platform that makes public schemes easier to access, widely known, and used. decreasing disparities in socioeconomic status and inclusive development as means of empowering rural populations. making use of modern technologies like AI and blockchain. surveying people in rural locations to get data on awareness, usage trends, and preferences. creation of an extensive website, increased community involvement, expedited application procedures, and improved awareness. When the goal gets carried out successfully, it assists in closing knowledge divides while promoting fair development in rural areas. The insights derived from data on the website provide policymakers with helpful data for maximizing the effectiveness of government initiatives. © 2024 IEEE.

关键词： Blockchain

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Emerging Trends and Obstacles in Predicting Preeclampsia: A Comprehensive review of Machine Learning and deep Learning techniques

Emerging Trends and Obstacles in Predicting Preeclampsia: A ...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Aruna, r. Sivaranjani, S. Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331522988

Preeclampsia, a serious hypertension condition during pregnancy, presents considerable hazards to both mother and fetal health, potentially resulting in catastrophic consequences if not promptly identified. Conventional diagnostic techniques depend on clinical evaluations and biomarkers, which may not consistently provide prompt or precise predictions. recently, deep learning algorithms have emerged as potent instruments for studying intricate, high-dimensional data, potentially improving illness prediction by uncovering patterns and features overlooked by traditional methods. recent research reveals the advancement of several machine learning and deep learning models for predicting preeclampsia from 2021 to 2024, employing a wide array of input data, including maternal demographics, clinical histories, and ultrasound pictures. research has indicated favorable outcomes, with these models exhibiting enhanced sensitivity, specificity, and overall accuracy relative to conventional methods. Nonetheless, obstacles persist in domains such as data integrity, model interpretability, and the applicability of findings across varied populations. This literature review highlights recent developments in deep learning methodologies for the detection of preeclampsia and examines potential enhancements for practical use. Keywords include preeclampsia, maternal demographics, clinical evaluations, biomarkers, ultrasound imaging, and fetal health. © 2024 IEEE.

关键词： Ultrasonic imaging

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