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8th International Conference on Inventive Communication and Computational technologies, ICICCT 2024

作者： Deepa, S. Arunkumar, M.S. Kanimozhi, T. Eswaran, balamurugan Duraivelu, Vivek Sweatha, M. Department of Computer Technology-PG Kongu Engineering College Tamil Nadu Erode Perundurai638060 India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600062 India Department of Computer Science and Engineering Bannariamman Institute of Technology Sathyamangalam India School of Information Technology Texila American University Lilayi Zambia Department of Computer Science and Engineering B V Raju Institute of Technology Telangana Narsapur502313 India Kongu Engineering College Tamil Nadu Erode Perundurai638060 India

ISBN: (纸本)9789819777099

The exponential expansion of e-learning has significantly broadened educational opportunities globally, empowering students to engage in learning experiences from anywhere. However, the abundance of online courses poses a challenge in customization, often resulting in diminished learning outcomes as learners struggle to select suitable courses tailored to their unique requirements. Personalized learning addresses this by adapting activities to individual needs, fostering the development of capabilities and personality. The proposed system integrates Markov Decision Processes (MDPs) and Reinforcement Learning (RL), leveraging deep Q-learning for sequential recommendations and MDP adaptability to tailor learning paths. This combined approach holds promise for revolutionizing e-learning by delivering personalized, adaptive experiences. Learners benefit from tailored courses, activities, and learning paths, optimizing their educational journey. Continuous adaptation and improvement, facilitated by the core MDP framework and user feedback, further enhance effectiveness in e-learning environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Federated learning

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Prevention from gas devastation

Prevention from gas devastation

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2020 International Online Conference on engineering Response to COVID-19, IOCER-COVID-19 2020

作者： Datta, Sanghita Sah, Ankita Gautam, Sidharth Das, Upamita Maity, Saikat B. Tech Computer Science Engineering JIS University India

LPG leakages are the most common issues found in household and gas plants. This issue is life threatening not only for the humans in the vicinity but also for the surrounding areas. The idea behind our project is to save the lives of those innocents who can get affected due to the carelessness of certain people. So, as soon as the gas gets detected by the system, the concerned people around will receive messages as well as the alarm rings. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Gas plants

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An iot and Random Forest-DT-based Early Enhanced Forest Fire Detection in Large and Small Targets 3

An IoT and Random Forest-DT-Based Early Enhanced Forest Fire...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Prabakaran, Manjula Wadhwani, Omesh Pandiaraj, V. Zainab, Kaneez Nishant, Neerav Justin, Z. Andhra Pradesh India Shri Ramdeobaba College of Engineering and Management Department of Information Technology Nagpur India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Mechanical Engineering Chennai India B.N. College of Engineering and Technology Department of Computer Science & Engineering Lucknow India Babu Banarasi das University School of Engineering Department of CSE Lucknow India Thiruvalluvar India

ISBN: (纸本)9798350381931

The world is confronted with more complex and severe environmental concerns than in the past. Extreme weather events have become more common and destructive in the last several decades, endangering forests and urban forests around the globe. Massive forest fires are among the most catastrophic natural catastrophes that affect Earth's climate and the diversity of life on it. Thus, in order to minimize harmful impacts on ecosystems and people, it is vital to have well-planned and coordinated plans for early warning, prevention, and reaction. Image preprocessing, feature extraction, and training the model are the initial steps in the suggested methodology. Histogram matching and picture smoothing are part of the image preparation procedure. The gray histogram is a way to see how many pixels fall into each grayscale level, and image smoothing is a way to enhance a large region of a picture using image processing. The GLCM and LbP histograms are employed in the feature extraction process. Training RF-Multivariate ARS models requires feature extraction as a prerequisite. The system improved accuracy to 97.29 percent after applying the approach. © 2024 IEEE.

关键词： Feature extraction

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Detection of Customer Interested Garments in Surveillance Video using computer Vision

arXiv

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arXiv 2025年

作者： Ijjina, Earnest Paul Joshi, Aniruddha Srinivas Kanahasabai, Goutham Department of Computer Science and Engineering National Institute of Technology Telangana Warangal506004 India B.Tech. Department of Computer Science and Engineering National Institute of Technology Telangana Warangal506004 India

One of the basic requirements of humans is clothing and this approach aims to identify the garments selected by customer during shopping, from surveillance video. The existing approaches to detect garments were developed on western wear using datasets of western clothing. They do not address Indian garments due to the increased complexity. In this work, we propose a computer vision based framework to address this problem through video surveillance. The proposed framework uses the Mixture of Gaussians background subtraction algorithm to identify the foreground present in a video frame. The visual information present in this foreground is analysed using computer vision techniques such as image segmentation to detect the various garments, the customer is interested in. The framework was tested on a dataset, that comprises of CCTV videos from a garments store. When presented with raw surveillance footage, the proposed framework demonstrated its effectiveness in detecting the interest of customer in choosing their garments by achieving a high precision and recall. Copyright © 2025, The Authors. All rights reserved.

关键词： Sales

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A Real Time ML technique for the Manufacturing and Image Classification System

A Real Time ML Technique for the Manufacturing and Image Cla...

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Advances in Modern Age technologies for Health and engineering science (AMATHE), International Conference on

作者： Thoutireddy Shilpa Nagendar Yamsani bura Vijay Kumar Shaik Mabasha Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Medak Telangana School of Computer Science and Artificial Intelligence SR UNIVERSITY Warangal School of Computer Science and Artificial Intelligence SR University Warangal India Dept. of CSE (AIML & IoT) VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Manufacturing analysis at many facilities is of fundamental significance nowadays and is increasing in the present setting of big Data in Industry. The chemometrics, machine learning and statistics are the areas were projected to offer methods can successfully manage numerous industrial data features. The undertaking of picture based item characterization is investigated in this examination. This is a supervised learning issue when the info is a picture, and the result is an extraordinary name relegated to the picture from a limited arrangement of marks which relate to the item classes accessible. This is a major challenge for the industry and recent deep learning technologies have proven beneficial in enhancing the accuracy of the image classification and in offering cutting edge outcomes. In this work, we hence utilize the capacity of profound deep neural networks(DNNs) to gain highlights from photographs consequently and test their exhibition for the expectation of the pellet structure in a certifiable modern climate. Transfer learning is used to speed the training of DNN, and previously a network is established for single process was designed to forecast the pellet form. In addition, the benefit of adoption of DNN in comparison to current classifiers is also examined in other less sophisticated techniques, such as partially less square discrimination analysis (PLS-DA) and random forests (RF).

关键词： Radio frequency Training Deep learning Filters Transfer learning Artificial neural networks Feature extraction

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A Feline-Inspired Optimizer Enhanced Through Self-Improvement, Coupled With Machine Learning, For the Identification Of Lung Cancer In CT Scans 2

A Feline-Inspired Optimizer Enhanced Through Self-Improvemen...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Arunachalam, P. Geetha, S. Jose, Naduvathezhath Nessariose Vivekanandan, G. Shrutika Shree, Sowmya H.S. Department of Biomedical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamilnadu600062 India Department of Computer Science and Engineering V.S.B. Engineering College Karur India Architect Denken Solutions Department of CSE Sri Sairam Institute of Technology Chennai India Department of Computer Science University of southern California Department Bangalore Institute of Technology K R Road V V Pura Karnataka Bengaluru560004 India

ISBN: (纸本)9798350383867

The study introduces an innovative breakthrough in the analysis of medical images, particularly focusing on the timely identification and categorization of lung cancer using computed tomography (CT) scans. Employing the pioneering Cat Mouse Optimizer algorithm, which has undergone enhancements via self-improving mechanisms, we' ve crafted an intricate framework. This framework seamlessly integrates cutting-edge machine learning algorithms to streamline the process of diagnosing lung cancer from CT scans with unparalleled precision and efficiency. Conventional methods for diagnosing lung cancer often hinge on manual interpretation of CT images by radiologists, a process susceptible to time constraints and human error. Our methodology tackles these challenges by capitalizing on the capabilities of artificial intelligence and deep learning methodologies. Through extensive training on extensive datasets of annotated CT scans, our model becomes adept at identifying nuanced patterns and indicators of lung cancer, leading to robust classification outcomes. The hallmark attributes of our system encompass its flexibility and scalability, enabling continual enhancement and adaptation over time. The self-enhancing features of the Cat Mouse Optimizer algorithm empower the system to autonomously fine-tune its algorithms and parameters, informed by feedback from new data and user interactions. This guarantees that the system remains abreast of the latest advancements in medical imaging and lung cancer research. Moreover, our framework underscores the importance of interpretability and transparency, furnishing clinicians with invaluable insights into the decision-making process of the AI model. This cultivates trust and collaboration between human experts and AI systems, culminating in more precise diagnoses and enhanced patient outcomes. Through rigorous experimentation and validation across varied datasets, we showcase the effectiveness and dependability of our methodology in re

关键词： computerized tomography

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An Ensemble Model to Predict Network Traffic Prediction Model Using Rb Multicast Protocol 7

An Ensemble Model to Predict Network Traffic Prediction Mode...

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

作者： Deepa, S. Sathishkumar, P. Duraivelu, Vivek Suguna, R. Arunkumar, M.S. Myleshan, M.b. Kongu Engineering College Perundurai Department of Computer Technology - PG Tamilnadu Erode638060 India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Narsapur502313 India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai-62 India Kongu Engineering College Perundurai Tamilnadu Erode638 060 India

ISBN: (纸本)9798350340600

A Vehicular Ad Hoc Network (VANET) is an effective method for addressing group-oriented services. It is possible to support such services through multicast routing. As a result, packet delivery ratios must be improved, delays must be reduced, and overheads must be reduced. Consequently, multicast routing protocols for VANETs must be stable and reliable. A Receiver based Multicast protocol is proposed in our paper that determines how multicast traffic should be forwarded. In the packet header, Rb Multicast stores a list of destinations. Multicast members to whom this packet is addressed are listed in this destination list. Consequently, there is no need to store tree state at intermediate nodes and no multicast tree is required. According to this study, the objective of the study was to predict network traffic by using the V2R dataset, and to use the receiving packets sent by vehicles to the RSUs as a measure of network activity. Our analysis of network traffic flow resulted in the prediction of network flow. Using ensemble classification metrics, we evaluated the data. Simulated results indicate that the proposed model has a superior performance in terms of execution time and prediction accuracy compared to other algorithms. © 2023 IEEE.

关键词： Vehicular ad hoc networks

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Analysis and Classification of Heart Disease and COVID 19 Using Electrocardiogram and Deep Learning

Analysis and Classification of Heart Disease and COVID 19 Us...

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Advances in Modern Age technologies for Health and engineering science (AMATHE), International Conference on

作者： Thoutireddy Shilpa bura Vijay Kumar Nagendar Yamsani A. Harshavardhan Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Medak Telangana School of Computer Science and Artificial Intelligence SR University Warangal India Dept. of CSE (AIML&IoT) VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

The health industry has access to a significant amount of data. All this data is frequently overlooked and underutilized. Many diseases can be diagnosed, prevented, or cured by employing such a large amount of data. Diseases such as cardiovascular diseases, cancer, tumors, Alzheimer's disease, and heart diseases pose a significant threat to humanity, claiming an estimated 17.9 million lives each year. Since its identification in December 2019, COVID-19 has become a pandemic. COVID-19-related deaths are continuing to rise, and early detection is becoming increasingly important. Utilizing ECG copies and deep learning algorithms, an ideal way for automatically diagnosing Corona virus and irregular heartbeats is proposed. ECGs are segmented. Each segment of the ECG is an attribute, and the photos constitute a dataset. Following that, the resulting scanned copies of ECG are given as input into a Convolutional Neural Network (CNN). Using deep learning system and ECG data, it will be possible to identify cardiovascular changes automatically. This along with the appearance of cardiovascular changes due to abnormalities in the heart but also recognizes impact made by COVID-19. This also suggests that the DL model could be used to detect COVID-19 and heart diseases. The proposed study, we hope, might provide a significant decision-making framework for healthcare practitioners.

关键词： COVID-19 Heart Deep learning Industries Pandemics Medical services Electrocardiography

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Development of Secure iot Ecosystems for Healthcare

Development of Secure IoT Ecosystems for Healthcare

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International Conference on Intelligent Communication and Computational techniques (ICCT)

作者： Pranav Shirgur Sandeep Chaurasia B. Tech Computer Science and Engineering Manipal University Jaipur Jaipur Rajasthan Department of Computer Science and Engineering Manipal University Jaipur Jaipur Rajasthan

The Internet of Things (iot) is a rapidly developing field of technology which entails a network of smart devices connected to each other and the internet. The TATCRb2industry is anticipated to increase by 18% to 14.4 billion active connections in 2022. There will likely be about 27 billion linked iot devices by 2025 as supply limitations, brought about by the current global semiconductor and chip shortage - loosen and demand quickens. iot has quickly penetrated the healthcare industry, this paper defines a framework that enables the development of secure and scalable iot healthcare platforms/applications. These platforms will also allow for secure cloud storage and analysis of patient data, helping professionals recognize latent parameters such as patient behavioral patterns that contribute to an ailment. This ultimately will enable the study of social and economic impact of a particular disease. This will greatly cull the survivorship bias in the health care industry - especially in testing times like a pandemic.

关键词： Industries Economics Cloud computing Ecosystems Medical services behavioral sciences Internet of Things

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Solar-Power Enabled Data-Driven iot Smart Garbage Pail for Individuals and Disabled

Solar-Power Enabled Data-Driven IoT Smart Garbage Pail for I...

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science, technology, engineering and Management (ICSTEM), International Conference on

作者： S.Appavu alias balamurugan Preethi b Mohamed Ashik Sheriff R Vigneshwar b R Sumaiya V Computer Science and Engineering Periyar Maniammai Institute of Science & Technology (Deemed to be University) Thanjavur Tamilnadu India Final Year B Tech Computer Science and Engineering Periyar Maniammai Institute of Science & Technology (Deemed to be University) Thanjavur Tamilnadu India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

According to the increasing population, our disposal of waste has been increased. The main aim is to provide help for the physically challenged people in the disposal of waste. In this motive we have created one smart garbage pail based on loT. To provide convenience in this task, we have developed the bin to move on its own path to collect the dust. Main function of the smart bin is to deliver the trash bin close to a user who are physically challenged. by adding the ultrasonic sensor, it avoids the obstacles and deviates its path. In addition to that, it can automatically get opened when the user approaches the dustbin. Another purpose of the garbage pail is voice recognition accessed. by accessing the voice, the dustbin will move accordingly. Arduino UNO is act as a heart component of our smart garbage pail. A smart garbage pail comes the user and collects the dust, we can access it through voice reignition. It can also avoid obstacles.

关键词： Heart Sociology Speech recognition Robot sensing systems Acoustics Waste disposal Task analysis

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