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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Agrawal, Navin Kumar Kumar, Sandeep Bisht, Girish Singh Srivastav, Akanksha Bansla, Vandna Jain, Arpit Gla University Department Of Computer Engineering And Applications U.P. Mathura India Tula's Institute Department Of Computer Science And Engineering Uttarakhand Dehradun India Shivalik Engineering College Department Of Computer Science And Engineering Uttarakhand Dehradun India Koneru Lakshmaiah Education Foundation Department Of Computer Science And Engineering AP Vaddeswaram India

ISBN: (纸本)9798350366846

A mobile ad hoc network (MANET) uses mobile devices and wireless networking to stream video. Several methods have been developed to address the challenges associated with video streaming over MANETs. This paper designed a new protocol for Mobile Video of Demand (MVOD) system architecture over the MANET environment. The proposed MVOD Model is used to improve the QoS parameters of the video system in wireless ad-hoc networks. The MVOD protocol uses client caching algorithms and a staggered broadcasting method, and it compares its performance with that of an existing traditional protocol for three-tier architecture. When compared to QoS parameters such as service delay and bandwidth utilization, the proposed MVOD model performs better than the traditional approach when compared with various scenarios. Based on the importance and complexity of evaluating the techniques, we conclude that comparability problems exist in research. © 2024 IEEE.

关键词： Quality of service

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TCOA: Triple-Check Offloading Algorithm for Roadside Units and Vehicular Microclouds in 5G Networks and Beyond

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IEEE Access 2023年 11卷 84985-85001页

作者： Qiu, Bo-Jun Hsieh, Cheng-Ying Chen, Jyh-Cheng Dressler, Falko National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan Tu Berlin School of Electrical Engineering and Computer Science Berlin10623 Germany

Next-generation intelligent transportation systems aim to achieve many cooperative perception and cooperative driving functions that require considerable computational resources. Offloading such tasks via mobile edge computing is considered part of the solution;this approach is currently being studied within the scope of 5G networks and beyond. In the automotive context, such edge systems could be roadside units (RSUs), which can easily be overloaded at peak times. Vehicular microcloud approaches have been proposed to overcome such problems by sharing the computational resources of nearby cars. In this study, we propose an offloading system architecture to enable such offloading in a vehicular microcloud interconnected by a 5G core network. We model the system as a queueing model to derive closed-form solutions for selected performance metrics. Based on our insights, we propose the triple-check offloading algorithm (TCOA) to obtain both the best offloading ratio of the vehicular microcloud to the entire offloading system and the optimal maximum number of the remaining vehicle instances in the vehicular microcloud. Our simulation results show that the proposed TCOA achieves better system performance than four other offloading schemes in terms of cost, response time, service rate, and cost response-time production service rate division (CRPSD). © 2013 IEEE.

关键词： 5G mobile communication systems

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Analyze and Detect Lung Disorders Using Machine Learning Approaches—A Systematic Review 2nd

Analyze and Detect Lung Disorders Using Machine Learning App...

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2nd International Conference on Computational Intelligence in Machine Learning, ICCIML 2022

作者： Shwetha, Sirikonda Ramana, N. Department of Computer Science and Engineering Kakatiya University Warangal India

ISBN: (纸本)9789819979530

The lung is a decisive organ for the human body’s functioning. If there is any problem with the functioning of the lungs, it is called a lung disorder. To reduce the risk factors and maintain healthy lungs, prevent this as early as possible. From 2019 onwards, due to COVID-19, so many people have been infected with lung disorders because the coronavirus affects the lungs. Lung disorders spread throughout the respiratory system gradually, so the initial prevention stage will help to achieve the best results. The increasing rate of lung disorders causes a growth in the death rate and requires pricey medicinal treatment. Because of the significance of these issues, researchers have implemented a variety of methods to detect lung disorders. This paper considered a detailed systematic review of AI methods for the early discovery of lung disorders. The key advantage of AI lies in its ability to autonomously learn, extract, and interpret features from diverse datasets like images, text, and videos, eliminating the need for conventional manual coding or rule-based approaches. The aim of the article is to provide a survey on deep learning–based techniques and available datasets to detect lung disorders, and we proposed a methodology that can detect the disease of lung as untimely as possible, which will assist the doctors to save the patient’s life. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Respiratory system

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A Review of Anomaly Detection using Machine Learning Techniques 1

A Review of Anomaly Detection using Machine Learning Techniq...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Kesharwani, Abhishek Shukla, Prashant United University Department of Computer Science and Engineering Prayagraj India

ISBN: (纸本)9798350367720

Anomaly detection is different in different areas or context and the definition of anomaly also changes from problem to problem. Classical techniques like classification and clustering are not effective when it comes to identify anomaly in graph structured data. This paper examines various anomaly detection methods, focusing on classification, clustering, time series data, and problems related to graph-based structures. On the basis of existing studies, we have emphasized that anomaly detection algorithms applied on graphs using Graph Neural network are much accurate. © 2024 IEEE.

关键词： Adversarial machine learning

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Research on the design of third-person 3D action role-playing games

Research on the design of third-person 3D action role-playin...

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2024 International Conference on Information Technology, Data science, and Optimization, I-DO 2024

作者： Xu, Hao-Xiang Chang, Kai-Ni Chiu, Chien-Sheng Wu, Shih-Jung Department of Computer Science and Information Engineering Tamkang University Taiwan

ISBN: (纸本)9798400709180

Affected by COVID-19 in recent years, many industries have suffered a lot in the near future. At the same time, computer hardware technology is still improving. The game industry has not decreased but increased under this development trend. Among the many game options, 3D action role-playing games are one of the most popular types of games today, and there are many well-known and excellent works on the market. For consumers, games are not just a tool to kill time, the entertainment they bring can also relieve people's physical and mental pressure. From the perspective of developers, we might as well study these games in depth. What sets these classic 3D action role-playing games special, and how to let people have a good gaming experience. Based on the experience of actually playing several games of the same type, I will share the structure and knowledge of studying the game. The research including implement of 3D action role-playing games, the design of program logic, the writing of plot of a play, etc., using Blender for 3D modeling, Unity3D games Developing the engine as an environment, in person making a 3D action game, and exploring the knowledge gained in the research. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： 3D modeling

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Machine Learning in Medical Diagnosis of Cancer 9

Machine Learning in Medical Diagnosis of Cancer

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Raj, Rishi Jyoti Singh, Antara Kumar, Karan Chandigarh University Department of Computer Science And Engineering Mohali India

ISBN: (纸本)9798350394474

This study compares machine learning (ML)-based medical diagnosis techniques with conventional medical diagnosis techniques. The first section of the study gives background information on conventional medical diagnosis techniques and the difficulties - such as poor accuracy, inefficiency, and high cost - that they encounter. The use of ML technology for medical diagnostics is then explained, along with some of its possible advantages, including improved accessibility, efficiency, and accuracy. The process for creating and implementing an ML-based medical diagnosis system is then described in the study, along with data collection and analysis methods. The analysis's findings demonstrate that ML-based medical diagnostic systems are superior to conventional techniques in a number of ways, including increased accuracy, quicker diagnosis times, and reduced *** study also compares the system's performance with conventional medical diagnosis techniques and assesses its effectiveness based on a number of criteria. According to the study, ML-based medical diagnosis systems provide a number of important benefits over conventional techniques, including improved early-stage cancer detection and more precise prognosis prediction. In conclusion, the research findings and their consequences are discussed, future directions for research are suggested, and the study's limitations are emphasized. Overall, this work offers insightful information on the possibilities of machine learning for medical diagnostics and establishes a foundation for future investigations. © 2024 IEEE.

关键词： Machine learning

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Personalized Blood Glucose Forecasting Models Using Machine Learning

Personalized Blood Glucose Forecasting Models Using Machine ...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Jyoti Saloni, Saumya Pathak, Mayank Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798350361537

Accurate blood glucose forecasting can enable improved decision support and glycaemic control for diabetes patients. However, complex patient-specific behaviours make reliable personalized predictions challenging. This work develops data-driven machine learning models to provide personalized blood glucose forecasts. Long short-term memory (LSTM) neural networks are adapted to incorporate patient context through modular architecture layers. The models are trained on continuous glucose monitoring (CGM) data from diabetes patients. Customization techniques such as transfer learning and similarity matching are introduced to tailor the forecasting models to individual patient characteristics and subgroups. Experiments demonstrate significantly improved predictive performance over population-based models, with over 85% accuracy on 30-minute ahead forecasts. The customized LSTM framework has the potential to provide more effective blood glucose predictions and strengthen personalized diabetes management. © 2024 IEEE.

关键词： Prediction models

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Maximizing Efficiency in Landfill Operations Using IoT and Random Forest Algorithms for Green Revolution

Maximizing Efficiency in Landfill Operations Using IoT and R...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Solainayagi, P. Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

This paper proposes a new approach to waste management that is more sustainable and eco-friendly. Important landfill factors including temperature, humidity, gas emissions, and waste accumulation may be monitored in real-time with this system, which combines the Random Forest algorithm with Internet of Things (IoT) technology. The IoT sensor has the potential to provide real-time data interchange and monitoring, while the Random Forest algorithm a machine learning approach might provide effective pattern identification and decision-making. The use of Random Forest in predictive modeling method for the creation of early warning systems that can combat potential issues such gas leaks, overfilling, and operational inefficiencies before they ever happen. Future possibilities and consequences of this technology convergence may also be covered in the paper, with an emphasis on the important factors to consider for the effective and scalable deployment of waste management systems. Privacy and security of data, development of infrastructure, cost-effectiveness, and involvement of stakeholders are all factors that may be taken into account. A performance indicator would be a prediction accuracy of more than 95% on the validation and test datasets. Wind turbines and solar panels, which produce power from waste gas, may be more easily integrated into landfill operations using this technology, significantly reducing the environmental impact of landfill operations. © 2024 IEEE.

关键词： Internet of things

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Blockchain Based Polling 11

Blockchain Based Polling

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Chauhan, Neeru Jain, Kavya Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798350350357

Blockchain-based polling is a new and innovative way of conducting polls that uses the security and transparency of blockchain technology. This method provides an immutable record of votes that is resistant to manipulation, fraud and hacking. In addition, it enables real- time results and increased availability, making the process more efficient and convenient for both administrators and poll participants. Voting on blockchain technology is a novel approach to safe, decentralized public opinion gathering. This paper discusses the differences between conventional method of voting which includes Ballot paper and Electronic Voting Machines (EVM). It also highlights the advantages of using Blockchain system for voting in contrast to EVM. A Blockchain based e-voting system is also proposed. © 2024 IEEE.

关键词： Blockchain

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Enhanced Text Summarization through Hybrid Integration of RoBERTa, T5, and Pegasus Models

Enhanced Text Summarization through Hybrid Integration of Ro...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Myla, Sameera Datta Saini, Ravinder Kapoor, Nitika Chandigarh University Department Of Computer Science And Engineering Mohali India

ISBN: (纸本)9798350371567

The domain of Natural Language Processing (NLP) confronts a pivotal challenge in text summarization, with the goal of condensing extensive texts into concise, informative summaries. This research presents a revolutionary approach to text summarizing that combines the best features of top NLP models, RoBERTa, T5, and Pegasus, into a unified framework. This method employs RoBERTa for precise sentence relevance determination, and T5 and Pegasus for crafting coherent summaries, thereby generating outputs that are both compact and closely aligned with the semantic content of the original texts. Our assessment across various datasets, utilizing established metrics such as METEOR, ROUGE, BLEU scores, readability metrics, and BERT score, reveals the superiority of our proposed system over conventional summarization methods, showcasing its efficacy in producing high-quality summaries. Furthermore, our system achieves an ideal balance in compression accuracy, adeptly preserving essential information while ensuring the brevity and readability of summaries. This study not only establishes a new standard in text summarization but also heralds future progress by demonstrating the advantages of amalgamating multiple NLP models. The encouraging outcomes highlight the promise of our neural network-based strategy in pushing the boundaries of text summarization, thereby opening new pathways for exploration in this critical segment of NLP. © 2024 IEEE.

关键词： Semantics

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