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1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Kumar, Gautam Dut, Yagya Kumar, Rajneesh Rajakumar, P. Indervati Galgotias University School of Computing Science & Engineering UP India

ISBN: (纸本)9798350356816

The internet is regarded as the most vital international data media. It allows consumers to browse and write information about the world around them. The development of the web has been continuously increasing over the years. In the past couple of years, it has been able to develop various versions. In 1991, the concept of the exemplary net was confirmed. This means that web1.0 is tied to statistics and is used to gather information. The rise of net 2.0 allowed individuals to create and distribute content. Web 3.0 is regarded as the most significant update to the internet. The goal of the semantic internet is to create and think about new information in a way that is most appropriate for humans. The rise of artificial intelligence (AI) is expected to make people more interested in facts. The goal of the Metaverse is to consolidate various virtual spaces. Through the utilization of elevated reality, people can indulge in various diversions and undertakings. The rise of the Internet has created various new obstacles and opportunities for education and online learning. The previous generation of the Internet was known as Web 1.0, which was initially designed as a Read-Only medium. Web 3.0, which is currently in development, is a more technologically advanced version of the web that enables people to perform various tasks and read and write. The development of Web 3.0 has led to the creation of new tools and technologies that can help support online learning and education. This essay aims to provide a brief overview of the various aspects of Web 3.0. The goal of Web 3.0 is to provide a more personalized and comprehensive education and learning experience. It also supports knowledge building through the use of the Semantic Web. © 2024 IEEE.

关键词： Traffic congestion

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House Price Prediction Using Machine Learning 1

House Price Prediction Using Machine Learning

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1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Mishra, Amandeep Jason Singh, Yogesh Rajakumar, P. Partheeban, N. Indervati Galgotias University School of Computing Science & Engineering UP India

ISBN: (纸本)9798350356816

Machine Learning (ML) has become increasingly prominent in various domains, including everyday conversations, product recommendations, and even the medical field. It has also played a vital role in enhancing customer services and ensuring safer transportation systems. Given ML's widespread impact, we aim to incorporate ML techniques into our project. The housing market, known for its competitiveness and constant fluctuations, poses challenges for individuals seeking to purchase a new home within their budget. The existing system lacks accurate predictions of future market trends, resulting in price increases. Therefore, our project's primary objective is to predict house prices with precision, eliminating any potential losses. To achieve this, we consider multiple factors that influence house prices and strive to provide customers with successful predictions that align with their budgets and preferences. Our proposed housing price prediction model employs AI algorithms such as Linear, Decision Tree, K-Means, and Random Forest Regression. The goal of this strategy is to eliminate the need for individuals to rely on intermediaries when making decisions on investments in real estate. The research findings highlight that Random Forest Regression offers the highest level of accuracy. © 2024 IEEE.

关键词： Houses

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Multi-response Optimization of Process Parameters in Stretch Blow Molding of PET Plastic Bottles

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Journal of The Institution of Engineers (India): Series D 2024年 1-15页

作者： Merga, Henok Hailu Sinha, Devendra Kumar Getachew, Aklilu Workneh, Melese Rathee, Devender Singh Department of Mechanical Engineering School of Mechanical Chemical and Materials Engineering Center of Excellence Advanced Manufacturing Engineering Adama Science and Technology University Adama Ethiopia Department of Electronics Engineering School of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia

Plastic waste in Polyethylene Terephthalate (PET) bottles is creating new challenges, which in today’s scenario is a major concern in every industry. The defect in PET bottles mainly occurs due to improper selection of levels of process parameters during the production process. Therefore, optimizations of process parameters are very important to minimize the defects produced due to improper stretch blow molding process parameter selections. The study is carried out on 2 L bottle since they have the highest demand and the highest defect rate in the PET bottling sector. The PET bottles have been developed from the preform at various blowing temperature, blowing speed and blowing angle on commercially available SBM setup. The design and plan of experiments have been done according to L9 orthogonal array of Taguchi method. The optimization of process parameters has been done through the hybrid Taguchi grey relational analysis optimization technique on four factors namely: blowing temperature, first blow angle, second blow angle and blowing speed. Each value of parameter was taken at three levels in combination. The testing of all samples such as tensile test, wall thickness measurement and hoop stretch ratio measurement have been carried out as per ASTM standards. The morphology of the samples has been carried out through scanning electron microscope. A maximum tensile strength of 65 MPa, minimum hoop stretch ratio of 5.45 and a maximum wall thickness 0.24 mm was found at varying process parameter combinations. It has been observed that among the process parameters: blowing temperature was the most significant factor for minimizing defects while second blow angle was the least significant parameter. It was found that optimized values of the process parameters yielded 15% reduction in defects on PET bottles. The present study is expected to contribute significantly for the industries working in this field for carrying out production at optimum levels of process parameter

关键词： Plastic bottles

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Intelligent patient monitoring through hybrid consensus algorithm based blockchain technology

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Multimedia Tools and Applications 2025年第18期84卷 19579-19600页

作者： Priyadharshini, A. Nithiya, S. Archana, H.A. Jaganathan, Subash Chandra Bose School of Engineering and Technology Bangaluru India Department of Computing Technology SRM Institute of Science and Technology Kattankulathur603203 India Department of Agronomy SRM College of Agricultural Science Chengalpattu603201 India School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India

This research aims to integrate IoT with blockchain technology to securely manage and monitor sensitive patient health data in critical care environments, thereby improving the reliability and efficiency of patient monitoring systems. A hybrid blockchain architecture is employed to store ICU patient data securely, linking each patient's information with their respective doctors' details. This system operates without third-party intermediaries, ensuring trusted communication channels between patients and doctors. The blockchain ensures authorized access and secure transmission of medical histories and treatment plans, even allowing for remote care by doctors outside hospital premises. The integration of blockchain technology with IoT devices facilitates rapid and secure data transmission, enhancing the speed and accuracy of critical care delivery. The decentralized nature of blockchain ensures data integrity and prevents unauthorized access, while the hybrid approach allows for efficient and trusted collaboration among healthcare professionals. By harnessing the power of hybrid blockchain technology, this system not only safeguards sensitive medical information but also improves the overall quality of critical care through timely and informed medical intervention. The proposed solution holds the promise of reducing patient mortality rates by ensuring secure, efficient, and collaborative healthcare delivery. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Enhancing Connectivity of AODV in Urban Scenarios for VANETs: A QoS-Aware Approach 4

Enhancing Connectivity of AODV in Urban Scenarios for VANETs...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Janet Kumari, J Jesy Thangam, S. Ghosh, Archishman Kolhe, Dhruv Vijay Ghosh, Soumish Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331523923

As cities expand, vehicles and congestion become more complex. Efficient vehicle-to-vehicle contact networks are needed for road safety and efficient traffic flow. Thus, Vehicular Ad Hoc Networks are needed to overcome all the issues. The Adhoc On-Demand Distance Vector (AODV) routing protocol is used in VANETs. However, this protocol has many challenges in urban settings due to high vehicle density, rapid topology changes, and non-line-of-sight conditions. This work improves AODV by integrating Quality of Service (QoS) metrics. The prioritizing factors such as overhead and end-to-end delay are used to create a more efficient protocol for urban traffic. The SUMO simulator is used for realistic traffic patterns and NS-2 for network analysis. An evaluation of the protocol against the classical AODV is done and important metrics for real-time communication are focused. The enhanced AODV showed marked improvements. It achieved lower overhead in route finding, an increase in the number of packets sent, and lower end-to-end delay. © 2025 IEEE.

关键词： Vehicular ad hoc networks

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Adapted Runge-Kutta Methods for Nonlinear First-Order Delay BVPs with Time-variable Delay

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Acta Mathematicae Applicatae Sinica 2025年第2期41卷 400-413页

作者： Cheng-jian ZHANG Yang WANG Hao HAN School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan 430074China

This paper deals with numerical solutions for nonlinear first-order boundary value problems(BVPs) with time-variable delay. For solving this kind of delay BVPs, by combining Runge-Kutta methods with Lagrange interpolation, a class of adapted Runge-Kutta(ARK) methods are developed. Under the suitable conditions, it is proved that ARK methods are convergent of order min{p, μ+ν +1}, where p is the consistency order of ARK methods and μ, ν are two given parameters in Lagrange interpolation. Moreover, a global stability criterion is derived for ARK methods. With some numerical experiments, the computational accuracy and global stability of ARK methods are further testified.

关键词： delay boundary value problems adapted Runge-Kutta method Lagrange interpolation error analysis global stability

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Effortless learning: leveraging summarization and tabulation for automated note making

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Multimedia Tools and Applications 2024年 1-26页

作者： Arivarasi, A. Srinivasan, R. Rajeswari, D. Govindasamy, Alagiri School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India Future Connect Technologies Pvt. Ltd Tamil Nadu Kanchipuram600034 India

In this fast-paced Digital Age, Natural Language Processing (NLP) can prove beneficial in consuming quality information efficiently. With the ever-growing number of learning resources, it is becoming onerous for students to choose the best-suited learning resource. Furthermore, many students find it difficult to understand and retain highly factual information. To overcome these issues, this paper proposes a novel Automated TExt Summarization of scholastic text in Table format (Auto-TEST) to provide information at a glance by mimicking the student’s style of note-making. The Auto-TEST framework is achieved through two steps which is Summarization and Tabulation. Initially, the Extractive Single Document Text Summarization (ESDTS) method is adopted to identify the salient information from the non-fictional (scholastic) text which is extracted and grouped together to form a concise summary. A Named Entity Recognition (NER) method is implemented to extract noun chunks and then arrange them in a tabulation format that aids in memorizing essential facts and figures by enabling effortless retrieval. The performance of the Auto-TEST framework is gauged by using scholastic domain-specific dataset using ROUGE as an evaluation metric. The extracted entities gives an average Recall of 71.40%, an average Precision score of 75.80% and an average F-score of 73.54% for the dataset respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Students

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Identification and Classification of Medicinal Plants using Machine Learning Algorithms 3

Identification and Classification of Medicinal Plants using ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Bhargavi, G. Jayaraman, Venkatesh Jabez, J. Deepa, R. Surendran, R. Srm Institute of Science and Technology Department of Computing Technologies Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Chennai600069 India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Tamilnadu Chennai603203 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509675

The increaing significance of plant life and botanical expertise extends beyond mere visual appreciation. With the growing interest in sustainable living and alternative remedies, there is a pressing demand for easily accessible and credible information on plant categorization, particularly focusing on medicinal plants. This innovative project synergizes machine learning capabilities, botanical proficiency, and user-friendly technology to give users the insights required to harness the benefits of medicinal plants responsibly and sustainably. The platform facilitates precise categorization and a comprehensive understanding of the therapeutic attributes of plants. © 2025 IEEE.

关键词： Medicinal chemistry

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A Derivative-Free Optimization Algorithm Combining Line-Search and Trust-Region Techniques

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Chinese Annals of Mathematics,Series B 2023年第5期44卷 719-734页

作者： Pengcheng XIE Ya-xiang YUAN State Key Laboratory of Scientific/Engineering Computing Institute of Computational Mathematicsand Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesUniversity of Chinese Academy of SciencesBeijing 100190China

The speeding-up and slowing-down(SUSD)direction is a novel direction,which is proved to converge to the gradient descent direction under some *** authors propose the derivative-free optimization algorithm SUSD-TR,which combines the SUSD direction based on the covariance matrix of interpolation points and the solution of the trust-region subproblem of the interpolation model function at the current iteration *** analyze the optimization dynamics and convergence of the algorithm *** of the trial step and structure step are *** results show their algorithm’s efficiency,and the comparison indicates that SUSD-TR greatly improves the method’s performance based on the method that only goes along the SUSD *** algorithm is competitive with state-of-the-art mathematical derivative-free optimization algorithms.

关键词： Nonlinear optimization Derivative-Free Quadratic model Line-Search Trust-Region

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Image Caption Generation with Python: A Deep Learning-Based Approach 5

Image Caption Generation with Python: A Deep Learning-Based ...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Krishna, Siram Chaitanya Reddy, Painti Nagi Kirubanantham, P. School of Computing SRM Institute of Science and Technology Kattankulathur Department of Computing Technologies Chennai India School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331518097

Today, the day-to-day generation of such a large amount of content on social media and other websites makes it impossible to maintain each image manually, so the need for automatically identifying sensitive images and filtering them out becomes inevitable. This project proposes a sensitive image classification application using automatic captions in identifying and filtering inappropriate content. Thus, the information extracted from images by ResNet50 in the caption generation process is allowed to integrate such features with a standard model through Senti-Long Short-Term Memory networks (SLSTM) for better captioning. Deep learning models bring about drastically improved quality of captions along with contextual understanding. The captions are tested rigorously with training loss metrics, BLEU scores, and sentiment analysis through the VADER Sentiment Analyzer. This system efficiently categorizes images involving violence, negativity, or dangerous situations into the class of negative. A Flask-based web application was developed for implementing a scalable and user-friendly interface, ensuring efficient content moderation. This project has illustrated the possibility of integrating deep learning techniques with natural language processing to yield beneficial content moderation, further ensuring safe and controlled online environments. © 2024 IEEE.

关键词： Long short-term memory

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