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National Conference on Technological Advancements in Waste Management: Challenges and Opportunities, TAWMCO 2023

作者： Pandey, Srijan Singh, Sudheer Kumar Singh, Ranjana Scholar Department of Economics National P. G. College UP Lucknow India Associate Professor Department of Computer Science and Engineering Galgotias University UP Greater Noida India Assistant Professor Department of Economics National P. G. College UP Lucknow India

ISBN: (纸本)9789819760237

Plastic pollution has become a global environmental crisis, with detrimental impacts on ecosystems, economy, and human health. To address this challenge, effective plastic waste management strategies are essential. This research paper focuses on the issue of plastic waste and the management strategies to be adopted in India. The paper emphasizes the necessity of innovative waste management techniques to tackle this problem on a larger scale. Despite notable efforts to tackle the problem in India, an alarming plastic waste approximately 3.8 million tons remains uncollected or improperly disposed of each year, which contributes to pollution. Drawing on existing literature and case studies, the paper examines the environmental, economic, and social dimensions of plastic waste management. The findings underscore the importance of holistic and collaborative efforts involving government policies, industry initiatives, public awareness campaigns, and technological innovations to achieve a circular economy and mitigate the adverse effects of plastic pollution. Key strategies include the implementation of extended producer responsibility (EPR) laws, the promotion of circular economy principles, investment in recycling infrastructure, public awareness campaigns, and international cooperation. The paper offers a set of recommendations that are based on solid evidence and propose a more sustainable and environmentally friendly approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Circular economy

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Comparative Evaluation of Deep Learning Models in Alzheimer’s Disease Diagnosis

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Procedia computer science 2025年 258卷 2352-2361页

作者： Leena Arya Yogesh Kumar Sharma Smitha Nayak Janaki Sivakumar Assistant Professor Computing Department Muscat College Oman Associate Professor Programme leader Computer security and forensics Department of Computer Science and Creative Technologies Global College of Engineering and Technology Muscat Oman

Alzheimer’s Disease (AD) is an ever-evolving neurodegenerative confusion that influences the neurons of the mind in a manner that hinders the patients’ ability to solve problems and remember issues and that requires timely and proper identification to enable appropriate management and treatment. Based on MRI medical images, deep learning models have become one of the most effective approaches for analyzing the signs of the early stages of AD. This research, therefore, carries out a comparative analysis of five deep learning models such as Convolutional Neural Networks (CNNs) like AlexNet, VGGNet, and ResNet, as well as Long Short-Term Memory (LSTM) networks and 3D Convolutional Neural Networks (3D-CNNs) in the finding of Alzheimer’s Sickness from medical imaging data. Each model’s arrangement parameters, for example, layer configurations, learning rate, batch size, optimizer, number of epochs, loss function, applying techniques for regularization, activation functions, and the input dimensions, using pre-trained weights, were carefully initialized to yield a proper comparison between the models. Diagnostic performances were evaluated utilizing precision, responsiveness(recall), specificity, and region under the receiver operating characteristic curve (AUC-ROC). ResNet outperformed other profound learning models in diagnosing Alzheimer’s Disease from MRI scans with a precision of 0.91 and AUC-ROC of 0.95. At the same time, VGGNet, AlexNet, LSTM, and 3D-CNNs demonstrated varying strengths and limitations in balancing accuracy, computational efficiency, and feature extraction.

关键词： Artificial Intelligence (AI) Cognitive Impairment Data Augmentation Diagnosis Accuracy Medical Imaging

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The Impact of the Blockchain on Academic Certificate Verification System-Review

EAI Endorsed Transactions on Energy Web

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EAI Endorsed Transactions on Energy Web 2021年第36期8卷 1-8页

作者： Kumutha, K. Jayalakshmi, S. Research Scholar VISTAS Assistant Professor Tagore College of Arts and Science Chennai India VISTAS Department of Computer Applications Chennai India

Blockchain innovation (BT) guarantees benefits in believe capacity, collaboration, organization, identifying proof, validity, and transparency. These solid suggestions for the long run of how to verify the authenticity of academic certificates because a paper-based certification is fallible to manipulation and susceptible to fraud. This literature review started by collecting a test of basically peer-reviewed sources, as well as an enlightening outline of articles from different other channels to analyse about approximately later approaches, techniques and later patterns on instruction which is being utilized in blockchain innovation on document verification from different sector such as banking, medical records and education system etc. This paper proposes a digital certificate verification blockchain based system employing anowner authentication scheme and time, space of the students are stored as blocks using blockchain technology. A distributed public record with tamper proof and immutable that preserves the state of the document, which creates security in the digital asset. This clearly stated that this technology is required to keep digital assets in secure and any one can access without loss of data and maintained it with minimum cost. © 2021 Kumutha.K et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

关键词： Authentication

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A World of Contradictions

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AMERICAN HISTORICAL REVIEW 2023年第2期128卷 703-718页

作者： [Anonymous] Hanna Holborn Gray Professor of History and Roman Family Director of the Neubauer Collegium University of Chicago US University of Vienna Austria Thomas Siebel Distinguished Professor in the History of Science Massachusetts Inst. of Technology US Clinical Assistant Professor Sam Houston State University US Assistant Professor of History and of Social Studies Harvard University US Associate Professor in the History Department Columbia University US Assistant Professor of History Dartmouth College US Assistant Professor of History Brandeis University US Research Associate and Lecturer University of Erfurt Germany

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Performance analysis of machine learning algorithms on automated sleep staging feature sets

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CAAI Transactions on Intelligence Technology 2021年第2期6卷 155-174页

作者： Santosh Satapathy D Loganathan Hari Kishan Kondaveeti RamaKrushna Rath Puducherry Research Scholar of Computer Science and EngineeringPondicherry Engineering CollegePuducherryIndia Professor of Computer Science and Engineering Pondicherry Engineering CollegePuducherryPuducherryIndia Assistant Professor of Computer Science and Engineering VIT UniversityAmaravatiAndhra PradeshIndia Research Scholar of Computer Science and Engineering Anna UniversityChennaiIndia

With the speeding up of social activities,rapid changes in lifestyles,and an increase in the pressure in professional fields,people are suffering from several types of sleep-related *** is a very tedious task for clinicians to monitor the entire sleep durations of the subjects and analyse the sleep staging in traditional and manual laboratory environmental *** the purpose of accurate diagnosis of different sleep disorders,we have considered the automated analysis of sleep epochs,which were collected from the subjects during sleep *** complete process of an automated approach of sleep stages5 classification is majorly executed through four steps:pre-processing the raw signals,feature extraction,feature selection,and *** this study,we have extracted 12 statistical properties from input *** proposed models are tested in three different combinations of features *** the first experiment,the feature set contained all the 12 *** second and third experiments were conducted with the nine and five best *** patient records come from the ISRUC-Sleep *** highest classification accuracy was achieved for sleep staging through combinations with the five feature *** the categories of the subjects,the reported accuracy results were found to exceed above 90%.As per the outcome from the proposed system the random forest classification techniques achieved best accuracy incomparable to that of the other two classifiers.

关键词： sleep staging diagnosis

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A Study of Deep Learning Techniques for Predicting Breast Cancer Types

A Study of Deep Learning Techniques for Predicting Breast Ca...

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International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2022

作者： Ashwini, P. Suguna, N. Vadivelan, N. Research Scholar Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Chidambaram India Associate Professor Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Chidambaram India Professor Department of Computer Science and Engineering Teegala Krishna Reddy Engineering College Hyderabad India

ISBN: (纸本)9789819947164

Breast cancer is today’s deadly health issue which causes high mortality in woman worldwide. The preliminary detection and classification may help for proper treatment of the same. Understanding the causes of this cancer using traditional machine learning techniques misread feature extraction are leading to a complication. Traditional machine learning models are best suited for feature-based raw data. The novel deep learning techniques have been introduced for diagnosis of breast abnormalities using various imaging modalities like mammogram, magnetic resonance imaging (MRI) and ultrasound with high accuracy. This survey focuses on challenges of classical machine learning models and supervene efficient prediction models using novel deep learning techniques. This review presents comparison analysis of traditional machine learning and deep learning models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance imaging

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Improving Robotic Arms Through Natural Language Processing, computer Vision, and Edge Computing

Improving Robotic Arms Through Natural Language Processing, ...

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Mechatronics, Control and Robotics (ICMCR), International Conference on

作者： Pascal Sikorski Kaleb Yu Lucy Billadeau Flavio Esposito Hadi AliAkbarpour Madi Babaias Undergraduate Research Assistant Computer Science Department Saint Louis University Saint Louis USA Undergraduate Research Assistant Aerospace & Mechanical Engineering Department Saint Louis University Saint Louis USA Associate Professor Computer Science Department Saint Louis University Saint Louis USA Assistant Professor Computer Science Department Saint Louis University Saint Louis USA CBL Assistant Professor of Robotics Aerospace & Mechanical Engineering Department Saint Louis University Saint Louis USA

ISBN: (数字)9798331544546

ISBN: (纸本)9798331544553

This paper introduces a prototype for a new approach to assistive robotics, integrating edge computing with Natural Language Processing (NLP) and computer vision to enhance the interaction between humans and robotic systems. Our proof of concept demonstrates the feasibility of using large language models (LLMs) and vision systems in tandem for interpreting and executing complex commands conveyed through natural language. This integration aims to improve the intuitiveness and accessibility of assistive robotic systems, making them more adaptable to the nuanced needs of users with disabilities. By leveraging the capabilities of edge computing, our system has the potential to minimize latency and support offline capability, enhancing the autonomy and responsiveness of assistive robots. Experimental results from our implementation on a robotic arm show promising outcomes in terms of accurate intent interpretation and object manipulation based on verbal commands. This research lays the groundwork for future developments in assistive robotics, focusing on creating highly responsive, user-centric systems that can significantly improve the quality of life for individuals with disabilities. For video demonstrations and source code, please refer to: https://***/EnhancedArmEdgeNLP.

关键词： computer vision Mechatronics Source coding Machine vision Large language models Prototypes Focusing Manipulators Natural language processing Edge computing

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Survey of Various Machine Learning Techniques for Analyzing IoMT-Based Remote Patient Monitoring System 1

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International Conference on Advances in Data science and Computing Technologies, ASDC 2022

作者： Johar, Sayyed Manjula, G.R. Assistant Professor Department of Artificial Intelligence and Machine Learning Jawaharlal Nehru New College of Engineering Karnataka Shivamogga India Professor Department of Computer Science and Engineering Jawaharlal Nehru New College of Engineering Karnataka Shivamogga India

ISBN: (数字)9789819936564

ISBN: (纸本)9789819936557

Internet of Medical Things (IoMT) consists of several medical devices used to capture patient health information. The recorded details are transmitted to the healthcare centers via computer networks. This transmitted information is stored in the cloud environment for making further clinical analysis. The continuous recordings of patient information help predict the patient’s chronic disease and help to initiate the respective solutions. This recorded information is processed by applying various machine learning and deep learning techniques to predict patient health conditions. Therefore, here the general discussion of IoMT and the impact of patient health monitoring systems are discussed. Along with this, various methodology’s respective pitfalls and advantages are analyzed to improve the healthcare system’s remote patient health monitoring process. The major goals of this study were to comprehend the role played by IoMT in remote patient monitoring and the effects of intelligent approaches on data analysis. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Quantum-Accelerated Hyperparameter Tuning for Dynamic NLP Models

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Procedia computer science 2024年 235卷 506-519页

作者： Ravikumar S Arockia Raj Y R. Babu Vijay K R. Ramani Associate Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India 600062 Assistant Professor Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Assistant Professor Department of Computational Intelligence Faculty of College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamilnadu India 603203 Assistant Professor Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai Tamilnadu India 602117 Associate Professor Department of Computer Science and Engineering P.S.R. Engineering College Sivakasi Tamilnadu India 626140

In the rapidly evolving field of natural language processing (NLP), performance optimization of large-scale NLP models is crucial. Through the application of Quantum-Accelerated Hyperparameter Tuning (QAHT), this abstract introduces a novel approach to addressing this issue. Our proposed framework leverages quantum computing capabilities to dynamically optimize NLP model hyperparameters in real-time, catering to the ever-changing character of textual data streams. Traditional hyperparameter optimisation methods usually rely on laborious grid searches or random exploration, which may not be suitable for dynamic NLP jobs. Contrarily, QAHT uses Quantum Neural Network (QNN) architectures that have been specially designed for hyperparameter optimisation. These QNNs improve performance and efficacy by dynamically modifying and improving model configurations. This abstract discusses the key elements of the QAHT architecture, including real-time model deployment, adaptive learning, and continuous data stream processing. In addition to speeding up the hyperparameter optimisation process, QAHT makes sure that NLP models are still flexible and responsive to shifts in the sentiment of the data and its distribution. This method has applications beyond NLP since it provides a foundation for effectively optimising machine learning models in complex, real-time situations. As quantum computing develops, QAHT represents a promising future in machine learning, where quantum-enhanced capabilities satisfy the needs of contemporary data-driven applications.

关键词： Hyperparameter optimization Quantum computing Natural language processing (NLP) Quantum Neural Networks (QNN) Real-time processing Adaptive learning

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Prediction of the Impact of Covid-19 Vaccine on Public Health Using Twitter

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2022年第1期49卷 19-29页

作者： Syed, Ayesha Ayub Gaol, Ford Lumban Suparta, Wayan Abdurachman, Edi Trisetyarso, Agung Matsuo, Tukoro Ph.D. student at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Associate Professor at Informatics Engineering and Information System at Bina Nusantara University Jakarta Indonesia Malaysia Associate Professor at the Department of Doctor of Research in Management at Bina Nusantara University Jakarta Indonesia Assistant Professor at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Professor at the Graduate School of Industrial Technology Advanced Institute of Industrial Technology Japan and Department of M-Commerce and Multimedia Applications Asia University Taiwan

Social media is a source of big data. Media like Twitter and Facebook has been used for collecting and analyzing user data for different purposes. The data can be used to analyze people opinions towards certain topics and incidents by applying sentiment analysis and then certain useful insights can be drawn from the analyzed data. During the current time of Covid-19, people have been sharing information regarding Covid-19 statistics, vaccines, and discussing the effects of the vaccine concerning public health. The purpose of this study is to analyze tweet data regarding the Covid-19 vaccine by applying sentiment analysis and predicting the impact of the vaccine on public health. Also, the tweets are analyzed for hidden topics by applying Topic Modelling using Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA). The source of data for this study is Twitter API. The coding and data analysis is done using Python programming language in the Spyder (Scientific Python Development Environment) that is an integrated development environment for scientific programming, testing, and data analysis. The results of the study indicate a greater positive sentiment reflecting a healthy public discussion about the Covid-19 vaccine, information, awareness, and public acceptance. With these results, a positive impact of the Covid-19 vaccine on public health is predicted. The results of topic modeling discovered 10 hidden topics from the tweet dataset. © 2022. All Rights Reserved.

关键词： Vaccines

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