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检索条件"任意字段=3rd International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2021"

共 2040 条记录，以下是21-30 订阅

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An Overview of data Aggregation Techniques with Special Sensing Intelligent Device Selection Approaches 3rd

An Overview of Data Aggregation Techniques with Special Sens...

引用

3rd international conference on data science, machine learning and applications, icdsmla 2021

作者： Nirmala, G. Guruprakash, C.D. Sri Siddhartha Academy of Higher Education Karnataka Tumakuru India Department of Computer Science and Engineering Sri Siddhartha Institute of Technology Karnataka Tumakuru India

ISBN: (纸本)9789811959356

The transfer of the data from one Sensing Intelligent Device (SID) to the other sensing intelligent devices (SIDs). The transmission of data happens from one area to a different area in a Sensing Intelligent Network (SIN). The data packets receiver is usually the destination Control Center. Various applications of SIN are disaster monitoring, war plane detection and sending the environmental hazards information along with various other applications. Due to the constraints which are involved in SIN like low battery, low transmission range it is important to avoid duplication of data as well as collect the data from the SIDs in an intelligent fashion. Each SIN has huge number of SIDs and hence they transmit data which is similar in nature and hence this causes the SIDs to choke and become irrelevant SIDs over a period. The number of such chocking instances can be reduced in the SIN with the help of data aggregation, intelligent selection of centralized SID for a specific SIN area with the help of machine learning techniques. This paper lists out various data aggregation techniques which are available in the literature and how they can be used to improve the relevant ratio value for SIN. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy utilization

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A Review on applications of IoT in Healthcare 3

A Review on Applications of IoT in Healthcare

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Thakare, Vedant Verma, Prateek Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

This article examines how the Internet of Things (IoT) is rapidly transforming the medical field. IoT technologies are revolutionizing patient care, increasing productivity, and boosting results in a variety of ways, from wearable technology and telemedicine to smart hospitals and personalized medicine. Predictive analytics, remote patient monitoring, and the use of AI and machine learning for more precise diagnosis and treatment regimens are important applications. The future of IoT in healthcare is extremely bright, with developments in 5G, AI, and other cutting-edge technologies set to further transform the sector and enhance human health, even while issues like data security and privacy still need to be resolved. © 2025 IEEE.

关键词： Electronic health record

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The 3rd international Workshop on machine learning on Graphs (MLoG) 23

The 3rd International Workshop on Machine Learning on Graphs...

引用

16th international conference on Web Search and data Mining

作者： Derr, Tyler Ma, Yao Rozemberczki, Benedek Shah, Neil Pan, Shirui Vanderbilt Univ Nashville TN 37235 USA New Jersey Inst Technol Newark NJ USA Isomorph Labs London England Snap Inc Santa Monica CA USA Griffith Univ Nathan Qld Australia

ISBN: (纸本)9781450394079

Graphs, which encode pairwise relations between entities, are a kind of universal data structure for a lot of real-world data, including social networks, transportation networks, and chemical molecules. Many important applications on these data can be treated as computational tasks on graphs. Recently, machine learning techniques are widely developed and utilized to effectively tame graphs for discovering actionable patterns and harnessing them for advancing various graph-related computational tasks. Huge success has been achieved and numerous real-world applications have benefited from it. However, since in today's world, we are generating and gathering data in a much faster and more diverse way, real-world graphs are becoming increasingly large-scale and complex. More dedicated efforts are needed to propose more advanced machine learning techniques and properly deploy them for real-world applications in a scalable way. Thus, we organize The 3rd international Workshop on machine learning on Graphs (MLoG)(1), held in conjunction with the 16th ACM conference on Web Search and data Mining (WSDM), which provides a venue to gather academia researchers and industry researchers/practitioners to present the recent progress on machine learning on graphs.

关键词： machine learning on Graphs

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Undersampled Random Forest: A Green Approach to Imbalanced learning 3

Undersampled Random Forest: A Green Approach to Imbalanced L...

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3rd international conference on Sustainable Mobility applications, Renewables and Technology, SMART 2024

作者： Kamalov, Firuz Elnaffar, Said El Khatib, Ziad Cherukuri, Aswani Kumar Jonnalagadda, Annapurna Department of Electrical Engineering Canadian University Dubai Dubai United Arab Emirates Department of Computer Science Canadian University Dubai Dubai United Arab Emirates School of IT & Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331532925

The increasing size of machine learning models and the datasets used for training has resulted in significantly higher computational demands. Modern large language models, in particular, consume vast amounts of energy during training, raising serious environmental concerns. In this paper, we propose a low-energy classifier, the Undersampled Random Forest (URF), designed specifically for imbalanced data. The URF method leverages undersampling in combination with a parallelizable random forest architecture. This approach reduces the size of the training data and accelerates computation, leading to more energy-efficient training processes. Moreover, the proposed method consistently achieves high recall rates on minority class instances, making it both effective and environmentally conscious. © 2024 IEEE

关键词： Adversarial machine learning

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Emotional AI-Based Analysis of Social Media Posts 3rd

Emotional AI-Based Analysis of Social Media Posts

引用

3rd international conference on data science, machine learning and applications, icdsmla 2021

作者： Binegde, Geleta Negasa Department of Computer Science Mattu University Mattu Ethiopia

ISBN: (纸本)9789811959356

Social media is a powerful tool for people to share their thoughts and feelings. People post their personal feelings and thoughts on any topic, person, policy or product for marketing or social attraction. This text information can be broken down into facts and opinions. Reflect people’s feelings about government policies, products, national and international personalities, the constitution, incidents and events. Sentiment analysis is an area of study that analyzes people’s opinions, ratings, attitudes and emotions in general from the written language. The texts of the reviews are edited to give an accurate description of the author’s opinion on the subject. This paper proposes an analysis of the feelings of the writers about the subject and comments of the users of social networks in the context of Ethiopia with Facebook as a platform. In this paper, natural language processing (NLP) and machine learning (ML) based on a novel emotional AI sentiment analysis and opinion mining framework used to conduct sentiment analysis and opinion mining of posts and user comments on social media via a Facebook application. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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A Review on Oversampling Techniques for the Treatment of Class Imbalance Problem 3

A Review on Oversampling Techniques for the Treatment of Cla...

引用

3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Anand, Apurv Biswas, Saroj Kr. Majumdar, Sounak Thounaojam, Dalton Meitei Department of Computer Science and Engineering National Institute of Technology Silchar Silchar India

ISBN: (纸本)9798331527549

The Class Imbalance Problem (CIP) is a critical challenge in machine learning, particularly in applications such as medical diagnosis and fraud detection, where minority classes are underrepresented but crucial. This research addresses the issue by introducing a novel classification of oversampling techniques into 11 distinct categories, with a unique distinction between duplication and derivation mechanisms. Through an in-depth review of each category, this study identifies specific challenges such as overfitting and noise in imbalanced datasets and demonstrates how these categorized strategies effectively enhance model performance. The outcomes highlight practical solutions to improving classification accuracy and robustness in imbalanced scenarios. © 2025 IEEE.

关键词： Contrastive learning

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A Deep Meta-model for Environmental Sound Recognition 1

引用

3rd international conference on data science, machine learning and applications, icdsmla 2021

作者： Arun, K.S. Department of Computer Science and Engineering Amal Jyothi College of Engineering Kottayam India

ISBN: (数字)9789811959363

ISBN: (纸本)9789811959356

Nowadays, sound serves as a crucial factor in all facets of human life. Staring from automating personal security systems to critical surveillance systems, sound is an indispensable component. The practical implementation of the present day automatic sound recognition systems in real-life settings is inadmissible due to their poor detection accuracy. However, deep learning-based systems overcome the incompetence of the traditional machine learning-based models, and it can be used to develop automatic sound classification systems. This work proposes a deep meta-model for categorizing environmental sounds on the basis of the spectrogram images generated from these sounds. In the proposed approach, spectrogram images of environmental sounds are used to train five different deep learning models, and the predictions from these base models are then stacked using the proposed deep meta-model. Experimental results on two benchmark datasets such as ESC-50 and UrbanSound 8K demonstrate the fact that the proposed deep meta-model is a promising alternative to the conventional approaches for environmental sound recognition. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Spectrographs

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Predictive Control Techniques for Induction Motor Drive for Industrial applications 3rd

Predictive Control Techniques for Induction Motor Drive for ...

引用

3rd international conference on data science, machine learning and applications, icdsmla 2021

作者： Penthala, Thanuja Kaliyaperumal, Saravanan Muddineni, Vishnu Prasad Bonala, Anil Kumar Department of Electrical and Electronics Engineering SRM Institute of Science and Technology SRM Nagar Tamil Nadu Kattankulathur603203 India Department of Electrical and Electronics Engineering Vaagdevi College of Engineering Bollikunta Warangal506005 India

ISBN: (纸本)9789811959356

In this paper, three popular model predictive control (MPC) techniques, namely predictive current control (PCC), predictive torque control (PTC), and predictive flux control (PFC), applied to induction motor (IM) drive are presented. These techniques directly use the inherent discrete nature of the power converter and evaluate control parameters for the finite switching states. These control techniques for IM can offer fast dynamic response, improved steady-state operation, and retain the decoupled nature. In case of PTC, weighting factor is used in cost function, whereas in other two control techniques, weighting factor is only required when additional control parameters are added. These control techniques are developed in MATLAB/SIMULINK, and results are presented for various operating conditions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Induction motors

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Real Time Poisoning Attacks and Privacy Strategies on machine learning Systems 3

Real Time Poisoning Attacks and Privacy Strategies on Machin...

引用

3rd international conference on Sentiment Analysis and Deep learning, ICSADL 2024

作者： Archa, A.T. Kartheeban, K. Kalasalingam Academy of Research and Education Computer Science and Engineering Tamil Nadu India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396157

machine learning aims to learn computer systems and predict output. Nowadays, Deep learning uses continuous training based on a large network of interconnected neurons to mimic the way humans think, analyze, and make decisions. It plays an important role in real-time applications. There are some security and privacy threats faced by deep learning systems. One of the major threats faced by such learning systems is attacks during training. The manipulation of training data leads to poisoning attacks. The resulting models are severely impacted by poisoning attacks, which may prevent them from converging or alter the outcomes of their predictions. The need to defend against such poisoning attacks is urgent and difficult. This study explains a thorough and detailed analysis of different types of poisoning attacks in real time applications. Its countermeasures to mitigate such attacks and various privacy-enhancing methods are also discussed here. © 2024 IEEE.

关键词： learning systems

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machine learning applications in Early Detection of Hepatic Steatosis Disease 3

Machine Learning Applications in Early Detection of Hepatic ...

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3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Patel, Vishal Rajendrabhai Dhanesha, Payal Soni, Parthi Dharmeshkumar Sankalchand Patel University Faculty of Computer Science Gujarat Visnagar India Gujarat Ahmedabad India Silver Oak University Silver Oak College of Computer Applications Gujarat Ahmedabad India

ISBN: (纸本)9798350375190

applications 'of machine learning (ML) have become vital for the early identification and treatment of hepatic steatosis illness, which is a common liver condition characterized by increased buildup of fat in the liver without a significant amount of alcohol use. This innovative approach takes use of a variety of patient-specific data, including body mass index (BMI), height, weight, gender, and follow-up time (futime), to develop prediction models and algorithms. These characteristics are used to generate models and algorithms. Patient outcomes can be improved by healthcare professionals through proactive intervention, the customization of treatment programs, and the identification of patterns, correlations, and risk factors associated with non-alcoholic fatty liver disease (NAFLD) through the analysis of multidimensional datasets by machine learning algorithms. By using machine learning (ML) to the process of identifying nonalcoholic fatty liver disease (NAFLD), diagnostic accuracy is improved, and customized medicine techniques are advanced. This, in turn, leads to advancements in the treatment and early management of this chronic liver textbf' disease. © 2024 IEEE.

关键词： Diseases

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