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检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

共 20255 条记录，以下是381-390 订阅

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Reinforcement learning-Based Bonobo Optimizer for Efficient Load Balancing in Cloud Computing 3

Reinforcement Learning-Based Bonobo Optimizer for Efficient ...

引用

3rd IEEE international conference on intelligent Technologies, CONIT 2023

作者： Adarsh, M.G. Bhargavi, K. Siddaganga Institute of Technology Department of Computer Science and Engineering Karnataka Tumakuru572103 India

ISBN: (纸本)9798350338607

The field of optimization has undergone a paradigm shift with the advent of reinforcement learning techniques, which are widely used for solving complex problems in various domains. In this paper, we propose a novel optimization algorithm, called the Bonobo Optimization. The algorithm is inspired by the social behaviour of Bonobo apes, which are known for their collaborative and communicative behaviour. This algorithm is designed to learn from its interactions with the environment and adapt to new situations, making it a robust and adaptive optimization tool. We demonstrate the effectiveness on several benchmark optimization problems, where it outperforms them. © 2023 IEEE.

关键词： Reinforcement learning

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Ensemble Based MRI Analysis for Early Detection of Alzheimer's Disease 3

Ensemble Based MRI Analysis for Early Detection of Alzheimer...

引用

3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Veerraju, Sampenga Varshini, Nakka Navya, Yadagiri Srinivas, Jonnakuti Venkata Siva Arasavali, Nalineekumari Putcha, Poornapriya Dadi Institute of Engineering & Technology Dept of Electronics and Communication Engineering Anakapalle India

ISBN: (纸本)9798331528201

The neurodegenerative disorder Alzheimer's disease is indicated by cognitive deficits and memory loss. Early identification and diagnosis of the disease contribute positively to therapeutic intervention opportunities, and can improve patient outcomes following diagnosis. Structural brain abnormalities linked with Alzheimer's can be reliably measured through magnetic resonance imaging, making it a strong imaging modality for cognitive impairment assessment, including Alzheimer's. This study develops an ensemble learning strategy to diagnose patients with Alzheimer's using MRI data. Specifically, an ensemble strategy uses the strengths of multiple less complex machine learning models to improve predictive accuracy and robustness locally or across populations, as compared to using a single model. The proposed ensemble diagnostic model utilizes pre-processed MRI images in order to extract relevant features of interest, and the software stack publishes the assessment of various machine learning models. The proposed method exhibits key performance metrics as accuracy-0.99, F1 score-0.98, recall-1.00 and precision-0.96. It shows that ensemble learning is a very powerful machine learning method to use for machine learning predictions when compared to traditional and individual approaches. © 2024 IEEE.

关键词： Contrastive learning

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DevOps Practices Into Machine learning 3

DevOps Practices Into Machine Learning

引用

3rd IEEE international conference on intelligent Systems, Smart and Green Technologies, ICISSGT 2024

作者： Jain, Samridhi Kumar, Puneet Chandigarh University Dept. of Computer Science & Engineering Mohali India

ISBN: (纸本)9798350304329

All industrial machine learning (ML) projects have their ultimate objective to quickly develop and deploy ML solutions. However, a lot of machine learning projects are failing, and never reach production. In order to address these concerns, this paper aims to investigate how manual ML processes can be automated and operationalised so that more ML concept can be put into production. The advantages of DevOps automation and machine learning capabilities are now being combined by industries. MLOps is the name of this combination. Our analysis found that while there are some similar aspects in defining MLOps, still there is a lack of standardisation in the term, which can occasionally lead to misunderstandings and uncertainty. We carried out the research, which includes a review of the literature and an evaluation of the MLOps tools, to fill this gap. These investigations have led us to define MLOps and identify unresolved issues in the field. In addition to this, we offer a workflow-based summary of all essential components. Finally, this paper gives guidance for applying DevOps practises to machine learning for researchers and practitioners. © 2024 IEEE.

关键词： DevOps Machine learning MLOps MLOps tools

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Federated learning for Enhanced and Private Autism Spectrum Disorder Detection in Children Across Diverse Populations 3

Federated Learning for Enhanced and Private Autism Spectrum ...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Madhavilatha, Idimadakala Krishna, Konda Hari Mohan Babu University Department of Cse Tirupathi India School of Computing Mohan Babu University Department of Cse Tirupathi India

ISBN: (纸本)9798331515911

The contribution of this work focuses on the results of applying Federated learning(Fl) to the improvement of performance and generalizability for Autism Spectrum Disorder (ASD) detection models, which include children, assuming high standards for data privacy. In FL, institutions can collaborate on model training by means of decentralized datasets that share knowledge without exposing sensitive information explicitly. This would be fully in line with all the regulations for privacy and would protect patient information. Our federated model achieved an accuracy of 91% and outperformed the traditional centralized way with a gain in generalization for several diverse demographic groups by 5% on the F1-score. FL combines five sources of input-eye-tracking data, speech prosody, EEG, and physiological signals-used by the clients into a staunch, adaptable model suitable for a wide range of populations. Our results suggest that FL might represent a game-changer in ASD Diagnostics, given the pursuit of a scalable and privacy-centric solution for sensitive health care contexts. © 2025 IEEE.

关键词： Federated learning

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Simulation of Predictive Maintenance Model of Power Enterprise Equipment based on Deep learning Algorithm 3

Simulation of Predictive Maintenance Model of Power Enterpri...

引用

3rd international conference on Energy and Power engineering, Control engineering (EPECE)

作者： Zhang, Biao Wu, Qinghua State Grid Informat & Telecommun Accenture Inform Beijing 100000 Peoples R China

ISBN: (纸本)9798350366105;9798350366099

With the increasing complexity of the power system, higher requirements have been put forward for the stable and efficient operation of equipment. Predictive maintenance, as a strategy aimed at identifying and correcting potential faults in advance, provides an effective means to ensure the stable operation of power equipment. This study delves into the application and effectiveness of DL (Deep learning) technology in predictive maintenance of equipment in power enterprises. The article first outlines the advantages and potential of DL in predictive maintenance, especially its unique advantages in handling large-scale, high-dimensional, and nonlinear data. Subsequently, we discussed in detail the key steps of data collection, preprocessing, and feature extraction, emphasizing the DL model's ability to automatically extract features. In the process of model design and validation, this article explores various DL algorithms and their practical application effects in predictive maintenance of power equipment. Finally, through simulation verification, the DL model shows higher prediction accuracy compared to traditional methods.

关键词： Deep learning Electric power enterprises Predictive maintenance Simulation verification

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Construction of Diabetes Prediction Model Based on Machine learning 3

Construction of Diabetes Prediction Model Based on Machine L...

引用

3rd international conference on intelligent Computing and Next Generation Networks, ICNGN 2024

作者： Xinping, Shang Ke, Jian Xuliang, Yang Yi, Wang Artificial Intelligence Dongguan City University Guangdong Dongguan China

ISBN: (纸本)9798331529222

Diabetes is a global epidemic of chronic diseases;early identification of high-risk groups can effectively reduce the incidence of diabetes and reduce the risk of complications. In recent years, the predictive model based on machine learning has been gradually applied to the early screening and prediction of diabetes due to its efficient data processing ability. In this study, a diabetes prediction model was constructed by analyzing and comparing common machine learning algorithms such as logistic regression, K-nearest neighbor, decision tree, random forest, neural network and XGBoost. The experimental results show that random forest and XGBoost have significant advantages in predicting diabetes mellitus with high prediction accuracy. This study provides an effective tool for clinical applications and provides a reference for the development of future medical prediction models based on machine learning. ©2024 IEEE.

关键词： Prediction models

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SmartBlend: Smart Video Recommendations on YouTube 3

SmartBlend: Smart Video Recommendations on YouTube

引用

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

作者： Singh, Kulvinder Maurya, Aditya Kumar Solanki, Drishti Chandigarh University Dept of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798350375190

In the dynamic era of online education, the pursuit of a personalized and effective learning experience is paramount. A transformative approach in online education by integrating Multimodal data Mining and data Synthesis to develop a Smart System for learning Video Recommendations. With a focus on Personalized learning and Adaptability, the methodology employs advanced techniques like Multimodal learning Analytics and Machine learning for a comprehensive analysis of learning materials. SmartBlend introduces a novel approach to personalized learning video recommendations on YouTube, leveraging Multimodal data Mining and data Synthesis. The SmartBlendEdu framework employs advanced techniques like Multimodal learning Analytics and Machine learning for a comprehensive analysis of learning materials. © 2024 IEEE.

关键词： data mining

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Predicting Digital Payment Platforms Adoption by Retail Vendors Using Machine learning Techniques 3

Predicting Digital Payment Platforms Adoption by Retail Vend...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Manjula Devi, P. Vinoth, S. Chinnasamy, Gopalakrishnan Cms Business School Jain Deemed-to-be University Faculty of Management Bengaluru India Jain Deemed-to-be University Faculty of Management Bengaluru India

ISBN: (纸本)9798331515911

The goal of this study is to use machine learning techniques to anticipate retail vendors' adoption of digital payment platforms. data was collected from 270 merchants throughout 10 major retail markets in Bangalore, with a focus on small and medium-sized businesses. Using multiple regression, exploratory factor analysis, and support vector machines (SVM), the study identifies significant factors driving adoption, including perceived usefulness, social influence, and compatibility. The data show that, while perceived usefulness and social influence are important drivers, other factors such as perceived ease of use and technical support have limited effect. The SVM model was moderately accurate in predicting platform adoption. The report also evaluates customer satisfaction across various digital payment platforms and predicts future trends using ARIMA models. These findings have significant implications for improving digital payment adoption tactics among retailers. © 2025 IEEE.

关键词： Adversarial machine learning

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Enhancing Decentralized Federated learning with User Feedback Loops: A Novel Approach for Personalized and Adaptive learning in IoT Environments 3

Enhancing Decentralized Federated Learning with User Feedbac...

引用

3rd international conference on Computing and Machine Intelligence (ICMI)

作者： Sarker, Arpita Jesser, Alexander Speidel, Markus Heilbronn Univ HHN Inst Intelligent Cyber Phys Syst ICPS Heilbronn Germany Heilbronn Univ HHN Heilbronn Germany

ISBN: (纸本)9798350372977;9798350372984

The burgeoning field of the Internet of Things (IoT) demands innovative approaches to data privacy and user-centric services. This study introduces a novel cross-platform application engineered using the Flutter framework, which orchestrates federated learning (FL) and natural language processing (NLP) to facilitate personalized event discovery within IoT environments. The application features an NLP-based chatbot for user interaction and employs Dendrite, a second-generation Matrix homeserver, to manage decentralized communication. Central to the system's design is the stringent upholding of data privacy: user data, including browsing history and application usage patterns, is processed locally on user devices to construct profiles that inform personalized event suggestions. The system's deployment of end-to-end encrypted communication underscores its commitment to user privacy and security. This integration of FL and NLP showcases a significant leap forward in the realm of privacy-preserving, personalized applications, charting a new course for user engagement in IoT.

关键词： federated learning natural language processing internet of things Flutter Dendrite artificial intelligence decentralized communication personalized event discovery end-to-end encryption

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Design of a Real-Time Crime Monitoring System using Machine learning 3

Design of a Real-Time Crime Monitoring System using Machine ...

引用

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

作者： Krishnaveni, A. Manikandan, C. Department ofComputer Science Karpagam Academy of Higher Education Coimbatore India

ISBN: (纸本)9798331527549

Crime in public areas poses risks to individuals and society at large. Traditional crime prevention methods face significant hurdles in detecting and tracking crime. Furthermore, the influx of large volumes of data overwhelms security systems and personnel making security enforcement harder. However, technological innovations have positively influenced different fields, including public safety and crime prevention. This study introduces an innovative system for crime prediction and analysis using machine learning, designed to assist law enforcement in identifying criminals in public spaces. The system enables authorities to input detailed information about criminals, including facial data, which is used to train an advanced recognition model. The system’s ability to provide accurate and timely alerts enhances the efficiency of law enforcement operations. This framework leverages state-of-the-art face recognition technology to offer a scalable and robust solution for crime prevention. By integrating machine learning algorithms with intelligent surveillance systems, the proposed approach addresses the growing need for proactive measures to ensure public safety. Further, this system also highlights the potential of technology to transform traditional crime detection methods, paving the way for smarter and more responsive law enforcement practices. Finally, the proposed system attained a 92% face detection accuracy score which was higher than existing approaches. © 2025 IEEE.

关键词： Crime prediction exact location facial data machine learning public surveillance recognition model

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