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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6844 条记录，以下是541-550 订阅

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Using an Extreme learning machine and an Extended Kalman Filter, State of Charge Estimation for Lithium-Ion Batteries 3

Using an Extreme Learning Machine and an Extended Kalman Fil...

引用

3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Sasikumar, M. Kumar, C. Mahendran, R. Suryamanikandan, P. Department Of Mechatronics Engineering Tiruchengode India

ISBN: (纸本)9798350318609

When discussing battery management systems (BMSs), one of their most crucial tasks is undoubtedly detecting the state of charge (SoC) of the battery. Background of electrochemical properties of lithium-ion batteries (LIBs), accurately estimating the SoC can be challenging. This study proposes an approach that combines the use of a machine learning technique, the Extreme learning machine (ELM), with an extended Kalman filter (EKF) to improve the accuracy of SoC estimation. First, we develop an accurate model of LIBs using ELM. EKF approach with the trained ELM model to estimate the SoC. To validate our approach, compare it with a traditional EKF methodology, the Equivalent Circuit Model (ECM)-EKF, using the Federal Urban Driving Schedule (FUDS) movement over three warming cycles. Results achieved show that the ELM can more accurately monitor voltage compared to the ECM, and the ELM-EKF-based SoC estimation provides more precise voltage estimates compared to ECM-EKF. © 2024 IEEE.

关键词： Lithium-ion batteries

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A Secure and Privacy-Preserving machine learning approach for waste management using federated learning 3

A Secure and Privacy-Preserving Machine Learning approach fo...

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Kumar, Vishal Pranao Amarnath Manthena, Vyshnavi Bhattacharjee, Sanjana Vruddhula, Akash Kumar Mohanty, Aparna Vellore Institute of Technology SENSE Vellore India Vellore Institute of Technology SELECT Vellore India

ISBN: (纸本)9798350372120

Efficient waste management remains a critical challenge in contemporary urban environments. Our research introduces a comprehensive waste management system integrating smart dustbins with federated learning and deep learning models. Federated learning, a technique that ensures privacy by training machine learning models across multiple decentralized edge devices or servers without revealing local data samples, is at the core of our approach. Operating on federated learning principles, our system employs selected deep learning models (ResNet50, VGG16, InceptionV3, and EfficientNet) for precise garbage categorization. Various optimization algorithms such as FedAvg, FedAMP, FedAdam, and FedMA enable collaborative model training across decentralized devices, eliminating the need to centralize sensitive data. The integration of smart dustbins significantly enhances garbage identification and classification capabilities, leading to improved waste sorting and operational efficiency. The chosen deep learning models play a pivotal role in this process, contributing to the system's advanced garbage categorization. Furthermore, our federated learning approach ensures privacy preservation by avoiding centralization of sensitive data, aligning with privacy-conscious methodologies. This research represents a notable advancement in modern waste management practices, emphasizing federated principles and privacy-aware methodologies. The findings underscore the potential impact of our system on waste management effectiveness and operational efficiency in urban environments. © 2024 IEEE.

关键词： Deep learning

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Intelligent Recognition Technology for Medical Laboratory Report Images Based on Deep learning 3

Intelligent Recognition Technology for Medical Laboratory Re...

引用

3rd international conference on Artificial Intelligence and Computer Information Technology, AICIT 2024

作者： Cai, Qianzhuo Zheng, Sheng Liu, Jianke School of electrical and information Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798350365443

In the medical field, the accurate identification and utilization of laboratory report information is crucial for enhancing the efficiency and accuracy of diagnosis and treatment. This study employs deep learning technology to systematically research and develop techniques for extracting information from medical laboratory reports. We designed and implemented a series of algorithms that effectively convert image-based laboratory reports into structured data through layout analysis, text detection, text recognition, and table recognition. Experimental results show that the proposed methods achieve high levels of accuracy, significantly enhancing the automation of laboratory report information processing. This provides substantial technical support for medical institutions in handling large volumes of laboratory report data. © 2024 IEEE.

关键词： Deep learning

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Improving Diagnostic Accuracy: A Deep Dive into Random Forest Optimization for Clinical data 3

Improving Diagnostic Accuracy: A Deep Dive into Random Fores...

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Modak, Masooda Jadhav, Madhavi Kadam, Riya Arwade, Manasi Mane, Samiksha SIES Graduate School Of Technology Department of Computer Engineering Nerul India

ISBN: (纸本)9798350372120

The healthcare industry produces a large amount of patient data, which makes it possible to use a variety of analytical techniques on a large dataset. A predictive system that can recognize several diseases at once has been created by utilizing machine learning techniques. Grid Search and Randomized Search strategies are used to further enhance the model that was developed using the Random Forest algorithm. This contrasts with widely used systems that frequently display a numerous illness prediction system without improving the model, producing erroneous and subpar results. Reduced accuracy and erroneous results have serious consequences that could endanger the health of the patient. Our system's present focus is on two different diseases: Diabetes and Parkinson's disease. Especially, the architecture of the system makes it possible to easily incorporate other illnesses in later stages. Users are required to input various disease-specific parameters, after which the system promptly generates output indicating the presence or absence of the specified condition. This research work carried out demonstrates the disease prediction system developed using Optimized machine learning algorithms such as Decision Tree classifier, Random forest classifier, Support Vector machine (SVM) and Logistic Regression along with Optimization techniques Grid Search and Randomized Search. The primary objective of the model is to enhance its recall capabilities through the incorporation of advanced methodologies and optimization techniques. The paper also presents comparative study of the results of the above algorithms used. © 2024 IEEE.

关键词： Forecasting

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Ocimum Sanctum Linn Plant Fertilizer and Insecticide Spraying System with Disease Prediction using machine learning 3

Ocimum Sanctum Linn Plant Fertilizer and Insecticide Sprayin...

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

作者： Aagaash, K.R. Ramalakshmi, K. Venkatesan, R. Sundar, G Naveen Nancy, Golden Shirly, S. Karunya Institute of Technology and Sciences Department of Computer Science Engineering Coimbatore641114 India Alliance University Department of Cse Bangalore562106 India Institute of Technology and Sciences Division of Cse Karunya Coimbatore641114 India Karunya Institute of Technology and Sciences Division of Aiml Coimbatore641114 India

ISBN: (纸本)9798350375190

An integrated system for spraying pesticides and fertilizers on plants, coupled with machine learning capabilities for disease prediction, revolutionizes crop management, transforming crop management. This innovative solution administers precise doses of nutrients and pesticides to optimize crop health autonomously. Leveraging CNN algorithms and image processing, it predicts diseases proactively, mitigating risks. By integrating machine learning into traditional practices, it enhances efficiency and sustainability in terrace farming, tailoring care to each crop's needs. Continuous monitoring allows real-time adjustment of spraying, responding to changing conditions. The system learns from extensive plant image datasets to recognize disease patterns, empowering preemptive action. This proactive approach minimizes crop losses, promotes sustainability, and improves yields in terrace farming. © 2024 IEEE.

关键词： Forecasting

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A Review on Meta-learning: How Artificial Intelligence and machine learning Can Learn to Adapt Quickly 3

A Review on Meta-Learning: How Artificial Intelligence and M...

引用

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

作者： Khadse, Shrikant Gourshettiwar, Palash Pawar, Adesh Faculty of Engineering and Technology Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Wardha442001 India Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

Meta-learning aims to create Artificial Intelligence (AI) systems that can adapt to new tasks and improve their performance over time without extensive retraining. The advent of meta-learning paradigms has fundamentally changed how Artificial Intelligence (AI) and machine learning (ML) are handled, constantly reshaping these fields. Research study reviews the broad field of meta-learning, highlighting its critical function in bridging the AI-ML divide to improve adaptability. The study starts with a review of past developments, following the development of meta-learning algorithms and their underpinning theories. The historical view lays the groundwork for understanding the course of this dynamic regulation, spanning from early efforts in probabilistic program induction to modern discoveries in model-agnostic meta-learning. The study explores multiple meta-learning applications in AI and ML, revealing how they affect financial forecasting, computer vision, natural language processing, autonomous cars, health informatics, robotics, and personalized recommendation systems. When seen from the perspective of adaptive intelligence, meta-learning may be effectively applied to conditions with limited data, hyperparameter optimization, few-shot learning, and quick adaption in reinforcement learning settings. © 2025 IEEE.

关键词： Contrastive learning

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Supervised learning Approaches for Vibration Analysis and Anomaly Detection in Rotating machines 3

Supervised Learning Approaches for Vibration Analysis and An...

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3rd international conference on Emerging Trends in Electrical, Control, and Telecommunication engineering, ETECTE 2024

作者： Ali, Syed Zulfiqar Ali, Syed Hasnat Javeed, Anees Ullah, Ihsan Memon, Sufyan Ali Manzoor, Sajjad Dept. of Electrical and Computer Engineering CUI Abbottabad Campus Abbottabad Pakistan Dept. of Defence Systems Engineering Sejong University Seoul Korea Republic of Dept. of Electrical Engineering MUST AJK Mirpur Pakistan

ISBN: (纸本)9798331504861

Safety and reliability are crucial for modern systems and technologies. This paper presents an approach for real-time anomaly detection in rotating machines using vibration analysis and machine learning algorithms for predictive maintenance. Rotating machines such as motors, engines, gearboxes and turbines are widely used systems and faults in these systems can result in complete shutdowns. Preventing unexpected downtime while ensuring equipment reliability and safety is essential. In this context, anomaly detection plays a vital role in monitoring the healthy operation of rotating machines. A test-bed was designed to generate datasets for both balanced (healthy) and unbalanced (faulty) motor conditions. The experimental setup consists of a three-phase induction motor, a signal acquisition system (Raspberry Pi), a vibration sensor (MPU6050) and a coupling system capable of adjusting motor balance. Supervised machine learning algorithms, Support Vector machine (SVM) and Random Forest (RF) were applied to classify and detect anomalies in the collected datasets. A grid search algorithm was employed for hyperparameter tuning of the SVM and RF models. The proposed technique achieved a maximum accuracy of approximately 98% using SVM and 100% using the RF algorithm leveraging nine optimally selected temporal features. ©2024 IEEE.

关键词： Rotating machinery

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Autism Spectrum Disorder Analysis and Detection System 3

Autism Spectrum Disorder Analysis and Detection System

引用

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

作者： Ananthajothi, K. Mohanapriya, E. Mukhilan, S.S. Department of CSE Engineering College Chennai India

ISBN: (纸本)9798331527549

Autism Spectrum Disorder is a neurodevelopmental disorder characterized by social interaction, communication impairments, and repetitive behaviors. In recent years, there has been an increase in prevalence, and it is important to identify individuals early for appropriate intervention to improve their quality of life. The following study was conducted to deepen the understanding and management of the disorder by employing a comprehensive two-module approach. Module one emphasizes the detection of ASD through sophisticated machine learning techniques for early and accurate diagnosis. Module two is on improving social and communication skills of children with ASD via game-based assessments, offering them an interactive yet enjoyable learning environment. This project, not only in contributing to the scientific community, but also through practical applications contributes and offers easy solutions for caregivers and educators. Integration of technology with both detection and intervention holds huge advancement in this area, promising better outcomes for ASD individuals. © 2025 IEEE.

关键词： Adversarial machine learning

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Next-Gen Maritime Security by Leveraging Advanced AIS Analytics for Precision Detection and Monitoring of Boats in Ocean 3

Next-Gen Maritime Security by Leveraging Advanced AIS Analyt...

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3rd international conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Keerthiga, K. Jolin Ashika, J. Narmadha, M. IFET College of Engineering Department of Electronics and Communications Engineering Villupuram India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391565

The enormous oceans of today are the worldwide arteries of commerce and transportation, making maritime security an issue of fundamental importance. The issue at stake is that conventional approaches of identifying and tracking ships at sea often fail to keep up with the ever-changing conditions of the ocean. These restrictions threaten international commerce, security, and ecological equilibrium by making countries more vulnerable to things like piracy, smuggling, illicit fishing, and pollution. Automatic Identification System (AIS) analytics are strengthened by a range of state-of-the-art machine learning and deep learning algorithms in our "Next-Gen Maritime Security"solution. Through the use of these methods, we are able to improve vessel identification, trajectory prediction, behavior grouping, vessel type classification, and anomaly detection. By doing so, we are able to prevent potential threats to maritime security from occurring, therefore protecting international shipping lanes, maritime workers, and marine ecosystems. Our novel strategy provides a solid answer to the problem of marine security in today's dynamic environment. © 2024 IEEE.

关键词： Deep learning

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DVC in Open Source ML-development: The Action and the Reaction 24

DVC in Open Source ML-development: The Action and the Reacti...

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IEEE/ACM 3rd international conference on AI engineering - Software engineering for AI (CAIN)

作者： Pacheco, Lorena Barreto Simedo Rahman, Musfiqur Rabbi, Fazle Fathollahzadeh, Pouya Abdellatif, Ahmad Shihab, Emad Chen, Tse-Hsun (Peter) Yang, Jinqiu Zou, Ying Concordia Univ Montreal PQ Canada Queens Univ Kingston ON Canada Univ Calgary Calgary AB Canada

ISBN: (纸本)9798400705915

machine learning (ML) systems are gaining popularity, reshaping various domains ranging from customer services to software engineering. The effectiveness of ML systems is dependent on the quality of their training data. Therefore, practitioners invest substantial time experimenting with different data, parameters, and models to guarantee the quality of the end system. Prior work highlighted unique challenges of developing ML systems, particularly concerning versioning data and models. Recently, various tools such as DVC and MLFlow have emerged to aid developers in the storage and tracking of data. Despite their growing popularity, very little is known about their usage patterns and impact on open-source software (OSS) systems. To address this gap, we conducted an empirical study on 56 GitHub OSS projects that use DVC to understand the DVC usage pattern and the impact of using DVC on the software development process. We found that Versioning and tracking is the most adopted DVC feature, being utilized by all 56 projects and being the only adopted feature in 85.7% of them. Furthermore, we found that DVC has a significant impact on the software development process indicators such as the number of created pull requests (PRs), and the number of bug-fix commits. For instance, our findings showed that DVC causes a peak in the number of commits and PRs at the moment of the adoption, followed by a long-term decrease. We believe that our findings can assist practitioners in tailoring tools to better meet user requirements and help organizations realize potential outcomes of adopting such tools.

关键词： Empirical Software engineering data Version Control Software Evolution SE4AI

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