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

共 20248 条记录，以下是111-120 订阅

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Exploring Hand Gesture Recognition: Trends, Technologies, and Application 3

Exploring Hand Gesture Recognition: Trends, Technologies, an...

引用

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

作者： Pohekar, Ayesha Pundge, Ajitkumar Shirsath, Vishal School of Engineering Ajeenkya DY Patil University Maharashtra Pune India

ISBN: (纸本)9798331527549

Hand Gesture Recognition (HGR) systems have garnered significant attention due to their applications in human-computer interaction, virtual reality, and assistive technologies. However, achieving adaptable, accurate, and user-friendly HGR solutions remains a challenge due to issues such as environmental variability and user adaptability. This survey explores state-of-the-art HGR techniques, encompassing traditional methods and advanced deep learning-based methodologies. Classification techniques are analyzed based on sensory-based, vision-based, and hybrid input approaches, highlighting their respective strengths and limitations. The impact of feature extraction methods, dataset quality, and real-time processing on the performance of classification algorithms is discussed. By addressing these challenges, this work identifies potential pathways for advancing HGR systems to enhance their usability and reliability across diverse applications. © 2025 IEEE.

关键词： Palmprint recognition

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Fusion learning Framework for Malignant and Benign Classification in Histopathology Images 3

Fusion Learning Framework for Malignant and Benign Classific...

引用

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

作者： Subramaniyam, P. Hari Krishnan, G. Department of ECE School of Engineering Mohan Babu University Tirupati India Department of EEE School of Engineering Mohan Babu University Tirupati India

ISBN: (纸本)9798331527549

This work presents a robust ensemble-based deep learning approach for classifying breast cancer histopathology images from the BreakHis dataset into Malignant and Benign categories. This study implements three ensemble strategies efficientNetB0 and ResNet50, EfficientNetB0 and VGG16, and VGG16 and ResNet50—the study highlights the potential of combining complementary deep learning architectures for improved classification performance. Each ensemble incorporates modifications to optimize feature extraction and generalization for the specific dataset, including dense layers, dropout, and feature concatenation techniques. The proposed methodology achieves high accuracy, precision, recall, and F1-scores across all methods, with the EfficientNetB0 and VGG16 ensemble demonstrating superior performance. Comparative analysis with state-of-the-art methods with robustness and adaptability of the proposed approach, offering a scalable and efficient framework for medical image classification. This work provides valuable insights into the application of ensemble learning for reliable cancer diagnosis. © 2025 IEEE.

关键词： Contrastive learning

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Pavement Crack Segmentation using Incomplete Supervision Network 24

Pavement Crack Segmentation using Incomplete Supervision Net...

引用

3rd international conference on Artificial Intelligence and intelligent Information Processing, AIIIP 2024

作者： Wu, Wenxiu Jin, Yihui Fang, Zhihua Zhang, Jianfeng Fan, Xiaqi Song, Xiaoliang Port and Transportation Management Center Zhejiang Jinhua China Lanxi Transportation Construction Investment Group Co. Ltd. Zhejiang Jinhua China Lanxi Tongda Highway Maintenance Engineering Co. Ltd. Zhejiang Jinhua China Lanxi SHUNDA Road and Bridge Engineering Co. Ltd. Zhejiang Jinhua China

ISBN: (纸本)9798400707308

Challenges such as the lack of baseline pavement disease databases and limited segmentation accuracy and speed impact the performance of pavement disease segmentation and its practical engineering applications. To explore the feasibility of incomplete supervised learning, an experimental study generated inaccurate labels in batches by combining traditional image segmentation algorithms with manual methods, and the Mean Teacher model was tested. For imprecise supervised learning, the test F1 accuracy reached 0.572 after adding imprecise labels to the training set of self-collected data, although external imprecise labels were not feasible. In incomplete supervised learning, the Mean Teacher model showed significant advantages, achieving a test accuracy (MIoU) of 0.796 and an F1 score of 0.596 on self-collected data. Using unlabeled external images can enhance network accuracy in semantic segmentation tasks. With a small number of accurately labeled images, incomplete supervised learning can achieve substantial prediction accuracy. © 2024 Copyright held by the owner/author(s).

关键词： Self-supervised learning

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Big data Analytics for Electrical Systems using Machine learning Algorithms 3

Big Data Analytics for Electrical Systems using Machine Lear...

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3rd IEEE international conference on Technology, engineering, Management for Societal Impact using Marketing, Entrepreneurship and Talent, TEMSMET 2023

作者： Satyanarayana, Salava V. Madhavi, Pillalamarri Hyderabad Institute of Technology and Management Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798350397819

Big data Analytics plays a vital role in every electrical industry to analyze the large amount of data received from their dispatch centers. Typically, data retrieved from electrical systems like Generation, Transmission, and distribution will be analyzed using many control systems like SCADA and HMI without any human intervention. To analyze this data as per industry 4.0, Every system should be automated by integrating the data into the internet of things along with cyber security systems. With consideration of industry 4.0 standards, in view of effective optimized operational maintenance of electrical systems in the future, this paper provides intelligent predicted data by analyzing current and existing data that is fetching from electrical systems i.e., Various state electrical utility data files. The available data of electrical systems can be analyzed by using various Supervised Machine learning algorithms and this paper verified the accuracy of various machine learning algorithms by analyzing the predicted data by each algorithm. © 2023 IEEE.

关键词： data Analytics

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Quantifying Effectiveness of Governmental Agriculture Policies using RNN-Transformer based Sentiment Analysis 3

Quantifying Effectiveness of Governmental Agriculture Polici...

引用

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

作者： Joshi, Vibhor Yede, Navinya Parihar, Prajwal Dhule, Chetan Agrawal, Rahul Morris, Nekita Chavhan Department of DIC G H Raisoni College of Engineering Maharashtra Nagpur India

ISBN: (纸本)9798331527549

The objective of this research paper is to understand the impact of governmental agricultural policies through a sentiment analysis approach, using advanced neural networks like RNNs and Transformer models. The study seeks to understand public sentiment on different policies and practices by analyzing emotional reactions from various stakeholders in the agricultural sector. The methodology employs farmer-targeted surveys for data collection, preprocessing of the tokens or the data, and the creation of a high-performance sentiment analysis model. This model uses RNN and Transformer techniques together to boost the predictive accuracy. The results are aimed at providing the policymakers with actionable information, thus, improving the decision-making process and the communication strategies in the agricultural community. © 2025 IEEE.

关键词： ensemble techniques Government agricultural policies machine learning policy evaluation Recurrent Neural Networks (RNN) sentiment analysis survey data collection textual data preprocessing Transformer models

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Deep learning-Based Automatic Identification System for Marine Plankton 24

Deep Learning-Based Automatic Identification System for Mari...

引用

3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Xiang, Su Shandong University of Science and Technology Shandong Qingdao China

ISBN: (纸本)9798400718120

Due to their diverse species and complex morphology, the automatic identification of marine plankton has always been a challenging task. In response to this problem, this study proposes an innovative deep learning-based intelligent identification system. The system integrates advanced computer vision and artificial intelligence technologies, possessing the capability of automatic feature extraction and pattern recognition. We extensively collected marine sampling data and combined it with public datasets to construct a large-scale database containing 200 species of plankton and 500,000 images. Through the training and optimization of convolutional neural network models, the system demonstrates outstanding classification performance of 94.2% on the test set. Not only does the system outperform traditional methods in accuracy, but its inference speed has also been optimized through model compression and parallel computing, capable of processing 620 images per second. By deploying containerization and automatic scaling strategies, the system ensures high availability. This research provides strong technical support for marine ecological conservation, fisheries management, and other related fields. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Advancements in AI-Powered Robotic Cleaning Systems: Autonomous Path Planning, Predictive Maintenance, and Cleanliness Assessment Frameworks 3

Advancements in AI-Powered Robotic Cleaning Systems: Autonom...

引用

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

作者： Jones Sarah, A. Nandhakumar, D. Kumar, Atchitha Magesh Pavithra, S. Department of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai600069 India

ISBN: (纸本)9798331527549

Artificial Intelligence (AI) is transforming robotic cleaning systems to be more efficient, safe, and adapt in various environments, including public spaces and industrial facilities. The novelty of these systems is the use of advanced sensing and deep learning to identify areas that need cleaning, plan paths, and save resources all while meeting high cleanliness standards required in sensitive areas like hospitals and pharmaceutical facilities. The objective is how recent developments in path planning, dirt detection, and predictive maintenance improve these systems, which reduce costs and perform without human supervision. Though these systems work well, challenges remain in deciding on cleaning paths for complex spaces, correctly detecting various types of dirt, adapting to changing environments, and maintaining to prevent breakdowns. © 2025 IEEE.

关键词： Predictive maintenance

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Research on Fire Alarm System Problems Based on Comprehensive Evaluation Method and Machine learning 3

Research on Fire Alarm System Problems Based on Comprehensiv...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Yang, Juncheng Chang'an University Xi'an China

ISBN: (纸本)9798350380989

Machine learning applied to fire alarm systems is an increasingly common optimization problem. In this paper, a method based on comprehensive evaluation and machine learning is proposed. Firstly, relying on the literature and data sets, 10 indicators such as the number of fires, the failure rate, the accuracy rate, etc. are extracted to comprehensively evaluate the performance of each type of alarm component, and the entropy weight method, the information weight method and the TOPSIS method are used to derive the performance level rating of each component, and the fire alarm intelligent research and judgment system is constructed. Then, a BP neural network was used to build a training model and test the alarm data set for a certain region, and finally predicted the real number of alarms of each test device with an accuracy of 95.4%. These results can help the government plan to select more reliable fire detector types and improve the fire alarm accuracy to further optimize the fire alarm system. © 2024 IEEE.

关键词： BP Neural Networks Fire Alarm System Machine learning TOPSIS

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Multi-fidelity deep neural network surrogate model for aerodynamic shape prediction based on multi-task learning 3

Multi-fidelity deep neural network surrogate model for aerod...

引用

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

作者： Wu, Pin Liu, Zhitao Zhou, Zhu Song, Chao Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China China Aerodynam Res & Dev Ctr State Key Lab Aerodynam Mianyang 621000 Sichuan Peoples R China

ISBN: (纸本)9798350366105;9798350366099

With the increasing demands of refined aerodynamic shape design for modern aircraft, it is very essential for aerodynamic shape optimization to obtain more accurate aerodynamic data. The widely used high-fidelity simulation is usually accurate but extremely time-consuming. Therefore, we propose an innovative multi-fidelity model based on multi-task learning for aerodynamic shape prediction. This model consists of Bezier-Auxiliary Classifier GAN (Bezier-ACGAN), Multi-gate Mixture-of-Experts and Multi-Fidelity (MMoE-MF). Firstly, Bezier-ACGAN is used to construct the subsonic and transonic datasets and is used as an intelligent parameterization method. Secondly, The MMOE-MF model is coupled with the parameters of Bezier-ACGAN to predict different-fidelity of aerodynamic data. The results show that the predicted results of the optimal airfoils agree with the results of high-fidelity simulation well. This method is a promising approach that can convert from low-fidelity data to high-fidelity data in a few seconds.

关键词： Bezier-Auxiliary Classifier GAN Multi-gate Mixtureof-Experts multi-task learning multi-fidelity surrogate model

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Stability Enhancement for PV and Wind Penetrated Power System Using Machine learning Prediction 3

Stability Enhancement for PV and Wind Penetrated Power Syste...

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3rd IEEE international conference on Power Electronics, intelligent Control and Energy Systems, ICPEICES 2024

作者： Sivaraman, P.R. Sivakumar, P. Amirthavarshini, A. Ashika, P.G. Hemanth Kumar, R. Jagan Balaji, T. Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Chennai India

ISBN: (数字)9798350387193

ISBN: (纸本)9798350387193

In modern power systems, the integration of distributed generation (DG) has introduced both opportunities and challenges. It has also gained significant attention due to its potential benefits in terms of renewable energy utilization, improved system reliability and reduced transmission losses. As PV and wind are intermittent variables of the power system, stability problems arise which can be recovered using conventional PID controllers through the advanced tuning methods which incorporate the AI based tuning parameter. The process of tuning of the parameter is approached through proper artificial intelligence based trained data. This approach will enhance the power angle stability of the existing system. © 2024 IEEE.

关键词： Adversarial machine learning

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