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

共 20256 条记录，以下是721-730 订阅

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Research on Machine Vision-Based intelligent Vehicle Obstacle Recognition and Range Measurement Algorithm 3

Research on Machine Vision-Based Intelligent Vehicle Obstacl...

引用

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

作者： Wang, Han Niu, Wenlai Liu, Yahui Tan, Ye Xiu, Chunlian Chen, Xiangyu College Of Computer Science And Engineering Chongqing University Of Technology Chongqing China School Of Vehicle Engineering Chongqing University Of Technology Chongqing China College Of Foreign Languages Chongqing University Of Technology Chongqing China

ISBN: (纸本)9798350318609

With the rapid development of autonomous driving technology, obstacle recognition and range measurement for intelligent vehicles have become critical research areas. This study aims to delve into the data collection, preprocessing, and annotation processes of the obstacle recognition model for intelligent vehicles. Additionally, it explores the utilization of various deep learning models, such as the FCN and DeepLabV2networks, to achieve accurate obstacle identification. Furthermore, we investigated distortion coefficient solving and calibration methods for binocular cameras to achieve accurate measurement of obstacle distances. This paper conducted experiments using the ACDC dataset. Through extensive experimentation and analysis, we found that DeepLabV2achieved a recognition accuracy as high as 0.839, with a detection speed of 0.539 images per second. Additionally, we provided a comparative table of recognition accuracy for each model to assist researchers and engineers in better understanding the performance of these models. The calibration method proposed in this paper for binocular cameras has an error proportion lower than 2%. The achievements of this study are expected to propel further advancements in intelligent vehicle technology, enhancing road safety and the feasibility of autonomous driving. © 2024 IEEE.

关键词： Cameras

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Efficient Rice Leaf Disease Detection Based on Convolutional Neural Networks 3rd

Efficient Rice Leaf Disease Detection Based on Convolutional...

引用

3rd international conference on Machine learning, Cloud Computing and intelligent Mining, MLCCIM 2024

作者： Mandal, Gouranga Sastry, Nageswara Ahmed, Nihal Jonnabathula, Kashyap Dayananda Sagar University Karnataka Bengaluru562112 India School of Computer Science and Engineering VIT-AP University Inavolu Andhra Pradesh Vijayawada Amaravati522237 India

ISBN: (纸本)9789819624676

The domain of agriculture has witnessed a transformative shift with the advent of automated disease detection systems. This paper delves into the realm of employing deep learning techniques to detect rice leaf diseases efficiently. Our focus centers on four prominent deep learning architectures: Convolutional Neural Networks (CNN), VGG-19, LeNet, and MobileNet. These architectures stand as pillars in the pursuit of accurate classification across diverse rice leaf disease classes. The dataset encompassing nine distinctive disease categories and a healthy state forms the cornerstone of our study. The experimentation phase is marked by meticulous evaluation, with performance metrics such as accuracy, precision, recall, and the F1-score taking center stage. Each architecture’s prowess in capturing intricate patterns plays a significant role in shaping the classification landscape. In an era where technology’s role in agriculture burgeons, this study serves as a significant milestone. Moreover, our exploration paves the way for further research, fuelling the engine of innovation in automated disease detection for a secure and prosperous agricultural future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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CT Tumor Image Segmentation Based on Deep learning 3

CT Tumor Image Segmentation Based on Deep Learning

引用

3rd international conference on Electronic Information engineering and Computer Science, EIECS 2023

作者： Yue, Sun Yong, Ding Jilin Communications Polytechnic Changchun China Changchun University of Science and Technology Changchun China

ISBN: (纸本)9798350316674

Computed Tomography (CT) images play a crucial role in tumor detection, as it directly impacts subsequent analysis and treatment procedures. To address the issue of decreased accuracy due to manual interference, leveraging the advancements in deep learning computer technology for automated segmentation of CT tumors holds significant promise. The U-Net network is an automatic segmentation algorithm that operates fully on its own, This paper proposes enhancements to the original U-Net network model by increasing the number of convolutional layers in the U-Net encoder and modifying the classification from three-classification to two-classification. Additionally, the training data set sample size is increased, and the system performance is compared with the original basic model. The results show a significant improvement in the Dice coefficient compared to the original model, which holds clinical significance. © 2023 IEEE.

关键词： CT images deep learning tumor segmentation U-Net model

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IsharaVerse: A Bidirectional, Context-Aware, Multilingual Sign Language System 3

IsharaVerse: A Bidirectional, Context-Aware, Multilingual Si...

引用

3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Kashish Chelsi Langdi, Ramjas Trivedi, Amit Kumar Thapar Institute of Engineering and Technology Department of Computer Science and Engineeering Patiala India

ISBN: (纸本)9798331523893

IsharaVerse, a Context-Aware Multilingual Sign Language Generation and Translation System, bridges communication gaps between signers (Deaf, Hard of Hearing, Mute communities) and non-signers. It supports Indian Sign Language (ISL), converting between digits, alphabets, words, and sentences. Text/audio input is converted to sign gestures using Rule-Based Mapping, while Sign Language Translation leverages CNN-based Deep learning. A multilingual module integrates Google Translator, and an Emotion Recognition feature uses a DistilRoBERTa model. Achieving 90% accuracy in sign-to-text and seamless multilingual support, IsharaVerse promotes inclusion in social, educational, and professional contexts. © 2025 IEEE.

关键词： Deep learning

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Federated learning with Discriminative Naive Bayes Classifier 25th

Federated Learning with Discriminative Naive Bayes Classifie...

引用

25th international conference on intelligent data engineering and automated learning

作者： Torrijos, Pablo Alfaro, Juan C. Gamez, Jose A. Puerta, Jose M. Univ Castilla La Mancha Inst Invest Informat Albacete Albacete 02071 Spain Univ Castilla La Mancha Dept Sistemas Informaticos Albacete 02071 Spain

ISBN: (纸本)9783031777370;9783031777387

Federated learning has emerged as a promising approach to train machine learning models on decentralized data sources while preserving data privacy. This paper proposes a new federated approach for Naive Bayes (NB) classification, assuming discrete variables. Our approach federates a discriminative variant of NB, sharing meaningless parameters instead of conditional probability tables. Therefore, this process is more reliable against possible attacks. We conduct extensive experiments on 12 datasets to validate the efficacy of our approach, comparing federated and non-federated settings. Additionally, we benchmark our method against the generative variant of NB, which serves as a baseline for comparison. Our experimental results demonstrate the effectiveness of our method in achieving accurate classification.

关键词： Federated learning Bayesian network classifiers Naive Bayes Discriminative learning

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data Analytics on Blitz E-sports: Enhancing Gaming Experience through Personalization and Prediction 3

Data Analytics on Blitz E-sports: Enhancing Gaming Experienc...

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

作者： Vaneeta, M. Kusuma, T. Shruthi, G. Avanish Rao, B.S. Anand, Nikhil Abhishek, M. Ramaiah Institute of Technology Department of Artificial Intelligence & Data Science Bangalore India Ramaiah Institute of Technology Department of Information Science Bangalore India

ISBN: (纸本)9798331515911

This paper delves into the comprehensive data analytics of Blitz E-sports, a dynamic gaming cafe, to enhance operational efficiency and customer satisfaction. The study focuses on three pivotal objectives: revenue optimization, game recommendations, and churn prediction. Through meticulous analysis of customer data, gaming trends, and operational metrics, this proposal equips Blitz E-sports with action able insights to enhance profitability, customer engagement, and long-term sustainability. The potential of data-driven decision-making in the competitive e-sports industry is demonstrated by leveraging advanced machine learning techniques and big data technologies. This paper aims to reveal significant improvements in personalized user experiences, optimized revenue strategies, and effective customer retention measures. © 2025 IEEE.

关键词： Sales

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A Silent Cardiac Atrial Fibrillation Detection and Classification using Deep learning Approach 3

A Silent Cardiac Atrial Fibrillation Detection and Classific...

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3rd international conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Rajesh, Banala Mohan, Allam Lovely Professinal University Department of Computer Science and Engineering Phagwara India

ISBN: (纸本)9798350336238

The electrocardiogram (ECG is a standard method for diagnosing irregular heart rhythms. Abnormalities, such as silent cardiac atrial fibrillation, which is caused by an irregular cardiac cycle, are detected with the aid of ECG signal data. Faster and more accurate results from automated classification and detection of the ECG arrhythmia signal are considered essential. Improvements in model speed and robustness have been achieved through the application of various pre-processing techniques and deep learning abilities. Many researchers have paid attention to the performance of various deep learning approaches on different datasets of ECG signals. But they have overlooked the significance of data pre-processing before feeding it to deep learning models. This research proposes a Residual Network (ResNet) architecture that increases training stability using a combination of resampling and data augmentation techniques. The results have proven that ResNet produces higher accuracy on the PhysioNet MIT-BIH Arrhythmia dataset for the classification of ECG data. © 2023 IEEE.

关键词： Electrocardiograms

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AutoML as a Catalyst for Predictive Maintenance Innovation: Strategies and Outcomes 3

AutoML as a Catalyst for Predictive Maintenance Innovation: ...

引用

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

作者： Jayasurya, B. Suguna, M. Saravanan, Pooja Revathi, M. Vellore Institute of Technology School of Computer Science Engineering Chennai India

ISBN: (纸本)9798350372120

The intersection of predictive maintenance and automated Machine learning (AutoML) ushers in a transformative era for industrial optimization, wherein the foresight of equipment failures becomes both attainable and actionable. Conventional methodologies for predictive maintenance often necessitate profound technical knowledge and comprehensive data manipulation. This proposed work introduces an innovative framework based on AutoML, employing tools such as AutoKeras, Sweetviz, NumPy, pandas, Streamlit, and PyCaret, to democratize the crafting of predictive models. The outlined methodology automates the stages of data preprocessing, feature selection, and model optimization, markedly simplifying the process of predictive maintenance. The application of this framework illustrates a streamlined model development cycle, attaining significant achievements in the prediction of equipment failures, thereby augmenting operational efficiency, and diminishing downtimes. Through practical deployment, the proposed work demonstrates how this strategy not only alleviates the complexity inherent in predictive maintenance but also yields considerable enhancements in maintenance strategy and resource allocation. This exploration validates the potential of AutoML to revolutionize predictive maintenance, enhancing its efficiency and accessibility. © 2024 IEEE.

关键词： Predictive analytics

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Implementation of Predictive Machine learning Algorithms on Heated Geothermal Bridge data 3

Implementation of Predictive Machine Learning Algorithms on ...

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

作者： Soleimani, Faezeh Bidi, Saeed Habibzadeh-Bigdarvish, Omid Yu, Xinbao Ball State Univ Dept Math Sci Muncie IN 47306 USA Queen Mary Univ London Wolfson Inst Populat Hlth London England Univ Texas Arlington Dept Civil Engn Arlington TX USA

ISBN: (纸本)9798350372977;9798350372984

In recent years, there has been a notable surge in exploring statistical methodologies and artificial intelligence (AI) approaches, such as machine learning and deep learning, across various domains, including engineering. These data-driven techniques hold promise for delivering faster and more accurate predictions, representing a significant avenue for progress. Building upon prior research, this study extends the scope by incorporating additional features and utilizing a distinct dataset for forecasting the average heated bridge deck surface temperature and the outlet fluid temperature from the hydronic heating loops. Leveraging machine learning algorithms on data collected from a bridge de-icing project in Texas, we examine the effectiveness of Multiple Linear Regression (MLR) and Support Vector Regression (SVR). Through rigorous comparison with field data, we validate the robustness of these methodologies in temperature forecasting tasks, noting high accuracy across all algorithms. Notably, while both MLR and SVR demonstrate commendable performance, MLR marginally outperforms SVR, achieving an R-2 value of 0.79.

关键词： temperature prediction machine learning multiple linear regression support vector regression geothermally heated bridge

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Efficient Ship Engine Condition Monitoring and Fault Diagnosis System based on intelligent Control 3

Efficient Ship Engine Condition Monitoring and Fault Diagnos...

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

作者： Luo, Changbo Sanya Vocational College of Aviation Tourism Hainan Province Sanya City572000 China

ISBN: (纸本)9798331509675

With the continuous iteration of the ship engine technology, related technologies have achieved great development. Ship engines run for a long time in harsh environments of high temperature, high pressure and high speed, which increases the possibility of failure. Therefore, how to detect and warn faults is an important research topic. In this study, the efficient ship engine condition monitoring and fault diagnosis system based on intelligent control is studied. In the early stage of system construction, this study analyzed the basic model of ship engine condition monitoring. The model combines neural networks and sensor data extraction algorithms, and analyzes the health status of gas turbines through modeling. At the same time, machine learning algorithms such as BP neural networks are used to predict the compressor characteristic curve. This attempt improves the traditional algorithm's ability to remember samples and improves the accuracy of the system. In terms of fault diagnosis modeling, this study uses a machine learning-based model and a signal processing technology study. Specifically, the time-frequency data of the turbine equipment is studied to identify anomalies. The characteristic frequency is extracted by fast Fourier transform and the energy concentration area is analyzed to achieve the potential fault detection. Experimental results show that the system can effectively improve the accuracy of ship engine fault diagnosis and achieve good results on both small and large data sets. © 2025 IEEE.

关键词： Ships

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