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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471-480 订阅

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Federated learning for Vietnamese SMS Spam Detection Using Pre-trained PhoBERT 25th

Federated Learning for Vietnamese SMS Spam Detection Using P...

引用

25th international conference on intelligent data engineering and automated learning

作者： Hoang Quang Anh Pham Tuan Anh Pham Son Nguyen Phan Duy Hung FPT Univ Hanoi Vietnam

ISBN: (纸本)9783031777301;9783031777318

SMS Spam Detection has increasingly garnered attention due to the widespread use of mobile devices. Currently, most SMS spam detection model training methods rely on centralized data collection, which poses numerous privacy threats and creates security vulnerabilities that expose sensitive information. This study aims to propose a training method that does not require data sharing between parties, based on a federated learning system. In this paper, we experiment with FedAvg, FedAvgM, and FedAdam algorithms using a fine-tuned PhoBERT model tailored for the SMS spam classification task. The results show that the FedAvg algorithm achieves high performance with an accuracy of 99.38% in the IID setting, while the FedAdam algorithm proves more effective in the Non-IID setting, yielding a model with an accuracy of up to 98.5%. This study demonstrates that models like PhoBERT trained with FL algorithms can achieve classification capabilities comparable to centralized data training methods, highlighting the significant potential of FL for natural language processing models without the need for centralized data collection.

关键词： Vietnamese SMS spam detection PhoBERT Federated learning

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A Composite intelligent Forecasting Method for Load Demand of Distribution Networks Based on Deep learning 3

A Composite Intelligent Forecasting Method for Load Demand o...

引用

3rd international conference on Electrical engineering and Mechatronics Technology, ICEEMT 2023

作者： Jiarula, Yasenjiang Zhang, Liyang Luo, Li Xinjiang University Urumqi830000 China

ISBN: (纸本)9798350303698

This paper proposes a composite intelligent prediction method for distribution network load demand based on deep learning. Firstly, the method preprocesses the distribution network operation information collected by sensors, supplements the missing values of data through cubic Spline interpolation fitting function, and realizes dimensionless data processing by standardization and interval scaling. Based on this, XGBoost algorithm is used to extract the characteristics of distribution network load demand data, and long-term and short-term memory networks in deep learning are used to build load demand prediction models, Complement the Dense layer in the long-term and short-term memory network to achieve composite prediction. The experimental results show that this method can effectively improve the accuracy and stability of load demand prediction. © 2023 IEEE.

关键词： Forecasting

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Improving Potato Disease Classification with Convolutional Neural Networks and Feature Fusion 3

Improving Potato Disease Classification with Convolutional N...

引用

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

作者： Deshmukh, Sakshi Parkhi, Anushka Gandhi, Shravani Ghusse, Diptee MIT Academy of Engineering School of E&TC Engineering Pune India MIT Academy of Engineering School of Computer Engineering Pune India

ISBN: (纸本)9798331523893

Accurate prediction of potato diseases plays a vital role in maintaining crop health and maximizing agricultural yield. This study presents a deep learning approach utilizing Convolutional Neural Networks (CNNs) integrated with advanced feature fusion strategies to enhance the precision of disease classification. The methodology employs three feature fusion strategies - Early, Intermediate, and Late Fusion - on a benchmark dataset of 2,152 potato leaf images categorized into Early Blight, Late Blight, and Healthy. The workflow includes data preprocessing, feature extraction using CNNs, and classification through fully connected *** indicate that Late Fusion outperforms other strategies, achieving the highest classification accuracy and robust generalization despite class imbalance. It effectively distinguishes between Early and Late Blight, while Early Fusion shows better performance for Healthy leaves. The study highlights the importance of feature fusion and data augmentation in enhancing disease diagnosis, paving the way for scalable and efficient agricultural solutions. © 2025 IEEE.

关键词： Convolutional neural networks

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Empowering Leather Quality Assurance: Leveraging Convolutional Neural Networks for Precise Defect Detection and Classification 3

Empowering Leather Quality Assurance: Leveraging Convolution...

引用

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

作者： Logeswaran, K. Suresh, P. Savitha, S. Sah, Swati Kr, Prasanna Kumar Rajadevi, R. Vasugi, M. Sujit, B. Akilesh, T. Kongu Engineering College Center of Excellence in Real-Time Analytic Department of Artificial Intelligence Tamilnaud Erode India Vellore Institute of Technology Department of Database Systems Tamil Nadu Vellore India K.S.R College of Engineering Department of Computer Science and Engineering Tamil Nadu Tiruchengode India Sharda University School of Engineering and Technology Uttar Pradesh Greater Noida India Kongu Engineering College Department of Information Technology Tamilnaud Erode India Kongu Engineering College Department of Artificial Intelligence Tamilnaud Erode India

ISBN: (纸本)9798331523893

Ensuring the development of high-quality leather products in the industrial sector requires a strong focus on leather quality assurance. In this study, a unique method for enhancing leather quality assurance using Convolutional Neural Networks (CNNs) is presented. Our suggested solution is able to detect and classify leather defects with amazing accuracy and efficiency by utilizing deep learning. CNN integration for leather fault categorization and detection offers a more reliable and affordable solution, ensuring better product quality and satisfied customers. Existing methods for inspecting leather products often suffer from inconsistent quality control, subjective manual inspection processes, and the complexity of accurately identifying and classifying various types of leather defects. By addressing these challenges, the proposed system aims to empower leather manufacturers with a reliable and efficient solution for ensuring consistent product quality and reducing production costs. Convolutional Neural Networks (CNNs) are integrated to provide a highly automated and effective solution that overcomes these scaling challenges. The proposed CNN-based method uses deep learning algorithms to decode images, which allows the system to swiftly and precisely analyse enormous volumes of visual data. To make sure the CNN can differentiate between various defect classes and keep a low rate of false positives and false negatives, its accuracy, precision, recall, and F1 score should be carefully assessed. © 2025 IEEE.

关键词： Fault tolerance

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Soil data Analytics using Machine learning Techniques - A Survey 3

Soil Data Analytics using Machine Learning Techniques - A Su...

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3rd IEEE international conference on intelligent Technologies, CONIT 2023

作者： Raghavendra Rao, R.V. Srinivasulu Reddy, U. Mohan Reddy, Ch Ram BMS College of Engineering Department of Computer Applications Bangalore India National Institute of Technology Department of Computer Applications Tiruchirappalli India

ISBN: (纸本)9798350338607

Food security is the most basic need with any individual all over the globe. With food security we can reduce malnutrition in children, who are the backbone of nation's development. To achieve this using machine learning techniques in agriculture domain is the new beginning. Machine learning techniques successfully implemented in innumerable domains, now entered in agriculture. Machine learning techniques establishes a novel way in data processing and data analytics, producing good results with larger data. In this work we carried out survey on machine learning technique's role in agriculture domain and challenges involved in it. We examined the models developed based on agricultural soil. In addition, we analyzed the associations between soil nutrients, which help in crop yield. These results specify that the accuracy of the model is more with ensemble methods. © 2023 IEEE.

关键词： Soils

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A Retrospective Survey on the Recent Trends in Brain Tumors and Their Detection Methodologies 3

A Retrospective Survey on the Recent Trends in Brain Tumors ...

引用

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

作者： Kiruthiga, G. Nithyasri, A. Saravanakumar, N.M. Department of Information Communication and Engineering Anna University Chennai600 025 India Department of AI & DS M.Kumarasamy College of Engineering Karur639 113 India Department of Information Technology Karpagam College of Engineering Coimbatore641 032 India

ISBN: (纸本)9798331527549

The abnormal cell growths in the brain are called tumours, and malignant tumours are referred to as "cancer". Scans using CT, MRI, or Positron Emission Tomography (PET) are commonly used to identify brain tissue that is malignant. This study addresses different types of brain tumours, how they are detected, and how to help people who are afflicted by early cancer identification. MRI and PET scans are used to diagnose brain tumours, and additional techniques such as molecular testing, lumbar puncture, and cerebral angiography are utilised to assess the disease stage. This survey investigation aims to: (i) identify abnormal photographs;(ii) segment tumour regions;and (iii) ascertain the cancer's stage. The proper brain tumour detection methodology will be identified with the aid of this survey investigation. © 2025 IEEE.

关键词： Positron emission tomography

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automated Assessment of Pizza Quality Using Computer Vision and Deep learning Techniques 3

Automated Assessment of Pizza Quality Using Computer Vision ...

引用

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

作者： Arakeri, Megha Lakshmana Manipal Academy of Higher Education Manipal Institute of Technology Bengaluru Department of Computer Science and Engineering Manipal India Seamovation Labs Pvt. Ltd Bangalore India

ISBN: (纸本)9798331523893

Quality control in the pizza-making process is essential to meet consumer expectations. In the age of online delivery, verifying the delivered pizza is necessary. The pizza usually consists of a base, sauce, cheese, vegetables, and meat. Low-quality pizza delivery may lead to consumer dissatisfaction and hinder pizza production business growth. Hence, this paper proposes a method to evaluate sauce spread, identify toppings, and assess their distribution on cooked and uncooked pizzas to address this issue. The proposed methodology uses a feature pyramid network for background and foreground separation in the pizza image. Then, the sauce spread thickness is classified into 3 categories using the U-Net model. Finally, a multi-label CNN and color segmentation were applied to the image to evaluate the spread of toppings on the pizza. Experiments were carried out on a standard image dataset, and it was observed that the proposed model achieved 95% accuracy in pizza quality evaluation. © 2025 IEEE.

关键词： Damage detection

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Predicting Employee Attrition in a Multi-company Setting 25th

Predicting Employee Attrition in a Multi-company Setting

引用

25th international conference on intelligent data engineering and automated learning

作者： Gomes, Adriano Silva, Luis M. Cruz, Joao Pedro Univ Aveiro Dept Econ Management Ind Engn & Tourism P-3810193 Aveiro Portugal Univ Aveiro Ctr Res & Dev Math & Applicat CIDMA Dept Math P-3810193 Aveiro Portugal

ISBN: (纸本)9783031777301;9783031777318

This paper describes the creation of a database and a machine learning model to predict employee attrition. Our proposal deals with attrition by considering 3 classes (voluntary, involuntary and no attritors) giving a more complete view of the loss of qualified personnel to the Human Resources Management. Of the several machine learning models tested to solve the problem, XGBoost stood out as the best performing one on a dataset with more than four thousand employees and twenty-one features collected from three independent companies from different industrial sectors. The model, evaluated on a 20-run experiment, achieved an overall mean accuracy of 78.5%, corresponding to the correct classification of 52.6% of the voluntary attritors, 78.9% of the involuntary attritors and 81.6% of the non-attritors, showing that voluntary attritors are harder to discriminate.

关键词： Employee attrition Machine learning Real data Human resources management Industry 4.0

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Advanced Deep learning Approaches for Fault Detection and Localization in Power Distribution Systems 3

Advanced Deep Learning Approaches for Fault Detection and Lo...

引用

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

作者： Sharma, Nitin Pankaj Rai, J.N. Nagarajan, S.T. Delhi Technological University Department of Electrical Engineering Delhi India

ISBN: (纸本)9798350387193

The usage of Artificial Neural Networks (ANN) in improving electricity gadget operations is a burgeoning field of study, specifically within the context of fault detection and localization in strength distribution networks. This studies investigates the utility of ANNs to plot more efficient and correct methodologies for pinpointing disruptions within energy traces. Employing the Levenberg-Marquardt algorithm inside a MAT LAB environment, the look at entails the development and schooling of an ANN version, which is intricately designed with various configurations of hidden layers and neurons. data preprocessing is a crucial step to nice-music the model's overall performance. The findings of this studies imply a vast improvement in the detection and localization of faults over traditional methods, with the ANN model showing superior precision and swiftness. These promising effects suggest for persisted studies into ANN programs in strength structures, suggesting a capability paradigm shift in future energy device management and upkeep protocols. © 2024 IEEE.

关键词： Power distribution faults

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IoT-Driven Smart Car Parking System: Optimizing Urban Mobility and Reducing Environmental Impact 3

IoT-Driven Smart Car Parking System: Optimizing Urban Mobili...

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

作者： Tanvi, Y. Lakshmi Sowmya, B. Prashanth, B.N. Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The rapid increase in urban vehicle numbers has significantly worsened traffic congestion, particularly in public parking spaces, where conventional parking systems often prove inefficient, leading to wasted time, excessive fuel consumption, and increased environmental pollution. A Smart Car Parking System is proposed to address these challenges by integrating advanced technologies, including the Internet of Things (IoT), to optimize parking operations. The system employs sensors embedded in parking spaces to detect vehicle presence and provide real-time data on availability. This enables drivers to locate free parking spaces efficiently, improving traffic flow. Additionally, the system offers valuable insights to city planners and facility managers, supporting optimal space allocation and dynamic pricing models. By reducing vehicle idling and unnecessary driving while searching for parking, the system helps minimize emissions, contributing to environmental sustainability. The study builds an automated smart car parking system for parking slot monitoring and gate control. The system ensures correct slot occupancy reading, status displayed in real time, and smooth operation of an opening and closing system. It manages additional features such as real-time sensor calibration and effective gate control with a high degree of accuracy and accountability. The proposed Smart Car Parking System represents a key innovation in urban mobility, offering a user-friendly, efficient, and ecofriendly solution to one of the most persistent challenges faced by modern cities. © 2025 IEEE.

关键词： Traffic congestion

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