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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691-700 订阅

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Transfer learning and Fine-tuned CNN Architecture for Dog Breed Classification 3

Transfer Learning and Fine-tuned CNN Architecture for Dog Br...

引用

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

作者： Gunaranjan, K. Vijay, K. Naveen, P. St. Joseph's College of Engineering Department of Artificial Intelligence and Data Science Chennai India SRM institute of science and technology Department of computational intelligence Chennai India

ISBN: (纸本)9798350372120

This study presents a comprehensive approach to dog breed classification, leveraging transfer learning algorithms and Convolutional Neural Networks (CNNs). The dataset utilized for training and evaluation is sourced from the Dog Breed Identification dataset, encompassing 120 distinct breeds. A pre-trained CNN model is employed, and through the application of transfer learning, the proposed models achieve an impressive accuracy rate of 99%. The primary objective is to enable users to submit images of dogs, with the model accurately identifying the specific breed from the diverse dataset. The research underscores the efficacy of transfer learning and pretrained models in achieving exceptional accuracy for the nuanced task of dog breed classification, contributing to the advancements in image recognition within the context of diverse canine breeds. © 2024 IEEE.

关键词： Convolutional neural networks

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Zero-shot data-to-text generation via dual learning 3

Zero-shot Data-to-text generation via dual learning

引用

3rd international conference on Electronic Information engineering and data Processing, EIEDP 2024

作者： Liao, Weiming Chen, Bo Zhao, Xiaobing School of Information Engineering Minzu University of China China

ISBN: (纸本)9781510680531

data-to-text task aims to convert structured data into natural language text. However, the lack of large parallel data is a major practical problem for many domains in data-to-text generation. To address these issues, this paper proposes a method based on dual learning. Dual learning simultaneously learns two mutually dual generator and extractor, where the generator is responsible for text generation and the extractor for information extraction. Through dual learning, we can effectively utilize the interrelationships between unaligned data, thereby improving the performance of the generation model. We conduct experiments on an advertising datasets, and compare with traditional generation models. Experimental results demonstrate that the dual learning-based method achieves nearly the same performance as fully supervised approaches for the data-to-text generation task validating its effectiveness and feasibility in data-to-text generation tasks. © 2024 SPIE.

关键词： Zero-shot learning

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Efficient net-based deep learning: A deep-fake image detection 3

Efficient net-based deep learning: A deep-fake image detecti...

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

作者： Singh, Rohan Sharma, Dilip Kumar Kumar Jain, Praphula Gla University Department of Computer Science and Engineering Mathura India Gla University Department of Computer Engineering and Application Mathura India Mits Gwalior Department of Computer Science and Engineering MP India

ISBN: (纸本)9798331528201

With the substantial volume of social media data generated daily and the concurrent emergence of powerful multimedia modification techniques, detecting manipulations in photographs and videos has become an important task. This study examines the strength of a deep learning-based efficient net model for detecting manipulation in multimedia images. This study examines the publicly accessible benchmark dataset DFDC to prove its efficiency. We also highlighted the real-world challenges and emerging trends to combat deep-fakes. The research experiment resulted in and confirmed the efficiency of Efficient-Net as it performed well in both the training and testing stages, exhibiting more accuracy and reduced loss. The test accuracy varies between 50 and 95, with epochs spanning from 0 to 10. © 2024 IEEE.

关键词： Deep learning Deepfakes Efficient Net Fake Image Video

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Deep learning methods in liver diseases 3

Deep learning methods in liver diseases

引用

3rd international conference on Biomedical and Intelligent Systems, IC-BIS 2024

作者： Bian, Xiangwei Bian, Qingxiang Wei, Xiaoning Li, Xiaokai Magnetic Resonance Imaging Center School of Sports and Health Shanghai University of Sport Shanghai China Department of Mechanical and Manufacturing Engineering Faculty of Engineering University Putra Malaysia Serdang Malaysia

ISBN: (纸本)9781510681279

With the booming development of artificial intelligence, deep learning, and machine learning, an important branch in the field of artificial intelligence, especially the intelligent algorithms represented by deep learning, has been gradually applied in the field of medicine. Liver cancer has been the focus of research in the field of medicine, and the development of deep learning technology is crucial for making precise diagnoses, lesion classification, risk factor prediction, and prognosis assessment of liver tumors. In this paper, we review the status of research related to deep learning in the field of liver cancer diagnosis. © 2024 SPIE.

关键词： Deep learning

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Prediction of mechanical properties of Q345qE steel welded joints based on machine learning 24

Prediction of mechanical properties of Q345qE steel welded j...

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3rd international conference on Signal Processing, Computer Networks and Communications, SPCNC 2024

作者： Liu, Chengqing Hou, Xiaolong Liu, Lijun Qiao, Zhu Qin, Zhiqi Wang, Shengbao Company Limited Heilongjiang Harbin China School of Architecture and Civil Engineering Harbin University of Science and Technology Heilongjiang Harbin China Heilongjiang Provincial Transportation Planning and Design Institute Group Co. Ltd. Heilongjiang Harbin China

ISBN: (纸本)9798400710834

The mechanical properties of welded joints are the key indicators to evaluate the welding quality, which are mainly subject to the rationality of the welding process and the technical level of the welders. Traditionally, enterprises screen out the optimal welding scheme by implementing a large number of welding procedure qualification tests, but this method is costly and inefficient. In this study, the prediction model of tensile strength and yield strength of Q345 qE steel welded joints was established based on the machine learning algorithm of BP neural network. The results show that the prediction accuracy of the model is high, the R2 value is more than 0.96, and the relative error is controlled within ± 3MPa (tensile strength) and ± 5MPa (yield strength). This prediction method is expected to reduce the dependence on laboratory tensile tests, reduce experimental costs, and has significant application value for actual production. © 2024 Copyright held by the owner/author(s).

关键词： Tensile strength

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Construction and research of forecasting method of electrical fault based on machine learning and decision tree 3

Construction and research of forecasting method of electrica...

引用

3rd international conference on Advanced Manufacturing Technology and Manufacturing Systems, ICAMTMS 2024

作者： Li, Yousong Yanbian University YBU Yanji China

ISBN: (纸本)9781510681804

Background: machine learning is progressively utilized within the realm of electrical fault detection, enhancing the accuracy of fault discrimination. Objective: This paper aims to utilize five machine learning algorithms to predict faults in three-phase electrical power system and compare the predictive performance of five classical machine learning algorithms. Method: Logistic Regression, Decision Tree, Random Forest, XGBoost, and Support Vector machine are employed to predict the existence and types of faults in three-phase electrical power system. The algorithms' performance is evaluated by comparing the predictive evaluation metrics. Results: In this paper, Decision Tree exhibited the optimal evaluation metrics, achieving an accuracy of 88% on the test set. Conclusion: The experimental results indicate that Decision Tree exhibits the best performance in predicting faults in power system. This study provides guidance and recommendations for decision-makers in relevant industries. © 2024 SPIE.

关键词： Contrastive learning

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Research on Residual Welding Rod Detection Technology Based on Deep learning 3

Research on Residual Welding Rod Detection Technology Based ...

引用

3rd international conference on Cloud Computing, Big data Application and Software engineering, CBASE 2024

作者： Min, Hengshi Zou, Huilan School of Information Engineering Central University for Nationalities China

ISBN: (纸本)9798331506582

In the domain of welding, conventional methods for quantifying residual welding rods predominantly depend on manual counting, which is not only labor-intensive but also susceptible to inaccuracies. To address this issue, this study proposes an instance segmentation model based on the improved Mask Region-based Convolutional Neural Network model, referred to as Point Mask R-CNN. By integrating an attention mechanism and the PointRend module, the model synergizes channel and spatial attention within the convolutional neural network, thereby augmenting its perceptual capabilities. Furthermore, it refines boundary delineation and detail segmentation through dynamic point sampling and localized refinement processes. Experimental findings demonstrate that this model not only marginally elevates the detection efficacy of the basic model but also markedly enhances segmentation performance, facilitating a more efficient and precise identification of residual welding rods. © 2024 IEEE.

关键词： Welding rods

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Detecting Lung Nodule Malignancy Using CNN-Based Deep learning Techniques 3

Detecting Lung Nodule Malignancy Using CNN-Based Deep Learni...

引用

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

作者： Khawar, Mishaal Taqi, Muhammad Ahmad, Faizan Tariq, Komal Anwer, Saba Department of Biomedical Engineering Riphah International University Lahore Pakistan

ISBN: (纸本)9798331504861

This study focuses on the development of a Convolutional Neural Network (CNN)-based deep learning model to detect cancerous lung nodules from computed-tomography (CT) scans. Timely identification of lung cancer is essential in improving survival rates, as it remains a leading cause of global cancer-related mortality. CNN models improve medical image analysis by automating the classification process to offer increased diagnostic accuracy. A custom-built CNN model is employed to distinguish between benign and malignant lung nodules using CT scans from the Kaggle’s data Science Bowl 2017 dataset. Our model reached a 92% accuracy in data classification. The findings show that this CNN-driven approach excels previous techniques in identifying lung nodule malignancy, allowing more precise diagnoses, and improved patient outcomes. Future research should explore using cascaded algorithms and leveraging multiple datasets to enhance prediction accuracy and generalization of malignancy, as well as reduce overfitting impact. ©2024 IEEE.

关键词： Lung cancer

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Cyberbullying Detection using Deep learning Algorithms 3

Cyberbullying Detection using Deep Learning Algorithms

引用

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

作者： Nirmala Devi, K. Jayanthi, P. Rajasekar, Vani Rathinasamy, Rahul Dhandapani, Pradeep Rasil Risam, F. Dept. of Computer Science Kongu Engineering College Erode India

ISBN: (纸本)9798331527549

Social media's widespread use has made it easier for people to communicate openly, but it has also fuelled the growth of cyberbullying, which has seriously harmed victims' emotional and mental health. The intricacy of human language and the variety of abusive behaviours make it difficult to detect cyberbullying. To solve this problem, numerous studies have used deep learning (DL) and machine learning (ML) algorithms over time. However, deep learning (DL) algorithms have transformed text analysis by automatically extracting features and comprehending contextual dependencies in language, while machine learning (ML) techniques are limited in their ability to handle complex and contextual linguistic patterns. Building upon this framework, this study investigates the use of transformer-based models—more especially, BERT—for the detection of cyberbullying. Out of the proposed models, DistlBERT gives the best accuracy, precision, recall, and F1-score of 98.87%,98.87%, 98.87 and 98.86% respectively. © 2025 IEEE.

关键词： Adversarial machine learning

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AI-Powered Crop Suggestion, Yield Prediction, Disease Detection, and Soil Monitoring 3

AI-Powered Crop Suggestion, Yield Prediction, Disease Detect...

引用

3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Raju, N. Santha Tamilkodi, R. Shekar, Vanaparthi Chandra Bharathi, Bogadi Jaya Kumar, Koviri dinesh Sumanth, Yanamala Godavari Global University A.P Rajahmundry India Godavari Institute of Engineering & Technology A.P Rajahmundry India

ISBN: (纸本)9798331532420

India's agricultural sector is crucial for food security. However, factors such as climate change, soil quality, and pest infestations can significantly impact crop yields. This research aims to develop a machine learning-based model to predict crop yields accurately. The proposed model leverages advanced techniques like Inception ResNet V2, AlexNet, Random Forest, Decision Tree, KNN, Naive Bayes, Simple RNN, Logistic Regression, and Voting Classifier to analyze historical data and identify key factors influencing crop yield. By considering variables such as area, crop variety, and climatic conditions, the model provides valuable insights to farmers. The proposed model achieved a high accuracy of 91%, demonstrating its potential to significantly improve agricultural practices. By enabling farmers to make informed decisions regarding crop selection, planting, and resource allocation, this research contributes to sustainable agriculture and food security. © 2024 IEEE.

关键词： Crop Suggestion Crop Yield Prediction Decision Tree Inception-ResNetv2 K-Nearest Neighbors Naive Bayes Plant Disease Detection Soil Moisture Monitoring machine learning Algorithms Voting Classifier

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