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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761-770 订阅

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CNN Image Forgery Detection: Machine learning Improves Photojournalism Accuracy and Robustness 3

CNN Image Forgery Detection: Machine Learning Improves Photo...

引用

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

作者： Thakur, Varsha Agarwal, Rohit Gla University Department of Computer Engineering & Applications Mathura India

ISBN: (纸本)9798331528201

The purpose of this study is to address the rising problem of picture modification and fraud, especially in the field of photojournalism, where the ability to recognize changed photos is essential to preserving faith in digital technology. By employing CNN-based approaches, the created forgery detection system exhibits its capacity to reliably discern between actual and AI-generated photos, reaching a balanced classification of 49% and 50%, respectively, with low misclassification. The purpose of this research is to investigate the development of digital picture forensics by using both conventional methods and contemporary innovations such as blockchain and deep learning. In order to achieve a higher level of detection accuracy, the suggested system makes use of machine learning models, such as transfer learning using pretrained models like ImageNet. The findings demonstrate that the system is capable of generalization and has a limited amount of overfitting, as seen by the high training accuracy of 98.44% and the validation accuracy of 98.92%. It is accepted that there is a need for future improvements in scalability, robustness to adversarial assaults, and interpretability, despite the fact that the findings have been somewhat encouraging. In order to successfully battle increasingly complex picture forgeries, future breakthroughs in digital image forensics will need cooperation between the government, industry, and academic institutions. © 2024 IEEE.

关键词： Adversarial machine learning

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Advancing Tomato Leaf Hygiene with Machine learning for Enhanced Plant Health and Productivity 3

Advancing Tomato Leaf Hygiene with Machine Learning for Enha...

引用

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

作者： Raman, Ramakrishnan Kumar, Vikram Sanjay, C.P. Rabadiya, Dhaval Rastogi, Shailesh Kumbhojkar, Neelesh Pune India Srv Media Pvt. Ltd. India India Faculty of Engineering and Technology Parul Institute of Technology Parul University India Symbiosis Institute of Business Management Nagpur India India

ISBN: (纸本)9798331515911

The implementation of a machine learning model to enhance hygiene permanence in tomato leaves has shown remarkable results, emphasizing the utility of convolutional neural networks (CNNs) for early disease detection. Our model, trained on a comprehensive dataset of several thousand annotated images, effectively identifies subtle changes in leaf condition before visible symptoms manifest. The CNN architecture, which incorporates convolutional, pooling, and fully connected layers, was meticulously optimized for both accuracy and computational efficiency. Techniques such as dropout and batch normalization were employed to prevent overfitting, ensuring robust performance across various conditions. The real-time monitoring system, integrated with the CNN, facilitated continuous data input and early intervention, significantly curtailing the spread of diseases. Field trials validated the CNN's effectiveness, with performance metrics showing an accuracy of 92.5%, precision of 90.3%, recall of 89.7%, and an F1-score of 90%. These results indicate a high reliability in detecting leaf degradation, demonstrating the model's proficiency in identifying true positive cases and its ability to catch a large percentage of actual positives. The study confirms that integrating advanced machine learning techniques into agricultural practices can transform plant disease management, promoting sustainable farming by reducing reliance on chemical interventions and improving crop resilience. The CNN model's continuous learning capability further supports its adaptability to evolving conditions, making it a valuable tool for enhancing tomato crop health and productivity. © 2025 IEEE.

关键词： Fruits

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Research on the Optimization of Supermarket Staff Scheduling Based on intelligent Algorithm Combined with Binary Coding 3

Research on the Optimization of Supermarket Staff Scheduling...

引用

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

作者： Chen, Zhitong Liu, Dongsheng Wang, Hexing Northeastern University at Qinhuangdao Qinhuangdao China

ISBN: (纸本)9798350380989

This paper delves into the core problem of supermarket staff scheduling optimization, especially in terms of reducing human resource costs and increasing corporate profits. Genetic algorithm is adopted as the solution method, and the raw data are exhaustively preprocessed and converted into formats at the initial stage. During the application of the genetic algorithm, the Binary 0-1 gene coding method and targeted fitness function were designed. The staff scheduling was successfully optimized through genetic crossover, mutation, and elimination mechanisms. On this basis, employee qualification and efficiency are introduced as new optimization indexes, and the model and fitness function are adjusted accordingly to further enhance the effectiveness of the scheduling scheme. © 2024 IEEE.

关键词： Corporate Profit Optimization data Preprocessing Employee Qualification & Efficiency Genetic Algorithms Staff Scheduling

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AMIMC: Based on Improved Meta-learning Contrast Network with Attention Mechanism for Few Shot Text Classification 3

AMIMC: Based on Improved Meta-Learning Contrast Network with...

引用

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

作者： Li, Senyan Dong, Ye Qin, Xizhong Xinjiang University Xinjiang Signal Detection and Processing Key Laboratory Urumqi830049 China

ISBN: (纸本)9798331506582

Currently, meta-learning is the mainstream approach to solving the problem of scarce data in few-shot text classification. Still, challenges remain, such as embedding vectors not being compact enough, suboptimal meta-task sampling strategies, and the rigidity of convolution operations in traditional Text CNNs. To address this, we propose the Attention Mechanism-based Improved Meta-learning Contrast Network (AMIMC), which enhances intra-class aggregation, increases inter-class separation and improves embedding quality. Additionally, Double Dynamic Similar Sampling (DDSS) generates more challenging meta-tasks, and the attention mechanism enhances the flexibility of Text CNNs, significantly boosting accuracy on five few-shot text classification datasets. © 2024 IEEE.

关键词： Contrastive learning

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Mini-SCXception: Balancing Accuracy and Efficiency in Real-Time Facial Expression Recognition 24

Mini-SCXception: Balancing Accuracy and Efficiency in Real-T...

引用

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

作者： Shao, Wenhao Huang, Xiaoning Xue, Bohuan Tang, Xiaoyu School of Data Science and Engineering Xingzhi College South China Normal University Guangdong Guangzhou China School of Electronic and Information Engineering Xingzhi College South China Normal University Guangdong Guangzhou China

ISBN: (纸本)9798400707308

In Facial Expression Recognition (FER), existing models often encounter challenges such as high parameter counts and significant computational demands, making real-time application on resource-constrained devices difficult. To address this issue, this paper proposes an improved Mini-SCXception model based on the Mini-Xception architecture. By integrating Spatial and Channel Reconstruction Convolution (SCConv), the proposed model reduces redundant computations while preserving the efficiency of Mini-Xception and enhancing feature extraction capabilities. On the FER2013 dataset, Mini-SCXception achieved an accuracy of 64.50%, representing a 1.05% improvement over the baseline model’s 63.83%. Experimental results demonstrate that Mini-SCXception balances accuracy and computational efficiency, providing a robust approach for real-time FER. © 2024 Copyright held by the owner/author(s).

关键词： Feature extraction

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Image Style Migration System based on Bootstrap-Flask 3

Image Style Migration System based on Bootstrap-Flask

引用

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

作者： Seng, Bingfeng Wang, Xiaomin Technologyy and Communication Qinghai Provincial Key Laboratory of Media Integration Xining China College of Computer Technology and Applications Qinghai University Xining China

ISBN: (纸本)9798331506582

Aiming at the current problem of single effect and same style for all kinds of style migration, the article develops a style migration system based on VGG-19 model. The system is developed with Flask as the back-end framework and Bootstrap as the front-end framework, combined with a MySQL database, utilizing deep learning technology, using the VGG-19 network model for training, completing the model migration and converting the style image and content image data acquired by JS, realizing the migration of the style of the user-selected image to the specified image, and at the same time, combining with other functions of the system, finally forming a system that integrates multi-user use, knowledge learning, sharing and discussion, as well as online style migration. At the same time, it combines with other functions of the system to form a system platform that integrates multi-user use, knowledge learning, sharing and discussion, online style migration and other functional modules. © 2024 IEEE.

关键词： Deep learning

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A Deep learning-Based Model for Indian Food Image Classification 3rd

A Deep Learning-Based Model for Indian Food Image Classifica...

引用

3rd international conference on Advanced Computing and intelligent Technologies, ICACIT 2023

作者： Ram, Rajravi Kumar Singh, Sunil Kumar Kumari, Reenu Department of Computer Science and Information Technology Mahatma Gandhi Central University Motihari India KSOM KIET Group of Institutions Ghaziabad India

ISBN: (纸本)9789819719600

Food picture classification provides considerable advantages for the food and medical industries, making it a topic of study that is rapidly increasing. Future uses for automated food identification algorithms are anticipated to include calorie estimates and diet tracking systems. This study recommends automated deep learning-based food classification systems. This study recommends automated deep learning-based food classification systems. These networks have been shown to perform considerably better when data are given and the hyperparameters are changed, making them appropriate for usage in real-world health and medical applications. Because SVM is a lightweight network, setting it up and using it is more accessible and appealing. SVM and DT can achieve a respectable accuracy of 100% even with a few parameters. The suggested CNN network will increase the efficacy of automatically categorizing food images. The accuracy of the suggested CNN has significantly risen and is currently 99.89% due to greater network depth. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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Sarcasm Detection on News Headline dataset Using Language Models 3

Sarcasm Detection on News Headline Dataset Using Language Mo...

引用

3rd IEEE international conference on intelligent Technologies, CONIT 2023

作者： Sharma, Abhilasha Pandey, Abhi Uday Gupta, Apoorv Delhi Technological University Software Engineering New Delhi India

ISBN: (纸本)9798350338607

Mechanized detection of sarcasm in textual data is a challenging task in NLP, as it involves recognizing the opposite of what is actually meant. This has become important today since many artificial intelligence tasks require understanding the user and answering appropriately. In this paper, we investigate multiple deep learning models for sarcasm detection on a news headline dataset, including BiLSTM and BERT. For the first time, hybrid BERT models have been used in our study to identify sarcasm in non-social media writings. The results show that the BERT and GBDT strategy combined performs better than all the other strategies listed. © 2023 IEEE.

关键词： Decision trees

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A Comprehensive Study on Computer Aided Approaches for Multiclass Neurological Disorder Classification 3

A Comprehensive Study on Computer Aided Approaches for Multi...

引用

3rd international conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2023

作者： Binu Rajan, M.R. Kavitha, K. Jayasudha, J.S. Annamalai University Department of Computer Science and Engineering Chidambaram India Central University of Kerala Department of Computer Science and Engineering Kasargod India

ISBN: (纸本)9798350306989

Visualizing anatomical structure benefits from medical imaging and analysis. However, employing basic imaging methods is challenging and ineffective. Brain abnormalities diagnosis and classification manually is especially a tedious task. Additionally, the approach currently in use experiences inter-observer variability while interpreting images. Diagnosis must be done with extreme care because even a small human error can have serious effects. An MRI can be used as a non-invasive method to find tumors. Early disease diagnosis can be accomplished by Computer Aided Diagnosis (CAD) systems, potentially improving survival odds and reducing the requirement for MRI analysis by a specialist. CNN have demonstrated to be quite efficient at finding tumors in brain MRIs. The automated diagnosis of brain MRI images using a unique computer-aided diagnosis method is described here. In recent decades, neuroimaging, especially MRI, has played a pivotal role in advancing our understanding of brain function and the various illnesses that can impact. Accurately identifying such illnesses from acquired neuroimaging data poses significant challenges due to the similarities in disease characteristics. This study analyses and evaluates the effectiveness of deep learning methods to identify neurological diseases. This study focuses on Alzheimer's, Picks, Huntington's, Cerebral Carcinoma and Tumors. © 2023 IEEE.

关键词： Magnetic resonance imaging

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CPAE-HRNet: A Deep learning Framework for Badminton Motion Posture Estimation 3rd

CPAE-HRNet: A Deep Learning Framework for Badminton Motion P...

引用

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

作者： Long, Hui Zhang, Yalu Yu, Haoli Li, Tao College of Information Science and Engineering Changsha Normal University Changsha China

ISBN: (纸本)9789819624676

In the traditional training and teaching process of badminton, coaches usually use the traditional way to teach, and this type of teaching method is prone to many problems such as high subjectivity and lack of data support. In this study, by incorporating the Attention Mechanism module to calculate the model loss with Euclidean distance, we propose a Combined Attention Mechanism Network with Euclidean Distance (CPAE-HRNet), which enhances the model’s ability to process and learn spatial and image details. Experiments on the COCO dataset and the self-constructed badminton action pose dataset show some improvement in the performance of CPAE-HRNet. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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