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

共 20691 条记录，以下是841-850 订阅

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Deep learning based HOG ResNet Model for Group Attendance System in Educational Institutions 3

Deep Learning based HOG ResNet Model for Group Attendance Sy...

引用

3rd international conference on Disruptive Technologies, ICDT 2025

作者： Varma, P. Ravi Kiran Sriram, A. Venkata Bharath, C.H. Reddy, B Sai Sagi Rama Krishnam Raju Engineering College Department of It Bhimavaram India Bhimavaram India

ISBN: (纸本)9798331519582

Traditional attendance management systems in educational institutions use manual methods that are time-consuming and prone to errors. This work proposes an automated attendance taking system implemented through group pictures using face recognition making the process much easier in classroom environments. For detection of faces from group photographs, the system employs Histogram of Oriented Gradients (HOG) available in the dlib library. For face recognition, Deep learning architecture known as ResNet is used to identify the students under different poses and lighting conditions. A real-time dataset was created using various photographs of the students from different angles with the most common scenarios that are likely to happen in the real classroom situation. The facial recognition model was trained on this custom real-time dataset thus addressing the challenge of photography in the classroom conditions. Using group images, the system recognizes students present classes, marks the attendance and registers them in the attendance database in real time. A user-friendly interface was developed for efficient management of attendance records. Developed Group picture attendance system demonstrates high accuracy in attendance management in various conditions, featuring seamless database integration and a user-friendly front-end interface for data retrieval. © 2025 IEEE.

关键词： Records management

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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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Application of CMAC feedforward control in the infrared temperature measurement rotorcraft device of intelligent power system station 3

Application of CMAC feedforward control in the infrared temp...

引用

3rd international conference on Energy, Power and Electrical Technology, ICEPET 2024

作者： Zhao, Xiaoxiao Zhang, Yanjie Ying, Huiyan Yun, Yuxin State Grid of China Technology College Shandong Jinan250002 China State Grid Shandong Electric Power Research Institute Shandong Jinan250002 China

ISBN: (纸本)9798350352658

In this article, the scheme and principle of neural network-controlled CMAC feedforward control were studied. A neural network control system was established based on the infrared temperature measurement system of the rotor flight chess in the intelligent system of power plants. Its performance and characteristics were simulated and studied. The pitch axis position tracking was also studied. The research showed that the model has good nonlinear mapping ability, self-learning ability and practical application processing ability for parallel information. The control effect is good, which provides a new approach to solving the uncertainty identification problem of nonlinear systems. © 2024 IEEE.

关键词： Feedforward control

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Recommendation Method Based on Multi-task learning and Graph Collaborative Filtering 3

Recommendation Method Based on Multi-task Learning and Graph...

引用

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

作者： Wang, Zhi-Min Zhu, Wen-Qiang School of Software and Internet of Things Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9798331506582

The rapid growth of Internet technology is continuously increasing information volume, leading to the problem of information overload. When faced with a massive amount of information, the focus is on how to quickly and accurately find the desired information, which has led to the emergence of recommendation systems. However, traditional collaborative filtering recommendations are often affected by issues such as cold start and data sparsity, while existing graph-based collaborative filtering methods also suffer from poor representation learning quality and data sparsity. So we propose a recommendation method based on Multi-Task learning and Graph Collaborative Filtering (MTLGCF). This method uses graph collaborative filtering recommendation as the main task, incorporates two contrastive learning tasks as auxiliary tasks, and introduces a data augmentation strategy, enabling the model to perform multi-task learning. By evaluating on three public datasets such as ml-latest-small and comparisons with nine recent recommendation methods, the proposed MTLGCF model shows significant improvements in Recall and NDCG metrics. © 2024 IEEE.

关键词： Contrastive learning

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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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Enhancing Healthcare Through Machine learning: A Rigorous Evaluation and Comparative Study of Algorithms 3

Enhancing Healthcare Through Machine Learning: A Rigorous Ev...

引用

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

作者： Sharma, Harshit Kumar, Rakesh Gupta, Meenu Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798350306989

The integration of machine learning in healthcare is rapidly growing and holds a great potential to transform the analysis and application of medical data. This study explores the latest developments in machine learning to significantly improve the speed and precision of disease diagnosis. Main goals of this research is to obtain current research landscape in this domain by identifying the existing research gaps, and then develop a potential solution for future investigation. By conducting a comprehensive search through Google Scholar database, 20-research publications were selected in order to address two key research questions, i.e. why researchers choose machine learning in healthcare and what are the limitation/weakness of ML in healthcare. Based on the analysis, it was determined that the PIMA and MESSIDOR datasets are the most frequently utilized datasets for diabetes classification. Additionally, in this work a machine learning algorithm that effectively reduce the time constraints while simultaneously improving diagnostic performance is proposed. © 2023 IEEE.

关键词： Classification (of information)

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FLDP: A differentiated privacy security approach based on flow model 3

FLDP: A differentiated privacy security approach based on fl...

引用

3rd IEEE international conference on intelligent Technology and Embedded Systems, ICITES 2023

作者： Zhao, Xing Wang, Yongli Chen, Yuke Tian, Fengjun School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350343755

Sensitive data in standard deep learning training can be subject to assaults. These attacks take advantage of the disclosure of gradients and intermediate representations during the training process, potentially resulting in data breaches and unauthorized access to sensitive information. Concerns about the privacy and security of sensitive data during the training process have grown in importance as deep learning has grown in popularity. This study describes a novel strategy FLDP for improving the privacy and security of deep learning training by combining differential privacy (DP) with stochastic gradient descent (SGD) and flow model. Our strategy improves privacy and security without compromising deep learning model training efficiency or scalability. Individual data points are indistinguishable in our methodology, reducing the danger of privacy *** of applying a consistent privacy level to all data points, our technique enables for privacy protection to be differentiated based on data sensitivity. This adaptive privacy approach promotes value while preserving the most sensitive data's privacy. © 2023 IEEE.

关键词： Sensitive data

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AI-Powered Predictive Cybersecurity in Identifying Emerging Threats through Machine learning 3

AI-Powered Predictive Cybersecurity in Identifying Emerging ...

引用

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

作者： Wang, Ruibin Dongying Vocational Institute Dongying China

ISBN: (纸本)9798350380989

With an emphasis on the incorporation of the Wasserstein Generative Adversarial Network (WGAN) algorithm, this study examines the revolutionary potential of AI-Powered Predictive Cybersecurity in detecting new risks using machine learning. The adoption of an innovative cybersecurity approach within CyberGuard Bank is investigated in this paper through the use of a fictitious case study set in the financial sector. With the use of cutting-edge machine learning techniques and WGAN-generated synthetic data, the model exhibits improved threat detection, false positive reduction, real-time threat mitigation, and scalability capabilities. The implementation places a strong emphasis on ethical issues, including as bias prevention and privacy compliance, which positions the company as a responsible steward of AI-driven cybersecurity. The results highlight the strategic importance of WGAN and AI in strengthening defences against the constantly changing and dynamic array of cyberthreats. © 2024 IEEE.

关键词： AI-Powered Predictive Cybersecurity Ethical Considerations Machine learning Real-Time Threat Mitigation Scalability Wasserstein Generative Adversarial Network (WGAN)

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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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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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