检索结果-内蒙古大学图书馆

Emerging Techniques in Computational Intelligence (ICETCI), International Conference on

作者： Aakash Suresh Lakshmi C Gopirajan PV K Dhanasekaran School of Computing SRM Institute of Science and Technology Chengalpattu Chennai TN India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chengalpattu Chennai TN India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chengalpattu Chennai TN India

ISBN: (数字)9798350387063

ISBN: (纸本)9798350387070

Humanity's exploration of the cosmos spans billions of years, prompting contemplation of our position in the universe. This study advocates for proactive communication with potential intelligent species, driven by uncertainty about their existence. The focus is on exploring Generative Adversarial Networks (GANs) to enhance exoplanet detection through data augmentation. Utilizing the extensive Kepler dataset, the study evaluates the accuracy of conventional machine learning techniques: Logistic Regression, Decision Trees, and Random Forests. (RF)-trained on original data. Subsequently, it assesses the effectiveness of models trained solely on GAN-generated synthetic data and compares their performance with that of models trained on original data. Lastly, it merges original and synthetic data to create a composite dataset, evaluating the ML model's accuracy. The results indicate that models trained solely on synthetic data achieve comparable accuracy to those trained on original data. Furthermore, the combined dataset yields improved accuracy-87%, 95%, and 96% in Logistic Regression, Decision Tree, and RF, respectively-highlighting the potential of GAN-based data augmentation in enhancing exoplanet detection. This study advances space exploration by integrating technology with astronomical research, facilitating more precise and reliable exoplanet detection methods. Leveraging GANs for data augmentation promises novel insights and discoveries about the universe.

关键词： Radio frequency Logistic regression Accuracy Uncertainty Computational modeling Heuristic algorithms Exoplanet data augmentation data models Synthetic data

来源：评论

学校读者我要写书评

暂无评论

Multi-Agent System based Medical Diagnosis Using Particle Swarm Optimization in Healthcare

Multi-Agent System based Medical Diagnosis Using Particle Sw...

引用

2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Dhatterwal, Jagjit Singh Singh Naruka, Mahaveer Kaswan, Kuldeep Singh Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Data Science AP Vaddeswaram India G L Bajaj Institute of Technology and Management Greater Noida India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350322309

Healthcare is an area that is challenging, dynamic, and diverse. Healthcare in the twenty-first century brings a variety of difficulties (population growth and rising healthcare costs). Compared to the conventional medical system, agent technology can offer superior healthcare for that. Agents in the hospital might deal with a variety of medical issues. We list collaboration across hospital wards, the development of diagnostics, the gathering of patient data, etc. as examples of issues that arise in hospitals. These issues can be resolved with a cooperative This paper proposes a generic architecture that integrates swarm intelligence with multi-agent healthcare systems to provide therapy as efficiently as possible. © 2023 IEEE.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Disease Identification in Cassava Plants Using RGB Imaging 6th

Deep Learning for Disease Identification in Cassava Plants U...

引用

6th International Conference on Recent Innovations in computing, ICRIC 2023

作者： Sutar, Shivam Mohite, Snehal Kamble, Tushar Kolhar, Shrikrishna Jagtap, Jayant Shastri, Rajveer Joshi, Shubham Vidya Pratishthan’s Kamalnayan Bajaj Institute of Engineering and Technology Maharashtra Baramati India Maharashtra Pune India Marik Institute of Computing Artificial Intelligence Robotics and Cybernetics NIMS University Rajasthan Jaipur303121 India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Maharashtra Pune411037 India

ISBN: (纸本)9789819778614

Agriculture is the most critical sector in the world economy. Food security and consistent food supply are the key challenges due to rapid growth in the world population and crop diseases. Crop yield gets significantly hampered due to diseases attacking large crop fields. The precise classification of plant diseases is essential to provide appropriate pesticide supplements. This study applies deep learning (DL) models to identify diseases in cassava plants using red–green–blue (RGB) images. Three models, namely Xception-net, MobileNetV2, and EfficientNetB0, are trained and evaluated on the cassava disease dataset publicly available on Kaggle. Since the dataset is imbalanced, five-fold cross-validation is used to check the consistency of the results. Transfer learning and random weight initialization were used for model training. The Xception-net is the clear winner among the three models, with an accuracy of 87.8% using transfer learning, 81.33% using random weight initialization, and 85% average accuracy using five-fold cross-validation. Further, this work is extended to develop web and mobile applications with DL models deployed at the backend to predict plant diseases from input images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Plant diseases

来源：评论

学校读者我要写书评

暂无评论

Development of Advanced Noise Filtering Techniques for Medical Image Enhancement 5

Development of Advanced Noise Filtering Techniques for Medic...

引用

5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Kushwaha, Sumit Amuthachenthiru, K. Geetha, K. Narasimharao, Jonnadula Dileep Kumar, M. Gadde, Sai Sudha University Institute of Computing Chandigarh University Department of Computer Applications Punjab 140413 India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam626117 India Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603 203 India Cmr Technical Campus Department of Computer Science and Engineering Telangana Hyderabad501401 India Hensard University Faculty of Management Sciences Bayelsa 561101 Nigeria Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Vaddeswaram500075 India

ISBN: (纸本)9798350385649

Medical imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), X-rays, and ultrasound, are extensively employed in the healthcare industry for diagnostic purposes. Noise, on the other hand, can disturb these approaches and lead to inaccurate diagnosis. Due to this problem, the significance of noise removal technologies has grown in the field of medical imaging, specifically for the examination and understanding of medical images and anatomical structures. To tackle these challenges, a variety of denoising techniques have been developed, such as the Weiner filter (WF), Gaussian filter, and median filter. This work employs a Deep Learning (DL) method known as Convolutional Autoencoder (CAE) to remove noise from medical images. We gather chest X-ray images for analysis and focus on common noise types that affect medical images, such as Poisson and Gaussian noise. We evaluate the effectiveness of the suggested denoising method CAE by comparing it to the commonly employed WF methodology. The Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) are employed to assess and compare the performance of the WF and CAE in reducing Poisson and Gaussian noise. The study's findings indicate that the suggested denoising method based on CAE shows promising results in terms of improving quality. © 2024 IEEE.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

LEARNING PARTIAL GRAPH MATCHING VIA OPTIMAL PARTIAL TRANSPORT

arXiv

引用

arXiv 2024年

作者： Ratnayaka, Gathika Nichols, James Wang, Qing School of Computing Australian National University Australia Biological Data Science Institute Australian National University Australia

Partial graph matching extends traditional graph matching by allowing some nodes to remain unmatched, enabling applications in more complex scenarios. However, this flexibility introduces additional complexity, as both the subset of nodes to match and the optimal mapping must be determined. While recent studies have explored deep learning techniques for partial graph matching, a significant limitation remains: the absence of an optimization objective that fully captures the problem’s intrinsic nature while enabling efficient solutions. In this paper, we propose a novel optimization framework for partial graph matching, inspired by optimal partial transport. Our approach formulates an objective that enables partial assignments while incorporating matching biases, using weighted total variation as the divergence function to guarantee optimal partial assignments. Our method can achieve efficient, exact solutions within cubic worst case time complexity. Our contributions are threefold: (i) we introduce a novel optimization objective that balances matched and unmatched nodes;(ii) we establish a connection between partial graph matching and linear sum assignment problem, enabling efficient solutions;(iii) we propose a deep graph matching architecture with a novel partial matching loss, providing an end-to-end solution. The empirical evaluations on standard graph matching benchmarks demonstrate the efficacy of the proposed approach. © 2024, CC BY.

关键词： Graph theory

来源：评论

学校读者我要写书评

暂无评论

Enhancing face recognition performance: a comprehensive evaluation of deep learning models and a novel ensemble approach with hyperparameter tuning

引用

Soft computing 2024年第20期28卷 12399-12424页

作者： Selvaganesan, Jana Sudharani, B. Shekhar, S. N. Chandra Vaishnavi, K. Priyadarsini, K. Raju, K. Srujan Rao, T. Srinivasa Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India Guntur Dist. Andhra Pradesh Vadlamudi India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad501301 India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering CMR Technical Campus Medchal Telangana Hyderabad501401 India Department of Electronics and Communication Engineering Aditya Engineering College Surampalem533437 India

In response to growing security concerns and the increasing demand for face recognition (FR) technology in various sectors, this research explores the application of deep learning techniques, specifically pre-trained Convolutional Neural Network (CNN) models, in the field of FR. The study harnesses the power of five pre-trained CNN models—DenseNet201, ResNet152V2, MobileNetV2, SeResNeXt, and Xception—for robust feature extraction, followed by SoftMax classification. A novel weighted average ensemble model, meticulously optimized through a grid search technique, is introduced to augment feature extraction and classification efficacy. Emphasizing the significance of robust data pre-processing, encompassing resizing, data augmentation, splitting, and normalization, the research endeavors to fortify the reliability of FR systems. Methodologically, the study systematically investigates hyperparameters across deep learning models, fine-tuning network depth, learning rate, activation functions, and optimization methods. Comprehensive evaluations unfold across diverse datasets to discern the effectiveness of the proposed models. Key contributions of this work encompass the utilization of pre-trained CNN models for feature extraction, extensive evaluation across multiple datasets, the introduction of a weighted average ensemble model, emphasis on robust data pre-processing, systematic hyperparameter tuning, and the utilization of comprehensive evaluation metrics. The results, meticulously analyzed, unveil the superior performance of the proposed method, consistently outshining alternative models across pivotal metrics, including Recall, Precision, F1 Score, Matthews Correlation Coefficient (MCC), and Accuracy. Notably, the proposed method attains an exceptional accuracy of 99.48% on the labeled faces in the wild (LFW) dataset, surpassing erstwhile state-of-the-art benchmarks. This research represents a significant stride in FR technology, furnishing a dependable and accurate

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Driven - Pest Classification and Sustainable Agriculture 3

Deep Learning Driven - Pest Classification and Sustainable A...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kumar, M. Sirish Usha, R. Reddy, B. Rama Kantha Ramachandra, S. Prakash, P. Jaya Swetha, B. Mohan Babu University School of Computing Dept. of Data Science India Madanapalle Institute of Technology and Science Madanapalle India Sri Venkateswara College of Engineering Tirupati India Mother Theresa Institute of Engineering and Technology Palamaner India Sri Venkateswara College of Engineering Department of It Tirupati India Mohan Babu University Dept. of Master of Computer Applications India

ISBN: (纸本)9798331509675

This study introduces a system designed to identify pests in crops and classify them as either beneficial or harmful. The project begins by providing a comprehensive overview of various pest identification methods, analyzing their strengths and limitations. To address the problem, a categorization approach is proposed that incorporates convolutional neural networks (CNN) to build the pest identification and classification model. The model was trained using a dataset containing images of different pest species. Extensive testing was conducted on a large dataset, and the system's performance was validated against multiple traditional classification models. The proposed framework achieved a classification accuracy of 90%, outperforming conventional methods. This research highlights agriculture's vital role in the economy and underscores the potential of advanced technologies in supporting sustainable farming practices. © 2025 IEEE.

关键词： Agriculture Sustainability Classification Accuracy Classification Model Convolutional Neural Network (CNN) Pest Classification

来源：评论

学校读者我要写书评

暂无评论

Enhancing Effective Learning Capability of SOAR Agent based Episodic Memory

Enhancing Effective Learning Capability of SOAR Agent based ...

引用

2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Kaswan, Kuldeep Singh Naruka, Mahaveer Singh Dhatterwal, Jagjit Singh Galgotias University School of Computing Science and Engineering Greater Noida India G L Bajaj Institute of Technology and Management Greater Noida India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence & Data Science AP Vaddeswaram India

ISBN: (纸本)9798350322309

In order to increase the agent's intelligence, the fundamental goal of the research paper is to learn from its experiences. In order to solve a problem, learning is accomplished with the aid of episodic memory, which stores previous experiences as episodes. This study illustrates the improvement in intelligence with the use of the application of an example in soar. Working memory elements (WMEs), which are episodes (past experiences) in the form, are discussed in this paper as a means of enhancing intelligence. The purpose of this work is to demonstrate that knowledge always leads to performance improvement, meaning that as an agent studies more, performance will extend. © 2023 IEEE.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

WNet: Multi-Task Artistic Portrait Generation Method

WNet: Multi-Task Artistic Portrait Generation Method

引用

IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Juan Li Binlong Wu Yuhui Lin Huang Zhang Xiangping Huang College of Computing and Information Science Fuzhou Institute of Technology Fuzhou China College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

Artistic portrait usually uses a set of sparse continuous lines to capture a person's unique appearance characteristics based on real people or photos. This paper addresses the issues of strong background noise and the loss of key details of human faces in artistic portraiture, leading to the inability to produce realistic and high quality portraits. A multi-task portrait painting model with hierarchical loss called WNet is proposed based on U 2 Net used in salient object detection. This model can generate highly abstract portrait painting that do not depend on the background. Compared to existing popular portrait painting generation methods, the images generated by our method have a better visual effect and retain the key details of the facial features better. The experimental results indicate that the proposed WNet is optimal in terms of PSNR, SSIM and MAE performance. Finally, The software communicating robotic arm can quickly spot-paint the fused art portraits.

关键词： Measurement Art Semantic segmentation Object detection Multitasking Manipulators Visual effects Software Faces Painting

来源：评论

学校读者我要写书评

暂无评论

Learning the sparse prior: Modern approaches

引用

Wiley Interdisciplinary Reviews: Computational Statistics 2024年第1期16卷 e1646-e1646页

作者： Peng, Guan-Ju Institute of Data Science and Information Computing National Chung Hsing University Taichung Taiwan

The sparse prior has been widely adopted to establish data models for numerous applications. In this context, most of them are based on one of three foundational paradigms: the conventional sparse representation, the convolutional sparse representation, and the multi-layer convolutional sparse representation. When the data morphology has been adequately addressed, a sparse representation can be obtained by solving the sparse coding problem specified by the data model. This article presents a comprehensive overview of these three models and their corresponding sparse coding problems and demonstrates that they can be solved using convex and non-convex optimization approaches. When the data morphology is not known or cannot be analyzed, it must be learned from training data, thereby formulating dictionary learning problems. This article addresses two different dictionary learning paradigms. In an unsupervised scenario, dictionary learning involves the alternating or joint resolution of sparse coding and dictionary updating. Another option is to create a recurrent neural network by unrolling algorithms designed to solve sparse coding problems. These networks can then be used in a supervised learning setting to facilitate the training of dictionaries via forward-backward optimization. This article lists numerous applications in various domains and outlines several directions for future research related to the sparse prior. This article is categorized under: Statistical Learning and Exploratory Methods of the data sciences > Modeling Methods Statistical and Graphical Methods of data Analysis > Modeling Methods and Algorithms Statistical Models > Nonlinear Models. © 2024 Wiley Periodicals LLC.

关键词： algorithm unrolling convex and non-convex optimization convolutional sparse model dictionary learning multi-layer convolutional sparse model recurrent neural network sparse coding sparse prior

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：