检索结果-内蒙古大学图书馆

8th international conference on Intelligent Informatics and Biomedical sciences, ICIIBMS 2023

作者： Zeku, Tian Yin, Chen We, Sun Ha, Gao Shanghai Institute of Technology School of Computer Science & Information Engineering Shanghai201418 China

ISBN: (纸本)9798350369564

To solve the problem of low accuracy of remote sensing image registration caused by different image colors in multi-temporal conditions, a remote sensing image registration algorithm based on generative adversarial network and cross attention is proposed. Firstly, the color of the image is adjusted based on the improved generative adversarial network before feature extraction to improve the feature similarity and reduce the difficulty of feature matching. Then the cross-attention network is added to enhance the feature extraction ability of the image. © 2023 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Thyroid Model for Prediction System

Machine Learning Thyroid Model for Prediction System

引用

2023 IEEE international conference on Device Intelligence, Computing and Communication Technologies, DICCT 2023

作者： Nayak, Chhaya Ajalkar, Deepika Shinde, Jayashri Prashant Barik, Swati Sucharita Pimpri Chinchwad College of Engineering Department of Computer Pune India G H Raisoni College of Engineering and Management Department of It Pune India Bhivarabai Sawant Institute of Technology and Research Department of Computer Engineering Pune India

ISBN: (纸本)9781665474924

Deep learning and cytopathology identification have trended in recent decades. Whole slide images (WSI) from clinic e-scanners help researchers classify slides (benign or malignant). In a thyroid WSI, the crucial area that supports the detection result may be smaller, and only the worldwide label may be gathered, making the supervised learning framework impracticable. Clinical thyroid cell identification requires many visual cues across several scales, therefore standard feature extraction may not work. This study proposes a poorly guided multi-instance-based learning technique for cytopathological thyroid diagnosis using multi-Scale Feature Fusion (MSF) and Convolutional Neural Network (CNN). WSIs are pouches with numerous occurrences for each section. Architecture learns to automatically categorise important sections. A feature fusion architecture that merges minimum features in the feature map with an instance-level awareness model improves classification results. The suggested model, trained and verified on clinical data, outperforms all existing methods with 93.2% accuracy. Our model outperformed ultra-modern deep multi-instance technique on a publically accessible histopathology dataset. © 2023 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

multimodal Bio Cryptography for Securing Cloud Computing using Convolutional Neural Network 2

Multimodal Bio Cryptography for Securing Cloud Computing usi...

引用

2nd international conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Mammo, Andualem Fantahun Kassahun, Habitamu Endalamaew Gandhi, Smiley Gupta, Shashi Kant Salau, Ayodeji Olalekan Onibonoje, Moses Oluwafemi Kishore Kanna, R. University of Gondar Electrical and Computer Engineering Gondar Ethiopia Galgotias University Greater Noida Uttar Pradesh Greater Noida201310 India Eudoxia Research University Department of Computer Science and Engineering United States Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Biomedical Engineering Chennai India

ISBN: (纸本)9798350364699

Cloud computing has grown in popularity as a storage platform that makes use of the internet to provide users with large amounts of data storage. The cloud's client base has grown throughout time, and access for consumers and businesses has expanded. At this time, security concerns develop among the cloud community. Different harmful assaults occur on well-known cloud services by exploiting weak password providers, causing them to lose confidentiality. Cryptography has evolved into an autonomous data encryption method, which allows users to manually cipher/decipher information. Secure keys can be generated using biometric key generation, eliminating the need for remembering. However, usability concerns persist since biometric credentials are required every time a file is encrypted or decrypted. This study focuses on multimodal biometrics, which will create a non-intrusive key for cryptography algorithms to encrypt/decrypt the file uploaded to the cloud by the client. To achieve this, this paper presents a convolutional neural networks approach which extract features from face and fingerprint biometrics and trains them to understand the patterns that are to be classified. multi-label categorization features were employed as output labels, which were hashed to 512 bits from each of the multimodal biometrics and could be combined to yield 1024 bits for RSA cryptography encryption and decryption. False acceptance and rejection rates were utilized to assess the whole system. As a result, the face exhibits 2.13% FAR and the fingerprint 2.71% FAR on average. © 2024 IEEE.

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

Text Analysis and Recognition of Emotional Content Using Deep Learning Methods and BERT 23

Text Analysis and Recognition of Emotional Content Using Dee...

引用

23rd IEEE/ACIS international conference on computer and Information science, ICIS 2023

作者： Andrikakis, Efthymios Perikos, Isidoros Paraskevas, Michael Hatzilygeroudis, Ioannis University of Patras Department of Computer Engineering & Informatics Patras Greece University of Patras Computer Technology Institute and Press Diophantus Department of Computer Engineering & Informatics Patras Greece Department of Electrical and Computer Engineering University of Peloponnese Computer Technology Institute and Press Diophantus Patras Greece

ISBN: (纸本)9798350345841

Recognizing the emotional content of Natural Language sentences can improve the way humans communicate with a computer system by enabling them to recognize and imitate emotional expressions. In this paper, deep learning, deep neural networks as well as Transformers were examined. Specifically, we designed and developed deep learning methods and BERT-based implementations for recognizing emotional content in user-generated data. Extensive experiments were conducted using these models on a variety of textual data and all the designed methods were evaluated. The results of the study show that BERT achieved the best overall performance, getting an F1-score of 0.86 on the Twitter Data dataset. Also, the Bi-LSTM with Attention Mechanism and Bi-LSTM using Word2vec performed quite well, achieving F1-scores of 0.83 and 0.82, respectively. © 2023 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

M-RAT: a multi-grained Retrieval Augmentation Transformer for Image Captioning 17th

M-RAT: a Multi-grained Retrieval Augmentation Transformer f...

引用

17th Asian conference on computer Vision, ACCV 2024

作者： Song, Jiayan Pan, Renjie Zhou, Jun Yang, Hua Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Lab of Digital Media Processing and Transmission Shanghai200240 China

ISBN: (纸本)9789819609079

Current encoder-decoder methods for image captioning mai-nly consist of an object detection module (two-stage), or rely on big models with large-scale datasets to improve the effectiveness, which leads to increasing computation costs and cannot introduce new external knowledge. In this paper, we propose a novel end-to-end method multi-grained Retrieval Augmentation Transformer (M-RAT) that innovatively fuses retrieved text derived from a changeable datastore with input visual feature through a multi-modal Aligned Encoder, and introduce a specialized attention mechanism, multi-MSA, to exploit both local and global interactions for delicate fine-grained details. Additionally, we enhance the decoder generation ability by employing low-level and high-level fused embeddings. Experiments demonstrate that M-RAT achieves comparable performance to state-of-the-art baselines with remarkable accuracy and details, as well as showing excellent domain adaptability for novel objects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

Coronary Artery Disease Prediction Using Enhanced multi Layer DCNN 10

Coronary Artery Disease Prediction Using Enhanced Multi Laye...

引用

10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Malik, Varun Sharma, Smita Mittal, Ruchi Rao, A. Kakoli Martin, R. John Khan, Akhilesh Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Amity School of Engineering and Technology Amity University Department of Electronics and Communication Engineering Greater Noida India Lloyd Institute of Engineering & Technology Greater Noida India Jazan University Faculty of Computer Science and Information Technology Saudi Arabia Lloyd Law College Greater Noida India

ISBN: (纸本)9798350382471

Coronary Artery Disease(CAD) is a prevalent and life-threatening cardiovascular condition affecting millions of individuals globally. Early detection and accurate prediction of CAD are vital for timely intervention and prevention of severe cardiac events. Convolutional Neural Networks (CNNs) and other deep learning approaches have recently demonstrated amazing effectiveness in a number of medical applications, including illness prediction. This research proposes an innovative approach, Enhanced multi-Layer Deep Convolutional Neural Network (EML-DCNN), for the prediction of Coronary Artery Disease. The proposed EML-DCNN architecture integrates advanced deep learning concepts, feature engineering, and data augmentation techniques to enhance the model's predictive performance. The multi-layer structure of EML-DCNN enables the automatic extraction of intricate patterns and features from medical imaging data, specifically focusing on coronary artery images. Additionally, the model incorporates attention mechanisms and residual connections, enhancing its ability to capture subtle details and relationships within the data. To validate the efficacy of the proposed EML-DCNN model, extensive experiments were conducted on a large and diverse dataset comprising coronary artery images obtained from various medical sources. The results demonstrate superior predictive accuracy, sensitivity, and specificity compared to existing CAD prediction models. Moreover, the model's interpretability is enhanced through visualization techniques, providing valuable insights into the features influencing the prediction process. © 2023 IEEE.

关键词： Coronary Artery Disease deep learning disease prediction Enhanced multi-Layer Deep Convolutional Neural Network

来源：评论

学校读者我要写书评

暂无评论

Privacy preserving data distillation in medical imaging with multidimensional matching on PATHMNIST

Privacy preserving data distillation in medical imaging with...

引用

international conference on computer Vision, Robotics, and Automation engineering, CRAE 2024

作者： Xiong, Yukun Wang, Yihan Zhang, Wenshuang Department of Computer Science Wenzhou Kean University Zhejiang Wenzhou325060 China

ISBN: (纸本)9781510682283

In the domain of medical image analysis, the privacy of patient data is paramount, yet the need for extensive datasets to train robust models is ever-increasing. This paper introduces a novel dataset distillation approach that leverages multidimensional matching methods, including distribution, gradient, and trajectory matching, to generate synthetic datasets that preserve the utility of original medical datasets while enhancing privacy protection. Applied to the PATHMNIST dataset, a colon pathology benchmark, our method not only achieves superior model performance compared to existing dataset distillation techniques but also significantly improves the privacy of the distilled images, as evidenced by lower L2 norms in pixel-level comparisons. Our findings demonstrate that our approach can serve as a robust framework for generating training-ready datasets that adhere to privacy constraints inherent in medical data applications. © 2024 SPIE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Water Quality Classification Using multi-cell RNN in Aquaculture Ponds for Catla Fish 5th

Water Quality Classification Using Multi-cell RNN in Aquacul...

引用

5th international conference on computer and Communication Technologies, IC3T 2023

作者： Arepalli, Peda Gopi Khetavath, Jairam Naik Department of Computer Science and Engineering National Institute of Technology Raipur Raipur India

ISBN: (纸本)9789819997039

In aquaculture, water quality has a crucial role in fish development and survival. The link between different water quality sections is not well captured by the water quality prediction systems now in use, and their capacity to take advantage of the temporal relationships and interactions between various physical and chemical properties of water parameters is constrained. In this study, we present a multi-cell RNN for catla fish that combines the benefits of LSTM and GRU to capture these temporal linkages and interactions in a deep learning model for water quality prediction in aquaculture catla fishponds. Compared to other models, our suggested approach performs better at categorizing data on water quality for catla fish. The experimental findings show that the suggested multi-cell RNN achieves a high accuracy of 99.88% and a low loss of 0.0192. This study offers a viable method for forecasting aquaculture water quality, which may help fish develop and survive and support sustainable aquaculture methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fish

来源：评论

学校读者我要写书评

暂无评论

YOLOv7-DSE: An Efficient Safety Equipment Detection Network

IAENG International Journal of Computer Science

引用

IAENG international Journal of computer science 2024年第6期51卷 572-581页

作者： Ren, Jiaxin Cui, Wenhua Tao, Ye Shi, Tianwei School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China

Safety equipment detection is an important application of object detection, receiving widespread attention in fields such as smart construction sites and video surveillance. Significant progress has been made in object detection due to the rapid development of deep learning. multi-scale targets and complex scenes increase the likelihood of false positives and missed detections, which can affect the accuracy of the detection. To address this issue, this study proposes YOLOv7-DSE. It is a small complex target scene detection network based on the improved YOLOv7. Also, we have created a private dataset of safety equipment. First, we enhanced the ELAN and MP backbone networks. Backbone is replaced by ordinary convolution by the depthwise separable convolution. We enabled the backbone network to extract deeper image features without increasing the amount of parameters and computation. Simultaneously, the model incorporates the EIOU loss function to improve its convergence speed and positioning effect. Secondly, we propose a new ELAN-SPD structure in the head network. Based on the ELAN structure, a space-to-depth convolutional layer is added to fully downsample the feature map, preserving all learnable features. Our network model can better detect objects with significant size differences faced with complex scenes. YOLOv7-DSE achieved the mAP of 82.38%, surpassing the original YOLOv7 with 2.64%. The YOLOv7-DSE model has a minor size compared to the baseline model. Our improvement has reduced the model parameters by 22.4%. © (2024), (international Association of Engineers). All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Dynamic Programming to Solve the Optimal Production Model

Dynamic Programming to Solve the Optimal Production Model

引用

2023 international conference on Applied Physics and Computing, ICAPC 2023

作者： Chen, Xiao Zhang, Pengbei Zhen, Zeming Liang, Wenqian Deng, Baijie Computer Engineering Technical College Guangdong Institute of Science and Technology Zhuhai China School of International Cooperation Guangdong Institute of Science and Technology Zhuhai China

This article mainly investigates the demand for small batch materials. It establishes a multi-objective dynamic programming model aimed at maximizing service level, minimizing inventory, and shortages by listing corre... 详细信息

ISBN: (纸本)9798350359244

关键词： Dynamic programming

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：