检索结果-内蒙古大学图书馆

3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Dhinakar, Yalla Reyana, A. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375190

Analyzing medical data effectively is critical for making educated decisions and providing patient care in this age of quickly improving healthcare technology. Natural language processing methods, particularly text classification and sentiment analysis in cancer patients' medical records is the primary objective of the proposed work. The goal is to help healthcare professionals diagnose and treat patients more effectively by extracting meaningful insights from textual data. Logistic Regression is used as separate text analysis method in the research. In order to get a full picture of patients' histories, these models are trained and tested to see how well they categorize medical information pertaining to cancer into certain predetermined groups. To make sure the input data is good, data preparation is essential. Remove stop words, special letters, and any unnecessary information from the text to make it cleaner. In addition, the dataset is examined to learn about its properties and distribution which understanding of the data that lie underneath. The suggested models are successful, according to the experimental data;the Bidirectional LSTM model had Healthcare practitioners must pay close attention to these results because they pave the way for improved decision support via the accurate and rapid identification of pertinent medical information. This study highlights the value of natural language processing methods in analyzing cancer patients' medical records, which is a contribution to healthcare analytics as a whole. In addition, it highlights how Bidirectional LSTM models may improve medical record classification, which in turn improves patient care and healthcare decision-making. © 2024 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Detecting Schizophrenia from Healthy control using Machine Learning and EEG Signals 4

Detecting Schizophrenia from Healthy control using Machine L...

引用

4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Tamilarasi, K. Pavitra, S. Kumar, Anuj School of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India

ISBN: (纸本)9798350385793

A brain condition called schizophrenia has a significant impact on people's thinking, behaviors, and lives on a global state. The prevalence of the condition rises along with the population, and those who suffer from schizophrenia lose their capacity for thought and behavior with time. All these issues arise because it is challenging to identify schizophrenia patients. As a result, proper care and quality patient treatment require an accurate and quick diagnosis procedure. This article aims to improve the accuracy of. detection by using EEG signals and Machine Learning algorithms. It is tested using the Resting state EEG dataset, which demonstrates a high level of capability in distinguishing between schizophrenia patients and healthy controls. The EEG data is converted to different forms, where the given machine learning techniques extract the EEG signals to identify patients with schizophrenia and healthy control. We are utilizing the 28-file public dataset in this study. In the file, there are 14 healthy individuals as well as 14 SCZ sufferers. In accordance with the findings, (1) the decision tree successfully distinguished SCZ patients from healthy individuals using EEG signals with an accuracy of 89.12%, and (2) logistic regression was also used to successfully identify the affected patient using EEG signals with an accuracy of 69.78%. (3) The SCZ patient was correctly identified by SVM with an accuracy of 65.51 %. Our research demonstrates the potential for machine learning to play a significant role in aiding in the diagnosis of schizophrenia. © 2024 IEEE.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

Multiple Disease Prediction from Retinal Images Using Deep Learning Algorithm

Multiple Disease Prediction from Retinal Images Using Deep L...

引用

2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Rishikesh, B. Karthikayani, K. Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani Campus Tamilnadu Chennai26 India

ISBN: (纸本)9798331508685

Retinal imaging serves as an essential tool for the early identification and diagnosis of numerous systemic diseases. This model framework is used for predicting multiple diseases simultaneously using retinal images. The implementation begins with the collection of annotated retinal images, enabling the identification of ailments as glaucoma, age-related macular degeneration, diabetic retinopathy, including hypertensive retinopathy. Preprocessing techniques are applied to enhance data quality and reduce noise. Feature extraction is conducted using the pretrained VGG16 convolutional neural network (CNNis essential for deep learning model training. Multi-label categorization and multi-task learning are employed to predict multiple diseases concurrently. Strict measures, including as accuracy, sensitivity, specificity, including the area under the receiver operating characteristic curve (AUC-ROC), are used to assess the system's performance. Clinical validation also evaluates its efficacy in practical settings. The incorporation of this predictive system into clinical workflows is facilitated by ensuring regulatory compliance and seamless communication with healthcare professionals. Experimental Results show that the suggested VGG16-based system accomplishes an impressive 98.7% accuracy in detecting multiple retinal diseases. The implementation underscores the transformative potential of leveraging advanced deep learning techniques in ophthalmology. Future efforts are focused on enhancing algorithm performance through larger datasets and refining models to further improve diagnostic precision, enabling timely and effective treatment strategies for retinal conditions. © 2025 IEEE.

关键词： Ophthalmology

来源：评论

学校读者我要写书评

暂无评论

What Happens to a Dataset Transformed by a Projection-based Concept Removal Method? 30

What Happens to a Dataset Transformed by a Projection-based ...

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Johansson, Richard Department of Computer Science and Engineering University of Gothenburg and Chalmers University of Technology Sweden

ISBN: (纸本)9782493814104

We investigate the behavior of methods that use linear projections to remove information about a concept from a language representation, and we consider the question of what happens to a dataset transformed by such a method. A theoretical analysis and experiments on real-world and synthetic data show that these methods inject strong statistical dependencies into the transformed datasets. After applying such a method, the representation space is highly structured: in the transformed space, an instance tends to be located near instances of the opposite label. As a consequence, the original labeling can in some cases be reconstructed by applying an anti-clustering method. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： concept removal language representation method analysis projection

来源：评论

学校读者我要写书评

暂无评论

Healthcare Analytics: Conceptualizing a Research Agenda

Healthcare Analytics: Conceptualizing a Research Agenda

引用

2023 International Conference on ENTERprise Information Systems, CENTERIS 2023 - International Conference on Project MANagement, ProjMAN 2023 - International Conference on Health and Social Care Information Systems and Technologies, HCist 2023

作者： Elragal, Rawan Elragal, Ahmed Habibipour, Abdolrasoul Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden

This research recognizes the pressing need for innovative research in healthcare, enabling the transition towards analytics, by explaining how previous studies utilized big data, AI, and machine learning to identify, address, or solve healthcare problems. Healthcare science methods are combined with contemporary data science techniques to understand the literature, identify research gaps, and posit research questions for researchers, academic institutions, and governmental healthcare organizations. We intend to explain how contemporary analytics have been used to address healthcare concerns as well as to posit several research questions for future studies based on gaps which we have identified. The study has multi-folds contribution areas: first, it provides a state-of-the-art review to healthcare analytics, second, it posits a research agenda to advance the knowledge in this area further. © 2024 The Author(s). Published by Elsevier B.V.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Grouped Guidance Convolutional Networks for Absolute 3D Human Pose and Joint Rotations Estimation 7

Grouped Guidance Convolutional Networks for Absolute 3D Huma...

引用

7th International Conference on Advanced Algorithms and Control engineering, ICAACE 2024

作者： Zhang, Hua Li, Shujie Jia, Wei Hefei University Of Technology Department Of Computer Science And Information Engineering Hefei China

ISBN: (纸本)9798350361445

In the current landscape of monocular single-person 3D human pose estimation, the primary focus lies in recovering the 3D coordinates of human joints relative to the root joint from monocular images or videos. However, there has been a disregard for the estimation of absolute 3D pose and joint rotations, limiting the application of human pose estimation in fields such as virtual reality and computer animation. In this paper, we address this issue by proposing a Grouped Guidance Convolutional Network capable of estimating 3D absolute pose and joint rotations concurrently. The input to the network consists of 2D joint coordinates, preprocessed using camera parameters. The Grouped Guidance Network extracts shared features between 3D joint coordinates and joint rotations, and through training, the network obtain joint rotations, while a Skeleton Length Network estimates the lengths of human bones. The joint rotations and skeleton lengths are then fed into a forward kinematics layer to obtain the root-relative pose. Simultaneously, the preprocessed data, along with foot contact information outputted by the Foot Contact Network, is input into a Trajectory Network. This network extracts potential connections between foot contact information and the trajectory of the human root joint, further outputting the coordinates of the root joint trajectory. Finally, by combining the root-relative pose and the root joint trajectory, the 3D absolute pose of the human body is obtained. Quantitative and qualitative experiments demonstrate that the proposed method achieves optimal results in absolute pose estimation, surpassing other methods capable of outputting joint rotations. © 2024 IEEE.

关键词： Trajectories

来源：评论

学校读者我要写书评

暂无评论

Campus Surveillance: Deep Learning-Based Rules Violation Detection 14

Campus Surveillance: Deep Learning-Based Rules Violation Det...

引用

14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Deepalakshmi, R. Senthil Murugan, K.R. Arunnachalam, R.S. Mohamed Asif, B. Velammal College of Engineering and Technology Dept of Computer Science Tamil Nadu India

ISBN: (纸本)9798350344837

Cutting edge Innovation had made big affect in areas in Technical, Trade and Mechanical Sectors. Deep learning calculations had created those innovation into great enhancement for the quick, exactness and effectiveness in information. Deep learning Methods could executed in Campus Areas to moves forward its headway in innovation. The paper proposes Deep Learning in college camera which recognizes run the rule violators successfully. Wear ID Card and Wearing Shoes is one of the important rules in Campus, but in today's world limited datasets for the recognizing identity card and shoes. For this issue, Dataset for recognizing the Rule Violators have been made with 5000 pictures. Enhanced deep learning calculations like YOLO for object recognition and Convolutional neural networks to facial detection. YOLO verifies the identity card and Shoes from Pictures and Video. It gives about 80.6% mAP,90.3% precision, and 76.5% recall. © 2024 IEEE.

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

Grainbee: A Quantum-Resistant Blockchain-Based Ration Distribution System with Hardware Security Modules

Grainbee: A Quantum-Resistant Blockchain-Based Ration Distri...

引用

2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Joni, Sohel Ahmed Rahat, Rabiul Tasnin, Nishat Ghose, Partho Mahbub-Or-Rashid, Md. Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

In developing countries, subsidized food programs aimed at supporting the poor are often undermined by corruption and mismanagement, leading to food waste and unequal distribution. To overcome these challenges, we introduce GrainBee, a blockchain-powered ration distribution system designed to ensure secure, transparent, and efficient food supply distribution. By leveraging blockchain's inherent security and immutability, GrainBee addresses the major security and transparency issues that plague traditional and existing blockchain-based *** proposed system employs a post-quantum secure, blockchain-based approach to combat corruption and fraudulent invoicing, guaranteeing integrity through a distributed, immutable ledger. To further fortify security, we integrate Hardware Security Modules (HSMs), providing an additional layer of protection for sensitive data and transactions. A built-in reservation and scheduling system streamlines the distribution process, while a user-friendly interface and machine translation services bridge language gaps, enhancing usability and accessibility. By harnessing the power of blockchain technology, GrainBee offers a robust solution to the complex challenges of food distribution. © 2024 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Experimental Investigation and Weibull Statistical Analysis on Tensile Behaviour of Carbon/Epoxy Composites

引用

Journal of The Institution of Engineers (India): Series D 2024年第3期105卷 1373-1383页

作者： Amar, Thakur Abhinav Kumar, Amit Yadav, D.K. Department of Production and Industrial Engineering Birla Institute of Technology Ranchi JH Mesra835215 India Department of Mechanical Engineering National Institute of Technology BH Patna800005 India Department of Computer Science and Engineering National Institute of Technology JH Jamshedpur831014 India

This paper is concerned with the experimental and theoretical evaluation of the tensile strength of carbon fabric-reinforced epoxy composites developed using hand lay-up and vacuum bagging techniques. The developed CFRP composite laminates were subjected to tensile tests as per ASTM D3039 at different strain rates of 1 s−1, 10 s−1, and 100 s−1. The statistical analysis of the experimental results was conducted using a two-parameter Weibull distribution function. The results showed an increase in tensile strength with an increase in strain rate for composite laminates produced using both fabrication techniques. The tensile modulus also showed an increasing trend with respect to strain rate, but the increase was marginal, while elongation at break showed a decreasing trend. For all cases of strain rate, the vacuum bag-fabricated composite laminates showed higher tensile strength and modulus values except for elongation at break when compared with the laminates fabricated with hand lay-up. Cumulative probability density data, along with Weibull parameters, were utilised to determine theoretical tensile strength. The projected tensile strength values closely matched the experimental results, with a minimal difference of approximately 6.2% observed in the context of composite laminates fabricated under vacuum bag conditions. Fracture analysis identified distinct failure mechanisms: adhesive failure at the fabric/matrix interface for the hand lay-up method and brittle fracture as the predominant failure mode for laminates produced using vacuum bagging techniques. © The Institution of Engineers (India) 2023.

关键词： Tensile strength

来源：评论

学校读者我要写书评

暂无评论

Image Splicing Detection Based on Hybrid CNN-Transfarmer Network 2

Image Splicing Detection Based on Hybrid CNN-Transfarmer Net...

引用

2nd International Conference on Machine Vision, Image Processing and Imaging technology, MVIPIT 2024

作者： Jiang, Ningjing Zhang, Dengyong Li, Feng School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China

ISBN: (纸本)9798331543037

-Image splicing is widely recognized as the predominant technique in image manipulation. Most current splicing detection methods have low accuracy and poor localization of small-sized splicing areas. Therefore, we design a hybrid CNN-Transformer network for image splicing detection. Firstly, a hybrid structure encoder is constructed, which first uses CNN to extract the input images' local features, and then inputs it to Transformer to calculate long-distance dependencies to obtain global feature representation. Then, a deformable attention enhancement module (DAEM) is designed to make the CNN local features at different scales at each stage more refined and improve the deformation adaptability of the network. Finally, features from the hybrid structured encoder are added to the decoder via skip connections, to make the decoder perform better feature reconstruction. Results on three datasets show that the network can more accurately locate tampered areas and has superior performance than other methods. ©2024 IEEE.

关键词： Network coding

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：