检索结果-内蒙古大学图书馆

Correction to: Advanced model based machine learning technique for early stage prediction of ankylosing spondylitis under timely analysis with featured textures

引用

Multimedia Tools and applications 2024年第26期83卷 68415-68420页

作者： Ahammad, Shaik Hasane Jayaraj, R. Shibu, S. Sujatha, V. Prathima, Ch Leo, L. Megalan Prabu, R. Thandaiah Hossain, Md.Amzad Rashed, Ahmed Nabih Zaki Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Data Science and Buisness Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Master of Computer Applications S.A. Engineering College Chennai India Department of Data Science School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Deptartment of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha University Chennai India Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore Bangladesh Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf Egypt

来源：评论

学校读者我要写书评

暂无评论

Decentralized Intrusion Detection in Peer-to-Peer Networks using Stacking Ensemble Model

Decentralized Intrusion Detection in Peer-to-Peer Networks u...

引用

Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： R. Renuga Devi Rame Riadhusin Anu D Swarnateja R R. Revathi Department of Computer Science and Applications SRM Institute of Science and Technology India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Artificial Intelligence and Data Science Nitte Meenakshi Institute of Technology Bengaluru India Department of Information Technology Dhanalakshmi Srinivasan College of Engineering and Technology India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In recent years, due to the rise in Peer to Peer (P2P) networks has brought a significant way in which the data is shared and services are offered over the internet. These P2P networks make use of decentralized architecture in which nodes directly interact and exchange resources. However, due to the use of decentralized architecture, the P2P networks are exposed to various security threats such as data breaches, and other malicious attacks. In this research, Stacking Ensemble Model (SEM) is proposed for network intrusion detection in P2P networks. Initially, the data is considered from network intrusion detection dataset and then preprocessed with data imputation and min-max normalization effectively handles the missing values and rescales the data into significant range. After that, Recursive Feature Elimination (RFE) is incorporated to select optimal features from preprocessed data based on feature importance. Finally, the proposed SEM effectively detects the intrusion in P2P networks which enhances the security. From the results, the proposed SEM model obtained a better outcome in terms of accuracy, precision, recall, and F1-score compared to the existing K-Nearest Neighbor (KNN) by accomplishing 97.20%, 94.71%, 96.62%, and 95.65%, respectively.

关键词： Computational modeling Stacking Network intrusion detection Predictive models Nearest neighbor methods Feature extraction Imputation data models Peer-to-peer computing Security

来源：评论

学校读者我要写书评

暂无评论

Extracting and Classifying 3D Medical Image Texture Characteristics using GLCM and LBP-Based Techniques

Extracting and Classifying 3D Medical Image Texture Characte...

引用

data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： R. Srinath R. Sharmila N. Raghavendran M. C. Babu K. Ramanan R. Saravanakumar Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Artificial Intelligence and Date Science RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Sciences Chennai Institute of Technology Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Recent advances in 3D sensor technology have advanced 3D imaging systems. This sparks interest in 3D picture feature extraction and categorization. According to the literature, textural content is essential to visuals. In this case, we extract texture characteristics from three-dimensional photos to improve feature differentiation. Category of volumetric data with texture is possible using this method. We employ feature vectors from Local Binary Patterns (LBP) and Gray-Level Co-occurrence Matrix methods to achieve this. The visual pattern and volumetric data contrast, homogeneity, and local anisotropy are described. The proposed approach was tested using a publicly accessible dataset of modified volumetric textured pictures. Generative Adversarial Networks (GAN) and recurrent Neural Networks (RNN) are classified. Our solution outperforms manually constructed techniques for extracting texture characteristics from 3D and 2D data and standard deep-learning networks. Even with few photographs per class, the recommended method improves class distinction and yields good results. Show exceptional accuracy. Integrating the Gray Level Co-occurrence Matrix (GLCM) with Local Binary Patterns (LBP) improved classification performance. The GLCM approach, which captures spatial relationships, and the LBP method, which depicts local texture, was advantageous. This hybrid method improved feature extraction and classification, notably for complex 3D medical pictures with subtle texture changes.

关键词： Deep learning Visualization Three-dimensional displays Machine learning algorithms Recurrent neural networks Liver Feature extraction Generative adversarial networks Classification algorithms Tumors

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Method for the Diagnosis of Retinal Diseases using Convolutional Neural Network

Machine Learning Method for the Diagnosis of Retinal Disease...

引用

data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Sumit Kushwaha Jayaram Boga Bairaboina Sai Sambasiva Rao Syed Noeman Taqui R G Vidhya J. Surendiran Department of Computer Applications University Institute of Computing Chandigarh University Punjab India Department of Computer science and Data science Marri Laxman Reddy Institute of Technology and Management Hyderabad Telangana India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of ECE HKBK College of Engineering Bangalore India

The early diagnosis of visual problems such as diabetic retinopathy and glaucoma relies on the successful completion of medical image processing tasks such as the segmentation of retinal blood vessels. The process of isolating retinal blood vessels from images of the retinal fundus is a challenging one because of the similarities in intensity that exist between blood vessels and the surrounding tissues. In addition, there are differences in the size, shape, and intensity of the vessels. In recent times, a number of different algorithms based on deep learning have been presented for the segmentation of retinal blood vessels. These techniques have been shown to be more accurate and dependable when compared to the traditional computer vision methods that have been used. In this abstract, we present the U-Net deep learning model as well as a technique for segmenting retinal blood vessels that is based on the programming language Python. A U-Net model is trained using a series of retinal fundus images so that the features of retinal blood vessels may be comprehended. As part of the inference process, the image of the retinal fundus is loaded into a trained version of the U-Net model. This model then generates a probability map that illustrates the chance that each pixel represents an artery in the human body. The final segmentation is produced by applying a threshold to the probability map. When it comes to the process of segmenting retinal blood vessels, our python-based solution outperforms methods that are considered to be state-of-the-art in studies comparing accuracy and speed. The scientific and medical communities have unrestricted access to the software that may be used to put the suggested strategy into action since it is an open-source initiative. In conclusion, the python-based approach for retinal blood vessel segmentation that makes use of U-Net provides a potentially helpful solution for identifying retinal illnesses and has the potential to serve as a stan

关键词：

来源：评论

学校读者我要写书评

暂无评论

Efficient Zero-Trust data Transmission for Consumer Electronic Using Holographic Counterparts in Internet of Things: Ciphertext-Policy Attribute-Based Signcryption With Equality Test

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Kuang, Ping Zhang, Xiyue Yang, Mengheng Xiong, Hu Feng, Chaosheng Wu, Deyan Wang, Weizheng Wahaballa, Abubaker University of Electronic Science and Technology of China Intelligent Information Technologies and Applications Lab Chengdu610054 China University of Electronic Science and Technology of China Network and Data Security Lab Chengdu610054 China Sichuan Normal University School of Computer Science Chengdu610101 China Sichuan Institute of Computer Sciences Chengdu610000 China City University of Hong Kong Department of Computer Science Hong Kong The University of Aizu Aizuwakamatsu Japan Arab East Colleges Department of Computer Science Riyadh53354 Saudi Arabia

Secure communication in consumer electronic devices, such as health monitoring systems, is essential as they process sensitive medical data. This paper proposes a Ciphertext-Policy Attribute-Based Signcryption with Equality Test (CP-ABSC-ET) scheme to address these security challenges. The scheme controls who can access data and ensures both encryption and signature verification are done together, keeping the data secure. The integration of equality test enables secure ciphertext verification without decryption, ensuring both data integrity and confidentiality. Compared to existing schemes, the proposed approach reduces the computational overhead. Comprehensive security analysis and performance evaluations demonstrate the scheme’s suitability for dynamic and resource-constrained environments. The CP-ABSC-ET scheme ensures strong data protection, better access control, and efficient communication, making it ideal for consumer electronics like health monitoring devices. © 1975-2011 IEEE.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Enhanced Framework for MRI Brain Tumor Recognition with Residual Learning and Intuitive Heatmap Visualization

Enhanced Framework for MRI Brain Tumor Recognition with Resi...

引用

Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： L. Velmurugan R. K Maheswari K. Janani K. Agalya M. Poornima K.C. Gayathri School of Computing Science and Engineering (SCOPE VIT Bhopal University Sehore Madhya Pradesh India Artificial Intelligence and Data Science M.A.M College of Enginnering and Technology Trichy Tamilnadu India Department of Data science and computer applications Manipal institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science Engineering Sri Eshwar College of Engineering Coimbatore India Department of Information Technology OASYS Institute of Technology Trichy Tamil Nadu India Chettinad School of physiotherapy (CSP Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE) Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Early detection of brain tumors is crucial for effective treatment, yet it poses a significant challenge due to the rapid nature of the disease's progression. This study explores the application of ResNet, aimed at improving the accuracy of brain tumor diagnosis in magnetic resonance imaging (MRI) scans. Our model was trained on a dataset comprising 253 MRI images, which included 155 confirmed cases of tumors. The residual learning framework of ResNet effectively captures intricate features and mitigates issues such as vanishing gradients. The results indicate that the ResNet model achieved an impressive accuracy rate of 97%, surpassing traditional detection methods. We evaluated the model's performance using metrics like accuracy and F1-score, demonstrating its robustness. To improve the interpretability of our model's predictions, we utilized Grad-CAM (Gradient-weighted Class Activation Mapping), which highlighted critical areas in the MRI scans that influenced the model's decisions. Our findings confirm ResNet’ s effectiveness in medical imaging and stress the importance of model interpretability in healthcare, allowing radiologists to make informed diagnostic choices.

关键词： Deep learning Accuracy Magnetic resonance imaging Brain tumors Medical services Predictive models Brain modeling Robustness Stress Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

AI Personalized Mental Health Monitoring System using Machine Learning, and Natural Language Processing

AI Personalized Mental Health Monitoring System using Machin...

引用

data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： S. Mohamed Jamal Irfan G. Dhivya N. Raghavendran M. C. Babu T. Jasperline R. Saravanakumar Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Artificial Intelligence and Date Science RMK College of Engineering and Technology Chennai Tamil Nadu India Artificial Intelligence and Data Sciences Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Dr. G. U. Pope College of Engineering Tuticorin Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

This study introduces an AI-driven customized mental health monitoring system that uses machine learning (ML) and natural language processing (NLP) to provide ongoing mental health evaluation and assistance. The system uses user-generated textual data, including journal entries, chat logs, and social media postings, to assess emotional states, stress levels, and mental health issues. The system uses sophisticated natural language processing methods to identify linguistic patterns associated with mental health problems such as depression, anxiety, and others. The machine learning algorithms customize monitoring by adjusting to the unique characteristics of each user and use past data to provide customized suggestions and preemptive alerts. The technology aims to enhance human interaction and function as a proactive tool in mental health treatment by providing real-time information. The system extracts emotional indicators from textual input using sentiment analysis methods and pre-trained natural language processing models such as BERT. The results are used with user demographics and history to build machine learning classifiers, such as Random Forest (RF) and Support Vector Machine (SVM), which predict mental health disorders. Supervised learning is used to develop the model, and suggestions are progressively improved using a tailored feedback system. The SVM and RF algorithms demonstrated an 88% and 92%accuracy rate in forecasting mood swings and early warning indicators of mental health disorders when evaluated on a set of anonymized mental health information. The 30% increase in user involvement demonstrated the efficacy of tailored suggestions in proactive mental health treatment.

关键词： Support vector machines Machine learning algorithms Accuracy Social networking (online) Supervised learning Mental health Natural language processing Monitoring Random forests Classification tree analysis

来源：评论

学校读者我要写书评

暂无评论

Uncovering Android Zero-Day Threats: The Zero-Vuln Approach with Deep and Zero-Shot Learning

Uncovering Android Zero-Day Threats: The Zero-Vuln Approach ...

引用

Computing and data Analytics (ICCDA), International Conference on

作者： Suresh Palarimath Pyingkodi Maran Wilfred Blessing N. R S. J Kavitha Cibi. A Subitha G. Sutherlin College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Applications Kongu Engineering College Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai India Data & Analytics / Visualization - Delivery Virtusa Consulting Services Private Limited Chennai India Department of Computer Science and Engineering Stella Mary's College of Engineering Kanyakumari India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

The growing occurrence of cybersecurity hazards, such as Android zero-day vulnerabilities, poses substantial difficulties because they are unattended and imperceptible. With the increasing popularity of Android smartphones, hackers are taking advantage of these weaknesses to quickly spread advanced malware. Conventional detection approaches frequently prove inadequate due to the absence of shown criteria for emergent threats. In order to address this crucial problem, this article presents the Zero-Vuln method, an advanced technique specifically developed to identify and categorize previously unidentified malware on Android platforms. Zero-Vuln combines sophisticated supervised deep learning methods with zero-shot learning algorithms to accurately detect zero-day threats. Through the utilization of comprehensive datasets, the method attains an average classification metric score of 84.22%, showcasing exceptional accuracy, precision, and recall. This advanced solution significantly improves the ability to identify and handle Android zero-day threats, representing a major progress in cybersecurity research and implementation. The Zero-Vuln strategy provides a strong and adaptable solution for the constantly changing mobile security risks, representing a notable advancement in the sector.

关键词： Measurement Instruments Zero shot learning Malware Mobile security Hazards Reliability Protection Smart phones Research and development

来源：评论

学校读者我要写书评

暂无评论

Hyper-heuristic Algorithm based on ε-Greedy for Solving the Online Seru Scheduling Problem *

Hyper-heuristic Algorithm based on ε-Greedy for Solving the...

引用

Chinese Control and Decision Conference, CCDC

作者： Fan Tu Yang Liu Qi Lou Fajun Liu Yang Yu Department of Intelligent Data and Systems Engineering Northeastern University Shenyang China Beijing Institute of Computer Technology and Applications Beijing China Shenyang City University Shenyang China Shanghai Science and Technology Museum Shanghai China School of Economics and Management Dalian University of Technology Dalian China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

We address an online seru scheduling problem with the uncertainty of new batch arrivals. First, the decision-making process for online seru scheduling is delineated. Subsequently, drawing on this process, we design three simple and effective scheduling rules that have different advantages. To optimize their inherent benefits, an ε-greedy based hyper-heuristic algorithm is designed to adaptively select the appropriate rule for online seru scheduling. Within this algorithmic framework, the three scheduling rules serve as low-level heuristics, while ε-greedy acts as a high-level strategy. The extensive experiments are conducted to demonstrate the performance of the proposed algorithm.

关键词： Uncertainty Heuristic algorithms Decision making Scheduling

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-based Alzheimer's Disease Prediction using Personalized Methods 3

Machine Learning-based Alzheimer's Disease Prediction using ...

引用

3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Lavanya, M. Chandan, Radha Raman Rajasekar, P. Rham, P. Rethik Deivakani, M. Mahesh Kumar, A.S. Master of Computer Applications Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India Shambhunath Institute of Engineering and Technology Department of Computer Science & Engineering Uttar PradeshJhalwa Prayagraj211015 India Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India Saveetha Medical College Tamil Nadu Chennai602105 India Psna College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624622 India Pes College of Engineering Department of Electronics and Communication Engineering Karnataka Mandya571401 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Alzheimer's disease (AD) is a neurological illness which occurs over time. Early detection reduces the likelihood of disease worsening condition. Unfortunately, the present research focuses on defining disease processes in their existing stages rather than forecasting disease progression. The Generative Personalized embedding model (GPE) is a multi-layer perceptron that is proficient of relating prior knowledge to the prediction process by realizing the patient's behavioural data and helps in forecasting the disease progression. This work suggest a prediction process based on GPE. As a result, a GPA network is built with the connectivity layers and activated layers to observe the possible correlation among information and next generation of Alzheimer's disease. Experiments reveal that the outcomes significantly outperformed the majority of previous designs. © 2022 IEEE.

关键词： Machine learning

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：