检索结果-内蒙古大学图书馆

Zero Trust Networks: Evolution and Application from Concept to Practice

computers, Materials & Continua 2025年第2期82卷 1593-1613页

作者： Yongjun Ren Zhiming Wang Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang School of Computer Science School of Cyber Science and EngineeringEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China Department of Computing Science University of AberdeenAberdeenAB243FXUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Sanya Institute of Hunan University of Science and Technology Sanya572024China

In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network *** paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key ***,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use *** paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.

关键词： Zero trust cybersecurity software-defined perimeter micro-segmentation internet of things

来源：评论

学校读者我要写书评

暂无评论

Energy Efficient and SecurityAware Task off Loading for Multi-Tier Edge Cloud Computing Systems 1

Energy Efficient and SecurityAware Task off Loading for Mult...

引用

1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Divya, S. Saranya, S. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

The massive progression of the multi-tier edge cloud computing systems integration has created the possibility of data orchestration and resource control in edge and cloud layers. However, there are many shortcomings, including inappropriate methods of scheduling tasks and security breaches that prevent scalability. To solve these problems, we designed an energy-efficient and security-aware task offloading framework for multi-tier edge cloud computing. Firstly, we estimate the energy ratio of tasks using a Fuzzy Logic Energy Management (FL-EM) algorithm and distribute resources according to different layers. Subsequently, the task-offloading parallel squirrel (TO-PS) method is used to effectively extend the task with better energy consumption and computational efficiency. Finally, it is decided to protect the work data using the Provable Energy-Aware Cryptographic Security (PE-ACS) algorithm. The proposed algorithm achieves a 96.7% success rate in protecting high-level data from cybercrime. Additionally, it enhances security and task performance while reducing energy consumption in multi-tier edge cloud computing systems. © 2025 IEEE.

关键词： Cloud security

来源：评论

学校读者我要写书评

暂无评论

AI-Based Bone Cancer Detection Using Image Processing and CNN 5th

AI-Based Bone Cancer Detection Using Image Processing and CN...

引用

5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Srividya, K. Reddy, Gangannagari Varunteja Bakki, Vishwaja Adilakshmi, T. Department of Computer Science and Engineering Vasavi College of Engineering Hyderabad India

ISBN: (纸本)9783031770777

This study presents a new Technology-driven approach for early detection of bone cancer using preliminary image processing technologies and neural networks (CNN) used for diagnosing cancer from pathological images. The main aim is to provide a powerful and reliable tool for cancer diagnosis done in clinics. Survival rates of cancer decrease with an increase in age, urging the need for early diagnosis and precaution. The project aims to use machine learning and image preprocessing to provide solutions for the detection of cancer, necessary for timely intervention. Using carefully selected datasets and advanced image preprocessing techniques, the project aims to provide doctors with reliable tools to accurately diagnose bone cancer. Analysis of existing literature demonstrates the effectiveness of CNN and deep learning algorithms in the analysis of medical images as the basis of the proposed method. The proposed methodology includes image enhancement, data acquisition, CNN development, training, validation, transformation, and model evaluation. The CNN model is evaluated on accuracy, precision, recall, and f1-scores of bone cancer diagnosis images. It provides insight into model development by addressing issues such as classification error, class inequality, and biases. While comparisons with existing literature demonstrate the model's uses, practical considerations are also discussed. The findings suggest a significant potential to improve bone cancer diagnosis and stimulate further research on improving CNN model architecture and developing real-world deployment strategies. The training accuracy of the CNN model came out to be 94.64%. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

CUET_Big_O@NLU of Devanagari Script Languages 2025: Identifying Script Language and Detecting Hate Speech Using Deep Learning and Transformer Model 1

CUET_Big_O@NLU of Devanagari Script Languages 2025: Identify...

引用

1st Workshop on Challenges in Processing South Asian Languages, CHiPSAL 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Hossan, Md. Refaj Sakib, Nazmus Hossain, Md. Alam Miah Jawad Hoque, Mohammed Moshiul Department of Computer Science and Engineering Chittagong University of Engineering and Technology Bangladesh

ISBN: (纸本)9798891762015

Text-based hate speech has been prevalent and is usually used to incite hostility and violence. Detecting this content becomes imperative, yet the task is challenging, particularly for low-resource languages in the Devanagari script, which must have the extensive labeled datasets required for effective machine learning. To address this, a shared task has been organized for identifying hate speech targets in Devanagari-script text. The task involves classifying targets such as individuals, organizations, and communities and identifying different languages within the script. We have explored several machine learning methods such as LR, SVM, MNB, and Random Forest, deep learning models using CNN, BiLSTM, GRU, CNN+BiLSTM, and transformer-based models like Indic-BERT, m-BERT, Verta-BERT, XLM-R, and MuRIL. The CNN with BiLSTM yielded the best performance (F1-score of 0.9941), placing the team 13th in the competition for script identification. Furthermore, the fine-tuned MuRIL-BERT model resulted in an F1 score of 0.6832, ranking us 4th for detecting hate speech targets. © 2025 International Committee on Computational Linguistics.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Toxic Comments Classification using Machine Learning and Word Embedding Techniques 2

Toxic Comments Classification using Machine Learning and Wor...

引用

2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Prabha, Sneh Yadav, Aditya Yadav, Aman Kumar, Akash Tyagi, Manu Department of Computer Science and Engineering Inderprastha Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

Toxic comments are comments which are disrespectful, unreasonable and infuriating that make the reading uncomfortable. Sometimes these comments are inappropriate and not good for the public. These comments make daily content consumers uncomfortable and sometime even cause emotional as well as emotional harm. There are some people whose purpose for coming online is to create trouble for others and make their work and daily life difficult by making inappropriate comments. This kind of behavior can be described as anti-social and harmful to society. Many steps have already been taken to catch these troublemakers and punish them accordingly by blocking users who are found guilty. This cannot be done personally by checking one by one, to achieve this purpose we need to make a model. We have applied three Machine Learning Models logistic regression, Naive bayes, SVM and three Word Embedding Techniques, Word2Vec, FastText and Glove which can classify comments, and the content creator take appropriate action against users who makes these comments by reporting and even by deleting their account if needed. Out of all six techniques SVM has outperformed for classification of toxic and non-toxic comments and normal text classification in general. SVM gives the best results with 93.61% accuracy and F1 score of 93%. Proposed technique has improvised accuracy score as compared to existing techniques. © 2025 IEEE.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

An Interactive Augmented Reality Application for Agriculture using Unity 17

An Interactive Augmented Reality Application for Agriculture...

引用

17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Maasthi, Manasvi J. Vidyavardhaka College of Engineering Department of Computer Science and Engineering Mysuru India

ISBN: (纸本)9798331531195

Augmented reality is defined as a 3D computer generated imagery that is embedded into the real world. There are numerous areas in which augmented reality can be applied, including: education, medical care, entertainment, & so on. Unlike VR, AR does not require the use of a HMD (Head Mounted Device);a projection can be performed on a mobile phone making it affordable. Most farmers are facing problems today, for instance, loss of crops. In this Paper an application has been created aimed at enabling farmers to know important things like when to grow certain crops and what to use to assist in soil fertility, through the development of augmented reality applications using Unity and Vuforia. That way, the farmers will have an idea of what other crops they should be able to grow during the other season. So that they will not be undergoing any kind of loss. © 2025 IEEE.

关键词： Virtual environments

来源：评论

学校读者我要写书评

暂无评论

Road Lane Detection using Region-based Convolutional Neural Network (RCNN) 1

Road Lane Detection using Region-based Convolutional Neural ...

引用

1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Thondralnayagi, A. Nandhakumar, T. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

Road lane markings are one of the main factors that cause traffic accidents and jeopardize the safety of both drivers and pedestrians. Both computer vision systems find it challenging to recognize road lanes. The irregular maintenance, insufficient drainage, and traffic patterns. It negatively affects driving comfort, road safety, and vehicle condition, and it could also lead to a lot of accidents. Recently, several machine learning algorithms have been used, although their accuracy and efficiency have not improved significantly. We suggest applying Region-Based Convolutional Neural Networks (RCNN) for path identification to tackle this issue. RCNNs are another kind of neural network that is capable of extracting significant information from picture and time series data. This makes it highly helpful for tasks requiring images, such as shape identification, object classification, and recognition. Image from a dataset with noise removed using a Wiener filter. The next step is to extract features from images using Linear Discriminant Analysis (LDA). Determine which lane line in the RCNN is efficient and provides accuracy, precision, recall, and F1-score as 95.6%, 94.05%, 94.58%, and 94.23%, respectively. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of Machine Learning Based On Optimized Feature Selection for Type II Diabetes Mellitus

引用

Multimedia Tools and Applications 2025年第8期84卷 4945-4964页

作者： Bhat, Salliah Shafi Ansari, Gufran Ahmad Ansari, Mohd Dilshad B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai48 India Department of Computer Science and Software Engineering University of Hail Hail55424 Saudi Arabia Department of Computer Science & Engineering SRM University Delhi-NCR Harayana Sonepat131029 India

Type 2 Diabetes Mellitus (T2DM) is a common chronic illness caused by variations in the secretion of insulin. T2DM will be treated early with the help of an early diagnosis reducing the risk of early death and controlling is course of illness. In this Paper Machine Learning Algorithms (MLA) for T2DM diagnosis are proposed. The major objective of this research is to assess use of MLA and its methods to forecast different illnesses at an initial stage. In this research, we examine most recent proposed strategies with some limitations and potential improvements for future work. The data is collected from Kaggle (University of California) *. Researchers highlight how important is to identify important features that enhance outcomes suggested by recent methods. Six MLA were used in this paper such as Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbour (KNN) were applied in Jupiter Notebook. Feature sets are selected using three feature selection methodologies such as information gain-based selection, correlation-based selection and sequential feature selection. On these feature subsets a variety of MLA are applied and optimal feature subset is chosen based on their performance. Finally a DT is suggested as the top model among the top six performances. The suggested method provides an improved performance label with an average accuracy of 96.10%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Early Stage Pepper Leaf Disease Detection using Deep Learning based Capsule Network with Convolutional Neural Network 1

Early Stage Pepper Leaf Disease Detection using Deep Learnin...

引用

1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Jeevitha, S. Venkadesh, R. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

Agriculture plays a vital part in the economy of some countries, providing benefits such as food, national income, and employment. This results in substantial growth in agricultural produce, financial losses, and a decline in quality and quantity. Farmers have traditionally detected diseases through eye tests, but this method has drawbacks. However, previous research suggests that binary classification needs improvement because some types are more challenging to determine. To address these issues, various pepper diseases are classified based on DL techniques to address these issues. First, image pre-processing uses a Median Filter (MF) to reduce noise and increase brightness. Furthermore, the Active Contour Segmentation (ACS) method detects affected and unaffected areas in the image. Moreover, the Texture Color Intensity Feature Selection (TCIFS) method is employed to select the optimal features of pepper disease. Finally, the proposed Capsule Network with Convolutional Neural Network (CapsNet-CNN) technique detects pepper leaf disease. The proposed method attains an accuracy of 98.32%, recall of 97.43%, and precision of 98.04% compared to other methods. © 2025 IEEE.

关键词： Median filters

来源：评论

学校读者我要写书评

暂无评论

Helmet and Equipment Detection with Worker's Mobility Tracker In Mining Sector Using YOLOv8 & LSTM

Helmet and Equipment Detection with Worker's Mobility Tracke...

引用

2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Ramyadevi, R. Keerthy, S. Catherina J, Sherin Joys Dhayanithi, T. Saveetha Engineering College Department of Computer Science and Engineering Kanchipuram India

ISBN: (纸本)9798331529833

Monitoring personal protective equipment and compliance with safety procedures for mining operations is essential to ensuring personnel safety. Nevertheless, sending humans underground is impractical, as mining conditions are fast-changing and dangerous. Most of the existing systems are still not able to detect helmets, PPE, and the worker mobility in real-time, which are two critical aspects of safety. To overcome these challenges, this project bases its approach on enhancing monitoring and security by integrating machine learning technologies. Using YOLOv8, a cutting-edge object detection model, the system enables workers' helmet and PPE detection in real-time. This work also uses Long Short-Term Memory (LSTM), a special type of RNN, to track the behavior of workers within and over time. By reading the frames at 10 millisecond intervals, the system detects movement deviations be it unsafe movements or fatigue induced deviations and provides proactive measures for safety. The devised method reaches an outstanding accuracy of 95.9%, combining YOLOv8 and LSTM, which reduces human error and improves safety at the workplace. This state-of-the-art solution is not only providing continuous monitoring but is also increasing operational efficiency, thereby transforming safety solutions in the mining space. © 2025 IEEE.

关键词： Protective clothing

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：