检索结果-内蒙古大学图书馆

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kulshreshtha, Kushagra Goel, Bhawna Kanyal, Hoshiyar Singh Singhal, Vikas Gla University Department of Computer Engineering & Applications UP Mathura India UP Ghaziabad India Abes Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India Greater Noida Institute of Technology Department of Information Technology UP Greater Noida India

ISBN: (纸本)9798350371055

Federated studying is a disbursed system learning method that lets a set of dispensed users, or clients, collaboratively learn a shared predictive model without sharing any statistics. Through taking part in the model education method, every purchaser contributes to the development of a worldwide, predictive model without sacrificing its very own information privacy. This privateness renovation may be achieved through various techniques, which include differential privateness, cozy multi-celebration computation, and information obfuscation. In this way, model schooling isn't always a task exclusively for centralized facts creditors, but as an alternative, a large-scale collaboration amongst multiple entities with disparate hobbies and facts silos. In the technology of privacy protection, federated studying represents a powerful tool that enables the construction of predictive fashions that might be knowledgeable by means of various information resources at the same time as retaining the integrity and privateness of the individual statistics units. © 2024 IEEE.

关键词： Collaboratively Construction Federated Privateness Technology

来源：评论

学校读者我要写书评

暂无评论

Integrating Cloud-based Data Mining Algorithms for Smart City Infrastructure Management and Decision Support Systems 4

Integrating Cloud-based Data Mining Algorithms for Smart Cit...

引用

4th International Conference on Soft Computing for Security applications, ICSCSA 2024

作者： Guru Vimal Kumar, M. Jude Moses Anto Devakanth, J. Selvapandian, D. Venkatesan, R. Revathi, J. Aruna, R. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Madanapalle Institute of Technology & Science Department of Computer Applications Chittoor India Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Bannari Amman Institute of Technology Department of Information Technology Erode India Kongu Engineering College Department of Mathematics Erode India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331515720

This study explores cloud-based data mining algorithm integration in elevating smart city infrastructure management and decision support systems. Specifically, the authors focus on optimizing traffic management through the analysis of real-Time traffic sensor data. Timestamped history records of vehicle count, traffic speed, density, weather condition, accidents, and traffic signals were utilized. Machine learning models such as Support Vector Machine, K-Nearest Neighbors, Decision Trees, and Random Forest were trained and tested. The analysis indicated SVM to be the most effective instrument, resulting in an accuracy of 98.98%. The model was also instrumental in reducing vehicle count by 16.7%, traffic density by 25%, and traffic speed was increased by 50%. KNN's overall accuracy experience was 94.5%, and the model was shown to be highly optimized for various forever traffic conditions. However, the overall impact on traffic speed and density was not as significant compared to the previously-discussed model. DT and RF demonstrated respective accuracies of 92.3% and 91.2%, offering varying levels of decision interpretation and ensemble learning opportunities. As a result, the study indicates that machine learning can play a critical role in improving urban traffic management via traffic congestion prediction and minimization, resulting in improved traffic flow, overall trip time reduction, and general urban mobility management. T. © 2024 IEEE.

关键词： Random forests

来源：评论

学校读者我要写书评

暂无评论

Multi-Criteria Decision-Making Model to Achieve Sustainable Developmental Goals in Industry 4.0 for Smart City Infrastructure

HighTech and Innovation Journal

引用

HighTech and Innovation Journal 2024年第4期5卷 1135-1153页

作者： Akila, D. Pal, Souvik Sarkar, Bikramjit Jayalaksshmi, S. Muthaiyah, Saravanan Anbananthen, Kalaiarasi Sonai Muthu Department of Computer Applications Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Management Information Systems Saveethas College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Computer Science and Engineering JIS College of Engineering Kalyani India Department of Information Technology Vels Institute of Science Technology and Advanced Studies India School of Business and Technology International Medical University Kuala Lumpur57000 Malaysia Faculty of Information Science and Technology Multimedia University Melaka75450 Malaysia

Due to a shortage of funding and other market challenges, Small and Medium-sized Enterprises (SMEs) face difficulties in adopting new technologies. Numerous technological obstacles negatively impact the long-term commercial achievement of SMEs. The deployment of Industry 4.0hopes to resolve these technological challenges. A sustainable city is a complex structure where economic, societal, and ecological components interact and compete. There is a scarcity of l methodologies for measuring interactions in this complex structure. Industry 4.0 aims to obtain higher performance effectiveness, profitability, and automation. The main goal is to develop a reliable method of evaluating small and medium-sized enterprises (SMEs) adopting Industry 4.0 technologies, particularly concerning smart city applications. This paper aims to determine the influence of Industry 4.0 in fostering economic efficiency and sustainability amongst these SMEs. The study introduces a multi-criteria decision-making (SC-MCDM) system designed to test an SME’s achievement of their targeted sustainable developmental goals. A technique for computing the interaction between various standards, i.e., (static interactions and dynamical pattern resemblance), as well as the weightage of variables of every indicator generated by the connection, is included within SC-MCDM. Furthermore, applying the suggested technique is validated by assessing the sustainable development goals of twelve Chinese cities within the Triple Bottom Line (TBL) paradigm. From a geographic-temporal viewpoint, spatial variations in city sustainability reveal regional sustainable inequalities. Indicator scores suggest that the most significant factors for most communities are the lack of research spending, falling financing in stationary assets, shortage of financial development, and inadequate shared transit. Furthermore, the growth of tertiary industries, improvement of energy performance, expansion of green areas, and reduction of poll

关键词： Sustainable city

来源：评论

学校读者我要写书评

暂无评论

Smart Health Management Application Using IoT and Smart Sensors 2

Smart Health Management Application Using IoT and Smart Sens...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Arnika Shaikh, Asmat Ara Kumar, Mukesh Sharma, Bhasker Yadav, Dinesh Kumar Aeri, Manisha Sharda School of Engineering and Technology Sharda University Department of Computer Science and Applications Up Greater Noida India Parle Tilak Vidyalaya Association's Institute of Management Department of Marketing Management Maharashtra Mumbai India Graphic Era Hill University Department of Computer Science & Engineering Uttarakhand Dehradun India Hitech Institute of Engineering and Technology Department of Computer Science UP Ghaziabad India Lloyd Institute of Engineering & Technology Department of Mechanical Engineering UP Greater Noida India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350371055

Clever fitness management software using the Internet of Factors (IoT) and smart sensors are a modern answer for powerful and personalized health care. This technology integrates innovative sensors with IoT, creating a community of connected gadgets that can music and screen a character's health and well-being. The utility collects facts in actual time, which are then analyzed and processed to provide valuable insights and recommendations for stepped-forward fitness management. The software uses diverse clever sensors, including health trackers, blood strain monitors, and glucose video display units, to constantly display the consumer's vital signs, symptoms, and physical activities. This record is then transmitted to the cloud for garage and analysis. © 2024 IEEE.

关键词： Garage Health care Management Sumptoms Transmitted

来源：评论

学校读者我要写书评

暂无评论

P2FLF: Privacy-Preserving Federated Learning Framework Based on Mobile Fog Computing

International Journal of Interactive Mobile Technologies

引用

International Journal of Interactive Mobile Technologies 2023年第17期17卷 72-81页

作者： Ankayarkanni, B. Pani, Niroj Kumar Anand, M. Malathy, V. Bhupati Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science Engineering and Applications Indira Gandhi Institute of Technology Odisha Sarang India Department of ECE Dr M.G.R Educational and Research Institute Tamil Nadu Chennai India Department of ECE SR University Telangana Warangal India Department of IoT KL Deemed to be University Andhra Pradesh Vaddeswaram Guntur India

Mobile IoT devices provide a lot of data every day, which provides a strong base for machine learning to succeed. However, the stringent privacy demands associated with mobile IoT data pose significant challenges for its implementation in machine learning tasks. In order to address this challenge, we propose privacy-preserving federated learning framework (P2FLF) in a mobile fog computing environment. By employing federated learning, it is possible to bring together numerous dispersed user sets and collectively train models without the need to upload datasets. Federated learning, an approach to distributed machine learning, has garnered significant attention for its ability to enable collaborative model training without the need to share sensitive data. By utilizing fog nodes deployed at the edge of the network, P2FLF ensures that sensitive mobile IoT data remains local and is not transmitted to the central server. The framework integrates privacy-preserving methods, such as differential privacy and encryption, to safeguard the data throughout the learning process. We evaluate the performance and efficacy of P2FLF through experimental simulations and compare it with existing approaches. The results demonstrate that P2FLF strikes a balance between model accuracy and privacy protection while enabling efficient federated learning in mobile IoT environments. © 2023 by the authors of this article. Published under CC-BY.

关键词： Fog computing

来源：评论

学校读者我要写书评

暂无评论

Cybersecurity Threat Detection in Smart Cities using Box Plot Sampling Isolation Forest 1

Cybersecurity Threat Detection in Smart Cities using Box Plo...

引用

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

作者： Shankari, S. Uma Mohameed, Haeedir Kulkarni, Megha Aravindh, S. Purushotham, N. Srm Institute of Science and Technology Department of Computer Science and Applications Ramapuram India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Civil Engineering Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Mohan Babu University Department of Computer Science and Engineering Tirupati India

ISBN: (纸本)9798331529246

Now a days, the standard of living in urban areas is advanced by leveraging technologies such as Cyber-Physical Systems (CPS) and Internet of Things (IoT) with physical infrastructures. However, smart cities highly rely on digital ecosystems, which makes them vulnerable to various cyber threats such as Distributed Denial of Service (DDoS) which leads in violations of privacy and threats to safety of citizens. To develop an effective and efficient system for detection of cybersecurity threats, Box plot Sampling Isolation Forest (BSIF) is proposed. Initially, the data was collected from the Botnet Traffic attack in Internet of Things (BoT-IoT) dataset which contains various normal traffic and Botnet traffic data. After that, Label Encoding (LE) was used for preprocessing the data, which assigns numerical labels to each category in the dataset. Furthermore, Recursive Feature Elimination (RFE) technique was utilized for iterative extraction and selection of features based on their weights is performed by Decision Tree (DT). Finally, the proposed BSIF model is used for detection of cybersecurity threats associated with smart cities, by leveraging box plot sampling method. The proposed obtain better results with 99.74% Precision, 99.92% Accuracy, 99.87% Recall and 99.84% F1-score, compared with Smart Attacks Learning Machine Advisor (SALMA) respectively. © 2025 IEEE.

关键词： Bot (Internet)

来源：评论

学校读者我要写书评

暂无评论

Analysis and study of IDSs for cybersecurity vulnerability detection and prevention: A Comprehensive Review 14

Analysis and study of IDSs for cybersecurity vulnerability d...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Shaji, Lijin Suji Pramila, R. Noorul Islam Center for Higher Education Thucklay Computer Applications Tamil Nadu 629180 India Mar Baselios Institute of Technology and Science Computer Science and Engineering Kerala Kothamangalam686693 India

ISBN: (纸本)9798350335095

Smart environments are worried about private keys and security problems. To prevent security attacks, which take benefit of a few of these intrusion detection systems (IDSs) and security vulnerabilities, were modeled. In the age of the Internet of Things (IoT), data generated by connected objects presents the fundamentals for big data analytics that could be used to recognize the anomalies and patterns in the data. Numerous cyber security systems use IDSs to detect intrusions that employ a variety of approaches and architectures. In this work, several research papers have been utilized based on diverse approaches used for detecting and preventing cyber security vulnerabilities. Moreover, this research work is reviewed by classifying them based on the detection approaches used in the preceding works. In addition, the research gaps and problems recognized in the previous works are stated;thus, the researchers can bring elucidation and improve their research in the future. The works in the literature are analyzed based on several features, such as performance evaluation measures, datasets utilized and approaches used. © 2023 IEEE.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

An Effective Analysis of Machine Learning Models for Prediction of Heart Disease 2

An Effective Analysis of Machine Learning Models for Predict...

引用

2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Patil, Rudragoud Kamkar, Shambahvi C. Goudar, Shwetha Indudhar K.L.S.Gogte Institute of Technology Dept of Computer Science and Engineering Karnataka Belagavi India K.L.S Gogte Institute of Technology Dept.of Master of Computer Applications Karnataka Belagavi India

ISBN: (纸本)9798350372892

Heart failure is a chronic illness that impacts millions of individuals worldwide. In order to forecast heart failure health status early and take the necessary activities to address this global issue an effective machine learning-based technique is required. Globally, heart disease is one of the leading causes of death. Medical professionals find it challenging to forecast since it demands experience and a deeper level of predictive knowledge. The healthcare industry operates in a knowledge-deficient but information-rich environment. Healthcare systems have access to a vast amount of data via the internet, but there aren't many useful tools for analyzing the data to find hidden patterns. Medical efficiency will increase with an automated system, which will also save money and time. This software uses information acquired from Kaggle to forecast the likelihood of a disease developing. To create a prediction model, the following algorithms are used: Random Forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), Decision Tree (DT), and Logistic Regression (LR). Phases for feature selection and data preprocessing were finished before the models were created. When the models were evaluated based on accuracy and the F1-score, the RF model performed the best. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Gesture-Based Virtual Keyboard: Enhancing Human-computer Interaction Through Hand Tracking

Gesture-Based Virtual Keyboard: Enhancing Human-Computer Int...

引用

2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Singh Yadav, Ajay Pratap Yadav, Ganesh Kumar Nagar, Sheetal Sharma, Brijesh Kumar Rawat, Daksh Bhakuni, Vihan Singh Uttar Pradesh Greater Noida201310 India JSS Academy of Technical Education Department of Computer Science and Engineering UP Noida201301 India IIMT College of Engineering Department of ECE Greater Noida India SRM Institute of Science and Technology Department of Computer Applications Modinagar India Graphic Era Hill University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331542344

In this study, an algorithm is developed that utilizes various OpenCV, cvzone and python libraries to provide a virtual keyboard. The program detects hand gestures, specifically the tips of fingers captured through a webcam, to simulate keystrokes corresponding to buttons on the virtual keyboard displayed on the screen, using Hand tracking module provided by Mediapipe. The virtual keyboard consists of keys of 'QWERTY' format and are represented by a graphical element. When the index finger approaches the corresponding key, it is highlighted and when distance between the tips of middle finger and index finger matches the required distance the corresponding keystroke is generated. The program captures video frames continuously from the webcam, identifies hand gestures, and accordingly updates the virtual keyboard's display. Additionally, the program outputs the text generated in a designated area of the screen using pynput library. The implementation uses object-oriented programming principles, with classes defined for buttons and hand detection functionality. © 2024 IEEE.

关键词： Object oriented programming

来源：评论

学校读者我要写书评

暂无评论

Modular irregular labelling in Network Analysis with Complete Bipartite Graph as Auto Proctoring 4

Modular irregular labelling in Network Analysis with Complet...

引用

4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Sivakumar, S. Vidyanandini, S. Nayak, Soumya Ranjan Aluvala, Srinivas Srm Institute of Science and Technology Kattankulathur Faculty of Science and Humanities Department of Computer Applications Chennai603203 India Srm Institute of Science and Technology Kattankulathur College of Engineering and Technology Department of Mathematics Chennai603203 India Kiit Deemed to Be University School of Computer Engineering Odisha Bhubaneswar751024 India Sr University Department of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798350349856

Modular irregular labeling is an edge irregular labeling of a graph in which the edges and vertices are labelled with 1, 2, 3,.. k such that the weight of all vertices are different. The complete bipartite graph is whose vertices are divided into two subsets such that no edge is in the same subset. In this paper the modular irregular labeling technique is applied in the complete bipartite graph. It is also planned to develop certain theorems in work of applying the modular irregular labeling in complete bipartite graph. An application in complete bipartite graph in the field of networking is also discussed. The aim of the work is to build the theorem and to promote an application in complete bipartite graph. The application is about the autoproctor for the online examination to make work easier for the examiner to examine students. © 2024 IEEE.

关键词： Graph theory

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：