检索结果-内蒙古大学图书馆

作者： Sivan, Durga Sellappa, Mohan Dinesh Peter, J. Department of Computer Science Engineering Karunya Institute of Technology and Sciences Coimbatore India College of Computer and Information Systems Al Yamamah University Riyadh Saudi Arabia

Deep learning is a powerful technology that enables intelligent data processing in the smart healthcare domain. Inspired by the tremendous processing power of cloud computing, the training process and the model repository of deep learning are moved to the cloud. Cloud-assisted deep learning applications enable smart mobile users to experience quick predictive results. Health professionals use smart mobile devices to convey recordings of the patient and to receive the best inference results. The mobility of these devices causes severe performance degradation as it increases the distance between its current location and the edge cloud where the virtual machines are provisioned. Therefore, mobility-based resource provisioning to identify a suitable server based on deadline constraints, available resources, and cost metrics is crucial. This paper proposes a proximity-based resource provisioning technique that guarantees minimal delay in obtaining inference results with a local mobile cloud system. The proposed technique comprises two algorithms (a) deadline-based initial resource provisioning and (b) resource migration and provisioning at suitable cloudlet during location change. The proposed technique is implemented in a mobile cloud platform running the inference method of a smart mobile healthcare application. The performance results show that the proposed technique outperforms the state-of-the-art techniques in terms of the response time, deadline meeting percentage, and system utilization. © 2020 John Wiley & Sons, Ltd.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Enabling Protection for Critical Infrastructure through Security and Privacy in the Industrial Internet of Things 9

Enabling Protection for Critical Infrastructure through Secu...

引用

9th International Conference on Communication and Electronics systems, ICCES 2024

作者： Yasotha, B. Vijayanandh, T. Thirumoorthy, P. Priya, M.Sangeetha Sasidaran, S. Prakalya, S.B. Srmist Department of Data Science and Business Systems Kattankulathur India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Avadi Tamil Nadu Chennai India Dhanalakshmi College of Engineering Department of Electrical and Electronics Engineering Chennai India Chennai India Panimalar Engineering College Department of Information Technology Chennai600123 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350377972

Industrial Internet of Things can improve critical infrastructure in energy, transportation, and manufacturing. However, IIoT device and system integration opened security weaknesses that bad actors may exploit, causing huge disruptions and financial losses. IoT network security and privacy must be prioritized to protect key infrastructure. IIoT security issues and solutions are examined in this research. First, we take a look at the state of security and the unique risks that industrial IoT devices are up against, including hacking, illegal access, and cyber-physical assaults. Then, to lessen the impact of these dangers, we investigate cutting-edge security solutions, such as authentication, encryption, and access control systems. Building a robust security architecture specifically for IIoT applications is one of the main goals of the project. To improve safety and anonymity, this system uses cutting-edge innovation like blockchain, AI, and edge computing. We also talk about how privacy-preserving methods like differential privacy and anonymization are important for keeping sensitive information safe while allowing data sharing for analysis and operations. We assess the efficacy of the suggested security protocols and procedures using a battery of case studies and simulations. Reducing the attack surface and improving the resilience of IIoT systems can be achieved by a multi-layered security approach that includes effective privacy protections. We conclude by outlining our research's larger implications, which include the importance of standardization, regulatory compliance, and stakeholder collaboration in ensuring the safe and privacy-protecting deployment of IIoT technologies in vital infrastructure. Protecting vital infrastructure from cyber threats is our ultimate goal, and our work helps build IIoT systems that are more secure and dependable. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

来源：评论

学校读者我要写书评

暂无评论

The Impact Of Social Media On The Academic Development Of School Students 10

The Impact Of Social Media On The Academic Development Of Sc...

引用

10th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2022

作者： Perera, U.D.Harshi Lahiruka Harshanath, S.M.B. Sri Lanka Institute of Information Technology Faculty of Graduate Studies Research Sri Lanka Sliit Department of Computer Science and Software Engineering Sri Lanka

ISBN: (数字)9781665401562

ISBN: (纸本)9781665401562

While social media has reached the peak of its popularity, the addiction to social media become a major issue. With the present pandemic the authorities are forced to close the schools and to shift to virtual classrooms. The students must therefore use a connected devise to participate to the classes which has contributed to the increased addiction to social media among the youth. Inappropriate or excessive usage social media may affect the studies as well as mental and physical health of the user. Spread of fake news through social media has also been a big problem. Mainly, due to its convenience of usage, social media, has become a daily necessity, especially among the youth. So, the primary goal of this study project is to determine the impact of social media on school students' academic performance and to suggest a separate social media platform which will meet their needs. © 2022 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

A New Method for Scene Classification from the Remote Sensing Images

引用

computers, Materials & Continua 2022年第7期72卷 1339-1355页

作者： Purnachand Kollapudi Saleh Alghamdi Neenavath Veeraiah Youseef Alotaibi Sushma Thotakura Abdulmajeed Alsufyani Department of CSE B V Raju Institute of TechnologyNarsapurMedakTelanganaIndia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Electronics and Communications DVR&DHS MIC Engineering CollegeKanchikacharlaVijayawadaA.P.India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of ECE P.V.P Siddhartha Institute of TechnologyVijayawadaIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

The mission of classifying remote sensing pictures based on their contents has a range of applications in a variety of *** recent years,a lot of interest has been generated in researching remote sensing image scene *** sensing image scene retrieval,and scene-driven remote sensing image object identification are included in the Remote sensing image scene understanding(RSISU)*** the last several years,the number of deep learning(DL)methods that have emerged has caused the creation of new approaches to remote sensing image classification to gain major breakthroughs,providing new research and development possibilities for RS image classification.A new network called Pass Over(POEP)is proposed that utilizes both feature learning and end-to-end learning to solve the problem of picture scene comprehension using remote sensing imagery(RSISU).This article presents a method that combines feature fusion and extraction methods with classification algorithms for remote sensing for scene *** benefits(POEP)include two *** multi-resolution feature mapping is done first,using the POEP connections,and combines the several resolution-specific feature maps generated by the CNN,resulting in critical advantages for addressing the variation in RSISU data ***,we are able to use Enhanced pooling tomake the most use of themulti-resolution feature maps that include second-order *** enablesCNNs to better cope with(RSISU)issues by providing more representative feature *** data for this paper is stored in a UCI dataset with 21 types of *** the beginning,the picture was pre-processed,then the features were retrieved using RESNET-50,Alexnet,and VGG-16 integration of *** characteristics have been amalgamated and sent to the attention layer,after this characteristic has been fused,the process of classifying the data will take *** utilize an ensemble classifier in our classification a

关键词： Remote sensing RSISU DL RESNET-50 VGG-16

来源：评论

学校读者我要写书评

暂无评论

An Ensemble Fake News Detection Model Using fakenewslingual dataset with Feature Selection 12

An Ensemble Fake News Detection Model Using fakenewslingual ...

引用

12th IEEE Conference on systems, Process and Control, ICSPC 2024

作者： Choong, Hoi Chun Chua, Hui Na Jasser, Muhammed Basheer Wong, Richard T.K. Aldharhani, Ghassan Saleh Sunway University No. 5 School of Engineering and Technology Department of Computing and Information Systems Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia UKM University Faculty of Information Science and Technology Selangor Bangi Malaysia

ISBN: (纸本)9798350391398

The rapid dissemination of information on social media acts as a double-edged sword, promoting the spread of accurate information and fake news, which can disrupt social cohesion and undermine trust in media and institutions. Existing fake news detection methods often lack feature comprehensiveness and struggle to generalize across different fake news types and sources. This study aims to enhance feature comprehensiveness by incorporating headline features into existing models and improve model generalization through ensemble techniques and hyperparameter tuning. The study expanded an existing dataset with headline, sentiment, and readability features. We assessed the impact of these additional features on model performance and explored various ensemble learning techniques (Bagging, AdaBoost, Gradient Boost) with tailored hyperparameters. An optimal model was then used to build a prototype for real and fake news classification. Integrating headline features showed marginally improved accuracy from 98.63% to 98.66%. While using ensemble learning models and hyperparameter tuning, accuracy increased significantly to 99.34%. This research enriched the dataset with headline features, enhancing the detection model's accuracy and generalization. The enhanced dataset, which we named fakenewslingual, has been made publicly available for further research. The study underscores the effectiveness of headline features and ensemble models in fake news detection. © 2024 IEEE.

关键词： Data accuracy

来源：评论

学校读者我要写书评

暂无评论

Bridging the Artificial Intelligence Knowledge and Skill Gaps in Africa: a Case of the 3rdGoogle Tensorflow Bootcamp and FEDGEN Mini-Workshop

Bridging the Artificial Intelligence Knowledge and Skill Gap...

引用

2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Adetiba, Emmanuel Wejin, John S. Oshin, Oluwadamilola Ifijeh, Ayodele H. Lawal, Comfort Thakur, Surendra Colin Awelewa, Ayokunle A. Kala, Raymond Jules Ajayi, Priscilla O. Akanle, Matthew B. Sweetwiliams, Faith O. Nnaji, Uche Owolabi, Emmanuel Idowu-Bismark, Olabode Sobola, Gabriel Ota Nigeria Durban University of Technology HRA Institute for Systems Science Durban South Africa Covenant University Department of Electrical and Information Engineering CApIC-ACE Jalingo Nigeria Taraba State University Department of Computer Science Jalingo Nigeria Covenant University Department of Electrical and Information Engineering CApIC-ACE Ota Nigeria Durban University of Technology Department of Information Technology Kzn E-Skills Co-Laboratory Durban4001 South Africa Covenant University Department of Electrical and Information Engineering Ota Nigeria International University of Grand Bassam Grand Bassam Ivory Coast Covenant University Center for Systems and Information Services CApIC-ACE Ota Nigeria Covenant University Department of Computer and Information Science CApIC-ACE Ota Nigeria Ltd 405 Justice Mahomed St. Gauteng Pretoria0002 South Africa

ISBN: (纸本)9798350358155

In transiting from one civilization to another, technology has played a vital and positive role. In the 21st century, one of the digital developments that is paving ways for human life improvement is machine-assisted technology using Artificial Intelligence (AI). Artificial Intelligence has successfully enhanced man's capacity in solving complex problems and processes. However, as developed nations continue to reap from the adoption of AI in various fields of human endeavors, the continent of Africa has remained behind, especially in AI-based skills and research. Various governments in developing nations have encouraged the adoption of AI, especially in institutions of learning. However, theoretical adoption without practical experience has remained an ineffective way of bridging the digital divide. In this paper we present the outcome of a practical approach to bridging the AI divide among students and researchers in Africa through funding support from the Google TensorFlow College Outreach Award. A 3-day hybrid bootcamp was organized (11th to 13th December, 2023) using the Google funding in order to equip postgraduate students and researchers with AI and collaborative research skills. A pre-survey method was employed to ascertain the knowledge level of the bootcamp participants. From the pre-surveyed feedback, training sessions on various AI domains were presented, and participant equipped with practical AI skills using a deployed AI-based cloud programming platform running on the private Federated Genomic Cloud (FEDGEN) infrastructure at Covenant University. A post-survey feedback was used to ascertain the effectiveness of this approach. A comparative analysis of the pre-survey and post-survey reveals a 70% improvement of AI skills among participants. This shows that having continuous training session for students and researchers is an effective method in closing the AI skills gap between developed and developing nations. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Computation of an Emptiable Minimal Siphon in a Subclass of Petri Nets Using Mixed-Integer Programming

引用

IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 219-226页

作者： Shouguang Wang Wenli Duo Xin Guo Xiaoning Jiang Dan You Kamel Barkaoui MengChu Zhou the School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China the Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China the Department of Computer Science Conservatoire National des Arts et MétiersParis 75141France the Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems Macao University of Science and TechnologyMacao 999078China

Deadlock resolution strategies based on siphon control are widely *** computational efficiency largely depends on siphon ***-integer programming(MIP)can be utilized for the computation of an emptiable siphon in a Petri net(PN).Based on it,deadlock resolution strategies can be designed without requiring complete siphon enumeration that has exponential *** to this reason,various MIP methods are proposed for various subclasses of *** work proposes an innovative MIP method to compute an emptiable minimal siphon(EMS)for a subclass of PNs named S^(4)*** particular,many particular structural characteristics of EMS in S4 PR are formalized as constraints,which greatly reduces the solution *** results show that the proposed MIP method has higher computational ***,the proposed method allows one to determine the liveness of an ordinary S^(4)PR.

关键词： Automated manufacturing systems deadlocks discrete event system mixed-integer programming(MIP) Petri nets(PN) siphons

来源：评论

学校读者我要写书评

暂无评论

Optimized Deep Learning Mechanism for Intrusion Detection: Leveraging RFE-Based Feature Selection and PCA for Improved Accuracy

Optimized Deep Learning Mechanism for Intrusion Detection: L...

引用

Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： M. Rajkumar Lakshi VS Karthik R Pavithra S Department of Smart Computing School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu India B. Tech-Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

With the growing demand for wireless networks, network traffic has surged, particularly involving large-scale data transmission. This results in an increased number of network security threats and the emergence of various cyber-attacks. To overcome these threats, the development of an efficient intrusion detection system (IDS) has become crucial. By leveraging the recent advancements in Artificial intelligence (AI), this project introduces an optimized deep learning mechanism utilizing a prevalent deep learning model, the Deep Neural Network (DNN) algorithm. This proposed method uses feature engineering techniques like feature selection and feature reduction. Given the high dimensionality of the selected datasets, these techniques are employed to prevent over-fitting. Recursive feature elimination performs feature selection followed by Principal Component Analysis for the feature reduction process, thus enhancing the model's efficiency. The system is assessed using two benchmark datasets: KDD and UNSW-NB15. The experimental results indicate that the presented DNN model on the KDD dataset has achieved 92.45% accuracy and 91.35% accuracy on the UNSW-NB15 dataset for intrusion classification.

关键词： Deep learning Accuracy Wireless networks Computational modeling Intrusion detection Artificial neural networks Feature extraction Data models Tuning Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Enhancing Cardiovascular Disease Analytics with Demographic and Behavioral Risk Features through Unsupervised and Ensemble Learning Techniques 12

Enhancing Cardiovascular Disease Analytics with Demographic ...

引用

12th IEEE Conference on systems, Process and Control, ICSPC 2024

作者： Sem, Li Xiang Chua, Hui Na Jasser, Muhammed Basheer Wong, Richard T.K. Aldharhani, Ghassan Saleh Sunway University No. 5 Jalan Universiti School of Engineering and Technology Department of Computing and Information Systems Bandar Sunway Selangor Darul Ehsan47500 Malaysia Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia UKM University Faculty of Information Science and Technology Selangor Bangi Malaysia

ISBN: (纸本)9798350391398

Cardiovascular diseases (CVD) are a leading health concern, projected to affect 23.6 million people by 2030. Despite extensive research, challenges remain in conventional diagnostic accuracy and limited healthcare access in low-income communities. Current computing methods for detecting CVD rely on costly medical tests. Subsequently, prior studies have documented many inconsistent insights on the development of CVD due to variations in study design and demographics. This research addresses these gaps by studying the potential of incorporating demographics and behavioral risk factors for CVD detection and experimenting with various modeling and sampling techniques to address imbalanced target distributions and improve detection accuracy. The resampled BRFSS dataset was subjected to regression analysis to capture demographic trends in CVD development, with two cross-referenced datasets for comparison. Association rule mining identified key risk factors linked to CVD. Classifiers, including XGBoost, Random Forest, SVM, Multilayer Perceptron, and Logistic Regression, were used to establish baseline performance, and these classifiers were integrated into CatBoost as a meta-classifier using a stacking ensemble approach. Our findings indicate that demographic factors significantly influence CVD development, but effects vary by region, culture, and lifestyle. Key risk factors such as diabetes, smoking, and stroke were identified as significant contributors to CVD. Our experiment results show that leveraging full features set with fine-tuned ensemble stacking techniques increases model performance and achieves an accuracy of 96%. Our research contributes valuable insights into the consistency of demographic effects and identifies key risk factors for CVD. The proposed ensemble model demonstrates exceptional performance, highlighting its potential as a cost-effective tool for early cardiac detection and improved healthcare accessibility. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

A Bibliometric Analysis and Research Landscape of Artificial Intelligence in Education 14

A Bibliometric Analysis and Research Landscape of Artificial...

引用

14th IEEE International Conference on Control System, Computing and engineering, ICCSCE 2024

作者： Ajibade, Samuel-Soma M. Issa, Bayan Jasser, Muhammed Basheer Hassan, Farrukh Aldharhani, Ghassan Saleh Al-Hadi, Ismail Ahmed Al-Qasem Akintoye, Kayode Akinlekan Tabuena, Almighty Cortezo School of Engineering and Technology Sunway University Department of Computing and Information Systems No. 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia Istanbul Ticaret University Department of Computer Engineering Istanbul Turkey University of Aleppo Faculty of Informatics Engineering Syria Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia UKM University Faculty of Information Science and Technology Selangor Bangi Malaysia The Federal Polytechnic Department of Computer Science Ado Ekiti Ekiti State Nigeria University of the City of Valenzuela College of Arts and Sciences Philippines

ISBN: (纸本)9798350364507

This study examines the academic field of Artificial Intelligence in Education (AI-ED) by examining the publications and indexed documents in Scopus from 2010 to 2023. Consequently, we examined the publishing trends, exemplary articles, and the most engaged participants and funding institutions in the domain of AI-ED research. A bibliometric analysis was performed to examine the network of co-authorships, keywords, and citations in the domain of AIED research. The Scopus search resulted in the discovery of 10,474 published documents on AI-ED. Additionally, trends analysis indicated a significant increase of over 3,000% in publications between 2010 and 2023. Conference papers are the most often utilized document type, including 6757 publications or 64.5% of the overall amount. The ACM International Conference Proceeding Series is the primary source for published materials, accounting for 675 publications or 6.44% of the total publications on the topic. The most prolific researchers in the field of AI-ED are Breazeal, C and Chai C.S, each having published 18 documents. Additionally, the two institutions that have produced the highest number of published documents are Carnegie Mellon University (CMU) and the University of NC State University (NCSU). The output of the affiliations active in the topic is primarily attributed to the funders, specifically the National science Foundation, which is the leading funding institution based in the United States and has contributed to 354 publications. An analysis of keyword co-occurrence revealed 6 main study areas that cover the essential tools, theories, methodologies, and socioeconomic and financial aspects of artificial intelligence in education (AIED). Future research in the field of artificial intelligence in education will focus on employing sophisticated deep learning, machine learning, and neural network algorithms to precisely predict students' learning patterns. © 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

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：