检索结果-内蒙古大学图书馆

AI-Driven Smart Healthcare for Society 5.0

作者： Mahesh Kumar Mishra Lalit Mohan Trivedi Mansoor Farooq Divya Nimma Malik Bader Alazzam B Kiran Bala Sr SAP Consultant Delta System and Software Inc Texas USA Department of ASH Moradabad Institute of Technology Moradabad UP India Department of Cybersecurity American University of Bahrain Bahrain PhD in Computational Science University of Southern Mississippi Data Analyst in UMMC USA Faculty of Information Technology Jadara University Irbid Jordan Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331536336

ISBN: (纸本)9798331536343

The rising use of IoT devices in Public Health information systems (PHIS) has revolutionized the way patient surveillance and record keeping is being done in real-time, but this has exposed systems to serious cybersecurity threats. Generated by using traditional approaches, like rule-based detection and basic artificial neural network-based models, is not capable of dealing with the dynamic and diverse nature of threats in IoT networks. These approaches are often inadequate in providing a detailed analysis of the attack especially because they do not have the ability to learn from sequences of features. This work presents an improved predictive analytics model using CNN with BiLSTM to improve cybersecurity in IoT connected PHIS. The CNN component performs feature extraction on spatial elements in the data received while the BiLSTM uncovers temporal dependencies and sequence of attacks, which enhance threats identification accuracy. The process of the proposed methodology includes dataset acquisition and cleansing of IoT healthcare datasets and finally, the training and testing of the model with the help of relevant parameter such as accuracy, precision, and recall values. Qualitative comparison with the baseline models also shows that the proposed hybrid CNN-BiLSTM model performs better, and it has fewer false alarms to detect a range of cyber threats. Accordingly, the results point to a feasible solution for enhancing security and reliability of IoT-based healthcare applications to protect patients’ record from cyber threats.

关键词： Training Accuracy Surveillance Bidirectional long short term memory Threat assessment Internet of Things Computer security Public healthcare information systems Testing

来源：评论

学校读者我要写书评

暂无评论

NLP for Sentiment Analysis in Social Media Posts to Detect Suspicious Behaviour

NLP for Sentiment Analysis in Social Media Posts to Detect S...

引用

Intelligent Algorithms for Computational Intelligence systems (IACIS), International Conference on

作者： Abhishek Awasthi Mohammad Bdair A. Nandha Kumar Sanjog Thapa Nalini Devi S Bharathi Ramesh Kumar Software Developer Sporta Technology pvt Ltd Maharashtra India School of Architecture Computing & Engineering University of East London London United Kindom Department of EEE Bannari Amman Institute of Technology Sathyamangalam India Department of Information System and Analysis Lamar University Texas USA Department of Management Studies St. Joseph's Institute of Technology Chennai India Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

In an era where social media is an almost omnipresent means of communication, online safety is crucial. Still, conventional sentiment analysis methods sometimes fall short of accurately spotting dubious conduct. A novel approach utilizing advanced Natural Language Processing (NLP) techniques, especially Deep Learning (DL) models like BERT (Bidirectional Encoder Representations from Transformers), is proposed to overcome these challenges. DL is used by the suggested method to identify nuanced attitudes in social media posts, which raises the accuracy of identifying suspicious activities. That is not the case with conventional systems that depend on basic algorithms. With detection rates of 88%, 85%, and 90% for hate speech, violent statements, and extreme opinions, respectively, the system accomplishes remarkable outcomes via meticulous data gathering, preprocessing, and model training. Through comparison, it outperforms existing systems in terms of F1 score (0.92), recall (0.91), precision (0.94), and accuracy (0.92). The proposed system effectively reduces potential risks and encourages a safer online environment by utilizing the effective analysis of sentiment in social media posts, therefore indicating a major advancement in online security.

关键词： Training Sentiment analysis Accuracy Social networking (online) Hate speech Bidirectional control Transformers Encoding Data models Security

来源：评论

学校读者我要写书评

暂无评论

DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis

arXiv

引用

arXiv 2024年

作者： Deng, Kaijun Zheng, Dezhi Xie, Jindong Wang, Jinbao Xie, Weicheng Shen, Linlin Song, Siyang Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China Department of Computer Science University of Exeter United Kingdom

Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code will be publicly available at https://***/CVI-SZU/DEGSTalk. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

来源：评论

学校读者我要写书评

暂无评论

Quantum reversible circuits for multiplicative inverse

引用

EPJ Quantum technology 2022年第1期9卷 1-17页

作者： Luo, Qing-bin Yang, Guo-wu Li, Xiao-yu Li, Qiang Department of Computer Science and Technology School of Information Engineering Hubei Minzu University Enshi China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Big data research Center & School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu China

The synthesis of quantum circuits for multiplicative inverse over $\operatorname{GF}(2^{8})$ are discussed in this paper. We first convert the multiplicative inverse operation in $\operatorname{GF}(2^{8})$ to arithmetic operations in the composite field $\operatorname{GF}((2^{4})^{2})$ , and then discuss the expressions of the square calculation, the inversion calculation and the multiplication calculation separately in the finite field $\operatorname{GF}(2^{4})$ , where the expressions of multiplication calculation in $\operatorname{GF}(2^{4})$ are given directly in $\operatorname{GF}(2^{4})$ and given through being transformed into the composite field $\operatorname{GF}((2^{2})^{2})$ . Then the quantum circuits of these calculations are realized one by one. Finally, two quantum circuits for multiplicative inverse over $\operatorname{GF}(2^{8})$ are synthesized. They both use 21 qubits, the first quantum circuit uses 55 Toffoli gates and 107 CNOT gates and the second one uses 37 Toffoli gates and 209 CNOT gates. As an example of the application of multiplication inverse, we apply these quantum circuits to the implementations of the S-box quantum circuit of the AES cryptographic algorithm. Two quantum circuits for implementing the S-box of the AES cryptographic algorithm are presented. The first quantum circuit uses 21 qubits, 55 Toffoli gates, 131 CNOT gates and 4 NOT gates and the second one uses 21 qubits, 37 Toffoli gates, 233 CNOT gates and 4 NOT gates. Through the evaluation of quantum cost, the two quantum circuits of the S-box of AES cryptographic algorithm use less quantum resources than the existing schemes.

关键词：

来源：评论

学校读者我要写书评

暂无评论

RETRACTED ARTICLE: Industrial-IoT-hardware security-improvement using plan load optimization method in cloud

引用

International Journal of system Assurance Engineering and Management 2023年第1期14卷 578-578页

作者： Basheer, Shakila Gopu, Magesh Mathew, Rincy Merlin Bivi, Maryam Aysha Prabu, M. Department of Information System College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Department of Software and Systems Engineering Vellore Institute of Technology Tamilnadu Vellore India Computer Science Department College of Science and Arts King Khalid University Khamis Mushayt Abha Saudi Arabia Department of Computer Science College of Computer Science King Khalid University Abha Saudi Arabia Department of Computer Science Christ University Karnataka Bangalore India

来源：评论

学校读者我要写书评

暂无评论

Analysis and Design of a Personalized Recommendation system Based on a Dynamic User Interest Model

arXiv

引用

arXiv 2024年

作者： Mao, Chunyan Huang, Shuaishuai Sui, Mingxiu Yang, Haowei Wang, Xueshe School of Information and Communication Engineering Shanghai Jiao Tong University Shanghai China University of Science and Technology of China Department of Software Software system design Anhui Hefei China University of Iowa Department of Mathematics Iowa CityIA United States University of Houston Cullen College of Engineering Indusrial Enginnering HoustonTX United States Duke University Pratt School of Engineering Mechanical Engineering DurhamNC United States

With the rapid development of the internet and the explosion of information, providing users with accurate personalized recommendations has become an important research topic. This paper designs and analyzes a personalized recommendation system based on a dynamic user interest model. The system captures user behavior data, constructs a dynamic user interest model, and combines multiple recommendation algorithms to provide personalized content to users. The research results show that this system significantly improves recommendation accuracy and user satisfaction. This paper discusses the system's architecture design, algorithm implementation, and experimental results in detail and explores future research directions. © 2024, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Classifiers for Network Intrusion Detection system: Comparative Study

Machine Learning Classifiers for Network Intrusion Detection...

引用

2021 International Conference on information technology, ICIT 2021

作者： Almomani, Omar Almaiah, Mohammed Amin Alsaaidah, Adeeb Smadi, Sami Mohammad, Adel Hamdan Althunibat, Ahmad The World Islamic Sciences and Education University Department of Information System and Networks Jordan College of Computer Science and Information Technology King Faisal University Al-Ahsa31982 Saudi Arabia The World Islamic Sciences and Education University Department of Computer Science Amman Jordan Al-Zaytoonah University of Jordan Department of Software Engineering Amman11733 Jordan

ISBN: (纸本)9781665428705

Network security risks are increasing at an exponential rate as Internet technology advances. Keeping the network protected is one of the most challenging of network security. Many security mechanisms were implemented to detect and identify any malicious activity on the network. Intrusion Detection system (IDS) one of the most used mechanisms to reduce the effects of these risks. Machine Learning (ML) classifiers are widely begin used to classify the network traffic as normal or abnormal. In this paper a comparative evaluation of the following ML classifiers: LogisticRegression, Multinomial Naive Bayesian, Gaussian Naive Bayesian, Bernoulli Naive Bayesian, k-Nearest Neighbors, Decision Tree, Adaptive Boosting, Random Forest, Multilayer Perceptron, and GradientBoosting is performed to specify the best classifier in identifying intrusion detection. The used evaluation metrics are accuracy, precision, and F-measure. The UNSW-NB15 dataset is used to assess ML classifiers. The experimental results show that the RandomForest classifier outperforms the other classifiers in terms of accuracy at 87%, precision 98%, and F-measure 84%. © 2021 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Multibiometric Identification of Computer Network Users in the Distance Learning Process

Multibiometric Identification of Computer Network Users in t...

引用

International Computer Sciences and information Technologies (CSIT)

作者： Tetiana Kovaliuk Nataliya Kobets Anastasia Shevchenko Nataliia Kunanets Software System and Technology Department Taras Shevchenko National University of Kyiv Kyiv Ukraine UNITY-BARS LLC Kyiv Ukraine Information Systems and Networks Department Lviv Polytechnic National University Lviv Ukraine

ISBN: (纸本)9798350334326

The article considers the multibiometric identification of a student to improve the quality and minimize errors in the process of his identification using voice and visual biometric features. Sound processing methods, models of neural networks and Gaussian mixtures for identifying a student by voice signs, and methods of visual identification of a person by video stream and photographs were studied. A software system has been developed for recognizing and indexing people on video while identifying a person by voice signs to use in the educational process to account for students attending remote classes. The system provides for the extraction of acoustic characteristics from a recording of a human language and the subsequent assignment of the obtained data to one of the specified classes (speakers). A multilayer neural network (MNN) was used as a classifier. The classifier was trained on a data set of 43832 audio files from 108 speakers. MHC on the test sample showed an accuracy of 91%. At the stage of processing the video stream frames, a face was detected in the frame, and the detected face was recognized. Face recognition in the system was based on the search for the most appropriate template of basic images stored in the database.

关键词： Visualization Face recognition Speech recognition Streaming media software systems Nonhomogeneous media Recording

来源：评论

学校读者我要写书评

暂无评论

Trainbot: A Conversational Interface to Train Crowd Workers for Delivering On-Demand Therapy 8th

Trainbot: A Conversational Interface to Train Crowd Workers ...

引用

8th AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2020

作者： Abbas, Tahir Khan, Vassilis-Javed Gadiraju, Ujwal Markopoulos, Panos Industrial Design Department Eindhoven University of Technology Eindhoven Netherlands Software Engineering Dept Mirpur University of Science & Technology Mirpur AJK Pakistan Web Information Systems Delft University of Technology Netherlands

ISBN: (纸本)9781577358480

On-demand emotional support is an expensive and elusive societal need that is exacerbated in difficult times – as witnessed during the COVID-19 pandemic. Prior work in affective crowdsourcing has examined ways to overcome technical challenges for providing on-demand emotional support to end users. This can be achieved by training crowd workers to provide thoughtful and engaging on-demand emotional support. Inspired by recent advances in conversational user interface research, we investigate the efficacy of a conversational user interface for training workers to deliver psychological support to users in need. To this end, we conducted a between-subjects experimental study on Prolific, wherein a group of workers (N=200) received training on motivational interviewing via either a conversational interface or a conventional web interface. Our results indicate that training workers in a conversational interface yields both better worker performance and improves their user experience in on-demand stress management tasks. © 2020, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： User interfaces

来源：评论

学校读者我要写书评

暂无评论

Analysis of Financial Risk Behavior Prediction Using Deep Learning and Big Data Algorithms

arXiv

引用

arXiv 2024年

作者： Yang, Haowei Cheng, Zhan Zhang, Zhaoyang Luo, Yuanshuai Huang, Shuaishuai Xiang, Ao University of Houston Cullen College of Engineering Industrial Engineering Houston United States University of California Irvine Donald Bren School of Information & Computer Science Master of Computer Science Irvine United States University of California San Diego Computational Science San Diego United States Southwest Jiaotong University School of Computing and Artificial Intelligence Computer Science and Technology Chengdu China University of Science and Technology of China Department of Software Software system design Hefei China Northern Arizona University Information Security and Assurance AZ United States

As the complexity and dynamism of financial markets continue to grow, traditional financial risk prediction methods increasingly struggle to handle large datasets and intricate behavior patterns. This paper explores the feasibility and effectiveness of using deep learning and big data algorithms for financial risk behavior prediction. First, the application and advantages of deep learning and big data algorithms in the financial field are analyzed. Then, a deep learning-based big data risk prediction framework is designed and experimentally validated on actual financial datasets. The experimental results show that this method significantly improves the accuracy of financial risk behavior prediction and provides valuable support for risk management in financial institutions. Challenges in the application of deep learning are also discussed, along with potential directions for future research. © 2024, CC BY.

关键词： Data accuracy

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：