检索结果-内蒙古大学图书馆

Multimodal signals subject authentication system 18

Multimodal signals subject authentication system

18th International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information technology, ECTI-CON 2021

作者： Alotaiby, Turky N. Alshebeili, Saleh A. Alotaibi, Gaseb King Abdulaziz City for Science and Technology The National Center for Data Analytics and Artificial Intelligence Riyadh Saudi Arabia King Saud University Dept. of Electrical Engineering Ryiadh Saudi Arabia Ministry of Interior Public Security Riyadh Saudi Arabia

ISBN: (纸本)9780738111278

The ever increasing number of e-services provided to users necessitates the efficient and accurate verification of user identity. In this work, we present a user authentication model based on multimodal signals. More specifically, we investigate the extraction of statistical features using various segment lengths of photoplethysmogram (PPG), electrocardiogram (ECG) and capnogram (CO2) signals and generate a feature vector using different fusion approaches. In addition, adding to the feature vector additional features extracted from the 1st derivative of the corresponding signal is explored. Finally, a classifier is used for identity authentication. The proposed authentication system achieved an average authentication accuracy of 99.76% using a 1 second segment length and a random forest classifier. © 2021 IEEE.

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Algorithms for Business Management: Ethical Considerations

Deep Learning Algorithms for Business Management: Ethical Co...

引用

Communication, Computer sciences and engineering (IC3SE), International Conference on

作者： Shiney Chib B Santhosh Kumar V Asha Navdeep Singh Melanie Lourens Dyuti Banerjee Management Datta Meghe Institute of Management Studies Nagpur Maharashtra Institute of Aeronautical Engineering Dundigal Hyderabad Department of Computer Applications New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Deputy Dean Faculty of Management Sciences Durban University of Technology South Africa Artificial intelligence & Data Science (AI&DS) Koneru Lakshmaiah Education Foundation Green Fields Vaddeswaram Guntur District Andhra Pradesh

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

By applying the right method to extract both structured and unstructured data, data science may add value. Accelerated deep learning advancement has led to substantial changes in data science operations. Deep learning is a sophisticated machine learning algorithmic method. It differs significantly from other machine learning procedures in that it uses a straightforward method to provide data to the system. To tackle difficult issues and make wise business decisions, deep learning leverages cutting-edge analytics. As smart manufacturing initiatives expand, deep leaning is seen as a potential avenue to boost efficiency and sustainability even further. In this study, we look into how DL models can enhance the application of business intelligence by organisational management. The results show that DL models may be trained on diverse data sources to identify linkages and patterns in the data. These models can assist decision-makers in making well-informed decisions based on accurate and reliable data.

关键词： Deep learning Accuracy Computational modeling Soft sensors Decision making data science Prediction algorithms

来源：评论

学校读者我要写书评

暂无评论

Assessing the Validity of Automated data Analysis Methods Based on artificial intelligence

Assessing the Validity of Automated Data Analysis Methods Ba...

引用

Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： K. Yuvaraj Balakumar P Prema S. Kadam Prakash Mishra Jambi Ratna Raja Kumar Anupama Verma Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Management Mangalayatan University Jabalpur India Department of Computer Engineering Genba Sopanrao Moze College of Engineering Balewadi Pune Maharashtra India Faculty of Business Mangagement & Commerce Usha Martin University Ranchi India

Automated records analysis strategies primarily based on artificial intelligence (AI) have become increasingly famous in recent years because of their capability to quickly and effectively method enormous quantities of facts. However, concerns about the validity of those methods and their potential for introducing bias or mistakes have been raised. This study aims to assess the validity of automatic facts evaluation methods based on AI. It entails evaluating those methods' accuracy, reliability, and consistency compared to conventional guide evaluation methods. An expansion of study methods may be used, which includes experimental research, case research, and surveys. Facts will be gathered from computerized AI techniques and manual strategies, after which they will be compared and evaluated to decide the extent of agreement between them. One capability mission in this research is the dearth of a gold standard for statistics analysis. Unlike in medical fields wherein there may be a clean popular for prognosis, information analysis methods can also vary depending on the unique dataset and research question. Expanding a hard and fast of criteria or metrics for evaluating the validity of different AI techniques is essential. Another challenge is the rapidly evolving nature of the AI era. As new techniques and algorithms constantly evolve, keeping up with modern-day improvements and their potential impact on records analysis could be challenging.

关键词：

来源：评论

学校读者我要写书评

暂无评论

CFGNet: Coarse-to-Fine Grained Network for Image Classification of Confusing Fly Species

CFGNet: Coarse-to-Fine Grained Network for Image Classificat...

引用

Pattern Recognition and artificial intelligence (PRAI), International Conference on

作者： Lin Niu Bo Wang Baomin Niu Jin Zhang Danning Liang Jingpeng Wu Rong Zhou Jianqiang Deng School of Biomedical Informatics and Engineering Hainan Medical University Haikou China Hainan Vocational College of Political Science and Law Hainan Gongping Forensic Center Haikou China School of Big Data and Artificial Intelligence Zhengzhou University of Science and Technology Zhengzhou China Center for Educational Technology Hainan Medical University Haikou China Department of Forensic Medicine of Hainan Medical University Haikou China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

In the realm of fine-grained image classification (FGIC), despite substantial technological advancements, models frequently misclassify certain fly species with exceedingly high inter-class similarity, thereby impacting overall classification accuracy. To address this challenge, we propose an innovative Coarse-to-Fine Grained Network Framework (CFGNet). This architecture tackles the issue through two stages: Initially, to alleviate classification complexity, closely related and easily confused categories are amalgamated. Subsequently, a lightweight coarse-grained information extraction network (CM) is introduced to enhance the classification accuracy of samples with relatively larger class differences. In the second stage, for precise identification of challenging samples, a fine-grained feature extraction network (FM) is proposed exclusively for difficult-to-distinguish images. Additionally, we introduce a novel feature reuse method to deeply mine and repurpose coarse-grained features from the previous stage to guide fine-grained classification and improve performance in this stage. To validate our approach, we curated a dataset based on merged confusion classes annotation (Flies-MCC). Experimental results demonstrate that our proposed network architecture achieves significant advancements in fine-grained fly classification and offers a pioneering solution for precise fly identification.

关键词： Accuracy Frequency modulation Annotations Network architecture Information retrieval Feature extraction Pattern recognition Complexity theory artificial intelligence Image classification

来源：评论

学校读者我要写书评

暂无评论

Prediction of ship fuel consumption based on Elastic network regression model

Prediction of ship fuel consumption based on Elastic network...

引用

2021 International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2021

作者： Li, Shanshan Li, Xinyu Zuo, Yi Li, Tieshan Dalian Maritime University Navig. Coll. Collab. Innov. Center of Maritime Big Data and Marine Artificial General Intelligence Dalian China University of Electronic Science and Technology of China College of Automation Engineering Chengdu China

ISBN: (纸本)9781665402453

Predicting the fuel consumption of ships sailing under different navigation conditions and improving the operation efficiency of shipping industry has become an important topic. There are many characteristic variables affecting ship fuel consumption during navigation, such as trim, draft, wind speed, wind direction and so on. And some variables are highly correlated, which is easy to produce multicollinearity problems. It makes the fuel consumption prediction complex. The study established an Elastic network regression model by combining the least absolute contraction and selection operator (LASSO) and Ridge regression algorithm. The model reduces the complexity and improves the interpretability and accuracy by selecting the characteristic variables affecting ship fuel consumption. The study is verified by the navigation data of a ferry within two months. The results show that compared with long short term memory (LSTM) and back-propagation neural network (BPNN), the Elastic network regression model can not only explain the relationship between fuel consumption and variables, but also predict fuel consumption more accurately and effectively. © 2021 IEEE.

关键词： Regression analysis

来源：评论

学校读者我要写书评

暂无评论

AoI-Sensitive data Forwarding with Distributed Beamforming in UAV-Assisted IoT

arXiv

引用

arXiv 2025年

作者： Lang, Zifan Liu, Guixia Sun, Geng Li, Jiahui Sun, Zemin Wang, Jiacheng Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

This paper proposes a UAV-assisted forwarding system based on distributed beamforming to enhance age of information (AoI) in Internet of Things (IoT). Specifically, UAVs collect and relay data between sensor nodes (SNs) and the remote base station (BS). However, flight delays increase the AoI and degrade the network performance. To mitigate this, we adopt distributed beamforming to extend the communication range, reduce the flight frequency and ensure the continuous data relay and efficient energy utilization. Then, we formulate an optimization problem to minimize AoI and UAV energy consumption, by jointly optimizing the UAV trajectories and communication schedules. The problem is non-convex and with high dynamic, and thus we propose a deep reinforcement learning (DRL)-based algorithm to solve the problem, thereby enhancing the stability and accelerate convergence speed. Simulation results show that the proposed algorithm effectively addresses the problem and outperforms other benchmark algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Intelligent Gen: An Efficient On-Demand Access Protocol in MANETs using Network Simulator - 2

Intelligent Gen: An Efficient On-Demand Access Protocol in M...

引用

International Conference on Computing for Sustainable Global Development (INDIACom)

作者： T. Ganesan N. Murali S. Palani Murugan M. Chandrakumar Peter P. Anand Raj S. Manikandan Department of Computer Science & Engineering E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Artificial Intelligence & Data Science E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Software Engineering Periyar Maniammai Institute of Science & Technology Thanjavur Tamil Nadu India Department of Computer Science and Business Systems E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Information Technology E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India

Mobile Ad-Hoc network is a self-configuring and multi-hop collection of mobile devices for making successful communication. The wireless and mobile communication process are encountered based on bandwidth, process control and energy conception. Energy conception is considered and dynamically changed to rely on life time of batteries and power. The life time of battery get exhausted very quickly the nodes are moved and changed rapidly across the network. This paper noticed the problem of life time of power, batteries and energy consumption problem in various routing stages. The proposed Intelligent Gen Algorithm is used to get optimal solution for ensuring high robustness, flexibility and Energy consumption factor. It presents a common framework to balance various routing requirements in MANETs dynamically. The following are the main contributions includes, i. introducing Intelligent GEN Algorithms to classify the throughout bandwidth, delay, data ratio. ii. Various routing performance are evaluated and viewed by speed, packetize and simulation time. The comparison evaluated by select routing algorithm using NS-2. The simulation results are recorded and the performance is monitored by using on demand access protocl. The result clearly demonstrated the proposed Intelligent Gen algorithm used in self-organized manner by wireless channel.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Efficient Diabetes Detection using Hybrid Machine Learning Model

Efficient Diabetes Detection using Hybrid Machine Learning M...

引用

Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： A S Gowri P Jose Karthik S M S Berlin Shaheema K M Karuppasway R. Balamurugan Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department for Applied Research The Gandhigram Rural Institute (Deemed to be University) Gandhigram India Department of Artificial Intelligence and Data Science Stella Mary’s College of Engineering Azhikal India Department of Mathematics and Statistics Bharath Institute of Higher Education and Research Chennai India Department of Information Technology DMI College of Engineering Chennai India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Diabetes is a metabolic disease that affects a large number of the global population and is incurable. The primary causes of death symptoms are kidney failure, heart attacks, strokes, and blindness. In this paper Principal component analysis used for pre-processing the data to extract features and The Efficient Hybrid Machine Learning Model Convolution Neural Network-Support Vector Machine is used to categorize the Pima Indian dataset. CNN and non-linear SVM used in a hybrid model to classify the diabetes data from the Pima Indians. To identify positive classes of pima Indians diabetes, the HCNN-SVM extract the features from the original data expression and learns diabetes data through a novel convolution network environment to the SVM classifier with RBF function and achieve the accuracy as 98,04%.

关键词： Support vector machines Accuracy Convolution Machine learning Feature extraction data models Diabetes data mining Convolutional neural networks Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Detection and Analysis of Depression in Women Through Advanced Machine Learning Techniques

Comprehensive Detection and Analysis of Depression in Women ...

引用

International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Manjunath H R V. K. Kolekar K. S. Bhuvaneshwari Romil Jain Ar Aravind Sudhanshu Dev School of Engineering and Technology Physics JAIN (Deemed to be University) Bangalore Karnataka India Computer Engineering Vishwakarma Institute of Technology Pune India Department of Artificial Intelligence and Data Science Karpagam College of Engineering Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of ECE Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Global Health data estimates that 450 million individuals throughout the globe deal with some kind of mental illness. Also, from their present position of fourth place, depressive disorders are projected to move to the position of most frequent ailment globally by 2020. A frequent and grave mental condition, depression causes a person to feel gloomy and apathetic all the time, making it hard for them to focus on their everyday tasks like work or school. Nearly half of all attempts to commit suicide are influenced by depression, which affects one in fifteen individuals annually. Roughly 30% of persons may experience anxiety throughout their lifetimes; it's a serious mental illness. We provide a method for diagnosing anxiety and depression, two of the most prevalent mental health issues. Machine learning algorithms provide its basis. It would be helpful to know the severity of the patient's condition. Talking to psychologists at different hospitals in India made it quite evident that when first diagnosing common mental problems, the majority of Indian psychologists utilize a standard scale. Bharath Multispecialty Hospital's psychology department gathered anxiety and depression standard scale questionnaires from urban patients (aged 18 to 35). Before the five machine learning approaches were applied to this data, they were pre-processed. When applied to datasets for anxiety and depression, our proposed model has shown five distinct AI strategies to be effective: Our CNN-based model outperforms the competition across a range of metrics, including accuracy, recall, and precision. With a 96.41% accuracy rate for anxiety and a 82.25% accuracy rate for depression, it notably surpasses current algorithms. Furthermore, among urban women aged 18–35, 7.4% suffer from severe anxiety and 15.6% from chronic depression, according to our study.

关键词： Support vector machines Accuracy Machine learning algorithms Anxiety disorders Mental health Machine learning Predictive models Depression Reliability Standards

来源：评论

学校读者我要写书评

暂无评论

GainNet: Coordinates the Odd Couple of Generative AI and 6G Networks

arXiv

引用

arXiv 2024年

作者： Chen, Ning Yang, Jie Cheng, Zhipeng Fan, Xuwei Liu, Zhang Huang, Bangzhen Zhao, Yifeng Huang, Lianfen Du, Xiaojiang Guizani, Mohsen The School of Informatics Xiamen University Xiamen361005 China The National Institute for Data Science in Health and Medicine Xiamen University Xiamen361102 China The School of Future Science and Engineering Soochow University Suzhou215006 China The Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ United States Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

The rapid expansion of AI-generated content (AIGC) reflects the iteration from assistive AI towards generative AI (GAI) with creativity. Meanwhile, the 6G networks will also evolve from the Internet-of-everything to the Internet-of-intelligence with hybrid heterogeneous network architectures. In the future, the interplay between GAI and the 6G will lead to new opportunities, where GAI can learn the knowledge of personalized data from the massive connected 6G end devices, while GAI’s powerful generation ability can provide advanced network solutions for 6G network and provide 6G end devices with various AIGC services. However, they seem to be an odd couple, due to the contradiction of data and resources. To achieve a better-coordinated interplay between GAI and 6G, the GAI-native networks (GainNet), a GAI-oriented collaborative cloud-edge-end intelligence framework, is proposed in this paper. By deeply integrating GAI with 6G network design, GainNet realizes the positive closed-loop knowledge flow and sustainable-evolution GAI model optimization. On this basis, the GAI-oriented generic resource orchestration mechanism with integrated sensing, communication, and computing (GaiRom-ISCC) is proposed to guarantee the efficient operation of GainNet. Two simple case studies demonstrate the effectiveness and robustness of the proposed schemes. Finally, we envision the key challenges and future directions concerning the interplay between GAI models and 6G networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Queueing networks

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：