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检索条件"主题词=Deep learning algorithms"

共 226 条记录，以下是81-90 订阅

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Leveraging deep learning on Multimodal Sensor Data for Wireless Communication: From mmWave Beamforming to Digital Twins

Leveraging Deep Learning on Multimodal Sensor Data for Wirel...

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作者： Salehihikouei, Batool Northeastern University

学位级别：Ph.D., Doctor of Philosophy

With the widespread Internet of Things (IoT) devices, a wide variety of sensors are now present in different environments. For example, self-driving vehicles and automated warehouses depend on sensor information for navigation and management of the robots, respectively. In this dissertation, we present methods, where these sensors are re-purposed to assist network management in wireless communication, especially when classic approaches fall short to provide the required quality of service (QoS). This thesis presents data-driven and AI-based methods, where the multimodal sensor information is used for two applications: (i) beamforming at the mmWave band and (ii) joint optimization of the navigation and network management in warehouse environments. In the first part, we study multimodal beamforming methods for mmWave vehicular networks. First, we present deep learning fusion algorithms, where the inputs from a multitude of sensor modalities such as GPS (Global Positioning System), camera, and LiDAR (Light Detection and Ranging) are combined towards predicting the optimum beam at the mmWave band. We prove that fusing the multimodal sensor data improves the prediction accuracy, compared to using single modalities. Second, we study the trade-off between the accuracy and cost of different learning strategies and demonstrate that federated learning is the most successful learning strategy, with respect to the communication overhead. Third, we propose algorithms to further optimize the communication overhead by incorporating a pruning strategy tailored to the disturbed nature of the federated learning systems. Fourth, we propose a modality-agnostic deep learning paradigm that operates on any possible combination of sensor modalities. In part two, we propose using digital twins to overcome the challenges of scarcity of data and close-world assumption in deep learning algorithms. A digital twin is a replica of a real world entity, which is typically used for studying the impa

关键词： Network resource management Reinforcement learning deep learning algorithms Sensor modalities Federated learning systems

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deep learning for Prediction of Cardiovascular Disease

Deep Learning for Prediction of Cardiovascular Disease

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Tito, Mokammel Hossain Arifuzzaman, Md Nasrin, Alifa Khan, Shahzad Asaduzzaman, Md Chohan, Muhammad Shahzad Al-Duais, Ali Nabil Bangabandhu Sheikh Mujibur Rahman Science and Technology University Animanl Science and Veterinary Medicine Gopalganj Bangladesh Civil and Environmental Engineering King Faisal University Al Hofuf Saudi Arabia Dhaka Bangladesh King Faisal University Department of Biomedical Sciences Al Hofuf Saudi Arabia National Heart Foundation Hospital & Research Institute Bangladesh

ISBN: (纸本)9798350372229

This study compares three deep learning algorithms for cardiovascular disease risk prediction. While RBFN boasts the highest accuracy (84.07%), wekadeeplearning4j excels in identifying high-risk individuals via better AUC and PRC area, valuable for prioritizing early intervention despite slightly lower overall accuracy (81.85%). Conversely, MLP's low mean absolute error indicates high precision in individual case prediction, ideal for personalized treatments. However, tradeoffs exist: wekadeeplearning4j requires longer training times, and MLP's precision may sacrifice sensitivity. Choosing the optimal algorithm depends on context and priorities. High accuracy and speed favor RBFN, while superior high-risk identification or precise individual predictions favor wekadeeplearning4j or MLP, respectively. Understanding these trade-offs is crucial for maximizing deep learning's effectiveness in cardiovascular disease risk prediction. © 2024 IEEE.

关键词： Cardiovascular Disease Risk Prediction deep learning algorithms MLP (Multi-Layer Perceptron) RBFN (Radial Basis Function Network) wekadeeplearning4j

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Research on Automatic Music Annotation Algorithm Based on deep learning 3

Research on Automatic Music Annotation Algorithm Based on De...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Li, Wenqi China Southwest Petroleum University School of Art Sichuan Province Chengdu610500 China

ISBN: (纸本)9798350319125

With the advent of the digital age, a large number of music works exist in digital form, which makes music automatic tagging an important research field. Automatic music tagging refers to the use of computer technology to classify, label and describe music in order to better organize and retrieve music resources. As an important technology in the field of artificial intelligence, deep learning has a profound impact on automatic music tagging. This paper will discuss in detail the influence, methods and measures of deep learning on automatic music tagging. © 2023 IEEE.

关键词： Artificial intelligence Automatic labeling deep learning algorithms music labels

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Research on Library Collection Resource Recommendation Algorithm based on deep learning

Research on Library Collection Resource Recommendation Algor...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Zhao, ZhenDong Wan, YuXin Shandong Institute of Commerce & Technology Shandong Jinan250103 China Shandong YingCai University Shandong Jinan250104 China

ISBN: (纸本)9798350383348

With the continuous development of deep learning technology, deep learning algorithms have also been widely used in library resource recommendation. The deep learning algorithm has powerful feature extraction and classification capabilities, which can effectively solve the data sparsity and cold start problems of library resource recommendation. Therefore, this paper proposes a deep learning algorithm to evaluate and analyze the innovative recommendation algorithm. Firstly, natural language processing technology is used to evaluate book resources, and the indicators are divided according to the evaluation requirements of the recommendation algorithm to reduce the interference factors in the evaluation of the recommendation algorithm. Then, natural language processing technology evaluates the recommendation algorithm of library collection resources, forms a recommendation algorithm evaluation scheme, and comprehensively analyzes the evaluation results of the recommendation algorithm. MATLAB simulation shows that under certain evaluation criteria, the evaluation accuracy and evaluation time of the recommendation algorithm of library collection resources by the deep learning algorithm are better than those of the traditional recommendation algorithm. © 2024 IEEE.

关键词： deep learning algorithms Library resources natural language processing technology recommendation algorithm

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Research on Music Information Retrieval Algorithm based on deep learning

Research on Music Information Retrieval Algorithm based on D...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Guo, Xiaochen Weifang Engineering Vocational College 262500 China

ISBN: (纸本)9798350383348

With the rapid development of digital music, users' demand for music retrieval is increasing day by day. The introduction of deep learning technology has brought new breakthroughs to music information retrieval. This paper systematically introduces the application of deep learning in music information retrieval, including music recommendation, music classification, emotion recognition and so on, and discusses the future development direction. © 2024 IEEE.

关键词： Classification of compositions convolutional neural network theory deep learning algorithms High error rate and labor cost music information retrieval

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deep learning-Based Object Detection System for Crowd Monitoring in High-Security Areas 2

Deep Learning-Based Object Detection System for Crowd Monito...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Pandey, Anurag Kandpal, Hitesh Rawat, Shubham Singh Vaidya, Himadri Rawat, Ruchira Kumar, Bura Vijay Computer Science and Engineering Graphic Era Hill University Dehradun India Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350306415

In order to maintain crowd control and assure safety, contemporary security systems require effective monitoring tools for sensitive and high-traffic locations. In particular, headcount estimate in sensitive areas like high-security buildings, crowded areas is the subject of this research's novel using deep learning algorithms for real-time object identification in surveillance settings. The main goal is to create a reliable system that can count the number of people in a certain area, recognize their presence, and sound an alarm when the number of people in the crowd rises over pre-established levels. YOLO (You Only Look Once), RetinaNet, SSD (Single Shot MultiBox Detector), and RCNN (Region-based Convolutional Neural Networks) are four of the cutting-edge deep learning models that are applied in this study of object recognition. In order to identify the best method for precise and effective headcount estimation, the study comprises a comparison review of various models. And of all the models examined, YOLO consistently showed the greatest results for precision, recall, and average precision, making it the most efficient model for this specific project. © 2024 IEEE.

关键词： deep learning algorithms Object detection RCNN RetinaNet SSD YOLO

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deep learning Algorithm in the Investigation of Low-Altitude Tourism Development Status and Innovation Path Exploration

Deep Learning Algorithm in the Investigation of Low-Altitude...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Liu, Kang Lian, Lijuan Sanya Aviation and Tourism College Hainan Sanya572000 China

ISBN: (纸本)9798350374933

fly tourism is an emerging form of local economic development. Local tourism needs to leverage the advantages of existing tourism resources to optimize the form and content of tourism, focusing on the content perspective involved in tourism. It not only involves tourism policies and systems, but also the rational development of tourism resources. Therefore, local tourism has great development potential. Based on this, I analyze the development of low altitude tourism and its key indicators, conduct statistical comparative analysis for the research object, verify the effectiveness of institutional tourism implementation and development, compare the rationality of local tourism, and find innovative ways to optimize local *** optimization results of low altitude tourism show that under certain criteria for evaluating the results, the deep learning algorithm has a good impact on low-altitude travel the accuracy of the investigation of development status and the exploration of innovation path, and the exploration time of development status investigation and innovation path are better than that of the recommendation algorithm. © 2024 IEEE.

关键词： deep learning algorithms low-altitude tourism tourism development theory Tourism innovation path

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Research on Smart Contract Vulnerability Detection Method of Power Equipment Based on deep learning Algorithm

Research on Smart Contract Vulnerability Detection Method of...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Zhang, Liang Fang, Yuan Shen, Yuexin Wang, Xiyin Information Communication Branch of State Grid Anhui Electric Power Co. Ltd. Anhui Hefei230022 China

ISBN: (纸本)9798350383348

With the rapid development of information technology, the problem of network security has become increasingly prominent. Camouflage intrusion, as a common means of network attack, has strong concealment and destructiveness, which brings great security threats to enterprises and organizations. In order to effectively deal with camouflage intrusion, more and more researchers apply machine learning and data mining technology to the field of intrusion detection. Among them, Random Forest (RF) algorithm, as an ensemble learning algorithm, has the advantages of high accuracy and low complexity, and has been widely concerned. However, the traditional RF algorithm still has some problems when dealing with camouflage intrusion detection, such as single feature selection, strong correlation between base classifiers and so on © 2024 IEEE.

关键词： computer technology deep learning algorithms power equipment smart contract vulnerability detection

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Research and Implementation of Duplicate Data Management Level based on deep learning Algorithm

Research and Implementation of Duplicate Data Management Lev...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Shan, Guangxing Hui, Yi Gong, Xu Chen, Yongxia Wuhan Real Estate Information Center Wuhan City430015 China Wuhan No. 1 Business School Wuhan City430022 China Wuhan Zhongshe Digital Technology Co. Ltd Wuhan City430040 China

ISBN: (纸本)9798350308167

The role of duplicate data management in data management is important, but there is a problem of inaccurate outcome evaluation. Traditional machine algorithms cannot solve the problem of duplicate data management in data management, and the evaluation is unreasonable. Therefore, this paper proposes a deep learning algorithm for repetitive data management analysis in data management. Firstly, the the duplicate data, and the indicators are divided according to the data management requirements to reduce the management of duplicate data Disturbing factors in. to manage duplicate data in data management, form a duplicate data management scheme, and manage the results of duplicate data Conduct a comprehensive analysis. Sim Manager simulation shows that under certain evaluation criteria, deep learning algorithms manage data The accuracy of duplicate data management and the time of duplicate data management are better than traditional machine algorithms. © 2023 IEEE.

关键词： data management deep learning algorithms Management management theory

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The Integration of deep learning Techniques and Big Data Analytics for Improved Breast Cancer Diagnosis and Treatment: A Systematic Review

The Integration of Deep Learning Techniques and Big Data Ana...

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2024 International Conference on Information and Communication Technology for Development for Africa, ICT4DA 2024

作者： Gebre, Hambisa Mitiku Wegari, Getachew Mamo Jimma University Faculty of Computing and Informatics Department of Information Science Jimma Ethiopia Jimma University Faculty of Computing and Informatics Department of Information Technology Jimma Ethiopia

ISBN: (纸本)9798331527792

Background:- Breast cancer is one of the most common and lethal diseases in the world. Traditional breast cancer diagnostic and prognosis procedures usually involve significant human talent and can be time-consuming and subjective, leading to potential errors and treatment delays. Two recent technical advancements, deep learning, and big data analytics are promising to improve breast cancer diagnosis and therapy. As a result, this systematic review aims to examine specific papers that describe deep learning techniques and big data analytics in the context of breast cancer diagnosis and ***: Using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), peer-reviewed articles published in the English language from January 2020 to August 2023 were selected from electronic databases such as PubMed, ACM, Digital Library, and Science Direct, as well as citations and manual searches. This review paper takes into account papers about deep learning algorithms, big data analytics, the efficacy of deep learning and big data analytics, as well as the problems and limitations of merging deep learning methods with big data analytics. Articles that were not original or in English were not ***: - Ten articles were identified for this review. The finding showed that deep-learning techniques play a great role in analyzing vast datasets to identify malignant cells or tumors, aiding radiologists in accurate diagnoses and improving patient outcomes. Big data analytics in breast cancer diagnosis and treatment can improve accuracy, efficiency, and patient-centered care. deep learning techniques are utilized with big data, enhancing screening test accuracy and guiding diagnostic procedures, especially for image-based scanning that leads to early breast cancer identification, improving patient outcomes, and potentially enhancing diagnostics. Obtaining huge volumes of high-quality data for training deep learning models is a challenge and limita

关键词： big data analytics breast cancer diagnosis and treatment deep learning algorithms

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