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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Fan, Zhen Guangdong University of Science and Technology College of Management Dongguan China

ISBN: (纸本)9798350315455

System optimization is the main content of big data technology, but in the process of big data technology management, data changes and computing methods will manage big data influence as well as minimize rationality of system optimization. This paper proposes a deep learning algorithm to optimize and analyze the data information technology, enhance a development level of big data technology, and shorten the time of data statistics formulation. Then, the big data is estimated comprehensively. Finally, continuous monitoring is used to optimize the data information technology. deep learning method can identify the relationship between information and data, better analyze data correlation, simplify the complexity between data and improve the accuracy of information analysis. The deep learning (DL) algorithm can accurately calculate parameters, improve the level of data information technology, and the rationality is greater, and better than traditional statistics law. Therefore, the deep learning algorithm can meet the requirements, and is appropriate for continuous examination of the development of information technology. © 2023 IEEE.

关键词： big data management big data technology deep learning algorithms System optimization traditional statistical methods

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Gait Recognition: A Cross - Dataset Evaluation of deep learning Models

Gait Recognition: A Cross - Dataset Evaluation of Deep Learn...

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2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Singloo, Hemal Kumar, Manoj Namdev, Simran Sonbawne, Kinsukh Suni Indoria, Nishant Vit Bhopal University Dept. Computer Science and Engineering Sehore India

ISBN: (纸本)9798350348460

This research paper investigates the effectiveness of deep learning models for gait recognition using a variety of data, including gait phase data and sensor data. This study evaluates the performance of convolutional neural networks (CNN) and long-term memory networks (LSTM), focusing on comparative analysis across multiple datasets to gain insight and contribute to different data analyses. The gait level dataset contains important movement patterns as a main area of research. Additionally, sensor-based data identifies various information needs, and complete analysis, for greater understanding and use. These comprehensive experimental results provide several observations on the variability and performance of CNNs and LSTMs across various datasets. The applicability of these models in recognizing activities in different products is presented, and their advantages and limitations in model recognition, display, and feature extraction are seen. This study distinguishes between different types of deep learning models and demonstrates their suitability for handling different types of data. These results lead to decisions regarding model selection and application, contributing to advances in analytics, biometrics, and human recognition applications. © 2024 IEEE.

关键词： CNN deep learning algorithms Gait data LSTM Machine learning

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An Attention-Based deep learning Approach to Knee Injury Classification from MRI Images 26

An Attention-Based Deep Learning Approach to Knee Injury Cla...

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26th International Conference on Computer and Information Technology, ICCIT 2023

作者： Nath, Kowshik Deb Rahman, A. F. M. Minhazur Hossain, Md. Ali Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350359015

Knee injuries, prevalent in athletic and aging populations, pose significant challenges to healthcare professionals due to their complex nature and the critical function of the knee joint. Early and accurate diagnosis is paramount to ensure effective treatment and minimize long-term complications. Traditional diagnostic methods, including physical examinations and imaging techniques like MRI, require expert interpretation and can sometimes be inconclusive. This study introduces an approach to knee injury classification using deep learning techniques by leveraging convolutional neural networks (CNNs) with Attention Mechanism. This research work integrates powerful feature extraction capabilities of CNN and feature refinement of attention mechanism for the binary and multi-class classification of knee MRI images, with the aim of accurately identifying specific knee injury types. Based on our experiment on two comprehensive knee MRI datasets, our custom CNN model achieved 88% testing accuracy on Dataset-1 (Binary classification) and 77% accuracy on Dataset-2 (Multi-class classification). Meanwhile, the Attention-based CNN model achieved 100% accuracy on Dataset- 1 (Binary Classification) and 91% accuracy on Dataset-2 (MultiClass Classification). This approach not only holds promise for enhancing diagnostic accuracy but also for reducing the time to diagnosis. © 2023 IEEE.

关键词： Attention Mechanism Binary Classification Convolutional Neural Networks(CNNs) Data augmentation deep learning algorithms Knee Injuries MRI Knee Images Multi-class classification Radiologists

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Research and Application of deep learning Algorithm Based on Multi-Source Data Fusion of Power Grid and Communication Network 3

Research and Application of Deep Learning Algorithm Based on...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Li, Li Zhou, Wei Ma, Liang Jiang, Yingjie Sun, Chao Zhang, Pu Information Telecommunications Company State Grid Shandong Electric Power Company Jinan250001 China State Grid Shandong Electric Power Company 250001 China

ISBN: (纸本)9798350354171

Multi-source data fusion plays a very important role in improving the intelligent level of power grid operation, but there is a problem that multi-source data fusion is not ideal. The previous operation mode could not solve the problem of data fusion in the intelligent operation of the power grid, and the processing was inaccurate. Therefore, this paper proposes a deep learning algorithm for multi-source data fusion analysis. Firstly, the signal processing and estimation theory are used to integrate the power grid communication data, and the indicators are carried out according to the requirements of multi-source data fusion Partitioning to reduce interference factors in multi-source data fusion. Then, signal processing and estimation theory intelligently process power grid communication to form an intelligent processing scheme and fuse multi-source data The results were comprehensively analyzed. MATLAB simulation shows that under the condition of certain processing standards, deep learning algorithms intelligently process power grid communication accuracy and multi-source data fusion time are better than previous operating modes. © 2024 IEEE.

关键词： data fusion deep learning algorithms Improve the intelligent level of power grid operation multi-source data fusion signal processing and estimation theory

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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 the Application of deep learning Algorithm in Face Expression Recognition

Research on the Application of Deep Learning Algorithm in Fa...

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

作者： Luo, Yanling Huang, Longhui Wuhan Technology and Business University Wuhan430065 China

ISBN: (纸本)9798350308167

Recognition technology is a kind of extensive technology applied by AI technology, which mainly uses expressions and related data for face recognition. The key points in face recognition technology need intelligent algorithm ***, this paper proposes an intelligent technology of face recognition, which analyzes its expressions. First, collect expression data, then compare expressions according to expression formats and samples, and carry out the actual *** recognition can find the actual expressions of different people and make judgments based on them. The research results show that face recognition can better analyze actions and expressions, and provide technology for related *** recognition technology can be widely used. © 2023 IEEE.

关键词： deep learning algorithms facial expression recognition Image recognition technology

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Extraction and Mapping of Cropland Parcels in Typical Regions of Southern China Using Unmanned Aerial Vehicle Multispectral Images and deep learning

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DRONES 2023年第5期7卷

作者： Wu, Shikun Su, Yingyue Lu, Xiaojun Xu, Han Kang, Shanggui Zhang, Boyu Hu, Yueming Liu, Luo South China Agr Univ Guangdong Prov Key Lab Land Use & Consolidat Guangzhou 510642 Peoples R China Hainan Univ Coll Trop Crops Haikou 570100 Peoples R China

The accurate extraction of cropland distribution is an important issue for precision agriculture and food security worldwide. The complex characteristics in southern China pose great challenges to the extraction. In this study, for the objective of accurate extraction and mapping of cropland parcels in multiple crop growth stages in southern China, we explored a method based on unmanned aerial vehicle (UAV) data and deep learning algorithms. Our method considered cropland size, cultivation patterns, spectral characteristics, and the terrain of the study area. From two aspects-model architecture of deep learning and the data form of UAV-four groups of experiments are performed to explore the optimal method for the extraction of cropland parcels in southern China. The optimal result obtained in October 2021 demonstrated an overall accuracy (OA) of 95.9%, a Kappa coefficient of 89.2%, and an Intersection-over-Union (IoU) of 95.7%. The optimal method also showed remarkable results in the maps of cropland distribution in multiple crop growth stages, with an average OA of 96.9%, an average Kappa coefficient of 89.5%, and an average IoU of 96.7% in August, November, and December of the same year. This study provides a valuable reference for the extraction of cropland parcels in multiple crop growth stages in southern China or regions with similar characteristics.

关键词： unmanned aerial vehicle (UAV) data deep learning algorithms cropland parcels multiple crop growth stages extraction

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deep learning based IoT and cloud-integrated state of charge estimation for battery powered electric vehicles

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JOURNAL OF ENERGY STORAGE 2024年 100卷

作者： Devi, B. Kumar, V. Suresh Karthick, T. Balasundar, C. Thiagarajar Coll Engn Dept EEE Madurai 625015 Tamil Nadu India Quantan Techserv Pvt Ltd Madurai 625016 Tamil Nadu India

Lithium-ion battery packs are widely used for automotive applications. An exact state of charge (SOC) estimation is essential to obstruct the battery from overcharge and discharge, increase the battery lifespan, and know the driving range. Even though a number of SOC estimation strategies have been presented by researchers, further exploration is necessary to identify an appropriate technique that can accommodate a variety of lithium-ion battery chemistries. In recent studies, it has been demonstrated that deep learning (DL), a well-known machine learning algorithm, performs better for SOC estimates than many other strategies. To get the most out of DL models, it is crucial to choose the proper hyperparameters and make effective use of relevant input characteristics. This work proposes a novel cloud-integrated battery management system (CIBMS) based on quantifiable features, including voltage, current, and temperature. The training and testing data is collected from the realtime electric vehicle drive set up using an Internet of Things (IoT) sensor and transferred to the cloud using an internet-connected Raspberry Pi 4B+ processor. The data is analyzed using various DL algorithms such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network with LSTM (CNN-LSTM), Convolutional Neural Network with Bi-directional LSTM (CNN BI-LSTM), and Convolutional Neural Network-Gated Recurrent Unit-Long Short-Term Memory (CNN-GRU-LSTM) for accurate battery status estimation. The proposed algorithm is incorporated to mitigate issues like pre- processing, overfitting challenges in real-time applications rather than conventional methods. The accomplishment of the algorithm has been evaluated in terms of mean square error (MSE) and root mean square error (RMSE) of the SOC. The obtained results clearly show that the proposed CNN-GRU-LSTM algorithm accurately estimated the battery status which is crucial for real-time decision-making in electric vehicles

关键词： Electric vehicle State of charge Internet of things deep learning algorithms Cloud-integrated battery management system

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Single and multiple drones detection and identification using RF based deep learning algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 187卷

作者： Sazdic-Jotic, Boban Pokrajac, Ivan Bajcetic, Jovan Bondzulic, Boban Obradovic, Danilo Univ Def Belgrade Mil Acad Veljka Lukica Kurjaka 33 Belgrade Serbia Mil Tech Inst Ratka Resanovica 1 Belgrade Serbia

Unmanned aerial systems, especially drones have gone through remarkable improvement and expansion in recent years. Drones have been widely utilized in many applications and scenarios, due to their low price and ease of use. However, in some applications drones can pose a malicious threat. To diminish risks to public security and personal privacy, it is necessary to deploy an effective and affordable anti-drone system in sensitive areas to detect, localize, identify, and defend against intruding malicious drones. This research article presents a new publicly available radio frequency drone dataset and investigates detection and identification methodologies to detect single or multiple drones and identify a single detected drone's type. Moreover, special attention in this paper has been underlined to examine the possibility of using deep learning algorithms, particularly fully connected deep neural networks as an anti-drone solution within two different radio frequency bands. We proposed a supervised deep learning algorithm with fully-connected deep neural network models that use raw drone signals rather than features. Regarding the research results, the proposed algorithm shows a lot of potentials. The probability of detecting a single drone is 99.8%, and the probability of type identification is 96.1%. Moreover, the results of multiple drones detection demonstrate an average accuracy of 97.3%. There have not been such comprehensive publications, to this time, in the open literature that have presented and enlightened the problem of multiple drones detection in the radio frequency domain.

关键词： Anti-drone system Classification deep learning algorithms Detection Drone Multiple drones

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Application of unsupervised learning and deep learning for rock type prediction and petrophysical characterization using multi-scale data

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2023年 230卷

作者： Iraji, Shohreh Soltanmohammadi, Ramin Matheus, Gabriela Fernandes Basso, Mateus Vidal, Alexandre Campane State Univ Campinas UNICAMP Dept Mech Engn FEM BR-13083860 Campinas SP Brazil State Univ Campinas UNICAMP Ctr Energy & Petr Studies CEPETRO BR-13083896 Campinas SP Brazil State Univ Campinas UNICAMP Geosci Inst IG Dept Geol & Nat Resources BR-13083855 Campinas SP Brazil

This study integrates well log data, routine core analyses, microcomputed X-ray tomography (mu CT) images, and sedimentary petrography to accurately characterize and evaluate the carbonate reservoirs of the Barra Velha formation (Aptian) of the Santos Basin within Brazilian pre-salt region. In these carbonate reservoirs, the porous system is extremely diverse and variable, making it challenging to establish rock typing with comparable petrophysical properties. Based on this integrated study, the reservoir sequences were characterized and a precise definition of four reservoir rock types (RRTs) was performed by integrating the petrophysical values from the plugs and their corresponding well log data of two cored wells using K-means unsupervised classification algorithm. The classification results were combined with various conventional techniques to evaluate the quality and geological characteristics of the studied sequence. This evaluation encompassed different parameters such as flow and storage capacity, reservoir quality index, flow zone indicator, pore spaces interpretation, and average pore and throat radius. The study involved a detailed analysis of thin sections to identify various facies, including shrubstones, reworked, and spherulitestone, and to classify various forms of porosity such as interparticle, intraparticle, intercrystalline, vug, moldic, fracture, and growth framework porosity. Pore Network Modeling from mu CT analysis of plugs was used specifically for the characterization of pores and throats of plug samples from each RRT. These datasets were utilized as supporting evidence to offer a more accurate and inclusive knowledge of reservoir quality. The study aimed to develop predictive models by implementing deep learning and machine learning algorithms trained on well log data to estimate plug porosity and rock type. Two deep learning models, ResNet and 1D CNN, were trained and evaluated for plug porosity prediction, with the 1D CNN model showing

关键词： Micro -computed tomography ( mu CT) images K -means unsupervised classification algorithm Reservoir rock types (RRTs) Pore network modeling deep learning algorithms ResNet 1D CNN XGBoost algorithm

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