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Classification of Fir Seeds Based on Feature Selection and Near-infrared Spectroscopy 5

Classification of Fir Seeds Based on Feature Selection and N...

引用

5th IEEE international conference on Artificial Intelligence and big data, ICAIBD 2022

作者： Lu, Jing Zhang, Yan Xie, Shanshan Liu, Jiang Lv, Danjv Huang, Biaosheng Yin, Yue Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China Southwest Forestry University School of Mathematics and Physics Kunming China

ISBN: (纸本)9781665499132

Fir trees account for about a quarter of the country's building materials. Fir trees grow quickly, and have certain economic, social, ecological and other benefits. Different fir trees require different growth environments, so it is necessary to accurately identify the fir seeds to cultivate them more specifically, so as to maximize the various benefits of the fir trees. In this paper, six kinds of fir seeds, namely Abies fabri, Cryptomeria japonica, Metasequoia glyptostroboides, Cathaya argyrophylla, Keteleeria fortune, Picea asperata, were used as experimental samples. the spectral data of six fir seeds were measured by Fourier transform near-infrared spectrometer. To reduce the high dimensions of spectra data, four different feature selection methods (InfoGain, Symmetric Uncert, Gainratio and ReliefF) were used to select effective feature subset for random forest classification. Experiments compared performances of the original data and the selected features cascade data with standard normal variate (SNV) method to preprocessing. the results show that the accuracy rate of the selected feature subset through SNV processing reaches 98.90%, 1.1% higher than that of the original data and the rate of dimensionality reduction reaches 97.7%. the study provides an effective classification method for fir seeds of NIRS data. © 2022 IEEE.

关键词： Near infrared spectroscopy

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DU-ResNet to Predict Survival in Patient of Heart Failure 16

DU-ResNet to Predict Survival in Patient of Heart Failure

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16th international conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2024

作者： Zhang, Chen-Lang Zhang, Chang-Jiang Tang, Fu-Qin Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua China Taizhou University School of Electronic and Information Engineering School of Big Data Science ZheJiang Taizhou China Hospital of Taizhou University Taizhou Central Hospital Taizhou China

ISBN: (纸本)9798331540241

Predicting survival in patients with heart disease clinically is a challenging task. In this paper, we propose a prediction model for the survival of heart failure patients based on deep learning combined with clinical data of heart failure patients. the proposed model is named DU-ResNet which is designed by combining ResUNet with ResNet50. In this paper, the clinical data of all original patients with heart failure were normalized first, and then, each normalized clinical data point was placed in a certain area of the grid image. therefore, according to the value of each clinical data point, a gray image with different brightness regions was constructed. After data enhancement was performed on the constructed image dataset of clinical data of heart failure patients to expand the number of samples, DU-ResNet is used to binary classify the expanded dataset, and ten-fold cross-validation and ablation experiments are performed on the dataset. then, ten-fold cross-validation was used to verify the performance of the proposed DU-ResNet model. the results show that the proposed DU-ResNet model has the best result with the use of four features, with the accuracy was 96.47%, the Specificity was 97.22%, the Sensitivity was 95.71%, the Precision was 96.87%, the F1-score was 96.27% and the MCC was 92.97% after ten-fold cross-validation. © 2024 IEEE.

关键词： Heart

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Optimization and Implementation of Vehicle Detection Algorithm for Tiny Objects 6

Optimization and Implementation of Vehicle Detection Algorit...

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6th international conference on Computer Information Science and Application technology, CISAT 2023

作者： Shi, Jing North China Institute of Computing Technology Beijing100083 China

ISBN: (纸本)9781510668546

To address the issue of current target detection models struggling to detect small vehicles and complex depth models being ill-suited for embedded platforms with limited computing power and memory, NWD and IoU are combined for tiny object detection, and the lightweight backbone network EfficientFormer is used to replace the YOLOv5 model backbone, which finally improves the tiny object detection effect while reducing the model parameters to achieve the lightweight depth model. the experimental results show that on the actual data set collected from the project, the mAP reaches 82.1%, and the number of parameters is reduced by 8.5MB. © 2023 SPIE.

关键词： Object detection

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A High Performance Bitcoin Trading Strategy Prediction Model 5

A High Performance Bitcoin Trading Strategy Prediction Model

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5th international conference on Computer Information Science and Artificial Intelligence, CISAI 2022

作者： Zhang, YunJing Sun, JiaXu Liu, HengBin Shi, ShaoQing Wang, QiuFeng School of Computer and Information Engineering Henan University of Economics and Laws Henan Province China Kansas International College Zhengzhou Sias College Henan Province China Mathematical Finance International College Zhengzhou University Henan Province China Communication Engineering International College Zhengzhou University Henan Province China Academy of Cyberspace Security International College Zhengzhou University Henan Province China

ISBN: (纸本)9781510662520

First released as open source in 2009 by the pseudonym Satoshi Nakamoto, Bitcoin is the longest running and best known cryptocurrency. Bitcoin's transaction history is characterised by openness and transparency, and Bitcoin has become an important part of financial transactions. therefore, it is increasingly important to be able to make accurate predictions about the development of the Bitcoin market. In this study, we construct a prediction model for bitcoin trading strategies based on the LightGBM algorithm, and show that our model has an accuracy of over 95.1%. the results show that our model achieves an accuracy of over 95.1% and has a higher performance compared to popular machine learning models. © 2023 SPIE.

关键词： Machine learning

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Design and implementation of big data platform based on data mining timing 5

Design and implementation of big data platform based on data...

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5th international conference on E-Business, Information Management and Computer Science, EBIMCS 2022

作者： Xiaofang, Qin Department of Mechanical and Electrical Engineering Hubei Water Resources Technical College China

ISBN: (纸本)9781450397834

the traditional collaborative filtering algorithm is improved, and an improved collaborative filtering algorithm is proposed. through comparative analysis, it is obtained that the new collaborative filtering algorithm has higher recommendation accuracy and a good speed-up ratio, and the larger the data set, the more its advantages are reflected, and it has good scalability. In addition, through supply and demand analysis, a big data platform based on data mining technology is established, and users can quickly complete the mining and analysis work for large data sets based on this platform. © 2022 ACM.

关键词： data mining

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A Novel Wi-Fi Mesh Network Framework for Efficient Mobile data Transmission 10

A Novel Wi-Fi Mesh Network Framework for Efficient Mobile Da...

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10th international conference on Internet of things, big data and Security, IoTBDS 2025

作者： Ou, Yu-Jie Chen, Yan-Ming Yeh, Chun-Chao Department of Computer Science and Engineering National Taiwan Ocean University Keelung202301 Taiwan

ISBN: (纸本)9789897587504

the current trend involves the use of robots and various IoT devices to assist in production and development. In the future, with the integration of AI technology, related applications will become even more widespread. Within this context, whether it is command transmission, equipment status reporting, or equipment condition monitoring, a stable network environment is essential. Presently, the technology can be mainly categorized into wired networks, wireless networks (Wi-Fi), and mobile networks. While wired networks are fast and stable, they are constrained by physical lines and cannot be used in environments requiring interlaced movement. Wireless networks face disconnection issues when crossing different APs. While mobile networks, particularly those using 5G, offer excellent real-time performance, the establishment of private networks is expensive and the signal sources are relatively singular. this study proposes the use of Wi-Fi technology combined with the UDP (User datagram Protocol) and QUIC (Quick UDP Internet Connections) protocols to create a new type of multipath network system. this system aims to reduce the adverse effects of physical environmental changes and wireless access point (AP) device transitions, achieving a low-cost, high-speed, highly mobile, and secure network environment in specific settings. the proposed scheme leverages the encryption and security features of the QUIC protocol to protect data privacy while supporting the needs of high-mobility applications. Copyright © 2025 by SCITEPRESS - Science and technology Publications, Lda.

关键词： Wireless mesh networks (WMN)

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Study on the Modelling Method Based on Testing data of the Frequency Control Electromechanical Transient Model of Doubly-fed Wind Turbine 12

Study on the Modelling Method Based on Testing Data of the F...

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12th international conference on Renewable Power Generation, RPG 2023

作者： Wang, Zehui Peng, Xiaotao He, Jing Li, Shaolin Miao, Fenglin Hubei Key Laboratory of Power Equipment & System Security for Integrated Energy School of Electrical Engineering and Automation Wuhan University China School of Electrical Engineering and Automation Wuhan University China China Electric Power Research Institute Haidian District Beijing China

It is important to study the electromechanical transient model of wind turbine participating in frequency regulation and its modelling method based on testing data to improve the analysis results of frequency regulation response characteristics of wind power grid-connected system. Based on this, this paper investigates the working principle of wind turbine frequency response in PSASP software, considers the principle of electromechanical transient time scale modelling, establishes the transfer function model of doubly-fed wind turbine considering inertia support and frequency response based on pitch angle adjustment, and then establishes the discrimination model for real measured signal modelling by differencing. In view of the multiple input and single output characteristics of the identification model, a model parameter identification method based on the niche particle swarm algorithm is proposed. Finally, the rationality of the proposed electromechanical transient model and the effectiveness of the parameter identification method are verified by using the active frequency disturbance of the improved WSCC-9BUS wind power system. the results of the testing data modelling show that the proposed method can realize the equivalent modelling of the frequency regulation characteristics of the doubly-fed wind turbine. © the Institution of engineering & technology 2023.

关键词： Wind turbines

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Study of Stochastic Resonance Parameters Optimization of Multiple-Frequency Weak Signals Based on Grey Wolf Algorithm 5

Study of Stochastic Resonance Parameters Optimization of Mul...

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5th international conference on Electronic engineering and Informatics, ICEEI 2023

作者： Xie, Zikang Zhang, Hongxin Tian, Liguo Wang, Yanchao Sun, Miao Tianjin University of Technology and Education Tianjin300222 China Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin300222 China

ISBN: (纸本)9781643685021

the stochastic resonance algorithm is widely used to process weak noisy signals with a single frequency. In practice, most signals are co-existing with multiple target frequencies, so stochastic resonance has certain limitations on processing multi-frequency signals. Adaptive adjustment of stochastic resonance parameters according to the target signal frequency is the key point to efficiently process weak multi-frequency signals. the simulation data illustrate that the Grey wolf algorithm can be used to optimize the structural parameters of the bi-stable stochastic resonance system. Aiming at the difference of multi-frequency signals, the Grey wolf algorithm can grope the best function parameters through realizing algorithm iteration and realize the efficient processing of multi-frequency weak signals. © 2024 the Authors.

关键词： Magnetic resonance

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Missing Values for Classification of Machine Learning in Medical data 5

Missing Values for Classification of Machine Learning in Med...

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5th IEEE international conference on Artificial Intelligence and big data, ICAIBD 2022

作者： Ren, Lijuan Wang, Tao Seklouli, Aicha Sekhari Zhang, Haiqing Bouras, Abdelaziz Lyon France Chengdu University of Information Technology School of Software Engineering Chengdu China

ISBN: (纸本)9781665499132

Missing values are an unavoidable problem for classification tasks of machine learning in medical data. With the rapid development of the medical system, large scale medical data is increasing. Missing values increase the difficulty of mining hidden but useful information in these medical datasets. Deletion and imputation methods are the most popular methods for dealing with missing values. Existing studies ignored to compare and discuss the deletion and imputation methods of missing values under the row missing rate and the total missing rate. Meanwhile, they rarely used experiment data sets that are mixed-type and large scale. In this work, medical data sets of various sizes and mixed-type are used. At the same time, performance differences of deletion and imputation methods are compared under the MCAR (Missing Completely At Random) mechanism in the baseline task using LR (Linear Regression) and SVM (Support Vector Machine) classifiers for classification with the same row and total missing rates. Experimental results show that under the MCAR missing mechanism, the performance of two types of processing methods is related to the size of datasets and missing rates. As the increasing of missing rate, the performance of two types for processing missing values decreases, but the deletion method decreases faster, and the imputation methods based on machine learning have more stable and better classification performance on average. In addition, small data sets are easily affected by processing methods of missing values. © 2022 IEEE.

关键词： Classification (of information)

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Research on the Application of Blockchain Digital Forensics Architecture Based on the DIKW Graph Model in the Police Forensic Process 25

Research on the Application of Blockchain Digital Forensics ...

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25th IEEE international conferences on High Performance Computing and Communications, 9th international conference on data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Liu, Tingwei Cheng, Hexiang Liu, Yanfei Duan, Yucong Wang, Zhenhua Chongqing Police College Department of Public Security ChongQing China Administrative Law School Southwest University of Political Science and Law Chong Qing China Tianjin University Chongqing Police College Department of Intelligence and Computing Department of Information Security ChongQing China School of Computer Science and Technology Hainan University Haikou China Chongqing Police College Department of Tactics ChongQing China

ISBN: (纸本)9798350330014

this study explores the application of a blockch-ain-based digital forensics architecture using the DIKW (data, Information, Knowledge, Wisdom) graph in police investigations. Current digital intelligence systems usually plan a route from data Governance to Wisdom Governance, but ignore or lack I nformation Governance. From a conceptual semantic perspectiv -e [1], the concepts underlying these domains include data, Information, Knowledge, Wisdom(DIKW) [2]. With the development of blockchain technology and the Internet of things, digital forensics plays a crucial role in criminal investigations. Existing methods face challenges in terms of flexibility in storing evidence on the blockchain and efficient handling of large-scale and multimodal forensic demands. To address these issues, the DIKW (data, Information, Knowledge, Wisdom) graph model is introduced. the DIKW graph is a multi-layered hierarchical structure for processing typed resources, consisting of data graph, information graph, knowledge graph, and wisdom graph [3]. It can be used to optimize the efficiency of storage, trans mission, and computation integration [4], as well as for privacy data protection [5]. the methods include the integration of the Internet of thi -ngs (IoT) and blockchain, as well as the design of a blockcha -in-based digital forensics architecture using the DIKW (data, Information, Knowledge, Wisdom) graph. the data layer is responsible for collecting and storing data generated b y IoT devices. the results show that this architecture im proves the efficiency and accuracy of digital forensics an d provides strong decision support for law enforcement. © 2023 IEEE.

关键词： Blockchain

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