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4th International Conference on Bioinformatics and Intelligent Computing (BIC)

作者： Xu, Menglu Wen, Hongjuan Changchun Univ Chinese Med Changchun 130117 Peoples R China

ISBN: (纸本)9798400716645

The COVID-19 outbreak, declared a public health emergency by the WHO, has intensified stress levels for medical students who were already under significant pressure. Medical students has been an increase in mental health issues and changes in health behaviors. This study utilized a logistic regression algorithm to determine factors that cause stress among medical students following public health emergencies in China. Data on demographic characteristics, mental health conditions, and health behaviors post-emergency were collected from 1927 medical students. Chi-square tests and logistic regression were used to analyze the factors that influenced stress levels among Chinese medical students following public health emergencies. Results showed that after public health emergencies, medical students experienced higher stress levels (44.84%). Psychological factors such as anxiety and loneliness, and health behaviors like sleep patterns, social isolation, and changes in daily routines were identified as primary contributors to increased stress levels (p<0.05). In conclusion, psychological factors and health behaviors are primary contributors to increased stress levels, the study highlights the importance of addressing various factors that contribute to stress among medical students, including enhancing stress management abilities and encouraging healthy behaviors to promote the physical and mental well-being of Chinese medical students during public health emergencies.

关键词： stress logistic regression algorithm medical students public health emergencies health behaviors machine learning

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A Study Based on logistic regression algorithm to Teaching Indicators 1

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1st International Artificial Intelligence Conference, IAIC 2023

作者： He, Yufang Shen, Kaiyue Zhang, Haiyang Duan, Wenjing Gong, Zhen Jia, Ruoyao Wang, He School of Health Management Changchun University of Chinese Medicine Changchun130117 China School of Medical Information Changchun University of Chinese Medicine Changchun130117 China

ISBN: (数字)9789819712809

ISBN: (纸本)9789819712793

Objective: This study aims to examine the factors that influence teachers’ choice regarding the importance of instructional indicators. Methods: Based on a logistic regression algorithm to survey university faculty on the importance of teaching indicators, and the resultant data were processed by binary logistic regression analysis. Results: 731 questionnaires were collected in total. We constructed 3 dimensions of basic construction development, practice cultivation construction, and innovation education development, concluding that the difference of age, education, education work time, and title category are the factors that influence the choice of teachers’ teaching goal emphasis level under each dimension, education constitutes an important influencing factor with the most profound level impact. Conclusion: Accelerate faculty gradient structure, strengthen teachers’ teaching awareness and enhance teachers’ sense of belonging in education career may become vital links to promote high quality construction and development of universities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： logistic regression algorithm Teachers Teaching indicators

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An Innovative Method to Enhance the Accuracy of Credit Card Fraud Detection Using logistic regression algorithm by Comparing Random Forest algorithm 1

An Innovative Method to Enhance the Accuracy of Credit Card ...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Poojitha, S. Malathi, K. Saveetha University Chennai India

ISBN: (纸本)9781607685395

The objective is to develop a model to predict which transactions could be fraud with a high accuracy rate by using machine learning(ML) methods such as the innovative Random Forest algorithm. F1 score and accuracy rate are performed on fraud rates transactions in a dataset. The two groups (LR) the sample size is (N=20) and (RF) is (N=20) with G power value=80. To perform the transactions in the credit card by utilizing the card number, Financial balance and client details and if it is offline the fraud has to steal the user's details in online transactions it is not easily recognized by the client and bank authority which prompts loss of sensitive data. (p © The Electrochemical Society

关键词： Card Name Credit Card Financial Balance Innovative Random Forest algorithm logistic regression algorithm Machine Learning

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A Hybrid Model Based on logistic regression algorithm and Extraction algorithm Using Reward Extremum to Real-Time Detect Blade Icing Alarm

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2019年第14期33卷

作者： Li, Tingshun Tan, Wen Liu, Zesan North China Elect Power Univ Sch Control & Comp Engn Beijing Peoples R China China Power Informat Technol Co Ltd Beijing Peoples R China

Generally, the blades of wind turbine installed in cold regions often encounter the situation of icing on their surface in winter, which affects the performance of wind turbine greatly and reduces power generation. Blade icing of wind turbine is a typical alarm related to multivariates, such as environment temperature, humidity, blade rotation and so on. To successfully achieve a supervised classification, it is necessary to extract the correlated features with blade icing. This paper proposes an extraction algorithm using reward extremism which can extract a group of variables correlated to blade icing from many monitoring variables of wind turbine, and they affect each other. This algorithm, using reward function which can measure the reward or value of alarm problem, can effectively distinguish the variables correlated to blade icing from the uncorrelated ones without knowing the mean of variables and the relations between them. Whether the blade of wind turbine is iced or not is a binary logic, so logistic regression algorithm is better choice to detect blade icing. Hence, this paper proposes a hybrid model using logistic regression algorithm and extraction algorithm using reward extremism, which can be built by machine learning with historic monitoring data. This model can real-time detect blade icing by real-time monitoring data. A correlation variable set can be obtained by the application of extraction algorithm using reward extremism on the historic monitoring data;and then the weights of the correlation variables in the set can be gained from the historic data by using logistic regression algorithm, so a blade icing detection pattern can be found using logistic regression algorithm;finally, whether the blade of wind turbine is iced in delay time or not can be real-time detected with the help of the hybrid model by real-time monitoring data. It is proved by wind turbine data that this hybrid model can work well not only to get correlation variable set but a

关键词： logistic regression algorithm blade icing alarm real-time detect extraction algorithm using reward extremism machine learning

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Risk Prediction of Power Battery Based on logistic regression algorithm

Risk Prediction of Power Battery Based on Logistic Regressio...

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2021中国汽车工程学会年会暨展览会

作者： Li Yaohua Ren Chao Wang Xiaoyu Chen Guixin Liu Zikun Liu Dongmei School of Automobile Chang'an University

Today,when we advocate the development of new energy vehicles,the improvement of battery management system is an important factor in the development of electric ***,the estimation of the state of power batteries has become the key factor to ensure the safe driving of the *** research on the low residual charge of power battery mostly uses the charge and discharge data of single battery under ideal experimental conditions,which may not be able to adapt to the real complex and changeable driving conditions and separate from the real driving *** solve this problem,this work relies on the national big data alliance of new energy vehicles and uses the data-driven method to build a time-varying,battery voltage,battery current and other angles to reflect the current working status of the remaining power(SOC) of the power *** type of power battery used in this paper is lithium iron phosphate battery,and the alarm group and prediction group are *** data of the alarm group are the collected power battery data detected as low residual power(SOC),and the data of the prediction group are the driving data of vehicles from May 9,2019 to November 5,*** logical regression algorithm is used to extract the battery characteristic information of low residual power,establish a model,and use the model to predict the data of the prediction group.

关键词： new energy vehicles power batteries low SOC logistic regression algorithm prediction

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logistic regression for Enhancing Scalability of Blockchain System

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Procedia Computer Science 2025年 252卷 146-153页

作者： Manjula K Pawar Prakashgoud Patil

Blockchain is a cutting-edge technology widely recognized for its applications to industry, business, and sustainable development goals. Its key features include tamper-proofing, decentralization, immutability, transparency, and security. Despite its numerous advantages, Challenges such as privacy and scalability persist. Scalability, is measured with respect to throughput, capacity, and latency, which are remains a crucial aspect of its functionality. The latency includes the timestamp of block inclusion in the blockchain. The proposed methodology suggests using a logistic regression method to enhance the scalability of a blockchain system. The suggested methodology involves analyzing the performance of logistic regression, resulting in higher transactions per second and lower latency time which is measured in milliseconds.

关键词： Blockchain Throughput Latency Scalability Consensus algorithm logistic regression algorithm

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Analysis of Frequent Trading Effects of Various Machine Learning Models

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COMPUTATIONAL ECONOMICS 2025年第3期65卷 1707-1740页

作者： Chen, Jiahao Li, Xiaofei Du, Junjie Yangtze Univ Sch Informat & Math Jingzhou 434020 Hubei Peoples R China Jingzhou Univ Sch Math & Phys Jingzhou 434020 Peoples R China

In recent years, high-frequency trading has emerged as a crucial strategy in stock trading. Notably, within China's distinctive market regulatory framework, stock transactions are limited to a daily. Consequently, this article delves into the exploration of daily-updated high-frequency stock trading strategies within this unique market context. This study aims to develop an advanced high-frequency trading algorithm and compare the performance of three different mathematical models: the combination of the cross-entropy loss function and the quasi-Newton algorithm, the FCNN (Fully Connected Neural Network) model, and the support vector machine model. The proposed algorithm employs neural network predictions to generate trading signals and execute buy and sell operations based on specific conditions. By harnessing the power of neural networks, the algorithm enhances the accuracy and reliability of the trading strategy. To assess the effectiveness of the algorithm, the study evaluates the performance of the three mathematical models. The combination of the cross-entropy loss function and the quasi-Newton algorithm is a widely utilized logistic regression approach. The FCNN model, on the other hand, is a deep learning algorithm that can extract and classify features from stock data. Meanwhile, the support vector machine is a supervised learning algorithm recognized for achieving improved classification results by mapping data into high-dimensional spaces. By comparing the performance of these three models, the study aims to determine the most effective approach for high-frequency trading. This research makes a valuable contribution by introducing a novel methodology for high-frequency trading, thereby providing investors with a more accurate and reliable stock trading strategy.

关键词： High-frequency trading Mathematical model logistic regression algorithm Fully Connected Neural Network model Support vector machine Stock data

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Hippocampal Functional Radiomic Features for Identification of the Cognitively Impaired Patients from Low-Back-Related Pain: A Prospective Machine Learning Study

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JOURNAL OF PAIN RESEARCH 2025年 18卷 271-282页

作者： Yang, Ziwei Liang, Xiao Ji, Yuqi Zeng, Wei Wang, Yao Zhang, Yong Zhou, Fuqing Nanchang Univ Affiliated Hosp 1 Jiangxi Med Coll Jiangxi Prov Key Lab Precis Pathol & Intelligent D 17 Yongwaizheng St Nanchang 330006 Jiangxi Peoples R China Jiangxi Prov Med Imaging Res Inst Neuroradiol Lab Nanchang 330006 Jiangxi Peoples R China Nanchang Univ Affiliated Hosp 1 Jiangxi Med Coll Dept Pain Clin 17 Yongwaizheng St Nanchang 330006 Jiangxi Peoples R China

Purpose: To investigate whether functional radiomic features in bilateral hippocampi can identify the cognitively impaired patients from low-back-related leg pain (LBLP). Patients and Methods: For this retrospective study, a total of 95 clinically definite LBLP patients (40 cognitively impaired patients and 45 cognitively preserved patients) were included, and all patients underwent functional MRI and clinical assessments. After calculating the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC) and degree centrality (DC) imaging, the radiomic features (n = 819) of bilateral hippocampi were extracted from these images, respectively. After feature selection, machine learning models were trained. Finally, we further analyzed the relationship between the hippocampal functional radiomic features and clinical measures, to explore the clinical significance of these features. Results: The combined radiomic features model logistic regression algorithm superior performance in distinguishing cognitively impaired patients from LBLP (AUC = 0.970, accuracy = 92.3%, sensitivity = 92.3%, specificity = 92.3%) compared to the other models. Additionally, radiomic wavelet features were correlated with Montreal Cognitive Assessment (MoCA) and Hamilton Anxiety Scale, present pain intensity scores in cognitively impaired LBLP patients (P < 0.05, with Bonferroni correction). Conclusion: Hippocampal functional radiomic features are valuable for diagnosing cognitively impaired patients from LBLP.

关键词： cognitive impairment resting-state functional MRI low-back-related leg pain radiomic logistic regression algorithm

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A prediction method of purchasing intention of e-commerce consumers in network marketing

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International Journal of Business Intelligence and Data Mining 2025年第1-2期26卷 161-173页

作者： Da, Hongyan Department of Applied Foreign Languages Ma An Shan Technical College Maanshan243000 China

In order to improve the accuracy of prediction results, a prediction method of purchasing intention of e-commerce consumers in network marketing is proposed. Firstly, collect consumer related data, standardise and handle outliers to eliminate dimensional differences between data. Secondly, a logistic regression algorithm is used to extract and select consumer features related to purchase intention, revealing consumers’ purchasing preferences. Finally, based on the feature extraction results, a random forest algorithm is used to predict purchase intention. Gini impurity and information gain are used as splitting criteria to construct multiple decision trees, and the prediction results of all decision trees are synthesised by voting or averaging to obtain the final prediction result. The experimental results show that the root mean square error of the proposed method is relatively low, with the highest normalised information value reaching 0.89, indicating that it can accurately reflect consumers’ purchasing intention. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： e-commerce logistic regression algorithm network marketing purchase intention random forest algorithm

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Analysis and elaboration of enterprise management innovation path based on logistic regression perspective

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年第2期8卷 1387-1400页

作者： Zhao, Jia Shanghai Normal Univ Sch Tourism Shanghai 200234 Peoples R China Shanghai Inst Tourism Sch Hospitality & Culinary Arts Management Shanghai 201418 Peoples R China

Based on the lack of management level and other problems in enterprise management innovation, this paper analyzes the innovation pathway of enterprise management by logistic regression algorithm. Firstly, the management pathway is set as a data set, and the feature vector of the model is defined, the probability model of logistic regression is derived from the input data vector, and the model defines the likelihood probability of the training set. Then the log-likelihood function of the logistic regression model with beta distribution is calculated, the feature weights of the data set are obtained using this function, and finally, the effective countermeasures of enterprise management innovation pathways under logistic regression are obtained according to these weights. The results show that by examining the variance comparison of different enterprise properties, private and sole proprietorship enterprises have the highest scores in the external organizational management factors, 3.86 and 3.72, respectively. The factors in the strategic technological capability have the lowest scores compared with other factors. It can be seen that the construction of comprehensive innovation management capability of enterprises is a long-term accumulation process, and the logistic regression algorithm helps enterprises to develop feasible innovation management pathway strategies according to their capabilities.

关键词： logistic regression algorithm Dataset feature weights Beta distribution Likelihood function

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