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International Conference on Modeling, Natural Language Processing and Machine Learning (CMNM)

作者： Song, Meirui Wu, Shixiao Huang, Rujuan Wuhan Business Univ Wuhan Peoples R China

ISBN: (纸本)9798400709760

breast cancer ranks first in female malignant tumors. Early detection and diagnosis is the key to treatment. This paper uses the open-source load_break_cancer breast cancer data set, mainly uses random forest, support vector machine, logical regression, Gauss naive Bayesian algorithm, BP neural network algorithm, k-neighborhood algorithm and XGBoost algorithm to classify and predict the breast cancer data set, conducts a lot of training and testing on the data set under a variety of machine learning algorithms, analyzes the learning curve in the training process, analyzes the training and testing results, and analyzes the performance of the algorithm processing data, which is of great significance for breast cancer diagnosis and treatment.

关键词： Random Forest algorithm Support Vector Machine algorithm logistic regression algorithm Gaussian Naive Bayes algorithm BP Neural Network algorithm k-Neighbor algorithm XGBoost algorithm

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Evaluating the Predictive Accuracy and Precision of AlexNet and Linear regression for Women's Education Post-Independence 2

Evaluating the Predictive Accuracy and Precision of AlexNet ...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Mary Joy Kinol, A. Bommi, R.M. Nalini, M. Uganya, G. Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai India Department of ECE Chennai Institute of Technology Chennai India Department of Electronics and Instrumentation Engineering Sri Sairam Engineering College Chennai India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798350340235

In this work, the conventional linear regression algorithm and the deep learning architecture AlexNet are compared and contrasted. With a focus on measuring accuracy and precision in the context of a real-time educational system, the main goal is to assess their effectiveness in anticipating changes in women's education following periods of independence. The findings indicate that the AlexNet algorithm's accuracy (84.22%) is much greater than the linear regression algorithm's accuracy (80.07%). The results show that, with a mean accuracy of 83.33% versus 79.27%, the Novel AlexNet algorithm beats the logistic regression technique in terms of accuracy. The findings of this comparative research will aid in the creation of data-driven solutions for women's education, giving stakeholders like policymakers, educators, and educators the ability to make decisions that are well-informed in real-time. © 2023 IEEE.

关键词： Higher education Literacy logistic regression algorithm Machine Learning Novel AlexNet algorithm Precision Women education

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Mental Health Predictive Analysis using Multiple Machine Learning Techniques 2

Mental Health Predictive Analysis using Multiple Machine Lea...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Tressa, Neethu Asha, V. Prasad, Arpana Prathith Pramodh Kumar, D. Saju, Binju New Horiznon College of Engineering Department of Master of Computer Applications Bengaluru India Adi Shankara Institute of Engineering & Technology Department of Computer Science - Ai Engineering Kerala Kalady India

ISBN: (纸本)9798350369960

The growth in mental health issues and the demand for high-quality medical care have prompted the requirement of machine learning techniques in the psychological research area. For the better prediction of mental health complications, this work presents an up-to-date thorough evaluation of machinelearning algorithms. This article reviews the difficulties, limitations and future directions of using machine learning approaches in psychological health. By searching reliable databases, relevant studies and publications on machine learning methods were collected for predicting mental health related issues. For the completion of this work, 40 research outcomes were analyzed using screening and classification procedures. The analyzed works were categorized according to mental health conditions such as schizophrenia, bipolar disorder, depression or anxiety, PTSD, and mental health conditions affecting children. While discussing the results, the difficulties and limitations that many researchers have encountered when studying mental health related problems were also considered. The paper also encompasses specific directions for future research work that are feasible and the need for utilizing varying approaches in machine learning for psychological health disorder predictions. © 2023 IEEE.

关键词： Decision Tree Classifier Flask Deployment K Nearest Neighbor Classifier logistic regression algorithm Naïve Bayes Random Forest Stacking

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Recommendation System for Adventure Tourism 4

Recommendation System for Adventure Tourism

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Pandey, Prachi Mayank, Kumar Sharma, Sachin Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9798350305258

Recommender systems are commonly used. We focus our emphasis mostly on how tourism is used. Conferences have been thoroughly sought for many years. We provide a comprehensive and current overview of this subject that takes into account the numerous method types, the diversity of recommendation algorithms, the features these systems have to offer, and their use of artificial intelligence techniques. Our poll also provided some recommendations for tour operators as well as the most promising areas for further research in this area. Information overload has emerged as a common occurrence and a serious issue for those looking for pertinent information in this age of the Internet because quick access to accurate information is the basis of our system. The effectiveness of the content on tourism websites has also been the subject of a large number of studies. Because of this, an intelligent smart tourism management system tries to reduce the gap by noting what a visitor thinks is important while connecting to tourism products on tourism websites. Because content is regarded as the essential element of an effective and intelligent website, our study focuses heavily on it. © 2023 IEEE.

关键词： Collaborative filtering Content based filtering K-Means algorithm logistic regression algorithm Recommendation System Review System

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Research on the Risk of Water Traffic Accidents Considering Risk Navigation Preference Behavior 8

Research on the Risk of Water Traffic Accidents Considering ...

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8th IEEE Information Technology and Mechatronics Engineering Conference, ITOEC 2025

作者： Zhang, Shukui Dai, Qibing Xiong, Ding Jiangsu Maritime Institute Navigation College Nanjing China Digital Engineering Technology Research and Development Center for Maritime Safety and Security Nanjing China

ISBN: (纸本)9798331517915

The prediction of ship traffic accidents has always been a hot topic in the industry. Although the traffic safety risk assessment model based on waterway operation status has achieved certain results, it is still unsatisfactory. Therefore, two traffic accident prediction models have been built in the article based on logistic regression algorithm. The first model only considers the main characteristics of waterway operation status as variables, while the second model considers both risk navigation preference behavior and the main characteristics of waterway operation status as variables. Actual data verification shows that compared with the prediction results of the first model, accuracy of the second model has increased by 5.20%, false alarm rate has decreased by 2.51%, false alarm rate has decreased by 6.62%, and Area Under Curve (AUC) value has increased by 0.04. Research results indicate that using the main characteristics of risk navigation preference behavior as variables in traffic accident prediction models yields better results. © 2025 IEEE.

关键词： Data mining logistic regression algorithm Machine learning Risk identification Risk navigation preference

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Predicting Purchasing Behavior on E-Commerce Platforms: A regression Model Approach for Understanding User Features that Lead to Purchasing

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International Journal of Communications, Network and System Sciences 2024年第6期17卷 81-103页

作者： Abraham Jallah Balyemah Sonkarlay J. Y. Weamie Jiang Bin Karmue Vasco Jarnda Felix Jwakdak Joshua College of Computer Science and Engineering Hunan University Changsha China Department of Health Inspection and Quarantine Xiangya School of Public Health Central South University Changsha China

This research introduces a novel approach to improve and optimize the predictive capacity of consumer purchase behaviors on e-commerce platforms. This study presented an introduction to the fundamental concepts of the logistic regression algorithm. In addition, it analyzed user data obtained from an e-commerce platform. The original data were preprocessed, and a consumer purchase prediction model was developed for the e-commerce platform using the logistic regression method. The comparison study used the classic random forest approach, further enhanced by including the K-fold cross-validation method. Evaluation of the accuracy of the model’s classification was conducted using performance indicators that included the accuracy rate, the precision rate, the recall rate, and the F1 score. A visual examination determined the significance of the findings. The findings suggest that employing the logistic regression algorithm to forecast customer purchase behaviors on e-commerce platforms can improve the efficacy of the approach and yield more accurate predictions. This study serves as a valuable resource for improving the precision of forecasting customers’ purchase behaviors on e-commerce platforms. It has significant practical implications for optimizing the operational efficiency of e-commerce platforms.

关键词： E-Commerce Platform Purchasing Behavior Prediction logistic regression algorithm

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Network security risk assessment of football matches based on data mining technology

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OPTIK 2022年 270卷

作者： Lu, Tao Leshan Normal Univ Sch Phys Educ Major Leshan 614000 Sichuan Peoples R China

Objective: Using the principles and advanced methods of data mining technology, this paper aims to achieve the analysis and assessment of network security risks under soccer tournaments as a way to help analyze and solve the objective problems such as system vulnerabilities and network viruses that exist in network security today. Faced with various network securities, this paper adopts quantitative risk assessment ***: In the evaluation of algorithms for constructing network security, the principles and laws of data mining techniques are drawn upon for the analysis and evaluation of network se-curity risks to help analyze the network security risks of soccer ***: It contains 494,020 network connection data, each of which contains 41 connection at-tributes, including 34 numeric and 7 character attributes. There were 97,277 normal network connections and 396,743 anomalous network connections. In the experiments of the anomaly detection module, although the false positive rate increases compared to all features, the accuracy is basically the same as that of all features with nearly 50% less modeling time and lower false positive rate. So this method is more stable. Therefore, the analysis and assessment of cyberse-curity risks are crucial in the present time.

关键词： Cybersecurity risk Football match Data mining technology logistic regression algorithm

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Elaboration of a multisequence MRI-based radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer: a double-center study

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EUROPEAN RADIOLOGY 2020年第9期30卷 4816-4827页

作者： Wang, Huanjun Xu, Xiaopan Zhang, Xi Liu, Yang Ouyang, Longyuan Du, Peng Li, Shurong Tian, Qiang Ling, Jian Guo, Yan Lu, Hongbing Sun Yat Sen Univ Affiliated Hosp 1 Dept Radiol 58 Zhongshan Rd 2 Guangzhou Guangdong Peoples R China Fourth Mil Med Univ Sch Biomed Engn Air Force Med Univ 169 Changle West Rd Xian 710032 Shaanxi Peoples R China Fourth Mil Med Univ Tangdu Hosp Dept Radiol Air Force Med Univ Xian Shaanxi Peoples R China

Objectives To develop a multisequence MRI-based radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer (BCa). Methods This retrospective study involved 106 eligible patients from two independent clinical centers. All patients underwent a preoperative 3.0 T MRI scan with T2-weighted image (T2WI) and multi-b-value diffusion-weighted image (DWI) sequences. In total, 1404 radiomics features were extracted from the largest region of the reported tumor locations on the T2WI, DWI, and corresponding apparent diffusion coefficient map (ADC) of each patient. A radiomics signature, namely the Radscore, was then generated using the recursive feature elimination approach and a logistic regression algorithm in a training cohort (n = 64). Its performance was then validated in an independent validation cohort (n = 42). The primary imaging and clinical factors in conjunction with the Radscore were used to determine whether the performance could be further improved. Results The Radscore, generated by 36 selected radiomics features, demonstrated a favorable ability to predict muscle-invasive BCa status in both the training (AUC 0.880) and validation (AUC 0.813) cohorts. Subsequently, integrating the two independent predictors (including the Radscore and MRI-determined tumor stalk) into a nomogram exhibited more favorable discriminatory performance, with the AUC improved to 0.924 and 0.877 in both cohorts, respectively. Conclusions The proposed multisequence MRI-based radiomics signature alone could be an effective tool for quantitative prediction of muscle-invasive status of BCa. Integrating the Radscore with MRI-determined tumor stalk could further improve the discriminatory power, realizing more accurate prediction of nonmuscle-invasive and muscle-invasive BCa.

关键词： MRI Bladder cancer Diffusion-weighted image Apparent diffusion coefficient logistic regression algorithm

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Study on learning effect prediction models based on principal component analysis in MOOCs

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第6-Sup期22卷 15347-15356页

作者： Zhang, Wei Qin, Shiming Yi, Baolin Tian, Peng Cent China Normal Univ Natl Engn Res Ctr E Learning Wuhan Hubei Peoples R China Cent China Normal Univ Coll Publ Adm Wuhan Hubei Peoples R China

In recent years, the rise and development of massive open online courses (MOOCs) have promoted the boom of online education and also promoted the research on learning analysis and mining based on big data of education. However, while offering a large number of high quality courses, there is also a phenomenon that the overall learning effect is not ideal. How to make effective use of MOOCs for teaching activities poses urgent practical requirements for educators and researchers. MOOCs store massive learners' learning behavior data, and mining these data is of great significance to learners' learning effects. Due to the deviation caused by the correlation between many behavior indexes, the paper analyze the nine measurable performance indexes by principal component analysis (PCA), and get three principal components factors in order to reduce the computational dimension and increase the comprehensibility, and then obtain a model of learning effect prediction, namely the PCA prediction model (PCA_PM). logistic regression algorithm has a good fitting effect on behavioral indexes. Therefore, the paper use the logistic regression method to construct another model of learning effect prediction, namely the principal component logistic regression prediction model (PCL_PM), and predictive effects of two models were compared and analyzed. The results show that it is an effective way to help learners to improve the passing rate of the course by establishing the predictive models. The prediction accuracy of both PCA_PM and PCL_PM is high, but overall, the prediction effect of PCA_PM is better.

关键词： Predictive models PCA logistic regression algorithm MOOCs

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Improved LDA Dimension Reduction Based Behavior Learning with Commodity WiFi for Cyber-Physical Systems

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ACM TRANSACTIONS ON CYBER-PHYSICAL SYSTEMS 2019年第4期3卷 1–19页

作者： Xiao, Fu Chen, Jing Li, Zhetao Huang, Haiping Sun, Lijuan Nanjing Univ Posts & Telecommun Jiangsu High Technol Res Key Lab Wireless Sensor 9 Wenyuan Rd Nanjing 210046 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Nanjing Jiangsu Peoples R China Xiangtan Univ Minist Educ Key Lab Intelligent Comp & Informat Proc Key Lab Hunan Prov Internet Things & Informat Sec North Second Ring Xiangtan Hunan Peoples R China Nanjing Univ Posts & Telecommun Jiangsu High Technol Res Key Lab Wireless Sensor Sch Engn Nanjing 210046 Jiangsu Peoples R China

In recent years, rapid development of sensing and computing has led to very large datasets. There is an urgent demand for innovative data analysis and processing techniques that are secure, privacy-protected and sustainable. In this article, taking human activities and interactions with Cyber-Physical Systems (CPS) into consideration, we propose a human behavior learning system based on Channel State Information (CSI) utilizing a series of algorithms for data analysis and processing. Aiming to recognize a set of gestures, our system is designed based on the observation that different gestures have different effects on signals and specific gesture signals have a unique energy spectrum. Specifically, an improved Linear Discriminant Analysis algorithm (I-LDA) is devised to reduce the dimension of human behavior signals. Additionally, behaviors are learned by logistic regression algorithm (LRA). Bandwidth ratios in an energy spectrum are selected as features to eliminate the impact of speed differences on results. The system is based on commercial off-the-shelf WiFi devices and we conduct a large number of experiments in a typical indoor environment to evaluate its performance. Experimental results show that our system is robust with average recognition accuracy of up to 96%.

关键词： Behavior learning WiFi channel state information improved linear discriminant analysis logistic regression algorithm

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