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International Core Journal of Engineering 2021年第5期7卷 222-228页

作者： Xudong Wei Zhaofei Li Zenan Wang Yumei Chen Xiaodong Tong Sijing Deng Wei Pu Yuanbo Zhang Xueshen Liu Li He Bin Liu

With the explosive growth of data, the use of big data technology machine learning classification algorithms to predict the results can improve the intelligent classification of data. It can provide data support for predicting classification in advance. Filter out the classification results to improve the efficiency of data processing and data realization. This article first introduces the development process of machine learning under big data, introduces the mainstream distributed processing framework spark, and then compares the advantages and disadvantages of classification algorithms under big data.

关键词： Big Data Spark Machine Learning classification algorithm

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Data-driven prediction-control system modeling for magnetorheological damping force

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JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES 2023年第2期34卷 155-167页

作者： Lv, Hongzhan Sun, Qi Ma, Weiguang Donghua Univ Coll Mech Engn 2999 North Renmin RdSongjiang Campus Shanghai 201620 Peoples R China Minist Educ Engn Res Ctr Text Equipments Shanghai Peoples R China

As a new type of intelligent damper, the magnetorheological damper has been widely used in robot, automobile NVH, and intelligent structure. However, for the intelligent response control from the structural excitation, it is the challenge to realize the intelligent control of the magnetorheological damping system. In this paper, the prediction-control mechanism of the magnetorheological damping system is modeled by a data-driven method, such as neural network and classification algorithm. The NARX (Nonlinear autoregressive with external input) neural network is used to predict the desired damping force required for the structural system in the forward direction, and the decision tree classification algorithm is used to reversely-control the desired current of the magnetorheological damping system in instant response to the structural system's damping force requirement. The analysis results show that the prediction-control method is feasible to realize the intelligent control of the damper based on the state data of the damped system, which provides a new idea for the intelligent control of the magnetorheological damper system.

关键词： Data-driven magnetorheological damping force prediction-control neural network classification algorithm

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Use of Machine Learning algorithms to Study the Hospitalization for Coronary Artery Bypass at University Hospital "Federico II" 6th

Use of Machine Learning Algorithms to Study the Hospitalizat...

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6th International Conference on Biomedical Engineering

作者： Marino, Marta Rosaria Borrelli, Anna Triassi, Maria Improta, Giovanni Univ Naples Federico II Dept Publ Hlth Naples Italy Federico II Univ Hosp Naples Italy Univ Naples Federico II Interdept Ctr Res Healthcare Management & Innovat Naples Italy

ISBN: (纸本)9783031803543;9783031803550

Coronary artery bypass graft (CABG) is a surgical treatment performed to treat coronary artery disease. During CABG, the patient's coronary arteries are cleaned out of obstructions and new, heart-blood-supplying arteries are implanted in their place. Following the CABG, the objective of postoperative care is aimed at fostering the patient's recovery by reducing the risk of complications. Given that a prolonged length of stay may result in a significant financial burden and ineffective management of hospital resources, the length of stay (LOS) in the hospital is an important component for a good organizational structure. The LOS of patients receiving bypass surgery has been predicted in this study using Machine Learning (ML) techniques. Computer systems can automatically learn from data without explicit programming thanks to a combination of mathematical models and statistical techniques called machine learning (ML). The dataset comprises 243 patients having heart bypass surgery in 2019-2020, was obtained from the University Hospital "Federico II" - Complex Operative Unit (C.O.U.) of Cardiology. The data were compared with those obtained from two other hospitals (University Hospital "San Giovanni di Dio e Ruggi d'Aragona" and those of the national hospital "Antonio Cardarelli") to identify the algorithm with the best performance in predicting length of stay of patient undergoing CABG by analyzing personal and clinical information like age, presence/absence of comorbidities.

关键词： Coronary Artery Bypass Machine Learning classification algorithm Length of Hospital Stay

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Comparative Analysis of Force-Sensitive Resistors and Triaxial Accelerometers for Sitting Posture classification

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SENSORS 2024年第23期24卷 7705页

作者： Liu, Zhuofu Shu, Zihao Cascioli, Vincenzo Mccarthy, Peter W. Harbin Univ Sci & Technol Higher Educ Key Lab Measuring & Control Technol & Harbin 150080 Peoples R China Murdoch Univ Chiropract Clin Murdoch 6150 Australia Univ South Wales Fac Life Sci & Educ Pontypridd CF37 1DL Wales Durban Univ Technol Fac Hlth Sci ZA-1334 Durban South Africa

Sedentary behaviors, including poor postures, are significantly detrimental to health, particularly for individuals losing motion ability. This study presents a posture detection system utilizing four force-sensitive resistors (FSRs) and two triaxial accelerometers selected after rigorous assessment for consistency and linearity. We compared various machine learning algorithms based on classification accuracy and computational efficiency. The k-nearest neighbor (KNN) algorithm demonstrated superior performance over Decision Tree, Discriminant Analysis, Naive Bayes, and Support Vector Machine (SVM). Further analysis of KNN hyperparameters revealed that the city block metric with K = 3 yielded optimal classification results. Triaxial accelerometers exhibited higher accuracy in both training (99.4%) and testing (99.0%) phases compared to FSRs (96.6% and 95.4%, respectively), with slightly reduced processing times (0.83 s vs. 0.85 s for training;0.51 s vs. 0.54 s for testing). These findings suggest that, apart from being cost-effective and compact, triaxial accelerometers are more effective than FSRs for posture detection.

关键词： sitting posture force-sensitive resistor triaxial accelerometers classification algorithm sensor verification accuracy computational efficiency

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Research and Application of Digital Media Object classification Method Based on Large Interval Distribution Learning 3

Research and Application of Digital Media Object Classificat...

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

作者： Pu, Li'e Ru, Huasuo Tang, Yufang Yunnan Land and Resources Vocational College Yunnan Kunming650500 China

ISBN: (纸本)9798350319125

Digital media object classification plays an important role in digital media, but there is a problem of low classification accuracy. Deep learning algorithms cannot improve the accuracy of digital media object classification. Therefore, this paper proposes a large-interval distribution learning algorithm to classify digital media objects. Digital media objects are analyzed through distributed learning, and indicators are divided according to classification requirements. The large interval distribution learning algorithm forms the digital media object classification scheme, and the formed classification scheme is comprehensively analyzed. The simulation results show that the classification accuracy and classification efficiency of the proposed algorithm are better than those of the deep learning algorithm. © 2023 IEEE.

关键词： arge interval distribution learning classification algorithm digital media object classification

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A Comparison of classification algorithms Based on The Number of Features 39

A Comparison of Classification Algorithms Based on The Numbe...

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39th Chinese Control Conference (CCC)

作者： Xiong, Peichen Ping, Wei Xian Jiaotong Liverpool Univ Comp Sci & Software Engn Suzhou 215123 Peoples R China Ningbo Univ Coll Sci & Technol Sch Informat Engn Ningbo 315300 Peoples R China

ISBN: (纸本)9789881563903

As the most widely known data milling algorithm, classification algorithms have attracted wide attention. K-Nearest Neighbor (KNN) algorithm and decision tree algorithm are the two widely known algorithms in classification algorithms. Sometimes, people not sure how to choose the suitable to solve the classification problems. In this paper, we establish KNN algorithm model and decision tree ID3 algorithm model to analyze the accuracy of the two algorithms in the same data set with different number of features. Through the learning curve and cross validation, we find ID3 algorithm is better than KNN algorithm, and when the number of feature increased the accuracy of KNN is increasing while ID3 is decreased.

关键词： KNN Decision Tree ID3 classification algorithm

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The Anatomy of Sorting-Evidence From Danish Data

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ECONOMETRICA 2023年第6期91卷 2409-2455页

作者： Lentz, Rasmus Piyapromdee, Suphanit Robin, Jean-Marc Univ Wisconsin Dept Econ Madison WI 53715 USA Univ Aarhus Dale T Mortensen Ctr Aarhus Denmark UCL Dept Econ London England Sci Po Sci Paris France

In this paper, we formulate and estimate a flexible model of job mobility and wages with two-sided heterogeneity. The analysis extends the finite mixture approach of Bonhomme, Lamadon, and Manresa (2019) and Abowd, McKinney, and Schmutte (2019) to develop a new classification Expectation-Maximization algorithm that ensures both worker and firm latent-type identification using wage and mobility variations in the data. Workers receive job offers in worker-type segmented labor markets. Offers are accepted according to a logit form that compares the value of the current job with that of the new job. In combination with flexibly estimated layoff and job finding rates, the analysis quantifies the four different sources of sorting: job preferences, segmentation, layoffs, and job finding. Job preferences are identified through job-to-job moves in a revealed preference argument. They are in the model structurally independent of the identified job wages, possibly as a reflection of the presence of amenities. We find evidence of a strong pecuniary motive in job preferences. While the correlation between preferences and current job wages is positive, the net present value of the future earnings stream given the current job correlates much more strongly with preferences for it. This is more so for short- than long-tenure workers. In the analysis, we distinguish between type sorting and wage sorting. Type sorting is quantified by means of the mutual information index. Wage sorting is captured through correlation between identified wage types. While layoffs are less important than the other channels, we find all channels to contribute substantially to sorting. As workers age, job arrival processes are the key determinant of wage sorting, whereas the role of job preferences dictate type sorting. Over the life cycle, job preferences intensify, type sorting increases, and pecuniary considerations wane.

关键词： Heterogeneity wage distributions employment and job mobility mutual information finite mixtures EM algorithm classification algorithm sorting decomposition of wage inequality

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Method of Physiological State Abnormality Discrimination Based on Improved KNN algorithm 10

Method of Physiological State Abnormality Discrimination Bas...

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10th International Conference on Big Data and Information Analytics

作者： Miao Yongfei Li Xiaolong Wu Shikai Zhao Xiaoqing Yang Yufeng Yu Yue Air Force Commun NCO Acad Dept Ground Air Nav Dalian Peoples R China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638;9798350354621

Physiological state abnormality due to genetic diseases, excessive exercise, etc. is becoming a fatal killer endangering people's life and safety because of its hidden characteristics. K-Nearest Neighbor(KNN) algorithm is widely used in various fields due to its simple implementation, but when the sample capacity is too large or the feature attributes are too many, the classification efficiency decreases significantly. This paper proposed an improved KNN(IKNN) algorithm based on clustering by hierarchically clustering the data in data pre-processing, which reduced the search space of the algorithm and effectively improved the search efficiency. When the improved KNN algorithm was applied in the physiological state abnormality discrimination field, which better improved the efficiency and accuracy of physiological abnormality discrimination. Results show that this could provide an effective guarantee for the early discovery of physiological parameter abnormality symptom, the timely adoption of dispositive measures, and the maintenance of people's life safety.

关键词： Physiological Abnormality classification algorithm Hierarchical Clustering KNN algorithm

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Analysis of Student Performance to Predict Career Specialization using Random Forest Data Mining Technique 24

Analysis of Student Performance to Predict Career Specializa...

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9th International Conference on Intelligent Information Technology (ICIIT)

作者： Hermogenes, Mary Grace G. Repaso, Jennifer Anne A. Perez, Joann G. Bulacan State Univ Malolos Philippines

ISBN: (纸本)9798400716713

Career specialization has emerged as a critical factor for students to attain success in today's highly competitive job market. Therefore, analyzing students' academic performance data to predict their career specialization can be a valuable tool for educational institutions. This study uses Random Forest to forecast graduates' job specialties. The study employed 360 records of IT graduates and the related grades from their technical/major subjects. The conducted tracer research was used to determine the graduates' present occupations. The model made use of 16 attributes in total. An accuracy rate of 87.19% was obtained from the prediction, which is quite good. The collected data also revealed that while students who perform fairly well in most of the major topics may have another relevant vocation, those who achieve very well will likely obtain a job as a developer.

关键词： Career Prediction classification algorithm Learning Analytics Prediction Random Forest

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Brain Microstructural Changes in Patients with Amnestic mild Cognitive Impairment Detected by Neurite Orientation Dispersion and Density Imaging (NODDI) Combined with Machine Learning

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CLINICAL NEURORADIOLOGY 2023年第2期33卷 445-453页

作者： Fu, Xiuwei Wang, Xiaonan Zhang, Yu Li, Tongtong Tan, Zixuan Chen, Yuanyuan Zhang, Xianchang Ni, Hongyan Tianjin Med Univ Gen Hosp Dept Radiol Tianjin Peoples R China Tianjin Med Univ Cent Clin Inst 1 Dept Radiol Tianjin Peoples R China Tianjin Med Univ Inst Med Imaging Tianjin Peoples R China Tianjin Univ Inst Med Engn & Translat Med Tianjin Peoples R China Siemens Healthineers Ltd MR Collaborat Beijing Peoples R China Tianjin First Cent Hosp Dept Radiol 24 Fukang Rd Tianjin 300192 Peoples R China

Purpose This study investigated brain microstructural changes in patients with amnestic mild cognitive impairment (aMCI) by retrospectively analyzing neurite orientation dispersion and density imaging (NODDI) data with machine learning *** A total of 26 aMCI patients and 24 healthy controls (HC) underwent NODDI magnetic resonance imaging (MRI) examinations. The NODDI parameters including neurite density index (NDI), orientation dispersion index (ODI), and volume fraction of isotropic water molecules (Viso) were estimated. Machine learning algorithms such as K-nearest neighbor (KNN), logistic regression (LR), random forest (RF), and support vector machine (SVM) were used to evaluate the diagnostic efficacy of NODDI parameters in predicting aMCI. The differences in the NODDI parameter values between the aMCI and HC groups were analyzed using the independent sample t-test, False discovery rate (FDR) correction was used for multiple testing. After adjusting for age, sex, and educational years, partial correlation analysis was used to evaluate the relationship between NODDI parameters and clinical cognitive status of aMCI *** The NDI, ODI, and Viso values of white matter (WM) and gray matter (GM) structure templates combined with the KNN, LR, RF and SVM machine learning algorithms accomplished the discrimination between aMCI and HC groups. The NDI and ODI values decreased (p value range, < 0.001-0.042) and Viso values increased (p value range, < 0.001-0.043) in the aMCI group compared to the HCs. The NDI, ODI, and Viso values of the WM and GM structure templates with significant differences were significantly correlated with mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA) *** NODDI combined with machine learning algorithms is a promising strategy for early diagnosis of aMCI. Moreover, NODDI parameters correlated with the clinical cognitive status of aMCI patients.

关键词： Cognitive impairment classification algorithm Diffusion weighted imaging Neurite density Orientation dispersion

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