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International Conference on Intelligent Human-machine Systems and Cybernetics

作者： Fang Miao Pu Zhang Libiao Jin Hongda Wu School of information engineering Communication University of China Beijing China

Text classification is the key technology for mining and organizing text information, which is the process of determining the text types automatically according to the content. Based on machine learning algorithm, text classification system includes four processes, namely text pretreatment, text representation, classifier training and classification. In this paper, a Chinese news text classification system model is designed. And in the classifier training part, we separately chose and compared K-nearest Neighbor, Naive Bayes, and Support Vector machine as our classification algorithm. Then, we tested and analyzed these classifiers with each other and finally got a conclusion. The experimental conclusion shows that the Chinese news text classification system can get satisfied results based on the machine learning algorithm.

关键词： Chinese text classification Chinese word segment Feature extraction machine learning algorithm

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Design and Application of machine learning algorithm Computer in Connect6 of Computer Games System

Design and Application of Machine Learning Algorithm Compute...

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第28届中国控制与决策会议

作者： Gui Wu Jun Tao Educational Administration office Jianghan University School of Mathematics and Computer Science Jianghan University Department of Electrical & Computer Engineering Rowan University

ISBN: (纸本)9781467397155

The paper introduces briefly some of the key structures of the computer games system in *** it mainly provides a further optimization for the current existing computer games system in *** machine learning algorithm can increase the adaptive width adjustment in the MTD（f） algorithm and the self-learning method by TDP algorithm in the evaluation function which could strengthen the "thinking" ability on the computer games system in *** advanced and modified algorithm is proved to be practical and applicative by experimentations and tests of computer games system in connect6 provided in this paper.

关键词： Connect6 machine learning algorithm Evaluation Function Computer Games

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Advanced machine learning algorithm Application for Rotating machine Health Monitoring

Advanced Machine learning Algorithm Application for Rotating...

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International Symposium on Future I&C for Nuclear Power Plants（ISOFIC2014）

作者： Shigeru KANEMOTO Masaya WATANABE Noritaka YUSA Graduate Department of Computer and Information System The University of Aizu Department of Quantum Science and Energy Engineering Tohoku University

The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health *** techniques include support vector machine,deep leaning neural network,*** inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of *** we cannot find remarkable difference of anomaly discrimination performance,some methods give us the very interesting eigen patterns corresponding to normal and abnormal *** results will be useful for future more sensitive and robust anomaly monitoring technology.

关键词： Health Monitoring Rotating machine Anomaly Diagnosis machine learning algorithm

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Research on Gender Recognition of Names Based on machine learning algorithm

Research on Gender Recognition of Names Based on Machine Lea...

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International Conference on Intelligent Human-machine Systems and Cybernetics

作者： Qianjun Shuai Runze Wang Libiao Jin Long Pang School of information engineering Communication University of China Beijing China

The name has a rich connotation. In both English and Chinese, people's names usually have strong distinctions of gender. We can guess that strangers are male or female from their name. And the accuracy is high. This paper mainly makes the study of the gender recognition of Chinese names but it begins from the study of English gender recognition, step by step, looking for the rules, and use a variety of machine learning algorithms to search for a better model.

关键词： Gender recognition of names machine learning algorithm Classification Feature extraction

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Application of machine learning algorithm in the internal and external hazards from industrial byproducts

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CLEANER ENGINEERING AND TECHNOLOGY 2023年 13卷

作者： Oyebisi, Solomon Owamah, Hilary Omeje, Maxwell Covenant Univ Dept Civil Engn PMB 1023Km 10Idiroko Rd Ota Nigeria Delta State Univ Dept Civil & Environm Engn Abraka Nigeria Covenant Univ Dept Phys PMB 1023Km 10Idiroko Rd Ota Nigeria

Natural radioactive substances that are produced because of industrial processes pose a risk to both the envi-ronment and people. An extensive analysis of the radiological properties of industrial byproducts was undertaken in this work, and the risks for both indoor and outdoor environments were assessed based on activity concen-trations. The machine learning technique of artificial neural networks was used with various training algorithms to predict the internal and external hazards from these industrial byproducts. The findings demonstrated that, with the exception of incinerated sewage sludge ash, metakaolin, marble powder, nickel slag, pyrite ash, silica fume, steel slag, and glass waste powder, every industrial byproduct examined poses potential indoor and out-door dangers. All backpropagation training algorithms that were used showed high prediction, according to the neural networks. However, when compared to the Bayesian Regularization and Scaled Conjugate Gradient backpropagation training algorithms, the Levenberg-Marquardt backpropagation technique had the best per-formance indicators for training, validation, and testing. The results can provide reference information for developing a framework for monitoring hazards and their accompanying precise management.

关键词： Cleaner production machine learning algorithm Recycling Responsible consumption and production Waste valorization

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Implications of Diagnosis Through a machine learning algorithm on Management of People With Familial Hypercholesterolemia

JACC: Advances

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JACC: Advances 2024年第9P2期3卷 101184-101184页

作者： Kim, Kain Faruque, Samir C. Lam, Shivani Kulp, David He, Xinwei Sperling, Laurence S. Eapen, Danny J. Department of Medicine Emory School of Medicine Atlanta GA United States Division of General Medicine Washington University School of Medicine St. Louis MO United States Department of Biology Emory University Wayne Rollins Research Center Atlanta GA United States Department of Biostatistics and Bioinformatics Rollins School of Public Health at Emory University Atlanta GA United States Emory Clinical Cardiovascular Research Institute Division of Cardiology Emory University Emory School of Medicine Atlanta GA United States Department of Medicine Emory School of Medicine Atlanta GA United States

Background: Familial hypercholesterolemia (FH) is an underdiagnosed genetic condition that leads to premature cardiovascular disease. Flag, Identify, Network, and Deliver (FIND) FH is a machine learning algorithm (MLA) developed by the Family Heart Foundation that identifies high-risk individuals in the electronic medical record for targeted FH screening. Objectives: The purpose of this study was to characterize the FH diagnostic coding status of patients detected by a MLA screening and assess for correlations with patterns in medical management and cardiovascular outcomes. Methods: We applied the FIND FH MLA to a retrospective, cross-sectional cohort within one large academic medical center. Individual patient charts were manually reviewed and stratified by diagnosis status. Variables including baseline characteristics, medical history, family history, laboratory values, medications, and cardiovascular outcomes were compared across diagnosis status. Results: The MLA identified 471 patients over 5.5 years with a high probability for FH. 121 (26%) previously undiagnosed patients met criteria for having “likely FH.” Those with established FH diagnoses (n = 32) had significantly more lipid panel monitoring, prescriptions for non-statin or combination lipid-lowering agents, visits with a cardiologist, and frequency of coronary artery calcium score (CACS) testing or lipoprotein(a) testing than undiagnosed patients with likely FH. The two groups had no significant differences in having had prior major adverse cardiovascular events. The remaining 318 patients were classified as having “suspected FH.” Conclusions: These findings suggest that implementation of a MLA approach such as FIND FH may be feasible for identifying undiagnosed individuals living with FH, as well as addressing treatment disparities in this population at increased cardiovascular risk. © 2024 The Authors

关键词： electronic health record familial hypercholesterolemia machine learning algorithm

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A prediction model using machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose during cesarean section

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BMC ANESTHESIOLOGY 2021年第1期21卷 116-116页

作者： Wei, Chang-na Wang, Li-ying Chang, Xiang-yang Zhou, Qing-he Jiaxing Univ Affiliated Women & Children Hosp Dept Anesthesia Jiaxing Zhejiang Peoples R China Jiaxing Univ Affiliated Hosp Dept Anesthesia Jiaxing Zhejiang Peoples R China

Background The intrathecal hyperbaric bupivacaine dosage for cesarean section is difficult to predetermine. This study aimed to develop a decision-support model using a machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose based on physical variables during cesarean section. Methods Term parturients presenting for elective cesarean section under spinal anaesthesia were enrolled. Spinal anesthesia was performed at the L3/4 interspace with 0.5% hyperbaric bupivacaine at dosages determined by the anesthesiologist. A spinal spread level between T4-T6 was considered the appropriate block level. We used a machine-learning algorithm to identify relevant parameters. The dataset was split into derivation (80%) and validation (20%) cohorts. A decision-support model was developed for obtaining the regression equation between optimized intrathecal 0.5% hyperbaric bupivacaine volume and physical variables. Results A total of 684 parturients were included, of whom 516 (75.44%) and 168 (24.56%) had block levels between T4 and T6, and less than T6 or higher than T4, respectively. The appropriate block level rate was 75.44%, with the mean bupivacaine volume [1.965, 95%CI (1.945,1.984)]ml. In lasso regression, based on the principle of predicting a reasonable dose of intrathecal bupivacaine with fewer physical variables, the model is "Y=0.5922+ 0.055117* X-1-0.017599*X-2" (Y: bupivacaine volume;X-1: vertebral column length;X-2: abdominal girth), with lambda 0.055, MSE 0.0087, and R-2 0.807. Conclusions After applying a machine-learning algorithm, we developed a decision model with R-2 0.8070 and MSE due to error 0.0087 using abdominal girth and vertebral column length for predicting the optimized intrathecal 0.5% hyperbaric bupivacaine dosage during term cesarean sections.

关键词： machine learning algorithm Bupivacaine dosage Cesarean section Spinal anesthesia Physical variables

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A machine-learning algorithm integrating baseline serum proteomic signatures predicts exercise responsiveness in overweight males with prediabetes

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CELL REPORTS MEDICINE 2023年第2期4卷 100944-100944页

作者： Diaz-Canestro, Candela Chen, Jiarui Liu, Yan Han, Hao Wang, Yao Honore, Eric Lee, Chi-Ho Lam, Karen S. L. Tse, Michael Andrew Xu, Aimin Univ Hong Kong State Key Lab Pharmaceut Biotechnol Hong Kong Peoples R China Univ Hong Kong Dept Med Hong Kong Peoples R China Univ Cote Azur CNRS INSERM Inst Pharmacol Mol & CellulaireLabex ICST Valbonne France Univ Hong Kong Ctr Sports & Exercise Hong Kong Peoples R China Univ Hong Kong Dept Pharmacol & Pharm Hong Kong Peoples R China

The molecular transducers conferring the benefits of chronic exercise in diabetes prevention remain to be comprehensively investigated. Herein, serum proteomic profiling of 688 inflammatory and metabolic biomarkers in 36 medication-naive overweight and obese men with prediabetes reveals hundreds of exer-cise-responsive proteins modulated by 12-week high-intensity interval exercise training, including regula-tors of metabolism, cardiovascular system, inflammation, and apoptosis. Strong associations are found between proteins involved in gas tro-intestinal mucosal immunity and metabolic outcomes. Exercise -induced changes in trefoil factor 2 (TFF2) are associated with changes in insulin resistance and fasting in-sulin, whereas baseline levels of the pancreatic secretory granule membrane major glycoprotein GP2 are related to changes in fasting glucose and glucose tolerance. A hybrid set of 23 proteins including TFF2 are differentially altered in exercise responders and non-responders. Furthermore, a machine-learning al-gorithm integrating baseline proteomic signatures accurately predicts individualized metabolic respon-siveness to exercise training.

关键词： proteomics exercise metabolic outcomes prediabetes insulin resistance personalized medicine machine learning algorithm

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Primary care clinician engagement in implementing a machine-learning algorithm for targeted screening of familial hypercholesterolemia

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AMERICAN JOURNAL OF PREVENTIVE CARDIOLOGY 2024年 19卷 100710页

作者： Kim, Kain Faruque, Samir C. Kulp, David Lam, Shivani Sperling, Laurence S. Eapen, Danny J. Emory Sch Med Atlanta GA 30306 USA Washington Univ Div Gen Med Sch Med St Louis MO 63110 USA Emory Univ Wayne Rollins Res Ctr Dept Biol Atlanta GA 30306 USA Emory Univ Hubert Dept Global Hlth Rollins Sch Publ Hlth Atlanta GA 30306 USA Emory Ctr Heart Dis Prevent Atlanta GA 30306 USA Emory Clin Cardiovasc Res Inst Atlanta GA 30306 USA Emory Sch Med Dept Med Atlanta GA 30306 USA

Objective: To assess the impact of a multi-pronged educational approach on the knowledge, attitudes, and behaviors regarding Familial Hypercholesterolemia (FH) management at a large academic medical center with the aim of empowering primary care clinicians (PCC) to diagnose and treat FH. Methods: A comprehensive educational program for PCCs on FH management was developed and piloted from July 2022 to March 2024. Components of our intervention included: 1. Implementation of a novel clinical decision support tool in the electronic medical record for FH management, 2. Development and dissemination of an interactive educational website focused on FH and its management, 3. Delivery of virtual instructional sessions to increase awareness of the tool, provide education on its use, and obtain support from institutional leadership, and 4. Direct outreach to a pilot subset of PCCs whose patients had been detected using the validated FIND FH (R) (R) machine learning algorithm. Participating clinicians were surveyed at baseline before the intervention and after the educational session. Results: 70 PCC consented to participate in the study with a survey completion rate of 79 % (n n = 55) and 42 % (n n = 23) for the baseline and follow-up surveys, respectively. Objective PCC knowledge scores improved from 40 to 65 % of responders correctly responding to at least 2/3rds of survey questions. Despite the fact that 87 % identified PCC's as most effective for early detection of FH, 100% of PCCs who received direct outreach chose to defer care to an outpatient cardiologist over pursuing workup in the primary care setting. Conclusion: Empowering PCCs in management of FH serves as a key strategy in addressing this underdiagnosed and undertreated potentially life-threatening condition. A systems-based approach to addressing these aims may include leveraging EMR-based clinical decision support models and cross-disciplinary educational partnerships with medical specialists.

关键词： Familial hypercholesterolemia machine learning algorithm Electronic health record

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Simulation of thermal radiation image monitoring based on machine learning algorithms in logistics economic service development

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THERMAL SCIENCE AND ENGINEERING PROGRESS 2025年 57卷

作者： Zhang, Xiaohong Suzhou Polytech Inst Agr Dept Econ & Management Suzhou 215000 Jiangsu Peoples R China

With the rapid development of modern logistics industry, traditional monitoring methods are limited by environmental factors and equipment costs, thermal radiation image monitoring can provide more accurate and realtime monitoring data, the advantages are more obvious. A set of thermal radiation image monitoring system based on machine learning algorithm is developed to simulate the real-time monitoring and management of logistics economy services. In this paper, the real-time image data in the logistics process is collected by thermal radiation imaging equipment, and the collected thermal radiation images are preprocessed. The key features in the thermal radiation images are extracted by machine learning algorithm, and the classifier and detector models are constructed and trained. The trained model is integrated into a monitoring system, and a complete thermal radiation image monitoring solution is developed. The performance of the monitoring system is tested in a simulated logistics environment, and its effect in practical applications is evaluated. The results show that the system can accurately identify and track the key events in the logistics process, and meet the needs of real-time logistics monitoring. This technology can not only provide high-precision real-time monitoring data, but also effectively reduce the operating costs of logistics enterprises and improve logistics efficiency.

关键词： machine learning algorithm Heat radiation Image monitoring Logistics economic services Development simulation

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