检索结果-内蒙古大学图书馆

Terahertz spectroscopy and machine learning algorithm for non-destructive evaluation of protein conformation

OPTICAL AND QUANTUM ELECTRONICS 2020年第4期52卷 225-225页

作者： Cao, Can Zhang, Zhaohui Zhao, Xiaoyan Zhang, Tianyao Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100081 Peoples R China Beijing Engn Res Ctr Ind Spectrum Imaging Beijing 100081 Peoples R China

Given the condition that protein conformation and activity are highly susceptible to environment factors such as temperature and pH, evaluation of protein conformation and activity is urgently needed in many fields. For example, most protein drugs need a stable and proper environment during production, storage and transportation, and it's an enormous challenge to maintain protein activity throughout the whole process. Therefore, it's necessary to ensure the safety and effectiveness of protein drugs by monitoring their activity before use. In our study, we presented an improved method for non-destructive evaluation of protein conformation and biological activity by terahertz spectroscopy combined with t-SNE-XGBoost. Firstly, bovine serum albumin (BSA) samples heated to different temperature were measured with THz-TDS. The obtained results indicated that native-conformation BSA will undergo transient states in the process of temperature induced denaturation. However, for any single given sample, it's difficult to identify its conformation and activity directly by using the measured raw terahertz data. Therefore, we applied several different algorithms to the raw data for recognition of BSA samples with different conformation and activity induced by temperature. Finally, the models obtained by different algorithms were evaluated by calculating the root mean standard error of prediction (RMSEP) and the correlation coefficient of prediction (Rp). The THz-TDS plus t-SNE-XGBoost proved to be an effective non-destructive and label-free method for evaluation of protein conformation and activity. It can provide a new technique in many applications, such as pharmaceutical industry, clinical diagnosis and quality control.

关键词： Terahertz time-domain spectroscopy (THz-TDS) Dimension reduction algorithm machine learning algorithm Non-destructive evaluation Protein conformation

来源：评论

学校读者我要写书评

暂无评论

Non-linearities mitigation with fuzzy neural networks using a machine learning algorithm in a CO-OFDM system

引用

IET OPTOELECTRONICS 2020年第1期14卷 44-51页

作者： Kaur, Gurpreet Kaur, Gurmeet Indian Inst Informat Technol ECE Una Himachal Prades India Punjabi Univ ECE Patiala Punjab India

This study presents a fuzzy neural network based non-linear equaliser to diminish the non-linearities in a coherent optical orthogonal frequency division multiplexing (CO-OFDM) system. The numerical results show that the proposed technique based CO-OFDM system outperforms the CO-OFDM system without non-linear equaliser by 5 and 7.5% EVM performance, and 2.36 and 4.87 dB Q-factor performance after 1000 km transmission and -3 dBm input launch power at a bit rate of 40 and 80 Gbps, respectively. Moreover, it has been generally accepted in statistics that the rank-based Wilcoxon methodology provide more robust results in a contradiction of outliers. Therefore, the aim of this study is to analyse fuzzy neural network based non-linear equaliser and compare the results with that of Wilcoxon approach fuzzy neural network based non-linear equaliser.

关键词： OFDM modulation fuzzy neural nets optical fibre communication Q-factor equalisers CO-OFDM system machine learning algorithm fuzzy neural networks coherent optical orthogonal frequency division multiplexing system nonlinear equaliser Q-factor performance input launch power rank-based Wilcoxon methodology Wilcoxon approach fuzzy neural network based non-linear equaliser

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

machine learning algorithm Helps Identify Non-Diagnosed Prodromal Alzheimer's Disease Patients in the General Population

引用

JPAD-JOURNAL OF PREVENTION OF ALZHEIMERS DISEASE 2019年第3期6卷 185-191页

作者： Uspenskaya-Cadoz, O. Alamuri, C. Wang, L. Yang, M. Khinda, S. Nigmatullina, Y. Cao, T. Kayal, N. O'Keefe, M. Rubel, C. IQVIA Cent Nervous Syst Ctr Excellence Med Strate Therapeut Sci & Strategy Unit St Ouen France IQVIA Analyt Ctr Excellence Durham NC USA IQVIA Project Leadership 500 Brook DrGreen Pk Reading RG2 6UU Berks England

BACKGROUND: Recruiting patients for clinical trials of potential therapies for Alzheimer's disease (AD) remains a major challenge, with demand for trial participants at an all-time high. The AD treatment R&D pipeline includes around 112 agents. In the United States alone, 150 clinical trials are seeking 70,000 participants. Most people with early cognitive impairment consult primary care providers, who may lack time, diagnostic skills and awareness of local clinical trials. machine learning and predictive analytics offer promise to boost enrollment by predicting which patients have prodromal AD, and which will go on to develop AD. OBJECTIVES: The authors set out to develop a machine learning predictive model that identifies prodromal AD patients in the general population, to aid early AD detection by primary care physicians and timely referral to expert sites for biomarker confirmation of diagnosis and clinical trial enrollment. DESIGN: The authors use a classification machine learning algorithm to extract patterns within healthcare claims and prescription data three years prior to AD diagnosis/AD drug initiation. SETTING: The study focused on subjects included within proprietary IQVIA US data assets (claims and prescription databases). Patient information was extracted from January 2010 to July 2018, for cohorts aged between 50 and 85 years. PARTICIPANTS: A total of 88,298,289 subjects aged between 50 and 85 years were identified. For the positive cohort, 667,288 subjects were identified who had 24 months of medical history and at least one record with AD or AD treatment. For the negative cohort, 3,670,254 patients were selected who had a similar length of medical history and who were matched to positive cohort subjects based on the prevalence rate. The scoring cohort was selected based on availability of recent medical data of 2-5 years and included 72,670,283 subjects between the ages of 50 and 85 years. INTERVENTION (if any): None. MEASUREMENTS: A list of clinic

关键词： Alzheimer's disease prodromal AD machine learning algorithm AD clinical trial recruitment

来源：评论

学校读者我要写书评

暂无评论

Hourly global solar forecasting models based on a supervised machine learning algorithm and time series principle

引用

INTERNATIONAL JOURNAL OF AMBIENT ENERGY 2020年第1期43卷 1707-1718页

作者： Belaid, Sabrina Mellit, Adel Boualit, Hamid Zaiani, Mohamed Ctr Dev Energies Renouvelables URAER Ghardaia Algeria Jijel Univ Renewable Energy Lab Jijel Algeria AS ICTP Trieste Italy

In this paper, predictive models of hourly global solar radiation (HGSR) at one-hour step ahead have been developed by adopting a new methodology. It consists of the association of a supervised machine learning algorithm named Support Vector machine (SVM) with time series principle. A dataset of HGSR, collected at Ghardaia city in the south of Algeria, has been used. In order to evaluate the accuracy of the developed models, we have used the Kolmogorov-Smirnov Integral (KSI) in addition to the conventional metrics (RMSE, NRMSE, NMBE, MAPE, and R). The results showed that accurate forecasts of HGSR at one-hour step ahead have been obtained by considering only its previous values at two-step forward. The predictive performances of the selected SVM model have been compared with those of some models available in the literature. This work proved the ability of the investigated machine learning algorithm with time series principle in predicting HGSR with good accuracy.

关键词： Solar radiation hourly forecasting time series machine learning algorithm persistent forecast new metrics

来源：评论

学校读者我要写书评

暂无评论

Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals

引用

BMC MEDICAL INFORMATICS AND DECISION MAKING 2020年第1期20卷 276-276页

作者： Burdick, Hoyt Pino, Eduardo Gabel-Comeau, Denise Gu, Carol Roberts, Jonathan Le, Sidney Slote, Joseph Saber, Nicholas Pellegrini, Emily Green-Saxena, Abigail Hoffman, Jana Das, Ritankar Cabell Huntington Hosp Huntington WV USA Marshall Univ Sch Med Huntington WV USA Dascena Inc POB 156572 San Francisco CA 94115 USA

Background Severe sepsis and septic shock are among the leading causes of death in the United States and sepsis remains one of the most expensive conditions to diagnose and treat. Accurate early diagnosis and treatment can reduce the risk of adverse patient outcomes, but the efficacy of traditional rule-based screening methods is limited. The purpose of this study was to develop and validate a machine learning algorithm (MLA) for severe sepsis prediction up to 48 h before onset using a diverse patient dataset. Methods Retrospective analysis was performed on datasets composed of de-identified electronic health records collected between 2001 and 2017, including 510,497 inpatient and emergency encounters from 461 health centers collected between 2001 and 2015, and 20,647 inpatient and emergency encounters collected in 2017 from a community hospital. MLA performance was compared to commonly used disease severity scoring systems and was evaluated at 0, 4, 6, 12, 24, and 48 h prior to severe sepsis onset. Results 270,438 patients were included in analysis. At time of onset, the MLA demonstrated an AUROC of 0.931 (95% CI 0.914, 0.948) and a diagnostic odds ratio (DOR) of 53.105 on a testing dataset, exceeding MEWS (0.725, P < .001;DOR 4.358), SOFA (0.716;P < .001;DOR 3.720), and SIRS (0.655;P < .001;DOR 3.290). For prediction 48 h prior to onset, the MLA achieved an AUROC of 0.827 (95% CI 0.806, 0.848) on a testing dataset. On an external validation dataset, the MLA achieved an AUROC of 0.948 (95% CI 0.942, 0.954) at the time of onset, and 0.752 at 48 h prior to onset. Conclusions The MLA accurately predicts severe sepsis onset up to 48 h in advance using only readily available vital signs extracted from the existing patient electronic health records. Relevant implications for clinical practice include improved patient outcomes from early severe sepsis detection and treatment.

关键词： machine learning algorithm Sepsis prediction Severe sepsis Diagnostic

来源：评论

学校读者我要写书评

暂无评论

Detecting risk of suicide attempts among Chinese medical college students using a machine learning algorithm

引用

JOURNAL OF AFFECTIVE DISORDERS 2020年第0期273卷 18-23页

作者： Shen, Yanmei Zhang, Wenyu Chan, Bella Siu Man Zhang, Yaru Meng, Fanchao Kennon, Elizabeth A. Wu, Hanjing Emily Luo, Xuerong Zhang, Xiangyang Cent South Univ Xiangya Hosp 2 Dept Psychiat Changsha 410011 Hunan Peoples R China Cent South Univ China Natl Clin Res Ctr Mental Disorders Xiangya China Natl Technol Inst Mental DisordersMental H Hunan Technol Inst PsychiatHunan Key Lab Psychia Changsha 410011 Hunan Peoples R China Beijing Jiaotong Univ Beijing Municipal Commiss Educ Key Lab Commun & Informat Syst Sch Elect & Informat Engn Beijing 100044 Peoples R China Univ British Columbia Dept Educ & Counselling Psychol & Special Educ Vancouver BC Canada Univ Texas Hlth Sci Ctr Houston Dept Psychiat & Behav Sci Houston TX 77030 USA

Background Suicide has become one of the most prominent concerns for public health and wellness; however, detecting suicide risk factors among individuals remains a big challenge. The aim of this study was to develop a machine learning algorithm that could effectively and accurately identify the probability of suicide attempts in medical college *** A total of 4,882 medical students were enrolled in this cross-sectional study. Self-report data on socio-demographic and clinical characteristics were collected online via website or through the widely used social media app, WeChat. 5-fold cross validation was used to build a random forest model with 37 suicide attempt predictors. Model performance was measured for sensitivity, specificity, area under the curve (AUC), and accuracy. All analyses were conducted in *** The random forest model achieved good performance [area under the curve (AUC) = 0.9255] in predicting suicide attempts with an accuracy of 90.1% (SD = 0.67%), sensitivity of 73.51% (SD = 2.33%) and specificity of 91.68% (SD = 0.82%).Limitation The participants are primarily females and medical *** This study demonstrates that the random forest model has the potential to predict suicide attempts among medical college students with high accuracy. Our findings suggest that application of the machine learning model may assist in improving the efficiency of suicide prevention.

关键词： Suicide attempts Prediction machine learning algorithm Random forest Anxiety

来源：评论

学校读者我要写书评

暂无评论

Adoption of machine learning algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention

引用

FRONTIERS IN NEUROROBOTICS 2021年 14卷 620378页

作者： Xu, Teng Tang, Lijun Shanghai Jiao Tong Univ Dept Phys Educ Shanghai Peoples R China Shanghai Normal Univ Phys Educ Coll Shanghai Peoples R China

In order to effectively prevent sports injuries caused by collisions in basketball training, realize efficient shooting, and reduce collisions, the machine learning algorithm was applied to intelligent robot for path planning in this study. First of all, combined with the basketball motion trajectory model, the sport recognition in basketball training was analyzed. Second, the mathematical model of the basketball motion trajectory of the shooting motion was established, and the factors affecting the shooting were analyzed. Thirdly, on this basis, the machine learning-based improved Q-learning algorithm was proposed, the path planning of the moving robot was realized, and the obstacle avoidance behavior was accomplished effectively. In the path planning, the principle of fuzzy controller was applied, and the obstacle ultrasonic signals acquired around the robot were taken as input to effectively avoid obstacles. Finally, the robot was able to approach the target point while avoiding obstacles. The results of simulation experiment show that the obstacle avoidance path obtained by the improved Q-learning algorithm is flatter, indicating that the algorithm is more suitable for the obstacle avoidance of the robot. Besides, it only takes about 250 s for the robot to find the obstacle avoidance path to the target state for the first time, which is far lower than the 700 s of the previous original algorithm. As a result, the fuzzy controller applied to the basketball robot can effectively avoid the obstacles in the robot movement process, and the motion trajectory curve obtained is relatively smooth. Therefore, the proposed machine learning algorithm has favorable obstacle avoidance effect when it is applied to path planning in basketball training, and can effectively prevent sports injuries in basketball activities.

关键词： machine learning algorithm intelligent robot basketball training sports injury athlete injury prevention

来源：评论

学校读者我要写书评

暂无评论

An explainable machine learning algorithm for risk factor analysis of in-hospital mortality in sepsis survivors with ICU readmission

引用

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021年 204卷 106040-106040页

作者： Jiang, Zhengyu Bo, Lulong Xu, Zhenhua Song, Yubing Wang, Jiafeng Wen, Pingshan Wan, Xiaojian Yang, Tao Deng, Xiaoming Bian, Jinjun Naval Med Univ Changhai Hosp Fac Anesthesiol 168 Changhai Rd Shanghai 200433 Peoples R China Naval Med Univ Naval Med Ctr Dept Anesthesiol Shanghai 200052 Peoples R China Heal Sci Technol Co Ltd BDA 1606Tower 52 Rong Hua South Rd Beijing 100176 Peoples R China

Background and objective: Patients who survive sepsis in the intensive care unit (ICU) (sepsis survivors) have an increased risk of long-term mortality and ICU readmission. We aim to identify the risk factors for in-hospital mortality in sepsis survivors with later ICU readmission and visualize the quantitative relationship between the individual risk factors and mortality by applying machine learning (ML) algorithm. Methods: Data were obtained from the Medical Information Mart for Intensive Care III (MIMIC-III) database for sepsis and non-sepsis ICU survivors who were later readmitted to the ICU. The data on the first day of ICU readmission and the in-hospital mortality was combined for the ML algorithm modeling and the SHapley Additive exPlanations (SHAP) value of the correlation between the risk factors and the outcome. Results: Among the 2970 enrolled patients, in-hospital mortality during ICU readmission was significantly higher in sepsis survivors ( n = 2228) than nonsepsis survivors ( n = 742) (50.4% versus 30.7%, P < 0.001). The ML algorithm identified 18 features that were associated with a risk of mortality in these groups;among these, BUN, age, weight, and minimum heart rate were shared by both groups, and the remaining mean systolic pressure, urine output, albumin, platelets, lactate, activated partial thromboplastin time (APTT), potassium, pCO2, pO2, respiration rate, Glasgow Coma Scale (GCS) score for eye-opening, anion gap, sex and temperature were specific to previous sepsis survivors. The ML algorithm also calculated the quantitative contribution and noteworthy threshold of each factor to the risk of mortality in sepsis survivors. Conclusion: 14 specific parameters with corresponding thresholds were found to be associated with the in-hospital mortality of sepsis survivors during the ICU readmission. The construction of advanced ML techniques could support the analysis and development of predictive models that can be used to support the decisions and

关键词： Sepsis Sepsis survivor Readmission machine learning algorithm Predictive modeling Critical care

来源：评论

学校读者我要写书评

暂无评论

Selection of a machine learning algorithm for OSHA fatalities

Selection of a machine learning algorithm for OSHA fatalitie...

引用

Annual IEEE Technology and Engineering Management Conference (TEMSCON)

作者： Mohammed, Jaby Mahmud, Md Jubaer Illinois State Univ Dept Technol Normal IL 61761 USA

ISBN: (纸本)9781728142241

Traditional programming has already cleared its path to machine learning. With the abundance of data that is available in different applications and fields, it has grown exponentially in the past years. With the available data, machine learning models can predict the output. The different models that have been used have transformed a wide range of real-world applications. Workplace safety is one of the most important factors that make an organization successful. Every organization has a plan that describes what employees should do in order to prevent injury, but still, accidents happen at the workplace. Accident related data is available with the Occupational Safety and Health Administration (OSHA). There are different machine learning algorithms that are used to predict the required output. In this paper, the authors are investigating different machine learning algorithms and their accuracy to the predicted results.

关键词： machine learning OSHA machine learning algorithm fatalities

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：