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machine learning algorithm optimization for intelligent prediction of rock thermal conductivity: A case study from a whole-cored scientific drilling borehole

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GEOTHERMICS 2023年第1期111卷

作者： Pang, Yumao Shi, Bingbing Guo, Xingwei Zhang, Xunhua Wen, Yonghang Yang, Guoxin Sun, Xudong Shandong Univ Sci & Technol Shandong Prov Key Lab Deposit Mineralizat & Sedime Qingdao 266590 Peoples R China Qingdao Natl Lab Marine Sci & Technol Lab Marine Mineral Resources Qingdao 266237 Peoples R China Qingdao Inst Marine Geol Qingdao 266071 Peoples R China Shandong Univ Inst Marine Sci & Technol Qingdao 266237 Peoples R China Ocean Univ China Qingdao 266100 Peoples R China

Rock thermal conductivity (TC) is a key parameter in geothermal, petroleum geology, and basin research. Although experimental analysis relying on core samples is currently an effective way to obtain the TC of rocks, it is not always feasible as most boreholes have no or limited cores, making it difficult to establish TC model of geological body efficiently and accurately. This study sheds light on how machine learning algorithms can be used to accurately predict rock TC with easily accessible and high-resolution logging data. Based on 295 measured TC data, logging data, and vertical seismic profile (VSP) data collected from the whole-cored CSDP-2 borehole, the models including random forest (RF), convolutional neural network (CNN), support vector regression (SVR), and particle swarm optimization-SVR (PSO_SVR), were applied to predict the TC of the entire borehole section. Using a confusion matrix analysis, the primary-wave velocity (PWV) from the VSP survey, measured density, and logging data, including shallow lateral resistivity (LLS), compensated neutron log (CNL), density (DEN), gamma rays (GR), spontaneous potential (SP), and acoustic transit time (AC), were used as the input variables for training models and TC prediction. The results showed that the geophysical parameters reflecting those properties related to mineral composition, porosity and reservoir fluids of geological body can be well used to predict TC through machine learning algorithms. Regardless of unconsolidated sediments or rocks, the RF model showed stronger applicability and higher accuracy in TC prediction compared to the PSO_SVR and CNN models, while the SVR model showed poor applicability in this case study. The PWV data can effectively improve the accuracy of TC prediction of all models, and the RF model finally yielded the excellent performance with a correlation coefficient (> 0.86) and root mean squared error (8%) between the predicted and measured values. Future research should focus on

关键词： thermal conductivity machine learning algorithm convolutional neural network support vector regression random forest particle swarm optimization

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Establishment and validation of a risk stratification model for stroke risk within three years in patients with cerebral small vessel disease using a combined MRI and machine learning algorithm

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SLAS TECHNOLOGY 2024年第5期29卷 100177页

作者： Yang, Xiaolong Chang, Hui Yanan Univ Affiliated Hosp Cardiocerebrovasc Dis Hosp Dept Radiol Yanan 716000 Shaanxi Peoples R China Yanan Univ Cardiocerebrovasc Dis Hosp Affiliated Hosp Dept Anesthesiol Operat Room Yanan 716000 Shaanxi Peoples R China

Background: Cerebral small vessel disease (CSVD) is a major cause of stroke, particularly in the elderly population, leading to significant morbidity and mortality. Accurate identification of high-risk patients and timing of stroke occurrence plays a crucial role in patient prevention and treatment. The study aimed to establish and validate a risk stratification model for stroke within three years in patients with CSVD using a combined MRI and machine learning algorithm approach. Methods: The assessment encompassed demographic, clinical, biochemical, and MRI-derived parameters. Correlation analysis, logistic regression, receiver operating characteristic (ROC) curve analysis, and nnet neural network algorithm were employed to evaluate the predictive value of machine learning algorithms and MRI parameters for stroke occurrence within 3 years in patients with CSVD. Results: MRI-derived parameters, including average WMH volume, perfusion deficit volume, ischemic core volume, microbleed count, and perivascular spaces, exhibited strong correlations with stroke occurrence (P < 0.001). MRI-derived parameters demonstrated high sensitivities (0.719 to 0.906), specificities (0.704 to 0.877), and AUC values (0.815 to 0.871). The combined model of machine learning algorithms and MRI parameters yielded an AUC value of 0.925, indicating significantly high predictive accuracy for identifying the risk of stroke within three years in CSVD patients. Conclusion: The integrated risk stratification model, incorporating machine learning algorithms and MRI parameters, demonstrated strong predictive potential for stroke within three years in patients with CSVD. This model offered valuable insights for personalized interventions and clinical decision-making in the management of CSVD.

关键词： Risk stratification model Stroke Cerebral small vessel disease MRI machine learning algorithm

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Application of machine learning algorithm in electronic book database management system

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SN APPLIED SCIENCES 2023年第11期5卷 287页

作者： Zhou, Jianghe Huang, Tangsen Hunan Univ Sci & Engn Lib Yongzhou 425199 Hunan Peoples R China Hunan Univ Sci & Engn Sch Informat Engn Yongzhou 425199 Hunan Peoples R China

Intelligent algorithms have excellent performance in managing large databases and mining effective knowledge, and many researchers have introduced these algorithms into research to improve the performance of book management systems. This article introduced the application of machine learning algorithms in electronic book database management systems. A linear classifier (linear regression algorithm is one of machine learning algorithms) was used to analyze the empirical loss value. This paper studied the application of machine learning algorithm in the database management of library special collection resources, aiming to optimize the management of library special collection resources database through machine learning algorithm, and provided students with better experience of using special collection resources. This paper verified the effectiveness of the machine learning algorithm from three aspects: the retrieval time before and after the special resource database, the number of crashes in a month and the number of library staff. The average time of the five sets using the machine learning algorithm was about 150% faster than the average search time of the five sets of special resource databases. The number of crashes after using machine learning algorithm was 2, 1, 1, 2, 1, which was much lower than that of special resource database. After using the machine learning algorithm, the five groups of workers were 7, 5, 9, 6, and 4, respectively, far fewer than the five groups of special collectors. Finally, by comparing the linear regression model, Support vector machine model, and random forest model, it was found that the accuracy of the linear regression model reached 98.2%, an increase of 8.2% compared to the random forest model, the precision rate reached 96.7%, and the recall rate reached 98.2%. 97.8%, F1 value reached 97.5%.It can be seen from the experimental data that the machine learning algorithm plays a good role in the database management of the library sp

关键词： Library special collection resources Database management machine learning algorithm Linear regression

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Development and validation of a machine learning algorithm-based risk prediction model of pressure injury in the intensive care unit

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INTERNATIONAL WOUND JOURNAL 2022年第7期19卷 1637-1649页

作者： Xu, Jie Chen, Danxiang Deng, Xiaofang Pan, Xiaoyun Chen, Yu Zhuang, Xiaoming Sun, Caixia Wenzhou Med Univ Dept Thorac Surg Affiliated Hosp 1 Wenzhou Peoples R China Wenzhou Med Univ Dept Breast Surg Affiliated Hosp 1 Wenzhou Peoples R China Wenzhou Med Univ Nursing Dept Wenzhou Peoples R China Wenzhou Med Univ Nursing Dept Affiliated Hosp 1 Wenzhou 325000 Zhejiang Peoples R China

The study aimed to establish a machine learning-based scoring nomogram for early recognition of likely pressure injuries in an intensive care unit (ICU) using large-scale clinical data. A retrospective cohort study design was employed to develop and validate a top-performing clinical feature panel accessibly in the electronic medical records (EMRs), which was in the mode of a quantifiable nomogram. Clinical factors regarding demographics, admission cause, clinical laboratory index, medical history and nursing scales were extracted as risk candidates. The performance improvement was based on the application of the machine learning technique, comprising logistic regression, decision tree and random forest algorithm with five-fold cross-validation (CV) technique. The comprehensive assessment of sensitivity, specificity and the area under the receiver operating characteristic curve (AUROC) was considered in the evaluation of predictive performance. The receiver operating characteristic curves revealed the top performance for the logistic regression model in respect to machine learning improvement, achieving the highest sensitivity and AUC among three types of classifiers. Compared against the 23-point Braden scale routinely recorded online, an incorporated nomogram of logistic regression model and Braden scale achieved the best performance with an AUC of 0.87 +/- 0.07 and 0.84 +/- 0.05 in training and test cohort, respectively. Our findings suggest that the machine learning technique potentiated the limited predictive validity of routinely recorded clinical data on pressure injury development during ICU hospitalisation. Easily accessible electronic records held the potentials to substitute the traditional Braden score in the prediction of pressure injury in intensive care unit. Preoperative prediction of pressure injury facilitates the exemption from the severe consequences.

关键词： electronic medical record machine learning algorithm pressure injury risk prediction

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

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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

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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

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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

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Database task processing optimization based on performance evaluation and machine learning algorithm

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SOFT COMPUTING 2023年第10期27卷 6811-6821页

作者： Deng, Aqin Hunan Polytech Environm & Biol Hengyang 421005 Hunan Peoples R China

One of the key components of artificial intelligence algorithms is machine learning, involving a variety of fields, and has been applied in many artificial intelligence systems, including computer vision algorithm, radio network algorithm, medical diagnosis algorithm and intelligent robot system algorithm. In the form of machine learning algorithm, the machine learning module of the algorithm is first used to calculate the consumption, the main performance modules are optimized and improved, and the system data under database optimization are obtained, and selected the optimized structure of the database for calculation, data analysis for the calculation results. Finally, in the design of the database optimization system, the separation of the database system storage engine is studied, and the database optimization form under the data processing structure is proposed. In terms of performance and functionality and reliability, it helps to solve the loss problem caused by big data processing in transmission. In the research of data intensive downward moving calculation process, the optimized solution of data processing is estimated. The results show that using computer terminal sampling comparison to select the executable data processing scheme. The result of this paper shows that it can improve the calculation efficiency of data optimization system query.

关键词： machine learning algorithm Database Task processing Model optimization

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Application of machine learning algorithm and data evaluation in computer network security situation awareness technology

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2024年第4期18卷 2827-2839页

作者： Zhang, Xuxia Chen, Weijie Wang, Jian Fang, Rang Shandong Elect Power Coll State Grid Tech Coll Jinan 250001 Shandong Peoples R China

With the rapid development of information technology and the rapid popularization of the Internet, while people enjoy the convenience and efficiency brought about by new technologies, they are also suffering from the harm caused by cyber attacks. In addition to efficiently thwarting network assaults, a high volume of complicated security event data might unintentionally increase the strain of policy makers. At present, NS threats mainly include network viruses, trojans, DOS (Denial-Of-Service), etc. For the increasingly complex Network Security (NS) problems, the traditional rule-based network monitoring technology is difficult to predict the unknown attack behavior. Environment-based, dynamic and integrated data fusion can integrate data from a macro perspective. In recent years, machine learning (ML) technology has developed rapidly, which could easily train, test and predict existing third-party models. It uses ML algorithms to find out the association between data rather than manually sets rules. Support vector machine is a common ML method, which can predict the security of the network well after training and testing. In order to monitor the overall security status of the entire network, NS situation awareness refers to the real-time and accurate reproduction of network attacks using the reconstruction approach. Situation awareness technology is a powerful network monitoring and security technology, but there are many problems in the existing NS technology. For example, the state of the network cannot be accurately detected, and its change rule cannot be understood. In order to effectively predict network attacks, this paper adopted a technology based on ML and data analysis, and constructed a NS situational awareness model. The results showed that the detection efficiency of the model based on ML and data analysis was 7.18% higher than that of the traditional NS state awareness model.

关键词： Network security machine learning algorithm situation awareness data analysis

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

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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

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The efficacy of machine learning algorithm for raw drug authentication in Coscinium fenestratum (Gaertn.) Colebr. employing a DNA barcode database

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PHYSIOLOGY AND MOLECULAR BIOLOGY OF PLANTS 2021年第3期27卷 605-617页

作者： Unnikrishnan, Remya Sumod, M. Jayaraj, R. Sujanapal, P. Dev, Suma Arun Kerala Forest Res Inst Forest Genet & Biotechnol Div Trichur 680653 Kerala India Kerala Forest Res Inst Sustainable Forest Management Div Trichur 680653 Kerala India Kerala Forest Res Inst Forest Ecol & Biodivers Div Trichur 680653 Kerala India Cochin Univ Sci & Technol Kochi Kerala India

Medicinal plants are a valuable resource for traditional as well as modern medicine. Consequently huge demand has exerted a heavy strain on the existing natural resources. Due to over exploitation and unscientific collection most of the commercially traded ayurvedic plants are in the phase of depletion. Adulteration of expensive raw drugs with inferior taxa has become a common practice to meet the annual demand of the ayurvedic industry. Although there are several recommended methods for proper identification varying from the traditional taxonomic to organoleptic and physiochemical, it is difficult to authenticate ayurvedic raw drugs available in extremely dried, powdered or shredded forms. In this regard, the study addresses proper authentication and illicit trade in Coscinium fenestratum (Gaertn.) Colebr. using CBOL recommended standard barcode regions viz. nuclear ribosomal-Internally Transcribed Spacer (nrDNA- ITS), maturase K (matK), ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit (rbcL), and psbA-trnH spacer regions. Further, an integrated analytical approach employing Maximum Likelihood phylogenetic tree and machine learning Approach, Waikato Environment for Knowledge Analysis was employed to prove efficacy of the method. The automated species identification technique, Artificial Intelligence uses the ability of computers to build models that can receive the input data and then conduct statistical analyses which significantly reduces the human labour. Concurrently, scientific management, restoration, cultivation and conservation measures should be given utmost priority to reduce the depletion of wild resources as well as to meet the rapidly increasing demand of the herbal industries.

关键词： machine learning algorithm Artificial intelligence DNA barcoding Threatened species Medicinal plants

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