Modeling metallurgical responses of coal Tri-Flo separators by a novel BNN: a “Conscious-Lab” development

AbstractTri-flo cyclone, as a dense-medium separation device, is one of the most typical environmentally friendly industrial techniques in the coal washery plants. Surprisingly, no detailed investigation has been conducted to explore the effectiveness of tri-flo cyclone operating parameters on their representative metallurgical responses (yield and recovery). To fill this gap, this work for the first time in the coal processing sector is going to introduce a type of advanced intelligent method (boosted-neural network “BNN”) which is able to linearly and nonlinearly assess multivariable correlations among all variables, rank them based on their effectiveness and model their produced responses. These assessments and modeling were considered a new concept called “Conscious Laboratory (CL)”. CL can markedly decrease the number of laboratory experiments, reduce cost, save time, remove scaling up risks, expand maintaining processes, and significantly improve our knowledge about the modeled system. In this study, a robust monitoring database from the Tabas coal plant was prepared to cover various conditions for building a CL for coal tri-flo separators. Well-known machine learning methods, random forest, and support vector regression were developed to validate BNN outcomes. The comparisons indicated the accuracy and strength of BNN over the examined traditional modeling methods. In a sentence, generating a novel BNN within the CL concept can apply in various energy and coal processing areas, fill gaps in our knowledge about possible interactions, and open a new window for plants' fully automotive process.

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Developing a Novel Method for Estimating the Speed of Sound in Biodiesel Known as Grey Wolf Optimizer Support Vector Machine Algorithm

BioMed Research International ◽

10.1155/2021/5368987 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Zhenzhen Lv ◽

Ming Hu ◽

Yixin Yang ◽

Jeren Makhdoumi

Keyword(s):

Support Vector Machine ◽

Speed Of Sound ◽

Support Vector ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Statistical Parameters ◽

Machine Learning Methods ◽

Robust Model ◽

Novel Method ◽

First Time

In the current study, our goal was to obtain a robust model to predict the speed of sound in biodiesel. For this purpose, an extensive databank has been extracted from previously published papers. Then, a Support Vector Machine (SVM) has been optimized by Grey Wolf Optimization (GWO) method to analyze these data and determine the correlation between speed of sound in biodiesel and its related properties including pressure, temperature, molecular weight, and normal melting point. The results were very satisfactory because the values of statistical parameters R 2 and RMSE were obtained 1 and 1.4024, respectively. Here, this is the first time that the sensitivity analysis is used to estimate this target value. This analysis shows that the pressure widely affects the output values with relevancy factor 87.92. Also, our proposed method is highly accurate than other machine learning methods used in papers employed for this objective.

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An evaluation of machine learning and latent semantic analysis in text sentiment classification

Czasopismo Techniczne ◽

10.37705/techtrans/e2020030 ◽

2020 ◽

pp. 1-11

Author(s):

Justyna Miazga ◽

Tomasz Hachaj

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Semantic Analysis ◽

Support Vector ◽

Data Sets ◽

R Language ◽

Machine Learning Methods ◽

Nearest Neighbours ◽

Artificial Neural Network Ann ◽

First Time

In this paper, we compare the following machine learning methods as classifiers for sentiment analysis: k – nearest neighbours (kNN), artificial neural network (ANN), support vector machine (SVM), random forest. We used a dataset containing 5,000 movie reviews in which 2,500 were marked as positive and 2,500 as negative. We chose 5,189 words which have an influence on sentence sentiment. The dataset was prepared using a term document matrix (TDM) and classical multidimensional scaling (MDS). This is the first time that TDM and MDS have been used to choose the characteristics of text in sentiment analysis. In this case, we decided to examine different indicators of the specific classifier, such as kernel type for SVM and neighbour count in kNN. All calculations were performed in the R language, in the program R Studio v 3.5.2. Our work can be reproduced because all of our data sets and source code are public.

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Identifying Cancer Targets Based on Machine Learning Methods via Chou’s 5-steps Rule and General Pseudo Components

Current Topics in Medicinal Chemistry ◽

10.2174/1568026619666191016155543 ◽

2019 ◽

Vol 19 (25) ◽

pp. 2301-2317 ◽

Cited By ~ 2

Author(s):

Ruirui Liang ◽

Jiayang Xie ◽

Chi Zhang ◽

Mengying Zhang ◽

Hai Huang ◽

...

Keyword(s):

Machine Learning ◽

Growth Rate ◽

Big Data ◽

Human Genome Project ◽

Genome Project ◽

Support Vector ◽

Successful Implementation ◽

Learning Methods ◽

Machine Learning Methods ◽

Vector Machines

In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of ‘big data’ derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.

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Integration of transcriptomic data identifies key hallmark genes in hypertrophic cardiomyopathy

BMC Cardiovascular Disorders ◽

10.1186/s12872-021-02147-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jing Xu ◽

Xiangdong Liu ◽

Qiming Dai

Keyword(s):

Machine Learning ◽

Hypertrophic Cardiomyopathy ◽

Heart Diseases ◽

Expression Patterns ◽

Support Vector ◽

Rna Seq ◽

Ppi Network ◽

Learning Methods ◽

Transcriptomic Data ◽

Machine Learning Methods

Abstract Background Hypertrophic cardiomyopathy (HCM) represents one of the most common inherited heart diseases. To identify key molecules involved in the development of HCM, gene expression patterns of the heart tissue samples in HCM patients from multiple microarray and RNA-seq platforms were investigated. Methods The significant genes were obtained through the intersection of two gene sets, corresponding to the identified differentially expressed genes (DEGs) within the microarray data and within the RNA-Seq data. Those genes were further ranked using minimum-Redundancy Maximum-Relevance feature selection algorithm. Moreover, the genes were assessed by three different machine learning methods for classification, including support vector machines, random forest and k-Nearest Neighbor. Results Outstanding results were achieved by taking exclusively the top eight genes of the ranking into consideration. Since the eight genes were identified as candidate HCM hallmark genes, the interactions between them and known HCM disease genes were explored through the protein–protein interaction (PPI) network. Most candidate HCM hallmark genes were found to have direct or indirect interactions with known HCM diseases genes in the PPI network, particularly the hub genes JAK2 and GADD45A. Conclusions This study highlights the transcriptomic data integration, in combination with machine learning methods, in providing insight into the key hallmark genes in the genetic etiology of HCM.

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Predictive modeling for peri-implantitis by using machine learning techniques

Scientific Reports ◽

10.1038/s41598-021-90642-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Tomoaki Mameno ◽

Masahiro Wada ◽

Kazunori Nozaki ◽

Toshihito Takahashi ◽

Yoshitaka Tsujioka ◽

...

Keyword(s):

Machine Learning ◽

Demographic Data ◽

Risk Indicators ◽

Machine Learning Techniques ◽

Support Vector ◽

Machine Learning Methods ◽

Complex Interactions ◽

Learning Techniques ◽

Increased Risk ◽

Vector Machines

AbstractThe purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 127 without peri-implantitis, from among 1408 implants with at least 4 years in function. Demographic data and parameters known to be risk factors for the development of peri-implantitis were analyzed with three models: logistic regression, support vector machines, and random forests (RF). As the results, RF had the highest performance in predicting the onset of peri-implantitis (AUC: 0.71, accuracy: 0.70, precision: 0.72, recall: 0.66, and f1-score: 0.69). The factor that had the most influence on prediction was implant functional time, followed by oral hygiene. In addition, PCR of more than 50% to 60%, smoking more than 3 cigarettes/day, KMW less than 2 mm, and the presence of less than two occlusal supports tended to be associated with an increased risk of peri-implantitis. Moreover, these risk indicators were not independent and had complex effects on each other. The results of this study suggest that peri-implantitis onset was predicted in 70% of cases, by RF which allows consideration of nonlinear relational data with complex interactions.

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Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain)

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10040199 ◽

2021 ◽

Vol 10 (4) ◽

pp. 199

Author(s):

Francisco M. Bellas Aláez ◽

Jesus M. Torres Palenzuela ◽

Evangelos Spyrakos ◽

Luis González Vilas

Keyword(s):

Machine Learning ◽

Performance Metrics ◽

Prediction Models ◽

Support Vector ◽

False Alarms ◽

Learning Approaches ◽

Learning Methods ◽

Machine Learning Methods ◽

Rías Baixas ◽

New Algorithms

This work presents new prediction models based on recent developments in machine learning methods, such as Random Forest (RF) and AdaBoost, and compares them with more classical approaches, i.e., support vector machines (SVMs) and neural networks (NNs). The models predict Pseudo-nitzschia spp. blooms in the Galician Rias Baixas. This work builds on a previous study by the authors (doi.org/10.1016/j.pocean.2014.03.003) but uses an extended database (from 2002 to 2012) and new algorithms. Our results show that RF and AdaBoost provide better prediction results compared to SVMs and NNs, as they show improved performance metrics and a better balance between sensitivity and specificity. Classical machine learning approaches show higher sensitivities, but at a cost of lower specificity and higher percentages of false alarms (lower precision). These results seem to indicate a greater adaptation of new algorithms (RF and AdaBoost) to unbalanced datasets. Our models could be operationally implemented to establish a short-term prediction system.

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Predicting plaque vulnerability change using intravascular ultrasound + optical coherence tomography image-based fluid–structure interaction models and machine learning methods with patient follow-up data: a feasibility study

BioMedical Engineering OnLine ◽

10.1186/s12938-021-00868-6 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Xiaoya Guo ◽

Akiko Maehara ◽

Mitsuaki Matsumura ◽

Liang Wang ◽

Jie Zheng ◽

...

Keyword(s):

Machine Learning ◽

Coronary Plaque ◽

Support Vector ◽

Single Factor ◽

Plaque Vulnerability ◽

Learning Methods ◽

Machine Learning Methods ◽

Vulnerability Prediction ◽

Biomechanical Factors

Abstract Background Coronary plaque vulnerability prediction is difficult because plaque vulnerability is non-trivial to quantify, clinically available medical image modality is not enough to quantify thin cap thickness, prediction methods with high accuracies still need to be developed, and gold-standard data to validate vulnerability prediction are often not available. Patient follow-up intravascular ultrasound (IVUS), optical coherence tomography (OCT) and angiography data were acquired to construct 3D fluid–structure interaction (FSI) coronary models and four machine-learning methods were compared to identify optimal method to predict future plaque vulnerability. Methods Baseline and 10-month follow-up in vivo IVUS and OCT coronary plaque data were acquired from two arteries of one patient using IRB approved protocols with informed consent obtained. IVUS and OCT-based FSI models were constructed to obtain plaque wall stress/strain and wall shear stress. Forty-five slices were selected as machine learning sample database for vulnerability prediction study. Thirteen key morphological factors from IVUS and OCT images and biomechanical factors from FSI model were extracted from 45 slices at baseline for analysis. Lipid percentage index (LPI), cap thickness index (CTI) and morphological plaque vulnerability index (MPVI) were quantified to measure plaque vulnerability. Four machine learning methods (least square support vector machine, discriminant analysis, random forest and ensemble learning) were employed to predict the changes of three indices using all combinations of 13 factors. A standard fivefold cross-validation procedure was used to evaluate prediction results. Results For LPI change prediction using support vector machine, wall thickness was the optimal single-factor predictor with area under curve (AUC) 0.883 and the AUC of optimal combinational-factor predictor achieved 0.963. For CTI change prediction using discriminant analysis, minimum cap thickness was the optimal single-factor predictor with AUC 0.818 while optimal combinational-factor predictor achieved an AUC 0.836. Using random forest for predicting MPVI change, minimum cap thickness was the optimal single-factor predictor with AUC 0.785 and the AUC of optimal combinational-factor predictor achieved 0.847. Conclusion This feasibility study demonstrated that machine learning methods could be used to accurately predict plaque vulnerability change based on morphological and biomechanical factors from multi-modality image-based FSI models. Large-scale studies are needed to verify our findings.

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Domain Adaptation Using a Three-Way Decision Improves the Identification of Autism Patients from Multisite fMRI Data

Brain Sciences ◽

10.3390/brainsci11050603 ◽

2021 ◽

Vol 11 (5) ◽

pp. 603

Author(s):

Chunlei Shi ◽

Xianwei Xin ◽

Jiacai Zhang

Keyword(s):

Machine Learning ◽

Domain Adaptation ◽

Recognition Accuracy ◽

State Of The Art ◽

Autism Spectrum ◽

Fmri Data ◽

Target Domain ◽

Sample Distribution ◽

Machine Learning Methods ◽

First Time

Machine learning methods are widely used in autism spectrum disorder (ASD) diagnosis. Due to the lack of labelled ASD data, multisite data are often pooled together to expand the sample size. However, the heterogeneity that exists among different sites leads to the degeneration of machine learning models. Herein, the three-way decision theory was introduced into unsupervised domain adaptation in the first time, and applied to optimize the pseudolabel of the target domain/site from functional magnetic resonance imaging (fMRI) features related to ASD patients. The experimental results using multisite fMRI data show that our method not only narrows the gap of the sample distribution among domains but is also superior to the state-of-the-art domain adaptation methods in ASD recognition. Specifically, the ASD recognition accuracy of the proposed method is improved on all the six tasks, by 70.80%, 75.41%, 69.91%, 72.13%, 71.01% and 68.85%, respectively, compared with the existing methods.

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Early warning of citric acid overdose and timely adjustment of regional citrate anticoagulation based on machine learning methods

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01489-8 ◽

2021 ◽

Vol 21 (S2) ◽

Author(s):

Huan Chen ◽

Yingying Ma ◽

Na Hong ◽

Hao Wang ◽

Longxiang Su ◽

...

Keyword(s):

Machine Learning ◽

Citric Acid ◽

Early Warning ◽

Regional Citrate Anticoagulation ◽

Support Vector ◽

Learning Methods ◽

Citrate Anticoagulation ◽

Replacement Fluid ◽

Machine Learning Methods ◽

Neutral Networks

Abstract Background Regional citrate anticoagulation (RCA) is an important local anticoagulation method during bedside continuous renal replacement therapy. To improve patient safety and achieve computer assisted dose monitoring and control, we took intensive care units patients into cohort and aiming at developing a data-driven machine learning model to give early warning of citric acid overdose and provide adjustment suggestions on citrate pumping rate and 10% calcium gluconate input rate for RCA treatment. Methods Patient age, gender, pumped citric acid dose value, 5% NaHCO3 solvent, replacement fluid solvent, body temperature value, and replacement fluid PH value as clinical features, models attempted to classify patients who received regional citrate anticoagulation into correct outcome category. Four models, Adaboost, XGBoost, support vector machine (SVM) and shallow neural network, were compared on the performance of predicting outcomes. Prediction results were evaluated using accuracy, precision, recall and F1-score. Results For classifying patients at the early stages of citric acid treatment, the accuracy of neutral networks model is higher than Adaboost, XGBoost and SVM, the F1-score of shallow neutral networks (90.77%) is overall outperformed than other models (88.40%, 82.17% and 88.96% for Adaboost, XGBoost and SVM). Extended experiment and validation were further conducted using the MIMIC-III database, the F1-scores for shallow neutral networks, Adaboost, XGBoost and SVM are 80.00%, 80.46%, 80.37% and 78.90%, the AUCs are 0.8638, 0.8086, 0.8466 and 0.7919 respectively. Conclusion The results of this study demonstrated the feasibility and performance of machine learning methods for monitoring and adjusting local regional citrate anticoagulation, and further provide decision-making recommendations to clinicians point-of-care.

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Prediction of Hanwoo Cattle Phenotypes from Genotypes Using Machine Learning Methods

Animals ◽

10.3390/ani11072066 ◽

2021 ◽

Vol 11 (7) ◽

pp. 2066

Author(s):

Swati Srivastava ◽

Bryan Irvine Lopez ◽

Himansu Kumar ◽

Myoungjin Jang ◽

Han-Ha Chai ◽

...

Keyword(s):

Machine Learning ◽

Support Vector ◽

Learning Methods ◽

Eye Muscle ◽

Important Species ◽

Machine Learning Methods ◽

Extreme Gradient Boosting ◽

Boosting Method ◽

Predictive Correlation ◽

Hanwoo Cattle

Hanwoo was originally raised for draft purposes, but the increase in local demand for red meat turned that purpose into full-scale meat-type cattle rearing; it is now considered one of the most economically important species and a vital food source for Koreans. The application of genomic selection in Hanwoo breeding programs in recent years was expected to lead to higher genetic progress. However, better statistical methods that can improve the genomic prediction accuracy are required. Hence, this study aimed to compare the predictive performance of three machine learning methods, namely, random forest (RF), extreme gradient boosting method (XGB), and support vector machine (SVM), when predicting the carcass weight (CWT), marbling score (MS), backfat thickness (BFT) and eye muscle area (EMA). Phenotypic and genotypic data (53,866 SNPs) from 7324 commercial Hanwoo cattle that were slaughtered at the age of around 30 months were used. The results showed that the boosting method XGB showed the highest predictive correlation for CWT and MS, followed by GBLUP, SVM, and RF. Meanwhile, the best predictive correlation for BFT and EMA was delivered by GBLUP, followed by SVM, RF, and XGB. Although XGB presented the highest predictive correlations for some traits, we did not find an advantage of XGB or any machine learning methods over GBLUP according to the mean squared error of prediction. Thus, we still recommend the use of GBLUP in the prediction of genomic breeding values for carcass traits in Hanwoo cattle.

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