Learning Classification Models Research Articles

Application of Deep Learning Classification Model for Regional Evaluation of Roof Pressure Support Evolution Effects Over Time in Coal Mining Face

Hydraulic support leg pressure serves as a crucial indicator for assessing work face support quality. Current evaluation methods for support quality primarily concentrate on static analyses-like inadequate initial support force, pressure overrun, and uneven bracket force-while neglecting dynamic column pressure changes. This paper introduces a model for assessing hydraulic support quality using deep learning techniques. Real-time data is preprocessed into a spatio-temporal pressure sub-matrix sample, which is then inputted into the model. This process assesses the support quality type and characterizes its dynamic evolution within the area. The model facilitates the identification of dynamic support quality effects in the working face area, aiding operators in making targeted adjustments to hydraulic support status. Experimental results revealed that the optimized LeNet-5 network-adjusting parameters like convolutional layer count, kernel size, and ReLU activation function-achieved the highest classification accuracy of 85.25% for support quality, surpassed other networks. Furthermore, the improved LeNet-5 network outperformed other networks in both F1 score and recall. Additionally, the improved LeNet-5 network achieved faster convergence to the optimal solution, accelerated training speed. This highlighted its advantages in evaluating the spatio-temporal support quality of hydraulic supports in smart mining operations.

Data & Semantic Analysis Fake News Detection Based Domain-Specific Auto Encoder Learning

Abstract: Efforts to create technology for automatically detecting fake news are actively underway as the spread of misinformation on social media continues to escalate. However, most of these approaches primarily concentrate on the linguistic and structural aspects of fake news, such as identifying sources or authors, message length, and the frequency of negative language. In contrast, our study introduces a phony news detection model leveraging machine learning that incorporates user behaviors, news, and social capital-based social network dynamics. We used the XG Boost model to evaluate each feature's significance and identify the critical elements affecting the identification of fake news to capture the variables related to the spread of phony news entirely. We implemented SVM, RF, LR, CART, and NNET, well-known machine learning classification models, using these identified factors and assessed how well they detected bogus news. This work used a cross-validation process to prevent overfitting and to generalize the established models. Additionally, the predicted accuracy of the established models was compared. The RF model had the best forecast accuracy, about 94%, while the NNET had the worst performance rate, about 92.1%. As disinformation is created and disseminated with growing sophistication, the results of this study should improve the effectiveness of false news detection systems.

A Study on the Screening of Children at Risk for Developmental Disabilities Using Facial Landmarks Derived From a Mobile-Based Application

Objective Early detection and intervention of developmental disabilities (DDs) are critical to improving the long-term outcomes of afflicted children. In this study, our objective was to utilize facial landmark features from mobile application to distinguish between children with DDs and typically developing (TD) children.Methods The present study recruited 89 children, including 33 diagnosed with DD, and 56 TD children. The aim was to examine the effectiveness of a deep learning classification model using facial video collected from children through mobile-based application. The study participants underwent comprehensive developmental assessments, which included the child completion of the Korean Psychoeducational Profile-Revised and caregiver completing the Korean versions of Vineland Adaptive Behavior Scale, Korean version of the Childhood Autism Rating Scale, Social Responsiveness Scale, and Child Behavior Checklist. We extracted facial landmarks from recorded videos using mobile application and performed DDs classification using long short-term memory with stratified 5-fold cross-validation.Results The classification model shows an average accuracy of 0.88 (range: 0.78–1.00), an average precision of 0.91 (range: 0.75–1.00), and an average F1-score of 0.80 (range: 0.60–1.00). Upon interpreting prediction results using SHapley Additive exPlanations (SHAP), we verified that the most crucial variable was the nodding head angle variable, with a median SHAP score of 2.6. All the top 10 contributing variables exhibited significant differences in distribution between children with DD and TD (p<0.05).Conclusion The results of this study provide evidence that facial landmarks, utilizing readily available mobile-based video data, can be used to detect DD at an early stage.

Neuroimaging Insights: Structural Changes and Classification in Ménière's Disease.

This study aimed to comprehensively investigate the neuroanatomical alterations associated with idiopathic Ménière's disease (MD) using voxel-based morphometry and surface-based morphometry techniques. The primary objective was to explore nuanced changes in gray matter volume, cortical thickness, fractal dimension, gyrification index, and sulcal depth in MD patients compared with healthy controls (HC). Additionally, we sought to develop a machine learning classification model utilizing these neuroimaging features to effectively discriminate between MD patients and HC. A total of 55 patients diagnosed with unilateral MD and 70 HC were enrolled in this study. Voxel-based morphometry and surface-based morphometry were employed to analyze neuroimaging data and identify structural differences between the two groups. The selected neuroimaging features were used to build a machine learning classification model for distinguishing MD patients from HC. Our analysis revealed significant reductions in gray matter volume in MD patients, particularly in frontal and cingulate gyri. Distinctive patterns of alterations in cortical thickness were observed in brain regions associated with emotional processing and sensory integration. Notably, the machine learning classification model achieved an impressive accuracy of 84% in distinguishing MD patients from HC. The model's precision and recall for MD and HC demonstrated robust performance, resulting in balanced F1-scores. Receiver operating characteristic curve analysis further confirmed the discriminative power of the model, supported by an area under the curve value of 0.92. This comprehensive investigation sheds light on the intricate neuroanatomical alterations in MD. The observed gray matter volume reductions and distinct cortical thickness patterns emphasize the disease's impact on neural structure. The high accuracy of our machine learning classification model underscores its diagnostic potential, providing a promising avenue for identifying MD patients. These findings contribute to our understanding of MD's neural underpinnings and offer insights for further research exploring the functional implications of structural changes.

Real-time invasive sea lamprey detection using machine learning classifier models on embedded systems

Real-time invasive sea lamprey detection using machine learning classifier models on embedded systems

Evaluating the Accuracy of Machine Learning Classification Models for Similar Herbal Medicine Using Hyperspectral Imaging

Evaluating the Accuracy of Machine Learning Classification Models for Similar Herbal Medicine Using Hyperspectral Imaging

Efektivitas Model Pembelajaran Problem Based Learning terhadap Hasil Belajar Matematika Kelas I SD Supriyadi 02 Semarang

Quality education must be supported by the selection of appropriate resources and learning models. The aim of this research is to determine the effectiveness of the Problem Based Learning model in improving Mathematics Learning Outcomes for Class I SD Supriyadi 02 Semarang. This research is quantitative research. The research design used was pre-experimental with a one group pretest posttest design system. The population in this study was 26 students with 14 female students and 12 male students. The sampling technique used was a saturated sample. Data collection techniques use tests. Data analysis techniques include normality tests, homogeneity tests and hypothesis tests. The results obtained were that the pretest score obtained an average score of 59.23 with a posttest score of 80.00. Based on the results of the t-test, the sig (2-tailed) value is 0.000 < 0.05, meaning that H0 is rejected and H1 is accepted. The research results show an increase in learning outcomes through the Problem Based Learning learning model in class I.

Ensemble Machine Learning Classification Models for Predicting Pavement Condition

Forecasting pavement performance condition is essential within the pavement management system to optimize decisions with regard to planning maintenance and rehabilitation projects. Accurate forecasts facilitate timely interventions and assist in formulating cost-effective asset management plans. Data-driven machine learning models that utilize historical data to improve forecasting precision have gained attention in the field of asset management. Although numerous studies have employed regression-based models to forecast pavement condition, transportation asset management often operates according to condition index ranges rather than exact values. Therefore, classification models are suitable for predicting pavement condition grades and determining the appropriate maintenance type for pavement assets. This research focuses on developing five machine learning classification models to predict pavement condition: random forest; gradient boost; support vector machine; k-nearest neighbors; and artificial neural network. To enhance prediction performance, these models are integrated using ensemble methods, including voting and stacking. The classification models are developed using a dataset from the Georgia Department of Transportation that documented the condition of asphalt pavements for predefined maintenance sections between 2017 and 2021. A voting ensemble model constructed with the two best-performing individual classification models reached the highest accuracy rate at 83%. Although the performance of individual models fluctuates, ensemble models consistently produce a top-tier performance regardless of the variations in data sampling. Therefore, ensemble methods are recommended for developing pavement condition prediction models to improve accuracy and achieve a more consistent quality of predictions. The findings of this research will provide transportation agencies with information to help them strengthen their forecasting practices in relation to pavement condition, thereby improving their maintenance planning and cost savings.

IMPROVING MATHEMATICS LEARNING OUTCOMES THROUGH THE IMPLEMENTATION OF PROBLEM-BASED LEARNING APPROACH IN SDN 147 PEKANBARU

Students' low interest in learning creates challenges in the learning process, where a lack of motivation can hinder understanding of the material and reduce students' active participation in teaching and learning activities. Therefore, it is essential to identify strategies that can increase students' interest in learning so that they are more involved and enthusiastic in participating in learning. This research aims to overcome the low learning outcomes of the Theme 1 Subtheme "My Daily Tasks at School" by applying the PBL (Problem-Based Learning) learning model in class II at SDN 147 Pekanbaru. The research method used is Classroom Action Research with four main stages: planning, action implementation, observation, and reflection, carried out in 2 cycles, each consisting of 2 meetings. Data collection techniques include observation, tests, documentation, and quantitative and qualitative data processing. The research results show that applying the PBL model has improved student learning outcomes. In the pre-cycle, only eight students, or 31%, achieved completeness, then this increased to 18 students, or the equivalent of 69% in cycle I, and in cycle II, it reached 26 students or 100% completion

MALDI‐TOF MS analysis of nasal swabs for the characterization of patients infected with SARS‐CoV‐2 Omicron

AbstractWith the ongoing mutation of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) leading to various variants, there is an urgent need for new diagnostic methods for SARS‐CoV‐2 infection. The existing nucleic acid test and antigen test suffer from long assay time and low sensitivity, respectively. Matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (MALDI‐TOF MS)‐based nasal swabs analysis have been demonstrated as a promising technique in SARS‐CoV‐2 infection screening. However, the applicability of the technique in the different variants of SARS‐CoV‐2 is uncertain. Given the prevalence of the Omicron variant since 2022, we developed a MALDI‐TOF‐based diagnosis method with nasal swab samples to detect the infection by this variant. We collected 325 SARS‐CoV‐2‐positive and 221 SARS‐CoV‐2‐negative nasal swab samples, and the molecular mass fingerprints were acquired from the samples by MALDI‐TOF MS. Using a random forest machine learning classification model to analyze the molecular mass fingerprints MALDI‐TOF mass spectra, the accuracy of 97%, false negative rate of 0%, and false positive rate of 7.6% were achieved for the diagnosis of SARS‐CoV‐2 infection. Combining the MALDI‐TOF analysis with top‐down proteomics, we identified four potential protein biomarkers, that is, humanin‐like 4, thymosin beta‐10, thymosin beta‐4 and statherin, in the nasal swab for the diagnosis of coronavirus disease 2019. It was further found that the four protein biomarkers can also differentiate the SARS‐CoV‐2 original strains infection and Omicron strains infection. These results suggest that the MALDI‐TOF MS‐based nasal swab analysis holds effective diagnostic capabilities of SARS‐CoV‐2 infection, and shows promising potential for global application and extension to other infectious diseases.

Exploring the Learning Analytics of Skill-based Course using Machine Learning Classification Models

This study explores learning analytics of a skill-based course using various machine learning classification models, including Random Forest, Logistic Regression, CatBoost, Support Vector Classification (SVC), and Naïve Bayes. The objective is to categorize student outcomes into four classes: Pass, Distinction, Withdrawn, and Fail. The research contributes to the growing body of knowledge in learning analytics and machine learning applications in education. The findings from this investigation offer educators and academic institutions a robust framework for early identification of students at risk of underperformance or withdrawal, thereby enabling timely intervention to enhance student success in skill-based courses

From deep learning to the discovery of promising VEGFR-2 inhibitors.

Vascular endothelial growth factor receptor 2 (VEGFR-2) stands as a prominent therapeutic target in oncology, playing a critical role in angiogenesis, tumor growth, and metastasis. FDA-approved VEGFR-2 inhibitors are associated with diverse side effects. Thus, finding novel and more effective inhibitors is of utmost importance. In this study, a deep learning (DL) classification model was first developed and then employed to select putative active VEGFR-2 inhibitors from an in-house chemical library including 187 druglike compounds. A pool of 18 promising candidates was shortlisted and screened against VEGFR-2 by using molecular docking. Finally, two compounds, RHE-334 and EA-11, were prioritized as promising VEGFR-2 inhibitors by employing PLATO, our target fishing and bioactivity prediction platform. Based on this rationale, we prepared RHE-334 and EA-11 and successfully tested their anti-proliferative potential against MCF-7 human breast cancer cells with IC50 values of 26.78±4.02 and 38.73±3.84 µM, respectively. Their toxicities were instead challenged against the WI-38. Interestingly, expression studies indicated that, in the presence of RHE-334, VEGFR-2 was equal to 0.52±0.03, thus comparable to imatinib equal to 0.63±0.03. In conclusion, this workflow based on theoretical and experimental approaches demonstrates effective in identifying VEGFR-2 inhibitors and can be easily adapted to other medicinal chemistry goals.

Classification of e-waste using machine learning-assisted laser-induced breakdown spectroscopy.

Waste management and the economy are intertwined in various ways. Adopting sustainable waste management techniques can contribute to economic growth and resource conservation. Artificial intelligence (AI)-based classification is very crucial for rapid and contactless classification of metals in electronic waste (e-waste) management. In the present research work, five types of aluminium alloys, because of their extensive use in structural, electrical and thermotechnical functions in the electronics industry, were taken. Laser-induced breakdown spectroscopy (LIBS), a spectral identifier technique, was employed in conjunction with machine learning (ML) classification models of AI. Principal component analysis (PCA), an unsupervised ML classifier, was found incapable to differentiate LIBS data of alloys. Supervised ML classifier was then trained (for 10-fold cross-validation) on randomly selected 80% and tested on 20% spectral data of each alloy to assess classification capacity of each. In most of the tested variants of K nearest neighbour (kNN) the resulting accuracy was lower than 30% but kNN ensembled with random subspace method showed improved accuracy up to 98%. This study revealed that an AI-based LIBS system can classify e-waste alloys rather effectively in a non-contactless mode and could potentially be connected with robotic systems, hence, minimizing manual labour.

Design and performance analysis of decision tree learning model for classification of dry and cooked rice samples

Design and performance analysis of decision tree learning model for classification of dry and cooked rice samples

Computer-assisted decision support for the usage of preventive antibacterial therapy in children with febrile pyelonephritis: a preliminary study

Urinary tract infection (UTI) is one of the most common infectious diseases among children, but there is controversy regarding the use of preventive antibiotics for children first diagnosed with febrile pyelonephritis. To the best of our knowledge, no studies have addressed this issue by the deep learning technology. Therefore, in the current study, we conducted a study using Tc99m−DMSA renal static imaging data to investigate the need for preventive antibiotics on children first diagnosed with febrile pyelonephritis under 2 years old. The self-collected dataset comprised 64 children who did not require preventive antibiotic treatments and 112 children who did. Using several classic deep learning models, we verified that it is feasible to screen whether the first diagnosed children with febrile pyelonephritis require preventive antibacterial therapy, achieving a graded diagnosis. With the AlexNet model, we obtained accuracy of 84.05%, sensitivity of 81.71% and specificity of 86.70%, respectively. The experimental results indicate that deep learning technology could provide a new avenue to implement computer-assisted decision support for the diagnosis of the febrile pyelonephritis.

Decoding Consumer Behavior: Predictive Analytics of Social Media Advertising Effectiveness

Decoding Consumer Behavior: Predictive Analytics of Social Media Advertising Effectiveness

Causality Analysis and Prediction of Riverine Algal Blooms by Combining Empirical Dynamic Modeling and Machine Learning Techniques

AbstractRiver algal blooms have become a global environmental problem due to their large impact range and environmental hazards. However, the complex mechanisms underlying these blooms make prediction and prevention challenging. Here, we employed empirical dynamic modeling (EDM) and machine learning to reveal the causes and predict diatom blooms from 2003 to 2017 in the Han River of China. The diatom cell density ranged from 0.1 to 5.1 × 107 cells L−1, whereas algal blooms often lasted for 10 days with density exceeding 107 cells L−1. The EDM results elucidated that, under consistent high nutrient concentrations, algal blooms were primarily regulated by eight environmental factors: water temperature in the Han River; water levels, flow velocities, and streamflow discharges in the Han River and the Yangtze River; and water level variation in the Han River. The poor performance (coefficient of determination R2 < 0) of the multiple linear regression, EDM, and random forest models indicated the challenge of predicting algae density. Therefore, we used machine learning classification models to predict algal blooms occurrences. With the resampling techniques to account for imbalanced data, machine learning models achieved perfect classification prediction (Kappa value = 1) of the 13 algae‐bloom events with a 10‐day lead time during a 15‐year period, providing an important reference for preemptive warning of riverine algal blooms.

Construction of an antidepressant priority list based on functional, environmental, and health risks using an interpretable mixup-transformer deep learning model

As emerging pollutants, antidepressants (AD) must be urgently investigated for risk identification and assessment. This study constructed a comprehensive-effect risk-priority screening system (ADRank) for ADs by characterizing AD functionality, occurrence, persistence, bioaccumulation and toxicity based on the integrated assignment method. A classification model for ADs was constructed using an improved mixup-transformer deep learning method, and its classification accuracy was compared with those of other models. The accuracy of the proposed model improved by up to 23.25% compared with the random forest model, and the reliability was 80% more than that of the TOPSIS method. A priority screening candidate list was proposed to screen 33 high-priority ADs. Finally, SHapley Additive explanation (SHAP) visualization, molecular dynamics, and amino acid analysis were performed to analyze the correlation between AD structure and toxic receptor binding characteristics and reveal the differences in AD risk priority. ADs with more intramolecular hydrogen bonds, higher hydrophobicity, and electronegativity had a more significant risk. Van der Waals and electrostatic interactions were the primary influencing factors, and significant differences in the types and proportions of the main amino acids in the interaction between ADs and receptors were observed. The results of the study provide constructive schemes and insights for AD priority screening and risk management. Environmental implicationThis study established an improved mixup-transformer deep learning classification model based on the functionality, occurrence, persistence, bioaccumulation and toxicity criteria (F-O-PBT criterion) to achieve a comprehensive-effect risk-priority screening for ADs. The classification efficacy was verified by comparison with traditional methods. An AD risk-priority screening candidate list was then generated to screen 33 high-priority ADs. SHapley Additive exPlanation (SHAP) visualization, molecular dynamics, and amino acid analysis were used to analyze the mechanisms of the AD structural and binding characteristics to dominant toxicity receptors and reveal the significant differences in AD risk priority and correlation between physicochemical properties and risk.

Factor annealing decoupling compositional training method for imbalanced hyperspectral image classification

Factor annealing decoupling compositional training method for imbalanced hyperspectral image classification

Machine learning prediction of hospitalization costs for coronary artery bypass grafting operations

BackgroundWith the steady rise in health care expenditures, the examination of factors that may influence the costs of care has garnered much attention. Although machine learning models have previously been applied in health economics, their application within cardiac surgery remains limited. We evaluated several machine learning algorithms to model hospitalization costs for coronary artery bypass grafting. MethodsAll adult hospitalizations for isolated coronary artery bypass grafting were identified in the 2016 to 2020 Nationwide Readmissions Database. Machine learning models were trained to predict expenditures and compared with traditional linear regression. Given the significance of postoperative length of stay, we additionally developed models excluding postoperative length of stay to uncover other drivers of costs. To facilitate comparison, machine learning classification models were also trained to predict patients in the highest decile of costs. Significant factors associated with high cost were identified using SHapley Additive exPlanations beeswarm plots. ResultsAmong 444,740 hospitalizations included for analysis, the median cost of hospitalization in coronary artery bypass grafting patients was $43,103. eXtreme Gradient Boosting most accurately predicted hospitalization costs, with R2 = 0.519 over the validation set. The top predictive features in the eXtreme Gradient Boosting model included elective procedure status, prolonged mechanical ventilation, new-onset respiratory failure or myocardial infarction, and postoperative length of stay. After removing postoperative length of stay, eXtreme Gradient Boosting remained the most accurate model (R2 = 0.38). Prolonged ventilation, respiratory failure, and elective status remained important predictive parameters. ConclusionMachine learning models appear to accurately model total hospitalization costs for coronary artery bypass grafting. Future work is warranted to uncover other drivers of costs and improve the value of care in cardiac surgery.