This study focuses on investigating the performance of different machine learning algorithms and corresponding comparative analysis in predicting cardiovascular disease. Globally this fatal disease causes a plethora o...
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This study focuses on investigating the performance of different machine learning algorithms and corresponding comparative analysis in predicting cardiovascular disease. Globally this fatal disease causes a plethora of mortality among mankind and so, machine learning algorithms can play a significant role in early detection which will ensure proper treatment for the patients and reduce severity in many cases. The University of California, Irvine (UCI) data repository is utilized for the training and testing of the model. Twelve machine learning algorithms were studied and the performances were observed for default hyperparameter (DHP), grid search cross validation (GSCV) and random search cross validation (RSCV) method. Moreover, computational time were also calculated for both GSCV and RSCV. An accuracy of 92% has been found in both hard and soft voting ensemble classifiers (EVCH and EVCS). However, it observed that Adaboost algorithm outperforms EVCH and EVCS in terms of precision and specificity . Hence, the overall comparative analyses among all the algorithms are carried out extensively where accuracy, precision, sensitivity, specificity, F1 score, and ROC-AUC are brought into action. Jupyter notebook 6.0.3 is utilized for simulation.
ObjectiveThis study aimed to identify osteoporosis-related core genes using bioinformatics analysis and machinelearning *** expression profiles of osteoporosis patients were obtained from the Gene Expression Profiles...
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ObjectiveThis study aimed to identify osteoporosis-related core genes using bioinformatics analysis and machinelearning *** expression profiles of osteoporosis patients were obtained from the Gene Expression Profiles (GEO) database, with GEO35958 and GEO84500 used as training sets, and GEO35957 and GSE56116 as validation sets. Differential gene expression analysis was performed using the R software "limma" package. A weighted gene co-expression network analysis (WGCNA) was conducted to identify key modules and modular genes of osteoporosis. Kyoto Gene and Genome Encyclopedia (KEGG), Gene Ontology (GO), and gene set enrichment analysis (GSEA) were performed on the differentially expressed genes. LASSO, SVM-RFE, and RF machine learning algorithms were used to screen for core genes, which were subsequently validated in the validation set. Predicted microRNAs (miRNAs) from the core genes were also analyzed, and differential miRNAs were validated using quantitative real-time PCR (qPCR) *** total of 1280 differentially expressed genes were identified. A disease key module and 215 module key genes were identified by WGCNA. Three core genes (ADAMTS5, COL10A1, KIAA0040) were screened by machine learning algorithms, and COL10A1 had high diagnostic value for osteoporosis. Four core miRNAs (has-miR-148a-3p, has-miR-195-3p, has-miR-148b-3p, has-miR-4531) were found by intersecting predicted miRNAs with differential miRNAs from the dataset (GSE64433, GSE74209). The qPCR experiments validated that the expression of has-miR-195-3p, has-miR-148b-3p, and has-miR-4531 was significantly increased in osteoporosis *** study demonstrated the utility of bioinformatics analysis and machine learning algorithms in identifying core genes associated with osteoporosis.
New challenges and complexities are continuously increasing in advanced driver assistance systems (ADAS) development (e.g. active safety, driver assistant and autonomous vehicle systems). Therefore, the health managem...
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New challenges and complexities are continuously increasing in advanced driver assistance systems (ADAS) development (e.g. active safety, driver assistant and autonomous vehicle systems). Therefore, the health management of ADAS' components needs special improvements. Since software contribution in ADAS' development is increasing significantly, remote diagnosis and maintenance for ADAS become more important. Furthermore, it is highly recommended to predict the remaining useful life (RUL) for the prognosis of ADAS' safety critical components;e.g. (Ultrasonic, Cameras, Radar, LIDAR). This paper presents a remote diagnosis, maintenance and prognosis (RDMP) framework for ADAS, which can be used during development phase and mainly after production. An overview of RDMP framework's elements is explained to demonstrate how/when this framework is connected to database servers and remote analysis servers. Moreover, Sensors fusion is used in RDMP to detect some sensor failures and even to predict their RUL. Additionally, some well-known machine learning algorithms (MLA) are used to predict RUL of ADAS' components, and different types of input attributes to these MLA are proposed for some basic ADAS' components. MLA use training data set, which shall be constructed ideally from actual records reported remotely by RDMP (Prognosis Analysis and Self-learning System). However, initial dataset before production of the vehicle can be created from ADAS laboratory tests (e.g. Assessments on test tracks), ADAS simulation and theoretical analytical methods. Also, experiments of using the proposed RDMP in some ADAS' components (Sensor fusion and Braking system as ADAS actuator) are presented. Summary, conclusion with proven results and future work are explained.
Urban greenways have been recognized as an important strategy to improve human-scale quality in high-density built environments. Nevertheless, current greenway suitability analysis mainly focuses on geographical and n...
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Urban greenways have been recognized as an important strategy to improve human-scale quality in high-density built environments. Nevertheless, current greenway suitability analysis mainly focuses on geographical and natural issues, failing to account for human-scale urban design factors. Accordingly, this study proposes a data-informed approach to planning urban greenway networks using a combination of classical urban design theories, multi-sourced urban data, and machine learning algorithms. Maoming City in China was used as a case study. Per classical urban design theories, specifically, Cervero and Ewing's 5D variables, density, diversity, design, dimensions of destination accessibility, and distance-to-transit, were selected as key factors. A series of new urban data, including points of interest (PoIs), location-based service (LBS) positioning data, and street view images, were applied in conjunction with machine learning algorithms and geographical information system (GIS) tools to measure these key factors at a human-scale resolution and generate an optimized greenway suitability analysis. This analytical approach is an attempt to take human-scale concerns into account on a city-wide scale regarding greenway network generation. It also pushes the methodological boundaries of greenway planning by combining classical urban design thinking with new urban data and new techniques.
The stock market is a popular investment option for investors because of its expected high returns. Stock market prediction is a complex task to achieve with the help of artificial intelligence. Because stock prices d...
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The growths in e-mobility are the factor that impetus the research in lightweight material with improved mechanical properties, especially strength. The research concentrates on developing a novel metal matrix nano-co...
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The growths in e-mobility are the factor that impetus the research in lightweight material with improved mechanical properties, especially strength. The research concentrates on developing a novel metal matrix nano-composite of high-quality AA8011 aluminium alloy reinforced with varying weight percentages of nano-particles of B4C (0, 0.3, 0.6, 0.9, 1.2 and 1.5 wt%). The high-energy electromagnetic frequency stir casting technique was used to fabricate nano-composite, which enhanced the wetting of matrix and reinforcement. The rupture strength of nano-composite primarily hinges on the structure and formation of nano-composite. The morphology and distribution of nano-particles in metal matrix nano-composites (MMNC) were characterized by using Field Emission Scanning Electron Microscope. The yield strength, yield point, tensile strength, elongation, and reduction of area of MMNC under uniaxial tensile stresses were determined by Universal Testing machine. The machine learning algorithms automatically characterized the fractured material texture in conjunction with the described techniques. The unique textural information extracted from each fractured sample distinguished the surfaces as agglomeration, brittle and ductile texture. The fractography analysis of MMNCs revealed the transition of composite from cup and cone to cleavage fracture, which ensured the property change of matrix by the addition of nano-reinforced particles.
Nonsteroidal anti-inflammatory drugs are the most used anti-inflammatory medicines in the world. Side effects still occur, however, and some inflammatory pathologies lack efficient treatment. Cyclooxygenase and lipoxy...
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Nonsteroidal anti-inflammatory drugs are the most used anti-inflammatory medicines in the world. Side effects still occur, however, and some inflammatory pathologies lack efficient treatment. Cyclooxygenase and lipoxygenase pathways are of utmost importance in inflammatory processes;therefore, novel inhibitors are currently needed for both of them. Dual inhibitors of cyclooxygenase-1 and 5-lipoxygenase are anti-inflammatory drugs with high efficacy and low side effects. In this work, 57 leaf extracts (EtOH-H2O 7:3, v/v) from Asteraceae species with in vitro dual inhibition of cyclooxygenase-1 and 5-lipoxygenase were analyzed by high-performance liquid chromatography-high-resolution-ORBITRAP-mass spectrometry analysis and subjected to in silico studies using machine learning algorithms. The data from all samples were processed by employing differential expression analysis software coupled to the Dictionary of Natural Products for dereplication studies. The 6052 chromatographic peaks (ESI positive and negative modes) of the extracts were selected by a genetic algorithm according to their respective anti-inflammatory properties;after this procedure, 1241 of them remained. A study using a decision tree classifier was carried out, and 11 compounds were determined to be biomarkers due to their anti-inflammatory potential. Finally, a model to predict new biologically active extracts from Asteraceae species using liquid chromatography-mass spectrometry information with no prior knowledge of their biological data was built using a multilayer perceptron (artificial neural networks) with the back-propagation algorithm using the biomarker data. As a result, a new and robust artificial neural network model for predicting the anti-inflammatory activity of natural compounds was obtained, resulting in a high percentage of correct predictions (81%), high precision (100%) for dual inhibition, and low error values (mean absolute error=0.3), as also shown in the validation test. Thus,
The study focuses on the preparation of landslide susceptibility maps in the Kali River valley, Kumaun Himalaya using three machine learning algorithms, namely K-nearest neighbour (KNN), random forest (RF) and extreme...
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The study focuses on the preparation of landslide susceptibility maps in the Kali River valley, Kumaun Himalaya using three machine learning algorithms, namely K-nearest neighbour (KNN), random forest (RF) and extreme gradient boosting (XGB). Fifteen landslide conditioning factors (LCFs) were selected and an inventory of 368 landslides was used for the analysis. Multicollinearity analysis using the variation inflation factor, tolerance and Pearson correlation coefficient (PCC) depicted less to no similarity between all factors. Evaluation of variable importance suggests LCFs such as slope, elevation and distance to thrust contributed significantly and consistently for all three models. Model accuracy was determined and compared using the area under the receiver operating characteristic curve and other statistical signifiers like accuracy, sensitivity, F-measure, accuracy, specificity and recall. The results show that the ensemble algorithms, XGB and RF, yield higher accuracy of approximately 85% compared to the KNN model with 81% accuracy.
The urban watershed of Guwahati is a highly flood-prone region and the fastest growing city situated on the bank of the Brahmaputra River. Therefore, this study aims to the urban flood susceptibility mapping of Guwaha...
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The urban watershed of Guwahati is a highly flood-prone region and the fastest growing city situated on the bank of the Brahmaputra River. Therefore, this study aims to the urban flood susceptibility mapping of Guwahati city using metaheuristic optimization algorithms integrated with random forest (RF) machinelearning algorithm. Further, the receiver operating characteristic (ROC) and multiple error measurements were applied to analyze the performances of the models used. The result showed that about one-third of the area of Guwahati city is under the high and very high flood risk while nearly 50% area comes under low and very low flood risk. The value of the area under curve (AUC) of ROC was above 0.80 for all the integrated models applied. However, the RF-bee colony (BCO) and the RF-based ant colony (ACO) are the two best flood susceptibility models that performed better in the analysis. The methodology adopted in the study is cost and time effective and can be used for the flood susceptibility modeling in other parts of the world. Further, the findings of this study can useful in the flood mitigation and planning process.
The study aimed to describe and test machinelearning (ML)-based algorithms to evaluate the unit price of drinking milk. The algorithms were applied to the data collected over 8 years in 2014 and 2021 related to the p...
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The study aimed to describe and test machinelearning (ML)-based algorithms to evaluate the unit price of drinking milk. The algorithms were applied to the data collected over 8 years in 2014 and 2021 related to the price of drinking milk in Turkey. The economic, social, and environmental factors that have an impact on the unit price of drinking milk were evaluated. Five ML algorithms, including random forest, gradient boosting, support vector machine (SVM), neural network, and AdaBoost algorithms, were utilized to predict the drinking milk unit price. ML also applied hyperparameter tuning with nested cross-validation to calculate the prediction accuracy for each algorithm. The results show that the random forest algorithm based on the features of the ML algorithms has the best performance, with the accuracy of 99.30% for training and 98.10% for testing the dataset. The average accuracy of gradient boosting, SVM, neural network, and AdaBoost are obtained as 97.30%, 96.15%, 95.65%, and 96.05%, respectively. Random forest performed best as the target variable with the lowest deviation values of mean squared error (MSE) (0.004), root mean square error (RMSE) (0.060), and mean absolute error (MAE) (0.029) in the training and MSE (0.009), RMSE (0.096), and MA (0.055) in the testing dataset. This study presents an interesting perspective with practical potential to adopt ML methods in the dairy industry. The developed ML algorithms can provide dairy investors and policymakers with important decision-support information. [EconLit Citations: C13, C53, L66, C88].
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