K-fold Cross Validation Technique Research Articles

Incorporating crumb rubber in slag-based geopolymer: Experimental work and predictive modelling

Crumb rubber (CR), a prevalent hazardous waste material, poses significant environmental challenges that necessitate innovative management strategies. Recent studies have focused on incorporating CR and ground granulated blast furnace slag (GGBS) into geopolymer concrete (GC) to address this challenge. This research presents a novel approach that combines experimental analysis with machine learning (ML) techniques to investigate the chemical effects of these materials on GC. Initially, compressive strength (CS) tests are conducted on rubberized geopolymer (RG) concrete, and the resulting data are further analyzed through ML algorithms to enhance the understanding of material behavior. Despite numerous attempts by researchers to integrate CR into geopolymer-based materials, the challenge of mitigating strength degradation persists. Therefore, it becomes imperative to construct a predictive model that relies on new variables influencing the strength characteristics. This research develops a predictive model to assess concrete compressive strength (CS). Key variables such as CR grade (particle size), incorporation percentage, and oxide ratio were analyzed for their impact on geopolymer strength. Employing ensemble techniques, namely Bagging (BA) and Gradient boosting (GB) with five learners (M5P, random forest (RF), random tree (RT), reduced error pruning tree (REPT), and support vector machine (SVM), the study found that M5P-GB models offered the most accurate CS predictions with the highest accuracy and lowest errors. Moreover, the dataset was checked via the k-fold cross-validation technique and confirmed the developed models' robustness, reliability, and effectiveness. Furthermore, uncertainty analysis revealed that GB-M5P-unpruned models-maintained uncertainty percentages of 14.769 % at 7 days and 11.277 % at 28 days, which was under the 35 % threshold. Additionally, the sensitivity analysis identified the CR grade and replacement percentage by volume of crusher dust (CD) as the most critical factors affecting CS. These findings underscore the importance of considering predictive model development in decision-making processes related to RG concrete properties, providing valuable insights into optimizing the model's robustness and reliability for practical applications. However, the study's reliance on specific input parameters and controlled laboratory conditions may limit the generalizability of the predictive models, necessitating further validation under diverse real-world conditions and variations in CR source and environmental factors.

SVOH: Rigorous Selection Approach for Optimal Hyperparameter Values

The problem we address in this paper is a model selection problem. We consider the k-fold cross-validation (KCV) technique, applied to the Gaussian support vector machine (SVM) classification algorithm. In the cross-vali- dation process, the value of k for the number of subsets is generally chosen and set aprioristically (without any ex- periment). However, the value of k affects the choice of the best compromise between the estimation error and the ap- proximation error of the model. In this way, the k value of the number of subsets can severely influence the optimal values of the SVM classifier's hyperparameters and conse- quently affect the performance of the selected model and its ability to generalize. In this work, we propose a rigorous approach for finding the values of the hyperparameters of the Gaussian SVM known as SVOH (Selection of Optimal Hyperparam- eter Values) in a context of protein-protein interaction (PPI) prediction, where it is necessary to classify the pairs of pro- teins that interact together and those that do not interact together. The proposed approach considers the k value of the number of subsets as an influential parameter of the model and therefore performs learning to find an optimal value of k.

Helicobacter Pylori Infection as the Predominant High-Risk Factor for Gastric Cancer Recurrence Post-Gastrectomy: An 8-Year Multicenter Retrospective Study.

The reappearance of gastric cancer, a frequent postoperative complication following radical gastric cancer surgery, substantially impacts the near-term and far-reaching medical outlook of patients. The objective of this research was to create a machine learning algorithm that could recognize high-risk factors for gastric cancer recurrence and anticipate the correlation between gastric cancer recurrence and Helicobacter pylori (H. pylori) infection. This investigation comprised 1234 patients diagnosed with gastric cancer, and 37 characteristic variables were obtained. Four machine learning algorithms, namely, extreme gradient boosting (XGBoost), random forest (RF), k-nearest neighbor algorithm (KNN), and multilayer perceptron (MLP), were implemented to develop the models. The k-fold cross-validation technique was utilized to perform internal validation of the four models, while independent datasets were employed for external validation of the models. In contrast to the other machine learning models, the XGBoost algorithm demonstrated superior predictive ability regarding high-risk factors for gastric cancer recurrence. The outcomes of Shapley additive explanation (SHAP) analysis revealed that tumor invasion depth, tumor lymph node metastasis, H. pylori infection, postoperative carcinoembryonic antigen (CEA), tumor size, and tumor number were risk elements for gastric cancer recurrence in patients, with H. pylori infection being the primary high-risk factor. Out of the four machine learning models, the XGBoost algorithm exhibited superior performance in predicting the recurrence of gastric cancer. In addition, machine learning models can help clinicians identify key prognostic factors that are clinically meaningful for the application of personalized patient monitoring and immunotherapy.

Improving the short-term prediction of dissolved carbon monoxide using a combination of Light GBM and meta-heuristic algorithms

In this study, the prediction of carbon monoxide pollutants on a short-term scale has been investigated according to some input data sources, comprising gas concentrations related to air quality and weather features. Utilizing a hybrid modeling approach that integrates the Light Gradient Boosting Machine with several meta-heuristic optimization algorithms such as Chaos Game Optimization, Aquila Optimizer, and others, we aimed to optimize the hyperparameters of the Light GBM to enhance predictive accuracy. The application of a K-fold cross-validation technique with K=5 helped in preventing overfitting. By conducting a case study on a real dataset collected from a gas multi-sensor device, it was found that the hybrid model combining the Light Gradient Boosting Machine with Chaos Game Optimization demonstrated superior performance compared to other models. The values of the coefficient of determination, Root Mean Square Error, Mean Absolute Error, and Mean Absolute Percentage Error for this model based on test data are 0.99, 0.0393, 0.0301, and 0.0052, respectively. These results underscore the effectiveness of the hybridization approach in providing highly accurate predictions for short-term carbon monoxide concentrations, offering a valuable tool for environmental monitoring and enhancing public health safeguards.

Comparison of the impact of dimensionality reduction and data splitting on classification performance in credit risk assessment

Credit risk assessment (CRA) plays an important role in credit decision-making process of financial institutions. Today, developing big data analysis and machine learning methods have marked a new era in credit risk estimation. In recent years, using machine learning methods in credit risk estimation has emerged as an alternative method for financial institutions. The past demographic and financial data of the person whose CRA will be performed is important for creating an automatic artificial intelligence credit score prediction model based on machine learning. It is also necessary to use features correctly to create accurate machine learning models. This article aims to investigate the effects of dimensionality reduction and data splitting steps on the performance of classification algorithms widely used in the literature. In our study, dimensionality reduction was performed using Principal Component Analysis (PCA). Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Naive Bayes (NB) algorithms were chosen for classification. Percentage splitting (PER, 66–34%) and k-fold (k = 10) cross-validation techniques were used when dividing the data set into training and test data. The results obtained were evaluated with accuracy, recall, F1 score, precision, and AUC metrics. German data set was used in this study. The effect of data splitting and dimension reduction on the classification of CRA systems was examined. The highest ACC in PER and CV data splitting was obtained with the RF algorithm. Using data splitting methods and PCA, the highest accuracy was observed with RF and the highest AUC with NB, with 13 PCs in which 80% of the variance was obtained. As a result, the data set consisting of a total of 20 features, expressed by 13 PCs, achieved similar or higher success than the results obtained from the original data set.

K-Nearest Neighbor Method for Early Detection of Diabetes Patients Based on Symptoms and Clinical Data

Diabetes is a chronic disease rarely detected and develops quickly. Diabetes can trigger other chronic diseases such as kidney failure and heart disease. Early detection is necessary to help patients treat diabetes before the disease becomes more severe. Various health examination methods to detect diabetes, but these examinations require medical expert action and cannot be carried out by anyone. In addition, examination costs are often unaffordable. This research aims to apply data mining methods, especially k-Nearest Neighbor (KNN), for early detection of diabetes patients based on disease symptoms and patient clinical data. KNN is used to classify patient symptoms and clinical data into two classes, diabetes and non-diabetes, calculating the distance between test data and training data using Euclidean Distance. The research results show that a lower k-value provides a higher accuracy value. However, accuracy at low k-values ​​is insufficient to conclude the performance of KNN for early diabetes detection. High accuracy at low k-values ​​has the potential for overfitting, and the model is not generalizing well. Apart from that, if you use a low k-value, the model only sees patterns from 1 or a few neighbors, which results in the pattern of the data not being captured by the KNN model using a k-value that is too high also risks the model becoming underfitting. The model is too general, which makes the model unreliable. This research made use of the k-fold cross-validation technique to circumvent these issues. It is possible to avoid overfitting in the constructed KNN model by employing this method. The researchers are employing k-fold=10 and k-fold=20 in their investigation. KNN This research carried out this analysis by looking at the accuracy of each iteration of the k and k-fold values. The higher the k-fold value, the more accuracy the KNN produces. Inversely proportional to the k-fold cross-validation value, the higher the k-value in KNN, the decreases the accuracy. The KNN method applied in this research provides an accuracy of 98.2692% with higher precision than recall. These findings suggest that KNN can be an effective and efficient tool for early diabetes detection.

DCNN for Pig Vocalization and Non-Vocalization Classification: Evaluate Model Robustness with New Data.

Since pig vocalization is an important indicator of monitoring pig conditions, pig vocalization detection and recognition using deep learning play a crucial role in the management and welfare of modern pig livestock farming. However, collecting pig sound data for deep learning model training takes time and effort. Acknowledging the challenges of collecting pig sound data for model training, this study introduces a deep convolutional neural network (DCNN) architecture for pig vocalization and non-vocalization classification with a real pig farm dataset. Various audio feature extraction methods were evaluated individually to compare the performance differences, including Mel-frequency cepstral coefficients (MFCC), Mel-spectrogram, Chroma, and Tonnetz. This study proposes a novel feature extraction method called Mixed-MMCT to improve the classification accuracy by integrating MFCC, Mel-spectrogram, Chroma, and Tonnetz features. These feature extraction methods were applied to extract relevant features from the pig sound dataset for input into a deep learning network. For the experiment, three datasets were collected from three actual pig farms: Nias, Gimje, and Jeongeup. Each dataset consists of 4000 WAV files (2000 pig vocalization and 2000 pig non-vocalization) with a duration of three seconds. Various audio data augmentation techniques are utilized in the training set to improve the model performance and generalization, including pitch-shifting, time-shifting, time-stretching, and background-noising. In this study, the performance of the predictive deep learning model was assessed using the k-fold cross-validation (k = 5) technique on each dataset. By conducting rigorous experiments, Mixed-MMCT showed superior accuracy on Nias, Gimje, and Jeongeup, with rates of 99.50%, 99.56%, and 99.67%, respectively. Robustness experiments were performed to prove the effectiveness of the model by using two farm datasets as a training set and a farm as a testing set. The average performance of the Mixed-MMCT in terms of accuracy, precision, recall, and F1-score reached rates of 95.67%, 96.25%, 95.68%, and 95.96%, respectively. All results demonstrate that the proposed Mixed-MMCT feature extraction method outperforms other methods regarding pig vocalization and non-vocalization classification in real pig livestock farming.

Hybrid machine learning modeling of nitrogen removal from wastewater using gas-liquid-solid circulating fluidized bed riser

Municipal wastewater generally contains high amounts of nitrogen (N), approximately 23–28 mg/L of total Kjeldahl Nitrogen (TKN), which causes eutrophication and pollution of groundwater. This article reports the generation of empirical models for estimating the nitrogen removal efficacy of two outputs (NH4-N and NO3-N) from the anoxic riser of a highly efficient municipal wastewater treatment system involving a gas-liquid-solid circulating fluidized bed (GLSCFB) bioreactor. To identify the important variables and discard irrelevant ones, the ReliefF algorithm is applied initially. Out of 10 independent variables, this method indicates that only three, such as flow rate, total Kjeldahl Nitrogen (TKN), and downer effluent of NO3-N, are meaningful for anoxic nitrogen removal. Then, a hybrid Bayesian Optimization Algorithm and Support Vector Regression (BOA-SVR) is implemented, considering only the essential variables. In this regard, real-life laboratory data are utilized to develop the models. The BOA approach and k-fold cross-validation technique are explicitly applied to optimize the model's hyperparameters and avoid overfitting. The established models provided sufficiently accurate predictions via their comparisons to the experimental observations. Besides, the BOA-SVR model outperforms the classical multiple linear regression (MLR). The BOA-SVR models' forecasted results for both the predicted amount of NH4-N and NO3-N are favorably compared with the laboratory trials (R2> 99 %). A comprehensive evaluation is further conducted to validate the performance of this model by calculating the root mean square error, mean absolute percentage error, fractional bias, and computational efficiency. Gaussian white noise is used to add three distinct noise levels (20 %, 40 %, and 60 %) to the testing data to evaluate the model's robustness. Moreover, the plots of relative deviations and residuals were dispersed across the zero-reference line with moderate fluctuations. Sensitivity analysis indicated the relative importance of the three independent variables on the anoxic nitrogen removal in the order of influent TKN > downer effluent NO3-N >flowrate. Overall, the developed tool has substantial potential in modeling other convoluted processes.

Comparative Analysis of Hybrid Model Performance Using Stacking and Blending Techniques for Student Drop Out Prediction In MOOC

Despite being in high demand as a lifelong learner and academic material supplement, the implementation of Massive Open Online Courses (MOOC) has problems, one of which is the dropout rate (DO) of students, which reaches 93%. As one of the solutions to this problem, machine learning can be utilized as a risk management and early warning system for students who have the potential to drop out. The use of ensemble techniques to build models can improve performance, but previous research has not reviewed the most optimal ensemble technique for this case study. As a form of contribution, this study will compare the performance of models built from stacking and blending techniques. The algorithms used in the base model are KNN, Decision Tree, and Naïve Bayes, while the meta-model uses XGBoost. These algorithms are used to build models with stacking and mixing techniques. The experimental results using stacking are 82.53% accuracy, 84.48% precision, 94.12% recall, and 89.04% F1 score. Meanwhile, the blend obtained 83.39% precision, 85.31% precision, 94.21% recall, and 89.54% F1-Score. These results are supported by model testing using k-fold cross-validation and confusion matrix techniques, which show the same results. That is, blending is 0.86% higher than stacking, so it can be concluded that blending performs better than stacking in the MOOC student dropout prediction case study.

A proposed hybrid model of ANN and KNN for solar cell defects detection and temperature prediction using fuzzy image segmentation

This paper presents a novel hybrid model employing Artificial Neural Networks (ANN) and Mathematical Morphology (MM) for the effective detection of defects in solar cells. Focusing on issues such as broken corners and black edges caused by environmental factors like broken glass cover, dust, and temperature variations. This study utilizes a hybrid model of ANN and K-Nearest Neighbor (KNN) for temperature prediction. This hybrid approach leverages the strengths of both models, potentially opening up new avenues for improved accuracy in temperature forecasting, which is critical for solar energy applications. The significance lies in the interconnectedness of temperature fluctuations and solar cell efficiency, leading to defects. The proposed model aims to predict temperatures accurately, providing insights into potential solar cell efficiency problems. Subsequently, this work studies the transitions to defect detection using Fuzzy C-Means (FCM) clustering and MM techniques. The hybrid model demonstrates accurate temperature prediction with Mean Absolute Percentage Error (MAPE) values of 0.92 %, 0.72 %, and 1.3 % for average, maximum, and minimum temperatures, respectively. The defect detection process yields a detection accuracy (CR) of 96 % and sensitivity of detection (SD) of 89 %. This work is validated compared to the literature work done and by using K-fold cross validation technique. The proposed work emphasizes the improvement in defect detection accuracy and the overall quality enhancement of solar cells.

Using machine learning algorithms and techniques for defining the impact of affective temperament types, content search and activities on the internet on the development of problematic internet use in adolescents' population.

By using algorithms and Machine Learning - ML techniques, the aim of this research was to determine the impact of the following factors on the development of Problematic Internet Use (PIU): sociodemographic factors, the intensity of using the Internet, different contents accessed on the Internet by adolescents, adolescents' online activities, life habits and different affective temperament types. Sample included 2,113 adolescents. The following instruments were used: questionnaire about: socio-demographic characteristics, intensity of the Internet use, content categories and online activities on the Internet; Facebook (FB) usage and life habits; The Internet Use Disorder Scale (IUDS). Based on their scores on the scale, subjects were divided into two groups - with or without PIU; Temperament Evaluation of Memphis, Pisa, Paris, and San Diego scale for adolescents (A-TEMPS-A). Various ML classification models on our data set were trained. Binary classification models were created (class-label attribute was PIU value). Models hyperparameters were optimized using grid search method and models were validated using k-fold cross-validation technique. Random forest was the model with the best overall results and the time spent on FB and the cyclothymic temperament were variables of highest importance for these model. We also applied the ML techniques Lasso and ElasticNet. The three most important variables for the development of PIU with both techniques were: cyclothymic temperament, the longer use of the Internet and the desire to use the Internet more than at present time. Group of variables having a protective effect (regarding the prevention of the development of PIU) was found with both techniques. The three most important were: achievement, search for contents related to art and culture and hyperthymic temperament. Next, 34 important variables that explain 0.76% of variance were detected using the genetic algorithms. Finally, the binary classification model (with or without PIU) with the best characteristics was trained using artificial neural network. Variables related to the temporal determinants of Internet usage, cyclothymic temperament, the desire for increased Internet usage, anxious and irritable temperament, on line gaming, pornography, and some variables related to FB usage consistently appear as important variables for the development of PIU.

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization.

The domain of human locomotion identification through smartphone sensors is witnessing rapid expansion within the realm of research. This domain boasts significant potential across various sectors, including healthcare, sports, security systems, home automation, and real-time location tracking. Despite the considerable volume of existing research, the greater portion of it has primarily concentrated on locomotion activities. Comparatively less emphasis has been placed on the recognition of human localization patterns. In the current study, we introduce a system by facilitating the recognition of both human physical and location-based patterns. This system utilizes the capabilities of smartphone sensors to achieve its objectives. Our goal is to develop a system that can accurately identify different human physical and localization activities, such as walking, running, jumping, indoor, and outdoor activities. To achieve this, we perform preprocessing on the raw sensor data using a Butterworth filter for inertial sensors and a Median Filter for Global Positioning System (GPS) and then applying Hamming windowing techniques to segment the filtered data. We then extract features from the raw inertial and GPS sensors and select relevant features using the variance threshold feature selection method. The extrasensory dataset exhibits an imbalanced number of samples for certain activities. To address this issue, the permutation-based data augmentation technique is employed. The augmented features are optimized using the Yeo-Johnson power transformation algorithm before being sent to a multi-layer perceptron for classification. We evaluate our system using the K-fold cross-validation technique. The datasets used in this study are the Extrasensory and Sussex Huawei Locomotion (SHL), which contain both physical and localization activities. Our experiments demonstrate that our system achieves high accuracy with 96% and 94% over Extrasensory and SHL in physical activities and 94% and 91% over Extrasensory and SHL in the location-based activities, outperforming previous state-of-the-art methods in recognizing both types of activities.

Workforce forecasting for state transportation agencies: A machine learning approach

A decline in the number of construction engineers and inspectors at State Transportation Agencies (STAs) to manage the ever-increasing lane miles has emphasized the importance of workforce planning in these agencies. Forecasting workforce requirements is crucial for effective planning in any industry or agency. This study developed machine learning models to estimate the person-hour requirements of STAs at the project level. The Arkansas Department of Transportation (ARDOT) was used as a case study, using its employee and project details data between 2012 and 2021. Machine learning regression models ranging from linear, tree ensembles, kernel-based, and neural network-based models were developed. These models were compared based on the accuracy of their predictions, the time taken for training the models and their prediction time. Predictions were tested based on the K-fold cross validation technique. The results indicated a high performance from the random forest regression model, a tree ensemble with bagging, which recorded a mean R-squared value of 0.91. Other machine learning models such as an ensemble neural networks model and the linear models also proved to be fit for the problem, attaining R squared value as high as 0.80 and 0.78 respectively. These findings underscore the capability of machine learning models to provide more accurate workforce demand forecasts for STAs and the construction industry. This enhanced accuracy in workforce planning will contribute to improved resource allocation and management.

Industrial Fault Detection Employing Meta Ensemble Model Based on Contact Sensor Ultrasonic Signal.

Ultrasonic diagnostics is the earliest way to predict industrial faults. Usually, a contact microphone is employed for detection, but the recording will be contaminated with noise. In this paper, a dataset that contains 10 main faults of pipelines and motors is analyzed from which 30 different features in the time and frequency domains are extracted. Afterward, for dimensionality reduction, principal component analysis (PCA), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) are performed. In the subsequent phase, recursive feature elimination (RFE) is employed as a strategic method to analyze and select the most relevant features for the classifiers. Next, predictive models consisting of k-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM) are employed. Then, in order to solve the classification problem, a stacking classifier based on a meta-classifier which combines multiple classification models is introduced. Furthermore, the k-fold cross-validation technique is employed to assess the effectiveness of the model in handling new data for the evaluation of experimental results in ultrasonic fault detection. With the proposed method, the accuracy is around 5% higher over five cross folds with the least amount of variation. The timing evaluation of the meta model on the 64 MHz Cortex M4 microcontroller unit (MCU) revealed an execution time of 11 ms, indicating it could be a promising solution for real-time monitoring.

An ensemble-based stage-prediction machine learning approach for classifying fetal disease

Fetal diseases often lead to the death of many babies during pregnancies. Machine learning and deep learning are promising technologies providing efficient and effective detection and treatment of various fetal diseases. We contribute to the medical field by addressing the critical challenge of fetal disease classification, a concern affecting females and infants. This study utilizes 22 features associated with fetal heart rate extracted from 2126 patient records within the Cardiotocography(CTG) datasets. Our classification system offers a cost-effective, efficient, and accurate solution. It classifies fetal diseases into three categories: Normal, Suspect, and Pathological, based on preprocessed data that underwent MinMax Scaling and employed dimensionality reduction techniques, including Principal Component Analysis(PCA) and Autoencoders. By incorporating dimensionality reduction techniques, the computation time has been reduced from 9 to 26 s to just 4 and 15 s, which is less than half of the original computation time. We assessed the performance of 11 standard machine learning algorithms and various performance metrics to identify the best classification model. We have applied the K-fold Cross-Validation technique to validate our model to improve machine learning models and identify the most effective algorithm. When the results are compared, it is observed that Extreme Gradient Boosting (XGBoost) gained the highest accuracy of 0.99% also highest precision 0.93% and outperformed all the other machine learning algorithms.

SlumberNet: deep learning classification of sleep stages using residual neural networks

Sleep research is fundamental to understanding health and well-being, as proper sleep is essential for maintaining optimal physiological function. Here we present SlumberNet, a novel deep learning model based on residual network (ResNet) architecture, designed to classify sleep states in mice using electroencephalogram (EEG) and electromyogram (EMG) signals. Our model was trained and tested on data from mice undergoing baseline sleep, sleep deprivation, and recovery sleep, enabling it to handle a wide range of sleep conditions. Employing k-fold cross-validation and data augmentation techniques, SlumberNet achieved high levels of overall performance (accuracy = 97%; F1 score = 96%) in predicting sleep stages and showed robust performance even with a small and diverse training dataset. Comparison of SlumberNet's performance to manual sleep stage classification revealed a significant reduction in analysis time (~ 50 × faster), without sacrificing accuracy. Our study showcases the potential of deep learning to facilitate sleep research by providing a more efficient, accurate, and scalable method for sleep stage classification. Our work with SlumberNet further demonstrates the power of deep learning in mouse sleep research.

Development of Machine Learning Models for Studying the Premixed Turbulent Combustion of Gas-To-Liquids (GTL) Fuel Blends

AbstractStudying the spatial and temporal evolution in turbulent flames represents one of the most challenging problems in the combustion community. Based on previous 3D numerical analyses, this study aims to develop data-driven machine learning (ML) models for predicting the flame radius evolution and turbulent flame speeds for diesel, gas-to-liquids (GTL), and their 50/50 blend (by volumetric composition) under different thermodynamic and turbulence operating conditions. Two ML models were developed in this study. Model 1 predicts the variations of the flame radius with time, equivalence ratio, and turbulence intensity, whereas model 2 predicts the variations of the turbulence flame speed with the operating parameters. The k-fold cross-validation technique is used for model training, and the developed neural network-based model is used to investigate the effects of operating parameters on the premixed turbulent flames. In addition, the possible minimum and maximum values of responses at the corresponding operating parameters are found using a genetic algorithm (GA) approach. Model 1 could capture the computational fluid dynamics (CFD) outputs with high precision at different flame radiuses and time instants with a maximum absolute error percentage of 5.46%. For model 2, the maximum absolute error percentage was 6.58%. Overall, this study demonstrates the applicability and promising performance of the proposed ML models, which will be used in subsequent research to analyze turbulent flames a posteriori.

Klasifikasi Penyakit Daun Tomat Menggunakan Algoritma K-NN Berdasarkan Ekstraksi Fitur GLCM dan LBP

Tomatoes are widely cultivated in Indonesia and are one of the crops with high economic value. According to the Central Bureau of Statistics, the Indonesian nation has the capacity to produce up to 1. 17 million tons of tomatoes in 2020. Tomatoes contain ingredients that the body needs. Additionally, grapes can also be consumed in different forms. However, tomato production in Indonesia decreased from 2013 to 2015 due to the spread of pests. Therefore, we conducted a study to classify tomato leaf diseases using the K-nearest neighbor method based on the grayscale coexistence matrix and the extraction of local binary pattern features. The data used was 9157 data obtained from the Plant Village database and classified into 6 classes (healthy, spot fungus, late blight, leaf mold, mosaic virus, spider mite, and target spot). The testing process was performed using the K-fold cross-validation technique, followed by performance calculations using the confusion matrix method. The highest accuracy was obtained at 86.8% when classifying using K9 and K10 with a precision value 40.6% and recall 49.2% when classifying using a value of K = 9, and precision 49.7% and recall of 49.3% when classifying using a value of K = 10.

O volume corporal pode predizer o peso corporal em borregas Pelibuey?

ABSTRACT: In traditional sheep production systems in tropical regions, the evaluation and monitoring of animal growth are not commonly practiced. This is primarily attributed to the limited adoption of technology and the high costs associated with acquiring livestock scales. The present study developed equations to predict body weight of Pelibuey ewe lambs using a Body Volume (BV) formula. BW and BV were obtained from 85 ewe lambs. The models evaluated were as follows: 1) linear, 2) quadratic, 3) cubic, 4) allometric, 5) exponential and 6) logarithmic. In addition, the models’ predictive ability for BW was assessed by k-fold (k=10) cross-validation. The BW and BV were 20.60 ± 8.73 kg and 17.54 ± 7.89 dm3, respectively. The quadratic model showed the highest r2 and the lowest values of mean square error, root mean square error, and bayesian information criterion, while the k-fold cross-validation technique also had the highest r2 and the lowest values of mean squared prediction error and mean absolute error. The quadratic model was the best performing mathematical model for predicting BW of growing Pelibuey ewe lambs using BV.

An Ensemble Machine Learning Method for Analyzing Various Medical Datasets

In recent years, machine learning (ML) has shown a significant impact in tackling various complicated problems in different application domains, including healthcare, economics, ecological, stock market, surveillance, and commercial applications. Machine Learning techniques are good enough to deal with a wide range of data, uncover fascinating links, offer insights, and spot trends. ML can improve disease diagnosis accuracy, predictability, performance, and reliability. This paper reviews various machine learning techniques applied to different medical datasets and proposes an ensemble method for helping in the early diagnosis of different diseases. The study compares existing machine learning techniques with the proposed ensemble method. The ensemble method uses the AdaBoost algorithm to combine the traits of choice trees, random forests, and support vector machines. Three feature selection techniques, Fisher’s score, information gain, and genetic algorithm, are used to select appropriate dataset features. The ensemble method also uses the K-fold cross-validation technique (where k=15) for validating results. SMOTE was employed to balance some of the datasets because they were quite unbalanced. All the methods used in this study are evaluated based on accuracy, AU Curve, Recall, Precision, and F1-score. The paper uses different medical datasets at the University of California Irvine and the Kaggle directory to compare machine-learning models with the proposed ensemble method. The encouraging results show that the ensemble method outperforms the existing machine-learning techniques. The paper thoroughly analyzes how machine learning is used in the medical industry, covering established technologies and their impact on medical diagnosis. An early diagnosis is needed to prevent people from deadly diseases. Hence, this study proposes an ensemble method that may be used to diagnose different diseases early.