Comparative Study of Different Classifiers Based on Extracted Facial Features for Supervised Age Estimation

This paper presents an overview of the machine learning techniques that can be used to predict a student’s placement performance. The ability to predicting the performance of a student is a very essential task of all educational institutions. Since this is the task of predicting student’s placement of undergraduate students. This paper can be used to predict the probability of an undergraduate student getting placed by applying different machine learning algorithms. In this system, multilayer perceptron (MLP), logistic model tree (LMT), sequential minimal optimization (SMO), simple logistic, and logistic classifiers are applied to predict student performance. These classifiers independently predict the results and then compare the accuracy of the algorithms, which is based on the data set. After performing analysis on different matrices (time taken to build classifier, correctly classified instances, root mean squared error, incorrectly classified instances, precision, recall, F-measure, ROC area) by different machine learning algorithms, we are able to find which algorithm is performing better than other on the student data set, so that we are able to make a guideline for future improvement of student placement performance in education.

DNA methylation markers in combination with skeletal and dental ages to improve age estimation in children

The majority of color histogram equalization methods do not yield uniform histogram in gray scale. After converting a color histogram equalized image into gray scale, the contrast of the converted image is worse than that of an 1-D gray scale histogram equalized image. We propose a novel 3-D color histogram equalization method that produces uniform distribution in gray scale histogram by defining a new cumulative probability density function in 3-D color space. Test results with natural and synthetic images are presented to compare and analyze various color histogram equalization algorithms based upon 3-D color histograms. We also present theoretical analysis for nonideal performance of existing methods.

Analyzing the hidden information from the images are helpful to identify the various causes. In general processing of images includes Pre-processing, segmentation, Feature extraction and Classification. Significance of classifier is essential since results are always based on the classifier. The ultimate aim is to explain how WEKA tool is used for rheumatoid arthritis and investigate the performance of the various classifiers for huge data. In our method we are distinctively give attention to the classification methods like ADTree, Best First Decision tree(BF), Decision stump, J48Pruned tree, J48 Graft Pruned tree, Least Absolute Deviation regression trees (LAD), Logistic Model Tree(LMT), Naïve-Bayes (NB), Random tree, Random forest tree, CART Decision tree. The features like Area, perimeter, circularity, integrated density, Median, Skewness, Raw integrated density, and Roundness and solidity are obtained from the Lymphocytes images and formed the data set. Different classifier is applied for RA facet for Validation. RA facet contains 108 rows and 10 columns. Using classifier to find out the various values like Relative Absolute Error, and Kappa Statistic, Mean Absolute Error, Root Mean Squared Error and Root Relative Squared Error. From those values compare with all the methods ADT classifier is suggested for use in huge data.

Globally, wind energy if properly harnessed, could serve as a source of energy generation in Africa. This study compared the performance of two Machine Learning (ML) algorithms (Linear regression and Random Forest) in predicting wind speed in five major cities in Africa (Yaoundé, Pretoria, Nairobi, Cairo and Abuja). Wind data were collected between January 1, 2000, and December 31, 2022, using the Solar Radiation Data Archive. The data preprocessing was carried out with 80% of the data used for training and 20% for validation. The performance of these ML algorithms was evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R2). The result shows that Nairobi (3.814795 m/s) closely followed by Cairo (3.606453 m/s) has the highest mean wind speed while Yaoundé (1.090512 m/s) has the lowest. Based on the performance metrics used, the two Machine Learning algorithms were competitive. Still, the Linear Regression (LR) algorithm outperformed the Random Forest Algorithm in predicting wind speed in all the selected major African cities. In Yaoundé (RMSE = 0.3892, MAE= 0.3001, MAPE =0.5030), Pretoria (RMSE=1.2339, MAE=0.9480, MAPE=0.7450) Nairobi (RMSE= 0.4223, MAE =0.6499, MAPE =0.1872), Nairobi (RMSE=0.6499, MAE=0.5171, MAPE =0.1872), Cairo (RMSE =1.0909, MAE =0.8544, MAPE =0.3541) and Abuja (RMSE = 0.70245, MAE =0.5441, MAPE= 0.4515) the Linear regression algorithms was found to outperformed Random Forest Regression. Therefore, the Linear regression algorithm is more reliable in predicting wind speed compared with the Random Forest regression.

Improving forecasts, particularly the accuracy, efficiency, and precision of time-series forecasts, is becoming critical for authorities to predict, monitor, and prevent the spread of the Coronavirus disease. However, the results obtained from the predictive models are imprecise and inefficient because the dataset contains linear and non-linear patterns, respectively. Linear models such as autoregressive integrated moving average cannot be used effectively to predict complex time series, so nonlinear approaches are better suited for such a purpose. Therefore, to achieve a more accurate and efficient predictive value of COVID-19 that is closer to the true value of COVID-19, a hybrid approach was implemented. Therefore, the objectives of this study are twofold. The first objective is to propose intelligence-based prediction methods to achieve better prediction results called autoregressive integrated moving average-least-squares support vector machine. The second objective is to investigate the performance of these proposed models by comparing them with the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average-support vector machine. Our investigation is based on three COVID-19 real datasets, i.e., daily new cases data, daily new death cases data, and daily new recovered cases data. Then, statistical measures such as mean square error, root mean square error, mean absolute error, and mean absolute percentage error were performed to verify that the proposed models are better than the autoregressive integrated moving average, support vector machine model, least-squares support vector machine, and autoregressive integrated moving average-support vector machine. Empirical results using three recent datasets of known the Coronavirus Disease-19 cases in Malaysia show that the proposed model generates the smallest mean square error, root mean square error, mean absolute error, and mean absolute percentage error values for training and testing datasets compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average-support vector machine models. This means that the predicted value of the proposed model is closer to the true value. These results demonstrate that the proposed model can generate estimates more accurately and efficiently. Compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average-support vector machine models, our proposed models perform much better in terms of percent error reduction for both training and testing all datasets. Therefore, the proposed model is possibly the most efficient and effective way to improve prediction for future pandemic performance with a higher level of accuracy and efficiency.

Breast cancer is one the most curable cancer types if it can be diagnosed early. Research efforts have reported with increasing confirmation that the computation methods have greater accurate diagnosis ability. An enhancement of multi classifiers voting technique based on histogram equalization as a preprocessing stage proposed in this paper. The methodology is based on five phases starting by mammogram images collection, preprocessing (histogram equalization and image cropping based region of interest (ROI)), features extracting, classification and last evaluating the classification results. An experimental conducted on different training-testing partitions of the dataset. The numerical results demonstrate that the proposed scheme achieves an accuracy rate of 81.25% and outperformed the accuracy of voting method without using histogram equalization.

Attention Deficit Hyperactivity Disorder (ADHD) is a neurobehavioral childhood impairment that wipes away the beauty of the individual from a very young age. Data mining classification techniques which are becoming a very important field in every sector play a vital role in the analysis and identification of these disorders. The objective of this paper is to analyze and evaluate ADHD by applying different classifiers like Naïve Bayes, Bayes Net, Sequential Minimal Optimization, J48 decision tree, Random Forest, and Logistic Model Tree. The dataset employed in this paper is the first publicly obtainable dataset ADHD-200 and the instances of the dataset are classified into low, moderate, and high ADHD. The analysis of the performance metrics and therefore the results show that the Random Forest classifier offers the highest accuracy on ADHD dataset compared to alternative classifiers. With the current need to provide proper evaluation and management of this hyperactive disorder, this research would create awareness about the influence of ADHD and can help ensure the proper and timely treatment of the affected ones. KeywordsADHDBayes netClassificationData miningJ48 decision treeLogistic model treeNaïve bayesRandom forestSequential minimal optimization

ObjectivesHuman brucellosis is a public health problem endangering health and property in China. Predicting the trend and the seasonality of human brucellosis is of great significance for its prevention. In...

Development and comparison of forensic interval age prediction models by statistical and machine learning methods based on the methylation rates of ELOVL2 in blood DNA

Effectiveness of three age estimation methods based on dental and skeletal development in a sample of young Brazilians

This study examines the comparison between Multiple Linear Regression and Principal Component Regression for black gram cultivation in Tamil Nadu. This research addresses the problem of accurately modeling and predicting the factors that influence the yield and productivity of black gram cultivation in Tamil Nadu. The challenge lies in identifying which statistical technique, Principal Component Regression (PCR) or Multiple Linear Regression (MLR), is more effective in capturing the complex relationships between various environmental, economic and agricultural variables that affect black gram production. Secondary data were collected from 1999 to 2022 (23 years). Considering yield as a dependent variable and the independent variables are Seed, Fertilizer, Manure, Human labour and Animal labour. According to Multiple Linear Regression, the regression coefficients of fertilizer and human labour significantly influence the yield. The coefficients of Fertilizer and Human labour are found to be 0.049 and 0.07 respectively. According to Principal Component Analysis, the 2 principal components are chosen because the eigenvalue is more than 1.0. These 2 components, PC1 and PC2, cover 36 % and 34 % respectively. The loadings revealed that fertilizer, manure and animal labour significantly contributed to PC1 and Seed and human labour contributed significantly in PC2. The Multiple Linear Regression and Principal Component regression are compared using R square, Adjusted R Square, Root Mean Square, Mean Absolute Error and Mean Absolute Percentage Error. The adjusted R square reveals that Principal Component Regression is better than Multiple Linear Regression. The lowest value of Root Mean Square Error, Mean Absolute Error and Mean Absolute Percentage Error shows the best model among the 2 models. The error is lower for Principal Component Regression compared to Regression. PC1 captures the relationship between fertilizer, manure and animal labor, representing an "input utilization efficiency" dimension. PC2 reflects the trade-off between human labor and seed usage, defining a spectrum between "labor-intensive" and "seed-reliant" farming strategies.

The purposes are to analyze the safety and real-time performance of intelligent transportation under big data and solve urban transportation safety problems. The existing problems of complex traffic data collection and single factor analysis are analyzed. Guangzhou target road section and its historical traffic flow are taken as the data source, the big data analytics (BDA) platform is constructed after preprocessing, and then the deep belief network (DBN) is introduced to build the intelligent transportation model based on BDA. Finally, the model is simulated to analyze its performance. The results reveal that the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of the proposed algorithm are 4.42%, 6.21%, and 8.03%, respectively compared to Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Neuro fuzzy c-means model (FCM), Deep Neural Network (DNN), and neural network (NN). The mean absolute error (MAE) and mean absolute percentage error (MAPE) are 4.42%, 6.21% and 8.03% respectively. The prediction accuracy is significantly better than that of other algorithms. The analysis of Accuracy, Precision, Recall and F1 suggests that the safety prediction accuracy of the proposed algorithm can reach up to 88.57%, which is at least 3.19% higher than that of other algorithms. Meantime, the proposed algorithm can effectively inhibit the spread of congestion, and achieve the effect of timely evacuation for traffic congestion. Regarding the data transmission performance, the delay is less than 40ms and the packet loss rate is less than 0.1, which is significantly better than other model algorithms in real-time performance. Hence, the constructed intelligent transportation algorithm based on BDA has high precision and excellent congestion evacuation performance, which can provide an experimental basis for constructing intelligent and safe transportation in the future.

This article uses machine learning techniques as fuzzy and neuro-fuzzy ANFISs, to develop and compare prediction models capable of relating pregnant women’s exposure to air pollutants, such as Nitrogen Dioxide and Particulate Matter, the mother’s age, and the number of prenatal consultations to the incidence of premature birth. In the current literature, studies can be found that relate prematurity to the exposure of pregnant women to NO2, O3, and PM10; to Toluene and benzene, mainly in the window 5 to 10 days before birth; and to PM10 in the week before birth. Both models used logistic regression to quantify the effects of pollutants as a result of premature birth. Datasets from Brazil—Departamento de Informatica do Sistema Único de Saúde (DATASUS) and Companhia Ambiental do Estado de São Paulo (CETESB)—were used, covering the period from 2016 to 2018 and comprising women living in the city of São José dos Campos (SP), Brazil. In order to evaluate and compare the different techniques used, evaluation metrics were calculated, such as correlation (r), coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and Mean Absolute Error (MAE). These metrics are widely used in the literature due to their ability to evaluate the robustness and efficiency of prediction models. For the RMSE, MAPE, MSE, and MAE metrics, lower values indicate that prediction errors are smaller, demonstrating better model accuracy and confidence. In the case of (r) and R2, a positive and strong result indicates alignment and better performance between the real and predicted data. The neuro-fuzzy ANFIS model showed superior performance, with a correlation (r) of 0.59, R2 = 0.35, RMSE = 2.83, MAPE = 5.35%, MSE = 8.00, and MAE = 1.70, while the fuzzy model returned results of r = 0.20, R2 = 0.04, RMSE = 3.29, MSE = 10.81, MAPE = 6.67%, and MAE = 2.01. Therefore, the results from the ANFIS neuro-fuzzy system indicate greater prediction capacity and precision in relation to the fuzzy system. This superiority can be explained by integration with neural networks, allowing data learning and, consequently, more efficient modeling. In addition, the findings obtained in this study have potential for the formulation of public health policies aimed at reducing the number of premature births and promoting improvements in maternal and neonatal health.

Mapping of groundwater salinization and modelling using meta-heuristic algorithms for the coastal aquifer of eastern Saudi Arabia