Analysis of Machine Learning Algorithms for Facial Expression Recognition

In this study, our aim is to predict sex using machine learning (ML) algorithms with morphometric variables taken from the vidian canal. The study was performed on Computed Tomography (CT) images of 137 women and 137 men aged 18–65 years. Seventeen morphometric variables were determined and recorded from axial and coronal skull images. These numerical data were used for sex determination with ML algorithms. As a result of the ML algorithm analysis of the data as 80% training set and 20% test set, the highest Accuracy (Acc.) rate was 0.89 with the Extra Tree Classifier (ETC) algorithm, and the lowest was 0.78 with Quadratic Discriminant Analysis (QDA). Among the parameters, the distance between the optic canal and the vidian canal was found to have the highest effect on sex determination with the Shapley Additive Explanations analyser. As a result of 10-fold cross-validation analysis of ML algorithms, the highest Acc rate was 0.82 with Random Forest (RF) algorithm, and the lowest was 0.73 with QDA. The findings of the study reveal that morphometric variables from the vidian canal show high accuracy in sex determination.

The global demand for electricity has visualized high growth with the rapid growth in population and economy. It thus becomes necessary to efficiently distribute electricity to households and industries in order to reduce power loss. Smart Grids (SG) have the potential to reduce such power losses during power distribution. Machine learning and artificial intelligence techniques have been successfully implemented on SGs to achieve enhanced accuracy in customer demand prediction. There exists a dire need to analyze and evaluate the various machine learning algorithms, thereby identify the most suitable one to be applied to SGs. In the present work, several state-of-the-art machine learning algorithms, namely Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Logistic Regression, Naive Bayes, Neural Networks, and Decision Tree classifier, have been deployed for predicting the stability of the SG. The SG dataset used in the study is publicly available collected from UC Irvine (UCI) machine learning repository. The experimentation results highlighted the superiority of the Decision Tree classification algorithm, which outperformed the other state of the art algorithms yielding 100% precision, 99.9% recall, 100% F1 score, and 99.96% accuracy.

Polycystic ovary syndrome (PCOS) is the most common endocrine disorder affecting many women in their child-bearing age groups. It leads to sterility and various kinds of disorders in women. The main signs of this disease are uneven mensuration cycle, obesity, oily skin, pimples, anxiety disorders. An estimated one in five women suffers from PCOS. It is seen that most women overlook the common indication of PCOS and visit the doctor only when they face difficulty conceiving. If not diagnosed in time, the condition can cause serious health issues. To overcome this problem this paper proposes to develop an application for the early prediction of PCOS using Machine Learning techniques. The required dataset is created by carrying a survey and is cleaned using Python and Google Colab. Gini importance is used to compute feature importance. Classification of PCOS is done using various machine learning techniques such as K-Nearest Neighbor(KNN), Naive Bayes, Decision Tree Classifier, Support Vector Machine(SVM), Logistic Regression(LR). Based on the accuracy and confusion matrix, the Decision Tree Classifier was found to be the most accurate model for PCOS prediction. A mobile application was developed which helps the user to predict PCOS at an early stage.

BackgroundDiffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and texture features from MRI Apparent Diffusion Coefficient (ADC) images of human brain tumors, identifying the distribution patterns of each feature and applying Machine Learning (ML) techniques to differentiate malignant from benign brain tumors.MethodsThis prospective study was carried out using 1599 labeled MRI brain ADC image slices, 995 malignant, 604 benign from 195 patients who were radiologically diagnosed and histopathologically confirmed as brain tumor patients. The demographics, mean pixel values, skewness, kurtosis, features of Grey Level Co-occurrence Matrix (GLCM), mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence and shade, were extracted from MRI ADC images of each patient. At the feature selection phase, the validity of the extracted features were measured using ANOVA f-test. Then, these features were used as input to several Machine Learning classification algorithms and the respective models were assessed.ResultsAccording to the results of ANOVA f-test feature selection process, two attributes: skewness (3.34) and GLCM homogeneity (3.45) scored the lowest ANOVA f-test scores. Therefore, both features were excluded in continuation of the experiment. From the different tested ML algorithms, the Random Forest classifier was chosen to build the final ML model, since it presented the highest accuracy. The final model was able to predict malignant and benign neoplasms with an 90.41% accuracy after the hyper parameter tuning process.ConclusionsThis study concludes that the above mentioned features (except skewness and GLCM homogeneity) are informative to identify and differentiate malignant from benign brain tumors. Moreover, they enable the development of a high-performance ML model that has the ability to assist in the decision-making steps of brain tumor diagnosis process, prior to attempting invasive diagnostic procedures, such as brain biopsies.

Due to climate change, windstorms are becoming increasingly common resulting in the destruction not only of extensive forest areas but, quite often, of small-sized and scattered forest lands, thereby adversely affecting both the productivity and the safety of workers employed in the harvest of windblown trees. In the present study, an attempt is made to identify and record areas in the northeastern forests of Greece covered by mixed stands of conifers and broadleaves that experienced massive windthrow following local storms. Our results reveal that where Pinus sylvestris was mixed either with Quercus sp. or with Fagus sylvatica, it had been substantially protected, while in plots where it stood on its own it had been extensively uprooted. On the other hand, Picea abies, even if it was mixed with Fagus sylvatica and Pinus sylvestris, had been blown down to a large extent. Based on tree-level data, local topographic features, forest characteristics, and the mechanical properties of green wood, a reliable model, to be used for the prediction of similar disturbances in the future, has been selected after a thorough comparative study of the most well-known intelligent Machine Learning (ML) algorithms. Specifically, Random Forest Classifier, k-Neighbors Classifier, Decision Tree Classifier, Light Gradient Boosting Machine, Gradient Boosting Classifier, Ada Boost Classifier, Ridge Classifier, Linear Discriminant Analysis, Logistic Regression, Naive Bayes, SVM – Linear Kernel, and Quadratic Discriminant Analysis were evaluated and compared using six performance measures (confusion matrix, accuracy, precision, recall, F1-score, and ROC).

Gesture recognition is a topic in computer science and language technology that aims to interpret human gestures with computer programs and many different algorithms. It can be seen as the way computers can understand human body language. Today, the main interaction tools between computers and humans are still the keyboard and mouse. Gesture recognition can be used as a tool for communication with the machine and interaction without any mechanical device such as a keyboard or mouse. In this paper, we present the results of a comparison of eight different machine learning (ML) classifiers in the task of human hand gesture recognition and classification to explore how to efficiently implement one or more tested ML algorithms on an 8-bit AVR microcontroller for on-line human gesture recognition with the intention to gesturally control the mobile robot. The 8-bit AVR microcontrollers are still widely used in the industry, but due to their lack of computational power and limited memory, it is a challenging task to efficiently implement ML algorithms on them for on-line classification. Gestures were recorded by using inertial sensors, gyroscopes, and accelerometers placed at the wrist and index finger. One thousand and eight hundred (1800) hand gestures were recorded and labelled. Six important features were defined for the identification of nine different hand gestures using eight different machine learning classifiers: Decision Tree (DT), Random Forests (RF), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) with linear kernel, Naïve Bayes classifier (NB), K-Nearest Neighbours (KNN), and Stochastic Gradient Descent (SGD). All tested algorithms were ranged according to Precision, Recall, and F1-score (abb.: P-R-F1). The best algorithms were SVM (P-R-F1: 0.9865, 0.9861, and 0.0863), and RF (P-R-F1: 0.9863, 0.9861, and 0.0862), but their main disadvantage is their unusability for on-line implementations in 8-bit AVR microcontrollers, as proven in the paper. The next best algorithms have had only slightly poorer performance than SVM and RF: KNN (P-R-F1: 0.9835, 0.9833, and 0.9834) and LR (P-R-F1: 0.9810, 0.9810, and 0.9810). Regarding the implementation on 8-bit microcontrollers, KNN has proven to be inadequate, like SVM and RF. However, the analysis for LR has proved that this classifier could be efficiently implemented on targeted microcontrollers. Having in mind its high F1-score (comparable to SVM, RF, and KNN), this leads to the conclusion that the LR is the most suitable classifier among tested for on-line applications in resource-constrained environments, such as embedded devices based on 8-bit AVR microcontrollers, due to its lower computational complexity in comparison with other tested algorithms.

Bearings are the most widely used mechanical parts in rotating machinery under high load and high rotational speeds. Operating continuously under such harsh conditions, wear and failure are imminent. Developing defects give rise to even-higher vibration and temperature levels. In general, mechanical defects in a machine cause high vibration levels. Therefore, bearing fault identification and early detection enables the maintenance team to repair the problem before it triggers catastrophic failure in the bearing. Machine downtime is thus avoided or minimized. This paper explores the use of Machine Learning (ML) integrated with decision-making techniques to predict possible bearing failures and improve the overall manufacturing operations by applying the correct maintenance actions at the right time. The accuracy of the Predictive Maintenance (PdM) module has been tested on real industrial production datasets. The paper proposes an effective PdM methodology using different ML algorithms to detect failures before they happen and reduce pump downtime. The performance of the tested ML algorithms is based on five performance indicators: accuracy, precision, F-score, recall, and an area under curve (AUC). Experimental results revealed that all tested ML algorithms are successful and effective. Furthermore, decision making with utility theory has been employed to exploit the probability of failures and thus help to perform the appropriate maintenance interventions. This provides a logical framework for decision-makers to identify the optimum action with the maximum expected benefit. As a case study, the model is applied on forwarding pumping stations belonging to the Sewerage Treatment Company (STC), one of the largest sewage stations in Qatar.

Recent advances in machine learning techniques have significantly influenced the development of early diabetes detection systems. In this paper, different machine learning (ML) algorithms are employed for diabetes detection based on hand movement. The spatial and temporal information for horizontal and vertical hand movement deviations for both diabetic and non-diabetic subjects are obtained using a data acquisition system. A dataset is constructed based on the obtained hand movement information along with other related attributes such as age, gender, weight and vision health. Binary classification of diabetic and non-diabetic subjects is achieved using several machine learning algorithms. These algorithms include Naïve Bayes (NB), Logistic Regression (LR), Linear Discriminate Analysis (LDA), Classification and Regression Tree (CART), k-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Multi-layer Perceptron (MLP). The considered ML algorithms are evaluated and ranked according to a set of key performance metrics that include accuracy, precision, recall and F-1 score. Algorithm ranking results demonstrate that LR, CART, KNN and MLP algorithms have achieved higher ranking positions as compared to other algorithms. The best ranked ML algorithms are then validated and thoroughly investigated to prove their ability to discriminate between diabetic and non-diabetic subjects. Validation results show that the best-ranked ML algorithms have achieved high classification accuracy, precision, recall and F-1 scores. Therefore, the considered machine learning algorithms can serve as efficient tools for diabetes detection based on the constructed dataset.

Machine Learning (ML) algorithms are used in many fields such as finance, education, industry, medicine and e-commerce. ML algorithms show performance differences depending on the dataset and processing steps. Choosing the right algorithm, preprocessing and postprocessing methods has great importance to achieve good results. In this paper, Random Forest Classifier, Logistic Regression Classifier, Decision Tree Classifier, Linear Discriminant Analysis, K-Nearest Neighbor Classifier and SVC methods are compared to predict mobile phone price class. The "Mobile Price Classification" dataset which is taken from Kaggle.com is used to evaluate methods. Firstly, all values at the dataset are checked to verify that there are no missing values. After that, scaling is applied to dataset in order to obtain more relevant data for ML algorithms. Then, feature selection methods which reduce the computational cost by reducing the number of inputs are performed to get meaningful features. Finally, the parameters of classification algorithms are tuned to improve the system accuracy. According to obtained results, it is seen that ANOVA f-test feature selection method is more convenient for this dataset. It gives satisfying accuracy with a minimum number of features. It is also seen that the SVC classifier has the highest test accuracy compared to other models.

ABSTRACT: The field of machine learning (ML) has made significant contributions to the medical domain, notably in the automatic classification of cancer types such as colon cancer. This type of cancer, while generally more common among the elderly, can affect people at any age. The incorporation of ML techniques has minimized the reliance on manual examination and enhanced the capacity to process large datasets in medical research. Utilizing advanced gene technology and analysis of gene expression data, this study addresses the challenges posed by the high dimensionality and limited size of such datasets. The findings reveal that most of the ML models employed here have surpassed expected accuracy levels, highlighting their effectiveness in the precise classification and diagnosis of colon cancer. Consequently, these developments hold promise for improving patient care and the overall efficiency of healthcare services in dealing with colon cancer.

Undernutrition among children under the age of five is a major public health concern, especially in developing countries. This study aimed to use machine learning (ML) algorithms to predict undernutrition and identify its associated factors. Secondary data analysis of the 2017 Multiple Indicator Cluster Survey (MICS) was performed using R and Python. The main outcomes of interest were undernutrition (stunting: height-for-age (HAZ) < -2 SD; wasting: weight-for-height (WHZ) < -2 SD; and underweight: weight-for-age (WAZ) < -2 SD). Seven ML algorithms were trained and tested: linear discriminant analysis (LDA), logistic model, support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), ridge regression, and extreme gradient boosting (XGBoost). The ML models were evaluated using the accuracy, confusion matrix, and area under the curve (AUC) receiver operating characteristics (ROC). In total, 8564 children were included in the final analysis. The average age of the children was 926 days, and the majority were females. The weighted prevalence rates of stunting, wasting, and underweight were 17%, 7%, and 12%, respectively. The accuracies of all the ML models for wasting were (LDA: 84%; Logistic: 95%; SVM: 92%; RF: 94%; LASSO: 96%; Ridge: 84%, XGBoost: 98%), stunting (LDA: 86%; Logistic: 86%; SVM: 98%; RF: 88%; LASSO: 86%; Ridge: 86%, XGBoost: 98%), and for underweight were (LDA: 90%; Logistic: 92%; SVM: 98%; RF: 89%; LASSO: 92%; Ridge: 88%, XGBoost: 98%). The AUC values of the wasting models were (LDA: 99%; Logistic: 100%; SVM: 72%; RF: 94%; LASSO: 99%; Ridge: 59%, XGBoost: 100%), for stunting were (LDA: 89%; Logistic: 90%; SVM: 100%; RF: 92%; LASSO: 90%; Ridge: 89%, XGBoost: 100%), and for underweight were (LDA: 95%; Logistic: 96%; SVM: 100%; RF: 94%; LASSO: 96%; Ridge: 82%, XGBoost: 82%). Age, weight, length/height, sex, region of residence and ethnicity were important predictors of wasting, stunting and underweight. The XGBoost model was the best model for predicting wasting, stunting, and underweight. The findings showed that different ML algorithms could be useful for predicting undernutrition and identifying important predictors for targeted interventions among children under five years in Ghana.

The Internet of Things (IoT) generates massive amounts of data everywhere through sensors of every kind which are disseminated in a variety of objects. This data contains incredibly valuable information useful for multiple applications. Knowing the context in which it was generated is extremely important and constitutes one of the first steps in extracting the knowledge it contains. Thereby, Context-Aware Learning (CAL) has become an important area of research as machine learning (ML) is a fast and ever-evolving technology. Wearable devices, ranging from accelerometers (ACC), frequently used, to magnetic field sensors, are used to monitor and recognize human activities (HA). Beyond ML Algorithms (MLA), accurate Human Activities Recognition (HAR) or context identification, depends not only on the kinds of sensors used but also on their location. In this paper, we study the impact of three types of sensors: ACC, gyroscope (GYR), and magnetometer (MAG); and their locations on the performance of MLA for HAR. Our results show that magnetic field sensors, less frequently used in the literature, placed at a specific location, provide the best performance in terms of HAR. Using a publicly available dataset, PAMAP2, we implement and evaluate the performance of HAR using five MLA: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QLA), K-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF). Our results show that the success rate of these algorithms is 98.3%, 90.4%, 97.6%, 99.9%, and 100% respectively, which exceeds the results obtained in a previous work based on the same dataset.

The aim of this paper is to review machine learning (ML) algorithms and techniques for hate speech detection in social media (SM). Hate speech problem is normally model as a text classification task. In this study, we examined the basic baseline components of hate speech classification using ML algorithms. There are five basic baseline components &#x2013; data collection and exploration, feature extraction, dimensionality reduction, classifier selection and training, and model evaluation, were reviewed. There have been improvements in ML algorithms that were employed for hate speech detection over time. New datasets and different performance metrics have been proposed in the literature. To keep the researchers informed regarding these trends in the automatic detection of hate speech, it calls for a comprehensive and an updated state-of-the-art. The contributions of this study are three-fold. First to equip the readers with the necessary information on the critical steps involved in hate speech detection using ML algorithms. Secondly, the weaknesses and strengths of each method is critically evaluated to guide researchers in the algorithm choice dilemma. Lastly, some research gaps and open challenges were identified. The different variants of ML techniques were reviewed which include classical ML, ensemble approach and deep learning methods. Researchers and professionals alike will benefit immensely from this study.

This paper presents a novel and effective method for facial expression recognition including happiness, disgust, fear, anger, sadness, surprise, and neutral state. The proposed method utilizes a regularized discriminant analysis-based boosting algorithm (RDAB) with effective Gabor features to recognize the facial expressions. Entropy criterion is applied to select the effective Gabor feature which is a subset of informative and nonredundant Gabor features. The proposed RDAB algorithm uses RDA as a learner in the boosting algorithm. The RDA combines strengths of linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). It solves the small sample size and ill-posed problems suffered from QDA and LDA through a regularization technique. Additionally, this study uses the particle swarm optimization (PSO) algorithm to estimate optimal parameters in RDA. Experiment results demonstrate that our approach can accurately and robustly recognize facial expressions.

Comparison of Machine Learning Techniques and Variables for Groundwater Dissolved Organic Nitrogen Prediction in an Urban Area