... sentiment analysis, translation and hate speech detection [ 9 ]. This paper focuses on automatically identifying Arabic dialects from social media text using machine learning (ML) and deep learning approaches. We examined Gulf Arabic – spoken in Saudi Arabia, Kuwait, Bahrain, Qatar, the UAE and Oman...

... . Pattern recognition Inverse problems Machine learning Deep neural network Data augmentation Despite advancements in deep learning (DL) for large-scale data analysis, many AI applications lack access to extensive datasets [ 1 ]. This challenge is evident in personalized systems, where DNNs rely...

...Nair Ul Islam; Ruqaiya Khanam Purpose This study evaluates machine learning (ML) classifiers for diagnosing Parkinson’s disease (PD) using subcortical brain region data from 3D T1 magnetic resonance imaging (MRI) Parkinson’s Progression Markers Initiative (PPMI database). We aim to identify top...

... stakeholders in scientific research. The software can be accessed for free in a code repository, the link to which is provided in the full text of the study. Stratos Moschidis can be contacted at: smos@iti.gr Data analytics Machine learning Visualization Software Multiple correspondence analysis...

... of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode Data mining Data visualization Machine learning Multiple correspondence analysis Dimension reduction Categorical data Dimension reduction methods seek to reduce the number of dimensions [ 1, 2...

.../legalcode . Machine learning Multilabel classifier Bidirectional long short-term memory ATC classification Learned features From start to market, the price for engineering new drugs, which can take decades before final approval, is now estimated to be 2.8 billion USD [ 1 ]. Of all drugs...

... develops a new approach harnessing applications of machine learning (ML) models on a dataset, that is publicly available, relevant to student attrition to identify potential students at risk. The dataset consists of the data generated by the interaction of students with LMS for their BL environment...

...Leila Ismail; Huned Materwala Purpose Machine Learning is an intelligent methodology used for prediction and has shown promising results in predictive classifications. One of the critical areas in which machine learning can save lives is diabetes prediction. Diabetes is a chronic disease and one...

.../approach In this study, the authors have presented machine learning models with feature selection, which can detect diabetes disease at its early stage. Also, the models presented are not costly and available to everyone, including those in the remote areas. Findings The study result shows...

... that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto...

... developing technologies and systems for cooling/lubricating of the cutting zone while maintaining machining efficiency. In the present study, three regression based machine learning techniques, namely, polynomial regression (PR), support vector regression (SVR) and Gaussian process regression (GPR) were...

.... With cricket being a very dynamic game, bettors and bookies are incentivised to bet on the match results because it is a game that changes ball-by-ball. This paper investigates machine learning technology to deal with the problem of predicting cricket match results based on historical match data of the IPL...

... and complex techniques deal with all objectives at once. This work reviews the use of Artificial Neural Networks and Machine Learning for data interpretation of Ground Penetrating Radar surveys. We show that these computational techniques have progressed GPR forward from locating and testing to imaging...

... Variability (HRV) parameters used to predict cardiac arrest in smokers using machine learning technique in this paper. Machine learning is a method of computing experience based on automatic learning and enhances performances to increase prognosis. This study intends to compare the performance of logistical...

... detection of diabetes is very important to maintain a healthy life. This disease is a reason of global concern as the cases of diabetes are rising rapidly. Machine learning (ML) is a computational method for automatic learning from experience and improves the performance to make more accurate predictions...

..., and decisions are combined by sum rule. These results are compared with standard methods, some deep learning approaches, and 185 human assessments. Our best machine learning methods are shown to outperform the human judges. The neonate’s face is detected using Discriminative Response Map Fitting (DRMF...

... and most failure prediction studies since then have adopted this approach. However, some of the succeeding studies, especially the most recent ones since around 2006, have used machine learning tools like ANN. The first study to develop a failure prediction model for construction firms was authored...