This systematic review broadly attempted to synthesize all relevant evidence residing in the Scopus, IEEE Xplore and MDPI databases, in order to inform the related Research Questions of this work. More precisely, the review protocol includes a broad and comprehensive search for eligible data sets from the Scopus, IEEE Xplore and MDPI databases, published from January 2017 to December 2022 by using inclusion/exclusion search criteria. Medical Education Research Study Quality Instrument (MERSQI) was commissioned to assess and analyze the quality of 69 quantitative studies. The findings generally received positive feedback and there was a discussion about the results. This work was an original contribution guided by pedagogical theory and the validity of the evaluation constitutes a proposal for future improvement.

The use of anthropometric measurements in machine learning algorithms for hypertension prediction enables the development of simple, non-invasive prediction models. However, different machine learning algorithms were utilized in conjunction with various anthropometric data, either alone or in combination with other biophysical and lifestyle variables. It is essential to assess the impacts of the chosen machine learning models using simple anthropometric measurements. We developed and tested 13 machine learning methods of neural network, ensemble, and classical categories to predict hypertension in adolescents using only simple anthropometric measurements. The imbalanced dataset of 2461 samples with 30.1% hypertension subjects was first partitioned into 90% for training and 10% for validation. The training dataset was reduced to eight simple anthropometric measurements: age, C index, ethnicity, gender, height, location, parental hypertension, and waist circumference using correlation coefficient. The Synthetic Minority Oversampling Technique (SMOTE) combined with random under-sampling was used to balance the dataset. The models with optimal hyperparameters were assessed using accuracy, precision, sensitivity, specificity, F1-score, misclassification rate, and AUC on the testing dataset. Across all seven performance measures, no model consistently outperformed the others. LightGBM was the best model for all six performance metrics, except sensitivity, whereas Decision Tree was the worst. We proposed using Bayes’ Theorem to assess the models’ applicability in the Sarawak adolescent population, resulting in the top four models being LightGBM, Random Forest, XGBoost, and CatBoost, and the bottom four models being Logistic Regression, LogitBoost, SVM, and Decision Tree. This study demonstrates that the choice of machine learning models has an effect on the prediction outcomes.

Speech Emotion Recognition (SER) is a common aspect of human-computer interaction and has significant applications in fields such as healthcare, education, and elder care. Although researchers have made progress in speech emotion feature extraction and model identification, they have struggled to create an SER system with satisfactory recognition accuracy. To address this issue, we proposed a novel algorithm called F-Emotion to select speech emotion features and established a parallel deep learning model to recognize different types of emotions. We first extracted emotion features from speech and calculated the F-Emotion value for each feature. These values were then used to determine the combination of speech emotion features that was optimal in speech emotion recognition. Next, a parallel deep learning model was established with the speech emotion feature combination as input to train and test for each type of emotion. Finally, decision fusion was applied to the parallel output results to obtain an overall recognition result. These analyses were conducted on two datasets, RAVDESS and EMO-DB, with the accuracy of speech emotion recognition reaching 82.3% and 88.8%, respectively. The results demonstrate that the F-Emotion algorithm can effectively analyze the correspondence between speech emotion features and emotion types.The MFCC feature best describes emotions of Neutral, Happiness, Fear and Surprise, and Mel best describes emotions of Angry and Sadness.The parallel deep learning model mechanism can improve the accuracy of speech emotion recognition.

Speech reflects people’s mental state and using a microphone sensor is a potential method for human–computer interaction. Speech recognition using this sensor is conducive to the diagnosis of mental illnesses. The gender difference of speakers affects the process of speech emotion recognition based on specific acoustic features, resulting in the decline of emotion recognition accuracy. Therefore, we believe that the accuracy of speech emotion recognition can be effectively improved by selecting different features of speech for emotion recognition based on the speech representations of different genders. In this paper, we propose a speech emotion recognition method based on gender classification. First, we use MLP to classify the original speech by gender. Second, based on the different acoustic features of male and female speech, we analyze the influence weights of multiple speech emotion features in male and female speech, and establish the optimal feature sets for male and female emotion recognition, respectively. Finally, we train and test CNN and BiLSTM, respectively, by using the male and the female speech emotion feature sets. The results show that the proposed emotion recognition models have an advantage in terms of average recognition accuracy compared with gender-mixed recognition models.

Real world objects are recognized by tracking less and tracking based techniques. Mobile augmented reality browsers are tracking less systems, which acquires location data using global positioning system and provide information in the form of maps or web links. Tracking based techniques recognize objects through markers or directly real world objects without markers. Marker based systems actually track the markers not the real objects and therefore, these approaches hides the reality. Marker- less (direct real object tracking) systems use client-server architecture. However, these are affected by network latency. The Smartphone is capable to recognize and track real world objects without any server and marker. It can guide the users about their location and also provide information in a convenient way. Therefore, an improved algorithm for tracking real world objects through natural features was formulated. The modified version of speed up robust features (SURF) was used for features extraction from live mobile camera image and recognition. The pose matrix from extracted features was calculated by Homography. The adapted algorithm was tested in a mobile AR-prototype application using iPhone. It was found from the results that the formulated algorithm recognized and tracked the real world objects from natural features in speedy, easy and convenient wa

Color segmentation has been widely applied in many computer vision systems nowadays. In one example of computer vision systems, the real - time automatic road sign detection and recognition system has mostly applied the RGB (i.e. Red, Green and Blue) and HSI (i.e. Hue, Saturation and Intensity) color model to segment road signs from images that were captured by the censoring device. Thus this paper aims to review the background of the real - time road sign detection based on the color; show the performance of RGB and HSI in identifying color of the road signs under different lighting conditions in a real - time video images through the proposed experiments and finally studies the performance of both RGB and HSI from the experiments for application such as automatic road sign detection system.