Objectives: This study explores different machine learning models (KNN: k-nearest neighbor, MLP: Multilayer Perceptron, SVM: Support Vector Machine) to identify the optimal model for accurate and rapid mental health detection among the recovered COVID-19 patients. Other techniques are also investigated, such as feature selection (Recursive Feature Elimination (RFE) and Extra Trees (ET) methods) and hyper-parameter tuning, to achieve a system that could effectively and quickly indicate mental health. Method/Analysis: To achieve the objectives, the study employs a dataset collected from recovered COVID-19 patients, encompassing information related to depression, anxiety, and stress. Machine learning models are utilized in the analysis. Additionally, feature selection methods and hyper-parameter tuning techniques are explored to enhance the model’s predictive capabilities. The performance of each model is assessed based on accuracy metrics. Findings: The experimental results show that SVM is the most suitable model for accurately predicting an individual’s mental health among recovered COVID-19 patients (accuracy ≥ 0.984). Furthermore, the ET method is more effective than the RFE method for feature selection in the anxiety and stress datasets. Novelty/Improvement:The study lies in the understanding of predictive modeling for mental health and provides insights into the choice of models and techniques for accurate and early detection. Doi: 10.28991/HEF-2024-05-01-01 Full Text: PDF

Objectives: This study aims to comprehensively explore trends, sentiments, and visualization of mental stress expressions in online communities, focusing on discussions within subreddits on the social media platform Reddit. Methods/Analysis: Advanced text analysis and statistical techniques are employed to achieve the study’s objectives. The research utilizes natural language processing (NLP) methods, sentiment analysis, and topic modeling to unravel the intricate layers of mental stress expressions found in posts across diverse subreddits. Additionally, engagement metrics, such as Redditors’ scores and the number of comments, are analyzed to discern distinctive information and patterns of interest. Findings: The research sheds light on prevalent trends, sentiments, and themes related to mental stress in online conversations before and after January 2020. The findings provide valuable insights into patterns of exciting topics, shared experiences of stress, coping mechanisms, and the significant role of virtual communities in offering support and understanding. Novelty/Improvement: The novelty lies in applying advanced text analysis techniques, including sentiment analysis with the majority voting method combining different machine learning techniques and topic modeling with semantic networks, to gain a deeper understanding of the dynamics of mental stress expressions in online communities. The research explores current patterns and distinguishes itself by examining temporal variations in stress-related posts and their correlation with engagement metrics, offering an innovative perspective on mental health discussions in the digital age. Doi: 10.28991/HEF-2024-05-02-01 Full Text: PDF

Due to the COVID-19 situation, all activities, including education, were shifted to online platforms. Consequently, instructors encountered increased challenges in evaluating students. In traditional assessment methods, instructors often face ambiguous cases when evaluating students' competencies. Recent research has focused on the effectiveness of fuzzy logic in assessing students' competencies, considering the presence of uncertain factors or multiple variables. Additionally, demographic characteristics, which can potentially influence students' performance, are not typically utilized as inputs in the fuzzy logic method. Therefore, analyzing students' performance by incorporating these factors is crucial in suggesting adjustments to teaching and learning strategies. In this study, we employ a combination of fuzzy logic and hierarchical linear regression to analyze students' performance. The experiment involved 318 students from various programs and showed that the hybrid approach assessed students' performance with greater nuance and adaptability when compared to a traditional method. Moreover, the findings in this study revealed the following: 1) There are differences in students' performance between traditional and fuzzy evaluation methods; 2) The learning method is an impact on students' fuzzy grades; 3) Students studying online do not perform better than those studying onsite. These findings suggest that instructors and educators should explore effective strategies being fair and suitable in assessment and learning.

Objectives: This study aims to develop an optimized machine learning (ML) framework for predicting stress levels via physiological signals collected during sleep via Internet of Medical Things (IoMT)-enabled biosensors. The primary goal is to increase the accuracy and efficiency of stress prediction by identifying the most significant features that influence stress classification. Method/Analysis: The proposed framework employs two feature selection methods: particle swarm optimization combined with the whale optimization algorithm (PSO-WOA) and an enhanced version incorporating Lévy flight (PSO-WOA with Lévy flight). These methods are designed to reduce feature dimensionality while maximizing classification performance. A set of single (LR, KNN, NB, MLP, and SVM) and ensemble (RF, XGBoost, and Voting) classifiers are evaluated via 10-fold cross-validation. The Sleep-IoMT stress dataset, comprising biosensor-based physiological signals, was used for experimental validation. Findings: The framework achieved high classification accuracy across all the models, with all the classifiers exceeding 0.98 accuracy. Compared with the PSO-WOA, the PSO-WOA with the Lévy flight method demonstrated superior performance in terms of both feature selection quality and training time efficiency. The results confirm that effective feature selection significantly improves model accuracy and interpretability. Novelty/Improvement: This research introduces a hybrid approach for feature selection (PSO-WOA) in the context of stress prediction from sleep-related IoMT data. The integration of Lévy flight into the PSO-WOA enhances exploration capabilities and reduces premature convergence, offering a robust solution for real-world healthcare applications, e.g., mobile stress monitoring and early intervention systems.

Aspect-based online information on social media plays a vital role in influencing people’s opinions when consumers concern with their decisions to make a purchase, or companies intend to pursue opinions on their product or services. Determining aspect-based opinions from the online information is necessary for business intelligence to support users in reaching their objectives. In this study, we propose the new aspect extraction and scoring system which has three procedures. The first procedure is normalizing and tagging part-of-speech for sentences of datasets. The second procedure is extracting aspects with pattern rules. The third procedure is assigning scores for aspects with SentiWordNet. In the experiments, benchmark datasets of customer reviews are used for evaluation. The performance evaluation of our proposed system shows that our proposed system has high accuracy when compared to other systems.

Online reviews are valuable sources of information to help companies to make good decisions for business intelligence. In this study, we propose an Automatic Aspect-based Sentiment Summarization (AAbSS) system that has two components and can generate a summary as an output. The first component is the Aspect-based Knowledge Representation and Selection (AKRS) used to represent reviews based on aspects and their polarities for selecting aspect-based knowledge. To represent and selection knowledge, a set of frequency of polarity opinion strength, a summation of frequency of aspect, and an information of aspect are initiated. The second component is the Summary Format Generation (SFG) used to automatically generate three kinds of formats. In this component (SFG), new representations for visual and structured summaries, and a new way of applied natural language generation for a textual summary are proposed. In the experiments, 13 domains from benchmark datasets of customer reviews, e.g. cell phone, digital camera, etc. are used. The proposed system not only fast generates summaries having good performance when compared to other summaries generated by other systems and easily updated when adding new reviews in the same domain but also does not spend memory capacity to save any raw data.