Coronavirus (covid 19) is one of the most dangerous viruses that have spread all over the world. With the increasing number of cases infected with the coronavirus, it has become necessary to address this epidemic by all available means. Detection of the covid-19 is currently one of the world's most difficult challenges. Data science and machine learning (ML), for example, can aid in the battle against this pandemic. Furthermore, various research published in this direction proves that ML techniques can identify illness and viral infections more precisely, allowing patients' diseases to be detected at an earlier stage. In this paper, we will present how ontologies can aid in predicting the presence of covid-19 based on symptoms. The integration of ontology and ML is achieved by implementing rules of the decision tree algorithm into ontology reasoner. In addition, we compared the outcomes with various ML classifications used to make predictions. The findings are assessed using performance measures generated from the confusion matrix, such as F-measure, accuracy, precision, and recall. The ontology surpassed all ML algorithms with high accuracy value of 97.4%, according to the results.

Sentiment analysis (SA) plays a central role in understanding the multidimensional nature of comments on social media platforms like YouTube, Twitter, Facebook, and health forums. This article presents a multi-aspect, multi-sentiment annotation of chronic disease-related comments, highlighting the importance of connecting aspects, such as disease category and treatment, with corresponding sentiments (positive, negative, and neutral). The integration of ontologies enhances semantic consistency, enabling a comprehensive understanding of user experiences related to chronic diseases. The purpose of this paper is to bridge the gap between unstructured patient-generated content and actionable insights for healthcare professionals. A structured approach employing two ontologies—one for chronic disease aspects and another for sentiment classification—enables the linking of disease-related comments with associated emotions, supporting comprehensive SA. A multi-label classification model is trained to simultaneously predict multiple aspects and sentiments within a single comment, addressing the native complexities of sentiment expression. The article concludes with an evaluation of the model’s predictions against real-world annotated data to assess its effectiveness. Our approach achieves promising results, demonstrating its ability to accurately link comments to their corresponding aspects and sentiments.

Many traffic-sign detection systems are available to assist drivers with particular conditions such as small and distant signs, multiple signs on the road, objects similar to signs, and other challenging conditions. Real-time object detection is an indispensable aspect of these detection systems, with detection speed and efficiency being critical parameters. In terms of these parameters, to enhance performance in road-sign detection under diverse conditions, we proposed a comprehensive methodology, SSAM_YOLOv5, to handle feature extraction and small-road-sign detection performance. The method was based on a modified version of YOLOv5s. First, we introduced attention modules into the backbone to focus on the region of interest within video frames; secondly, we replaced the activation function with the SwishT_C activation function to enhance feature extraction and achieve a balance between inference, precision, and mean average precision (mAP@50) rates. Compared to the YOLOv5 baseline, the proposed improvements achieved remarkable increases of 1.4% and 1.9% in mAP@50 on the Tiny LISA and GTSDB datasets, respectively, confirming their effectiveness.

Depression profoundly affects emotional states, behaviors, and overall quality of life. In the digital era, patients increasingly share their experiences through online comments, offering valuable yet complex emotional narratives. However, traditional sentiment analysis methods, which often assign a single emotional label to an entire text, fail to capture the nuanced intra-comment emotional fluctuations, overlooking the evolving and sometimes contradictory nature of emotional expression. This study introduces a novel sentiment analysis framework that models intra-comment emotional flow as a Brownian-like trajectory. Inspired by the random motion of particles in physics, the proposed method treats each sentence as a time step and assigns it a sentiment polarity score. The cumulative sequence of these scores constructs an emotional curve that reflects the dynamic affective progression throughout the comment, effectively simulating a Brownian path. From these trajectories, quantitative indicators—including drift, variance, -signal changes, trajectory length ( ), maximum emotional level ( ), minimum emotional level ( ), and amplitude — are extracted, providing a detailed and structured understanding of emotional instability. To uncover patterns across comments, the K-Means clustering algorithm is applied, enabling the automatic grouping of comments with similar emotional dynamics. In addition, Principal Component Analysis (PCA) is used to reduce dimensionality and facilitate the visualization and interpretation of emotional profiles. The proposed approach is applied to a corpus of comments from depressed patients to identify characteristic emotional patterns and examine differences according to the type of treatment. This work highlights the relevance of stochastic modeling for advancing sentiment analysis in complex psychological contexts and offers a tool to support remote monitoring, personalized interventions, and early detection of emotional relapse.