Diabetes is one of the chronic diseases, which is increasing from year to year. The problems begin when diabetes is not detected at an early phase and diagnosed properly at the appropriate time. Different machine learning techniques, as well as ontology-based ML techniques, have recently played an important role in medical science by developing an automated system that can detect diabetes patients. This paper provides a comparative study and review of the most popular machine learning techniques and ontology-based Machine Learning classification. Various types of classification algorithms were considered namely: SVM, KNN, ANN, Naive Bayes, Logistic regression, and Decision Tree. The results are evaluated based on performance metrics like Recall, Accuracy, Precision, and F-Measure that are derived from the confusion matrix. The experimental results showed that the best accuracy goes for ontology classifiers and SVM.

This document Among all the various types of mental and psychosocial illnesses, the most commonly occurring type is depression. It can cause serious problems such as suicide. Therefore, early detection is important to stop the progression of this disease that could endanger human lives. Predicting and detecting early-stage depression using machine learning (ML) techniques is a promising strategy. This study’s main purpose is to assess which ML techniques are highly appropriate and accurate regarding such diagnoses. Six supervised ML techniques namely: K-nearest neighbor (KNN), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Support vector machine (SVM) and Naive Bayes (NB) were applied on dataset collected from Kaggle and compared for their accuracy (ACC) and performance in predicting depression. The performance of each model was evaluated using 10-fold cross-validation and evaluated in terms of ACC, F1-score, Precision (PR), and Sensitivity (SEN). Based on the experimental results analysis, we can conclude that SVM and LR performed better than all other methods with an ACC of 83,32%. Therefore, we found that a simple ML algorithm can be used to assist clinicians and practitioners predict depression at an early stage, with excellent potential utility and a considerable degree of ACC.

The rising frequency and complexity of cybersecurity threats necessitate robust monitoring and rapid response capabilities to safeguard digital assets effectively. As a result, many organizations are increasingly establishing Security Operations Centers (SOCs) to actively detect and respond to cybersecurity incidents. This paper addresses the intricate process of setting up a SOC, emphasizing the need for careful planning, substantial resources, and a strategic approach. This study outlines the essential steps involved in defining the SOC’s objectives and scope, selecting appropriate technologies, recruiting skilled cybersecurity professionals, and developing processes throughout the SOC lifecycle. This paper aims to provide a comprehensive understanding of the SOC’s threat detection capabilities and use cases. It also highlights the importance of choosing technologies that integrate seamlessly with existing IT infrastructure to ensure broad coverage of SOC activities. Furthermore, this study offers actionable insights for organizations looking to enhance their SOC capabilities, including a technical overview of SOC use case coverage and a gap assessment of detection rules. This assessment is based on an alignment with the MITRE ATT&CK framework and an analysis of events generated by the company’s existing IT devices and products. The findings from this research elucidate the indispensable role that SOCs play in bolstering organizational cybersecurity and resilience.

The increasing complexity and sophistication of cyber threats underscore the critical need for advanced threat detection mechanisms within Security Operations Centers (SOCs) to effectively mitigate risks and enhance cybersecurity resilience. This study enhances the capabilities of Wazuh, an open-source Security Information and Event Management (SIEM) system, by addressing its primary limitation: high false-positive rates in rule-based detection. We propose a hybrid approach that integrates machine learning (ML) techniques—specifically, Random Forest (RF) and DBSCAN—into Wazuh’s detection pipeline to improve both accuracy and operational efficiency. Experimental results show that RF achieves 97.2% accuracy, while DBSCAN yields 91.06% accuracy with a false-positive rate of 0.0821, significantly improving alert quality. Real-time deployment requirements are rigorously evaluated, with all models maintaining end-to-end processing latencies below 100 milliseconds and 95% of events processed within 500 milliseconds. Scalability testing confirms linear performance up to 500 events per second, with an average processing latency of 45 milliseconds under typical SOC workloads. This integration demonstrates a practical, resource-efficient solution for enhancing real-time threat detection in modern cybersecurity environments.

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.