Cyberbullying is a form of abuse, manipulation, or humiliation directed against a single person via the Internet. CB makes use of nasty Internet comments and remarks. It occurs when someone publicly mocks, insults, slanders, criticizes, or mocks another person while remaining anonymous on the Internet. As a result, there is a rising need to create new methods for sifting through data on social media sites for symptoms of cyberbullying. The goal is to lessen the negative consequences of this condition. This article discusses a soft computing-based methodology for detecting cyberbullying in social multimedia data. This model incorporates social media data. Normalization is performed to remove noise from data. To improve a feature, the Particle Swarm Optimization Technique is applied. Feature optimization helps to make cyberbullying detection more accurate. The LSTM model is used to classify things. With the help of social media data, the PSO LSTM model is getting better at finding cyberbullying. The accuracy of PSO LSTM is 99.1%. It is 2.9% higher than the accuracy of the AdaBoost technique and 10.4% more than the accuracy of the KNN technique. The specificity and sensitivity of PSO-based LSTM is also higher in percentage than KNN and AdaBoost algorithm.

This study looks at the changing women’s contribution to formal leadership roles in Saudi Arabia and assesses how their greater presence affects cross-sector organizational performance. By synthesizing available literature, analyzing the latest statistics, and reviewing pertinent case studies, this paper highlights the status of women in leadership positions within Saudi organizations, as well as the performance indicators associated with these organizations. The results indicate that organizations with greater proportions of women in leadership tend to show better results in terms of innovation, financial performance, employee engagement, and organizational culture. This advocacy research adds to the growing literature on women and gender issues, particularly concerning leadership in Saudi Arabia, offering contextual relevance to policymakers and organizational leaders focused on performance improvement through women’s inclusive leadership strategies.

Internet of medical things (IoMT) ties medical sensors, actuators, wearable, and communication technologies to provide an improved healthcare delivery process by means of constant patient monitoring and decision-making. The trend has resulted in an increase in its use because of the increasing prevalence of chronic diseases and aging, and the need to have cost-effective healthcare services. Nonetheless, the networks provided by the IoMT are prone to cyberattacks under the considerable threat as both process huge volumes of sensitive health data, and do not have the resources to do it. In this paper, the author suggests a new adaptive intrusion detection system (IDS) as a way to enhance the security of IoMT-enabled healthcare environments. The proposed model the pre-treats NSL-KDD dataset, filling gaps and transmitting nominal values. Particle swarm optimization (PSO) is used in the optimal selection of features and artificial neural network (ANN), K-nearest neighbors (KNN), and random forest (RF) classifiers are used in intrusion detection. Experimental performance proves that the PSO-ANN model can perform better with a high accuracy of 99.54%, sensitivity of 99.31% and specificity of 97.32% on average with high protection of intelligent healthcare systems.