Social media sites, such as Twitter, provide the means for users to share their stories, feelings, and health conditions during the disease course. Anemia, the most common type of blood disorder, is recognized as a major public health problem all over the world. Yet very few studies have explored the potential of recognizing anemia from online posts. This study proposed a novel mechanism for recognizing anemia based on the associations between disease symptoms and patients' emotions posted on the Twitter platform. We used k-means and Latent Dirichlet Allocation (LDA) algorithms to group similar tweets and to identify hidden disease topics. Both disease emotions and symptoms were mapped using the Apriori algorithm. The proposed approach was evaluated using a number of classifiers. A higher prediction accuracy of 98.96 % was achieved using Sequential Minimal Optimization (SMO). The results revealed that fear and sadness emotions are dominant among anemic patients. The proposed mechanism is the first of its kind to diagnose anemia using textual information posted on social media sites. It can advance the development of intelligent health monitoring systems and clinical decision-support systems.

Persistent threats (APTs) pose a significant challenge to cybersecurity due to their incredible evasive nature. Identification systems often fail to recognize APTs, resulting in significant data breaches and lost revenue. This study aims to solve this problem by developing a machine learning based intrusion detection system (IDS) specifically designed for APT detection This study aims to evaluate the performance of different machine learning algorithms for APT detection, IDS integration of these algorithms are efficient. The system includes integration and evaluation of system performance under real-world conditions. A major contribution of this research includes a comprehensive investigation of machine learning methods for APT recognition, IDS reprogramming, extensive empirical validation using real-world data Findings show that the proposed IDS greatly improves detection accuracy while reducing false positives for.

Timetabling at large covering many different types of problems which have their own unique characteristics. In education, the three most common academic timetabling problems are school timetable, university timetable and exam timetable. Exam timetable is crucial but difficult to be done manually due to the complexity of the problem. The main problem includes dual academic calendar, increasing student enrolments and limitations of resources. This study presents a solution method for exam timetable problem in centre for foundation studies and extension education (FOSEE), Multimedia University, Malaysia. The method of solution is a heuristic approach that include graph colouring, cluster heuristic and sequential heuristic.