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Education field is a sign of advancement over the countries that can adopt technology to serve it. It will help to improve and enhance future achievements and be in touch with the development of technology utilizing solutions that extract student data, including their school records and other vital information about their performance, which can facilitate this process. These data are then analyzed to identify factors that affect the academic performance of the students at the school by expanding data mining techniques to enhance student academic performance. These factors are examined to develop a predictive model. Machine learning (ML) is one artificial intelligence (AI) field that can use such a model that supports educational institutions and decision-makers. A predictive method is applied using the data mining (DM) technique to take proactive action in identifying and anticipating the student's path. The data was analyzed, and the findings showed that the decision tree algorithm recorded the fastest training time for every 1000 rows. Also, the fast-scoring time for 1000 rows was in the decision tree algorithm, which was around 195 milliseconds, and the longest scoring time occurred in the random forest algorithm, which was two seconds. The top percent of classification errors reached 51% for the logistic regression algorithm and around +-1.5% of standard deviation. It took 520 mile-second for scoring time with 690 Gains for 67 m/s training time in every 1000 rows of the datasets. The findings of this study can help parents and teachers better understand the factors that influence students' academic performance and support them in assisting students with improving their academic performance.

The study's objective is to investigate the business intelligence (BI) capabilities of public sector organizations as well as the essential success variables associated with those skills. The research was carried out due to the fact that there has been a growth in the usage of BI, and a large number of the tools that are utilized in its operations are not reviewed, despite the fact that the influence of the user is considered to be one of the primary concerns associated with it. The study used a quantitative research method to look at Malaysian organizations and come up with some hypotheses to test the variables related to BI's capabilities in the public sector of an organization. There were good, reasonable samples of people who took part in the research. A questionnaire was used to get the information. When examined, the findings confirmed the correctness of every hypothesis made. Therefore, there is a connection between elements involving infrastructure, teams, data, top-level support, and user participation in data quality. The findings of this study helped determine whether or not there is a correlation between data quality and the speed with which business data is updated across geographies, the precision with which data is captured, the timeline with which it is processed and reported, and the ease with which key performance measures can be established at various operational levels within the company.

A huge amount of crucial information is contained in documents. The vast increase in the number of E-documents available for user access makes the utilization of automated text classification essential. Classifying or arranging documents into predefined groups is called Text classification. Feature selection (FS) is needed for minimizing the dimensionality of high-dimensional data and extracting only the features that are most pertinent to a particular task. One of the widely used algorithms for feature selection in text classification is the Evolutionary algorithm. In this paper, the filter method chi-square and the Artificial Bee Colony (ABC) algorithm were both used as FS methods. The chi-square method is a useful technique for reducing the number of features and removing those that are superfluous or redundant. The ABC technique considers the chi-square method's chosen features as viable solutions (food sources). The ABC algorithm searches for the most efficient selection of features that increase classification performance. Support Vector Machine and Naïve Bayes classifiers were used as a fitness function for the ABC algorithm. The experiment results demonstrated that the proposed feature selection method was able of decreasing the number of features by approximately 89.5%, and 94%, respectively when NB and SVM were used as fitness functions in comparison to the original dataset, while also enhancing classification performance