This research work explores the application of Artificial Intelligence in SAP S/4 HANA FICO for automating internal control hence compliance risk and financial analysis. To overcome the drawbacks of conventional manual controls and introduce concepts such as machine learning and analytics that improve the real-time control for compliance and anti-fraud, as well as increase operational effectiveness. By using case studies and the evaluation framework adopted in his study, the author proves that AI solutions enhance decision-making; critically address legal concerns; and minimise risks in effective financial management, which offers an effective way at being compliant with the regulations at a proactive level. Global corporate investment in AI has expanded in recent years. AI in SAP S/4HANA was pivotal in creating in-depth data analysis. These insights enable decision-makers to highlight underlying patterns and market trends in data that were not immediately obvious. Therefore, AI-driven recommendations in SAP were major in creating a more user-friendly experience, increasing productivity and efficiency in the workflow.   

AI-enabled technologies enable automation of the routine tasks, enhance fraud detection, and also support predictive decision making in the regulatory frameworks. Studies in JPMorgan Chase, HSBC, and Pfizer show that AI can simplify and improve the compliance processes, verify facts and eliminate manual errors. Although it has its perks, there are also obstacles in implementing AI as ethical concerns, data safety hasards, and complicated integration. These challenges need the support from strong governance frameworks, standardisation and regulators' technology firms working together to overcome these challenges. By exploiting the strategic role of AI in regulatory transformation, this study also provides recommendations for its adoption, so that it results in revising, reinvigorating and improving compliance mechanisms transparently and efficiently.

Kubernetes architecture has become a preferred platform for managing workload applications that handle extensive customer data, including mission-critical applications within software containers and multi-tenant systems. However, the security of Kubernetes systems must be strengthened to prevent misuse and potential anomalies. This research introduces an advanced machine learning-based approach for detecting misuse in Kubernetes environments. The proposed Machine Learning (ML) framework encompasses data pre-processing, feature engineering, model training, and evaluation stages for effective anomaly detection.
The primary objectives of this study include transforming raw traffic flow data into structured features suitable for machine learning models, capturing the sequential nature of traffic flows using time-series models to understand normal and misuse behaviours, developing advanced machine learning models for classifying normal and malicious traffic flow sequences, and evaluating the performance of the proposed model against other relevant models using appropriate metrics.
To achieve these goals, the raw traffic flow data, including timestamps, traffic flow names, categories, and arguments, undergoes pre-processing and feature extraction. Time-series models, such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), are employed to identify sequential patterns and detect deviations indicative of misuse. Supervised learning algorithms like Random Forests, Support Vector Machines (SVMs), and Neural Networks are utilized to classify anomalies in traffic flow sequences. The performance of the proposed model is assessed using metrics like accuracy, precision, recall, F1 score, and AUC-ROC.
This research aims to enhance Kubernetes security by effectively detecting misuses and potential threats in real-time, thereby fortifying the overall system's resilience.

The worldwide CoVid-19 pandemic poses great impact to human lives remarkably to psychological well-being. Thus, this study aimed to investigate and determine the prevalence of depression, anxiety, and stress (DAS) among students during the pandemic. A descriptive quantitative research design was utilized in the study. In gathering the data, the study utilized the standardized survey questionnaire of Depression, Anxiety, and Stress Scales 42 (DASS-42) to evaluate the students DAS level. The data compilation used online questionnaires through the platform Google Forms. The study reported a significantly high level of depression, anxiety and stress from the sample population. The result of the study found that levels of depression, anxiety and stress vary from age, gender, educational level and grade/strand/course program. Younger age groups (adolescents and young adults) suffer higher DAS level among other age groups while gender reveals that females compared to males are far more vulnerable to anxiety and stress. Moreover, students regardless of educational level and strand/course program experience significantly severe level of anxiety. In conclusion, COVID-19 is an epidemiological crisis that is casting a shadow to students’ DAS level

During the process of leading the country, Ho Chi Minh soon outlined strategic directions for the country’s development, including the issue of environmental protection. Educating environmental protection awareness for all classes of people, especially the young generation, is of great significance. The young generation, including youth and students, is a part of society that the Communist Party of Vietnam always pays attention to training and nurturing; they will become the core force shouldering the responsibility of the country’s future. The purpose of this study is to clarify Ho Chi Minh’s Ideology on environmental protection, and the significance of that Ideology in educating environmental protection awareness for students today. To achieve this goal, the study uses disciplinary and interdisciplinary research methods in social sciences and humanities. Research results have shown that Ho Chi Minh’s ideology on environmental protection is still valuable today. At the same time, inheriting those values for environmental protection education activities for students will bring about great changes in the awareness and actions of Vietnamese students. Proposing necessary solutions will help universities improve the quality of environmental protection education for students at present and in the future

This study investigated the impact of AI-Powered Personalized Learning Platforms on the Academic Achievement and Motivation of Computer Science senior secondary school students in Abuja Municipal Area Council (AMAC) FCT-Abuja, Nigeria. The study raised two research questions and two corresponding hypotheses which were tested at 0.05 level of significance. The study adopted a mixed research method; of both the quasi-experimental design and descriptive survey research design. The population of the study comprised all the public senior secondary school students in Abuja Municipal Area Council (AMAC), Federal Capital Territory (FCT). A sample of 100 students was selected through a combination of purposive and simple random sampling techniques for the study. The researchers adopted Code.org as a treatment instrument while the instrument for data collection was a researchers’ designed Computer Achievement Test (CAT) and Motivation Based Questionnaire (MBQ). A draft of the questionnaire was validated through expert judgment involving three lecturers. This was done to establish the face and construct validity. The reliability of the instrument was established through a test-re-test method. A reliability co-efficient of 0.85 and 0.80 was established for both instruments (CAT & MBQ). The data collected were analysed by using independent t-test statistics to test the two formulated hypotheses. The results indicated that Code.org significantly improved the academic achievement of secondary school students compared to traditional learning methods. Furthermore, Code.org positively impacted the motivation of secondary school students. The study recommended amongst others that schools should adopt AI-powered personalized learning platforms such as Code.org for teaching and learning

The study investigated Effect of Reflective Instructional Strategy on students’ performance in Cell-Physiology Concepts among Senior Secondary Students in Zaria. Quasi-experimental pretest, post test control group design. The population comprised of one thousand two hundred and twenty five Biology SS11 students from 11 public Senior Secondary Schools in Zaria metropolis. A sample of 137 students from two schools was randomly selected. The study involves two groups- experimental and a control group. The experimental group was taught Cell Physiology using Reflective Instructional Strategy while the control group was exposed to the same concept using Conventional Method. One instrument was used for data collection Namely Cell- Physiology Performance Test. The instrument was duly validated and have reliability coefficient of 0.87, using Pearson Product Moment Correlation statistic. Two research questions and two null hypotheses were formulated and answered using Mean and Standard deviation statistics and Independent Samples t-test at alpha 0.05 level of significance. The major findings reveals a significant difference between mean academic performance scores in favor of the experimental group. Based on the findings, it was recommended among others, that workshops, seminars and conferences should be organised for Biology teachers on the use of Reflective Instructional among secondary schools in Zaria metropolis

This study aimed to investigate the enrollment trends, motivations, and future aspirations of female undergraduate students in Science, Technology, Engineering, and Mathematics (STEM) courses at the Federal University Gusau in Nigeria. A survey design was employed, and a 36-item questionnaire was used to collect data from a sample of 350 female undergraduate students studying STEM courses at the university. The questionnaire had three sections focusing on demographic information, motivations and future career aspirations of the respondents in STEM fields. The questionnaire was validated and reliability coefficient of 0.72 was obtained using Cronbach alpha. The data collected were analyzed using mean and standard deviation. The results showed that female students' overall enrollment in STEM courses was relatively low. However, there were variations across disciplines, with some courses having as high as 50.70% (Biotechnology) and 49.43% (Microbiology), and as low as 8.36% (Physics), 9.06% (Geology) and 11.06% (Mathematics) of female enrollment. The overall enrollment rose from 23% in the 2014/2015 academic year to 31.4% in the 2019/2020. The study also found that personal interest, background knowledge, and academic success in STEM-related subjects were the key motivators for females to pursue STEM courses. On the other hand, lack of family and community support, lack of confidence in problem-solving, and the need for role models and mentors were significant barriers. It is recommended among others that, universities and policymakers provide more resources and support to female students in STEM fields, such as mentorship programmes, scholarships, and initiatives to promote gender equity.