Predicting students’ academic performance is very essential to produce high-quality students. The main goal is to continuously help students to increase their ability in the learning process and to help educators as well in improving their teaching skills. Therefore, this study was conducted to predict mathematics students’ performance using Artificial Neural Network (ANN). The secondary data from 382 mathematics students from UCI Machine Learning Repository Data Sets used to train the neural networks. The neural network model built using nntool. Two inputs are used which are the first and the second period grade while one target output is used which is the final grade. This study also aims to identify which training function is the best among three Feed-Forward Neural Networks known as Network1, Network2 and Network3. Three types of training functions have been selected in this study, which are Levenberg-Marquardt (TRAINLM), Gradient descent with momentum (TRAINGDM) and Gradient descent with adaptive learning rate (TRAINGDA). Each training function will be compared based on Performance value, correlation coefficient, gradient and epoch. MATLAB R2020a was used for data processing. The results show that the TRAINLM  function is the most suitable function in predicting mathematics students’ performance because it has a higher correlation coefficient and a lower Performance value.

A disorder known as mental illness alters a person’s emotions, thoughts, or behaviour. Any of these elements alone or in combination may cause it. If stress cannot be managed, people of all ages, races, religions, sexes, and nationalities can develop mental illnesses. The development of mental diseases is significantly influenced by stress. Risk factors for mental illness and social stress include academic stress, socioeconomic position, and financial difficulties. All of these risk variables are challenging to pinpoint because they come from many environments. The purpose of this study is to identify the key aspects that students experience that contribute to mental illness and social stress, as well as to rank those factors by using Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). This study ranks social life, academic life, and financial status as the three factors that have the greatest influence on mental illness and social stress among college students. The following evaluation criteria are used to grade the components: family background, educational attainment, physical health, and mode of study. Additionally, Fuzzy TOPSIS is used to rank the variables according to correlation. The issues in this study are evaluated by three decision-makers using linguistic characteristics ranging from “very affected” to “not affected”. With a proximity value of 0.469, the research demonstrates that academic life has a significant impact on student’s mental health and social stress. With a closeness coefficient of 0.358, social life is the least significant factor. The results of this study may be useful to many people, including parents, counsellors, and the kids themselves. The project’s scope could be expanded in the future by adding a range of criteria and options.

Crude oil is one of the important commodities to Malaysia. As a producer and exporter of oil and gas, Malaysia has gained high Gross Revenue from this sector. Crude oil is the global commodity and highly demanded. Therefore, major price changes on the commodity have a significant influence on world economy. Market sentiment, demand, and supply are some elements directly influencing the oil prices. Since crude oil is the backbone of businesses and is extremely important to the economy, it is essential to study the price of crude oil for future planning purposes. For that reason, this study proposes the use of the Fuzzy Time Series Cheng to predict crude oil price in Malaysia. In this study, Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) are used to evaluate the forecast performance. The result shows that Fuzzy Time Series Cheng is able to produce a good result in forecasting since the analyses shows that the low value of RMSE and MAPE (less than 10 percent). Although this is the fundamental study but the finding may assist many sectors in Malaysia, such as governments, enterprises, investors, and businesses to produce a better economic planning in the future especially after the pandemic covid-19 phase.

Many workplaces encounter complex problems in preparing an optimal work scheduling to meet the 24 hours work demand especially in shift working hours. The schedule needs to consider many constraints and multi objectives at the same time. A mathematical model such as Goal programming is able to cater this kind of problems. Thus, this study was designed to provide a systematic and optimal schedule for police officers at Criminal Unit, IPD Kuala Muda, Kedah. This study is aimed to formulate the best model for the shift rotating schedule of the police officers and to find the best way to optimize the police scheduling related to the limitations, requirements of the police   station and the preferences of the police. Lingo software is used to run the model. However, only one out of three goals set for the study was achieved. The new schedule obtained shows that all police officers have the same number of working days, which is 21 days in the 28-day planning period. The new schedule produced is better than the previous manual schedule since it takes less time to prepare it without neglecting the constraints involved. To improve efficiency and flexibility on the generated schedules, it is recommended to use other methods such as hybrid swarm-based optimization and many new limitations and preferences should be also considered in the analysis.

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