There are three income categories for Malaysians: the top 20% (T20), the middle 40% (M40), and the bottom 40% (B40). The government has extended B40′s access to higher education to eliminate socioeconomic disparities and improve their lives. The number of students enrolled in bachelor’s degree programmes at universities has risen annually. However, not all students who enrolled graduated. Machine learning approaches have been widely used and improved in education. However, research studies related to unsupervised learning in education are generally lacking. Therefore, this study proposes a clustering-based approach for classifying B40 students based on their performance in higher education institutions (HEIs). This study developed three unsupervised models (k-means, BIRCH, and DBSCAN) based on the data of B40 students. Several data pre-processing tasks and feature selection have been conducted on the raw dataset to ensure the quality of the training data. Each model is optimized using different tuning parameters. The observational results have shown that the optimized k-means on Model B (KMoB) achieved the highest performance among all the models. KMoB produced five clusters of B40 students based on their performance. With KMoB, this study may assist the government in reducing HEI drop-out rates, increasing graduation rates, and eventually boosting students’ socioeconomic status.

Fire incident is a disaster that can potentially cause the loss of life, property damage and permanent disability to the affected victim. They can also suffer from prolonged psychological and trauma. Fire fighters are primarily tasked to handle fire incidents, but they are often exposed to higher risks when extinguishing fire, especially in hazardous environments such as in nuclear power plant, petroleum refineries and gas tanks. They are also faced with other difficulties, particularly if fire occurs in narrow and restricted places, as it is necessary to explore the ruins of buildings and obstacles to extinguish the fire and save the victim. With high barriers and risks in fire extinguishment operations, technological innovations can be utilized to assist firefighting. Therefore, this paper presents the development of a firefighting robot dubbed QRob that can extinguish fire without the need for fire fighters to be exposed to unnecessary danger. QRob is designed to be compact in size than other conventional fire-fighting robot in order to ease small location entry for deeper reach of extinguishing fire in narrow space. QRob is also equipped with an ultrasonic sensor to avoid it from hitting any obstacle and surrounding objects, while a flame sensor is attached for fire detection. This resulted in QRob demonstrating capabilities of identifying fire locations automatically and ability to extinguish fire remotely at particular distance. QRob is programmed to find the fire location and stop at maximum distance of 40 cm from the fire. A human operator can monitor the robot by using camera which connects to a smartphone or remote devices.

The purpose of our study is to develop the flexible and lightweight actuator and apply it into a flexible robot arm. In this paper, the master-slave attitude control and the trajectory control of the flexible robot arm are proposed. This robot arm has three degree-of-freedom that is bending, expanding and contracting and will be applied into a rehabilitation device for human wrist. The master-slave control system which is proposed in this paper is necessary when a physical therapist wants to give a rehabilitation motion to a patient. While the trajectory control system is necessary when a sequential rehabilitation motion is applied to a patient. In this paper, an analytical model of a flexible robot arm is proposed for master-slave attitude control and trajectory control. Then, a compact and inexpensive control system is developed and tested. The system consists of a flexible robot arm, a low-cost embedded controller, accelerometers and small-sized quasi-servo valves which are developed by using the on/off control valve in our laboratory. The results from these experiments demonstrate that the master-slave attitude control can be realized by using an accelerometer and a simple analytical model of the robot arm. The trajectory control also was realized for a square trajectory by using an analytical model and a compact control system. The error between the desired trajectory and measured one is relatively large compared with typical robot arm. This is because by a friction that is existing in a rod-less type flexible pneumatic cylinder. The control performance can be improved by reducing the friction or by improving the control scheme

The primary agricultural pursuit in Malaysia centres around banana cultivation; however, this vital crop faces the daunting challenge of multiple diseases that hinder its growth. The adverse consequences of these diseases extend beyond the farms to impact the nation’s economy. To empower farmers with the tools to promptly identify and categorize these diseases, image processing techniques offer a valuable solution. This research leverages deep learning Convolutional Neural Networks (CNN) implemented through MATLAB in conjunction with a DJI drone. By harnessing this technology, the system can automatically detect and classify major banana diseases. The study meticulously fine-tuned several hyperparameters to achieve impressive training and testing accuracy levels. The results revealed that the model attained its highest training accuracy of 81.27% at epoch 8 and its lowest accuracy of 78.40% at epoch 4, demonstrating its potential to aid in early disease detection and classification in banana crops.