The manufacturing industries have been searching and developing new solutions to increase the product quality and to decrease the time taken and costs of production. Defect detection methodologies consume much time in manufacturing and manual inspections for quality enhancement. The existing systems cannot handle the data other than the trained ones as they followed the comparison process with the dataset which is of more time consuming and lack of effective depth representation. In the proposed system, multi scale saliency defect detection algorithm is implemented to obtain the boundaries and range of defect in the surface of industrial products. The defect in the products can be detected using pre-processing defect image with color channels, detecting uneven illumination and post processing the defect image thereby splitting out the defect part from original image with edge detection and contours. Hence the output will be of more robust and accurate comparing to the existing systems.

Despite learning English formally in primary classrooms, most of the Malaysian primary learners are incapable of conveying their ideas accurately in the target language since they have limited vocabulary. Action research based on the Kemmis and McTaggart Action Research Model was carried out to alleviate the vocabulary learning challenges. Thus, the study aims are to investigate the effects of the Poly Category Mind Map for vocabulary development of third-year students. Thirty students from the third-year suburban school in Mersing, Johor participated in the study. Vocabulary pre-test and post-tests used as the instruments of collecting the data. The results of the post-test at the end of the second cycle showed a significant difference between the mean scores before and after using Poly Category Mind Map to develop vocabulary (p>0.05).

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Owing to the corona pandemic, the government has insisted on wearing a safety mask and maintaining 6 feet distance to get rid of CoronaVirus. The detection of people with or without masks is a challenge due to the impact of Covid pandemic. There are some models / systems which really reduce the manpower to notify the people. The existing system runs on the model: Yolov3, V G G, for face detection and MobileNetv2 for face recognition, object detection, and semantic segmentation inorder to detect the people with and without masks. The proposed system holds an approach of detecting human’s faces and classifying them into people with and without masks which has been done using image processing and deep learning and our project runs u.3nder a model called Faster RCNN. Moreover, Faster R-CNN is more accurate while other models are faster. Being effective is not important but being efficient is way more important.