Chest x-ray image analysis is the common medical imaging exam needed to assess different pathologies. Having an automated solution for the analysis can contribute to minimizing the workloads, improve efficiency and reduce the potential of reading errors. Many methods have been proposed to address chest x-ray image classification and detection. However, the application of regional-based convolutional neural networks (CNN) is currently limited. Thus, we propose an approach to classify chest x-ray images into either one of two categories, pathological or normal based on Faster Regional-CNN model. This model utilizes Region Proposal Network (RPN) to generate region proposals and perform image classification. By applying this model, we can potentially achieve two key goals, high confidence in the classification and reducing the computation time. The results show the applied model achieved higher accuracy as compared to the medical representatives on the random chest x-ray images. The classification model is also reasonably effective in classifying between finding and normal chest x-ray image captured through a live webcam.

This paper investigates the application of deep Convolutional Neural Network (CNN) for herbal plant recognition through leaf identification. Traditional plant identification is often time-consuming due to varieties as well as similarities possessed within the plant species. This study shows that a deep CNN model can be created and enhanced using multiple parameters to boost recognition accuracy performance. This study also shows the significant effects of the multi-layer model on small sample sizes to achieve reasonable performance. Furthermore, data augmentation provides more significant benefits on the overall performance. Simple augmentations such as resize, flip and rotate will increase accuracy significantly by creating invariance and preventing the model from learning irrelevant features. A new dataset of the leaves of various herbal plants found in Malaysia has been constructed and the experimental results achieved 99% accuracy

Abstract-Religious non-profit organizations (RNPOs) exist to provide a variety of programs and services. They have an important niche in a wide range of social, human, education, health, and community services to their stakeholders. The reason for RNPOs' existence is its mission and thus, mission becomes the central thrust for RNPOs' existence and operations. Therefore, it is appropriate to focus on the financial management of RNPOs in their association with mission based approach to ratio analysis. A mission based approach to ratio analysis for RNPOs is one of the ways for a long way of having the financial performance measures for RNPOs. Such analysis through the use of ratios helps identifies the financial performance of RNPOs in actual faith of mission. The measures of financial performance concerning performance efficiency ratio (PER) and operating expense ratio (OER) were constructed in this study. The ratios were generated for 60 RNPOs and were considered a good measure of the efficiency in which the organization fulfilled its mission.

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This study investigates the factors that influence the students’ performance in the first computer programming course taught using the blended learning approach.  A blended learning model proposed by Hadjerrouit was adapted, which consists of conceptualization, construction, and dialogue phases. The objective of this study is to identify which phase in the adapted blended learning model has an influence on the performance of the students.  A cross-sectional population study with simple random sampling was conducted at Universiti Teknologi MARA (UiTM).  Stepwise multiple linear regression revealed that only the construction phase of the blended learning model was significant towards students’ performance. Keywords: Blended Learning; Face-to-Face Learning; Programming; Online Learning eISSN: 2398-4287© 2020. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v5iSI3.2559