Mobile application has been identified as the best platform for the expert system tool to reach as many users as possible. The main contribution of this paper is the development of an expert system tool for evaluating the ripeness of banana fruit. Utilizing Google Cloud Platform, the application sends the sample of banana image through Google Cloud Vision Application Programming Interface to get attribute readings from the sample image. The result of the analysis is compared with application's database of attributes datasets to determine the ripeness of the banana sample image. In this work, the ripeness of the banana is classified into three different class of maturity; unripe, ripe and overripe systematically based on their key attributes value. This work also involved the process of collecting samples of banana with different level of ripeness, application development and evaluation to improve the accuracy of the developed applications classification results using image processing and data mining techniques.

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Retinal blood vessel segmentation is crucial as it is the earliest process in measuring various indicators of retinopathy sign such as arterial-venous nicking, and focal arteriolar and generalized arteriolar narrowing. The segmentation can be clinically used if its accuracy is close to 100%. In this study, a new method of segmentation is developed for extraction of retinal blood vessel. In this paper, we present a new automated method to extract blood vessels in retinal fundus images. The proposed method comprises of two main parts and a few subcomponents which include pre-processing and segmentation. The main focus for the segmentation part is two morphological reconstructions which are the morphological reconstructions followed by the morphological top-hat transform. Then the technique to classify the vessel pixels and background pixels is Otsu’s Thresholding. The image database used in this study is the High Resolution Fundus Image Database (HRFID). The developed segmentation method accuracies are 95.17%, 92.06% and 94.71% when tested on dataset of healthy, diabetic retinopathy (DR) and glaucoma patients respectively. Overall, the performance of the proposed method is comparable with existing methods with overall accuracies were more than 90 % for all three different categories: healthy, DR and glaucoma.

Data-driven management of electric energy systems could provide major returns to system operation and control. This paper explores the potential applications of big data analytics in electricity grids. The primary sources of data in electric utilities are first outlined. These include phasor measurement units (PMUs), smart meters, intelligent electronic devices (IEDs), weather data, geographic information system (GIS), and electricity market data. Potential applications, relating to fault analysis, state estimation, security assessment, variable renewable energy, and power market operation are further described.

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