Vegetables industry plays an important role especially in providing the abundant fresh agricultural products. Forecasting the vegetable price is vital in agriculture sector for effective decision making. In Malaysia, the problems faced by the farmers are not only their age, but also their competitive skill where the wholesale market and the hypermarket/supermarket are prioritized by the consumers in Malaysia for the fresh vegetables and fruits. This review article helps to recognize the current problems faced by the agricultural sector of Malaysia and study the relationship between the agriculture and E-Commerce. Recent researchers have mentioned the growth of the E-Agribusiness and the authors found the potential of an Agricultural E-Commerce platform with price forecasting model in solving the current national issue. This research reviews the existing agricultural E-Commerce platforms in worldwide and try to compare with the local one. After the reviews have been done, the authors bring up an idea in constructing the time analysis model in hybrid approach for veggies price forecasting in an agricultural E-Commerce platform which can be used by the government in deriving their policies.

Image processing is the most effective method for enhancement and segmentation of tuberculosis bacilli in sputum smear samples. Improper straining can result in poor screening results such as over-staining, under-staining, and blurred images. The goal is to find an image enhancement and segmentation technique that will prepare the image for feature extraction. There are still some shortcomings with existing method when it is implemented on Ziehl Neelsen images. In normal images, TB bacilli can be identified easily, but in blur and images with dark background, TB bacilli are sometimes hidden behind the sputum cells. Hence, the basic method of contrast enhancement is not enough to improve the contrast of TB bacilli as the object of interest within the image. In this study, the combination of local and partial contrast enhancement is proposed as the best method for image enhancement. Image segmentation can be accomplished using Otsu thresholding technique. Otsu's method is presented as most suitable image processing techniques in this paper. The goal of the Otsu Threshold is to find a threshold value that distinguishes the object of interest from the background. Experiment shows that the combination of local and partial contrast enhancement followed by Otsu’s method achieve an average segmentation accuracy of 98.93% when applied on 50 images of sputum smear.

Integrated Multi-Trophic Aquaculture (IMTA) is an advance method of aquaculture which combines species with different nutritional needs to live together. The combination between aquatic live and crops is called aquaponics. Aquatic waste that normally removed by biofilters in normal aquaculture practice will be absorbed by crops in this practice. Aquaponics have few common components and growbed provide the best filtration function. In growbed a siphon act as mechanical structure to control water fill and flush process. Water to the growbed comes from fish tank with multiple flow speeds based on the pump specification and height. Too low speed and too fast flow rate can result in siphon malfunctionality. Pumps with variable speed do exist but it is costly. Majority of the aquaponic practitioner use single speed pump and try to match the pump speed with siphon operational requirement. In order to remove the matching requirement some control need to be introduced. Preliminarily this research will show the concept of fill-and-flush for multiple pumping speeds. The final aim of this paper is to show how water level management can be done to remove the speed dependency. The siphon tried to be controlled remotely since wireless data transmission quite practical in vast operational area. Fog architecture will be used in order to transmit sensor data and control command. This paper able to show the water able to be retented in the growbed within suggested duration by stopping the flow in once predefined level.

Among the top 10 leading causes of mortality, tuberculosis (TB) is a chronic lung illness caused by a bacterial infection. Due to its efficiency and performance, using deep learning technology with FPGA as an accelerator has become a standard application in this work. However, considering the vast amount of data collected for medical diagnosis, the average inference speed is inadequate. In this scenario, the FPGA speeds the deep learning inference process enabling the real-time deployment of TB classification with low latency. This paper summarizes the findings of model deployment across various computing devices in inferencing deep learning technology with FPGA. The study includes model performance evaluation, throughput, and latency comparison with different batch sizes to the extent of expected delay for real-world deployment. The result concludes that FPGA is the most suitable to act as a deep learning inference accelerator with a high throughput-to-latency ratio and fast parallel inference. The FPGA inferencing demonstrated an increment of 21.8% in throughput while maintaining a 31% lower latency than GPU inferencing and 6x more energy efficiency. The proposed inferencing also delivered over 90% accuracy and selectivity to detect and localize the TB.

Image contrast and brightness for sputum slide images are among important factors that can affect the accuracy of image analysis process. Microscopic images usually have the problem of low contrast and blurry, which are caused by a couple of factors such as uneven thickness of the slides and manual preparation of the slide in the first place. In this research, image enhancement procedure has been considered for either increasing the image quality, increasing the visibility of tubercle bacilli or to ease the segmentation process. Partial contrast technique is considered for the contrast enhancement method. The results show that the modified technique helps to improve tubercle bacilli visibility. Hence the resulted images would become useful for the microbiologists and technologists for further analysis of Ziehl-Neelsen (ZN) sputum slide.

The organizer of sport events often facing problems such as wrong calculations of marks and scores, as well as difficult to create a good and reliable schedule. Most of the time, the issues about the level of integrity of committee members and also issues about errors made by human came into the picture. Therefore, the development of sport tournament automated scheduling system is proposed. The system will be able to automatically generate the tournament schedule as well as automatically calculating the scores of each tournament. The problem of scheduling the matches of a round robin and knock-out phase in a sport league are given focus. The problem is defined formally and the computational complexity is being noted. A solution algorithm is presented using a two-step approach. The first step is the creation of a tournament pattern and is based on known graph-theoretic method. The second one is an assignment problem and it is solved using a constraint based depth-first branch and bound procedure that assigns actual teams to numbers in the pattern. As a result, the scheduling process and knock down phase become easy for the tournament organizer and at the same time increasing the level of reliability.