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Aquaculture is a fast-growing food-production sector that accounts for almost 50% of the world's fish used for consumption. Aquaculture refers to the cultivation of fish in cages. The fish in the cage must be fed at regular intervals, for which the number of fish helps in estimating the amount of feed to be put in the cage. The behavior of fish in a caged environment reflects their health. In the absence of an ambient atmosphere, fish are stressed, which results in frantic movement. The frantic behavior of fish can be identified using recent advancements in image and video processing. In this study, we have focused on frantic behavior detection, fish detection, and counting. For this, a RAS with Tilapia fish has been setup, and the videos of the fish are captured. The detection and counting have been achieved by using the YOLOv5 model. The model has resulted in a precision, Recall and F1-measure of 81%. The results are compared with the ground truth, which indicates that the model has been successful in counting the fish. The frantic movement of the fish has been detected by developing an optical flow model. The results are encouraging and can be used for frantic behavior detection.

Brain is one of the most important part of the body. Brain Hemorrhage is a severe head injury that deteriorates the performance and function of an individual. Brain Hemorrhage can be detected through CT (Computer Tomography) scan of the brain. CT scan uses narrow X-ray beam which rotates around the part of the body and provides a set of images from different angles and the computer creates a cross-sectional view. It is challenging to detect and segment the region of the brain having Hemorrhage. Hence an automated system would be handy at those times. In the proposed work an attempt has been made to segment and identify the hemorrhaged region of the brain in the CT scan slices of the image. Brain hemorrhage segmentation helps to identify the region of brain hemorrhage which in turn helps to treat the patients at an early stage. The region of brain hemorrhage is appropriately identified from the proposed algorithm.

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