Motif discovery can be used to categorize unknown DNA sequences into their corresponding families. For this study, PSO was modified for discovering motif. The modified Linear-PSO is chosen even though it is a slower because linear search is not a choice but a necessary criteria for identifying motif of pig (Sus Scrofa). Pig motif identification is a critical for halal authentication. The modified Linear-PSO algorithm used linear number for population initializing and next position updating. For each cycle, only a particle called 'target motif' was selected and compared with other DNA sequences for fitness calculation. Motif discovered can be used as a standard motif for species identification. Experimental results show that the modified algorithm is able to identify motifs as expected. This study showed that a slower algorithm is still needed and has value based on how critical the problem is.
Edge detection is important in image analysis to form the shape of an object. Edge is the boundary between different textures, which helps with object segmentation and recognition. Currently, several edge detection techniques are able to identify objects but are unable to localize the shape of an object. To address this problem, this paper proposes a fusion of selected edge detection algorithms with mathematical morphology to enhance the ability to detect the object shape boundary. Edge detection algorithm is used to simplify image data by minimizing the amount of pixel to be processed, whereas the mathematical morphology is used for smoothing effects and localizing the object shape using mathematical theory sets. The discussion section focuses on the improved edge map and boundary morphology (EmaBm) algorithm as a new technique for shape boundary recognition. A comparative analysis of various edge detection algorithms is presented. It reveals that the LoG's edge detection embedded in EmaBM algorithm performs better than the other edge detection algorithms for fruit shape boundary recognition. Implementation of the proposed method shows that it is robust and applicable for various kind of fruit images and is more accurate than the existing edge detection algorithms.
Recent years, vision-based fruit grading system is gaining importance in fruit classification process. In developing the fruit grading system, image segmentation is required for analyzing the fruit objects automatically. Image segmentation is a process that divides a digital image into separate regions with the aim to obtain only the interest objects and remove the background. Currently, there are several segmentation techniques which have been used in object identification such as thresholding and clustering techniques. However, the conventional techniques have difficulties in segmenting fruit images which captured under natural illumination due to the existence of non-uniform illumination on the object surface. The presence of different illuminations influences the appearance of the interest objects and thus misleads the object analysis. Therefore, this research has produced an innovative segmentation algorithm for fruit images which is able to increase the segmentation accuracy. The developed algorithm is an integration of modified thresholding and adaptive K-means method. The integration of both methods is required to increase the segmentation accuracy for fruits images with different surface colour. The results showed that the innovative method is able to segment the fruits images with high accuracy value,
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