Nowadays, pre-collision warning is one of the substantial aspects of the transportation sector. One of the steps to detect the collisions is by classifying and predicting the collisions. There are many supervised machine learning algorithms used, such as Least Square – Support Vector Machine (LS-SVM). Radial Based Function (RBF) is one of the LS-SVM kernels, which is a well-known method to support reliable performance. However, C and Gamma of its parameters are chosen randomly. This makes the performance of the classifier less optimal. To overcome that problem, this paper proposed a cuckoo search algorithm for optimizing two parameters to get optimal accuracy. The proposed approach is applied to 8437 transportation records and evaluated by using Accuracy. In addition, the performance of the proposed method is compared to other well-known meta-heuristic optimization algorithms, namely: Bat Algorithm (BA-SVM) and Firefly Algorithm (FA-SVM). Experimental results show that the Cuckoo Search Algorithm (CSA-SVM) yields the best performance for each of the 10-folds cross-validation by reaching 84.817% for accuracy, compared to the Bat Algorithm and Firefly Algorithm.
Automatic classification of tuna image needs a good segmentation as a main process. Tuna image is taken with textural background and the tuna’s shadow behind the object. This paper proposed a new weighted thresholding method for tuna image segmentation which adapts hierarchical clustering analysisand percentile method. The proposed method considering all part of the image and the several part of the image. It will be used to estimate the object which the proportion has been known. To detect the edge of tuna images, 2D Gabor filter has been implemented to the image. The result image then threshold which the value has been calculated by using HCA and percentile method. The mathematical morphologies are applied into threshold image. In the experimental result, the proposed method can improve the accuracy value up to 20.04%, sensitivity value up to 29.94%, and specificity value up to 17,23% compared to HCA. The result shows that the proposed method cansegment tuna images well and more accurate than hierarchical cluster analysis method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.