Diabetic retinopathy is a disease, which forms a severe threat on sight. It may reach to blindness among working age people. By analyzing and detecting of vasculature structures in retinal images, we can early detect the diabetes in advanced stages by comparison of its states of retinal blood vessels. In this paper, we present blood vessel segmentation approach, which can be used in computer based retinal image analysis to extract the retinal image vessels. Mathematical morphology and K-means clustering are used to segment the vessels. To enhance the blood vessels and suppress the background information, we perform smoothing operation on the retinal image using mathematical morphology. Then the enhanced image is segmented using K-means clustering algorithm. The proposed approach is tested on the DRIVE dataset and is compared with alternative approaches. Experimental results obtained by the proposed approach showed that it is effective as it achieved average accuracy of 95.10% and best accuracy of 96.25%

This paper presents a hybrid ontology-XLNet sentiment analysis classification approach for sentence-level aspects. The main objective of the proposed approach allows discovering user social data considering the extracted in-depth inference about sentiment depending on the context. Thus, in this paper, we investigate the contribution of utilizing the lexicalized ontology to improve the aspect-based sentiment analysis performance through extracting the indirect relationships in user social data. The XLNet model is utilized for extracting the neighboring contextual meaning and concatenating it with each embeddings word to produce a more comprehensive context and enhance feature extraction. In the proposed approach, Bidirectional Long Short Term Memory (Bi-LSTM) networks are used for classifying the aspects in online user reviews. Various experiments considering Adverse Drug Reactions (ADRs) discovery are conducted on six drug-related social data real-world datasets to evaluate the performance of the proposed approach using several measures. Obtained experimental results show that the proposed approach outperformed other tested state-of-the-art related approaches through improving feature extraction of unstructured social media text and accordingly improving the overall accuracy of sentiment classification. A significant accuracy of 98% and F-measure of 96.4% are achieved by the proposed ADRs aspect-based sentiment analysis approach.

Commonly, aquatic experts use traditional methods such as casting nets or underwater human monitoring for detecting existence and quantities of different species of fish. However, the recent breakthrough in digital cameras and storage abilities, with consequent cost reduction, can be utilized for automatically observing different underwater species. This article introduces an automatic classification approach for the Nile Tilapia fish using support vector machines (SVMs) algorithm in conjunction with feature extraction techniques based on Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) algorithms. The core of this approach is to apply the feature extraction algorithms in order to describe local features extracted from a set of fish images. Then, the proposed approach classifies the fish images using a number of support vector machines classifiers to differentiate between fish species. Experimental results obtained show that the support vector machines algorithm outperformed other machine learning techniques, such as artificial neural networks (ANN) and k-nearest neighbor (k-NN) algorithms, in terms of the overall classification accuracy.