2022-10

Science Journal of University of Zakho (Issue : 12) (Volume : 10)

Besides social network analysis, the Link-Prediction (LP) problem has useful applications in information retrieval, bioinformatics, telecommunications, microbiology, and e-commerce as a forecast of future links in a given context to find what possible connections are based on a local and global statistical analysis of the given graph data. However, in Academic Social Networks (ASNs), the LP issue has recently attracted a lot of attention in academia and called for a variety of link prediction techniques to predict co-authorship among researchers and to examine the rich structural and associated data. As a result, this study investigates the problem of LPG in ASNs to forecast the upcoming co-authorships among researchers. In a systematic approach, this review presents, analyses, and compares the primary taxonomies of topological-based, content-based, and hybrid-based approaches, which are used for computing similar scores for each pair of unconnected nodes. Then, this study ends with findings on challenges and open problems for the community to work on for further development of the LP problem of scholarly social networks.

2022-08

Biomedical Signal Processing and Control

Heart Disease (HD) is often regarded as one of the deadliest human diseases. Therefore, early prediction of HD risks is crucial for prevention and treatment. Unfortunately, current clinical procedures for diagnosing HD are costly and often require an expert level of intervention. In response to this issue, researchers have recently developed various intelligent systems for the automated diagnosis of HD. Among the developed approaches, those based on artificial neural networks (ANNs) have gained more popularity due to their promising prediction results. However, to the authors’ knowledge, no research has attempted to exploit ANNs for feature extraction. Hence, research into bridging this gap is worthwhile for more excellent predictions. Motivated by this fact, this research proposes a new approach for HD prediction, utilizing a pre-trained Deep Neural Network (DNN) for feature extraction, Principal Component Analysis (PCA) for dimensionality reduction, and Logistic Regression (LR) for prediction. Cleveland, a publicly accessible HD dataset, was used to investigate the efficacy of the proposed approach (DNN + PCA + LR). Experimental results revealed that the proposed approach performs well on both the training and testing data, with accuracy rates of 91.79% and 93.33%, respectively. Furthermore, the proposed approach exhibited better performance when compared with the state-of-the-art approaches under most of the evaluation metrics used.