The global spread of Coronavirus (COVID-19) has prompted imperative research into scalable and effective detection methods to curb its outbreak. The early diagnosis of COVID-19 patients has emerged as a pivotal strategy in mitigating the spread of the disease. Automated COVID-19 detection using Chest X-ray (CXR) imaging has significant potential for facilitating large-scale screening and epidemic control efforts. This paper introduces a novel approach that employs state-of-the-art Convolutional Neural Network models (CNNs) for accurate COVID-19 detection. The employed datasets each comprised 15,000 X-ray images. We addressed both binary (Normal vs. Abnormal) and multi-class (Normal, COVID-19, Pneumonia) classification tasks. Comprehensive evaluations were performed by utilizing six distinct CNN-based models (Xception, Inception-V3, ResNet50, VGG19, DenseNet201, and InceptionResNet-V2) for both tasks. As a result, the Xception model demonstrated exceptional performance, achieving 98.13% accuracy, 98.14% precision, 97.65% recall, and a 97.89% F1-score in binary classification, while in multi-classification it yielded 87.73% accuracy, 90.20% precision, 87.73% recall, and an 87.49% F1-score. Moreover, the other utilized models, such as ResNet50, demonstrated competitive performance compared with many recent works.

An honest peer-review process is a key for producing high quality scientific research. However, this process depends on two main factors: (1) the expertise of reviewers in the topic of a submitted paper and (2) the relationships between reviewers and authors. To satisfy the first factor, editors and conferences chairs manually select reviewers. Whereas to prevent any conflict of interest (CoI) between reviewers and authors to satisfy the second factor, reviewers and authors are asked to declare any CoI manually. Such a solution is tedious to all actors and error-prone. To solve this problem and satisfy those two factors, we have developed a novel framework that (1) recommend expert reviewers and (2) resolve the CoIproblem. To develop our framework, we have represented the DBLP citation network dataset as a graph database using Neo4J. A Cypher queries used to select expert reviewers. Various link prediction algorithms, especially the Adamic Adar and the Common Neighbors algorithms, have been utilized to resolve any notential conflict of interest.