The outbreak of a health crisis, such as Covid-19, leads to decisions that must combine efficiency and speed. Often there is a trade-off between these two values, as the faster a decision is made, the less information is considered. This paper presents a deep learning model pipeline that balances these two values with the primary goal of classifying human lung X-rays into three categories: pneumonia, covid-19 and normal. Through this process, we tried to explore whether the quality of an image can enhance the learning process to a greater extent as opposed to having larger number of images. For this purpose, we follow two approaches by viewing quality and quantity as competing objectives to increasing the level of information obtained. The first is through increasing the number of X-ray images in the dataset, and the second is through improving the quality of the X-ray images. In the first approach, our goal is achieved using a Generative Adversarial Network (GAN) to generate plasmatic covid-19 class X-rays, while in the second approach, we improve the resolution of the X-ray images. To find the hyperparameters in both approaches that lead to better system performance, we exploit the Particle Swarm Optimization (PSO) algorithm. Rapid training and hyperparameter tuning better perform through this algorithm. Our experiments depict the performance that our models, based on the two approaches, achieved. Accuracy reaches 93% while sensitivity reaches 90% over Covid-19 cases. Finally, we conclude which characteristic, quality or quantity, is most useful in our case.

Since the beginning of 2020, the whole world has been plagued by the coronavirus pandemic. During the last sixteen months, almost every country in the world has faced several epidemic waves. An intriguing question that arises is whether neighboring countries, similar in regard to their socioeconomic status and the restrictions employed to counter the spread of the virus, showcase similarities in their respective number of cases and deaths. To that end, in this paper we form three clusters of similar countries (European and USA, African-Asian and Latin American) and we use their cumulative data as training data for machine learning models (RNN family, TCN and Attention) that predict the respective cases and deaths of 4 fixed neighboring countries, namely Cyprus, Greece, Italy and Spain. The results of the experiments conducted show that these 4 countries accent bigger similarity with the European cluster, as expected. Thus, evidence is provided bolstering the claim that similar neighboring countries exhibit alike behavior regarding the repercussions of the COVID-19.

We propose a novel method for automatic annotation, indexing and annotation-based retrieval of images. The new method, that we call Markovian Semantic Indexing (MSI), is presented in the context of an online image retrieval system. Assuming such a system, the users' queries are used to construct an Aggregate Markov Chain (AMC) through which the relevance between the keywords seen by the system is defined. The users' queries are also used to automatically annotate the images. A stochastic distance between images, based on their annotation and the keyword relevance captured in the AMC, is then introduced. Geometric interpretations of the proposed distance are provided and its relation to a clustering in the keyword space is investigated. By means of a new measure of Markovian state similarity, the mean first cross passage time (CPT), optimality properties of the proposed distance are proved. Images are modeled as points in a vector space and their similarity is measured with MSI. The new method is shown to possess certain theoretical advantages and also to achieve better Precision versus Recall results when compared to Latent Semantic Indexing (LSI) and probabilistic Latent Semantic Indexing (pLSI) methods in Annotation-Based Image Retrieval (ABIR) tasks.