A Machine-Learning-Based Approach to Prediction of Biogeographic Ancestry within Europe.

Data obtained with the use of massive parallel sequencing (MPS) can be valuable in population genetics studies. In particular, such data harbor the potential for distinguishing samples from different populations, especially from those coming from adjacent populations of common origin. Machine learning (ML) techniques seem to be especially well suited for analyzing large datasets obtained using MPS. The Slavic populations constitute about a third of the population of Europe and inhabit a large area of the continent, while being relatively closely related in population genetics terms. In this proof-of-concept study, various ML techniques were used to classify DNA samples from Slavic and non-Slavic individuals. The primary objective of this study was to empirically evaluate the feasibility of discerning the genetic provenance of individuals of Slavic descent who exhibit genetic similarity, with the overarching goal of categorizing DNA specimens derived from diverse Slavic population representatives. Raw sequencing data were pre-processed, to obtain a 1200 character-long binary vector. A total of three classifiers were used-Random Forest, Support Vector Machine (SVM), and XGBoost. The most-promising results were obtained using SVM with a linear kernel, with 99.9% accuracy and F1-scores of 0.9846-1.000 for all classes.

Deploying digitalisation and artificial intelligence in sustainable development research.

Many industrialised countries have benefited from the advent of twenty-first century technologies, especially automation, that have fundamentally changed manufacturing and industrial production processes. The next step in the evolution of automation is the development of artificial intelligence (AI), i.e. intelligence which is demonstrated by machines and systems, which cannot only perform tasks but also work synergistically with humans and nature. Intelligent systems that can see, analyse situations and respond sensitively to real-time cues, from human gestures and facial expressions to pedestrians crossing a busy street, will reshape transportation, precision agriculture, biodiversity conservation, environmental modelling, public health, construction and manufacturing, as well as initiatives designed to promote prosperity on Earth. This paper explores the connections between AI systems and sustainable development (SD) research. By means of a literature review, world survey, and case studies, ways in which AI can support research on SD and, inter alia, contribute to a more sustainable and equitable world, are identified.

Influence of augmentation on the performance of the double ResNet-based model for chest X-ray classification.

Purpose: A pandemic disease elicited by the SARS-CoV-2 virus has become a serious health issue due to infecting millions of people all over the world. Recent publications prove that artificial intelligence (AI) can be used for medical diagnosis purposes, including interpretation of X-ray images. X-ray scanning is relatively cheap, and scan processing is not computationally demanding. Material and methods: In our experiment a baseline transfer learning schema of processing of lung X-ray images, including augmentation, in order to detect COVID-19 symptoms was implemented. Seven different scenarios of augmentation were proposed. The model was trained on a dataset consisting of more than 30,000 X-ray images. Results: The obtained model was evaluated using real images from a Polish hospital, with the use of standard metrics, and it achieved accuracy = 0.9839, precision = 0.9697, recall = 1.0000, and F1-score = 0.9846. Conclusions: Our experiment proved that augmentations and masking could be important steps of data pre-processing and could contribute to improvement of the evaluation metrics. Because medical professionals often tend to lack confidence in AI-based tools, we have designed the proposed model so that its results would be explainable and could play a supporting role for radiology specialists in their work.

Advances in Computer Recognition, Image Processing and Communications.

This Special Issue aimed to gather high-quality advancements in theoretical and practical aspects of computer recognition, pattern recognition, image processing and machine learning (shallow and deep), including, in particular, novel implementations of these techniques in the areas of modern telecommunications and cybersecurity [...].

Who Will Score? A Machine Learning Approach to Supporting Football Team Building and Transfers.

BACKGROUND: the machine learning (ML) techniques have been implemented in numerous applications, including health-care, security, entertainment, and sports. In this article, we present how the ML can be used for building a professional football team and planning player transfers. METHODS: in this research, we defined numerous parameters for player assessment, and three definitions of a successful transfer. We used the Random Forest, Naive Bayes, and AdaBoost algorithms in order to predict the player transfer success. We used realistic, publicly available data in order to train and test the classifiers. RESULTS: in the article, we present numerous experiments; they differ in the weights of parameters, the successful transfer definitions, and other factors. We report promising results (accuracy = 0.82, precision = 0.84, recall = 0.82, and F1-score = 0.83). CONCLUSION: the presented research proves that machine learning can be helpful in professional football team building. The proposed algorithm will be developed in the future and it may be implemented as a professional tool for football talent scouts.

Pre-processing methods in chest X-ray image classification.

BACKGROUND: The SARS-CoV-2 pandemic began in early 2020, paralyzing human life all over the world and threatening our security. Thus, the need for an effective, novel approach to diagnosing, preventing, and treating COVID-19 infections became paramount. METHODS: This article proposes a machine learning-based method for the classification of chest X-ray images. We also examined some of the pre-processing methods such as thresholding, blurring, and histogram equalization. RESULTS: We found the F1-score results rose to 97%, 96%, and 99% for the three analyzed classes: healthy, COVID-19, and pneumonia, respectively. CONCLUSION: Our research provides proof that machine learning can be used to support medics in chest X-ray classification and improving pre-processing leads to improvements in accuracy, precision, recall, and F1-scores.

A Novel Lightweight Approach to COVID-19 Diagnostics Based on Chest X-ray Images.

BACKGROUND: This paper presents a novel lightweight approach based on machine learning methods supporting COVID-19 diagnostics based on X-ray images. The presented schema offers effective and quick diagnosis of COVID-19. METHODS: Real data (X-ray images) from hospital patients were used in this study. All labels, namely those that were COVID-19 positive and negative, were confirmed by a PCR test. Feature extraction was performed using a convolutional neural network, and the subsequent classification of samples used Random Forest, XGBoost, LightGBM and CatBoost. RESULTS: The LightGBM model was the most effective in classifying patients on the basis of features extracted from X-ray images, with an accuracy of 1.00, a precision of 1.00, a recall of 1.00 and an F1-score of 1.00. CONCLUSION: The proposed schema can potentially be used as a support for radiologists to improve the diagnostic process. The presented approach is efficient and fast. Moreover, it is not excessively complex computationally.