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The heart is the most vital organ of the human body; thus, its improper functioning has a significant impact on human life. Coronary artery disease (CAD) is a disease of the coronary arteries through which the heart is nourished and oxygenated. It is due to the formation of atherosclerotic plaques on the wall of the epicardial coronary arteries, resulting in the narrowing of their lumen and the obstruction of blood flow through them. Coronary artery disease can be delayed or even prevented with lifestyle changes and medical intervention. Long-term risk prediction of coronary artery disease will be the area of interest in this work. In this specific research paper, we experimented with various machine learning (ML) models after the use or non-use of the synthetic minority oversampling technique (SMOTE), evaluating and comparing them in terms of accuracy, precision, recall and an area under the curve (AUC). The results showed that the stacking ensemble model after the SMOTE with 10-fold cross-validation prevailed over the other models, achieving an accuracy of 90.9 %, a precision of 96.7%, a recall of 87.6% and an AUC equal to 96.1%. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) are now the leading cause of death, as the quality of life and human habits have changed significantly. CVDs are accompanied by various complications, including all pathological changes involving the heart and/or blood vessels. The list of pathological changes includes hypertension, coronary heart disease, heart failure, angina, myocardial infarction and stroke. Hence, prevention and early diagnosis could limit the onset or progression of the disease. Nowadays, machine learning (ML) techniques have gained a significant role in disease prediction and are an essential tool in medicine. In this study, a supervised ML-based methodology is presented through which we aim to design efficient prediction models for CVD manifestation, highlighting the SMOTE technique's superiority. Detailed analysis and understanding of risk factors are shown to explore their importance and contribution to CVD prediction. These factors are fed as input features to a plethora of ML models, which are trained and tested to identify the most appropriate for our objective under a binary classification problem with a uniform class probability distribution. Various ML models were evaluated after the use or non-use of Synthetic Minority Oversampling Technique (SMOTE), and comparing them in terms of Accuracy, Recall, Precision and an Area Under the Curve (AUC). The experiment results showed that the Stacking ensemble model after SMOTE with 10-fold cross-validation prevailed over the other ones achieving an Accuracy of 87.8%, Recall of 88.3%, Precision of 88% and an AUC equal to 98.2%. [ABSTRACT FROM AUTHOR]

The coronavirus disease (COVID-19) pandemic was caused by the SARS-CoV-2 virus and began in December 2019. The virus was first reported in the Wuhan region of China. It is a new strain of coronavirus that until then had not been isolated in humans. In severe cases, pneumonia, acute respiratory distress syndrome, multiple organ failure or even death may occur. Now, the existence of vaccines, antiviral drugs and the appropriate treatment are allies in the confrontation of the disease. In the present research work, we utilized supervised Machine Learning (ML) models to determine early-stage symptoms of SARS-CoV-2 occurrence. For this purpose, we experimented with several ML models, and the results showed that the ensemble model, namely Stacking, outperformed the others, achieving an Accuracy, Precision, Recall and F-Measure equal to 90.9% and an Area Under Curve (AUC) of 96.4%. [ABSTRACT FROM AUTHOR]

The rapid growth in wireless communications, coupled with insufficient utilization of the spectrum, led to the development of new wireless services and the promising technology of cognitive radio (CR) networks, which facilitate periodic access to the unoccupied spectrum bands and thus increases spectral efficiency. A fundamental task in CR networks is spectrum sensing, through which unauthorized secondary users (SUs) detect unoccupied bands in the spectrum. To achieve this, an accurate estimate of the power spectrum is necessary. From this perspective, and given that many other factors can affect individual detection, such as pathloss and receiver uncertainty, we aim to improve its estimate by exploiting the spatial diversity in the SUs' observations. Spectrum sensing is treated as a parameters estimation problem, assuming that the parameters' vector of each SU consists of some global and partially common parameters. To exploit this modeling, distributed and cooperative spectrum sensing is the subject of interest in this study. Diffusion techniques, and especially the Adapt-Then-Combine (ATC) method will be exploited, where each SU cooperates with a group of nodes in its neighborhood that share the same parameters of interest. We consider a network of three static PUs with overlapping power spectrums, and thus, frequency bands. The performance of the employed method will be evaluated under two scenarios: (i) when the PUs spectrum varies, since some frequency bands are not yet utilized, and (ii) when the frequency bands of the PUs are fixed, but there is a mobile SU in the network, changing regions and parameters of interest. Experimental results and performance analysis reveal the ATC algorithm robustness and efficiency. [ABSTRACT FROM AUTHOR]

Cholesterol is a waxy substance found in blood lipids. Its role in the human body is helpful in the process of producing new cells as long as it is at a healthy level. When cholesterol exceeds the permissible limits, it works the opposite, causing serious heart health problems. When a person has high cholesterol (hypercholesterolemia), the blood vessels are blocked by fats, and thus, circulation through the arteries becomes difficult. The heart does not receive the oxygen it needs, and the risk of heart attack increases. Nowadays, machine learning (ML) has gained special interest from physicians, medical centers and healthcare providers due to its key capabilities in health-related issues, such as risk prediction, prognosis, treatment and management of various conditions. In this article, a supervised ML methodology is outlined whose main objective is to create risk prediction tools with high efficiency for hypercholesterolemia occurrence. Specifically, a data understanding analysis is conducted to explore the features association and importance to hypercholesterolemia. These factors are utilized to train and test several ML models to find the most efficient for our purpose. For the evaluation of the ML models, precision, recall, accuracy, F-measure, and AUC metrics have been taken into consideration. The derived results highlighted Soft Voting with Rotation and Random Forest trees as base models, which achieved better performance in comparison to the other models with an AUC of 94.5%, precision of 92%, recall of 91.8%, F-measure of 91.7% and an accuracy equal to 91.75%. [ABSTRACT FROM AUTHOR]

Diabetes mellitus is a chronic condition characterized by a disturbance in the metabolism of carbohydrates, fats and proteins. The most characteristic disorder in all forms of diabetes is hyperglycemia, i.e., elevated blood sugar levels. The modern way of life has significantly increased the incidence of diabetes. Therefore, early diagnosis of the disease is a necessity. Machine Learning (ML) has gained great popularity among healthcare providers and physicians due to its high potential in developing efficient tools for risk prediction, prognosis, treatment and the management of various conditions. In this study, a supervised learning methodology is described that aims to create risk prediction tools with high efficiency for type 2 diabetes occurrence. A features analysis is conducted to evaluate their importance and explore their association with diabetes. These features are the most common symptoms that often develop slowly with diabetes, and they are utilized to train and test several ML models. Various ML models are evaluated in terms of the Precision, Recall, F-Measure, Accuracy and AUC metrics and compared under 10-fold cross-validation and data splitting. Both validation methods highlighted Random Forest and K-NN as the best performing models in comparison to the other models. [ABSTRACT FROM AUTHOR]

A stroke is caused when blood flow to a part of the brain is stopped abruptly. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. In this research work, with the aid of machine learning (ML), several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence. The main contribution of this study is a stacking method that achieves a high performance that is validated by various metrics, such as AUC, precision, recall, F-measure and accuracy. The experiment results showed that the stacking classification outperforms the other methods, with an AUC of 98.9%, F-measure, precision and recall of 97.4% and an accuracy of 98%. [ABSTRACT FROM AUTHOR]

Smart devices have become an integral part of people's lives. The most common activities for users of such smart devices that are energy sources are voice calls, text messages (SMS) or email, browsing the World Wide Web, streaming audio/video, and using sensor devices such as cameras, GPS, Wifi, 4G/5G, and Bluetooth either for entertainment or for the convenience of everyday life. In addition, other power sources are the device screen, RAM, and CPU. The need for communication, entertainment, and computing makes the optimal management of the power consumption of these devices crucial and necessary. In this paper, we employ a computationally efficient linear mapping algorithm known as Concurrent Brightness & Contrast Scaling (CBCS), which transforms the initial intensity value of the pixels in the YCbCr color system. We introduce a methodology that gives the user the opportunity to select a picture and modify it using the suggested algorithm in order to make it more energy-friendly with or without the application of a histogram equalization (HE). The experimental results verify the efficacy of the presented methodology through various metrics from the field of digital image processing that contribute to the choice of the optimal values for the parameters a , b that meet the user's preferences (low or high-contrast images) and green power. For both low-contrast and low-power images, the histogram equalization should be omitted, and the appropriate a should be much lower than one. To create high-contrast and low-power images, the application of HE is essential. Finally, quantitative and qualitative evaluations have shown that the proposed approach can achieve remarkable performance. [ABSTRACT FROM AUTHOR]