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Heart and circulatory system diseases are often referred to as cardiovascular disease (CVD). The health and efficiency of the heart are crucial to human survival. CVD has become a primary cause of demise in recent years. According to data provided by the World-Health-Organization (WHO), CVD were conscientious for the deaths of 18.6M people in 2017. Biomedical care, healthcare, and disease prediction are just few of the fields making use of cutting-edge skills like machine learning (ML) and deep learning (DL). Utilizing the CVD dataset from the UCI Machine-Repository, this article aims to improve the accuracy of cardiac disease diagnosis. Improved precision and sensitivity in diagnosing heart disease by the use of an optimization algorithm is possible. Optimization is the process of evaluating a number of potential answers to a problem and selecting the best one. Support-Machine-Vector (SVM), K-Nearest-Neighbor (KNN), Naïve-Bayes (NB), Artificial-Neural-Network (ANN), Random-Forest (RF), and Gradient-Descent-Optimization (GDO) are just some of the ML strategies that have been utilized. Predicting Cardiovascular Disease with Intelligence, the best results may be obtained from the set of considered classification techniques, and this is where the GDO approach comes in. It has been evaluated and found to have an accuracy of 99.62 percent. The sensitivity and specificity were likewise measured at 99.65% and 98.54%, respectively. According to the findings, the proposed unique optimized algorithm has the potential to serve as a useful healthcare examination system for the timely prediction of CVD and for the study of such conditions.

The purpose of this research study is to measure the impact of governance on the organizational performance of schools in administrative, financial and pedagogical aspects. This governance approach will be approached using a methodology respecting several principles in terms of participation, transparency, accountability and evaluation (CSEFRS, 2015). Furthermore, we added in our frame of reference the primordial aspect of good governance, which is the new technologies. Considering the difficulty of an etymological and polysemic consensus on the concept of governance. It would be wise to set up operational indicators for applying this notion of governance with a perspective to enhance management skills, multi-stakeholder strategic planning, internal collaborative management, effective and efficient steering, control and evaluation. Methodology / approach: Our methodological approach based on a descriptive exploratory study. The researcher carried out an empirical study involving 20 principals of qualifying secondary schools from the Nouaceur-Casablanca region. Our measurement instrument is related to a survey which is appraised as one of the five principles of effective governance (transparency, accountability, participation, evaluation and technology). Findings: data analysis shows difficulties in applying governance principles, influencing organizational performance in administrative management. Additionally, the educational management and financial management of these establishments have seen a positive application of these governance mechanisms. It would be wise to apply the multidimensional aspects of governance, through the harmonious, complete and balanced integration of the three management mechanisms: administrative, pedagogic and financial management. The relationship between the study variables is demonstrated by the application of the five principles of governance, the results of the study recommended that we apply them to contribute significantly to improve the effectiveness of strategic planning, collaborative management and community steering.  

Despite all blockchain's advantages, it has experienced some challenges and disadvantages. Identifying these challenges and minimizing them are important steps in advancing the goals of this technology. The purpose of this study is to investigate and identify the challenges of using blockchain technology in international markets with regard to the risks of such newfound technologies. This is an applied research in terms of purpose and a mixed research in terms of methodology. The effective factors in using blockchain technology have been discovered by the meta-synthesis method at first; then the ranking of these factors has been done by the fuzzy pairwise comparison. A systematic search was performed in the database of Science Direct, Emerald, and Google Scholar. Also, the seven-step method of Sandlowski and Barroso (2007) was applied. All the related articles and the challenges of using blockchain in international markets were identified and coded. The comparison method between the two coders was used to adjust the reliability and the kappa coefficient was used to calculate the correlation coefficient between the two codes. The findings of the extracted sources were finally proposed and 41 challenges were classified into 6 categories of technical, educational, structural, market, infrastructure and legal which divided in both internal and external areas.

People are used to exploring grayscale images in their family albums but it is difficult to grasp the reality without colours. Luckily, with advancements in Machine Learning it has been possible to solve problems previously thought impossible. The authors aim to automatically colourize grayscale images using a subset of Machine Learning called Deep Learning. The system will be trained on an image dataset and given an input grayscale image the model will be able to assign aesthetically believable colours. A grayscale photograph has been provided; our approach solves the problem of visualizing a reasonable colour version of the grayscale picture. This issue is undoubtedly under controlled; therefore earlier methods to this problem have either counted majorly on user interaction or it leads to in unsaturated colourizations. The authors put forward a completely automatic approach that will try to produce realistic and vibrant colourizations as much as possible. The proposed system has been applied as a feed-forward in a Convolutional Neural Network and has been trained on over twenty thousand colour images currently.