In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.

Leadership and the academic freedom of the Universities in a digitally changing world are the generators of innovation in society. This study is a qualitative and quantitative empirical research of the Leadership at the public and private Higher Education Institutions (HEIs) in Kosovo, that examines their communication, authoritarian or liberal communication, and dominant perceptions and attitudes towards social, political, and financial strategies in HEI as a basis of social and economic wellbeing. The theory of research, as elaborated by Tight (2022), emphasizes the evolving nature of academic inquiry and the significance of context in shaping research practices. Waite (2013) highlights the pivotal role of communication strategies in determining the effectiveness of both democratic and authoritarian leadership styles. Effective communication in democratic leadership fosters transparency and collaboration, while in authoritarian leadership, it can be used to consolidate control and manage dissent The research was conducted at public and private HEI, through personal interviews and a structured questionnaire, which was carried out by the staff of higher management of HEI, academic staff, administrative staff, and students of the public and private Universities. The results demonstrated that academic and financial autonomy has a high impact on academic ethics and academic integrity and has a high impact on the increase of the economy and well-being in society, compared with the lack of academic and financial autonomy and interference of politics in the management of HEI which has an impact on lower quality and integrity of HEI in society. Leaders of Universities need to think about new leadership models more socially responsible and more ecologically sensible consumption oriented, from Society, to society for society.

In this study, we utilized a convolutional neural network (CNN) trained on microscopic images encompassing the SARS-CoV-2 virus, the protozoan parasite “plasmodium falciparum” (causing of malaria in humans), the bacterium “vibrio cholerae” (which produces the cholera disease) and non-infected samples (healthy persons) to effectively classify and predict epidemics. The findings showed promising results in both classification and prediction tasks. We quantitatively compared the obtained results by using CNN with those attained employing the support vector machine. Notably, the accuracy in prediction reached 97.5% when using convolutional neural network algorithms.