Chronic Kidney Disease is a serious lifelong condition that induced by either kidney pathology or reduced kidney functions. Early prediction and proper treatments can possibly stop, or slow the progression of this chronic disease to end-stage, where dialysis or kidney transplantation is the only way to save patient's life. In this study, we examine the ability of several machine-learning methods for early prediction of Chronic Kidney Disease. This matter has been studied widely; however, we are supporting our methodology by the use of predictive analytics, in which we examine the relationship in between data parameters as well as with the target class attribute. Predictive analytics enables us to introduce the optimal subset of parameters to feed machine learning to build a set of predictive models. This study starts with 24 parameters in addition to the class attribute, and ends up by 30 % of them as ideal sub set to predict Chronic Kidney Disease. A total of 4 machine learning based classifiers have been evaluated within a supervised learning setting, achieving highest performance outcomes of AUC 0.995, sensitivity 0.9897, and specificity 1. The experimental procedure concludes that advances in machine learning, with assist of predictive analytics, represent a promising setting by which to recognize intelligent solutions, which in turn prove the ability of predication in the kidney disease domain and beyond.

Background: Machine learning is a branch of Artificial Intelligence that is concerned with the design and development of algorithms, and it enables today’s computers to have the property of learning. Machine learning is gradually growing and becoming a critical approach in many domains such as health, education, and business. Methods: In this paper, we applied machine learning to the diabetes dataset with the aim of recognizing patterns and combinations of factors that characterizes or explain re-admission among diabetes patients. The classifiers used include Linear Discriminant Analysis, Random Forest, k–Nearest Neighbor, Naïve Bayes, J48 and Support vector machine. Results: Of the 100,000 cases, 78,363 were diabetic and over 47% were readmitted.Based on the classes that models produced, diabetic patients who are more likely to be readmitted are either women, or Caucasians, or outpatients, or those who undergo less rigorous lab procedures, treatment procedures, or those who receive less medication, and are thus discharged without proper improvements or administration of insulin despite having been tested positive for HbA1c. Conclusion: Diabetic patients who do not undergo vigorous lab assessments, diagnosis, medications are more likely to be readmitted when discharged without improvements and without receiving insulin administration, especially if they are women, Caucasians, or both.

Under the influence of the COVID-19 pandemic, there is an accelerated transition from the traditional form of knowledge transfer to online learning. Our study of 344 automotive students showed that the success of this transition depends on the readiness to introduce special digital tools for organizing knowledge and conducting practical forms of classes. In this regard, a modern digital form of organizing and transferring knowledge to automotive service engineers in the form of virtual laboratories was developed and presented in the article. The work scenarios, functionality, and minimum technical requirements of virtual laboratories as software systems are described and reviewed in the paper. The rationale for the effectiveness of the application, based on the results of using 109 university students in training practice, is presented as a result of the research. An analysis of the distributions of the student survey results and their training progress revealed differences at the p = 0.05 significance level. This confirmed the hypothesis that the use of methods for teaching engineers special disciplines and language skills using VR technologies is much more effective than the traditional one. An increase in students’ interest in learning was revealed, and their performance improved markedly. This proves that the immersive nature of VR technology makes it possible to better assimilate the studied material, increase the level of motivation of future car service specialists, and also allow the organization of the transfer of knowledge online. The very process of knowledge transfer becomes the point of acquiring new digital competencies necessary for high-tech industries.

The risk assessment of any Network or Security systems has a high level of uncertainties because usually probability and statistics were used to evaluate the security of different cyber security systems. In this paper we will use Shannon entropy to represent the uncertainty of information used to calculate systems risk and entropy weight method since the weight of the object index is normally used and point to the significant components of the index. We evaluate the risk of security systems in terms of different security layers and protections. The information system is analysed by perimeter, network, host, application and data layers' protections. The capability of protections is measured by introducing the concept of protection effectiveness. We write the security evaluations algorithm to normalized the protection matrix and calculate the entropy and the entropy weight, then we will use the weight and paths to evaluate and calculate the total risk in the system and give the systems administrator a clear guidance on the vulnerable security entities. We try to develop a novel approach to evaluate the cyber security suitable for the majority of cyber systems by introducing the term of security entities.

Electronic Governance (e-Governance) is the application of the Information and Communication Technology (ICT) with the aim to simplify and support the governance across different parties including public government organizations, business and citizens. Through the adoption and use of Information and Communication technology which will connect all of these three together to support the overall government's processes and operations. It's anticipated that e-Governance shall bring boundless improvements towards strategic planning, proper monitoring of government programs, investments, projects and activities. The e-Governance will provide easy access and delivery of government services to the citizens and reduce associated costs of transactions that occur across government entities. In the recent years, some of the new technological advancement concepts that include Gamification becomes one of the solutions that can be attached with the e-Governance implementation to sustain the effective adoption of government services delivery. Gamification is an evolution that supports people interactions with implemented government electronic services. It can be widely used within public organizations for training of new hires at workplaces, help employees to perform certain tasks and carry their day-to-day activities more efficiently by using Gamification tools which government entities has to offer in order to facilitate e-Governance implementation and services adoption by publics. The developed mobile application is based on a Gamification platform for employees at public government organizations for the purpose of training and learning. In this research, different variables were measured including productivity, motivational engagement, performance, training, support and services, collaboration, innovation, skills development, personal development and behavior changes.