Artificial Intelligence (AI) has been growing considerably over the last ten years. Machine Learning (ML) is probably the most popular branch of AI to date. Most systems that use ML methods use them to perform predictive analysis. This paper aims to conduct a literature review of trends and methods of machine learning used for predictive analysis. To do this, we carried out a collection of research papers from three scientific databases. We then considered selection criteria in order to study only papers published in the last five years, prioritizing those published in peer-reviewed scientific journals. This process led to the selection of 30 research papers that were considered for this review. The purpose of this study is to provide researchers, companies or anyone wishing to perform predictive analysis with clues that will enable them to choose the best ML method(s) according to its field of application, based on the latest research works in the literature. This study highlighted the most used methods by field of application: DT and ANN in education, LR, RF and DT in building, DT in botany, RF and ANN in social science and RF in medicine.

The Covid-19 pandemic crisis has made companies consider alternatives to the usual operating methods in order to ensure the continuity of their employees work and the offer of products and services to their customers. The current economic situation has therefore anchored the positioning of digital transformation as a lever for economic revival and evolution, and has reminded companies of the need to conduct this transformation in order to remain competitive. This paper is part of this debate and is in line with previous work on digital transformation frameworks. It develops the digital reflection from a strategic and above all multidimensional perspective. By adopting this vision, and starting from a global digital-oriented business strategy, it is proposed to decline it into business strategies through a digital roadmap. The article also proposes a framework of digital transformation based on these strategic guidelines and making all the dimensions that must be involved in a transformative process interact with each other.

Plenty data are generated each second under different locations and ways. In return, several valuable data are not used due to neglect or a wrong criteria selection, affecting the results. To have the accurate decision the exact information is needed. But, in case of lack of data, an overall vision about all existing findings make a big difference especially in Medicine. The approach of Systematic Literature Review give the possibility to have this clear vision related to a specific topic, for the right input. We aim to identify all important steps to conduct a high quality of systematic literature review, independently of research domain. We first explain the reason behind our research and the significance of Systematic Literature Review in research fields, one of the way for minimizing the loss of information. Since our goal is to have an explicit methodology, we select several papers mentioning the procedure in different years to have a general view. Subsequently, we explain the approaches used to select the key steps to follow for selected articles. We settle this study with a complete step resuming all finding key phases. This research highlight also the importance of the use of an explicit methodology to vanish any misunderstanding or ignoring basic points to accomplish a valuable and high quality work. This paper make a clear complete methodology to follow, in any domain and specially medicine, to conduct a best quality of Systematic Literature Review and reach the right gain using best practices.

Data management is becoming increasingly complex, especially with the emergence of the Big Data era. The best way to manage this data is to dispose a data lifecycle from creation to destruction. This paper proposes a new Data LifeCycle (DLC) called Smart DLC that helps to make from raw and worthless data to Smart Data in a Big Data context. In order to do this, we have followed a method which consists firstly in identifying and analyzing the lifecycles from a literature review, and then in defining the phases of our cycle and finally in modeling it. The cycle is modeled in the form of a process cartography resulting from the ISO 9001: 2015 standard and the CIGREF framework to facilitate its implementation within companies. Smart DLC is qualified as a set of management, realization and support processes that could be addressed by an Information System urbanization approach. The advantage of modeling the phases such as processes is to be concerned not only with the technical activities but also with management, which is a major player for the success of the technique.

To evaluate and improve the delivery of health care services and programs; to collect and share information on agro-biodiversity; to fill the gaps in the data of artisanal or industrial fisheries, the use of computer tools of data collection is a major asset. It is now obvious that most of these tools are difficult to use in developing countries. And this is because of the complexity of use, either because they are commercial solutions or simply because these tools require a stable internet connection. This study is the result of thorough research on Open Data Kit (ODK), a suite of tools fully compatible with the context of developing countries and fully open source software. This article traces the research work done for the design, implementation, and improvement of ODK. A description of the different ODK extensions that can be found in the literature is given in addition to the reasons that explain the success of ODK in developing countries. To highlight the utility of ODK, an analysis of the areas of use of ODK has been made. This analysis concludes that ODK is mainly used to improve the intervention effectiveness of health system agents in developing countries.