Maroun Jneid’s research while affiliated with Antonine University and other places

As the COVID-19 pandemic outbreaks all over the world, several clinical medical centers are currently testing numerous medicines in clinical trials. However, there is no powerful medication recommended so far, in addition to the possibility of clinical trials failure in the context of unbeneficial investments and undesired outcomes. To address this gap, a new quantitative approach has been proposed in the current paper to monitor the medicine-based treatment development. Precisely, the cure and mortality rates of a potential treatment can be predicted systematically by using convenient predictive multidimensional neural networks and based on the quantitative analysis of Big Data for different indicators. More specifically, the diseases’ symptoms, along with the corresponding medicines, the active ingredients that compose these medicines, the related patents and publications data, the related reports in clinical trials, etc. can be all considered as effective key performance indicators in evaluating medicine-based product success. The present methodology is applied on two candidate diseases: “Multiple Myeloma” for the testing phase and “COVID-19” for the prediction phase, using three types of Multidimensional Neural Networks for comparison purposes: “Wide model” (WNN), “Deep model” (DNN) and “Wide and Deep model” (WDNN). The findings show that the WDNN model achieves higher prediction accuracy and outperforms WNN and DNN, with a significant prediction accuracy equal to 98.79%. Consequently, addressing this estimation represents a decision support for pharmaceutical and clinical medical centers and a crucial prerequisite step before proceeding with investments decisions in clinical trials and medicines production.

Aiming to retain their position in the marketplace, organizations are constantly enhancing research and development-based digital innovation activities in order to constantly develop new products and deploy new technologies. However, innovative trends and products are prone to failure, leading to undesired repercussions. In addition, when evaluating a product life-cycle, many decision-makers confront unprecedented challenges related to the estimation of potential disruptive innovation. To address this gap and to tackle the opportunities of digitalization, we conduct quantitative study to investigate the usage of research and development activities that can represent a main economic driver for new product/service development. A new approach for predicting innovative technology-based product success is proposed using Neural Networks models and based on the analysis of patents, publications and technologies revenues which are considered major key performance indicators in measuring technology-based product power. The proposed methodology consists of two main steps: forecasting patents and publications growths separately for a specific candidate technology using a common predictive Neural Network regression model, then integrating the results into a Multi-dimensional Neural Network classifier model in order to predict future revenue growth for this candidate technology. The present methodology is applied using two different types of Neural Networks for comparison purpose: “Wide and Deep Neural Networks” and “Recurrent Neural Networks”. Consequently, addressing this estimation represents a decision support and a crucial prerequisite step before proceeding with investments, where organizations can improve decision making in innovative technology-based product/service development. The findings show that the Recurrent Neural Networks models achieve higher prediction accuracy, and outperform the Wide and Deep Neural Networks, proving to be a more reliable model that can enhance digital innovation development.

E-justice projects are facing major challenges in different countries although results from these projects differ by country. Obviously, a better understanding of stakeholders’ expectations, and their outlooks towards e-justice is an important research issue. This article analyses challenges facing digital transformation in justice based on a case-study research methodology, then proposes a conceptual model of e-justice success that places domain specific characteristics as the core for any e-justice strategy. The result is a domain specific conceptual model for success that extends generic egovernment and on Information Systems success models. This model contributes to success of digital transformation projects in justice undertaken by governments, local and international organizations working in the field of support to the modernization and capacity building of the administration of justice in countries at different stages of development more specifically under-development. It also reinforces the existing literature on digital transformation in justice since it is almost non-existent. Firstly, we present the specificities of the European judicial system, secondly we describe developments in ejustice, and thirdly we discuss some major challenges faced in some e-justice projects in countries in Europe and eventual transformation in the functioning of the judicial system through new technologies. Finally, we present and discuss the conceptual model by describing key factors contributing to e-justice success

The temporal dynamic growth of technology patents for a time sequence is a major indicator to measure the technology power and relevance in innovative technology-based product/service development. A new method for predicting success of innovative technology is proposed based on patent data and using Neural Networks models. Technology patents data are extracted from the United States Patent and Trademark Office (USPTO) and used to predict the future patent growth of two candidate technologies: "Cloud/Client computing” and “Autonomous Vehicles”. This approach is implemented using two Neural Networks models for accuracy comparison: a Wide and Deep Neural Network (WDNN) and a Recurrent Neural Network (RNN). As a result, RNN achieves a better performance and accuracy and outperforms the WDNN in the context of small datasets.