Implementation of a decision support process for evaluating the correlation between IT investment and of information systems success

The main objective of this paper is to study the correlation between investment in information technologies and especially information systems and information system success based on data collection and a multi-criteria decision-making approach using technique for order preference by similarity to ideal solution (TOPSIS) and analytical hierarchy process (AHP) methods. The criteria of the hierarchical model for evaluating the information system success are chosen from Delone and McLean information systems (IS) success model. The proposed approach has been implemented in 3 sectors recognized by their variation in the use of information systems: the financial sector, the service companies sector, and the construction industry sector. Therefore, the results of this implementation show that massive investment in information systems does not always guarantee good information system success, and information system success is not always the result of massive investment in the information system

Elasticity characteristics of a bio-load of renewable resources

The use of composite materials in industry has become more important thanks to the diversity of applications (aeronautics, rail, naval, automobile, etc.). The current concentration is moving towards bio-loading composites due to its environmental, economic and social benefits. In this work, the numerical modeling by the finite element method has been able to confirm the experimental tests on a material of animal origin with parallel , 45 ° and at 90° in the direction of the fibers. The results of the numerical simulation have illustrated that moving away from the parallel direction of fibers, the rigidity decreases and the behavior changes, which reinforces the hypotheses of the anisotropy of the material of animal origin studied and the high rigidity in the direction of the fibers due to the Keratin which gives it this behavior

An adaptation of Text2Onto for supporting the French language

The ontologies are progressively imposing themselves in the field of knowledge management. While the manual construction of an ontology is by far the most reliable, this task has proved to be too tedious and expensive. To assist humans in the process of building an ontology, several tools have emerged proposing the automatic or semi-automatic construction of ontologies. In this context, Text2Onto has become one of the most recognized ontology learning tools. The performance of this tool is confirmed by several research works. However, the development of this tool is based on Princeton WordNet (PWN) for English. As a result, it is limited to the processing of textual resources written in English. In this paper, we present our approach based on JWOLF, a Java API to access the free WordNet for French that we have developed to adapt this tool for the construction of ontologies from corpus in French. To evaluate the usefulness of our approach, we assessed the performance of the improved version of Text2Onto on a simplistic corpus of French language documents. The results of this experiment have shown that the improved version of Text2Onto according to our approach is effective for the construction of an ontology from textual documents in the French language

The impact of IT investment on firm performance based on MCDM techniques

In the recent past years, researchers have presented conflicting results regarding the impact of information technology investment on firm performance. Almost all studies on information technology productivity and it role for companies performance are based on data collected and meta-analysis and do not offer a methodology or prototype of analysis in any field This study presents an attempt to adopt a multi-criteria decision making approach to evaluate the non-financial performance of companies using two famous methods. Furthermore, our results try to investigate the effects of information technology investments on firms’ non-financial performance. Finding show that investment in information systems is not necessarily related to achieving a good non-financial performance at the firm level

Improving Student Engagement and Success in Computer Programming Courses through Social Learning in Online Environments

This paper addresses the necessity to enhance the effectiveness of online learning (OL) environments by examining the influence of social interactions on learner motivation, engagement, and success, specifically focusing on online computer science education (CSE). Acknowledging the limitations of peer-to-peer and learner-to-teacher interactions on online platforms, this paper delves into the potential of social learning (SL) organized through learner groups, communities, or networks to significantly enrich the learning experience. To test the hypothesis that SL positively impacts student outcomes, an experiment was conducted with 49 engineering students divided into two groups. The first group undertook an object-oriented programming course in C++ using the Moodle platform, while the second group, in addition to the online course, participated in asynchronous group activities through forums and synchronous interactions via chat. The results reveal a notable positive impact of SL on student outcomes, with participants in the second group reporting higher satisfaction levels and achieving superior results compared to those in the first group. The findings underscore the significance of integrating social interaction into OL environments, with potential implications for enhancing the overall quality of education and student performance in society

Onto2DB: towards an eclipse plugin for automated database design from an ontology

Ontologies are spreading more and more in the field of information technologies as a privileged solution allowing the formalization of knowledge. The theoretical model of ontologies is most promising. They are increasingly ubiquitous given the benefits they present. Despite the proliferation of research proposing approaches dedicated to the design of a database from an ontology, the tools to design a database from an ontology are rare or inaccessible. Thus, in this contribution, we present our approach for the development of an Eclipse Plug-in, in order to automatically generate a conceptual model of a relational database from an ontology. To evaluate the usefulness of our approach, we used our resulting Eclipse Plug-in to automatically generate a conceptual model of a relational database from an ontology, customize it, and automatically generate the corresponding SQL script for Data Definition. The results of this experiment showed that our Plug-in constitutes a concretization of our approach and a means of automatic translation from the ontological model to the relational model

A fuzzy-based prediction approach for blood delivery using machine learning and genetic algorithm

Multiple diseases require a blood transfusion on daily basis. The process of a blood transfusion is successful when the type and amount of blood is available and when the blood is transported at the right time from the blood bank to the operating room. Blood distribution has a large portion of the cost in hospital logistics. The blood bank can serve various hospitals; however, amount of blood is limited due to donor shortage. The transportation must handle several requirements such as timely delivery, vibration avoidance, temperature maintenance, to keep the blood usable. In this paper, we discuss in first section the issues with blood delivery and constraint. The second section present routing and scheduling system based on artificial intelligence to deliver blood from the blood-banks to hospitals based on single blood bank and multiple blood banks with respect of the vehicle capacity used to deliver the blood and creating the shortest path. The third section consist on solution for predicting the blood needs for each hospital based on transfusion history using machine learning and fuzzy logic. The last section we compare the results of well-known solution with our solution in several cases such as shortage and sudden changes

Implementation of artificial intelligence in the prediction of the elastic characteristics of bio-loaded polypropylene with bamboo fibers

Artificial intelligence is the current trend in the world, which has taken the opportunity to advance in all its fields, particularly in scientific research. In materials engineering, the results obtained from classic methods such as experimentation, homogenization methods, or finite element methods have become input and validation elements for intelligent models to obtain more effective results in an optimal time frame. In this article, we discuss the use of artificial neural networks to determine the mechanical properties of biocomposites, which are the subject of much research due to the advantages they represent. The properties of these complex materials depend on various parameters, such as the behavior of the constituent materials, the percentage of the mixture, and the manufacturing process. In this work, our goal is to predict how polypropylene behaves elastically when reinforced with 15% various natural fillers. and we will study the impact of bamboo on polypropylene to test and validate our model. By exploiting the results of the Mori-Tanaka model, we were able to generate our dataset, with which we feed our feedforward backpropagation neural network and demonstrate that our biocomposite gained in terms of stiffness, marked by an increase in Young's modulus to 550.3 MPa, with better performance validation and a very good regression coefficient

Dysgraphia detection based on convolutional neural networks and child-robot interaction

Dysgraphia is a disorder of expression with the writing of letters, words, and numbers. Dysgraphia is one of the learning disabilities attributed to the educational sector, which has a strong impact on the academic, motor, and emotional aspects of the individual. The purpose of this study is to identify dysgraphia in children by creating an engaging robot-mediated activity, to collect a new dataset of Latin digits written exclusively by children aged 6 to 12 years. An interactive scenario that explains and demonstrates the steps involved in handwriting digits is created using the verbal and non-verbal behaviors of the social humanoid robot Nao. Therefore, we have collected a dataset that contains 11,347 characters written by 174 participants with and without dysgraphia. And through the advent of deep learning technologies and their success in various fields, we have developed an approach based on these methods. The proposed approach was tested on the generated database. We performed a classification with a convolutional neural network (CNN) to identify dysgraphia in children. The results show that the performance of our model is promising, reaching an accuracy of 91%

A design of a multi-agent recommendation system using ontologies and rule-based reasoning: pandemic context

Learners attend their courses in remote or hybrid systems find it difficult to follow one size fits all courses. These difficulties have increased with the pandemic, lockdown, and the stress they cause. Hence, the role of adaptive systems to recommend personalized learning resources according to the learner's profile. The purpose of this paper is to design a system for recommending learning objects according learner's condition, including his mental state, his COVID-19 history, as well as his social situation and ability to connect to the e-learning system on a regular basis. In this article, we present an architecture of a recommendation system for personalized learning objects based on ontologies and on rule-based reasoning, and we will also describe the inference rules required for the adaptation of the educational content to the needs of the learners, taking into account the learner’s health and mental state, as well as his social situation. The system designed, and validated using the unified modeling language (UML). It additionally allows teachers to have a holistic view of learners’ progress and situations