La veille (informationnelle) et le crowdsourcing pour l’étude, la promotion et le suivi du géopatrimoine Algérien

International audienceAujourd'hui, avec le développement ininterrompu des moyens de communication et la croissance exponentielle de la masse d'informations, la veille s'avère un outil indispensable pour soutenir toute organisation dans ses prises de décision. Nous présentons, dans ce travail, une application de veille dédiée au domaine du géopatrimoine. Nous proposons un outil de veille destiné à la collecte et la recherche des documents et de photographies portant sur les sites géologiques algériens. Le premier objectif de ce projet est d'assister les géoscientifiques et les autorités locales dans la conservation et la promotion du géopatrimoine. Le deuxième objectif consiste à déterminer l'évolution des sites géologiques dans le temps, et cela en triant les différentes photographies récupérées. Pour ce faire, nous faisons recours à l'un des domaines émergents de la gestion des connaissances qui est le ' games with a purpose', Il s'agit d'une forme particulière de jeux faisant appel aux compétences humaines pour réaliser une tâche sérieuse

Pengaruh Pembiayaan Mikro Syariah Terhadap Perkembangan Usaha Mikro Kecil Dan Menengah (umkm) Pada Bmt Agromadani Kabupaten Rokan Hilir

Sharia Micro Financing which is a product of BMT Agromadani is financing that is channeled to customers who need funds as working capital or investment. With the existence of this financing product, it can facilitate MSME players who need funds to develop their business. However, there are still business people who have not been able to optimize Islamic microfinance to develop their business. This study aims to determine whether there is the influence of Islamic microfinance on the development of micro, small and medium enterprises (MSMEs) in Rokan Hilir's BMT Agromadani. Data is obtained directly from business actors as the subject of research with a method of distributing questionnaires with the number of customers of 50 people as respondents. The analysis technique used is simple linear regression analysis. The results in this study indicate that there is a positive and significant influence between Islamic microfinance and the development of MSMEs, with regression results which are obtained by t-count> t-table. The amount of microfinance that has been channeled by the agronomic sector BMT in 2014-2017 has increased every year

Veille et crowdsourcing pour promouvoir et mise en valeur du géopatrimoine alégérien

International audienc

Efficient Assamese Word Recognition for Societal Empowerment: A Comparative Feature-Based Analysis

The preservation and digitization of historical data are crucial for ensuring the continuity and accessibility of information over successive generations. The present study investigates the utilization of machine learning methodologies in the identification of Assamese words, focusing specifically on their distinctive visual characteristics. The main aim of this project is to improve word recognition technologies in Indic languages, specifically focusing on Assamese, in order to preserve and provide access to Assamese literature for future generations. The classification procedure entails the examination of 19 shape-related attributes through a range of machine learning algorithms, such as Logistic Regression, Decision Trees, Random Forest, Support Vector Machine (SVM) with various kernels, K Nearest Neighbors, and Gradient Boosting. The assessment of the model involves the utilization of various metrics such as Accuracy, Precision, Kappa, F1-score, Model Build Time, and Model Run Time to evaluate the computational efficiency. Additionally, the metrics of Area under the Curve (AUC) and Receiver Operating Characteristic (ROC) are also considered in the evaluation process. Out of the four datasets analyzed, Dataset 3 exhibits the highest level of performance. It is worth noting that Gradient Boosting demonstrates the highest level of accuracy, reaching 96.03% for conventional machine learning appraoches. Logistic Regression and SVM with RBF kernel closely trail behind, achieving accuracies of 95.64% and 95.60% respectively. Furthermore, the research conducted in this study also employs multiple layers of Convolutional Neural Networks (CNN), resulting in a remarkable recognition accuracy of 97.3%. This finding demonstrates that the CNN model and the proposed feature-set are in close proximity to one another in terms of the evaluation metrics

Just Suspended Speed Simulation in Torus Reactor Using Multiple Non-Linear Regression Model

In the chemical and water treatment industries, it is necessary to achieve maximum contact between the solid and liquid phase, thus promoting the mass and heat transfer, to obtain a homogeneous solution. Increasing stirring speed is the most recommended solution in different types of reactors: stirred tank, column, and tubular. However, this inadvertently increases the energy consumption of the industry. Determination of the minimum speed, labeled the just suspended speed (Njs) and crucial to attaining homogeneity, has been widely investigated. Numerous studies have been carried out to assess formulas for determining the solid particle speed in various reactor types. Given the limitations of the existing formulations based on a generalization of a unique equation for computing Njs for all soil classifications, it appears that most formulas can only approximate complex phenomena that depend on several parameters. A novel formula was developed, and the results given in this paper demonstrate the effectiveness of generating significant uncertainties for the estimation of Njs. The purpose of this study was the elaboration of experiment-based data-driven formulas to calculate Njs for different particle size classes. Nonlinear multiple regression (MNLR) models were used to generate the new formulas. The gradient descent optimization algorithm was employed to solve the hyperparameters of each novel equation, utilizing supervised learning. A comparison of the data indicated that the unique formulas presented in this study outperformed empirical formulas and provide a useful means for lowering energy consumption, while increasing the heat and mass transfer in torus type reactors

Kinetic Evaluation of Tin-POMS Catalyst for Urethane Reactions

We report the activity for a new tin-polyhedral oligomeric metal silsesquioxane (POMS) catalyst in 1-butanol and 2-butanol model reactions with 4,4\u27-methyle-nebis(cyclohexylisocyanate) (H12MDI) in toluene and N,N-dimethylformamide (DMF). Kinetic rate constants for varying levels of tin-POMS ranging between 100 ppm and 1000 ppm tin are reported. We observed urethane reactions in toluene to follow second order reaction kinetics, whereas similar reactions in DMF followed first order reaction kinetics. We determined tin-POMS is an efficient catalyst system for urethane reactions and found the new catalyst to be easy to handle, soluble, and very effective for catalyzing urethane reactions. By direct comparison of a model reaction between tin-POMS and dibutyltin dilaurate (DBTDL), tin-POMS was found to be quite similar to DBTDL for urethane catalytic activity. In addition, we show the efficacy for tin-POMS to be an excellent polyurethane reaction catalyst through a model reaction of H12MDI with 2000 g/mol poly(epsilon-caprolactone) diol. (c) 2008 Wiley Periodicals, Inc. J Appl Polym Sci 110: 3683-3689, 200