32,120 research outputs found

    Relation Structure molĂ©culaire - Odeur Utilisation des RĂ©seaux de Neurones pour l’estimation de l’Odeur Balsamique

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    Les molĂ©cules odorantes (parfums ou flaveurs) sont utilisĂ©es dans une grande variĂ©tĂ© de produits de consommation, pour inciter les consommateurs Ă  associer les impressions favorables Ă  un produit donnĂ©. La Relation Structure molĂ©culaire-Odeur (SOR) est cruciale pour la synthĂšse de ces molĂ©cules mais est trĂšs difficile Ă  Ă©tablir due Ă  la subjectivitĂ© de l’odeur. Ce travail prĂ©sente une approche de prĂ©diction de l'odeur des molĂ©cules basĂ©e sur les descripteurs molĂ©culaires. Les techniques d’analyse en composantes principales (PCA) et de d’analyse de colinĂ©aritĂ© permettent d’identifier les descripteurs les plus pertinents. un rĂ©seau de neurones supervisĂ©5 Ă  deux couches (cachĂ©e et sortie) est employĂ© pour corrĂ©ler la structure molĂ©culaire Ă  l’odeur. La base de donnĂ©es dĂ©crite prĂ©cĂ©demment est utilisĂ©e pour l’apprentissage. Un ensemble de paramĂštres est modifiĂ© jusqu’à la satisfaction de la meilleure rĂ©gression. Les rĂ©sultats obtenus sont encouragent, ainsi les descripteurs molĂ©culaires convenables corrĂšlent efficacement l'odeur des molĂ©cules. C’est la premiĂšre Ă©tape d’un modĂšle gĂ©nĂ©rique en dĂ©veloppement pour corrĂ©ler l'odeur avec les structures molĂ©culaire

    Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques

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    In recent years, there has been an increase in the number of applicants seeking admission into architecture programmes. As expected, prior academic performance (also referred to as pre-enrolment requirement) is a major factor considered during the process of selecting applicants. In the present study, machine learning models were used to predict academic success of architecture students based on information provided in prior academic performance. Two modeling techniques, namely K-nearest neighbour (k-NN) and linear discriminant analysis were applied in the study. It was found that K-nearest neighbour (k-NN) outperforms the linear discriminant analysis model in terms of accuracy. In addition, grades obtained in mathematics (at ordinary level examinations) had a significant impact on the academic success of undergraduate architecture students. This paper makes a modest contribution to the ongoing discussion on the relationship between prior academic performance and academic success of undergraduate students by evaluating this proposition. One of the issues that emerges from these findings is that prior academic performance can be used as a predictor of academic success in undergraduate architecture programmes. Overall, the developed k-NN model can serve as a valuable tool during the process of selecting new intakes into undergraduate architecture programmes in Nigeria

    Assessment of Virgin Olive Oil Adulteration by a Rapid Luminescent Method

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    The adulteration of virgin olive oil with hazelnut oil is a common fraud in the food industry, which makes mandatory the development of accurate methods to guarantee the authenticity and traceability of virgin olive oil. In this work, we demonstrate the potential of a rapid luminescent method to characterize edible oils and to detect adulterations among them. A regression model based on five luminescent frequencies related to minor oil components was designed and validated, providing excellent performance for the detection of virgin olive oil adulteration

    Would credit scoring work for Islamic finance? A neural network approach

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    Purpose – The main aim of this paper is to distinguish whether the decision making process of the Islamic financial houses in the UK can be improved through the use of credit scoring modeling techniques as opposed to the currently used judgmental approaches. Subsidiary aims are to identify how scoring models can reclassify accepted applicants who later are considered as having bad credit and how many of the rejected applicants are later considered as having good credit; and highlight significant variables that are crucial in terms of accepting and rejecting applicants which can further aid the decision making process. Design/methodology/approach – A real data-set of 487 applicants are used consisting of 336 accepted credit applications and 151 rejected credit applications make to an Islamic finance house in the UK. In order to build the proposed scoring models, the data-set is divided into training and hold-out sub-set. The training sub-set is used to build the scoring models and the hold-out sub-set is used to test the predictive capabilities of the scoring models.70 percent of the overall applicants will be used for the training sub-set and 30 percent will be used for the testing sub-set. Three statistical modeling techniques namely Discriminant Analysis (DA), Logistic Regression (LR) and Multi-layer Perceptron (MP) neural network are used to build the proposed scoring models. Findings – Our findings reveal that the LR model has the highest Correct Classification (CC) rate in the training sub-set whereas MP outperforms other techniques and has the highest CC rate in the hold-out sub-set. MP also outperforms other techniques in terms of predicting the rejected credit applications and has the lowest Misclassification Cost (MC) above other techniques. In addition, results from MP models show that monthly expenses, age and marital status are identified as the key factors affecting the decision making process. Research limitations/implications – Although our sample is small and restricted to an Islamic Finance house in the UK the results are robust. Future research could consider enlarging the sample in the UK and also internationally allowing for cultural differences to be identified. The results indicate that the scoring models can be of great benefit to Islamic finance houses in regards to their decision making processes of accepting and rejecting new credit applications and thus improve their efficiency and effectiveness. Originality/value –Our contribution is the first to apply credit scoring modeling techniques in Islamic Finance. Also in building a scoring model our application applies a different approach by using accepted and rejected credit applications instead of good and bad credit histories. This identifies opportunity costs of misclassifying credit applications as rejected

    Tracing the geographical origin of Argentinean lemon juices based on trace element profiles using advanced chemometric techniques

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    This study examines the application of chemometric techniques associated with trace element concentrations for origin evaluation of lemon juice samples. Seventy-four lemon juice samples from three different provinces of Argentina were evaluated according to their microelement contents to identify differences in patterns of elements in the three provinces. Inductively coupled plasma mass spectrometry (ICP-MS) was used for the determination of twenty-five elements (Ag, Al, As, Ba, Bi, Co, Cr, Cu, Fe, Ga, In, La, Li, Mn, Mo, Ni, Rb, Sb, Sc, Se, Sn, Sr, Tl, V, and Zn). Once the analytical data were collected, supervised pattern recognition techniques were applied to construct classification/discrimination rules to predict the origin of samples on the basis of their profiles of trace elements. Namely, linear discriminant analysis (LDA), partial least square discriminant analysis (PLS-DA), k-nearest neighbors (k-NN), random forest (RF), and support vector machine with radial basis function Kernel (SVM). The results indicated that it was feasible to attribute unknown lemon juice samples to its geographical origin. SVM had better performance compared to RF, k-NN, LDA and PLS-DA, listed in descending order. Eventually, this study verifies that trace element pattern is a powerful geographical indicator when identifying the origin of lemon juice samples by analyzing trace element data with the help of SVM technique. This level of accuracy provides an interesting foundation to propose the combination of trace element contents with SVM technique as a valuable tool to evaluate the geographical origin of lemon juice samples produced in Argentina.Fil: Gaiad, José Emilio. Universidad Nacional del Nordeste; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Båsica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Båsica y Aplicada del Nordeste Argentino; ArgentinaFil: Hidalgo, Melisa Jazmin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Båsica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Båsica y Aplicada del Nordeste Argentino; Argentina. Universidad Nacional del Nordeste; ArgentinaFil: Villafañe, Roxana Noelia. Universidad Nacional de San Luis; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Química de San Luis. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Instituto de Química de San Luis; ArgentinaFil: Marchevsky, Eduardo Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Química de San Luis. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Instituto de Química de San Luis; Argentina. Universidad Nacional de San Luis; ArgentinaFil: Pellerano, Roberto Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Båsica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Båsica y Aplicada del Nordeste Argentino; Argentina. Universidad Nacional del Nordeste; Argentin

    Forecasting creditworthiness in retail banking: a comparison of cascade correlation neural networks, CART and logistic regression scoring models

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    The preoccupation with modelling credit scoring systems including their relevance to forecasting and decision making in the financial sector has been with developed countries whilst developing countries have been largely neglected. The focus of our investigation is the Cameroonian commercial banking sector with implications for fellow members of the Banque des Etats de L’Afrique Centrale (BEAC) family which apply the same system. We investigate their currently used approaches to assessing personal loans and we construct appropriate scoring models. Three statistical modelling scoring techniques are applied, namely Logistic Regression (LR), Classification and Regression Tree (CART) and Cascade Correlation Neural Network (CCNN). To compare various scoring models’ performances we use Average Correct Classification (ACC) rates, error rates, ROC curve and GINI coefficient as evaluation criteria. The results demonstrate that a reduction in terms of forecasting power from 15.69% default cases under the current system, to 3.34% based on the best scoring model, namely CART can be achieved. The predictive capabilities of all three models are rated as at least very good using GINI coefficient; and rated excellent using the ROC curve for both CART and CCNN. It should be emphasised that in terms of prediction rate, CCNN is superior to the other techniques investigated in this paper. Also, a sensitivity analysis of the variables identifies borrower’s account functioning, previous occupation, guarantees, car ownership, and loan purpose as key variables in the forecasting and decision making process which are at the heart of overall credit policy

    The Relationship between Social Responsibility and Business Performance: An Analysis of the Agri-Food Sector of Southeast Spain

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    This study aims to contribute to the existing debate on the impact of corporate social responsibility (CSR) orientation on different measures of business performance through the proposal of a conceptual model. Drawing on stakeholder theory, we conceptualize CSR as a broad and multidimensional construct with seven dimensions: employees, partners, customers, farmers, environment, community, and competition. We also extend the concept of business performance, which includes tangible variables, namely financial performance (FP) and export performance (EXP), as well as intangible variables, namely image and reputation (IR) and the satisfaction of relevant stakeholders (SS). The research context of this study is the agri-food sector in southeastern Spain. This sector has been the focus of attention of numerous researchers due to the relevance that social and environmental aspects have had in its development. To test the proposed model, the partial least-squares technique (PLS-SEM) was applied to data collected by means of a survey from a sample of 107 companies, which represent 81.4% of the turnover of the sector analyzed. The results show that CSR has a positive effect on financial performance, improves the volume and performance of exports, positively affects the corporate image and reputation, and increases the level of satisfaction of relevant stakeholders. Further research should examine the model from the perceptions of other stakeholders (e.g., customers, employees, and suppliers), using a longitudinal research design and exploring other contexts
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