Siamese convolutional neural network and fusion of the best overlapping blocks for kinship verification

Multimedia Tools and Applications - Analysis of facial images decoding familial features has been attracting the attention of researchers to develop a computerized system interested in determining...     

Flexible Distribution-Based Regression Models for Count Data: Application to Medical Diagnosis

Abstract

Data mining techniques have been successfully utilized in different applications of significant fields, including medical research. With the wealth of data available within the health-care systems, there is a lack of practical analysis tools to discover hidden relationships and trends in data. The complexity of medical data that is unfavorable for most models is a considerable challenge in prediction. The ability of a model to perform accurately and efficiently in disease diagnosis is extremely significant. Thus, the model must be selected to fit the data better, such that the learning from previous data is most efficient, and the diagnosis of the disease is highly accurate. This work is motivated by the limited number of regression analysis tools for multivariate counts in the literature. We propose two regression models for count data based on flexible distributions, namely, the multinomial Beta-Liouville and multinomial scaled Dirichlet, and evaluated the proposed models in the problem of disease diagnosis. The performance is evaluated based on the accuracy of the prediction which depends on the nature and complexity of the dataset. Our results show the efficiency of the two proposed regression models where the prediction performance of both models is competitive to other previously used regression models for count data and to the best results in the literature.     

Simple N‐Heterocyclic Carbenes as Ligands in Ullmann‐Type Ether and Thioether Formations

A simple N‐heterocyclic carbene (NHC) derived from 1‐methyl‐3‐ethylimidazolium tetrafluoroborate was found to be an efficient ligand for a range of copper‐catalyzed cross‐coupling reactions, leading to the formation of aromatic ethers and thioethers.

     

Unexploited Thapsia garganica,Orlaya maritima,and Retama raetam Seeds: Potential Sources of Unsaturated Fatty Acid and Natural Antioxidants

Total lipid contents, fatty acid compositions, phenolic profiles and antioxidants activities of seeds from Thapsia garganica, Orlaya maritima, and Retama raetam were investigated. The oil values were more than 26 %, except seeds of R. raetam (ca. 3 %). Unsaturated fatty acids accounted for the majority of the fatty acids (more than 75 %). Oleic and linoleic acid were the predominant fatty acids. Total phenolic compounds (24–104 mg GAE g^?1 DR), total flavonoids (4–102 mg QE g^?1g DR), total tannins (28–85 mg GAE g^?1 DR) and condensed tannins (0.62–131 mg CE g^?1 DR) were also determined. The antioxidant activities using different assays were evaluated. The predominant detected classes were the phenolic acids (42–85 %) and the flavonoids (11–48 %). The major phenolic acids were caffeic, trans‐4‐hydroxy‐3‐methoxycinnamic, p‐coumaric, and gallic acid. The predominant flavonoids were quercetin, luteolin, naringin, apigenin, and kaempferol. This study brings attention to the medicinal importance of these species as a source of oil and antioxidant molecules.     

An inverse Black–Scholes problem

Optimization and Engineering - In this work, we provide a new Black–Scholes model, where the weak formulation at stake is done in the case of a general class of finite Radon measures. A...     

A heuristic to solve the synchronized log-truck scheduling problem

We present a synchronized routing and scheduling problem that arises in the forest industry, as a variation of the log-truck scheduling problem. It combines routing and scheduling of trucks with specific constraints related to the Canadian forestry context. This problem includes aspects such as pick-up and delivery, multiple products, inventory stock, multiple supply points and multiple demand points. We developed a decomposition approach to solve the weekly problem in two phases. In the first phase we use a MIP solver to solve a tactical model that determines the destinations of full truckloads from forest areas to woodmills. In the second phase, we make use of two different methods to route and schedule the daily transportation of logs: the first one consists in using a constraint-based local search approach while the second one is a hybrid approach involving a constraint programming based model and a constraint-based local search model. These approaches have been implemented using COMET2.0. The method, was tested on two industrial cases from forest companies in Canada.     

Optimization of friction stir welding process using NSGA-II and DEMO

In welding processes, the selection of optimal process parameter settings is very important to achieve best weld qualities. In this work, neuro-multi-objective evolutionary algorithms (EAs) are proposed to optimize the process parameters in friction stir welding process. Artificial neural network (ANN) models are developed for the simulation of the correlation between process parameters and mechanical properties of the weld using back-propagation algorithm. The weld qualities of the weld joint, such as ultimate tensile strength, yield stress, elongation, bending angle and hardness of the nugget zone, are considered. In order to optimize those quality characteristics, two multi-objective EAs that are non-dominated sorting genetic algorithm II and differential evolution for multi-objective are coupled with the developed ANN models. In the end, multi-criteria decision-making method which is technique for order preference by similarity to the ideal solution is applied on the Pareto front to extract the best solutions. Comparisons are conducted between results obtained from the proposed techniques, and confirmation experiments are performed to verify the simulated results.

     

A GIS Database for Sustainable Management of Shallow Water Resources in the Tulul al Ashaqif Region, NE Jordan

The Tulul al Ashaqif region is an arid area in northeastern Jordan that contains renewable shallow perched aquifer water. The study of these aquifers has led to better understanding of the recharge process as well as other hydrological issues related to management of water resources in similar areas. The use of geographic information system (GIS)-based predictive mapping to locate areas of high potential for shallow perched aquifer sites is explored in this paper. Knowledge of the hydrologic, geologic and geomorphic variables influencing the development of shallow aquifer formation is used to produce GIS layers representing the spatial distribution of those variables. The GIS layers are then analyzed to identify locations where combinations of environmental variables match patterns observed at known sites. In addition, information can be deduced on the volume of water that is available and the best locations to site recharge facilities. Moreover, future development of these resources requires consideration of possible adverse affects of usage on these resources. The database developed can be used for this purpose as well.