Solving the Weighted Constraint Satisfaction Problems Via the Neural Network Approach

A wide variety of real world optimization problems can be modelled as Weighted Constraint Satisfaction Problems (WCSPs). In this paper, we model this problem in terms of in original 0-1 quadratic programming subject to leaner constraints. View it performance, we use the continuous Hopfield network to solve the obtained model basing on original energy function. To validate our model, we solve several instance of benchmarking WCSP. In this regard, our approach recognizes the optimal solution of the said instances

Accumulation and Tissue Distribution of Domoic Acid in the Common Cuttlefish, Sépia Officinalis from the South Moroccan Coast.

Domoic acid (DA) is a phycotoxin produced by some diatoms, mainly from the Pseudo-nitzschia genus, and has been detected throughout the marine food web. In Morocco, many mollusc species are subject to regular monitoring of levels of contamination by toxins via Network Observation of the safety of the Moroccan coast (RSSL) implemented by National Fisheries Research Institute (INRH). Among these toxins, AD which has been frequently found in the bivalve molluscs, little known about DA accumulation  in cephalopod.This study presents the first data showing concentrations of DA that exceed health limits detected in the common cuttlefish, Sepia officinalis from the south Atlantic coast of Morocco.  Domoic acid was found throughout 2014 and 2015 in the digestive gland and flesh of cuttlefish reaching concentrations of 50 mg DA kg-1. The highest DA values  were  detected during autumn month. Evaluation of DA tissue distribution showed elevated DA concentrations in the digestive gland. The common cuttlefish, like other cephalopod species, plays a central position in the food web and might be a new DA vector to top predators like marine mammals.  Human intoxications are not expected as long as DA was only detected in the flesh at levels (16 mg DA kg-1) not exceed regulatory value. However, in some countries, whole juvenile animals are consumed (without evisceration), and in this case they might represent a risk to human health as the AD accumulation is more significant in the digestive gland than in the flesh. This study reveals a new member of the marine food web able to accumulate DA in Morocco

Solving the Weighted Constraint Satisfaction Problems Via the Neural Network Approach

A wide variety of real world optimization problems can be modelled as Weighted Constraint Satisfaction Problems (WCSPs). In this paper, we model this problem in terms of in original 0-1 quadratic programming subject to leaner constraints. View it performance, we use the continuous Hopfield network to solve the obtained model basing on original energy function. To validate our model, we solve several instance of benchmarking WCSP. In this regard, our approach recognizes the optimal solution of the said instances

A robust and consistent stack generalized ensemble-learning framework for image segmentation

Abstract In the present study, we aim to propose an effective and robust ensemble-learning approach with stacked generalization for image segmentation. Initially, the input images are processed for feature extraction and edge detection using the Gabor filter and the Canny algorithms, respectively; our main goal is to determine the most feature descriptions. Subsequently, we applied the stacking generalization technique, which is generally built with two main learning levels. The first level is composed of two algorithms that give good results in the literature, namely: LightGBM (Light Gradient Boosting Machine) and SVM (support vector machine). The second level is the meta-model in which we use a predictor model that takes the base-level predictions to improve the accuracy of the final prediction. In the stacked generalization process, we use the Extreme Gradient Boosting (XGBoost); it takes as input the sub-models’ outputs to better classify each pixel of the image to give the final prediction. Today, several research works exist in the literature using different machine learning algorithms; in fact, instead of trying to find a single efficient and optimal learner, ensemble-based techniques take the advantage of each basic model; they integrate their outputs to obtain a more consistent and reliable learner. The result obtained from the models of individuals and our proposed approach is compared using a set of evaluation measures for image quality such as IoU, DSC, CC, SSIM, SAM, and UQI. The evaluation and a comparison of the results obtained showed more consistent predictions for the proposed model. Thus, we have made a comparison with some recent deep learning-based unsupervised segmentation methods. The evaluation and a comparison of the results obtained showed more coherent predictions for our stacked generalization in terms of precision, robustness, and consistency

The Mediterranean region under climate change

This book has been published by Allenvi (French National Alliance for Environmental Research) to coincide with the 22nd Conference of Parties to the United Nations Framework Convention on Climate Change (COP22) in Marrakesh. It is the outcome of work by academic researchers on both sides of the Mediterranean and provides a remarkable scientific review of the mechanisms of climate change and its impacts on the environment, the economy, health and Mediterranean societies. It will also be valuable in developing responses that draw on “scientific evidence” to address the issues of adaptation, resource conservation, solutions and risk prevention. Reflecting the full complexity of the Mediterranean environment, the book is a major scientific contribution to the climate issue, where various scientific considerations converge to break down the boundaries between disciplines