Applying the Upper Integral to the Biometric Score Fusion Problem in the Identification Model

This paper presents a new biometric score fusion approach in an identification system using the upper integral with respect to Sugeno's fuzzy measure. First, the proposed method considers each individual matcher as a fuzzy set in order to handle uncertainty and imperfection in matching scores. Then, the corresponding fuzzy entropy estimates the reliability of the information provided by each biometric matcher. Next, the fuzzy densities are generated based on rank information and training accuracy. Finally, the results are aggregated using the upper fuzzy integral. Experimental results compared with other fusion methods demonstrate the good performance of the proposed approach

Soil Erosion Evaluation and Mapping Based on Geomatic Techniques in Wadi El Malleh Watershed

Soil erosion by water considered is serious problem in the Mediterranean region due to the climate aggressiviness of the mountainous terrain, the traditional farming practices and other anthropogenic pressure on its land and soil. The present study was scheduled to use Geographical Information System (GIS), Remote Sensing data and the Revised Universal Soil Loss Equation (RUSLE) model to evaluate the annual average soil loss and sedimentation rate from Wadi El Malleh watershed, which is located in the Northern-Fez (Morocco), and covers an area of 34 km2 . In fact, RUSLE and SEDIMENTATION models were combined with GIS techniques to predict the spatiotemporal distribution of soil erosion and deposition under different land uses. The land use was assessed using the Google Earth image, which was taken in 2013. The image was first geo-referenced and projected into Moroccan coordinates system and classified by ArcGIS software. The use of RUSLE model allowed the estimation of static soil loss. Then the results of RUSLE were applied in the deposition modelling calculations to assess the spread of soil loss downstream by SEDIMENTATION model. The values of the annual net soil erosion obtained by this study were (81.86 t/ha/y) in bad land, (-19.19 t/ha/y) in irrigated agriculture areas and (-13.66 t/ha/y) in reforestation land where the negative values indicated deposition

Application Géomatique Pour La Cartographie De La Vulnérabilité Environnementale Engendrée Par Les Déchets Miniers De La Mine Ferrifere De Nador (Nord-est du Maroc)

The field work has been done to study the impact of acid mine drainage on the environment. Space pollution measurement was carried out on the basis of interesting physicochemical and geomatic analyses of water and solid region outcrops. The study showed that the phenomenon under study represents an alarming problem that is attributed to the presence of huge stocks of mining waste sulphur, directly exposed to a Mediterranean climate and thus remains capable of accelerating the oxidation process. The classification of zones according to the levels of fragility is entailed by the necessity to opt for a model that is compatible with different intrinsic and extrinsic parameters. The study was concluded by the superposition of six thematic layers charting the environmental vulnerability map by the GIS. The findings demonstrated that most vulnerable zones near the points of exploitation are very oxidized in bottomlands or in places well apart from carbonate facies (pH <3

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

Towards an Efficient Implementation of Human Activity Recognition for Mobile Devices

The availability of diverse and powerful sensors embedded in modern Smartphones/mobile devices has created exciting opportunities for developing context-aware applications. Although there is good capacity for collecting and classifying human activity data with such devices, data pre-processing and model building techniques that achieve this goal are required to operate while meeting hardware resource constraints, particularly for real-time applications. In this paper, we present a comparison study for HAR exploiting feature selection approaches to reduce the computation and training time needed for the discrimination of targeted activities while maintaining significant accuracy. We validated our approach on a publicly available dataset. Results show that Recursive Feature Elimination method combined with Radial Basis Function Support Vector Machine classifier offered the best tradeoff between training time/recognition performance

Concrete Bridge Crack Image Classification Using Histograms of Oriented Gradients, Uniform Local Binary Patterns, and Kernel Principal Component Analysis

Bridges deteriorate over time, which requires the continuous monitoring of their condition. There are many digital technologies for inspecting and monitoring bridges in real-time. In this context, computer vision has extensively studied cracks to automate their identification in concrete surfaces, overcoming the conventional manual methods that rely on human judgment. The general framework of vision-based techniques consists of feature extraction using different filters and descriptors and classifier training to perform the classification task. However, training can be time-consuming and computationally expensive, depending on the dimension of the features. To address this limitation, dimensionality reduction techniques are applied to extracted features, and a new feature subspace is generated. This work used histograms of oriented gradients (HOGs) and uniform local binary patterns (ULBPs) to extract features from a dataset containing over 3000 uncracked and cracked images covering different patterns of cracks and concrete surface representations. Nonlinear dimensionality reduction was performed using kernel principal component analysis (KPCA), and three machine learning classifiers were implemented to conduct the classification. The experimental results show that the classification scheme based on the support-vector machine (SVM) model and feature-level fusion of the HOG and ULBP features after KPCA application provided the best results as an accuracy of 99.26% was achieved by the proposed classification framework

Forecasting solar energy production: A comparative study of machine learning algorithms

The use of solar energy has been rapidly expanding as a clean and renewable energy source, with the installation of photovoltaic panels on homes, businesses, and large-scale solar farms. The increasing demand for sustainable energy sources has pushed the growth of the solar industry, as well as advancements in technology, making solar panels more efficient and cost-effective. The implementation of solar energy not only reduces our reliance on non-renewable fossil fuels but also helps to mitigate the effects of climate change by reducing carbon emissions. This paper presents a complete and comparative study of solar energy production forecasting in Morocco using six machine learning (ML) algorithms : Support Vector Regression (SVR), Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF), Generalized Additive Model (GAM) and Extreme Gradient Boosting (XGBOOST), based on Solar Power Plant daily data installed in Benguerir city of Morocco between January and December 2022. The models were trained, tested, and then evaluated. In order to assess the models performance four metrics were used in this study, namely root mean squared error (RMSE), mean absolute error (MAE), mean absolute scaled error (MASE)and R-squared (R2). The performance of the models reveals ANN to be the most effective predictive model for energy forecasting in similar cases with the lowest value of RMSE, MSAE and the highest value of R-squared, which are accepted as one of the most important performance criteria by the ANN model. The findings of this study not only validate the effectiveness of the ANN algorithm but also offer the appropriate parameters for achieving the best results in predicting solar energy production. By identifying the optimal configuration of the ANN algorithm, we provide valuable insights that can be directly applied in real-world applications, thereby enhancing the optimization of solar energy systems and contributing to a sustainable future, particularly the integration of these results in an edge device for the predictive maintenance of photovoltaic power plants

Concrete Bridge Crack Image Classification Using Histograms of Oriented Gradients, Uniform Local Binary Patterns, and Kernel Principal Component Analysis

Bridges deteriorate over time, which requires the continuous monitoring of their condition. There are many digital technologies for inspecting and monitoring bridges in real-time. In this context, computer vision has extensively studied cracks to automate their identification in concrete surfaces, overcoming the conventional manual methods that rely on human judgment. The general framework of vision-based techniques consists of feature extraction using different filters and descriptors and classifier training to perform the classification task. However, training can be time-consuming and computationally expensive, depending on the dimension of the features. To address this limitation, dimensionality reduction techniques are applied to extracted features, and a new feature subspace is generated. This work used histograms of oriented gradients (HOGs) and uniform local binary patterns (ULBPs) to extract features from a dataset containing over 3000 uncracked and cracked images covering different patterns of cracks and concrete surface representations. Nonlinear dimensionality reduction was performed using kernel principal component analysis (KPCA), and three machine learning classifiers were implemented to conduct the classification. The experimental results show that the classification scheme based on the support-vector machine (SVM) model and feature-level fusion of the HOG and ULBP features after KPCA application provided the best results as an accuracy of 99.26% was achieved by the proposed classification framework