A combined method based on CNN architecture for variation-resistant facial recognition

Identifying individuals from a facial image is a technique that forms part of computer vision and is used in various fields such as security, digital biometrics, smartphones, and banking. However, it can prove difficult due to the complexity of facial structure and the presence of variations that can affect the results. To overcome this difficulty, in this paper, we propose a combined approach that aims to improve the accuracy and robustness of facial recognition in the presence of variations. To this end, two datasets (ORL and UMIST) are used to train our model. We then began with the image pre-processing phase, which consists in applying a histogram equalization operation to adjust the gray levels over the entire image surface to improve quality and enhance the detection of features in each image. Next, the least important features are eliminated from the images using the Principal Component Analysis (PCA) method. Finally, the pre-processed images are subjected to a neural network architecture (CNN) consisting of multiple convolution layers and fully connected layers. Our simulation results show a high performance of our approach, with accuracy rates of up to 99.50% for the ORL dataset and 100% for the UMIST dataset

Method of optimization of the fundamental matrix by technique speeded up robust features application of different stress images

The purpose of determining the fundamental matrix (F) is to define the epipolar geometry and to relate two 2D images of the same scene or video series to find the 3D scenes. The problem we address in this work is the estimation of the localization error and the processing time. We start by comparing the following feature extraction techniques: Harris, features from accelerated segment test (FAST), scale invariant feature transform (SIFT) and speed-up robust features (SURF) with respect to the number of detected points and correct matches by different changes in images. Then, we merged the best chosen by the objective function, which groups the descriptors by different regions in order to calculate ‘F’. Then, we applied the standardized eight-point algorithm which also automatically eliminates the outliers to find the optimal solution ‘F’. The test of our optimization approach is applied on the real images with different scene variations. Our simulation results provided good results in terms of accuracy and the computation time of ‘F’ does not exceed 900 ms, as well as the projection error of maximum 1 pixel, regardless of the modification

Study of the effect of chromium on the germination parameters of Fenugreek (Trigonella foenum-gracium L.) and Lens (Lens culinaris)

Received: September 21st, 2022 ; Accepted: January 2nd, 2023 ; Published: February 8th, 2023 ; Correspondence: [email protected] contamination by heavy metals is a global environmental problem. This contamination affects agricultural crops in the area concerned. In the present study, chromium, which is a heavy metal, is evaluated for its diverse effects on seed germination and lateral growth of fenugreek and lens seeds. A chromium solution was prepared at increasing concentrations: 0, 0.02, 0.04, 0.06, 0.08, 0.1, and 0.2 mg L-1 for the addition of germinating seeds in petri dishes for ten days. After two days, the germination rate is calculated. For the following days the length of radicle, stem, and number of leaves are measured. The germination rate of fenugreek varies between 100 and 73.33% for the control and 0.02 mg L-1 of chromium respectively. However, the germination rate of the lens varies between 100% for the control and 90% for the 0.02 mg L-1 . The elongation of fenugreek radicle with chromium solutions shows a significant effect. However, there is no significant difference in the lens at the different concentrations. For the growth of the fenugreek stalk, it is noticed that the concentration 0.02 shows a length of 2.83 cm compared to their control which is 2.30 cm. Consequently, chromium at 0.02 mg L-1 stimulates growth, but at 0.2 mg L-1 , it inhibits it. For lens the length of the stems shows also a significant difference compared to their control. So the effect of chromium on germination parameters depends on their concentrations, as well as on the seed response itself. For our research the response of fenugreek compared to the lens at the same concentrations is different

Liberal outcomes through undemocratic means: the reform of the Code de statut personnel in Morocco

The 2004 reform of the family code in Morocco has been held as one of the most significant liberal reforms undertaken in the country, and has led scholars and policy makers to argue that this demonstrates the democratic progress Morocco and the King are making. At the same time, the role of the women's movement in getting the reform approved has seemingly confirmed that associational life is crucial in promoting democratisation. This paper, building on theoretical work questioning the linkage between a strong civil society and democratic outcomes, argues that civil society activism does not necessarily lead to democratisation, and may reinforce authoritarian practices. Far from demonstrating the centrality of civil society, the process through which the new family code was passed highlights the crucial institutional role of the monarch, whose individual decision-making power has driven the whole process. Authoritarianism finds itself strengthened in Morocco despite the liberal nature and outcome of the reform

Fertilizing power evaluation of different mixtures of organic household waste and olive pomace

Received: February 15th, 2022 ; Accepted: June 27th, 2022 ; Published: August 12th, 2022 ; Correspondence: [email protected] the perspective of sustainable agriculture established by the Green Morocco Plan, it is interesting to direct research more towards the agronomic valorization of olive pomace, to give birth to a clean olive growing which leads to a viable economy thus respecting a pillar of sustainable development. Several studies have shown the effectiveness of using olive pomace as a soil amendment. Therefore, in this study we want to increase the agricultural performance of olive pomace by composting by mixing it with other waste. Morocco is considered one of the major olive-producing countries with an annual production of 1.41 million tonnes (MT), part of it is dedicated to olive oil production. Morocco produces approximately 26.8 MT of waste annually, 8.3 MT are household waste, 70% are organic household waste (5.8 MT). The current production of organic household waste in urban areas is estimated at 4.8 million tonnes per year, or an average of 0.76 kg hab-1 day-1 , and in rural areas 1 million tonnes per year, or an average of 0.30 kg hab-1 day-1 (SNRVD, 2015). Agri-food industry waste is around 3 million tonnes with 600,000 to 700,000 tonnes of olive oil waste (pomace) (Agricultural Development Agency, 2018). The rejection of this waste without any prior treatment contributes to the environment deterioration. However, a large part of this waste remains recoverable, which would reduce both waste volume to be eliminated and the associated management cost. This; will contribute to reducing the negative impacts on receiving environments and the cost of restoring the environment state, and ensuring a transition towards a circular economy. Our work is part of the context of solid waste management and recovery, in particular organic waste from household and food-processing activities, and is oriented towards the pomace recovery by composting, mixing it with different percentages of organic household waste. This work consists on composting olive pomace from the three phases system with another structural agent (organic household waste). Comparing the mixtures (6 treatments) with different concentrations in terms of composting process parameters (pH, electrical conductivity, organic matter temperature, etc.), organic matter evolution and composts quality, with manual aeration of the compost, in order to increase the agricultural yield of the olive pomace. Residues from the fermentation process can be used in agriculture. All the different mixtures of the different percentages are characterized at the initial state and at the end of the composting process in order to highlight their nutritional values

Histiocytose langerhansienne mono-focale chez l’enfant : à propos d’un cas: Monofocal Langerhans cell histiocytosis in child: case report

Langerhans cell histiocytosis, affecting both sexes at any age, is a rare disease that progresses by consecutive flares. The lesions can be unifocal or multifocal.  The organs mostly affected are: bone, lung, skin, and endocrine system. We report a case of a 13-year-old child with unifocal clavicular langerhansian histiocytosis under medical surveillance with good clinical and radiological standing. We report a case of a young 13-year-old child with single focal clavicular langerhansian histiocytosis kept under surveillance with good clinical and radiological progress. L’histiocytose à cellules de Langerhans est une maladie rare évoluant par poussées pouvant toucher les 2 sexes à n’importe quel âge de la vie. Les atteintes peuvent être mono-tissulaires uni ou multifocales, les organes les plus fréquemment touchés sont l’os, le poumon, la peau et le système endocrinien. Nous rapportons un cas d’un jeune enfant de 13 ans qui présente une histiocytose langerhansienne claviculaire mono focale gardé sous surveillance avec une bonne évolution clinique et radiologique

Face recognition method combining SVM machine learning and scale invariant feature transform

Facial recognition is a method to identify an individual from his image. It has attracted the intention of a large number of researchers in the field of computer vision in recent years due to its wide scope of application in several areas (health, security, robotics, biometrics...). The operation of this technology, so much in demand in today's market, is based on the extraction of features from an input image using techniques such as SIFT, SURF, LBP... and comparing them with others from another image to confirm or assert the identity of an individual. In this paper, we have performed a comparative study of a machine learning-based approach using several classification methods, applied on two face databases, which will be divided into two groups. The first one is the Train database used for the training stage of our model and the second one is the Test database, which will be used in the test phase of the model. The results of this comparison showed that the SIFT technique merged with the SVM classifier outperforms the other classifiers in terms of identification accuracy rate

Detection of COVID-19 from chest radiology using histogram equalization combined with a CNN convolutional network

The world was shaken by the arrival of the corona virus (COVID-19), which ravaged all countries and caused a lot of human and economic damage. The world activity has been totally stopped in order to stop this pandemic, but unfortunately until today the world knows the arrival of new wave of contamination among the population despite the implementation of several vaccines that have been made available to the countries of the world and this is due to the appearance of new variants. All variants of this virus have recorded a common symptom which is an infection in the respiratory tract. In this paper a new method of detection of the presence of this virus in patients was implemented based on deep learning using a deep learning model by convolutional neural network architecture (CNN) using a COVID-QU chest X- ray imaging database. For this purpose, a pre-processing was performed on all the images used, aiming at unifying the dimensions of these images and applying a histogram equalization for an equitable distribution of the intensity on the whole of each image. After the pre-processing phase we proceeded to the formation of two groups, the first Train is used in the training phase of the model and the second called Test is used for the validation of the model. Finally, a lightweight CNN architecture was used to train a model. The model was evaluated using two metrics which are the confusion matrix which includes the following elements (ACCURACY, SPECIFITY, PRESITION, SENSITIVITY, F1_SCORE) and Receiver Operating Characteristic (the ROC curve). The results of our simulations showed an improvement after using the histogram equalization technique in terms of the following metrics: ACCURACY 96.5%, SPECIFITY 98.60% and PRESITION 98.66%

Fracture de l’omoplate : une observation inhabituelle en orthopédie pédiatrique : Scapula fracture: an unusual fracture in pediatric orthopedics

The scapula fractures are exceptional in pediatric orthopedics. They are mainly due to high kinetic energy mechanisms; traffic accidents are the biggest contributors. They encompass the extra-articular fractures which interest the body of the scapula, and the articular fractures affecting the glenoid surface. We report a case of a young 11-year-old child who presents a right scapula comminuted fracture treated orthopedically with a good clinical and radiological evolution. Les fractures de l’omoplate sont des fractures exceptionnelles en orthopédie pédiatrique. Elles sont principalement dues à des mécanismes de haute cinétique d’énergie, les accidents de la voie publique en sont les plus grands pourvoyeurs. On distingue les fractures extra-articulaires intéressant le corps de l’omoplate, et les fractures articulaires intéressant la surface glénoïdienne. Nous rapportons un cas d’un jeune enfant de 11 ans ayant présenté une fracture comminutive du scapula droit traitée orthopédiquement dont l’évolution clinique et radiologique a été favorable

Discriminative Approach Lung Diseases and COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks: A Promising Approach for Accurate Diagnosis

Medical imaging treatment is one of the best-known computer science disciplines. It can be used to detect the presence of several diseases such as skin cancer and brain tumors, and since the arrival of the coronavirus (COVID-19), this technique has been used to alleviate the heavy burden placed on all health institutions and personnel, given the high rate of spread of this virus in the population. One of the problems encountered in diagnosing people suspected of having contracted COVID-19 is the difficulty of distinguishing symptoms due to this virus from those of other diseases such as influenza, as they are similar. This paper proposes a new approach to distinguishing between lung diseases and COVID-19 by analyzing chest x-ray images using a convolutional neural network (CNN) architecture. To achieve this, pre-processing was carried out on the dataset using histogram equalization, and then we trained two sub-datasets from the dataset using the Train et Test, the first to be used in the training phase and the second to be used in the model validation phase. Then a CNN architecture composed of several convolution layers and fully connected layers was deployed to train our model. Finally, we evaluated our model using two different metrics: the confusion matrix and the receiver operating characteristic. The simulation results recorded are satisfactory, with an accuracy rate of 96.27%