Search results for: Feriel Lalaoui

Authors: Malika Yaici, Feriel Lalaoui, Lydia Belhoul

Abstract:

As a new distributed point-to-point (P2P) technology, blockchain has become a very broad field of research, addressing various challenges including privacy preserving as is the case in all other technologies. In this work, a study of the existing solutions to the problems related to private life in general and in blockchains in particular is performed. User anonymity and transaction confidentiality are the two main challenges for the protection of privacy in blockchains. Mixing mechanisms and cryptographic solutions respond to this problem but remain subject to attacks and suffer from shortcomings. Taking into account these imperfections and the synthesis of our study, we present a mixing model without trusted third parties, based on group signatures allowing reinforcing the anonymity of the users, the confidentiality of the transactions, with minimal turnaround time and without mixing costs.

Keywords: anonymity, blockchain, mixing coins, privacy

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Authors: Faroudja Mohellebi, Fayrouz Khalida Kies, Moncef Rezzik El Marhoun, Feriel Yahiat

Abstract:

The purpose of this study is to valorize an agro-food waste (orange peels) by its use as an adsorbent in the treatment of water loaded with pharmaceutical micropollutant present in aquatic environments, oxytetracycline. The tests, carried out in batch mode, made it possible to study the influence on the sorptive capacity of calcined orange peels of several parameters: the contact time, the initial concentration of oxytetracycline, the adsorbent dose, and the initial pH of the solution. The pseudo-second-order model is best adapted to represent the adsorption kinetics. The Langmuir model describes the adsorption isotherm of oxytetracycline. The adsorption is favored in a basic environment.

Keywords: adsorption, emerging pollutants, oxytetracycline, water treatment

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Authors: Akrpoum Souad, Lalaoui Korrichi

Abstract:

Vicia faba L.,Vaccinium macrocarpon, Punica granatum, Lavandula officinalis, Artemisia absinthium, Linum capitatum and Camellia sinensis were frequently used in our alimentation. In this study, we have tested the antimicrobial activity of their ethanolic and methanolic extracts on some pathogen bacteria, then their ability to in vivo inhibit the growth of Strepcoccus pneumonia. The phytochemical screening has given the composition of the most active extracts. According to the obtained results, the ethanolic extract of Lavendula. officinalis and A absinthium has shown an inhibition of all the tested strains of becteria3. The ethanolic extract of L. officinalis has given the highest activity against S. pneumoniae, followed by the methanolic extract of C. sinensis 1, 2 and P. granatum. The phytochemical screening showed that the most active extracts contained mainly naturels compounds.

Keywords: plants, extracts, antimicrobial activity, streptococcus pneumoniae, phytochemical screening

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Authors: Mohammed Bnebaghdad, Feriel Betouche, Malika Semmani

Abstract:

Image processing become more sophisticated over time due to technological advances, especially artificial intelligence (AI) technology. Currently, AI image processing is used in many areas, including surveillance, industry, science, and medicine. AI in medical image processing can help doctors diagnose diseases faster, with minimal mistakes, and with less effort. Among these diseases is malaria, which remains a major public health challenge in many parts of the world. It affects millions of people every year, particularly in tropical and subtropical regions. Early detection of malaria is essential to prevent serious complications and reduce the burden of the disease. In this paper, we propose and implement a scheme based on AI image processing to enhance malaria disease diagnosis through automated analysis of blood smear images. The scheme is based on the convolutional neural network (CNN) method. So, we have developed a model that classifies infected and uninfected single red cells using images available on Kaggle, as well as real blood smear images obtained from the Central Laboratory of Medical Biology EHS Laadi Flici (formerly El Kettar) in Algeria. The real images were segmented into individual cells using the watershed algorithm in order to match the images from the Kaagle dataset. The model was trained and tested, achieving an accuracy of 99% and 97% accuracy for new real images. This validates that the model performs well with new real images, although with slightly lower accuracy. Additionally, the model has been embedded in a Raspberry Pi4, and a graphical user interface (GUI) was developed to visualize the malaria diagnostic results and facilitate user interaction.

Keywords: medical image processing, malaria parasite, classification, CNN, artificial intelligence

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