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A New Speed Limit Recognition Methodology Based on Ensemble Learning:Hardware Validation

Computers, Materials & Continua 2024年第7期80卷 119-138页

作者： mohamed karray Nesrine Triki mohamed Ksantini ESME ESME Research LabIvry Sur Seine94200France CEM Lab ENISUniversity of SfaxSfax3038Tunisia

Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the *** Sign Recognition System(TSRS)is one of themost important components *** the challengeswith TSRS is being able to recognize road signs with the highest accuracy and the shortest processing ***,this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed ***,the Speed Limit Detection(SLD)module uses the Haar Cascade technique to generate a new SL detector in order to localize SL signs within captured ***,the Speed Limit Classification(SLC)module,featuring machine learning classifiers alongside a newly developed model called DeepSL,harnesses the power of a CNN architecture to extract intricate features from speed limit sign images,ensuring efficient and precise *** addition,a new Speed Limit Classifiers Fusion(SLCF)module has been developed by combining trained ML classifiers and the DeepSL model by using the Dempster-Shafer theory of belief functions and ensemble learning’s voting *** rigorous software and hardware validation processes,the proposedmethodology has achieved highly significant F1 scores of 99.98%and 99.96%for DS theory and the votingmethod,***,a prototype encompassing all components demonstrates outstanding reliability and efficacy,with processing times of 150 ms for the Raspberry Pi board and 81.5 ms for the Nano Jetson board,marking a significant advancement in TSRS technology.

关键词： Driving automation advanced driver assistance systems(ADAS) traffic sign recognition(TSR) artificial intelligence ensemble learning belief functions voting method

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Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging,Laboratory VNIR Spectroscopy and Their Combination

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Computer Modeling in Engineering & Sciences 2023年第8期136卷 1399-1425页

作者： Emna karray Hela Elmannai Elyes Toumi mohamed Hedi Gharbia Souham Meshoul Hamouda Aichi Zouhaier Ben Rabah Center of Mapping and Remote Sensing Tunis1073Tunisia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Digital Research Centre of Sfax Sfax3021Tunisia Higher School of Agriculture of Mograne LR03AGR02SPADDUniversity of CarthageCarthage1054Tunisia

Pedo-spectroscopy has the potential to provide valuable information about soil physical,chemical,and biological ***,wemay predict soil properties usingVNIRfield imaging spectra(IS)such as Prisma satellite data or laboratory spectra(LS).The primary goal of this study is to investigate machine learning models namely Partial Least Squares Regression(PLSR)and Support Vector Regression(SVR)for the prediction of several soil properties,including clay,sand,silt,organic matter,nitrate NO3-,and calcium carbonate CaCO_(3),using five VNIR spectra dataset combinations(%IS,%LS)as follows:C1(0%IS,100%LS),C2(20%IS,80%LS),C3(50%IS,50%LS),C4(80%IS,20%LS)and C5(100%IS,0%LS).Soil samples were collected at bare soils and at the upper(0–30 cm)*** data set has been split into a training dataset 80%of the collected data(n=248)and a validation dataset 20%of the collected data(n=61).The proposed PLSR and SVR models were trained then tested for each dataset *** to our results,SVR outperforms PLSR for both:C1(0%IS,100%LS)and C5(100%IS,0%LS).For Soil Organic Matter(SOM)prediction,it achieves(R^(2)=0.79%,RMSE=1.42%)and(R^(2)=0.76%,RMSE=1.3%),*** data fusion has improved the soil property *** highest improvement was obtained for the SOM property(R^(2)=0.80%,RMSE=1.39)when using the SVR model and applying the second Combination C2(20% of IS and 80%LS).

关键词： Soil VNIR field imaging spectroscopy PLSR SVR VNIR data combination

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Olive Mill Waste Water Management Study by Using Principal Component Analysis

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International Journal of Geosciences 2013年第2期4卷 444-453页

作者： Houda Sahnoun mohamed Moncef Serbaji Boubaker karray Khaled Medhioub Département des Sciences de la Terre Faculté des Sciences de Sfax Université de Sfax Sfax Tunisie Ecole Nationale d’Ingénieurs de Sfax-LARSEN Université de Sfax Sfax Tunisie Institut Préparatoire Aux Etudes d’Ingénieurs de Sfax Université de Sfax Sfax Tunisie Institution de la Recherche et l’Enseignement Supérieur Agricoles Sfax Tunisie

Olive mill waste water (OMWW) is a by-product issued after triturating olives. In Sfax, its management is different from urban to farming area. In this paper we treat it through a statistical analysis study during the season 2005-2006. Principal Component Analysis (PCA) and Hierarchical Classification (HC) methods are carried out on this work. Applied to variables issued from an exhaustive questionnaire including 274 mills, four Principal Components (PCs) are found to be significant, explaining 67% of the total variance. The coordinates of the 13 active variables retained by PCA were used to create a typology relative to the OMWW management and offered 7 groups of individuals which have the same characteristics, explaining 70% of the total inter-variance. This study showed that OMWW management in farming area could causes environmental problems because oleifactors haven’t controlled tanks and could evacuated OMWW on soil (causing oil deposit, waterproofing and possible asphyxia) or on public sewage network (causing corrosion, flow reduction). So, mills transfer from urban to farming areas in the form of agro-industrial complex is needed in the Sfax region.

关键词： Olive Mill Waste Water Management Sfax Principal Component Analysis Hierarchical Classification

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Managing Salinity in Tunisian Oases

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Journal of Life Sciences 2014年第9期8卷 775-782页

作者： mohamed Hachicha Imed Ben Aissa Laboratory of Valorization of non Conventional Water National Institute of Rural Engineering Waters and Forest Ariana 2080 17 Rue Hedi Karray Tunisia Regional Research Centre of Horticulture and Biological Agriculture Chott Meriem BP 57-4042 Sousse Tunisia

The Tunisian oases face serious problems of waterlogging and salinization caused by mismanagement of water and soil resources and the reduced discharge of drainage water. The oases space is based on a fragile balance between water, soil and man, which is now changed by modem irrigation and drainage systems. The oases pump most of their water from deep aquifers and only to a small degree from shallow aquifers. The quality of the irrigation water and the presence of a shallow saline water table are the main causes of salinization of the oases. Concerning the salt-affected landscapes and hydro-saline dynamic, the authors distinguish an equilibrium dynamic of salts to the parcel which depends on water management, and an equilibrium dynamic at the level of the basin watershed which is powered by drilling and ending in hypersaline depressions. For the management of salinization and waterlogging, a combination of agricultural management techniques are used, the first being modem methods of irrigation and drainage. Other, less used methods are sandy amendment, the reuse of drainage waters, geothermal waters and of treated wastewater.

关键词： Water irrigation drainage salinity oases Tunisia.

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Oxidative Stress, Biochemical and Histopathological Alterations in the Liver and Kidney of Female Rats Exposed to Low Doses of Deltamethrin (DM):A Molecular Assessment

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Biomedical and Environmental Sciences 2012年第6期25卷 672-683页

作者： CHARGUI Issam GRISSA Intissar BENSASSI Fatma HRIRA mohamed Yahia HAOUEM Samir HAOUAS Zohra BENCHEIKH Hassen Laboratory of Histology-Cytology and Genetics (02/UR/08-03) Faculty of Medicine 5019 Monastir Tunisia Laboratory of Research on Biologically Compatible Compounds Faculty of Dentistry Street Avicenne 5019 Monastir Tunisia Laboratory of Plant Protection The National Institute for Agricultural Research INRA Tunisia Street Hedi Karray 2049 Ariana Tunisia Laboratory of Biochemistry (UR 07/06) Faculty of pharmacy 5019 Monastir Tunisia Laboratory of Physiology Faculty of Medicine 5019 Monastir Tunisia

Objective To evaluate histopathological alterations of the liver and kidney of female rats exposed to low doses of DM and its potential genotoxic activity. Methods Female Wistar rats were randomly assigned to control （3 groups, 6 rats in each） and treatment groups （3 groups, 6 rats in each）. They were subjected to subcutaneous injections of DM （at doses of 0.003, 0.03, and 0.3 mg/kg bw/d） after 30, 45, and 60 d, respectively. Results Significant alterations were recorded in liver parenchyma induced by hepatic vacuolization, fragmented chromatin in nuclei, dilatation of sinusoids and congestions. Lesions within proximal and distal tubules were observed in the kidneys. Tissue congestions and severe alterations within glomeruli were visible. DM as a pyrethroid insecticide induced significant increase （P〈_O.05） of plasma MDA concentrations after 45 d. A significant increase （P_〈0.05） in plasma ALT （after 45 and 60 d） and AST （after 60 d） concentrations was recorded as compared to controls. During the whole experimental period the toxic agent provoked significant DNA damages （P〈0.05）, especially in the dominance of classes 3 and 4 of obtained comet. Conclusion DM even at a very low dose displays harmful effects by disrupting hepatic and renal function and causing DNA damages in puberscent female rats. Low doses of DM are hepatotoxic and nephrotoxic.

关键词： Deltamethrin Rats Liver Kidney Histopathology Comet assay DNA damage.

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