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Variability in patterns of macro-epiphytic leaf community of Posidonia oceanica in the Islands of Kuriate:Western coast of Tunisia

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Journal of Coastal Life Medicine 2016年第3期4卷 211-216页

作者： Ben Brahim Mounir Abdennahder Moufida Feki-Sahnoun Wafa Mahfoudi Mabrouka hamza asma Faculty of Sciences of Sfax Road of Soukra km 4BP 802-3038 SfaxTunisia National Institut of Sciences and Technology of the Sea Center of SfaxBP 1035-CP 3018 SfaxTunisia

Objective:To test the response of the epiphyte community structure and biomass of the Posidonia oceanica(***)leaves to natural ***:Sampling of *** was carried in winter and summer on three sites in Kuriate Islands(western coast of Tunisia)subject to different environments *** of *** were preserved in seawater-formalin(5%)solution for macro-epiphytes species identification in the *** samples were examined for leaf surface per shoot and the coverage(expressed as a percentage of leaf surface)of each morphological group,then carefully scraped with a razor *** and scraped leaves were oven-dried at 60℃for 48 *** was expressed as g dry weight/***:The biomass and the percentage cover of macro-epiphytic leaves showed seasonal *** highest values of epiphytic leaves were detected in summer whereas the lowest values were registered during *** showed that Kuriate Islands functioned as a single ecosystem in terms assemblage of macro-epiphytic leaves since no significant variation was detected for biomass and percentage cover at the scale *** study showed that natural disturbance had no effect on the assemblage distribution and the biomass of macro-epiphyte on the leaves of *** between the scales of site,whereas variability at the smallest scale was *** showed that exposure to wind and current had no effect on the biomass of macro-epiphytes ***:Biomass and assemblages of macro-epiphytic leaves of *** were high in summer and homogenous between all sites *** disturbances such as exposure to wind have no effect on the distribution and the biomass of epiphytes on the shallow meadow.

关键词： Macro-epiphyte Assemblage Variability Posidonia oceanica Tunisia

Factor driving heterotrophic dinoflagellate in relation to environment conditions in Kerkennah Islands(eastern coast of Tunisa)

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Journal of Coastal Life Medicine 2015年第9期 686-690页

作者： Mounir Ben Brahim Morsi Feki Wafa Feki-Sahnoun Mabrouka Mahfoudi asma hamza Faculty of Sciences of Sfax Road of Soukra km 4BP 802-3038 SfaxTunisia National Institute of Sciences and Technologies of the Sea Center of Sfax-BP 1035-CP.3018.SfaxTunisia

Objective:To study the seasonal variability of heterotrophic dinoflagellate in the station of Cercina(southern coast of Tunisia).Methods:Sampling was done in 2007 in Cercina station located in the western coast of Kerkennah(34°41'27''N;11°07'45''E)(Southern Tunisia).Three replicates of water samples were taken during 10 days of each *** variables and nutrients were measured in ***:A significant seasonal difference was observed for temperature and water *** highest values were observed in spring and *** significant seasonal difference was,however,detected for nitrite,nitrate,ammonia,silica and ***-five species of dinoflagellate were identified in the station of *** of dinoflagellates fluctuated between seasons with values showing a significant seasonal and monthly *** highest mean abundance was recorded in spring in April,while the lowest abundance was detected in December in *** granii was the main species contributing to the dissimilarity between spring and winter with 13.98%followed by Peridinium *** 12.5%of dissimilarity and by Polykrikos *** 10.58%.Conclusions:Heterotrophic dinoflagellates proliferate in spring and *** increase was justified by the nutrient *** granii and Polykrikos kofoidii were the main heterotrophic dinoflagellate making difference between seasons and their densities were positively correlated with both temperature and salinity.

关键词： Heterotrophic dinoflagellate Variability Seasonal Kerkennah Tunisia

Artificial Intelligence-Based Fusion Model for Paddy Leaf Disease Detection and Classification

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Computers, Materials & Continua 2022年第7期72卷 1391-1407页

作者： Ahmed SAlmasoud Abdelzahir Abdelmaboud Taiseer Abdalla Elfadil Eisa Mesfer Al Duhayyim asma Abbas Hassan Elnour Manar Ahmed hamza Abdelwahed Motwakel Abu Sarwar Zamani Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Science and ArtsKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityAflaj16828Saudi Arabia Computer Science Department Community College-Girls SectionKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

In agriculture,rice plant disease diagnosis has become a challenging issue,and early identification of this disease can avoid huge loss incurred from less crop *** of the recently-developed computer vision and Deep Learning(DL)approaches can be commonly employed in designing effective models for rice plant disease detection and classification *** this motivation,the current research work devises an Efficient Deep Learning based FusionModel for Rice Plant Disease(EDLFM-RPD)detection and *** aim of the proposed EDLFM-RPD technique is to detect and classify different kinds of rice plant diseases in a proficient *** addition,EDLFM-RPD technique involves median filtering-based preprocessing and K-means segmentation to determine the infected *** study also used a fusion of handcrafted Gray Level Co-occurrence Matrix(GLCM)and Inception-based deep features to derive the ***,Salp Swarm Optimization with Fuzzy Support Vector Machine(FSVM)model is utilized for *** order to validate the enhanced outcomes of EDLFM-RPD technique,a series of simulations was *** results were assessed under different *** obtained values infer the improved performance of EDLFM-RPD technique over recent approaches and achieved a maximum accuracy of 96.170%.

关键词： Rice plant disease classification model artificial intelligence deep learning fusion model parameter optimization

Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

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Computers, Materials & Continua 2023年第5期75卷 3239-3255页

作者： Majdy M.Eltahir Adil Yousif Fadwa Alrowais Mohamed K.Nour Radwa Marzouk Hatim Dafaalla asma Abbas Hassan Elnour Amira Sayed A.Aziz Manar Ahmed hamza Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Arts and Science Najran UniversitySharourahSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Applied SciencesKing Khalid UniversityMuhayil63772Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event *** is especially applicable in the case of elderly or disabled people who live self-reliantly in their *** sensors produce a huge volume of physical activity data that necessitates real-time recognition,especially during *** is one of the most important problems confronted by older people and people with movement *** previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled ***,the costs incurred upon installation and operation are high,whereas the technology is relevant only for indoor ***,commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s *** this background,the current study develops an Improved WhaleOptimizationwithDeep Learning-Enabled Fall Detection for Disabled People(IWODL-FDDP)*** presented IWODL-FDDP model aims to identify the fall events to assist disabled *** presented IWODLFDDP model applies an image filtering approach to pre-process the ***,the EfficientNet-B0 model is utilized to generate valuable feature vector ***,the Bidirectional Long Short Term Memory(BiLSTM)model is used for the recognition and classification of fall ***,the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method,which shows the novelty of the *** experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.

关键词： Fall detection disabled people deep learning improved whale optimization assisted living