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Genetic Variability and Phenotypic Correlations Study among Grain Quality Traits and Mineral Elements Concentrations in Colored and Non-Colored Rice (Oryza sativa L.)

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Phyton-International Journal of Experimental Botany 2024年第7期93卷 1733-1748页

作者： Adel A.Rezk mohamed M.El-Malky heba I.mohamed Hossam S.El-Beltagi Agricultural Biotechnology Department College of Agricultural and Food ScienceKing Faisal UniversityAl-Ahsa31982Saudi Arabia Plant Pathology Research Institute Agriculture Research CenterGiza12619Egypt Rice Research and Training Center Field Crop Research InstituteAgriculture Research Center33717Egypt Biological and Geological Sciences Department Faculty of EducationAin Shams UniversityCairo11511Egypt Biochemistry Department Faculty of AgricultureCairo UniversityGiza12613Egypt

Twenty-four rice genotypes were examined to assess genetic variability,heritability,and correlations for seven-grain quality traits,eight nutritional elements,and *** revealed signiﬁcant differences for the quality traits *** every trait under study,the phenotypic coefﬁcient of variation was higher than the correspon-dence genotypic coefﬁcient of *** in a broad sense varied from 29.75%for grain length to 98.31%for the elongation *** percentage recovery had a highly signiﬁcant positive correlation with milling and head rice ***,milling percentage had a highly positive correlation with head rice *** amylose percentage,all the genotypes belonged to low amylose except the Hassawi-1 variety,which had intermediate amylose *** nutrition contents of magnesium(Mg),sodium(Na),potas-sium(K),calcium(Ca),copper(Cu),manganese(Mn),zinc(Zn),iron(Fe),or protein percentage gave different variations for 24 rice genotypes under all the nutritional *** the 24 genotypes,ten rice genotypes–HighNutrient-1,HighNutrient-2,HighNutrient-9,HighNutrient-8,HighNutrient-3,Hassawi-2,HighNutrient-7,HighNutrient-6,Hassawi-1,and HighNutrient-4–had the highest heist value for all nutritional and protein con-tents,and could be used as a donor to improving new *** was a positive and signiﬁcant correlation between magnesium Mg,K,Zn and ***,K had a positive correlation with zinc Zn,Fe,and protein *** analysis was divided into two groups:theﬁrst group included all genotypes rich in nutri-ents,while the remaining genotypes with low nutritional content were included in the second group.

关键词： Amylose cluster analysis genotypes grain quality mineral nutrition rice

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Chaotic Elephant Herd Optimization with Machine Learning for Arabic Hate Speech Detection

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Intelligent Automation & Soft Computing 2024年第3期39卷 567-583页

作者： Badriyya B.Al-onazi Jaber S.Alzahrani Najm Alotaibi Hussain Alshahrani mohamed Ahmed Elfaki Radwa Marzouk heba Mohsen Abdelwahed Motwakel Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies Riyadh12735Saudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqra11911Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in Egypt New CairoNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipAl-Kharj16278Saudi Arabia

In recent years,the usage of social networking sites has considerably increased in the Arab *** has empowered individuals to express their opinions,especially in ***,various organizations that operate in the Arab countries have embraced social media in their day-to-day business activities at different *** is attributed to business owners’understanding of social media’s importance for business ***,the Arabic morphology is too complicated to understand due to the availability of nearly 10,000 roots and more than 900 patterns that act as the basis for verbs and *** speech over online social networking sites turns out to be a worldwide issue that reduces the cohesion of civil *** this background,the current study develops a Chaotic Elephant Herd Optimization with Machine Learning for Hate Speech Detection(CEHOML-HSD)model in the context of the Arabic *** presented CEHOML-HSD model majorly concentrates on identifying and categorising the Arabic text into hate speech and *** attain this,the CEHOML-HSD model follows different sub-processes as discussed *** the initial stage,the CEHOML-HSD model undergoes data pre-processing with the help of the TF-IDF ***,the Support Vector Machine(SVM)model is utilized to detect and classify the hate speech texts made in the Arabic ***,the CEHO approach is employed for fine-tuning the parameters involved in *** CEHO approach is developed by combining the chaotic functions with the classical EHO *** design of the CEHO algorithm for parameter tuning shows the novelty of the work.A widespread experimental analysis was executed to validate the enhanced performance of the proposed CEHOML-HSD *** comparative study outcomes established the supremacy of the proposed CEHOML-HSD model over other approaches.

关键词： Arabic language machine learning elephant herd optimization TF-IDF vectorizer hate speech detection

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Prevelance of Bovine Cysticercosis in Egypt and the Cysticidal Effect of Two Extracts Obtained from Balanites aegyptiaca and Moringa oleifera on Mice Model Affected with T. saginata Cysticerci

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Open Journal of Animal Sciences 2024年第2期14卷 39-55页

作者： Omnia M. Kandil Noha M. F. Hassan Doaa Sedky Hatem A. Shalaby heba M. Ashry Nadia M. T. Abu El Ezz Sahar M. Kandeel mohamed S. Abdelfattah L. Ying Ebtesam M. Al-Olayan Parasitology and Animal Diseases Department Veterinary Research Institute National Research Centre Dokki Cairo Egypt Chemistry of Natural Compounds Department Pharmaceutical and Drugs Industries Research Institute National Research Centre Dokki Cairo Egypt Chemistry Department Faculty of Science Helwan University Cairo Egypt State Key Laboratory of Plateau Ecology and Agriculture Qinghai University Xining China College of Agriculture and Animal Husbandry Qinghai University Xining China Department of Zoology Collage of Science King Saud University Riyadh Saudi Arabia

The aim of the present study was to determine the prevalence of bovine cysticercosis in both cattle and buffloas, in Egypt and to assess the cysticidal efficacy of Balanites aegyptiaca fruits (B. aegyptiaca) and Moringa oleifera seeds (M. oleifera) extracts in experimentally infected mice. The study detected the level of tumor necrosis factor (TNF-α) to monitor the immune and inflammatory responses of experimentally infected mice. Through meat inspection, a total number of 2125 male bovine, 2 to 5 years old, (1125 cattle and 1000 buffloes) were examined under the authority of Albsatine and Alwaraq official abattoirs in Cairo Governorate, Egypt covering the period extended from March 2022 to April 2023. The overall prevalence of the disease among bovine was 7.8% (6.31% of cattle and 9.5% of buffloes). Besides, B. aegyptiaca and M. oleifera extracts showed cysticidal activity in experimentally infected mice. A decrease in the numbers of cysticerci was found in all treated mice groups, and up to 88% reduction was achieved in the B. aegyptiaca-treated group;higher than that was recorded in both M. oleifera (72.23%) and albendazole-treated ones (80.56%). Postmortem findings proved that M. oleifera and B. aegyptiaca reduced cysticerci numbers comparable to a commercial anthelmintic. The study showed a significant decrease (P 0.001) in TNF-α levels after treatment with Balanites and Moringa extracts, compared with the untreated control and the albendazole-treated groups.

关键词： Prevalence Balanites aegyptiaca Moringa oleifera Mice T. saginata Cysticerci

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Optimized Deep Learning Approach for Efficient Diabetic Retinopathy Classification Combining VGG16-CNN

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Computers, Materials & Continua 2023年第11期77卷 1855-1872页

作者： heba M.El-Hoseny heba F.Elsepae Wael A.mohamed Ayman S.Selmy Department of Computer Science the Higher Future Institute for Specialized Technological StudiesObour11828Egypt Department of Electrical Engineering Benha Faculty of EngineeringBenha UniversityBenha13511Egypt

Diabetic retinopathy is a critical eye condition that,if not treated,can lead to vision *** methods of diagnosing and treating the disease are time-consuming and ***,machine learning and deep transfer learning(DTL)techniques have shown promise in medical applications,including detecting,classifying,and segmenting diabetic *** advanced techniques offer higher accuracy and *** Diagnosis(CAD)is crucial in speeding up classification and providing accurate disease ***,these technological advancements hold great potential for improving the management of diabetic *** study’s objective was to differentiate between different classes of diabetes and verify the model’s capability to distinguish between these *** robustness of the model was evaluated using other metrics such as accuracy(ACC),precision(PRE),recall(REC),and area under the curve(AUC).In this particular study,the researchers utilized data cleansing techniques,transfer learning(TL),and convolutional neural network(CNN)methods to effectively identify and categorize the various diseases associated with diabetic retinopathy(DR).They employed the VGG-16CNN model,incorporating intelligent parameters that enhanced its *** outcomes surpassed the results obtained by the auto enhancement(AE)filter,which had an ACC of over 98%.The manuscript provides visual aids such as graphs,tables,and techniques and frameworks to enhance *** study highlights the significance of optimized deep TL in improving the metrics of the classification of the four separate classes of *** manuscript emphasizes the importance of using the VGG16CNN classification technique in this context.

关键词： No diabetic retinopathy(NDR) convolution layers(CNV layers) transfer learning data cleansing convolutional neural networks a visual geometry group(VGG16)

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Metabolites Composition of Bacillus subtilis HussainT-AMU Determined by LC-MS and Their Effect on Fusarium Dry Rot of Potato Seed Tuber

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Phyton-International Journal of Experimental Botany 2023年第3期92卷 783-799页

作者： Touseef Hussain Abrar A.Khan heba I.mohamed Plant Pathology and Nematology Section Department of BotanyAligarh Muslim UniversityAligarhIndia Biological and Geological Sciences Department Faculty of EducationAin Shams UniversityCairoEgypt

Fusarium dry rot is considered to be the most critical soilborne and postharvest disease that damages potato tubers worldwide when they are stored for a long *** study was performed to demonstrate the effect of crude extract,culture filtrate,and cell suspension obtained from the bacterium Bacillus subtilis Hussain T-AMU,on the net house and *** oil-contaminated soil,through the serial dilution method,biosurfactant bacteria were isolated on nutrient agar *** isolate and screen the prospective biosurfactant strain,various biosurfactant screening methods were *** protocols were carried out for morphological,molecular,and chemical *** disease incidences were significantly decreased under net house and field conditions after treatment with biosurfactant extract(20%,70%),culture filtrate(23%,49%),and bacterial cell suspension(46%,27%),*** results indicate a significant effect of *** HussainT-AMU against Fusarium *** characterization of the partially purified extract by gas-chromatography indicated the existence of a high amount of methyl ester fatty *** quantitative analysis determined the presence of lipopeptide-surfactin,fengycin,and iturin in the biosurfactant *** electron microscopy revealed that morphological variation was revealed in the structure of stomata size in leaves and in the mycelia of the pathogen that was *** promising biocontrol agent can be a plausible option for further research and can also be used for the production of green biomolecules at an additional industrial level.

关键词： Biosurfactant biocontrol agents disease severity lipopeptide stomata

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Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

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Computer Systems Science & Engineering 2023年第9期46卷 3103-3119页

作者： Khaled M.Alalayah Fatma S.Alrayes mohamed K.Nour Khadija M.Alaidarous ibrahim M.Alwayle heba Mohsen ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of Computer Science College of Science and ArtsNajran UniversitySharurahSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Malware is a‘malicious software program that performs multiple cyberattacks on the Internet,involving fraud,scams,nation-state cyberwar,and *** malicious software programs come under different classifications,namely Trojans,viruses,spyware,worms,ransomware,Rootkit,botnet malware,*** is a kind of malware that holds the victim’s data hostage by encrypting the information on the user’s computer to make it inaccessible to users and only decrypting it;then,the user pays a ransom procedure of a sum of *** prevent detection,various forms of ransomware utilize more than one mechanism in their attack flow in conjunction with Machine Learning(ML)*** study focuses on designing a Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection(LBAAA-OMLMD)approach in Computer *** presented LBAAA-OMLMDmodelmainly aims to detect and classify the existence of ransomware and goodware in the *** accomplish this,the LBAAA-OMLMD model initially derives a Learning-Based Artificial Algae Algorithm based Feature Selection(LBAAA-FS)model to reduce the curse of dimensionality ***,the Flower Pollination Algorithm(FPA)with Echo State Network(ESN)Classification model is *** FPA model helps to appropriately adjust the parameters related to the ESN model to accomplish enhanced classifier *** experimental validation of the LBAAA-OMLMD model is tested using a benchmark dataset,and the outcomes are inspected in distinct *** comprehensive comparative examination demonstrated the betterment of the LBAAAOMLMD model over recent algorithms.

关键词： Computer networks machine learning security malware detection feature selection ransomware

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Binary Oriented Feature Selection for Valid Product Derivation in Software Product Line

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Computers, Materials & Continua 2023年第9期76卷 3653-3670页

作者： Muhammad Fezan Afzal Imran Khan Javed Rashid Mubbashar Saddique heba G.mohamed Department of CS&SE International Islamic UniversityIslamabad44000Pakistan Information Technology Services University of OkaraOkara56300Pakistan Departmet of Computer Science MLC LabOkara56300Pakistan Department of Computer Science&Engineering University of Engineering&Technology LahoreNarowal CampusNarowal51601Pakistan Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Software Product Line(SPL)is a group of software-intensive systems that share common and variable resources for developing a particular *** feature model is a tree-type structure used to manage SPL’s common and variable features with their different relations and problem of Crosstree Constraints(CTC).CTC problems exist in groups of common and variable features among the sub-tree of feature models more diverse in Internet of Things(IoT)devices because different Internet devices and protocols are ***,managing the CTC problem to achieve valid product configuration in IoT-based SPL is more complex,time-consuming,and ***,the CTC problem needs to be considered in previously proposed approaches such as Commonality VariabilityModeling of Features(COVAMOF)andGenarch+tool;therefore,invalid products are *** research has proposed a novel approach Binary Oriented Feature Selection Crosstree Constraints(BOFS-CTC),to find all possible valid products by selecting the features according to cardinality constraints and cross-tree constraint problems in the featuremodel of ***-CTC removes the invalid products at the early stage of feature selection for the product ***,this research developed the BOFS-CTC algorithm and applied it to,IoT-based feature *** findings of this research are that no relationship constraints and CTC violations occur and drive the valid feature product configurations for the application development by removing the invalid product *** accuracy of BOFS-CTC is measured by the integration sampling technique,where different valid product configurations are compared with the product configurations derived by BOFS-CTC and found 100%*** BOFS-CTC eliminates the testing cost and development effort of invalid SPL products.

关键词： Software product line feature model internet of things crosstree constraints variability management

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Biological Control of Root-Knot Nematode Meloidogyne incognita in Psoralea corylifolia Plant by Enhancing the Biocontrol Efficacy of Trichoderma harzianum Using Press Mud

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Phyton-International Journal of Experimental Botany 2022年第8期91卷 1757-1777页

作者： Yasar Nishat Mohammad Danish heba I.mohamed Hisamuddin Shaikh Abeer Elhakem Section of Plant Pathology and Nematology Department of BotanyAligarh Muslim UniversityAligarh202002India Department of Biological and Geological Sciences Faculty of EducationAin Shams UniversityCairo11341Egypt Department of Biology College of Science and Humanities in Al-KharjPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Meloidogyne incognita is a plant pathogen causing root-knot disease and loss of crop *** present study aimed to use Trichoderma harzianum as a biocontrol agent against plant-parasitic nematodes and used press mud,which is a solid waste by-product of sugarcane,as a biocontrol agent and ***,the combined application of *** and press mud may enhance nematode control and plant *** analysis of press mud using scanning electron microscopy(SEM)integrated with an Energy Dispersive X-ray(EDX)analyzer revealed the presence of different elements such as C,O,Mg,Si,P,K,Ca,Cu and *** addition,a greenhouse study was conducted to investigate the combined effects of press mud and *** on *** reproduction and growth and the biochemical features of Psoralea *** results showed that plant length,dry biomass,leaf area,the number of seeds per plant,chlorophyll a,chl b,carotenoid content,nitrate reductase,carbonic anhydrase,and nitrogen content were significantly increased(P≤0.05)in the T2 plants(plants were treated with 100 g of press mud+50 mL *** before one week of *** inoculation),over inoculated plants(IC).Antioxidant enzyme activity of ascorbate peroxidase(APX),catalase(CAT),peroxidase(POD),and superoxide dismutase(SOD)in the foliage of *** was significantly increased when plants were treated with press mud+***.A significant reduction in the number of egg masses,nematode population,and root-knot index(RKI)was found in plants with T2 *** results suggest that the combined application of *** and press mud has the potential to control the *** infection and can be used as an environmentally safe alternative to chemical nematicides and also help in the removal of sugarcane waste that causes environmental pollution.

关键词： Growth improvement antioxidant enzymes nitrate reductase carbonic anhydrase root-knot index

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Hyperparameter Tuned Deep Learning Enabled Cyberbullying Classification in Social Media

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Computers, Materials & Continua 2022年第12期73卷 5011-5024页

作者： Mesfer Al Duhayyim heba G.mohamed Saud S.Alotaibi Hany Mahgoub Abdullah mohamed Abdelwahed Motwakel Abu Sarwar Zamani mohamed I.Eldesouki Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computers and Information Computer Science DepartmentMenoufia UniversityEgypt Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl KharjSaudi Arabia

Cyberbullying(CB)is a challenging issue in social media and it becomes important to effectively identify the occurrence of *** recently developed deep learning(DL)models pave the way to design CB classifier models with maximum *** the same time,optimal hyperparameter tuning process plays a vital role to enhance overall *** study introduces a Teacher Learning Genetic Optimization with Deep Learning Enabled Cyberbullying Classification(TLGODL-CBC)model in Social *** proposed TLGODL-CBC model intends to identify the existence and non-existence of CB in social media ***,the input data is cleaned and pre-processed to make it compatible for further *** by,independent recurrent autoencoder(IRAE)model is utilized for the recognition and classification of ***,the TLGO algorithm is used to optimally adjust the parameters related to the IRAE model and shows the novelty of the *** assuring the improved outcomes of the TLGODLCBC approach,a wide range of simulations are executed and the outcomes are investigated under several *** simulation outcomes make sure the improvements of the TLGODL-CBC model over recent approaches.

关键词： Social media deep learning cyberbullying cybersecurity hyperparameter optimization

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Water Wave Optimization with Deep Learning Driven Smart Grid Stability Prediction

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Computers, Materials & Continua 2022年第12期73卷 6019-6035页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim heba G.mohamed Mohammad Alamgeer mohamed K.Nour Anas Abdelrahman Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science and Bioinformatics Singhania UniversityPacheri BariJhnujhunuRajasthanIndia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Mechanical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt

Smart Grid(SG)technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures,control systems,and communication *** SGs,user demand data is gathered and examined over the present supply criteria whereas the expenses are then informed to the clients so that they can decide about electricity *** the entire procedure is valued on the basis of time,it is essential to perform adaptive estimation of the SG’s *** advancements inMachine Learning(ML)andDeep Learning(DL)models enable the designing of effective stability prediction models in *** this background,the current study introduces a novel Water Wave Optimization with Optimal Deep Learning Driven Smart Grid Stability Prediction(WWOODL-SGSP)*** aim of the presented WWOODL-SGSP model is to predict the stability level of SGs in a proficient *** attain this,the proposed WWOODL-SGSP model initially applies normalization process to scale the data to a uniform ***,WWO algorithm is applied to choose an optimal subset of features from the pre-processed ***,Deep Belief Network(DBN)model is followed to predict the stability level of ***,Slime Mold Algorithm(SMA)is exploited to fine tune the hyperparameters involved in DBN *** order to validate the enhanced performance of the proposedWWOODL-SGSP model,a wide range of experimental analyses was *** simulation results confirmthe enhanced predictive results of WWOODL-SGSP model over other recent approaches.

关键词： Smart grid stability prediction deep learning energy systems machine learning metaheursitics

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