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Sorghum:Nutritional Factors,Bioactive Compounds,Pharmaceutical and Application in Food Systems:A Review

Phyton-International Journal of Experimental Botany 2022年第7期91卷 1303-1325页

作者： heba I.Mohamed Eman M.Fawzi Abdul Basit Kaleemullah Rafiq Lone Mahmoud R.Sofy Biological and Geological Sciences Department Faculty of EducationAin Shams UniversityCairo11575Egypt Department of Horticulture Faculty of Crop Production SciencesUniversity of AgriculturePeshawar25120Pakistan Department of Agricultural Mechanization and Renewable Energy Technologies Faculty of Crop Production SciencesUniversity of AgriculturePeshawar25120Pakistan Department of Botany Central University of KashmirGanderbal474011India Botany and Microbiology Department Faculty of ScienceAl-Azhar UniversityCairo11884Egypt

After wheat,rice,maize,and barley,sorghum is the fifth most widely grown cereal on the *** to its high production,drought resistance,and heat tolerance,this crop is replacing maize in some *** is available in a variety of colors,including cream,lemon-yellow,red,and even *** principal grain anatomical components are pericarp,germ or embryo and *** review provides an overview of key sorghum grain components,including starches,fiber,proteins,lipids,and ***,we summarized phenolic compounds,flavonoids,tannins,carotenoids,vitamin E,amines,Policosanols and Phytosterols in sorghum *** is used to manufacture bread and porridge,and it provides a significant source of energy and nutrition for humans;sorghum is extensively farmed for animal ***,because the natural components in sorghum are useful in the development of healthy and functional foods,sorghum farming for both biofuel production and human consumption is gaining *** sorghum grain is high in antioxidants such as polyphenols,primarily tannins,which have a variety of health benefits,including antiproliferative properties linked to the prevention of certain cancers,antioxidant activities linked to the prevention of diseases linked to oxidative stress,and anti-inflammatory effects,as well as improving glucose *** these chemicals cannot be assimilated,their application in the food business has been limited,as sorghum is regarded as a lownutritional grain due to the presence of anti-nutritional components such as strong tannins,which form complexes with proteins and iron,limiting their *** review aims to show the utilization of sorghum as a source of bioactive chemicals and the value they bestow on human health due to the general biological potential it possesses.

关键词： Antioxidant anticancer food anti-inflammatory phenolic compounds sorghum

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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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Seasonal electric vehicle forecasting model based on machine learning and deep learning techniques

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Energy and AI 2023年第4期14卷 398-414页

作者： heba-Allah I.El-Azab R.A.Swief Noha H.El-Amary H.K.Temraz Faculty of Engineering Ahram Canadian University(ACU)GizaEgypt Faculty of Engineering Ain Shams UniversityCairo 11517Egypt Arab Academy for Science Technology and Maritime Transport(AASTMT)Cairo 2033Egypt

In this paper,multiple featured machine learning algorithms and deep learning algorithms are applied in fore-casting the electric vehicles charging load profile from real datasets of Spain’s electrical *** study aims to provide realistic datasets of electric vehicle load profiles to cope with the potential increase in electric vehicle penetration taking into consideration the seasonality *** issues are caused by the distribution network of the electricity grid;such as the huge charging power and stochastic charging behaviors of the drivers of electric vehicles due to the mass rollout of electric *** electric vehicles’load profile is necessary to face challenges to solve the problem of the potential mass rollout of electric vehicles ***,Electric vehicle is considered one of the most promising solutions that develops faster than other stochastic renewable solution to reduce greenhouse *** seasonality effect is one of the huge chal-lenges on electrical loads,so it is investigated by creating four separate forecasting networks to increase system accuracy and studying the effect of seasonal factors such as temperature fluctuation in the four seasons affecting the electric vehicles’battery in charging and draining *** factors are affecting the accuracy of the forecasting *** featured algorithms are *** Neural Networks and Adaptive Neuro-Fuzzy Inference Systems are applied as machine learning algorithms,and Long Short-Term Memory and the Gated Recurrent Units are also utilized as deep learning *** Gated Recurrent Units model performs slightly better than the long short-term memory employed on the hourly average daily historical data of charging electric *** the Adaptive Neuro-Fuzzy Inference System gathers both Artificial Neural Network and Fuzzy Inference System advantages.

关键词： Adaptive neuro-fuzzy inference system Deep learning Electric vehicles Gated recurrent units Long short-term memory Neural network Short-term load forecasting

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Intelligent Classification Model for Biomedical Pap Smear Images on IoT Environment

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Computers, Materials & Continua 2022年第5期71卷 3969-3983页

作者： CSS Anupama T.J.Benedict Jose heba FEid Nojood O Aljehane Fahd N.Al-Wesabi Marwa Obayya Anwer Mustafa Hilal Department Electronics and Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Computer Applications Government Arts&Science CollegeKanyakumari629401India Faculty of Science AL-Azhar UniversityCairo11651Egypt Faculty of Computers and Information Technology University of Tabuk47512Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid University62529Saudi Arabia&Faculty of Computer and ITSana’a University31220Yemen Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman University11564Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharj16278Saudi Arabia

Biomedical images are used for capturing the images for diagnosis process and to examine the present condition of organs or *** image processing concepts are identical to biomedical signal processing,which includes the investigation,improvement,and exhibition of images gathered using x-ray,ultrasound,MRI,*** the same time,cervical cancer becomes a major reason for increased women’s mortality *** cervical cancer is an identified at an earlier stage using regular pap smear *** this aspect,this paper devises a new biomedical pap smear image classification using cascaded deep forest(BPSIC-CDF)model on Internet of Things(IoT)*** BPSIC-CDF technique enables the IoT devices for pap smear image *** addition,the pre-processing of pap smear images takes place using adaptive weighted mean filtering(AWMF)***,sailfish optimizer with Tsallis entropy(SFO-TE)approach has been implemented for the segmentation of pap smear ***,a deep learning based Residual Network(ResNet50)method was executed as a feature extractor and CDF as a classifier to determine the class labels of the input pap smear *** order to showcase the improved diagnostic outcome of the BPSICCDF technique,a comprehensive set of simulations take place on Herlev *** experimental results highlighted the betterment of the BPSICCDF technique over the recent state of art techniques interms of different performance measures.

关键词： Biomedical imaging pap smear images internet of things deep learning cervical cancer disease diagnosis

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Automated Autism Spectral Disorder Classification Using Optimal Machine Learning Model

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Computers, Materials & Continua 2023年第3期74卷 5251-5265页

作者： hanan Abdullah Mengash hamed Alqahtani Mohammed Maray Mohamed K.Nour Radwa Marzouk Mohammed Abdullah Al-hagery heba Mohsen Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial Intelligence and Unit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Autism Spectrum Disorder (ASD) refers to a neuro-disorder wherean individual has long-lasting effects on communication and interaction *** information technologywhich employs artificial intelligence(AI) model has assisted in early identify ASD by using pattern *** advances of AI models assist in the automated identification andclassification of ASD, which helps to reduce the severity of the *** study introduces an automated ASD classification using owl searchalgorithm with machine learning (ASDC-OSAML) model. The proposedASDC-OSAML model majorly focuses on the identification and classificationof ASD. To attain this, the presentedASDC-OSAML model follows minmaxnormalization approach as a pre-processing stage. Next, the owl searchalgorithm (OSA)-based feature selection (OSA-FS) model is used to derivefeature subsets. Then, beetle swarm antenna search (BSAS) algorithm withIterative Dichotomiser 3 (ID3) classification method was implied for ASDdetection and classification. The design of BSAS algorithm helps to determinethe parameter values of the ID3 classifier. The performance analysis of theASDC-OSAML model is performed using benchmark dataset. An extensivecomparison study highlighted the supremacy of the ASDC-OSAML modelover recent state of art approaches.

关键词： Autism spectral disorder machine learning owl search algorithm feature selection id3 classifier

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Artificial Algae Optimization with Deep Belief Network Enabled Ransomware Detection in IoT Environment

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Computer Systems Science & Engineering 2023年第8期46卷 1293-1310页

作者： Mesfer Al Duhayyim heba G.Mohamed Fadwa Alrowais Fahd N.Al-Wesabi Anwer Mustafa Hilal Abdelwahed Motwakel Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityRiyadhSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science and Art at MuhaeylKing Khalid UniversityAbhaSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The Internet of Things(IoT)has gained more popularity in research because of its large-scale challenges and *** security was the main concern when witnessing the fast development in its applications and *** was a dreary task to independently set security systems in every IoT gadget and upgrade them according to the newer ***,machine learning(ML)techniques optimally use a colossal volume of data generated by IoT *** Learning(DL)related systems were modelled for attack detection in *** the current security systems address restricted attacks and can be utilized outdated datasets for *** study develops an Artificial Algae Optimization Algorithm with Optimal Deep Belief Network(AAA-ODBN)Enabled Ransomware Detection in an IoT *** presented AAAODBN technique mainly intends to recognize and categorize ransomware in the IoT *** presented AAA-ODBN technique follows a three-stage process:feature selection,classification,and parameter *** the first stage,the AAA-ODBN technique uses AAA based feature selection(AAA-FS)technique to elect feature ***,the AAA-ODBN technique employs the DBN model for ransomware *** last,the dragonfly algorithm(DFA)is utilized for the hyperparameter tuning of the DBN technique.A sequence of simulations is implemented to demonstrate the improved performance of the AAA-ODBN *** experimental values indicate the significant outcome of the AAA-ODBN model over other models.

关键词： Internet of things deep learning cybersecurity ransomware detection feature selection

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Coati Optimization-Based Energy Efficient Routing Protocol for Unmanned Aerial Vehicle Communication

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Computers, Materials & Continua 2023年第6期75卷 4805-4820页

作者： hanan Abdullah Mengash hamed Alqahtani Mohammed Maray Mohamed K.Nour Radwa Marzouk Mohammed Abdullah Al-hagery heba Mohsen Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial Intelligence and Unit of CybersecurityKing Khalid UniversityAbha62529Saudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Sciences College of Computing and Information SystemsUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Computer Science College of ComputerQassim UniversityBuraydah52571Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

With the flexible deployment and high mobility of Unmanned Aerial Vehicles(UAVs)in an open environment,they have generated con-siderable attention in military and civil applications intending to enable ubiquitous connectivity and foster agile *** difficulty stems from features other than mobile ad-hoc network(MANET),namely aerial mobility in three-dimensional space and often changing *** the UAV network,a single node serves as a forwarding,transmitting,and receiving node at the same ***,the communication path is multi-hop,and routing significantly affects the network’s performance.A lot of effort should be invested in performance analysis for selecting the optimum routing *** this motivation,this study modelled a new Coati Optimization Algorithm-based Energy-Efficient Routing Process for Unmanned Aerial Vehicle Communication(COAER-UAVC)*** presented COAER-UAVC technique establishes effective routes for communication between the *** is primarily based on the coati characteristics in nature:if attacking and hunting iguanas and escaping from ***,the presented COAER-UAVC technique concentrates on the design of fitness functions to minimize energy utilization and communication delay.A varied group of simulations was performed to depict the optimum performance of the COAER-UAVC *** experimental results verified that the COAER-UAVC technique had assured improved performance over other approaches.

关键词： Artificial intelligence unmanned aerial vehicle data communication routing protocol energy efficiency

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A Neural Network-Based Trust Management System for edge Devices in Peer-to-Peer Networks

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Computers, Materials & Continua 2019年第6期59卷 805-815页

作者： Alanoud Alhussain heba Kurdi Lina Altoaimy Computer Science Department King Saud UniversityRiyadh11451Saudi Arabia Information Technology Department King Saud UniversityRiyadh11451Saudi Arabia

edge devices in Internet of Things(IoT)applications can form peers to communicate in peer-to-peer(P2P)networks over P2P *** P2P networks ensures scalability and removes the need for centralized ***,due to the open nature of P2P networks,they often suffer from the existence of malicious peers,especially malicious peers that unite in groups to raise each other’s *** compromises users’safety and makes them lose their confidence about the files or services they are *** address these challenges,we propose a neural networkbased algorithm,which uses the advantages of a machine learning algorithm to identify whether or not a peer is *** this paper,a neural network(NN)was chosen as the machine learning algorithm due to its efficiency in *** experiments showed that the NNTrust algorithm is more effective and has a higher potential of reducing the number of invalid files and increasing success rates than other well-known trust management systems.

关键词： Trust management neural networks peer to peer machine learning edge devices

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New trends in pharmacological treatment of acute kidney injury

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Asian Pacific Journal of Tropical Biomedicine 2021年第7期11卷 285-297页

作者： heba M.I.Abdallah Department of Pharmacology Medical Research DivisionNational Research CentreGiza 12622Egypt

Acute kidney injury,previously known as acute renal failure(AKI),is defined as an abrupt decrease in kidney function that occurs within hours or *** new nomenclature opens a new door for possibility of treatment of developing renal injury before progression to unresolved renal *** arises due to diverse etiologic factors that rely mainly on three categories namely,prerenal,intrinsic renal,and post-renal factors with different clinical pictures,and confers a spectrum of injury ranging from mild to severe and sometimes leads to end-stage renal *** of pathogenesis and other factors generate barriers to developing effective treatments despite a large number of experimental and clinical *** this review,recent advances in the potential of the currently used drugs for renoprotection,novel pharmacological targets,and prospective therapeutics for AKI are *** information in this review was extracted from electronic resources(Pub Med,Google Scholar,Wiley,Science Direct,Springer),and English scientific books by using keywords including kidney,injury,recent therapy,and pharmacological *** articles were carefully checked for their relevance to the current *** targets of cellular repair or regenerative processes involved in AKI such as autophagy,ferroptosis inhibition,and p53 antagonism seem to be effective in disease *** may help researchers and clinicians to understand how to target the interrelated molecular and cellular mechanisms underlying the pathogenesis of AKI.

关键词： Acute kidney injury Recent therapy Promising pharmacological targets

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Battle Royale Optimization with Fuzzy Deep Learning for Arabic Sentiment Classification

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Computer Systems Science & Engineering 2023年第8期46卷 2619-2635页

作者： Manar Ahmed hamza hala J.Alshahrani Jaber S.Alzahrani heba Mohsen Mohamed I.Eldesouki Mohammed Rizwanullah Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkah 24211Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Aspect-Based Sentiment Analysis(ABSA)on Arabic corpus has become an active research topic in recent *** refers to a fine-grained Sentiment Analysis(SA)task that focuses on the extraction of the conferred aspects and the identification of respective sentiment polarity from the provided *** of the prevailing Arabic ABSA techniques heavily depend upon dreary feature-engineering and pre-processing tasks and utilize external sources such as *** literature,concerning the Arabic language text analysis,the authors made use of regular Machine Learning(ML)techniques that rely on a group of rare sources and *** sources were used for processing and analyzing the Arabic language content like ***,an important challenge in this domain is the unavailability of sufficient and reliable *** this background,the current study introduces a new Battle Royale Optimization with Fuzzy Deep Learning for Arabic Aspect Based Sentiment Classification(BROFDL-AASC)*** aim of the presented BROFDL-AASC model is to detect and classify the sentiments in the Arabic *** the presented BROFDL-AASC model,data pre-processing is performed at first to convert the input data into a useful ***,the BROFDL-AASC model includes Discriminative Fuzzy-based Restricted Boltzmann Machine(DFRBM)model for the identification and categorization of ***,the BRO algorithm is exploited for optimal fine-tuning of the hyperparameters related to the FBRBM *** scenario establishes the novelty of current *** performance of the proposed BROFDL-AASC model was validated and the outcomes demonstrate the supremacy of BROFDL-AASC model over other existing models.

关键词： Arabic corpus aspect based sentiment analysis arabic language deep learning battle royale optimization natural language processing

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