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

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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Weather Forecasting Prediction Using Ensemble machine Learning for Big Data Applications

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Computers, materials & Continua 2022年第11期73卷 3367-3382页

作者： Hadil Shaiba Radwa marzouk mohamed K nour Noha Negm Anwer mustafa Hilal Abdullah mohamed Abdelwahed motwakel Ishfaq Yaseen Abu Sarwar Zamani mohammed Rizwanullah Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Science Mathematics and Computer Science DepartmentMenoufia UniversityEgypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt

The agricultural sector’s day-to-day operations,such as irrigation and sowing,are impacted by the ***,weather constitutes a key role in all regular human *** forecasting must be accurate and precise to plan our activities and safeguard ourselves as well as our property from ***,wind speed,humidity,wind direction,cloud,temperature,and other weather forecasting variables are used in this work for weather *** research works have been conducted on weather *** drawbacks of existing approaches are that they are less effective,inaccurate,and *** overcome these issues,this paper proposes an enhanced and reliable weather forecasting *** well as developing weather forecasting in remote *** data analysis and machine learning techniques,such as Gradient Boosting Decision Tree,Random Forest,Naive Bayes Bernoulli,and KNN Algorithm are deployed to anticipate weather conditions.A comparative analysis of result outcome said in determining the number of ensemble methods that may be utilized to improve the accuracy of prediction in weather *** aim of this study is to demonstrate its ability to predict weather forecasts as soon as *** evaluation shows our ensemble technique achieves 95%prediction ***,for 1000 nodes it is less than 10 s for prediction,and for 5000 nodes it takes less than 40 s for prediction.

关键词： Weather forecasting KNN random forest gradient boosting decision tree naive bayes bernoulli

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Energy Aware Secure Cyber-Physical Systems with Clustered Wireless Sensor Networks

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Computers, materials & Continua 2022年第9期72卷 5499-5513页

作者： masoud Alajmi mohamed K.nour Siwar Ben Haj Hassine mimouna Abdullah Alkhonaini manar Ahmed Hamza Ishfaq Yaseen Abu Sarwar Zamani mohammed Rizwanullah Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recently,cyber physical system(CPS)has gained significant attention which mainly depends upon an effective collaboration with computation and physical *** greatly interrelated and united characteristics of CPS resulting in the development of cyber physical energy systems(CPES).At the same time,the rising ubiquity of wireless sensor networks(WSN)in several application areas makes it a vital part of the design of *** security and energy efficiency are the major challenging issues in CPES,this study offers an energy aware secure cyber physical systems with clustered wireless sensor networks using metaheuristic algorithms(EASCPSmA).The presented EASCPS-mA technique intends to attain lower energy utilization via clustering and security using intrusion *** EASCPSmA technique encompasses two main stages namely improved fruit fly optimization algorithm(IFFOA)based clustering and optimal deep stacked autoencoder(OSAE)based intrusion ***,the optimal selection of stacked autoencoder(SAE)parameters takes place using root mean square propagation(RmSProp)*** extensive performance validation of the EASCPS-mA technique takes place and the results are inspected under varying *** simulation results reported the improved effectiveness of the EASCPS-mA technique over other recent approaches interms of several measures.

关键词： Intrusion detection system metaheuristics stacked autoencoder deep learning cyber physical energy systems clustering wsn

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Relations between Low-Frequency modes of Climate Variability and Air-Sea Heat Flux at the mediterranean Interface

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Journal of Earth Science and Engineering 2014年第2期4卷 101-106页

作者： Lamri Nacef nour El Islam Bachari Department of Ecology and Environment Faculty of Biological Sciences Houari Boumediene University of Science and TechnologyAlgiers 16111 Algeria

The main objective of this work is to examine statistical causality relationships between low-frequency modes of climate variability and winter （December to February） anomaly of net heat flux at the mediterranean air-sea interface. The introduction of the concept of Granger causality allowed us to examine the influence of these climates indices on the net heat flux anomaly and to select mediterranean surface regions that really influenced by each index. Results show that the winter anomaly of the net heat flux in the Algerian basin south and the gulf of Lion is mainly caused by the Arctic Oscillation. El Nifio-Southern Oscillation influences much more the Algerian basin north and the northern lonian Sea. The Quasi-Biennial Oscillation affects only the Alboran and the Tyrrhenian Seas. But the Adriatic and Levantine basin are impacted by any climate index. They also show that these climate indices can increase explained variance in winter variations of air-sea net heat flux by 10% to 15%, with a lag of three seasons. These relationships are less persistent and spatially limited.

关键词： mediterranean Sea winter net heat flux air-sea interface climate index causality analysis.

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Gaussian Optimized Deep Learning-based Belief Classification model for Breast Cancer Detection

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Computers, materials & Continua 2022年第11期73卷 4123-4138页

作者： Areej A.malibari marwa Obayya mohamed K.nour Amal S.mehanna manar Ahmed Hamza Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed motwakel Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Digital media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

With the rapid increase of new cases with an increased mortality rate,cancer is considered the second and most deadly disease *** cancer is the most widely affected cancer worldwide,with an increased death rate *** to radiologists’processing of mammogram images,many computer-aided diagnoses have been developed to detect breast *** detection of breast cancer will reduce the death rate *** early diagnosis of breast cancer using the developed computer-aided diagnosis(CAD)systems still needed to be enhanced by incorporating innovative deep learning technologies to improve the accuracy and sensitivity of the detection system with a reduced false positive *** paper proposed an efficient and optimized deep learning-based feature selection approach with this *** model selects the relevant features from the mammogram images that can improve the accuracy of malignant detection and reduce the false alarm *** learning is used in the extraction of features *** ext,a convolution neural network,is used to extract the *** two feature vectors are fused and optimized with enhanced Butterfly Optimization with Gaussian function(TL-CNN-EBOG)to select the final most relevant *** optimized features are applied to the classifier called Deep belief network(DBN)to classify the benign and malignant *** feature extraction and classification process used two datasets,breast,and *** to the existing methods,the optimized deep learning-based model secured 98.6%of improved accuracy on the breast dataset and 98.85%of improved accuracy on the mIAS dataset.

关键词： Breast cancer detection computer-aided diagnosis(CAD) deep learning CNN entropy butterfly optimization

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A Curious Case of Recurrent Umbilical Swelling: Case Report and Literature Review

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Surgical Science 2018年第12期9卷 480-486页

作者： Shahd nour Najam Husain Naseem Waraich Department of General Surgery Queen’s Hospital Burton NHS Foundation Trust Burton-on-Trent UK

Background: Umbilical endometriosis, a rare benign condition, is clinically difficult to differentiate from other diseases that result in the formation of an umbilical nodule. It is caused by the presence of ectopic endometrial tissue in the umbilicus, which overtly presents as a painful, discoloured swelling in the umbilicus. However, in our case the presentation wasn’t as obvious. The endometrial tissue was embedded within an umbilical hernia. Case Summary: Our patient was a 37-year-old female with a past medical/surgical history of a subtotal colectomy for ulcerative colitis in 2001 followed by an ileo-rectal anastomosis. 17 years later, she was referred to the surgical outpatient clinic from GP practice with a painful umbilical/incisional hernia surrounding the previous scar and just above the umbilicus, this pain, particularly worsening during her menstrual cycle. multiple blood tests and ultrasound scans of her abdomen failed to diagnose a cause for her agony which mandated an elective excision of the swelling. This successfully resolved her symptoms. Conclusion: Umbilical endometriosis is a potential diagnosis in females with a painful umbilical swelling, imaging has no diagnostic role and excision is the definitive treatment.

关键词： Umbilical Endometrioma Endometriosis Case Report Total Umbilical Resection

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Deep Transfer Learning Driven Oral Cancer Detection and Classification model

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Computers, materials & Continua 2022年第11期73卷 3905-3920页

作者： Radwa marzouk Eatedal Alabdulkreem Sami Dhahbi mohamed K.nour mesfer Al Duhayyim mahmoud Othman manar Ahmed Hamza Abdelwahed motwakel Ishfaq Yaseen mohammed Rizwanullah Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Oral cancer is the most commonly occurring‘head and neck cancers’across the *** of the oral cancer cases are diagnosed at later stages due to absence of awareness among *** earlier identification of disease is essential for improved outcomes,Artificial Intelligence(AI)and machine Learning(mL)models are used in this *** this background,the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification model(AIDTLOCCm).The primary goal of the proposed AIDTL-OCCm model is to diagnose oral cancer using AI and image processing *** proposed AIDTL-OCCm model involves fuzzy-based contrast enhancement approach to perform data *** by,the densely-connected networks(DenseNet-169)model is employed to produce a useful set of deep ***,Chimp Optimization Algorithm(COA)with Autoencoder(AE)model is applied for oral cancer detection and ***,COA is employed to determine optimal parameters involved in AE model.A wide range of experimental analyses was conducted on benchmark datasets and the results were investigated under several *** extensive experimental analysis outcomes established the enhanced performance of AIDTLOCCm model compared to other approaches with a maximum accuracy of 90.08%.

关键词： Oral cancer detection image classification artificial intelligence machine learning deep learning

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Effect of Partially Replacing Corn meal by Wet Date on Growth Performance in Nile Tilapia(Oreochromis niloticus)Fingerlings,Diets Supplemented with Digestarom

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Journal of Geoscience and Environment Protection 2014年第2期2卷 60-67页

作者： m.m.Gaber E.H.Labib E.A.Omar m.A.Zaki A.m.nour National Institute of Oceanography and Fisheries CairoEgypt Departments of Animal and Fish Production Faculty of AgricultureAlexandria UniversitySaba BashaEgypt Departments of Animal and Fish Production Faculty of AgricultureAlexandria UniversityEl ShatbyEgypt

A 3-month randomized factorial design 4 × 2 (four levels 0, 15, 30, and 45%) of wet Date (WD), two levels (0 and 0.03%) of Digestarom? (D) and three replicates. The trial was conducted in twenty four glass aquaria. Ten fingerlings of Nile tilapia were placed in each aquarium with an average weight 15.40±0.3 g fish. Fish were fed twice daily (six days a week) at a rate of 3% of body weight. The results revealed that, mean final weight (g/fish), SGR (%/day), feed conversion ratio, PPV and PER, were significantly (p ≤ 0.05) affected by the levels of WD and level of D. And the best diet achieved which containing 30% WD supplemented with 0.03% D. From the above results and the economic information of these studies it can be concluded that, diet containing 30% of WD with 0.03% D exhibited the highest net profit and would seem to be the most desirable level of WD and D in the system studied.

关键词： Nile Tilapia Wet Date Digestarom

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Optimal Deep Learning model Enabled Secure UAV Classification for Industry 4.0

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Computers, materials & Continua 2023年第3期74卷 5349-5367页

作者： Khalid A.Alissa mohammed maray Areej A.malibari Sana Alazwari Hamed Alqahtani mohamed K.nour marwa Obbaya mohamed A.Shamseldin mesfer Al Duhayyim SAUDI ARAmCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Emerging technologies such as edge computing,Internet of Things(IoT),5G networks,big data,Artificial Intelligence(AI),and Unmanned Aerial Vehicles(UAVs)empower,Industry 4.0,with a progressive production methodology that shows attention to the interaction between machine and human *** the literature,various authors have focused on resolving security problems in UAV communication to provide safety for vital *** current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification(CSODL-SUAVC)model for Industry 4.0 *** suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image ***,the proposed CSODL-SUAVC method involves the following methods such as multi-Level Discrete Wavelet Transformation(mL-DWT),CSO-related Optimal Pixel Selection(CSO-OPS),and signcryption-based *** proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover *** secret images,encrypted by signcryption technique,are embedded into cover ***,the image classification process includes three components namely,Super-Resolution using Convolution Neural Network(SRCNN),Adam optimizer,and softmax *** integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV *** proposed CSODLSUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct *** simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.

关键词： Unmanned Aerial Vehicles Artificial Intelligence emerging technologies Deep Learning Industry 4.0 image steganography

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Amino Acid Requirements of Gilthead Bream (Sparus aurata) Juveniles

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World Journal of Engineering and Technology 2016年第3期4卷 18-24页

作者： m. m. Gaber m. El-S. Salem m. A. Zaki A. m. nour National Institute of Oceanography and Fisheries Cairo Egypt National Institute of Oceanography and Fisheries Alexandria Egypt Departments of Animal and Fish Production Faculty of Agriculture Alexandria University El Shatby Egypt

In one trial 360 gilthead bream fingerlings received two different feed mixtures containing two different levels of protein (diet A 400 and B 360 g?kg?1). The average initial live weight was about 2.2 ± 0.1 g fish?1 in each replicate of all treatments. Sixty fish per tank were stocked randomly in 6 fiberglass tanks (1 m3 each). Fish were fed with experimental diets at rate 8% of the body weight daily. The Amino Acids (AA) content was analyzed in the diets, whole fish and muscle at the end of the growth period (60 days). The results indicated that at the end of experiment gilthead bream fingerlings had reached an average live weight 18.5 and 16.2 g fish?1 for fish fed diet A and B respectively. Also the increase of protein levels in the feed to 40% caused a significant increase in the content of Indispensable Amino Acid (IAA) and dispensable amino acid (as %v of whole fish and muscle of wet weight). Two methods were subsequently used to estimate the quantitative IAA requirements of gilthead bream fry based on the hypothesis that (a) the dietary requirement pattern of IAA reflected the tissue pattern and (b) the rate of daily deposition in the fish could be equated with the dietary requirement as percentage of 100 g?1 diet. The quantitative IAA requirement (%) of gilthead bream (Sparus aurata) diets was as follow: Lysine 2.27, methionine 1.09, Therionine 0.61, Leucine 2.4, Isoleucine 0.47, Histidine1.6, Arginine 1.39, Phenylalanine 1.43, Valine 1.22 and Tryptophane 0.42.

关键词： Amino Acid Requirements Gilthead Bream Whole Body Tissues

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