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Formulating layered adjustable autonomy for unmanned aerial vehicles

International Journal of Intelligent Computing and Cybernetics 2017年第4期10卷 430-450页

作者： salama a.mostafa Mohd Sharifuddin Ahmad Aida Mustapha Mazin Abed Mohammed Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaBatu PahatMalaysia College of Information Technology Universiti Tenaga NasionalKajangMalaysia Planning and Follow Up Department University of AnbarAnbarIraq

Purpose–The purpose of this paper is to propose a layered adjustable autonomy(LAA)as a dynamically adjustable autonomy model for a multi-agent system.It is mainly used to efficiently manage humans’and agents’shared control of autonomous systems and maintain humans’global control over the agents.Design/methodology/approach–The authors apply the LAA model in an agent-based autonomous unmanned aerial vehicle(UAV)system.The UAV system implementation consists of two parts:software and hardware.The software part represents the controller and the cognitive,and the hardware represents the computing machinery and the actuator of the UAV system.The UAV system performs three experimental scenarios of dance,surveillance and search missions.The selected scenarios demonstrate different behaviors in order to create a suitable test plan and ensure significant results.Findings–The results of the UAV system tests prove that segregating the autonomy of a system as multidimensional and adjustable layers enables humans and/or agents to perform actions at convenient autonomy levels.Hence,reducing the adjustable autonomy drawbacks of constraining the autonomy of the agents,increasing humans’workload and exposing the system to disturbances.Originality/value–The application of the LAA model in a UAV manifests the significance of implementing dynamic adjustable autonomy.Assessing the autonomy within three phases of agents run cycle(taskselection,actions-selection and actions-execution)is an original idea that aims to direct agents’autonomy toward performance competency.The agents’abilities are well exploited when an incompetent agent switches with a more competent one.

关键词： Unmanned aerial vehicle Multi-agent system Adjustable autonomy Autonomous agent

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DeepIoT.IDS:Hybrid Deep Learning for Enhancing IoT Network Intrusion Detection

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Computers, Materials & Continua 2021年第12期69卷 3945-3966页

作者： Ziadoon K.Maseer Robiah Yusof salama a.mostafa Nazrulazhar Bahaman Omar Musa Bander Ali Saleh Al-rimy Faculty of Information and Communication Technology Universiti Teknikal MalaysiaMelaka76100Malaysia Center of Intelligent and Autonomous Systems Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn MalaysiaJohor86400Malaysia Faculty of Business and Technology UNITAR International UniversitySelangor47301Malaysia School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaJohor81310Malaysia

With an increasing number of services connected to the internet,including cloud computing and Internet of Things(IoT)systems,the prevention of cyberattacks has become more challenging due to the high dimensionality of the network traffic data and access points.Recently,researchers have suggested deep learning(DL)algorithms to define intrusion features through training empirical data and learning anomaly patterns of attacks.However,due to the high dynamics and imbalanced nature of the data,the existing DL classifiers are not completely effective at distinguishing between abnormal and normal behavior line connections for modern networks.Therefore,it is important to design a self-adaptive model for an intrusion detection system(IDS)to improve the detection of attacks.Consequently,in this paper,a novel hybrid weighted deep belief network(HW-DBN)algorithm is proposed for building an efficient and reliable IDS(DeepIoT.IDS)model to detect existing and novel cyberattacks.The HW-DBN algorithm integrates an improved Gaussian–Bernoulli restricted Boltzmann machine(Deep GB-RBM)feature learning operator with a weighted deep neural networks(WDNN)classifier.The CICIDS2017 dataset is selected to evaluate the DeepIoT.IDS model as it contains multiple types of attacks,complex data patterns,noise values,and imbalanced classes.We have compared the performance of the DeepIoT.IDS model with three recent models.The results show the DeepIoT.IDS model outperforms the three other models by achieving a higher detection accuracy of 99.38%and 99.99%for web attack and bot attack scenarios,respectively.Furthermore,it can detect the occurrence of low-frequency attacks that are undetectable by other models.

关键词： Cyberattacks internet of things intrusion detection system deep learning neural network supervised and unsupervised deep learning

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A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

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Computers, Materials & Continua 2021年第4期67卷 51-71页

作者： Mohamed Elhoseny Mazin Abed Mohammed salama a.mostafa Karrar Hameed Abdulkareem Mashael S.Maashi Begonya Garcia-Zapirain Ammar Awad Mutlag Marwah Suliman Maashi Department of Computer Science College of Computer Information TechnologyAmerican University in the Emirates503000United Arab Emirates College of Computer Science and Information Technology University of AnbarRamadi31001Iraq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaJohor86400Malaysia College of Agriculture Al-Muthanna UniversitySamawah66001Iraq Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia eVIDA Lab University of DeustoBilbao48007Spain Department of Pure Science Ministry of EducationGeneral Directorate of CurriculaBaghdadIraq Department of Medical Laboratory Science Faculty of Applied Medical ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia

Heart disease(HD)is a serious widespread life-threatening disease.The heart of patients with HD fails to pump sufcient amounts of blood to the entire body.Diagnosing the occurrence of HD early and efciently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment.Classical methods for diagnosing HD are sometimes unreliable and insufcient in analyzing the related symptoms.As an alternative,noninvasive medical procedures based on machine learning(ML)methods provide reliable HD diagnosis and efcient prediction of HD conditions.However,the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classication features from patients with HD.In this study,we propose an automated heart disease diagnosis(AHDD)system that integrates a binary convolutional neural network(CNN)with a new multi-agent feature wrapper(MAFW)model.The MAFW model consists of four software agents that operate a genetic algorithm(GA),a support vector machine(SVM),and Naïve Bayes(NB).The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classication.A nal tuning to CNN is then performed to ensure that the best set of features are included in HD identication.The CNN consists of ve layers that categorize patients as healthy or with HD according to the analysis of optimized HD features.We evaluate the classication performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using across-validation technique and by assessing six evaluation criteria.The AHDD system achieves the highest accuracy of 90.1%,whereas the other ML and conventional CNN models attain only 72.3%–83.8%accuracy on average.Therefore,the AHDD system proposed herein has the highest capability to identify patients with HD.This system can be used by medical practitioners to diagnose HD efciently。

关键词： Heart disease machine learning multi-agent feature wrapper model heart disease diagnosis HD cleveland datasets convolutional neural network

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A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images

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Computers, Materials & Continua 2021年第3期66卷 3289-3310页

作者： Mazin Abed Mohammed Karrar Hameed Abdulkareem Begonya Garcia-Zapirain salama a.mostafa Mashael S.Maashi Alaa S.Al-Waisy Mohammed Ahmed Subhi Ammar Awad Mutlag Dac-Nhuong Le College of Computer Science and Information Technology University of AnbarRamadi31001Iraq College of Agriculture Al-Muthanna UniversitySamawah66001Iraq eVIDA Lab University of DeustoBilbao48007Spain Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaJohor86400Malaysia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia Faculty of Engineering and Built Environment Universiti Kebangsaan MalaysiaBangi43600Malaysia Pure Science Department Ministry of EducationGeneral Directorate of CurriculaBaghdad10Iraq Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam

The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.

关键词： Coronavirus disease COVID-19 diagnosis machine learning convolutional neural networks resnet50 artificial neural network support vector machine X-ray images feature transfer learning

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Plenty of analytical and semi-analytical wave solutions of shallow water beneath gravity

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Journal of Ocean Engineering and Science 2022年第3期7卷 237-243页

作者： mostafa M.A.Khater SamirA.salama Department of Mathematics Faculty of ScienceJiangsu UniversityZhenjiang 212013China Department of Mathematics Obour High Institute For Engineering and TechnologyCairo 11828Egypt Division of Biochemistry Department of PharmacologyCollege of PharmacyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia

This article studies novel soliton wave solutions of the nonlinear fractional ill-posed Boussinesq(NLFIPB)dynamic wave equation by applying the extended Riccati-expansion(ERE)method.Jacques Hadamard has formulated the investigated model to figure out the dynamic characterizations of waves in shallow water under gravity.The obtained solutions are explained through some sketches in 2D and 3D and contour plots.At the same time,the results’accuracy is checked by comparing the obtained solutions with semianalytical solutions through the well-known Adomian decomposition(AD)method.The superiority of the ERE method over the original method is explained.All constructed solutions are checked by submitting them back into the original model through Mathematica 12 software.

关键词： Ill-posed Boussinesq dynamical wave Analytical and semi-analytical simulations

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Semi-analytical and numerical simulations of the modified Benjamin-Bona-Mahony model

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Journal of Ocean Engineering and Science 2022年第3期7卷 264-271页

作者： mostafa M.A.Khater SamirA.salama Department of Mathematics Faculty of ScienceJiangsu UniversityZhenjiang212013China Department of Mathematics Obour High Institute For Engineering and TechnologyCairo11828Egypt Division of Biochemistry Department of PharmacologyCollege of PharmacyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia

In this article,semi-analytical and numerical simulations of the well-known modified Benjamin-Bona-Mahony(mBBM)equation are processed.This study targets to check the accuracy of the obtained analytical solutions of the mBBM model that have been obtained in[1]through three recent analytical schemes(extended simplest equation(ESE)method,modified kudryashov(mKud)method,and SechTanh(ST)expansion method).The considered model describes the propagation of long waves in the nonlinear dispersive media in a visual illusion.The Homotopy iteration(HI)method,exponential cubic-B-spline(ECBS)method,and trigonometric-quantic-B-spline(TQBS)method are employed to construct novel semi-analytical and accurate numerical solutions.The obtained solutions’accuracy has been checked through some different types of two-dimensional graphs.

关键词： Modified Benjamin-Bona-Mahony equation Approximate solutions

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How New Individuals Behave in a Heterogeneous Community: A Computational Approach to Norm Assimilation Using Agent-Based Systems

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Journal of Systems Science & Complexity 2020年第4期33卷 849-881页

作者： MAHMOUD Moamin AHMAD Mohd Sharifuddin mostafa salama SUBRAMAINAN Latha College of Computing and Informatics Universiti Tenaga NasionalKajangSelangorMalaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaParit RajaMalaysia

Heterogeneous community entails a number of social groups that adopt similar/different social norms.In such community,new individuals who join a new social group should be able to decide with which group they could assimilate based on their capabilities/values/manners.Otherwise,they would be penalized by other members in the group for violating some norms which they cannot comply.Using this approach,software agents would have better reasoning in simulating human society.In this paper,the authors propose a norms assimilation theory,in which a new agent attempts to assimilate with a social group’s norms.This theory builds an approach to norm assimilation,analyzes the cases for an agent to decide to assimilate with a social group and develops a mathematical model to measure the assimilation cost and the agent’s ability.The approach is developed based on the agent’s internal belief about its ability and desire,and its external belief about the cost of assimilating with a number of social groups.The significance of this research is two-fold.Firstly,the study paves the way to future design of intelligent systems,i.e.,software agents or robots,to closely mimic human social interactions.Secondly,the norm assimilation using agent-based system could be potentially utilized to simulate some social issues such as immigrants,new students,expatriate etc.The experiments that have been conducted demonstrate that an agent in the domain is able to calculate the assimilation cost and decide which social group to join.

关键词： Agent-based simulation heterogeneous community individuals norms norm assimilation normative systems social group

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Urinary Continence after Orthotopic Detubularized Ileocaecal Pouch Versus Ileal Neobladder:Does it Differ?

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Advances in Modern Oncology Research 2018年第5期4卷 1-4页

作者： Abdalla El Azab Sameh Kotb Amro mostafa Ahmed Mahmoud Shouman Mohamed salama National cancer institute Cairo universityCairoEgypt Urology Department Kasr Al-Ainy HospitalsCairo UniversityCairoEgypt

Objective:To compare both techniques of detubularized ileocaecal and ileal neobladder post radical cystectomy with regards to functional results in the form of continence(day and night)with the use of urodynamics(for assessment of voiding volume and pouch capacity).Methods:This is a prospective randomized clinical trial that was done at the National Cancer Institute,Cairo University from August 2016 and January 2018.Sixty-two patients presented with muscle invasive urinary bladder cancer and no history of urinary incontinence or urethral stricture were included in this study.All patients underwent preoperative cystoscopic examination.Radical cystectomy was done,and patients were divided into two groups according to the type of diversion.Group A included 31 patients with orthotopoic detubularized ileal neobladder and group B included 31 patients with orthotopic detubularized ileocaecal neobladder.Urodynamic studies were done to 27 patients in group A and 27 patients in group B at just 6 and 12 months postoperatively.Results:The mean age in group A was 59.2±8.0 while in group B was 57.8±6.6.In each group,we compared day and night continence in a period of 3 and 6 months.Postoperatively in group A,day continence after 3 months was 81.5% which improved to 96.3% after 6 months postoperatively with significant P-value(0.025).Night continence after 3 months was fair in 66.7%,unsatisfactory in 33.3% and 0% for good state which improved to 70% for fair state,18% unsatisfactory and 11% for good state after 6 months with significant P-value(0.035)while in group B,day continence after 3 months and 6 months postoperatively was 100% and night continence after 3 months was good in 0.0%,fair in 78.6% and unsatisfactory in 21.4% which improved to 10.7% for good state,85.7% for fair state and 3.6% for unsatisfactory after 6 months with significant P-value(0.005).Conclusion:There are no significant differences between detubularized ileocaecal and ileal neobladder in relation to continence.However,there is significant change in each

关键词： Bladder cancer radical cystectomy ileocaecal ileal neobladder urodynamic

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Transient elastography compared to liver biopsy and morphometry for predicting fibrosis in pediatric chronic liver disease: Does etiology matter?

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World Journal of Gastroenterology 2016年第16期22卷 4238-4249页

作者： Behairy El-Sayed Behairy mostafa Mohamed Sira Khaled Refat Zalata El-Sayed Ebrahem salama Mohamed Ahmed Abd-Allah Department of Pediatric Hepatology National Liver Institute Menofiya University Department of Pathology Faculty of Medicine Mansura University

AIM: To evaluate transient elastography(TE) as a noninvasive tool in staging liver fibrosis compared with liver biopsy and morphometry in children with different chronic liver diseases.METHODS: A total of 90 children [50 with chronic hepatitis C virus(HCV), 20 with autoimmune hepatitis(AIH) and 20 with Wilson disease] were included in the study and underwent liver stiffness measurement(LSM) using TE. Liver biopsies were evaluated for fibrosis, qualitatively, by Ishak score and quantitatively by fibrosis area fraction(FAF) using digital image analysis(morphometry). LSM was correlated with fibrosis and other studied variables using spearman correlation. A stepwise multiple regression analysis was alsoperformed to examine independent factors associated with LSM. Different cut-off values of LSM were calculated for predicting individual fibrosis stages using receiveroperating characteristic curve. Cut-off values with optimal clinical performance(optimal sensitivity and specificity simultaneously) were selected.RESULTS: The majority of HCV group had minimal activity(80%) and no/mild fibrosis(72%). On the other hand, the majority of AIH group had mild to moderate activity(70%) and moderate to severe fibrosis(95%) and all Wilson disease group had mild to moderate activity(100%) and moderate to severe fibrosis(100%). LSM correlated significantly with both FAF and Ishak scores and the correlation appeared better with the latter(r = 0.839 vs 0.879, P < 0.0001 for both). LSM discriminated individual stages of fibrosis with high performance. Sensitivity ranged from 81.4% to 100% and specificity ranged from 75.0% to 97.2%. When we compared LSM values for the same stage of fibrosis, they varied according to the different etiologies. Higher values were in AIH(16.15 ± 7.23 k Pa) compared to Wilson disease(8.30 ± 0.84 k Pa) and HCV groups(7.43 ± 1.73 k Pa). Multiple regression analysis revealed that Ishak fibrosis stage was the only independent variable associated with higher LSM(P < 0.0001).CONCLUSION: TE appears reliable in dist

关键词： Autoimmune hepatitis Chronic hepatitis C Liver fibrosis Liver stiffness Morphometry Pediatrics Trans

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T-Helper I Cell/T-Helper 2 Cell Balance with Anti Inflammatory Therapy in Partly Controlled Asthmatic Children

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Journal of Life Sciences 2012年第11期6卷 1206-1213页

作者： Gamal Abdel Naser Yamamah Hala Hamdy Shaaban Emad Ezzat salama Nevine El Helaly Solaf Kamel Elham mostafa Pediatrics Department National Research Center (NRC) Cairo 12622 Egypt Pediatrics Department Faculty of Medicine Cairo University Cairo 11562 Egypt Clinical and Chemical Pathology Department National Research Center Cairo 12622 Egypt

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