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Explainable Conformer Network for Detection of COVID-19 Pneumonia from Chest CT Scan: From Concepts toward Clinical Explainability

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Computers, Materials & Continua 2024年第1期78卷 1171-1187页

作者： mohamed Abdel-Basset Hossam Hawash mohamed abouhawwash S.S.Askar Alshaimaa A.Tantawy Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Statistics and Operations Research College of ScienceKing Saud UniversityP.O.Box 2455Riyadh11451Saudi Arabia

The early implementation of treatment therapies necessitates the swift and precise identification of COVID-19 pneumonia by the analysis of chest CT scans.This study aims to investigate the indispensable need for precise and interpretable diagnostic tools for improving clinical decision-making for COVID-19 diagnosis.This paper proposes a novel deep learning approach,called Conformer Network,for explainable discrimination of viral pneumonia depending on the lung Region of Infections(ROI)within a single modality radiographic CT scan.Firstly,an efficient U-shaped transformer network is integrated for lung image segmentation.Then,a robust transfer learning technique is introduced to design a robust feature extractor based on pre-trained lightweight Big Transfer(BiT-L)and finetuned on medical data to effectively learn the patterns of infection in the input image.Secondly,this work presents a visual explanation method to guarantee clinical explainability for decisions made by Conformer Network.Experimental evaluation of real-world CT data demonstrated that the diagnostic accuracy of ourmodel outperforms cutting-edge studies with statistical significance.The Conformer Network achieves 97.40% of detection accuracy under cross-validation settings.Our model not only achieves high sensitivity and specificity but also affords visualizations of salient features contributing to each classification decision,enhancing the overall transparency and trustworthiness of our model.The findings provide obvious implications for the ability of our model to empower clinical staff by generating transparent intuitions about the features driving diagnostic decisions.

关键词： Deep learning COVID-19 multi-modal medical image fusion diagnostic image fusion

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MOALG: A Metaheuristic Hybrid of Multi-Objective Ant Lion Optimizer and Genetic Algorithm for Solving Design Problems

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Computers, Materials & Continua 2024年第3期78卷 3489-3510页

作者： Rashmi Sharma Ashok Pal Nitin Mittal Lalit Kumar Sreypov Van Yunyoung Nam mohamed abouhawwash Department of Mathematics Chandigarh UniversityGharuanMohali140413India Deaprtment of Industry 4.0 Shri Vishwakarma Skill UniversityPalwal121102India Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea Department of Computational Mathematics Science and Engineering(CMSE)College of EngineeringMichigan State UniversityEast LansingMI 48824USA Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt

This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic Algorithm(GA).MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions.The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO.A first-time hybrid of these algorithms is employed to solve multi-objective problems.The hybrid algorithm overcomes the limitation of ALO of getting caught in the local optimum and the requirement of more computational effort to converge GA.To evaluate the hybridized algorithm’s performance,a set of constrained,unconstrained test problems and engineering design problems were employed and compared with five well-known computational algorithms-MOALO,Multi-objective Crystal Structure Algorithm(MOCryStAl),Multi-objective Particle Swarm Optimization(MOPSO),Multi-objective Multiverse Optimization Algorithm(MOMVO),Multi-objective Salp Swarm Algorithm(MSSA).The outcomes of five performance metrics are statistically analyzed and the most efficient Pareto fronts comparison has been obtained.The proposed hybrid surpasses MOALO based on the results of hypervolume(HV),Spread,and Spacing.So primary objective of developing this hybrid approach has been achieved successfully.The proposed approach demonstrates superior performance on the test functions,showcasing robust convergence and comprehensive coverage that surpasses other existing algorithms.

关键词： Multi-objective optimization genetic algorithm ant lion optimizer metaheuristic

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A Novel Deep Learning-Based Model for Classification of Wheat Gene Expression

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Computer Systems Science & Engineering 2024年第2期48卷 273-285页

作者： Amr Ismail WalidHamdy Aya MAl-Zoghby Wael AAwad Ahmed Ismail Ebada Yunyoung Nam Byeong-Gwon Kang mohamed abouhawwash Faculty of Science Port Said UniversityPort SaidEgypt Modern Academy for Computer Science and Management Technology Cairo11742Egypt Faculty of Computers and Artificial Intelligence Damietta UniversityNew DamiettaEgypt Department of ICT Convergence Soonchunhyang UniversityAsan31538South Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Deep learning(DL)plays a critical role in processing and converting data into knowledge and decisions.DL technologies have been applied in a variety of applications,including image,video,and genome sequence analysis.In deep learning the most widely utilized architecture is Convolutional Neural Networks(CNN)are taught discriminatory traits in a supervised environment.In comparison to other classic neural networks,CNN makes use of a limited number of artificial neurons,therefore it is ideal for the recognition and processing of wheat gene sequences.Wheat is an essential crop of cereals for people around the world.Wheat Genotypes identification has an impact on the possible development of many countries in the agricultural sector.In quantitative genetics prediction of genetic values is a central issue.Wheat is an allohexaploid(AABBDD)with three distinct genomes.The sizes of the wheat genome are quite large compared to many other kinds and the availability of a diversity of genetic knowledge and normal structure at breeding lines of wheat,Therefore,genome sequence approaches based on techniques of Artificial Intelligence(AI)are necessary.This paper focuses on using the Wheat genome sequence will assist wheat producers in making better use of their genetic resources and managing genetic variation in their breeding program,as well as propose a novel model based on deep learning for offering a fundamental overview of genomic prediction theory and current constraints.In this paper,the hyperparameters of the network are optimized in the CNN to decrease the requirement for manual search and enhance network performance using a new proposed model built on an optimization algorithm and Convolutional Neural Networks(CNN).

关键词： Gene expression convolutional neural network optimization algorithm genomic prediction wheat

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Efficient Model for Emergency Departments:Real Case Study

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Computers, Materials & Continua 2022年第2期70卷 4053-4073页

作者： mohamed Abdel-Basset Abduallah Gamal Rehab mohamed mohamed abouhawwash Abdulwahab Almutairi Osama M.ELkomy Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA School of Mathematics Unaizah College of Sciences and ArtsQassim University51452Saudi Arabia

There are several challenges that hospitals are facing according to the emergency department(ED).Themain two issues are department capacity and lead time.However,the lack of consensus on performance criteria to evaluateEDincreases the complication of this process.Thus,this study aims to evaluate the efficiency of the emergency department in 20 Egyptian hospitals(12 private and 8 general hospitals)based on 13 performance metrics.This research suggests an integrated evaluation model assess ED under a framework of plithogenic theory.The proposed framework addressed uncertainty and ambiguity in information with an efficient manner via presenting the evaluation expression by plithogenic numbers.Data Envelopment Analysis(DEA)technique is used in order to measure the efficiency of the emergency department of 20 hospitals according to the number of treated patients and effect on patient’s life quality based on 11 factors.Using the Analytic Hierarchy Process(AHP),the weight of efficiency factors will be measured based on neutrosophic linguistic scale pairwise comparison.Plithogenic operations provide more accurate aggregation result according to contradiction degree between criteria values.The results show that ten of the hospitals are providing efficient service in their emergency department,while the other ten are less efficient.The analysis of the results shows that 58%of private hospitals emergency department is operating efficiently,while the efficient general hospitals represent 38%.

关键词： Emergency department plithogenic set data envelopment analysis analytic hierarchy process

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Scheduling Multi-Mode Resource-Constrained Projects Using Heuristic Rules Under Uncertainty Environment

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Computers, Materials & Continua 2022年第1期70卷 847-874页

作者： mohamed Abdel-Basset Ahmed Sleem Asmaa Atef Yunyoung Nam mohamed abouhawwash Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast Lansing48824MIUSA

Project scheduling is a key objective of many models and is the proposed method for project planning and management.Project scheduling problems depend on precedence relationships and resource constraints,in addition to some other limitations for achieving a subset of goals.Project scheduling problems are dependent on many limitations,including limitations of precedence relationships,resource constraints,and some other limitations for achieving a subset of goals.Deterministic project scheduling models consider all information about the scheduling problem such as activity durations and precedence relationships information resources available and required,which are known and stable during the implementation process.The concept of deterministic project scheduling conflicts with real situations,in which in many cases,some data on the activity’s durations of the project and the degree of availability of resources change or may have different modes and strategies during the process of project implementation for dealing with multi-mode conditions surrounded by projects and their activity durations.Scheduling the multi-mode resource-constrained project problem is an optimization problem whose minimum project duration subject to the availability of resources is of particular interest to us.We use the multi-mode resource allocation and schedulingmodel that takes into account the dynamicity features of all parameters,that is,the scheduling process must be flexible to dynamic environment features.In this paper,we propose five priority heuristic rules for scheduling multi-mode resource-constrained projects under dynamicity features for more realistic situations,in which we apply the proposed heuristic rules(PHR)for scheduling multi-mode resource-constrained projects.Five projects are considered test problems for the PHR.The obtained results rendered by these priority rules for the test problems are compared by the results obtained from 10 well-known heuristics rules rendered for the same test problems.The results in many cases

关键词： Constrained resource scheduling multi-mode durations dynamic scheduling resource allocation heuristic rules

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Network Analysis for Projects with High Risk Levels in Uncertain Environments

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Computers, Materials & Continua 2022年第1期70卷 1281-1296页

作者： mohamed Abdel-Basset Asmaa Atef mohamed abouhawwash Yunyoung Nam Nabil M.AbdelAziz Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast Lansing48824MIUSA Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

The critical path method is one of the oldest and most important techniques used for planning and scheduling projects.The main objective of project management science is to determine the critical path through a network representation of projects.The critical path through a network can be determined by many algorithms and is useful for managing,monitoring,and controlling the time and cost of an entire project.The essential problem in this case is that activity durations are uncertain;time presents considerable uncertainty because the time of an activity is not always easily or accurately estimated.This issue increases the need to use neutrosophic theory to solve the critical path problem.Real-world problems are characterized by a lack of precision,consistency,and completeness.The concept of neutrosophic sets has been introduced as a generalization of fuzzy,intuitionistic fuzzy,and crisp sets to overcome the ambiguity surrounding real-world problems.Truth-,falsity-,and indeterminacy-membership functions are used to express neutrosophic elements.This study was performed to examine a neutrosophic event-oriented algorithm for determining the critical path in activity-on-arc networks.The activity time estimates are presented as trapezoidal neutrosophic numbers,and score and accuracy functions are used to obtain a crisp model of the problem.An appropriate numerical example is then used to explain the proposed method.

关键词： Project management project life cycle project planning project scheduling CPM neutrosophic set theory trapezoidal neutrosophic number

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Improved Hybrid Swarm Intelligence for Optimizing the Energy in WSN

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Computer Systems Science & Engineering 2023年第8期46卷 2527-2542页

作者： Ahmed Najat Ahmed JinHyung Kim Yunyoung Nam mohamed abouhawwash Department of Computer Engineering Lebanese French UniversityErbil44001Iraq B LIFE Inc. Suwon16463Korea Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

In this current century,most industries are moving towards automation,where human intervention is dramatically reduced.This revolution leads to industrial revolution 4.0,which uses the Internet of Things(IoT)and wireless sensor networks(WSN).With its associated applications,this IoT device is used to compute the receivedWSN data from devices and transfer it to remote locations for assistance.In general,WSNs,the gateways are a long distance from the base station(BS)and are communicated through the gateways nearer to the BS.At the gateway,which is closer to the BS,energy drains faster because of the heavy load,which leads to energy issues around the BS.Since the sensors are battery-operated,either replacement or recharging of those sensor node batteries is not possible after it is deployed to their corresponding areas.In that situation,energy plays a vital role in sensor survival.Concerning reducing the network energy consumption and increasing the network lifetime,this paper proposed an efficient cluster head selection using Improved Social spider Optimization with a Rough Set(ISSRS)and routing path selection to reduce the network load using the Improved Grey wolf optimization(IGWO)approach.(i)Using ISSRS,the initial clusters are formed with the local nodes,and the cluster head is chosen.(ii)Load balancing through routing path selection using IGWO.The simulation results prove that the proposed optimization-based approaches efficiently reduce the energy through load balancing compared to existing systems in terms of energy efficiency,packet delivery ratio,network throughput,and packet loss percentage.

关键词： Wireless sensor networks(WSN) internet of things(IoT) energy efficiency load balance sensors grey wolf optimization social spider optimization

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Hybrid Optimisation with Black Hole Algorithm for Improving Network Lifespan

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Intelligent Automation & Soft Computing 2023年第2期35卷 1873-1887页

作者： S.Siamala Devi Chandrakala Kuruba Yunyoung Nam mohamed abouhawwash Department of Computer Science and Engineering Sri Krishna College of TechnologyCoimbatore641042India Department of Computer Science and Engineering Vignan Nirula Institute of Technology and ScienceGuntur522009India Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Wireless sensor networks(WSNs)are projected to have a wide range of applications in the future.The fundamental problem with WSN is that it has aﬁnite lifespan.Clustering a network is a common strategy for increasing the life-time of WSNs and,as a result,allowing for faster data transmission.The cluster-ing algorithm’s goal is to select the best cluster head(CH).In the existing system,Hybrid grey wolf sunﬂower optimization algorithm(HGWSFO)and optimal clus-ter head selection method is used.It does not provide better competence and out-put in the network.Therefore,the proposed Hybrid Grey Wolf Ant Colony Optimisation(HGWACO)algorithm is used for reducing the energy utilization and enhances the lifespan of the network.Black hole method is used for selecting the cluster heads(CHs).The ant colony optimization(ACO)technique is used toﬁnd the route among origin CH and destination.The open cache of nodes,trans-mission power,and proximity are used to improve the CH selection.The grey wolf optimisation(GWO)technique is the most recent and well-known optimiser module which deals with grey wolves’hunting activity(GWs).These GWs have the ability to track down and encircle food.The GWO method was inspired by this hunting habit.The proposed HGWACO improves the duration of the net-work,minimizes the power consumption,also it works with the large-scale net-works.The HGWACO method achieves 25.64%of residual energy,25.64%of alive nodes,40.65%of dead nodes also it enhances the lifetime of the network.

关键词： Energy efﬁciency power consumption lifespan of the network black hole method ant colony optimisation routing and cluster heads(CHs)

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Healthcare Monitoring Using Ensemble Classifiers in Fog Computing Framework

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Computer Systems Science & Engineering 2023年第5期45卷 2265-2280页

作者： P.M.Arunkumar Mehedi Masud Sultan Aljahdali mohamed abouhawwash Department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Nowadays,the cloud environment faces numerous issues like synchronizing information before the switch over the data migration.The requirement for a centralized internet of things(IoT)-based system has been restricted to some extent.Due to low scalability on security considerations,the cloud seems uninteresting.Since healthcare networks demand computer operations on large amounts of data,the sensitivity of device latency evolved among health networks is a challenging issue.In comparison to cloud domains,the new paradigms of fog computing give fresh alternatives by bringing resources closer to users by providing low latency and energy-efficient data processing solutions.Previous fog computing frameworks have various flaws,such as overvaluing response time or ignoring the accuracy of the result yet handling both at the same time compromises the network community.In this proposed work,Health Fog is integrated with the Optimized Cascaded Convolution Neural Network framework for diagnosing heart disease.Initially,the data is collected,and then pre-processing is done by Linear Discriminant Analysis.Then the features are extracted and optimized using Galactic Swarm Optimization.The optimized features are given into the Health Fog framework for diagnosing heart disease patients.It uses ensemble-based deep learning in edge computing devices,which automatically monitors real-life health networks such as heart disease analysis.Finally,the classifiers such as bagging,boosting,XGBoost,Multi-Layer Perceptron(MLP),and Partitions(PART)are used for classifying the data.Then the majority voting classifier predicts the result.This work uses FogBus architecture and evaluates the execution of power usage,bandwidth of the network,latency,execution time,and accuracy.

关键词： Healthfog FogBus cascaded convolution neural network cloud computing heart disease automatic health monitoring internet of things

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Paillier Cryptography Based Message Authentication Code for IoMT Security

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Computer Systems Science & Engineering 2023年第3期44卷 2209-2223页

Health care visualization through Internet of Things(IoT)over wireless sensor network(WSN)becomes a current research attention due to medical sensor evolution of devices.The digital technology-based communication system is widely used in all *** of medical thing(IoMT)assisted health-care application ensures the continuous health monitoring of a patient and provides the early awareness of the one who is suffered without human participation.These smart medical devices may consume with limited resources and also the data generated by these devices are large in size.These IoMT based applications suffer from the issues such as security,anonymity,privacy,and interoper-ability.To overcome these issues,data aggregation methods are the solution that can concatenate the data generated by the sensors and forward it into the base station through fog node with efficient encryption and decryption.This article proposed a well-organized data aggregation and secured transmission approach.The data generated by the sensor are collected and compressed.Aggregator nodes(AN)received the compressed data and concatenate it.The concatenated and encrypted data is forward to fog node using the enhanced Paillier cryptogra-phy-based encryption with Message Authentication code(MAC).Fog node extracts the forwarded data from AN using Fog message extractor method(FME)with decryption.The proposed system ensures data integrity,security and also protects from security threats.This proposed model is simulated in Net-work Simulator 2.35 and the evaluated simulation results proves that the aggregation with MAC code will ensures the security,privacy and also reduces the communication cost.Fog node usages in between Aggregator and base station,will reduce the cloud server/base station computational overhead and storage cost.The proposed ideology is compared with existing data aggregation schemes in terms of computational cost,storage cost,communication cost and energy cost.Cost of communication takes 18.7 ms which is much lesser than existing schemes.

关键词： Fog IoMT wireless sensor network cloud aggregation encryption decryption energy

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