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Medical Image Compression Based on Wavelets with Particle Swarm Optimization

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Computers, Materials & Continua 2021年第5期67卷 1577-1593页

作者： monagi h.alkinani E.A.Zanaty Sherif M.Ibrahim Department of Computer Science and Artificial Intelligence Faculty of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabi Department of Computer Science Faculty of Computers and InformationSohag UniversitySohagEgypt Department of Computer Science and Mathematics Faculty of ScienceSouth Valley UniversityQenaEgypt

This paper presents a novel method utilizing wavelets with particle swarm optimization(PSO)for medical image *** method utilizes PSO to overcome the wavelets discontinuity which occurs when compressing images using *** transfers images into subband details and approximations using a modified haar wavelet(MhW),and then applies a *** is applied for selecting a particle assigned to the threshold values for the *** positions assigned to particles values are used to represent *** particle updates its position depending on the global best position(gbest)(for all details subband)and local best position(pbest)(for a subband).The fitness value is developed to terminate PSO when the difference between two local best(pbest)successors is smaller than a prescribe *** experiments are applied on five different medical image types,i.e.,MRI,CT,and *** show that the proposed algorithm can be more preferably to compress medical images than other existing wavelets techniques from peak signal to noise ratio(PSNR)and compression ratio(CR)points of views.

关键词： Image compression wavelets haar wavelet particle swarm algorithm medical image compression PSNR and CR

An Efficient Method for Covid-19 Detection Using Light Weight Convolutional Neural Network

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Computers, Materials & Continua 2021年第11期69卷 2475-2491页

作者： Saddam Bekhet monagi h.alkinani Reinel Tabares-Soto M.hassaballah Faculty of Commerce South Valley UniversityQenaEgypt Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Department of Electronics and Automation Universidad Autónoma de ManizalesManizales170001Colombia Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt

The COVID-19 pandemic is a significant milestone in the modern history of civilization with a catastrophic effect on global wellbeing and *** situation is very complex as the COVID-19 test kits are limited,therefore,more diagnostic methods must be developed urgently.A significant initial step towards the successful diagnosis of the COVID-19 is the chest X-ray or Computed Tomography(CT),where any chest anomalies(e.g.,lung inflammation)can be easily *** hospitals possess X-ray or CT imaging equipments that can be used for early detection of *** by this,various artificial intelligence(AI)techniques have been developed to identify COVID-19 positive patients using the chest X-ray or CT ***,the advance of these AI-based systems and their highly tailored results are strongly bonded to high-end GPUs,which is not widely available in several *** paper introduces a technique for early COVID-19 diagnosis based on medical experience and light-weight Convolutional Neural Networks(CNNs),which does not require a custom hardware to run compared to currently available CNN *** proposed deep learning model is built carefully and fine-tuned by removing all unnecessary parameters and layers to achieve the light-weight attribute that could run smoothly on a normal CPU(0.54%of AlexNet parameters).This model is highly beneficial for countries where high-end GPUs are *** outcomes on some new benchmark datasets shows the robustness of the proposed technique robustness in recognizing COVID-19 with 96%accuracy.

关键词： Artificial intelligence COVID-19 chest CT chest X-ray deep learning

A Genetic Algorithm to Solve Capacity Assignment Problem in a Flow Network

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Computers, Materials & Continua 2020年第9期64卷 1579-1586页

作者： Ahmed Y.hamed monagi h.alkinani M.R.hassan Department of Computer Science Faculty of Computers and InformationSohag UniversitySohag82524Egypt Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Department of Mathematics and Computer Science Faculty of ScienceAswan UniversityAswan81528Egypt

Computer networks and power transmission networks are treated as capacitated flow networks.A capacitated flow network may partially fail due to ***,the capacity of each edge should be optimally assigned to face critical situations-i.e.,to keep the network functioning normally in the case of failure at one or more *** robust design problem(RDP)in a capacitated flow network is to search for the minimum capacity assignment of each edge such that the network still survived even under the edge’s *** RDP is known as ***,capacity assignment problem subject to system reliability and total capacity constraints is studied in this *** problem is formulated mathematically,and a genetic algorithm is proposed to determine the optimal *** optimal solution found by the proposed algorithm is characterized by maximum reliability and minimum total *** numerical examples are presented to illustrate the efficiency of the proposed approach.

关键词： Flow network capacity assignment network reliability genetic algorithms

Ant Colony Optimization for Multi-Objective Multicast Routing

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Computers, Materials & Continua 2020年第6期63卷 1159-1173页

In the distributed networks,many applications send information from a source node to multiple destination *** support these applications requirements,the paper presents a multi-objective algorithm based on ant colonies to construct a multicast tree for data transmission in a computer *** proposed algorithm simultaneously optimizes total weight(cost,delay and hop)of the multicast *** results prove the proposed algorithm outperforms a recently published Multi-objective Multicast Algorithm specially designed for solving the multicast routing ***,it is able to find a better solution with fast convergence speed and high reliability.

关键词： Multimedia communication multicast routing multicast tree quality of service ant colony

Task Scheduling Optimization in Cloud Computing by Rao Algorithm

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Computers, Materials & Continua 2022年第9期72卷 4339-4356页

作者： A.Younes M.KhElnahary monagi h.alkinani hamdy h.El-Sayed Faculty of Computers and Information Department of Computer ScienceSohag UniversitySohag82524Egypt Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia

Cloud computing is currently dominated within the space of highperformance distributed computing and it provides resource polling and ondemand services through the ***,task scheduling problem becomes a very important analysis space within the field of a cloud computing environment as a result of user’s services demand modification *** main purpose of task scheduling is to assign tasks to available processors to produce minimum schedule length without violating precedence *** heterogeneous multiprocessor systems,task assignments and schedules have a significant impact on system *** the heuristic-based task scheduling algorithm,the different processes will lead to a different task execution time(makespan)on a heterogeneous computing ***,a good scheduling algorithm should be able to set precedence efficiently for every subtask depending on the resources required to reduce(makespan).In this paper,we propose a new efficient task scheduling algorithm in cloud computing systems based on RAO algorithm to solve an important task and schedule a heterogeneous multiple processing *** basic idea of this process is to exploit the advantages of heuristic-based algorithms to reduce space search and time to get the best *** evaluate our algorithm’s performance by applying it to three examples with a different number of tasks and *** experimental results show that the proposed approach significantly succeeded in finding the optimal solutions than others in terms of the time of task implementation.

关键词： heterogeneous processors RAO algorithm heuristic algorithms task scheduling multiprocessing cloud computing

Enhancing Cloud Performance Using File Format Classifications

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Computers, Materials & Continua 2022年第2期70卷 3985-4007页

作者： Muhammad Junaid Adnan Sohail monagi h.alkinani Adeel Ahmed Mehmood Ahmed Faisal Rehman Department of Computing Iqra UniversityIslamabadPakistan Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Information Technology The University of Haripur Department of Computing COMSATS UniversityAbbottabad CampusPakistan

Metaheuristic approaches in cloud computing have shown significant results due to theirmulti-objective *** approaches are now considering hybridmetaheuristics combining the relative optimized benefits of two or more algorithms resulting in the least tradeoffs among several *** critical factors such as execution time,throughput time,response time,energy consumption,SLA violations,communication overhead,makespan,and migration time need careful attention while designing such dynamic *** improve such factors,an optimizedmulti-objective hybrid algorithm is being proposed that combines the relative advantages of Cat Swarm Optimization(CSO)with machine learning classifiers such as Support Vector Machine(SVM).The adopted approach is based on SVMone to many classification models of machine learning that performs the classifications of various data format types in the cloud with best *** CSO,grouping phase is used to divide the data files as audio,video,image,and text which is further extended by polynomial Kernel function based on various input features and used for optimized load ***,proposed approach works well and achieved performance efficiency in evaluated QoS metrics such as average energy consumption by 12%,migration time by 9%,and optimization time by 10%,in the presence of competitor baselines.

关键词： Classification load balancing optimization cloud computing

Detecting Information on the Spread of Dengue on Twitter Using Articial Neural Networks

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Computers, Materials & Continua 2021年第4期 1317-1332页

作者： Samina Amin M.Irfan Uddin M.Ali Zeb Ala Abdulsalam Alarood Marwan Mahmoud monagi h.alkinani Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan College of Computer Science and Engineering University of JeddahJeddah21959Saudi Arabia Faculty of Applied Studies King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Articial Intelligence College of Computer Sciences and EngineeringUniversity of JeddahJeddahSaudi Arabi

Social media platforms have lately emerged as a promising tool for predicting the outbreak of epidemics by analyzing information on them with the help of machine learning *** analytical and statistical models are available to infer a variety of user sentiments in posts on social *** amount of data generated by social media platforms,such as Twitter,that can be used to track diseases is increasing *** paper proposes a method for the classication of tweets related to the outbreak of dengue using machine learning *** articial neural network(ANN)-based method is developed using Global Vector(GloVe)embedding to use the data in tweets for the automatic and efcient identication and classication of *** proposed method classies tweets related to the outbreak of dengue into positives and *** were conducted to assess the proposed ANN model based on performance evaluation matrices(confusion matrices).The results show that the GloVe vectors can efciently capture a sufcient amount of information for the classier to accurately identify and classify tweets as relevant or irrelevant to dengue *** proposed method can help healthcare professionals and researchers track and analyze epidemic outbreaks through social media in real time.

关键词： Articial neural network classication social media GloVe social networking sites