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Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation

Computer Modeling in Engineering & Sciences 2025年第1期142卷 909-931页

作者： Adel Binbusayyis mohemmed sha Department of Software Engineering College of Computer Engineering and Sciences in Al-Kharj Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid *** the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the *** also possesses a better impact on averting overloading and permitting effective energy *** though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized *** overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning *** accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data ***,the pre-processed data are taken for training and *** that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in *** PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed *** hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on *** results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.

关键词： Smart Grid machine learning particle swarm optimization XGBoost dynamic inertia weight update

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A Cloud Based Sentiment Analysis through Logistic Regression in AWS Platform

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Computer Systems Science & Engineering 2023年第4期45卷 857-868页

作者： mohemmed sha Department of Software Engineering College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The use of Amazon Web Services is growing rapidly as more users are adopting the *** has various functionalities that can be used by large corporates and individuals as *** analysis is used to build an intelligent system that can study the opinions of the people and help to classify those related *** this research work,sentiment analysis is performed on the AWS Elastic Compute Cloud(EC2)through Twitter *** data is managed to the EC2 by using elastic load *** collected data is subjected to preprocessing approaches to clean the data,and then machine learning-based logistic regression is employed to categorize the sentiments into positive and negative *** accuracy of 94.17%is obtained through the proposed machine learning model which is higher than the other models that are developed using the existing algorithms.

关键词： Sentiment analysis AWS cloud data analytics machine learning

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Functionality Aware Dynamic Composition of Web Services

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Computer Systems Science & Engineering 2021年第1期36卷 201-211页

作者： mohemmed sha Abdalla Alameen Department of Computer Science College of Arts and SciencePrince Sattam bin Abdulaziz UniversityWadi Al dawaser11991Saudi Arabia

The composition of the web service is a common technique to attain the best results of complex web *** selection of appropriate web services,linking those services in the action flow and attaining the actual functionality of the task are the important factors to be *** though different frameworks and methods have been proposed to dynamically compose web services,each method has its advantage and disadvantage over the *** of the methods give much importance to the Quality of Service(QoS)but fail to achieve the actual functionality after *** paper proposes a functionalityoriented composition technique for composing web ***,this method helps reach the extreme functionality of each web service in the composition towards customer *** from considering the overall QoS of every single service,the non-functional parameters associated with these services are also considered for achieving the expected *** of these nonfunctional parameters has a vital role in the functional performance of the web *** web services that satisfy the non-functional requirements are chosen to form the composition to attain the best *** list of services in the proposed composition method is different from the conventional one,which is composed based on the overall *** non-functional parametric values,the QoS of each web service and the overall QoS after composition are evaluated for the proposed method and experimentally analyzed to prove their advantage over the others.

关键词： Web service service composition quality of service dynamic composition

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Hybrid Cloud Architecture for Higher Education System

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Computer Systems Science & Engineering 2021年第1期36卷 1-12页

作者： Omar Nooh Almotiry mohemmed sha Mohamudha Parveen Rahamathulla Omer Salih Dawood Omer Department of Arabic Language and Literature College of Arts and SciencePrince Sattam bin Abdulaziz UniversityWadi Al dawaser11991Saudi Arabia Department of Computer Science College of Arts and SciencePrince Sattam bin Abdulaziz UniversityWadi Al dawaser11991Saudi Arabia Department of Medical Lab Sciences College of Applied Medical SciencesPrince Sattam bin Abdulaziz UniversityWadi Al dawaser11991Saudi Arabia

As technology improves,several modernization efforts are taken in the process of teaching and *** effective education system should maintain global connectivity,federate security and deliver self-access to its *** cloud computing services transform the current education system to an advanced *** exist several tools and services to make teaching and learning more *** the higher education system,the data flow and basic operations are almost the *** systems need to access cloud-based applications and services for their operational advancement and *** a suitable cloud-based education system will leverage all the benefits of the cloud to its *** the same time,educational institutions want to keep their sensitive information more *** that,they need to maintain their on-premises data center along with the cloud *** paper proposes an advanced,flexible and secure hybrid cloud architecture to satisfy the growing demands of an education *** sharing the proposed cloud infrastructure among several higher educational institutions,there is a possibility to implement a common education system among ***,this research demonstrates how a cloud-based education architecture can utilize the advantages of the cloud resources offered by several providers in a hybrid cloud *** addition,a reference architecture using Amazon Web Service(AWS)is proposed to implement a common university education system.

关键词： Educational cloud hybrid cloud cloud services cloud data center cloud education system

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Automated White Blood Cell Disease Recognition Using Lightweight Deep Learning

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Computer Systems Science & Engineering 2023年第7期46卷 107-123页

作者： Abdullah Alqahtani Shtwai Alsubai mohemmed sha Muhammad Attique Khan Majed Alhaisoni Syed Rameez Naqvi College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer Science HITEC University TaxilaTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

White blood cells(WBC)are immune system cells,which is why they are also known as immune *** protect the human body from a variety of dangerous diseases and outside *** majority of WBCs come from red bone marrow,although some come from other important organs in the *** manual diagnosis of blood disorders is difficult,it is necessary to design a computerized *** have introduced various automated strategies in recent years,but they still face several obstacles,such as imbalanced datasets,incorrect feature selection,and incorrect deep model *** proposed an automated deep learning approach for classifying white blood disorders in this *** data augmentation approach is initially used to increase the size of a ***,a Darknet-53 pre-trained deep learning model is used and finetuned according to the nature of the chosen *** the fine-tuned model,transfer learning is used,and features engineering is done on the global average pooling *** retrieved characteristics are subsequently improved with a specified number of iterations using a hybrid reformed binary grey wolf optimization *** that,machine learning classifiers are used to classify the selected best features for final *** experiment was carried out using a dataset of increased blood diseases imaging and resulted in an improved accuracy of over 99%.

关键词： White blood cells augmentation deep features feature selection classification

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