Simulator for Scheduling Real-Time Systems with Reduced Power Consumption

Al-Fareed, Hakeem; Ali, Omar; Alshuraya, Abdulaziz; Alqahtani, Majed; Alwasfer, Saud; Aljomea, Ahmed; Rahman, Atta-ur; Aljameel, Sumayh S.; Krishnasamy, Gomathi

doi:10.18280/mmep.090509

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…Prior to utilizing the World Energy Consumption dataset, we employed a thorough pre-processing procedure to ensure the integrity and accuracy of the data [27][28][29][30][31][32]. Specifically, we removed any missing values by replacing them with a more appropriate constant value.…”

Section: Data Preprocessingmentioning

confidence: 99%

Predicting Global Energy Consumption Through Data Mining Techniques

Rahman,

Abahussin,

Alghamdi

et al. 2024

IJDNE

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With the explosion of the global population and technological progress, the electricity demand has skyrocketed. To ensure a consistent flow of power, it's essential to accurately predict energy usage ahead of time. Failure to do so could lead to potential outages and disrupt our daily lives. This research reviews previous research in the field of using data mining techniques to analyze electricity consumption data, optimize energy performance of buildings, and predict energy consumption in various industries. The study also aims to uncover patterns, correlations, and rules in electricity consumption worldwide using data mining techniques. The analysis is performed using various data mining techniques, such as simple K-Means and Expectation Maximization (EM). This selection is based on their prominent applications for similar problems in literature. The simple K-Means and EM algorithms showed successful outcomes on the dataset, which is evident in the clustering plots. Further, the performance of the Hierarchical Clustering algorithm was not up to the desired standard. This is probably due to the nature of the available dataset. These outcomes of the analysis will provide a valuable resource for decision-makers and stakeholders in the energy sector, as it will provide a deeper understanding of electricity consumption patterns and trends. This could lead to a sustainable future.

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Section: Data Preprocessingmentioning

confidence: 99%

Predicting Global Energy Consumption Through Data Mining Techniques

Rahman,

Abahussin,

Alghamdi

et al. 2024

IJDNE

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“…Cyber-Physical Systems (CPS) are orchestrated systems that incorporate computation, networking, and physical processes as well as security services against several cyber-attack vectors [2]. CPS combines the dynamics of the physical processes with those of the software and networking components, providing abstractions and modeling, design, and analysis techniques for the integrated real-time CPS [3,4]. As such, technological evolution has transformed traditional vehicles into autonomous vehicles that play an essential role in developing smart cities [5].…”

Section: Introductionmentioning

confidence: 99%

Analytical Study for Cyber Dynamic Continuous Time Vehicle Model with Networked PID Controller

Al‐Haija¹

2022

MMEP

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This paper uses different physical scenarios to analyze the cyber dynamic continuous time vehicle model with a networked PID controller. First, we model the problem of stateful continuous-time components for the dynamic physical vehicle motion on a graded and flat road without using a PID controller. Second, we model the problem of stateful continuous-time components for the dynamic physical vehicle motion on a graded road using a PID controller in different specification scenarios. Third, we model the problem of stateful continuous-time components for the cyber-physical dynamic vehicle model motion using a networked PID controller at different specification scenarios. The simulation results indicated the superiority and efficacy of the cyber-physical dynamic vehicle model providing high stability and controllability in the vehicle motion using a PID controller and Transceiver (network node).

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“…presents a confusion matrix for a binary problem classification. Where the symbols TP, TN, FP, and FN indicates True Positive, False Positive, False Positive, and False Negative respectively[47][48][49][50].…”

mentioning

confidence: 99%

Machine Learning Approach to Users’ Age Prediction: A Telecom Company Case Study in Saudi Arabia

Gollapalli,

Rahman,

Youldash

et al. 2023

MMEP

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The accurate prediction of demographic attributes of users, including age and gender, is a pivotal challenge in personalized search, ad targeting, and other related fields. This information enables companies to refine their target audience and enhance the overall user experience and quality of service (QoS). Among these, the Saudi Telecommunication Company (STC), a premier telecommunications provider in Saudi Arabia, Middle East, and Africa, recognizes the substantial role of age-prediction systems. This study, therefore, explores the application of machine learning (ML) techniques to predict user age, thus assisting in the delivery of age-appropriate ads and offers. We utilized a dataset provided by STC, comprising three million samples with key user and device features. Four ML algorithms were employed in this analysis: the Artificial Neural Network (ANN), Random Forest (RF), Gradient Boosting (GB), and Decision Tree (DT). These models were compared and evaluated based on their predictive performance. The ANN emerged as the optimal classifier, achieving an accuracy of 60%, comparable to similar studies conducted within the telecommunications industry. The implications of these findings suggest that ML techniques can effectively predict user information, thereby enabling service providers to tailor their offerings to the specific age demographics of their users. The findings from this study contribute to the broader understanding of user age prediction and its practical implications for telecommunications companies. Future research could extend this work by exploring other demographic prediction challenges and applying the ML approach to other sectors.

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Simulator for Scheduling Real-Time Systems with Reduced Power Consumption

Cited by 3 publications

References 18 publications

Predicting Global Energy Consumption Through Data Mining Techniques

Predicting Global Energy Consumption Through Data Mining Techniques

Analytical Study for Cyber Dynamic Continuous Time Vehicle Model with Networked PID Controller

Machine Learning Approach to Users’ Age Prediction: A Telecom Company Case Study in Saudi Arabia

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