Smart Home Energy Management through ARIMA Model Forecasting: Leveraging Weather Data for Improved Efficiency

El-Sayed M.; Marwa M.; Abdelhameed; Osama

doi:https://doi.org/10.54216/JAIM.080202

International Journal of Neutrosophic Science | 2025

Neutrosophic fuzzy metric spaces and fixed points results with integral contraction type

In this study, we introduce fixed point theorems related to integral type contractions, framed within the advanced context of neutrosophic fuzzy metric spaces. Additionally, we derive multiple fixed point results that are relevant to this particular setting.

groups

Anwar Bataihah mail -

Ayman A. Hazaymeh mail

link https://doi.org/10.54216/IJNS.250344

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2024

The Mathematical Formulas of 2-Cyclic Refined Duplets and Triplets

This work is dedicated to studying the problem of computing 2-cyclic refined neutrosophic duplets and triplets in the 2-cyclic refined neutrosophic ring of real numbers, where we present four different formulas that describe all possible duplets in this extended ring. Also, we present four different formulas for the computation of related triplets in the same ring.

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Josef Al Jumayel mail -

Ahmad Khaldi mail

link https://doi.org/10.54216/GJMSA.0110206

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2024

The Dominator Coloring of Some Graph Classes

A proper vertex coloring of a graph 𝐺(𝑉,𝐸) is an assignment of colors to the vertices of 𝐺 so that no two adjacent vertices have the same color. A dominator coloring of 𝐺 is a proper vertex coloring for which every vertex is adjacent to all the vertices of at least one color class. The minimum number of colors required to establish a proper dominator coloring on 𝐺 is called the dominator coloring number and is denoted by 𝜒𝑑(𝐺). In this paper, we determine the dominator coloring number of strong grid graphs 𝑃𝑚⊠𝑃𝑛 when 𝑚,𝑛≥3. We also determine the dominator coloring number of the Queen graph 𝑄2,𝑛 for 𝑛≥2.

groups

Ramazan Yasar mail

link https://doi.org/10.54216/GJMSA.0110207

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2024

Irreversible k-Threshold Conversion Number of Strong Grids for k>3

An irreversible k-threshold conversion process on a graph 𝐺=(𝑉,𝐸) is a dynamic, iterative process which begins by choosing a set 𝑆0&sube;𝑉. For each step 𝑡(𝑡=1,2,…,), 𝑆𝑡 is obtained from 𝑆𝑡−1 by adjoining all vertices that have at least k neighbors in 𝑆𝑡−1. We call 𝑆0 the seed set of the k-threshold conversion process and if 𝑆𝑡=𝑉(𝐺) for some 𝑡≥0, then 𝑆0 is called an irreversible k-threshold conversion set (IkCS) of 𝐺. The k-threshold conversion number of 𝐺 (denoted by (𝐶𝑘(𝐺)) is the minimum cardinality of all the IkCSs of 𝐺. In this paper, we study Irreversible k-threshold conversion processes on strong grids 𝑃𝑚⊠𝑃𝑛. We determine 𝐶𝑘(𝑃3⊠𝑃𝑛) for 𝑘=5,6,7 and 𝐶𝑘(𝑃4⊠𝑃𝑛) for 𝑘=6,7. We also present upper bounds for 𝐶4(𝑃3⊠𝑃𝑛), 𝐶4(𝑃4⊠𝑃𝑛),𝐶5(𝑃3⊠𝑃𝑛), then we determine 𝐶8(𝑃𝑚⊠𝑃𝑛) for arbitrary 𝑚,𝑛.

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Ali Kassem mail -

Ramy Shaheen mail -

Suhail Mahfud mail

link https://doi.org/10.54216/GJMSA.0110208

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

Prospects for Applied Mathematics and Data Analysis | 2024

Characteristics Neutrosophic Homomorphism for Neutrosophic Rings: On Review

The objective of this paper is to present and study elementary properties for concept a neutrosophic ring homomorphism and isomorphism which introduced by Florentine Smarandache in 2006. We will use a concept ring homomorphism and isomorphism in classical ring.

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Shawqi Al-lkami mail -

Adel Al-odhari mail

link https://doi.org/10.54216/PAMDA.030105

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2025

EfficientDense-ViT: APT Detection via Hybrid Deep Learning Framework with Hybrid Dipper Throated Sine Cosine Optimization Algorithm (HDT-SCO)

Advanced Persistent Threats (APT) are intelligent, sophisticated cyberattacks that frequently evade detection by gradually interfering with vital systems or focusing on sensitive data. It is proposed herein the new approach of the Hybrid Dipper Throated Sine Cosine Optimization Algorithm (HDT-SCO) for APT detection in association with the EfficientDense-ViT model. It handles the class imbalance issue with advanced processing Adaptive Synthetic Minority Oversampling Technique (ADASYN), including min-max scaling for normalization, and median imputation for missing values. In terms of feature engineering, ResNet-152 and Symbolic Aggregate Approximation (SAX) are adopted for statistical, deep, and time series feature extraction. HDT-SCO optimizes the selection of relevant features to refine by integrating into it the three approaches: PCA, RFE, RF Feature Importance, and L1 Regularization (Lasso). Compared to current detection techniques, the best detection model shows high performance and efficiency through the hybrid deep learning model known as EfficientDense-ViT, which is a combination of EfficientNet, DenseNet, and Vision Transformers (ViT) that can detect APTs reliably. This method shows considerable improvement in both accuracy (0.98741 for the 70/30 split and 0.99143 for the 80/20 split) and efficiency as compared to existing models in the detection of APTs in cybersecurity.

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Khaled Almasoud mail

link https://doi.org/10.54216/JCIM.150212

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Real-time Prediction Model for Heart Disease Risk during Medical Consultations and Health Monitoring

In the realm of cardiovascular health, early detection and proactive management of heart disease are critical for improving patient outcomes. This paper introduces a novel real-time prediction model designed to assess heart disease risk during medical consultations and continuous health monitoring. Leveraging advanced machine learning techniques and a diverse dataset comprising patient demographics, medical history, and biometric measurements, our model provides immediate, actionable insights into an individual’s cardiovascular health. The model integrates seamlessly with electronic health record (EHR) systems and wearable health devices, offering real-time risk assessments that aid healthcare professionals in making informed decisions and tailoring personalized treatment plans. Through extensive validation and testing, our model demonstrates high accuracy and reliability, with potential to significantly enhance early intervention strategies and patient engagement in heart disease prevention. This research underscores the transformative potential of real-time predictive analytics in clinical practice and highlights pathways for future development and integration of intelligent health monitoring solutions.

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Yerraginnela Shravani mail -

Ashesh K. mail

link https://doi.org/10.54216/JCIM.150213

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Artificial Intelligence Based Hybrid ASFO-ESVM for Load Demand Prediction in Micro Grid Energy Management

Predicting load demand is relevant when used in microgrid energy management systems to address issues such as nonlinear and dynamic consumption data. In this research, the author presents a fusion of Adaptive Sunflower Optimization (ASFO) and Enhanced Support Vector Machine (ESVM) methods to predict the load demand in micro grid environment. The ASFO algorithm enhances the efficiency of the ESVM through a fine-tuning meta-heuristic algorithm based on the sunflower natural organisms. This integration of ASFO and ESVM eliminates many of the drawbacks associated with the basic performance of the task, namely low speed of convergence, overtraining, and the presence of local minima in choosing the parameters. Some of the general parameters used in training and validating the model include load and meteorological data features involving, weather, temporal, load histories are the main contributors in the analysis. Comparisons with other ML algorithm ‘shave been made in respect of relative performance against established methods, such as Random Forest (RF) and Particle Swarm Optimization based with ESVM (PSO-ESVM). The findings infer that lower values of Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) and higher consistency index (d) are yielded by the proposed hybrid ASFO-ESVM model. For instance, even on working days of the week, the precision of the load forecasts was higher with the hybrid model than with the other options. The outcomes do prove that the proposed ASFO-ESVM model is very reliable and precise in its concerning aspect of load demand forecasting as it can be seen in the results obtained for different situations. Relatively, this work estimates a cost effective and feasible method for micro grid energy predictions which can enhance decisions in matters concerning power production, distribution, and control of energy. The study shows how these techniques are relevant towards the complexity and dynamism of the contemporary energy systems.

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Priyamvada Chandel mail

link https://doi.org/10.54216/JISIoT.140218

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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Journal of Artificial Intelligence and Metaheuristics | 2024

Comparative Analysis of Machine Learning Models for Daytime Power Generation Prediction

This paper proposes to evaluate how different machine learning techniques can be used to predict daytime power generation based on the "Daily Power Generation Data" data set. As a result of six models, which contain Random Forest Regressor, Decision Tree Regressor, Nearest Neighbors, Linear Regression, MLP Regressor, and SVR, a clear understanding has been accomplished by assessing the performance using multiple metrics. First, the Random Forest Regressor turned out to be the best in terms of the Mean Squared Error (MSE) of 3.57E-06, which was the lowest among the three ML models. The introduction of the paper highlights the role of precise planning of the power market and the consecutive sections describing the topic mathematically. The table below, with a total list of performance issues, explains why the Random Forest Regressor is the superior full-proof model using the lowest MSE, highest explained variance, and great resistance to outlying samples. The paper thus gave various useful approval criteria that we can largely choose the best model out of them because the Random Forest Regressor was able to get the highest performance metrics.

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Nima Khodadadi mail -

Benyamin Abdollahzadeh mail

link https://doi.org/10.54216/JAIM.080201

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2024

Smart Home Energy Management through ARIMA Model Forecasting: Leveraging Weather Data for Improved Efficiency

This study pursues machine learning models for the task of smart homes' energy management with the use of a dataset that combines smart meter readings and weather conditions at the same time. The assessment of the Baseline Qualification and ARIMA models is done using various criteria, such as MSE, RMSE, and others. Most telling, the best performance is shown by ARIMA, which gets the lowest MSE score, 0.0693, in this instance. They show that such a model is optimal in forecasting energy consumption dynamics, and while they could be better, weather information helps improve the accuracy of the forecasts. The conduct helps uncover priceless information, allowing for the development of new smart home operating systems with a prospect of energy efficiency enhancement as well as a sustainable environment.

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El-Sayed M. El-Kenawy mail -

Marwa M. Eid mail -

Abdelhameed Ibrahim mail -

Osama Alabedallat mail

link https://doi.org/10.54216/JAIM.080202

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new