Integration of advanced methods in decision making: plithogenic logic and neutrosophic AHP

Marco P. Villa; Merly C. Moran; Pablo O. Piray; Lorenzo; Sanjar

doi:https://doi.org/10.54216/JISIoT.130120

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.

groups

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.

groups

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.

groups

Priyamvada Chandel mail

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

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

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.

groups

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.

groups

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

Journal of Artificial Intelligence and Metaheuristics | 2024

Heart Attack Diagnosis System Based on Artificial Intelligence and Optimization Algorithms

Heart attacks, or myocardial infarctions, are a primary cause of mortality worldwide, underscoring the importance of early and accurate diagnosis to improve patient outcomes. This paper reviews various Artificial Intelligence (AI) and Machine Learning (ML) techniques for heart attack diagnosis, focusing on both traditional algorithms and more complex models. The traditional algorithms are Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR), and Decision Trees (DT). More complex models are Convolutional Neural Networks (CNN), Extreme Gradient Boosting (XGBoost), Auto-encoders, Artificial Neural Networks (ANN), and TSK Fuzzy Inference System (TANFIS). Additionally, the integration of optimization techniques, including the Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), and Jellyfish Optimization Algorithm (JOA) is explored to enhance model accuracy by selecting the most important features. Our findings indicate that ensemble and hybrid models, which combine ML with metaheuristic optimization, show significant potential in improving diagnostic performance and reducing overfitting. However, challenges remain, particularly regarding computational complexity and interpretability. This study provides insights into the strengths and limitations of different AI-based diagnostic models, contributing to the advancement of automated heart disease prediction systems.

groups

Bahaa El-Din Waleed mail -

El-Sayed M. El-Kenawy mail -

Sherif Ibrahim mail -

Asmaa H.rabie mail -

Hossam El-Din Moustafa mail

link https://doi.org/10.54216/JAIM.080203

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2024

Advances in Machine Learning for Predicting and Detecting Influenza Outbreaks: A Review

Influenza is often associated with millions of cases and hundreds of thousands of deaths each year, thus constituting a serious threat to public health. Traditional surveillance techniques employed in epidemiology are limited in forecasting impending outbreaks as caused by delays in receiving the relevant information and the dynamic nature of political environments. This review focuses on the available literature on the use of machine learning (ML) techniques in understanding and controlling influenza with an accent on all the sources of information available, including clinical papers, social networking sites and others. Applicable practices in classifying predictive modeling techniques, including deep learning and others, ensemble techniques, time series analysis, etc., have increased the speed and precision of the earlier results. Even so, the achievements made so far have not come on a silver platter as there are challenges, but not limited to data issues, model explain ability and strict validation processes. Some research areas are enhancing the present models to accommodate diverse virulent strains of the viruses and advancing extensive data analysis methods. It is noted in this review that machine learning strategies are essential in combating health issues and, thus, why such technologies can be deployed within a concise duration in the context of influenza epidemics for effective forecasting and resource management to salvage lives.

groups

Ehsan khodadadi mail

link https://doi.org/10.54216/JAIM.080204

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2024

Harnessing AI for Accurate Detection and Prediction of Ebola Virus Epidemics

In this review paper, the authors discuss the development and application of methods for modeling and control and comparison of viral spreading in society with fractional-order and ML techniques for data analysis. Some of the most well-known epidemiological models are based on traditional approaches to describing disease diffusion and often need to be more sufficient when mapping the realistic disease distribution. However, fractional-order models give more flexibility and accuracy due to the memory incorporated and interaction factors. Moreover, the amalgamation of ML and artificial intelligence allows the analysis of considerable and heterogeneous amounts of data, enabling real-time prediction and favorable outbreak response measures. This paper outlines some benefits of integrating these sophisticated techniques while discussing issues such as the quality of inputs, problems in the methods deployed, and issues of visibility of the methods deployed. Finally, it proposes better epidemic preparedness and response through interdisciplinary approaches that emphasize the role of these technologies in a society that is more vulnerable to epidemic diseases.

groups

Ehsaneh khodadadi mail

link https://doi.org/10.54216/JAIM.080205

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Coverless Image Steganography Based on Machine Learning Techniques

Image steganography is a technique used to conceal secret information within digital images in such a way that the existence of the hidden data is not perceptible to the human eye. This method leverages the vast amount of data contained in image files, embedding the secret message by altering certain pixel values in a manner that is undetectable. The primary goal of image steganography is to ensure that the embedded information is secure and invisible, maintaining the original image's appearance and quality. Applications of image steganography include secure communication, digital watermarking, and copyright protection. Advanced methods often employ complex algorithms and machine learning models to enhance the robustness and imperceptibility of the hidden data, making it resistant to detection and manipulation.. The main idea of the proposed work is to utilize features extracted from images to construct a Hash Table, which will be employed for concealing and revealing a secret message. Since the same CNN model and input image (i.e., cover image) produce identical features, even if the cover image is slightly affected by noise, the same features (and consequently the same Hash Table) will be generated. The work demonstrated promising results in regenerating images when the cover image is slightly affected. However, as the noise level increases on the cover image, the regenerated images begin to lose more details.

groups

Teba Hassan AlHamdani mail -

Suhad A. Ali mail -

Majid Jabbar Jawad mail

link https://doi.org/10.54216/JCIM.150214

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Integration of advanced methods in decision making: plithogenic logic and neutrosophic AHP

In this paper, we undertake the intriguing task of uniting the advanced methods in the domain of decision making. It is about the fusion of plithogenic logic and neutrosophic HPA. It appears that in an environment where the nature of strategic decisions includes high levels of uncertainty and complexity, it is a time to seek more comprehensive methods that help in overcoming contradictions and ambiguities. This is not the first time efforts have been made to bridge the divide between decision making and artificial intelligence, but for some reason, a holistic approach to these tools is still absent. What is missing from the current literature is a framework that would tackle the two challenges of the decision-making processes, and that chaos of human judgments which is often the order of the day. To this end, we seek to articulate the missing literature, suggesting a methodology that may be useful in addressing these problems. The purpose of this model is to integrate plithogenic logic, which in its nature is a model that enables the integration of varied perspectives, and neutrosophic HPA that is theorized to be in tune with uncertainty and those fundamental contradictions in expert judgment. When this combined methodology is implemented, it seems that the outcomes achieved not only enhance the value of decision analysis, but they also allow for a more versatile and elaborate framework for evaluation of alternatives in seemingly ambiguous contexts. The truth is that the contribution of this study could be rather considerable. Theoretically, it may expand the way decision making is perceived. And more practically, it provides a way which can be more useful and comprehensible, particularly in the fields of business strategic management, policy formulation and implementation, and even project management.

groups

Marco P. Villa Zura mail -

Merly C. Moran Giler mail -

Pablo O. Piray Rodriguez mail -

Lorenzo Cevallos-Torres mail -

Sanjar Mirzaliev mail

link https://doi.org/10.54216/JISIoT.130120

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

Details open_in_new