Neutrosophic decision making using Saaty's AHP method and VIKOR

Sheila B. Esparza; Luis A.; Haro Teran Lilian; Dinara

doi:https://doi.org/10.54216/JISIoT.120113

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

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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.

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Ehsan khodadadi mail

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

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

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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.

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Ehsaneh khodadadi mail

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

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

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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.

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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

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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

Journal of Cybersecurity and Information Management | 2025

Smart Grid intrusion detection system based on AI techniques

Smart grids (SGs) are integral to modern utility systems, managing power generation, energy consumption, and communication networks. However, as these systems become increasingly interconnected, they are exposed to sophisticated cyber threats that can compromise their functionality and security. To address these challenges, this paper presents an AI-driven detection framework designed to significantly enhance cybersecurity in smart grids. The proposed system combining Recurrent Neural Networks (RNNs) with Support vector classifier to improve detection accuracy, recognition capabilities, and system robustness. The methodology comprises four main stages: (1) data preprocessing to ensure high-quality input for analysis, (2) traffic detection using RNNs to capture temporal patterns, (3) classification of traffic as normal or abnormal via support vector classifier (SVC), and (4) identification of specific attack types through another SVC for refined threat categorization. This integrated approach enables real-time detection of both known and emerging threats, focusing on minimizing false positives and maximizing detection precision. The system was evaluated on three comprehensive benchmark datasets: UNSW_NB15 and BoT-IoT, achieving an average accuracy of 100%. These results underscore the superiority of this AI-based solution over traditional intrusion detection systems, providing a robust and scalable framework for securing smart grids and other critical infrastructures.

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Mounir Mohammad Abou-Elasaad mail -

Samir G. Sayed mail -

Mohamed M. El-Dakroury mail

link https://doi.org/10.54216/JCIM.150215

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2024

Enhancing Decision-Making in Uncertain Environments: The Role of Neutrosophic Cognitive Maps in Analyzing Complex Systems

Using Neutrosophic Cognitive Maps (NCM), this research tackles the very core of one of the main problems that exist in the analysis of complex structures and systems: how to represent and model decision making in situations of uncertainty, or where there is contradiction and ambiguity. This problem gets even worse in the areas of knowledge management, strategic evaluation, or design of public policies since orthodox methods do not always possess required versatility for combining partially available or contradicting information. To address this challenge, the researchers recommend Neutrosophic Cognitive Maps (NCM) as a more appropriate technique considering that the neutrosophic logic can depict and study more intricate relationships in the presence of indeterminacy. There is an iterative learning of cognitive maps which is coupled with neutrosophic analysis techniques enabling the construction of a comprehensive model capturing both certainties and the undefined and disputed areas of the evaluated systems. The findings obtained in this research demonstrate how effective NCMs are in the spatial and analytic representation of complex and multifactorial situations providing features that go beyond the conventional structure models. Apart from broadening theorization on decision-making processes in an uncertain situation, this research provides practical tools applicable in sectors such as strategic planning, complex problem solving and organizational management. In short, the study shows that neutrosophic, used as a methodological catalyst, not only expands the possibilities of analysis but also transforms how complex systems are conceptualized and managed in the academic and practical fields.

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Jose Luis Robalino Villafuerte mail -

Sheila Belen Esparza Pijal mail -

Mónica Isabel Mora Verdezoto mail -

Lorenzo Cevallos-Torres mail -

Sanjar Mirzaliev mail

link https://doi.org/10.54216/JISIoT.130121

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2025

Securing the Future: Real-Time intrusion Detection in IIoT Smart Grids through Innovative AI Solutions

The world is witnessing an unprecedented boom in the development of information technology, which has come to encompass all aspects of life, Smart networks based on the Industrial Internet of Things (IIoT) are among the latest technologies used in various industries, contributing to improved production efficiency, reduced costs, and enhanced security, With the increasing reliance on this technology, the challenge of complex cyberattacks are also on the rise, These attacks are considered one of the major challenges facing smart networks, as attackers can exploit vulnerabilities in systems to access sensitive data or disrupt industrial operations, To counteract these threats, advanced intrusion detection systems should be developed, leveraging artificial intelligence and big data analytics to effectively detect and respond to attacks in real-time. Therefore, it is imperative to strive towards developing advanced and intelligent security systems to combat cyberattacks, ensuring the safety of industrial operations and data protection. This paper provides two IDS based on AI that are developed to negate the raising sophisticated cyberattacks. IN the first technique, Group of ML techniques such as Decision tree, Random Forrest classifiers, support vector classifier, and K_Nearest Neigbor are used with Feature reduction algorithms classifying network traffic subspecies to enhancing the accuracy and efficiency of detection systems. The second proposed technique for specifying the type of intrusion advantage various methodologies, particularly in the context of IoT networks and deep learning, the two algorithms are trained and tested using three well-known datasets to investigate wide domain of cyberattacks targeting the IIoT infrastructure. Results of the simulation show that the algorithm proposed in this work provides high improvement in detection of cyberattacks. The first algorithm achieved an accuracy of 99.9% and a very low false positive rate of just 0.1%. In addition, the second proposed algorithm identifies type of attack with a detection ratio of 99.76%. These results demonstrate how the proposed IDS based on AI algorithms can effectively detect network intrusion, and significantly enhance the security of IIoT system

groups

Mounir Mohammad Abou-Elasaad mail -

Samir G. Sayed mail -

Mohamed M. El-Dakroury mail

link https://doi.org/10.54216/JCIM.150216

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2024

A Neutrosophic Multicriteria Analysis of Economic Recovery Systems

The article investigates the neutrosophic multicriteria analysis of the post-pandemic economic recovery, an intricate and multi-dimensional problem, which persistently affects several nations in a globalized setting with high levels of uncertainty. This study delves into how recovery measures including policies and strategies should be developed and implemented considering the newly emerging complexities that characterize the mod-ern world and its politics. The main concern is the absence of any tools that may seek to correlate the various factors that are necessarily involved in any recovery process, all of which have variable post- pandemic eco-nomic, social, and political conditions. In addition, there is clearly an importance of this issue since it has been so timely for governments and/or organizations to look for strategies and policies that would ensure a just and environmentally sound reconstruction phase. By providing a neutrosophic multicriteria analysis that has not been applied before in this context, the paper contributes to the existing literature by outlining various factors involved in economic recovery such as fiscal policy and health and social measures. This study takes the neu-trosophic perspective and forms of analysis to explore the various uncertainties and heterogeneous views con-cerning economic strategies, thus enabling an intricate analysis of the strategic options put forth. The findings emphasize the necessity for a creative and cross-cutting strategy towards the reconstruction of the economy, emphasizing the fact that the solutions must be dynamic to the changing circumstances. Making an academic contribution, this research not only proposes a new theoretically based framework for the understanding of recovery, but also has practical recommendations that may help in formulating policies that are more robust and effective in times of crises.

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Carlos Wilman Maldonado Gudiño mail -

Maria Fernanda Jara Campoverde mail -

Maria Belen Carlosama Ponce mail -

Marina Abdurashidova mail

link https://doi.org/10.54216/JISIoT.120112

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2024

Neutrosophic decision making using Saaty's AHP method and VIKOR

This study analyzes the difficulties that arise in multicriteria decision making in condition that bear uncertainty, ambiguity, and contradictions at the very core. The key issue is the shortage of instruments allowing for not only ranking of alternatives but also efficiently combining qualitative and quantitative information in management decision making. The relevance of this research is due to the growing number of critical situations in a variety of disciplines, including organizational management and public policies, which have a limited number of traditional methodologies and thus need more effective evaluation processes. Still, concerning such aspects as the integration of approaches that tend to discuss a lot of the quite fuzzy context in a structured and dynamic way, there are significant gaps in the existing literature. A methodological framework for managing uncertainties inherent in expert judgments and for prioritizing alternatives was developed through the integration of Saaty’s AHP method and the VIKOR approach from the perspective of neutrosophic logic. The results demonstrate that this integration not only improves the efficiency of ranking and selection of alternatives under complex environment but also enhances sensitivity to differences among evaluations. This progress is of central importance regarding practical implications of this advance, particularly in strategies design.

groups

Sheila B. Esparza P mail -

Luis A. Crespo-Berti mail -

Haro Teran Lilian Fabiola mail -

Dinara Turaeva mail

link https://doi.org/10.54216/JISIoT.120113

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

Details open_in_new