A Multi-Criteria Decision-Making Analysis for COVID-19 in Public Health under Neutrosophic Set: Case Study

Heba R.; Shereen; Mahmoud M.; Mohamed; Shimaa

doi:https://doi.org/10.54216/IJNS.230404

Pure Mathematics for Theoretical Computer Science | 2024

Some Results About αc– Continuous Functions

The main aim of this paper is to study new class of continuous function is called  – continuous function. for this aim, the nation of - open and pre – open sets and  - compact space are introduced. and we shall study the relationship between  – continuous and pc– continuous functions.

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Ahmed Rida Fares mail

link https://doi.org/10.54216/PMTCS.030103

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2024

RETRACTED ARTICLE: On the Characterization of Some m-Plithogenic Vector Spaces and Their AH-Substructures Under the Condition 6 ≤ dim SPV ≤10

This paper is concerned with studying symbolic m-plithogenic vector spaces with finite orders between 6 and 10, where it defines and characterizes the AH-subspaces, AH-kernels, and AH- linear transformations in five different symbolic m-plithogenic spaces (6-plithogenic, 7-plithogenic,10-plithogenic vector spaces). Also, we prove many theorems that describe the computation of the kernels and direct images of the plithogenic AH-linear transformations.

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Ibraheem Abu Falahah mail -

Abdallah Al-Husban mail -

Mutaz M. Abbas Ali mail -

Ahmed A. Alsaraireh mail -

Amani Shatarah mail -

Norah Mousa Alrayes mail

link https://doi.org/10.54216/IJNS.240322

Volume & Issue

Vol. Volume 24 / Iss. Issue 3

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Journal of Artificial Intelligence and Metaheuristics | 2024

Advances and Challenges in Feature Selection Methods: A Comprehensive Review

The feature selection area in data analytics is explored through a comprehensive literature review, and the increasing areas that have a data dependency problem and are being resolved with feature selection are highlighted. Review topics of this course cover the foundations to present use cases, for example, cybersecurity, healthcare, and finance. Particularly crucially for the healthcare domain, it reduces the dimensionality and elucidates complex causal links. The further investigation overlaps contemporary techniques, including optimization-based methods, swarm intelligence and algorithms for the diagnosis of heart diseases. The conclusion builds on the practical assessment and underlines research gaps, serving as a basis to set a diversified technological review. This also exhibits new techniques that have released their efficiency in classification environments, for example, hybrid Ant Colony Optimization and the Gray Wolf Optimizer. The ISSA algorithm stands out as a swarm intelligence technique that is best among others. The paper concludes by demonstrating that feature selection goes beyond the preprocessing stage, but it instead stands as a vital part of the fields of machine learning and data science and thus aids the researchers in both retrospective analysis and forthcoming projects.

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Mohamed Ziad Ali mail -

Abdulrahman Abdullah mail -

Ahmed Mohamed Zaki mail -

Faris H. Rizk mail -

Marwa M. Eid mail -

Elsayed M. El-Kenway mail

link https://doi.org/10.54216/JAIM.070105

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

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Journal of Artificial Intelligence and Metaheuristics | 2024

Exploring Optimization Algorithms: A Review of Methods and Applications

This article review focuses on feature selection as the main parameter that plays a major role in tuning machine learning models. Several algorithms of optimization such as MFO (Moth-Flame Optimization), the GA-GSA algorithm’s hybrid type, SOA (Seagull Optimization Algorithm), WOA (Whale Optimization Algorithm), GOA (Grasshopper Optimization Algorithm), HGSO (Henry Gas Solubility Optimization), and SafeOpt are widely used in engineering design, power systems scheduling, The paper stresses the importance of optimization in improving efficiency, lessening mistakes and increasing understandability of machine learning models. The literature addresses the widest directions in the usage of optimization for the following fields of science such as structural engineering, additive manufacturing, and landslide susceptibility mapping. A comprehensive summary table is generated, which shows an overview of each study, algorithm, focus, and methodology and has a stoke of key findings. The conclusions reveal the adaptiveness, competitiveness and compossibility of the optimization algorithms applied to a wide range of domains. The summary shows how optimization has the potential to change decision-making processes and activities by being a decisive factor that determines the future of branches of various industries. The main objective of this work is to direct researchers and practitioners by providing them with some innovative ideas and approaches and offering insights on the existing cutting-edge approaches while laying the groundwork for future innovations in optimization.

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Abdulrahman Abdullah Farag mail -

Ziad Mohammed Ali mail -

Ahmed Mohamed Zaki mail -

Faris H.Rizk mail -

Marwa M. Eid mail -

El-Sayed M. El-Kenawy mail

link https://doi.org/10.54216/JAIM.070201

Volume & Issue

Vol. Volume 7 / Iss. Issue 2

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Fusion: Practice and Applications | 2024

Exploring CIE Lab Color Characteristics for Skin Lesion Images Detection: A Novel Image Analysis Methodology Incorporating Color-based Segmentation and Luminosity Analysis

Accurate classification of malignant and benign skin lesions is crucial in dermatology. In this novel research, we propose robust image analysis methodology for skin lesion classification that integrates color-based segmentation with luminosity analysis. Our approach is evaluated on a dataset of 400 skin images, with equal representation of malignant and benign samples. By computing mean color values for the Red Channel Color (RCC), Green Channel Color (GCC), and Blue Channel Color (BCC) in groups of 10 samples, we establish a classification range for precise diagnosis, this research introduces a novel dimension by harnessing the potential of the CIE Lab Color characteristics for skin lesion detection as the most reliable scale for distinguishing between benign and malignant samples. The smaller and more thought variety ranges saw in the glow examination improve difference and perceivability, consequently working with prevalent sore separation. By featuring the meaning of mean histograms for each variety channel, this complete exploration adds to propelling the area of dermatology and presents an imaginative methodology that holds guarantee for PC helped conclusion frameworks in skin malignant growth discovery.

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Marwa Mawfaq M. Al-Hatab mail -

Ahmed S. Ibrahim Al-Obaidi mail -

Mohammad Abid Al-Hashim mail

link https://doi.org/10.54216/FPA.150108

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2024

Zero Trust-Based Blockchain Based IoT Security with Consensus and Access Control Framework

As the Internet and computer technology develop, more gadgets are linked wirelessly, expanding the Internet of Things (IoT). IoT is a huge network of sensors and gateways that links them. IoT devices generate images, music, video, digital signals, and more by interacting with their surroundings. To exchange resources and information, all IoT equipment and apps may connect to the Internet. Everything is connected in our world. Due to the broad deployment and massive size of IoT devices, access control of device resources is problematic. Obtaining IoT device resources unlawfully will have major implications since they include personal and sensitive information. Many systems and situations employ access control technologies to secure resources. Discriminatory, identity-based, and MAC access control schemes are traditional (mandatory access control). However, these centralized methods have single-point failure, scalability issues, poor dependability, and low throughput. IoT devices may belong to several organizations or people, be mobile, and function badly, making centralized access management problematic. Another innovative data management solution is blockchain, which uses distributed storage to stabilize data. A transaction writes the data reading or modification record into a block, and the blocks are connected as a chain using a hash to maintain data integrity. It synchronizes data between nodes via a peer-to-peer network and consensus process, assuring data consistency for blockchain network participants. Zero Trust-Based Blockchain, an open source blockchain development platform, offers more efficient consensus methods, larger throughputs, smart contracts, and support for different organizations and ledgers. Proposed work build the fabric-IoT access control system using Zero Trust-Based Blockchain to apply blockchain technology to IoT access control in this study. Distributed processing and storage for IoT data may solve these critical issues with blockchain. Thus, developing distributed IoT-based e-healthcare services using blockchain technology may have been feasible. FabricIoT can keep records, handle dynamic access control, and solve the IoT access control problem using distributed architecture.

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Ahmad Raza Khan mail

link https://doi.org/10.54216/JISIoT.120109

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2024

Orthogonal distance and similarity for single-valued neutrosophic fuzzy soft expert environment and its application in decision-making

A soft expert set(SES) is a concept that combines elements of soft sets and expert systems. It aims to incorporate expert knowledge and uncertainty-handling capabilities into the analysis and decision-making processes. On the other hand, the idea of single neutrosophic sets (SVNSs) and fuzzy sets (FSs) are imported models for handling the uncertainty data. In this work, the authors combine the critical features of FSs and SVNSs under expert systems in one model. Accordingly, this model worked to provide decision-makers with more flexibility in the process of interpreting uncertain information. From a scientific point of view, the process of evaluating this high-performance SVNFSES disappears. Therefore, in this paper, we initiated a new approach known as single-valued neutrosophic fuzzy soft expert sets (SVNFSESs) as a new development in a fuzzy soft computing environment. We investigate some fundamental operations on SVNFSESS along with their basic properties. Also, we investigate AND and OR operations between two SVNFSESS as well as several numerical examples to clarify the above fundamental operations. Finally, we have given an Orthogonal Distance and Similarity for SVNFSESs to construct a new algorithm to demonstrate the method’s effectiveness in handling some real-life applications.

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Faisal Al-Sharqi mail -

Ashraf Al-Quran mail -

Hamiden Abd El- Wahed Khalifa mail -

Haifa Alqahtani mail -

Badria A. Ali Yousif mail -

Rawan A. shlaka mail -

Mona Aladil mail

link https://doi.org/10.54216/IJNS.230401

Volume & Issue

Vol. Volume 23 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2024

Secondary K-Range Symmetric Neutrosophic Fuzzy Matrices

This paper introduces and explores the concept of secondary k-Range Symmetric (RS) Neutrosophic Fuzzy Matrices (NFM) and establishes its properties and relationships with other symmetric and secondary symmetric NFMs. The study defines secondary k-RS NFMs and provides insightful numerical examples to illustrate their characteristics. The paper investigates the interconnections among s-k-RS, s-RS, k-RS, and RS NFMs, discuss on their mutual relations. Additionally, the necessary and sufficient conditions for a given NFM to qualify as a s-k-RS NFM are identified. The research demonstrates that k-symmetry implies k-RS, and vice versa, contributing to a comprehensive understanding between different types of symmetries in NFMs. Graphical representations of RS, column symmetric, and kernel symmetric adjacency and incidence NFMs are presented, unveiling intriguing patterns and relationships. While every adjacency NFM is symmetric, range symmetric, column symmetric, and kernel symmetric, the incidence matrix satisfies only kernel symmetric conditions. The study further establishes that every range symmetric adjacency NFM is a kernel symmetric adjacency NFM, though the converse does not hold in general. The existence of multiple generalized inverses of NFMs in Fn is explored, with additional equivalent conditions for certain g-inverses of s-κ-RS NFMs to retain the s-κ-RS property. We conclude by characterizing the generalized inverses belonging to specific sets {1, 2}, {1, 2, 3}, and {1, 2, 4} of s-k-RS NFMs, providing a comprehensive framework for understanding the structure and properties of secondary k-Range Symmetric Neutrosophic Fuzzy Matrices. This research contributes to the mathematical literature by introducing a novel class of NFMs and establishing their fundamental properties and relationships, presenting new perspectives on matrix theory in the context of neutrosophic fuzzy logic.  

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M. Anandhkumar mail -

H. Prathab mail -

S. M. Chithra mail -

A. S. Prakaash mail -

A. Bobin mail

link https://doi.org/10.54216/IJNS.230402

Volume & Issue

Vol. Volume 23 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2024

Golden Jackal Optimization with Neutrosophic Rule-Based Classification System for Enhanced Traffic Sign Detection

Traffic signs detection is a critical function of automatic driving and assisted driving is a significant part of Cooperative Intelligent Transport Systems (CITS). The drivers can obtain the data attained via automated traffic sign detection to improve the comfort and security of motor vehicle driving and regulate the behaviors of drivers.  Recently, deep learning (DL) has been utilized in the fields of traffic sign detection and achieve better results. But there are two major problems in traffic sign recognition to be immediately resolved. Some false sign is always detected due to the interference caused by bad weather, and illumination variation. Some traffic signs of smaller size are increasingly complex to identify than larger size hence the smaller traffic signs go unnoticed. The objective is to achieve the accuracy and robustness of traffic sign detection for detecting smaller traffic signs in a complex environment. Thus, the study presents a Golden Jackal Optimization with Neutrosophic Rule-Based Classification System (GJO-NRCS) technique for Enhanced Traffic Sign Detection. The GJO-NRCS technique aims to detect the presence of distinct types of traffic signs. In the GJO-NRCS technique, DenseNet201 model is exploited for feature extraction process and the GJO algorithm is used for hyperparameter tuning process. For final recognition of traffic signals, the GJO-NRCS technique applies NRCS technique. The simulation values of the GJO-NRCS method can be examined using benchmark dataset. The experimental results inferred that the GJO-NRCS method reaches high efficiency than other techniques.

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Mohammed Assiri mail

link https://doi.org/10.54216/IJNS.230403

Volume & Issue

Vol. Volume 23 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2024

A Multi-Criteria Decision-Making Analysis for COVID-19 in Public Health under Neutrosophic Set: Case Study

This paper proposed a framework for assessing the COVID-19 response in Egypt. COVID-19 plays a vital role in public health, and selecting the best response can decrease the impact of the disease. This study used the type 2 neutrosophic set as a framework for dealing with uncertainty. The assessment of the COVID-19 response has various conflicting criteria, so the concept of multi-criteria decision-making (MCDM) is used to deal with COVID-19 criteria. The MCDM methods are adopted with the type-2neutrosophic set—the typ-2 neutrosophic AHP-COPRAS-VIKOR methodology framework. The AHP is used to compute the criteria weights. The COPRAS and VIKOR methods are used to rank the alternatives. The case study in Egypt is conducted to show the best response to COVID-19. Five main criteria and nineteen sub-criteria are used in this paper. The methodologies employed in this paper aid as examples for future research endeavors, inspiring a continued dialogue on refining and advancing MCDM methodologies in public health disasters.

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Heba R. Abdelhady mail -

Shereen Zaki mail -

Mahmoud M. Ismail mail -

Mohamed Emad mail -

Shimaa Said mail

link https://doi.org/10.54216/IJNS.230404

Volume & Issue

Vol. Volume 23 / Iss. Issue 4

Details open_in_new