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International Journal of Neutrosophic Science | 2024

Neutrosophic Social Structures and Neutrosophic 2-tuples Technique for Studying Labor Insertion and Gender Inequality

This study explores the dynamics of job placement and gender inequality at the Universidad Peruana Los Andes in Huancayo, Peru, with a focus on the application of neutrosophic methods. Recognizing the nuanced differences in professional opportunities for men and women, we employ the Smarandachean theory of neutrosophic social structures to examine these disparities. During 2021-2022, we conducted surveys among university graduates, utilizing the 2-tuple linguistic neutrosophic model to measure their satisfaction levels. This approach, grounded in neutrosophy, allows for a more precise capture of the participants' thoughts and feelings by effectively incorporating the inherent indeterminacies of social phenomena. The use of these neutrosophic tools provides a deeper understanding of the complex interplay between job placement and gender in professional settings.

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Michael R. Vásquez-Ramírez mail -
Ketty M. Moscoso-Paucarchuco mail -
Percy T. Avila-Zanabria mail -
Omar A. Vivanco-Nuñez mail -
Paul C. Calderon-Fernandez mail
link https://doi.org/10.54216/IJNS.240220

Volume & Issue

Vol. Volume 24 / Iss. Issue 2

Details open_in_new
International Journal of Neutrosophic Science | 2024

Enhancing Cybersecurity in Financial Services using Single Value Neutrosophic Fuzzy Soft Expert Set

Cybersecurity has become a primary concern as the financial sectors generally handle increasing cyber-attacks and an increasing danger of financial crime. Recently, ransomware attacks have intensified, affecting enterprises, and crucial infrastructure worldwide. Ransomware employs sophisticated encryption techniques to encrypt data on the targeted device, then requests payment for decrypting the data. Artificial intelligence (AI) approaches involving ML were progressively employed in the domain of cybersecurity and significantly subsidized to preventing and detecting variety of threats. On the other hand, the several researchers that employed ML to identify ransomware are still constrained by the accuracy of models, the complication of malware, the high false-positive rate, and the lack of setting up the appropriate analysis environment. Therefore, there is a need to design efficient ransomware detection based on ML algorithms. This work introduces a modified Single Value Neutrosophic Fuzzy Soft Expert Set (M-SVNFSES) technique for cyberattack detection. The main purpose of the M-SVNFSES system is to detect and recognize the existence of cyberattacks in the financial sectors. In the M-SVNFSES technique, min-max normalization is used as an initial pre-processing stage. For the identification of cyberattacks in the financial sectors, the M-SVNFSES technique uses the SVNFSES model. To enhance its performance, the M-SVNFSES technique makes use of a bat optimization algorithm (BOA). The performance of the M-SVNFSES methodology was extensively studied using financial datasets. The experimental outcomes depicted that the M-SVNFSES method reaches optimal detection performance in attack detection process

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Alsadig Ahmed mail
link https://doi.org/10.54216/IJNS.240222

Volume & Issue

Vol. Volume 24 / Iss. Issue 2

Details open_in_new
International Journal of Neutrosophic Science | 2024

Modelling of Green Human Resource Management using Pythagorean Neutrosophic Bonferroni Mean Approach

Green Human Resource Management (GHRM) state a determination of the association using crossing points of employees to stimulate environment performance activity, increase the employee awareness and sustainable activities, consequently, increasing the employee awareness towards environmental challenges.  The hotel industry is developing quickly in emerging nations owing to an upsurge in the tourism business; but, conversely, the hotel industry is mainly growing the problem of the environment. As a result, owing to the enormous amount of conservation problems that hotel business has faced, there is a growing potency to pay an accurate response to environmental problems and performing sustainable industry performance like the adoption of GHRM practice provides a win-win situation for its stakeholders and the organization. Accordingly, it indicates the requirement to scrutinize how GHRM performs will augment the environment in the hotel business. This manuscript models the design of GHRM using Pythagorean Neutrosophic Bonferroni Mean (GHRM-PNBM) approach. The presented GHRM-PNBM method objectives are to evaluate the limitation of hotel GRHM. Moreover, the presented technique constructs an expert system analysis technique for assessing the performance of hotel GHRM. Adaptive optimization of hotel GHRM assessment can be done using the PNBM technique, and the parameter selection method can be done using Quasi-Oppositional-Teaching-Learning-Based Optimization (QTLBO) method. The empirical analysis reports that the performance calculation of hotel GHRM has good confidence level and high accuracy

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Alsadig Ahmed mail -
Mamoun Badawy mail -
Gubarah Farah Gubarah mail
link https://doi.org/10.54216/IJNS.240223

Volume & Issue

Vol. Volume 24 / Iss. Issue 2

Details open_in_new
International Journal of Neutrosophic Science | 2024

A New Paradigm for Decision Making under Uncertainty in Signature Forensics Applications based on Neutrosophic Rule Engine

One of the most popular and legally recognized behavioral biometrics is the individual's signature, which is used for verification and identification in many different industries, including business, law, and finance. The purpose of the signature verification method is to distinguish genuine from forged signatures, a task complicated by cultural and personal variances. Analysis, comparison, and evaluation of handwriting features are performed in forensic handwriting analysis to establish whether or not the writing was produced by a known writer. In contrast to other languages, Arabic makes use of diacritics, ligatures, and overlaps that are unique to it. Due to the absence of dynamic information in the writing of Arabic signatures, it will be more difficult to attain greater verification accuracy. On the other hand, the characteristics of Arabic signatures are not very clear and are subject to a great deal of variation (features’ uncertainty). To address this issue, the suggested work offers a novel method of verifying offline Arabic signatures that employs two layers of verification, as opposed to the one level employed by prior attempts or the many classifiers based on statistical learning theory. A static set of signature features is used for layer one verification. The output of a neutrosophic logic module is used for layer two verification, with the accuracy depending on the signature characteristics used in the training dataset and on three membership functions that are unique to each signer based on the degree of truthiness, indeterminacy, and falsity of the signature features. The three memberships of the neutrosophic set are more expressive for decision-making than those of the fuzzy sets. The purpose of the developed model is to account for several kinds of uncertainty in describing Arabic signatures, including ambiguity, inconsistency, redundancy, and incompleteness. The experimental results show that the verification system works as intended and can successfully reduce the FAR and FRR.

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Oday Ali Hassen mail -
Shahlaa Mashhadani mail -
Iptehaj Alhakam mail -
Saad M. Darwish mail
link https://doi.org/10.54216/IJNS.240224

Volume & Issue

Vol. Volume 24 / Iss. Issue 2

Details open_in_new
International Journal of Wireless and Ad Hoc Communication | 2024

Comprehensive Detection of Security Threats in Wireless Ad Hoc Networks: Bridging Healthcare 4.0

Healthcare 4.0, which is the integration of digital technologies in healthcare, promises to bring about revolutionary advancements but also introduces significant cybersecurity challenges. This research seeks to address the growing concerns by investigating security threats in healthcare 4.0 systems. The study uses a multifaceted methodology that includes a comprehensive literature review and empirical analysis using advanced algorithms such as Random Forest. Using visualization techniques, data distribution analysis, and intrusion detection experiments, the research identifies common vulnerabilities and patterns in healthcare 4.0 environments. The findings highlight the need for proactive measures and strong policies to protect patient data integrity, safeguard medical infrastructure, and ensure continuous provision of health care services. This study calls for a holistic approach to cyber security with an emphasis on collaborative efforts toward strengthening Healthcare 4.0 systems against emerging threats.

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Shereen Zaki mail -
Heba R. Abdelhady mail -
Ahmed A. Metwaly mail -
Mahmoud M. Ismail mail
link https://doi.org/10.54216/IJWAC.080204

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new
International Journal of Wireless and Ad Hoc Communication | 2024

Enhancing Wireless Ad-Hoc Network Security by Mitigating Distributed Denial-of-Service (DDoS) Attacks

The increasing threat landscape of Distributed Denial-of-Service (DDoS) attacks makes network security a major concern. These attacks are a serious challenge to the stability and integrity of digital infrastructures. This research paper is an in-depth study on how to enhance network security through the detection and mitigation of DDoS attacks. The study reviews existing literature on DDoS attack mitigation strategies, emphasizing the evolving nature of these threats and the imperative for robust defense mechanisms. The research uses statistical analysis and logistic regression to provide a detailed methodology for distinguishing DDoS attacks from normal network activities. The results show that logistic regression is an effective classification model, providing insights into improved detection measures. Finally, the study concludes by recommending a multi-faceted approach that combines theoretical insights with empirical validation, highlighting the need for stronger network security measures against DDoS attacks and enhancing digital resilience.

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Mahmoud M. Ismail mail -
Ahmed A. Metwaly mail
link https://doi.org/10.54216/IJWAC.080205

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new
Journal of Cybersecurity and Information Management | 2024

Insider Threat Detection: Exploring User Event Behavior Analytics and Machine Learning in Security Reviews

With the exponential increase in technology use, insider threats are also growing in scale and importance, becoming one of the biggest challenges for government and corporate information security. Recent research shows that insider threats are more costly than external threats, making it critical for organizations to protect their information security. Effective insider threat detection requires the use of the latest models and technologies. Although a large number of insider threats have been discovered, the field is still limited by many issues, such as data imbalance, false positives, and a lack of accurate data, which require further research. This survey investigates the existing approaches and technologies for insider threat detection. It finds and summarizes relevant studies from different databases, followed by a detailed comparison. It also examines the types of data used and the machine learning models employed to detect these threats. It discusses the challenges researchers face in detecting insider threats and future trends in the field.

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Ruba Altuwaijiri mail -
Hanan AlShaher mail
link https://doi.org/10.54216/JCIM.130213

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new
International Journal of Neutrosophic Science | 2024

Multiple Attribute Group Decision-Making with Neutrosophic Environment for Carbon Emission Prediction on Sustainable Urban Management

Sufficient CO2 is indispensable for vegetation, but space and oceanic vehicles, industrial chimneys and land use tons of extreme CO2 and are typically accountable for global warming, climate variations and greenhouse effect. Owing to COVID19, CO2 discharge was in 2020 at its lower level than first ten years. However, the time taken is not known to decrease, increase or change carbon emissions to an endurable point. Precise predicting of carbon production has real consequences for selecting the optimal ways of decreasing carbon emissions. A pressing necessity to control these carbon emissions is needed. The preliminary step is to precisely recognize the milestones and threat levels. Specific thresholds should be mapped that formulate the maximum levels of CO2 namely – the point of no return, risk point, and so on. This article focuses on the development of Multiple Attribute Group Decision-Making with Neutrosophic Environment for Carbon Emission Prediction (MAGDM-NECEP) method on Sustainable Urban Management. The MAGDM-NECEP architecture proficiently manages the multi-criteria nature of emission calculation, while neutrosophic logic accommodates ambiguity and uncertainty in input dataset. Furthermore, GSO enhances model parameters, improving prediction performance. The MAGDM synergy and neutrosophic logic offer strong decision-making abilities, whereas GSO fine-tuned the model parameter for superior outcomes. Empirical analysis establishes the efficiency of the presented technique in precisely predicting carbon emission, providing valuable insight for the environmentalist and policymaker in developing efficient mitigation strategy

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Mesfer Al Duhayyim mail
link https://doi.org/10.54216/IJNS.240127

Volume & Issue

Vol. Volume 24 / Iss. Issue 1

Details open_in_new
International Journal of Neutrosophic Science | 2024

Two-Person Intuitionistic Neutrosophic Soft Games with Harris Hawks Optimizer based Tweets Classification on NLP Applications

With the widespread usage of social media in our day-to-day lives, it becomes a platform for persons to express and share their feelings, views, thoughts, and opinions. Recognizing emotions has numerous applications extending from dynamic advertisement to behavior analyses. People express their emotional state in a language that is often complemented by figures of speech and ambiguity, making it problematic even for human beings to understand. Categorizing tweets is a dynamic application of NLP, allowing the scrutiny of topical discussions, user opinions, and trends in real-time. Leveraging techniques such as word embeddings, machine learning, and text preprocessing approaches, tweet classification enables tasks like spam detection, sentiment analysis, and topic modeling. This ability assists companies in understanding client feedback and allows policymakers and researchers to track emerging issues and gauge public opinion on social networking media. This study presents a Two-Person Intuitionistic Neutrosophic Soft Games with Harris Hawks Optimizer (TINSG-HHO) based Tweets Classification on NLP Applications. The purpose of the TINSG-HHO technique is to detect the existence of different kinds of emotions or sentiments in the tweets. The TINSG-HHO technique begins with preprocessing of tweets to convert them into useful format.  Then, FastText embedding represents words as contextual similarities, dense vectors, and capturing semantic nuances. Leveraging the embedding, the Neutrosophic classification model proficiently handles vagueness and uncertainty intrinsic in deceptive content detection tasks. Moreover, the HHO technique enhances the parameter of the Neutrosophic classifier, improving its generalization capabilities and performance. Based on the hunting strategy of Harris's hawks, HHO discovers the parameter range to search for optimum configurations for the classifier. Experimental evaluations carried out on different datasets illustrate the effectiveness of the DCRM in precisely detecting the deceptive content

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Mohammad Mahzari mail -
Aisha H. Abdalla Hashim mail -
Khalid M. Osman Saeed mail -
Mohammed M. Osman Mokhtar mail
link https://doi.org/10.54216/IJNS.240128

Volume & Issue

Vol. Volume 24 / Iss. Issue 1

Details open_in_new