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Journal of Cybersecurity and Information Management | 2024

Heterogeneous Wireless Sensor Network Design with Optimal Energy Conservation and Security through Efficient Routing Algorithm

A heterogeneous wireless sensor network (H-WSN) comprises multiple sensor nodes having varied abilities, like diverse processing power and sensing range. H-WSN deployment and topology control seem to be more difficult than homogeneous WSNs. Research on H-WSNs has increased in the last few years to improve real-time sensor networks' reliability and deliver better networking services than a homogenous WSN does. When it comes to H-WSN's energy consumption and security, the major problem remains the efficient routing process.  To that end, this research aims at demonstrating how an efficient routing algorithm of hierarchical H-WSN can greatly enhance the network's performance. It is important to note that the nodes' capabilities mostly determine the suitability of a given routing algorithm. Hence, the H-WSN design issues for routing in a heterogeneous environment are discussed in this paper. This research designs an Optimal Energy Conservation and Security-aware Routing Algorithm (OECS-RA) for H-WSN using clustering and a secure-hop selection scheme. In this proposed model, the optimal cluster head selection and routing have been found through various computational stages based on the energy conservation of each sensor node. It further secures the transmission by selecting the secured node with credential factor computation and comparing each hop of the optimal route. The MATLAB simulation scenario finds the significant performance of the routing mechanism with security compared to existing models. The proposed OECS-RA gives highly recognizable throughput, lifetime, energy efficiency, and reliability. With these results, this proposed algorithm is suggested for real-time implementation in the medical industry, transportation, education, business, etc.

groups
D. Bhanu mail -
R. Santhosh mail
link https://doi.org/10.54216/JCIM.130211

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new
Journal of Cybersecurity and Information Management | 2024

Prediction of Skin Lesions Using Integrated Multi-Layered Network Model with Baseline Learning Approaches

Skin cancer has become more common in recent decades, raising severe concerns about world health. Creating an automated system to distinguish between benign and malignant images is challenging because of the subtle variations in how skin lesions appear. This study introduces Computer-Aided Diagnosis (CAD) system that offers high classification accuracy while maintaining low computing complexity for categorizing skin lesions. The system incorporates a pre-processing stage that uses morphological filtering to remove hair and artefacts. With the least minimum of human interaction, deep learning techniques are employed to separate skin lesions automatically. Image processing methods are currently being utilized to investigate the automated implementation of the prediction criteria for distinguishing between benign and malignant melanoma lesions. Various pre-trained convolutional neural networks (CNNs) with multi-layered (ML-CNN) are under examination for the classification of skin lesions as either benign or malignant. The best performance is achieved when RF, k-NN and XGBoost are combined, according to average 5-fold cross-validation findings. The outcomes also demonstrate that data augmentation works better than acquiring novel images for training and testing purposes. The experiment results show that the suggested diagnostic framework performs better than existing methods when used on actual clinical skin lesions, with accuracy at 97.5%, F1-score at 91.3%, precision at 96.5%, sensitivity at 89.2% and specificity at 96.7%. It also takes 2.6 seconds to complete with the MNIST dataset and accuracy at 98.2%, F1-score at 92.5%, precision at 98.4%, sensitivity at 92.3% and specificity of 97.2% with the ISIC dataset. This indicates that medical professionals can benefit from using the suggested framework to classify various skin lesions.

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Arpita Roy mail -
Shaik Razia mail
link https://doi.org/10.54216/JCIM.130212

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new
International Journal of Neutrosophic Science | 2024

Neutrosophic ANFIS Machine Learning Model and Explainable AI Interpretation in Identification of Oral Cancer from Clinical Images

This paper introduces a new Neutrosophic Adaptive Neuro-Fuzzy Inference System paired with Explainable Artificial Intelligence to classify oral cancer from clinical photos. The ANFIS model’s interpretability and accuracy have been enhanced in resolving challenging medical images by deploying Neutrosophic logic on a 1000-image dataset to solve the word indeterminacy. A combination of Neutrosophic sets addresses ambiguity, enabling an adaptive neuro-fuzzy network to learn from data to accurately classify oral cancer. This exhibits the benefits of fuzzy logic and neural networks in action. The parameters of this model have been changed meticulously to increase sensitivity, specificity, and accuracy toward diagnostic readiness. These results reflect a substantive enhancement in the model’s ability to distinguish between benign and malignant lesions by delivering accurate and understandable diagnostic decisions existence for clinical adoption. AI medical diagnostic confidence increases the understanding of how the model makes decisions. The ideal objective is to develop a strong, dependable, and easy-to-understand tool to diagnose cancer early. The experimentation on this model can be improved as it may lead to real-time testing, more data for the testing dataset, and using how many types of cancer the model can be applied.

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Sakshi Taaresh Khanna mail -
Sunil Kumar Khatri mail -
Neeraj Kumar Sharma mail
link https://doi.org/10.54216/IJNS.240218

Volume & Issue

Vol. Volume 24 / Iss. Issue 2

Details open_in_new
International Journal of Neutrosophic Science | 2024

Applying Neutrosophic Chi-Square Test and Social Structures to Analyze Gender Parity

This paper examines the disparities in job opportunities and social prosperity based on gender within Peruvian universities, particularly focusing on the Universidad Peruana Los Andes during 2021-2022. Utilizing Neutrosophic Social Structures and the Neutrosophic 2-tuples Technique, we statistically analyze the entrenched biases that categorize careers by gender, contributing to power imbalances and unequal employment rates between men and women. By modeling student data through intervals or neutrosophic numbers as per Smarandache's theory, we address the unique engagement of each student with their academic environment. Neutrosophic contingency tables are employed to present this data, and a neutrosophic chi-square test is applied to examine the correlation between students' gender and their major fields of study, which include Administrative and Accounting Sciences, Health Sciences, Law and Political Sciences, Engineering Sciences, and Pedagogical Sciences. This neutrosophic approach allows for a nuanced understanding of the indeterminate and complex nature of social phenomena, providing a clearer insight into gender parity in academic professional development.

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Ketty M. Moscoso-Paucarchuco mail -
Michael R. Vásquez-Ramírez mail -
Percy T. Avila-Zanabria mail -
Kathy L. Javier-Palacios mail -
Paul C. Calderon-Fernandez mail
link https://doi.org/10.54216/IJNS.240219

Volume & Issue

Vol. Volume 24 / Iss. Issue 2

Details open_in_new
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

groups
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