The Integration of Symbolic 2-Plithogenic and Symbolic 3-Plithogenic Rational Functions

Jenan; wael mahmoud mohammad; Ahmad A.; Esraa; Ahmed Atallah; S.

doi:https://doi.org/10.54216/IJNS.270206

Journal of Cognitive Human-Computer Interaction | 2025

Artificial Intelligence in Healthcare: A Review

Artificial Intelligence (AI) is reshaping healthcare by transforming disease diagnosis, treatment planning, and preventive care. Its origins trace back to the 1970s with expert systems like MYCIN, which pioneered the integration of computational intelligence into clinical decision-making. Today, AI harnesses machine learning, natural language processing, and computer vision to process large-scale medical data, detect intricate patterns, and generate precise insights. This paper presents a detailed review of AI’s progression in healthcare, focusing on its foundational technologies, significant applications, and persistent challenges. Key aspects explored include AI’s contributions to medical imaging, drug development, robotic-assisted procedures, and patient care, emphasizing its role in improving accuracy and efficiency in healthcare services. Additionally, this review examines pressing concerns such as data security, ethical dilemmas, and biases in AI models, while discussing strategies to address these challenges. By analyzing current advancements and future possibilities, this study highlights AI’s expanding role in shaping healthcare innovations and enhancing global medical outcomes.

groups

Sneha K. mail -

Akifulla Kha mail -

Hanan Abdul Razack mail -

Ibaad Khan mail

link https://doi.org/10.54216/JCHCI.100103

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

Journal of Cognitive Human-Computer Interaction | 2025

Real-Time Student Identification and Data Retrieval System Powered By Haarcascade and OpenCV

Face recognition technology is increasingly integrated into daily life, from unlocking smartphones to taking attendance in classrooms, despite challenges like lighting, occlusion, and posture variety in real-world scenarios. Therefore, this study aims to develop an Automated Face Recognition System for Data Retrieval and Management using OpenCV. Using a camera, the system records users' photos in real time. Computer vision techniques are then applied, particularly the face identification and recognition functions of the Local Binary Pattern Histogram (LBPH) and the Haar Cascade algorithm, which are implemented using OpenCV. The system correctly recognizes people and makes it easier to handle student information by comparing the faces it detects with a database of photographs of students that has been stored. Improved face recognition accuracy, real-time data retrieval, and efficient data management procedures are the main goals. Although the system performed satisfactorily in normal lighting, difficulties with low light were shown to affect the accuracy of detection and recognition. The primary causes of these constraints were changes in the quality of the camera and lighting. Subsequent developments will concentrate on optimizing the accuracy and overall performance of the system, maybe by incorporating better cameras and more sophisticated processing. The study highlights how computer vision and facial recognition technology can revolutionize data management procedures in a variety of applications. In conclusion, the suggested system effectively makes use of cutting-edge methods for dependable and effective data retrieval.

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S. Hemamalini mail -

J. Beryl Sharon mail -

M. Dharshini mail -

M. Indu mail

link https://doi.org/10.54216/JCHCI.100104

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

Journal of Cognitive Human-Computer Interaction | 2025

Machine Learning Rehabilitation for Stroke Patients

This study explores the use of algorithmic for learning (ML) techniques in stroke rehabilitation to enhance patient outcomes and care. Machine learning offers potential uses in outcome prediction, progress tracking, customized treatment planning, and assessment. Algorithms based on machine learning (ML) can assist doctors with seriousness of stroke assessment, which is treatment plan customization, monitoring of progress, and long-term result prediction by leveraging a range of data sources, such as sensor data, doctor's notes, and medical images. Through personalized interventions and timely feedback, machine learning (ML) can optimize rehabilitation efforts and improve the standard of life for stroke patients. Interdisciplinary cooperation and ethical considerations are required to ensure the responsible and effective application of ML in physiotherapy after a stroke treatment. This study highlights the significant impact on the treatment of patients and their outcomes as it investigates the potential applications of algorithms for learning (ML) in recovery from stroke. These applications include result prediction, customized treatment planning, assessment methods, and progress monitoring. Through a convergence of current research findings and technological advancements, we illustrate how machine learning (ML) approaches can exploit many information modalities to assist professionals in providing tailored rehabilitation therapies and optimizing patient care. Despite the benefits that seem obvious, adoption needs to be fair and responsible. Problems like algorithmic bias, concerns about data privacy, and barriers to integrating clinical information need to be fixed.

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Ramesh Prabhakaran R. mail -

Angel Maanu P. mail -

Niranjan G. mail -

Karthika K. mail

link https://doi.org/10.54216/JCHCI.100105

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

Journal of Cognitive Human-Computer Interaction | 2025

Neural Engineering Informatics: A Review

Neuroengineering Informatics (NEI) is an interdisciplinary field combining neuroscience, engineering, data science, and informatics to understand and control neural systems. It leverages advanced technologies like brain-computer interfaces (BCIs), neuroimaging, and artificial intelligence (AI) to decode brain function and drive clinical breakthroughs. BCIs enable direct communication between the brain and devices, aiding individuals with neurological conditions, while neuroimaging methods such as fMRI, EEG, and MEG generate vast data used to uncover neural patterns linked to cognition, emotion, and disease. AI, particularly deep learning, enhances data analysis, enabling disease prediction, personalized treatment, and decision- making insights. NEI also employs neuroinformatics platforms for data sharing and collaboration, advancing innovations like adaptive neuroprosthetics and brain stimulation techniques such as TMS and DBS to treat conditions like epilepsy, Parkinson’s, and depression. Computational neuroscience contributes further by modeling brain functions to explore learning, memory, and decision-making mechanisms. Despite challenges like integrating diverse datasets and ethical concerns around privacy and fair ness, advancements in cloud computing and parallel processing are addressing these issues, accelerating discoveries while ensuring responsible innovation. NEI’s transformative applications ex tend beyond healthcare to rehabilitation, cognitive enhancement, and human-machine integration, reshaping our understanding and interaction with the brain.

groups

Naheem M. R. mail -

Adithya V. mail -

Dhanush H. S. mail -

Harsh Vishwakarma mail

link https://doi.org/10.54216/JCHCI.100106

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2026

Improved Correlation Coefficients of Fermatean Quadripartitioned Neutrosophic Sets for MADM

A correlation coefficient is a statistical measure, which contributes measure, whichhe degree to which changes in one variable predict changes in another. In this article, we analyze the characteristics of Fermatean Quadripartitioned Neutrosophic sets with improved correlation coefficients. We have also used the same approach in multiple attribute decision-making methodologies including one with a Fermatean Quadripartitioned Neutrosophic environment. Finally, we implemented for above technique to the problem of multiple attribute group decision making.

groups

S. Murali mail -

M. Ramya mail -

R. Radha mail

link https://doi.org/10.54216/IJNS.270201

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

Comprehensive Framework for Financial Transaction Fraud Detection via Dimensionality Reduction with an Explainable Artificial Intelligence Approach

The expanding growth of financial transactions has resulted in the development of fraud systems. These progressions have considerably improved overall productivity, improved corporate management, and reduced operational costs. With the expanded utilization of automated financial transaction, organization and businesses have progressed to digital platform, convert their financial operation. Still, such a change in addition revealed financial systems to new threats, mainly through fraudulent activity and cybercrime. The large datasets, incorporated with the limits of conventional fraud detection techniques, provide a chance to accept Artificial Intelligence (AI) methods. The fraud detection problem is addressed by using Explainable AI (XAI) to give specialists with explained AI predictions over different explanation models. This paper proposes a Financial Transaction Fraud Detection via Dimensionality Reduction with an Explainable Artificial Intelligence Approach (FTFD-DRXAIA) technique. The aim is to develop an effective and intelligent system for accurate fraud detection in financial transaction utilizing progressive deep learning (DL) methods. Initially, the min-max method is used for data pre-processing to convert raw data into an appropriate format. Furthermore, the recursive feature elimination (RFE) system is applied for feature selection. For financial fraud detection process, the Elman recurrent neural network (ERNN) has been utilized. Moreover, the wildebeest herd optimization (WHO) method fine-tunes the ERNN model's hyperparameters, resulting in improved classification performance. Finally, the XAI technique applies LIME and SHAP to interpret complex AI models, enabling auditors and analysts to detect suspicious transaction patterns with greater clarity and confidence. The experimental outcome of FTFD-DRXAIA system is examined under the financial fraud detection database. The comparison analysis of FTFD-DRXAIA algorithm demonstrated an optimum precision value of 98.96% over recent methods.

groups

Lyudmila Chernikova mail -

Svetlana Dreving mail -

Olga Borisova mail -

Tatiana Tazikhina mail

link https://doi.org/10.54216/IJNS.270202

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

Modeling Extreme Industrial Events under Indeterminacy Using Neutrosophic Fréchet Distribution

This work presents a neutrosophic extension of the Fréchet distribution to enhance the modeling of extreme values under conditions of indeterminacy and uncertainty. While the classical Fréchet distribution is widely used in fields such as finance, hydrology, and environmental sciences to model extreme maximum values, it does not fully accommodate imprecise, vague, or conflicting data commonly encountered in real-world scenarios. By incorporating the principles of neutrosophic logic the proposed neutrosophic Fréchet distribution provides a more flexible and realistic approach to representing extreme phenomena. The paper introduces its theoretical formulation, outlines key statistical properties, and proposes an estimation method based on maximum likelihood. Through simulations and numerical illustrations, the robustness and applicability of the model are described, especially in contexts where data is incomplete, uncertain, or contradictory. A real industrial dataset is employed to illustrate the applicability of the proposed model.

groups

Fuad S. Alduais mail -

Zahid Khan mail

link https://doi.org/10.54216/IJNS.270203

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

An Adaptive Intelligent Decision Support Framework for Business-to-Business Sales Estimation Using Generalized Q-rung Neutrosophic Soft Set

The neutrosophic set (NS) is a powerful tool for representing uncertain information in decision-making, extending conventional, fuzzy sets (FS), and intuitionistic fuzzy sets (IFS) by incorporating three degrees: truth, falsity, and indeterminacy. Sales prediction analysis wishes for intellectual data mining systems with precise predictive methods and higher trustworthiness. In the majority of cases, business depends heavily on information in addition to demand prediction of sales performance. The B2B data can offer information on how a business has to manage its products, sales team, and budget flows. Clear prediction techniques were analysed and examined using the model of machine learning (ML) to improve future sales predictions. It is challenging to manage sales prediction precision and big data (BD) when the technique of classic prediction is applied. Thus, the ML method can also be used to analyze the B2B sales reliability. This study proposes an Intelligent Business to Business Sales Estimation Framework Using Neutrosophic Soft Set (IB2BSEF-NSSS) method. The primary purpose of IB2BSEF- NSSS method is to develop an effective system for B2B sales estimation using advanced techniques for greater predictive precision. Initially, the min-max method is adopted in the data pre-processing phase to normalize input data. Additionally, the IB2BSEF-NSSS model leverages the zebra optimization algorithm (ZOA) technique for feature selection. Additionally, the generalized q-rung neutrosophic soft set (GqRNSSS) methodology is exploited for the sales prediction operation. To further increase prediction performance, the Kepler Optimizer Algorithm (KOA) model is employed for model fine-tuning, assuring optimum hyperparameter selection for upgraded accuracy. To expose the better performance of the IB2BSEF- NSSS technique, a wide-ranging experimental analysis is conducted under the B2B sales and customer insight analysis dataset. The comparison study of the IB2BSEF- NSSS technique exposed greater predictive performance, accomplishing the lowest MSE of 0.00670, indicating its efficacy over each other evaluated techniques.

groups

Ilyos Abdullayev mail -

Jamshid Pardaev mail -

Mansur Eshov mail -

Sanat Chuponov mail -

Elena Klochko mail

link https://doi.org/10.54216/IJNS.270204

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

Neutrosophic Extension of the Transmuted Lindley Distribution: Theory and Properties

In this paper, we introduce a new extension of the transmuted Lindley distribution (TLD) by utilizing neutrosophic logic to handle uncertainties that are often found in real life data. As classical probability models are not flexible enough for dealing with vague, imprecise, ambiguous, and incomplete information, neutrosophic theory is more general as it handles indeterminacy part associated with data. The proposed neutrosophic transmuted Lindey distribution (NTLD) combines indeterminacy concept, yielding a powerful statistical distribution, which is suitable for modeling both randomness and indeterminacy. Major functions such as probability density function (PDF), cumulative function (CDF), reliability function (RF) and hazard rate function (HRF) are established in this framework. Graphical analysis and simulated data are used to illustrate the performance of the model. Moreover, important moments such as mean, variance, skewness, and kurtosis are computed for different values of the neutrosophic parameters. The proposed distribution provides a generalized approach to model complex and uncertain data in reliability engineering, survival analysis, and decision-making. A real electricity consumption data from energy sector is utilized to show the proposed model applicability.

groups

Afrah Al Bossly mail

link https://doi.org/10.54216/IJNS.270205

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026