Neutrosophic regular and normal subspaces in Neutrosophic Topological Spaces

Raed; Wadei; Mohammad Ali Ahmad

doi:https://doi.org/10.54216/IJNS.260406

Fusion: Practice and Applications | 2025

Personalized Cognitive Behavioral Therapy for Adults Using Machine Learning: A Multi-Factor, Reinforcement-Based Approach

This paper presents a novel machine-learning framework designed to personalize Cognitive Behavioral Therapy (CBT) for adult patients by leveraging a multi-dimensional, adaptive approach. The proposed system integrates historical clinical data, real-time behavioral indicators, and contextual factors to generate a comprehensive psychological profile for each adult patient. A reinforcement learning mechanism underpins therapy selection, allowing the model to iteratively refine treatment strategies based on individual responses and therapeutic outcomes. An embedded optimization process enables dynamic adaptation of interventions, improving predictive accuracy and fostering patient-centered care. The framework incorporates a multi-factor assessment model that synthesizes psychological, behavioral, and physiological variables to enhance therapeutic effectiveness, sustainability, and responsiveness to change. Comparative evaluations demonstrate that this approach outperforms traditional CBT planning methods, as well as existing deep learning, hybrid, and reinforcement-based models, in terms of accuracy, interpretability, computational efficiency, and patient outcome optimization for adults. Furthermore, the system emphasizes fairness and equity in treatment personalization, supporting real-time clinical decision-making while minimizing ineffective therapeutic pathways. This research underscores the transformative potential of machine learning in mental health care by enabling scalable, data-driven, and continuously improving interventions tailored to the nuanced needs of adult patients undergoing CBT.

groups

Mohammed Awad Alasmrai mail -

Ramadan Mohamed Ismail mail -

Mohammed Hasan Ali Al-Abyadh mail

link https://doi.org/10.54216/FPA.200205

Volume & Issue

Vol. Volume 20 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Machine Learning for Free Space Optical Communication: A Systematic Review with Emphasis on NOMA and Massive MIMO Integration

Advancements in high-speed communication networks, such as 5G and 6G, display the shortcomings of earlier Radio Frequency (RF) systems due to their limited access to the electromagnetic spectrum. Optical Wireless Communication (OWC) gives access to an unlimited optical spectrum that can address the demands in 6G networks. One key thing about Free Space Optical (FSO) is that it uses the near-infrared spectrum to transfer large amounts of data over several kilometers. FSO systems can be found in a large number of places, ranging from home and outdoor use to important roles in the military and in medical settings. These systems, however, struggle to transmit signals clearly and reliably when the distance is very long due to effects of the atmosphere. One solution to these problems is to rely on advanced channel modeling and using Multiple-Input Multiple-Output (MIMO) schemes, as they improve reliability and efficiency. The latest research efforts are centered on Massive MIMO-FSO networks that make use of spatial diversity to fight atmospheric fading and guarantee a sturdier connection. Importantly, Machine Learning (ML) is transforming the way research is carried out. Channel estimation, turbulence prediction, signal demodulation, and adaptive modulation can now be done using ML, which reduces the need for many calculations and makes things run more smoothly. Using information from data, ML helps optimize FSO systems in different channel conditions. This study provides a review of how machine learning is applied in Massive MIMO-FSO systems. It sorts out highlighting current strategies, explaining their strengths, weaknesses, and how to use them. The main goal of this review is to give an in-depth look at how ML-assisted optical wireless systems can fulfill the needs of future communication networks.

groups

Hasan Farooq Radeef mail -

Lwaa F. Abdulameer mail -

Heba M. Fadhil mail

link https://doi.org/10.54216/FPA.200206

Volume & Issue

Vol. Volume 20 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

A Two-Stage System for Surveillance Video Summarization and Unsupervised Abnormal Event Detection in Educational Institutions

Surveillance cameras play a pivotal role in educational institutions. They monitor the educational process, detect violations, and protect students from potential injuries or dangers. Continuous recording generates a massive amount of video data. Human observers spend significant time and effort reviewing the footage. Reviewing aims to detect and quickly address abnormal events. Abnormal events are rare in educational environments. Observers may become bored during continuous monitoring. This may cause fatigue and loss of attention. To overcome these challenges, this paper proposes an intelligent system that combines summarization and abnormal event detection in surveillance video. It is divided into two stages: The first stage starts with the extraction of static, feature-based key frames that highlight the video's most significant content. In the second stage, Convolutional Autoencoder (CAE) network used to detect abnormal events from the key frames generated by the summary stage. The proposed system produces two separate videos: a general summary and a dedicated abnormal events video sent to the relevant individuals. The proposed system was tested on some benchmark datasets. The experimental results demonstrated that the proposed system was effective in reducing browsing time and effort, as well as in detecting abnormal events within an educational context.

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M. E. ElAlmi mail -

M. M. Lotfy mail -

M. M. Ghoniem mail

link https://doi.org/10.54216/FPA.200207

Volume & Issue

Vol. Volume 20 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Generative AI Fusion in Digital Learning: Educators Insights in Revolutionising Modern Education

The advancement and expansion of artificial intelligence (AI) has revolutionized traditional education paradigms. The ability of language models to process human language has revolutionized the field of artificial intelligence. This had led to the integration of language models such as Generative AI (GAI) into learning as it can understand and process human language efficiently. Fusion of these models has significantly enhanced education and research development leading to academic progress. There is gap in the learning needs of the students. Traditional teaching methods often fail to provide personalised adaptive environments and hence to fill this gap this research focusses on integration of AI tools in classrooms.  The objective of this paper is to explore and analyze the applications of integration of generative AI strategies in teaching and to examine the impact from educators’ perspective. The objective of the study is to evaluate the effectiveness of GAI powered integration in teaching and learning by analyzing the feedback scores gathered by students and teachers of an undergraduate course. Data was collected and analyzed using standard mean comparisons. Results of the analysis demonstrate that generative AI assisted teaching facilitated adaptive learning, automated content generation, enhanced student engagement and the quality of dynamic learning when compared with conventional strategies. Using quantitative analysis, the study validates GAI fusion, and the data is analyzed using standard mean scores.  The improvement performance of students and educators feedback for traditional and GAI is 56.63% and 54.41% respectively, which suggests a positive shift of moving from traditional to GAI approaches. This strong score shows the GAI approach is more effective and student-centered. The results reveal that though challenges exist, strategic guided integration of GAI significantly enhances pedagogical factors of education and thus plays a crucial role in shaping AI education as AI models evolve.

groups

Moosa Ahmed Hassan Bait Ali Sulaiman mail -

Anita Venugopal mail

link https://doi.org/10.54216/FPA.200208

Volume & Issue

Vol. Volume 20 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2025

A Study of Some Neutrosophic Derivatives Problems Based On Newton's BDF and CDF Numerical Methods

This paper is dedicated to study for the first time the applications of neutrosophic BDF and CDF Newton's methods for finding the numerical solutions of some different problems related to the derivations from first and second order applied on neutrosophic-tabulated functions, where we apply those novel methods on some problems and list the solutions by using the numerical tables. In addition, we provide a theoretical discussion and description of these methods to be applicable on other numerical problems.

groups

Ahmad A. Abubaker mail -

Mayada Abualhomos mail -

Ahmed Atallah Alsaraireh mail -

Abdallah Al-Husban mail

link https://doi.org/10.54216/IJNS.260401

Volume & Issue

Vol. Volume 26 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2025

Asymptotically Stability Concept for Perturbed Bilinear Time Varying Controlled Differential-algebraic Systems and Applications under Neutrosophic Environment

Starting from semi-explicit perturbed bilinear time varying neutrosophic differential – algebraic equations (PBTVDAs). We develop a method for the stabilization of this controlled bilinear time varying neutrosophic differential – algebraic equations and prove that the controlled perturbed system can be stabilized by putting specific conditions on the proposed control. This method transfers the system to standard canonical form and uses the exponential stability concept. Therefore, the stabilization of this system is achieved finally; we present numerical results for the battery model, which confirm the theoretical results.

groups

Ghazwa F. Abd mail

link https://doi.org/10.54216/IJNS.260402

Volume & Issue

Vol. Volume 26 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2025

Jordan Endo Bi-AntiDerivation of 2-Torison Free Rings and Neutrosophic Rings

Let  be the direct product of an associative ring . In the work the concepts of Endo Bi-Antiderivation, Jordan Endo Bi-Antiderivation and Quasi Endo Bi-Antiderivation on a ring  are introduced, furthermore the relations between these bi-additive mappings are given. As essential point, we searched for appropriate conditions that make equivalence between Jordan Endo Bi-Antiderivation and Quasi Endo Bi-Antiderivation. Also, we prove the same results for the generalized case of neutrosophic rings.

groups

Ali Ibrahim Mansour mail -

Amal A. Ibrahim mail -

Auday Hekmat Mahmood mail

link https://doi.org/10.54216/IJNS.260403

Volume & Issue

Vol. Volume 26 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2025

Machine Learning and Linguistic Neutrosophic Hypersoft based Techniques Integration in Smart Farming in the Context of Weather Uncertainty

This paper proposes a novel smart farming decision-making framework that integrates machine learning (ML) techniques Support Vector Machine (SVM), Fuzzy C-Means (FCM) clustering, with the generalized distance and similarity measures in a linguistic neutrosophic hypersoft set environment. ML processes real-time sensor data to predict weather patterns, while linguistic neutrosophic terms capture uncertainty, indeterminacy, and falsity, allowing for a more precise analysis of imprecise information. Through the application of generalized similarity measures, the framework ranks the cities suitable for farming strategies based on multiple criteria such as temperature, wind speed, and humidity. The use of linguistic neutrosophic terms offer enhanced flexibility in managing weather-related uncertainty compared to existing methods. The outcomes demonstrate that this integrated approach optimizes decision-making under uncertain environmental conditions, enabling more efficient resource management and improving resilience in farming practices. Future research will further explore the inclusion of additional environmental factors and improve similarity measures to increase decision accuracy among broader agricultural contexts. This model also has the potential to be applied to other domains where uncertainty management is crucial, such as climate resilience and environmental sustainability.

groups

Muhammad Saqlain mail -

Poom Kumam mail -

Wiyada Kumam mail

link https://doi.org/10.54216/IJNS.260404

Volume & Issue

Vol. Volume 26 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2025

A Neutrosophic Interpretation of Data Cube Sparsity for Improved Machine Learning Preprocessing

Multidimensional data cubes are essential components in data warehouses, enabling rich, OLAP-based analysis across dimensions such as time, location, and product category. However, the complexity that supports such analytical flexibility often leads to extreme sparsity—where the majority of cube cells remain empty or only partially filled. This sparsity can hinder the performance of downstream machine learning models, especially when valuable but infrequent patterns are lost during preprocessing. This paper introduces a neutrosophic-based framework for evaluating and managing sparse regions within OLAP cubes. Instead of treating all sparsity as noise, we propose a typology that distinguishes between three forms: semantic sparsity (expected and justifiable absences), non-informative sparsity (regions with little analytical value), and informative sparsity (sparse areas that still carry meaningful insights). Each substructure is modeled using neutrosophic logic, which assigns degrees of truth, indeterminacy, and falsity to reflect its analytical potential. A dedicated Neutrosophic Evaluation Algorithm is developed to classify each region using metrics such as semantic confidence, entropy, and a context-aware informativeness score. These metrics allow for nuanced decisions: preserving informative sparsity, eliminating irrelevant regions, and flagging ambiguous areas for further review. This approach shows how neutrosophic logic can offer a novel and effective way to handle sparsity in OLAP cubes, improving the relevance and robustness of machine learning pipelines trained on multidimensional data.

groups

Wiem Abdelbaki mail

link https://doi.org/10.54216/IJNS.260405

Volume & Issue

Vol. Volume 26 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2025

Neutrosophic regular and normal subspaces in Neutrosophic Topological Spaces

The main purpose of this paper is to define the notion of neutrosophic based normal and regular spaces. This study investigates and open new class and conception of generalization of classical regular and normal spaces. The hereditary and topological properties of neutrosophic based normal and regular spaces have been analyzed and investigated. It also examines neutrosophic topological subspaces, providing insights into their characteristics. Furthermore, the paper investigates neutrosophic regular spaces and demonstrates their hereditary nature, specifically focusing on R_1, R_2, R_3 and R_4. Additionally, we explain some example of a neutrosophic-regular based space X which is a neutrosophic based normal-space but it is not necessary to neutrosophic -〖T〗_1 spaces. Eventually, it is shown that under certain conditions that the images are preserved in neutrosophic based normal and regular spaces.

groups

Raed Hatamleh mail -

Wadei Al-Omeri mail -

Mohammad Ali Ahmad Al-Qudah mail

link https://doi.org/10.54216/IJNS.260406

Volume & Issue

Vol. Volume 26 / Iss. Issue 4

Details open_in_new