An Intelligent DGGO-Based Machine Learning Framework for Predicting Student Adaptability in Online Education

Osama Alabedallat

doi:https://doi.org/10.54216/JAIM.110107

International Journal of Neutrosophic Science | 2025

A Study on NCT-Filters in NCT-Topological Spaces

In this research, we introduce and develop new concepts in the field of Neutrosophic Topology (NCT). Particularly our study is focusing on the filter and its properties. Also, we present the properties of convergence of -filter, a specialized filter that incorporates neutrosophic values, providing a robust approach to handle uncertainty in topological spaces. Additionally, we explore the concept of adherent points in neutrosophic crisp triple topological spaces, offering a new perspective on the study of these spaces. Moreover, our findings contribute to expanding the understanding and application of neutrosophic theories in topology that will provide a solid foundation for future research in this area. Furthermore, this work opens new avenues for the study of topological spaces under uncertainty, with potential Applications in various domains, including data analysis, decision-making, and artificial intelligence, among others.

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Dheargham Ali Abdulsada mail -

Audy Hatim Saheb mail -

Rasheed Al-Salih mail -

Mohammed Hadi Lafta mail

link https://doi.org/10.54216/IJNS.260324

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2025

Computational Approaches for Nonlinear Fractional Differential Problems Utilizing Chebyshev Polynomial Approximations Space with Neutrosophic Applications

Applying Chebyshev polynomial approximate results, this paper applies the idea of neutrophilic logic to the approach to partially differential equations (FPDEs). Three elements make up the Neutrosophic technique: Indeterminacy (I), Falsehood (F), and Truth (T). These three elements are appropriate for issues where precise values or distinct limits are lacking since they are utilized to represent ambiguity, vagueness, and imperfect truth in mathematical models. We improve the depiction of real-world occurrences that could contain unclear or ambiguous information by adding these values to the coefficients of FPDEs. In domains like material science, mechanical engineering, and biological phenomena, where uncertainty is inevitable, the use of neutrophilic logic enables a more thorough and precise approximation of approaches to complicated fractional differential equations. The findings show that when working with systems that have unknown characteristics, the Neutrosophic technique increases the accuracy and dependability of computations.

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Abdulsalam Al-Dulaimi mail -

Amirah Azmi mail -

Yaseen S. R. mail

link https://doi.org/10.54216/IJNS.260325

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2025

On Class of Bi-univalent functions involving Neutrosophic 𝓆-Poisson distribution Series

This paper introduces and investigates a new class of bi-univalent functions constructed through the Neutrosophic 𝓆-Poisson distribution series. The study focuses on estimating the upper bounds of the basic coefficients |a_2 |and |a_3 |   in the Taylor series expansion of these functions.

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Banin Shaker Jubeir mail -

Mohammad El-Ityan mail -

Rafid Habib Buti mail -

Mohammed Hassan Hamza mail

link https://doi.org/10.54216/IJNS.260326

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new

Journal of Sustainable Development and Green Technology | 2025

Sustainable Economic Development and Principles of Green Economy of Uzbekistan in 2017–2024

The study examines Uzbekistan’s economic development and the implementation of green economy principles from 2017 to 2024. During this period, the country achieved notable progress in economic diversification, the adoption of renewable energy sources, and ensuring environmental sustainability. Nevertheless, challenges such as waste management and the efficient use of water resources remain pressing issues that require future attention and resolution.

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Khodjieva Dilrabo mail

link https://doi.org/10.54216/JSDGT.050103

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

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American Journal of Business and Operations Research | 2025

Data-Driven Pricing Decisions for Ensuring the Success of Strategic Product Development

By effectively including diffusion into the framework, this study further illustrates whether Optimal Control Theory may be used to identify and address the control of prices issue of technology items. There is a three-stage paradigm that it uses to describe the procedure of adoption process: consciousness, inspiration, and adopting itself. The process is described by the diffusion logistic functions; furthermore, the model takes into account price fluctuations’ sensitivity. The selling price is a choice variable constrained such that the total profit over the relevant planning horizon is optimised. In this paper, the Hamiltonian function is used in obtaining necessary optimality conditions with spectral learning effects and Costate equations complemented by the adoption rates by use of Pontryagin’s Maximum Principle. As the problem is formulated as the continuous optimization problem, it is discretized for its practical applications, and the model is solved with the help of LINGO 15.0 software. The data used to validate the model implemented was derived from historical sales records of the electronics and semiconductor industries to obtain a measure of realism. Analysed sensitivity studies show how variations in adoption parameters including the price elasticity and customer attrition affect adoption rates and profitability. As such, the study offers managerial implications for the management of private sector schemes to focus on the application of dynamic pricing strategies as the optimal balance between consumers’ perceived value and firm revenues. It provides managers with strong tools for the implementation of adoption into a new generation of technology-enabled markets, maximization of revenues, and sustaining of competitive advantage. Outperforming all analysed models, the suggested technique employing Optimal Control Theory obtains an accuracy of 96%. This proves that the suggested strategy is the best at forecasting when a product will be adopted.

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Ramazan Yasar mail -

Sergey Drominko mail

link https://doi.org/10.54216/AJBOR.120205

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new

Journal of Cognitive Human-Computer Interaction | 2025

An Intelligent Framework for Flavor Recommendation and Cost Optimization in Hybrid Cloud Autoscaling

This research presents a flavor recommendation framework that intends to be used in hybrid clouds to address resource provisioning and cost issues. A cloud “flavor” is an instance type that assigns values for CPU, memory, storage, and networking. Today the flavor selection process is manual, and no dynamic technique is used, therefore, the process is inefficient because some flavors are underutilized. The proposed framework also provides flavor recommendations for autopiloted dynamic capacity provisioning using predictor analysis of workload and cost proportional to different CSPs. It uses an RNN_LSTM-based Proactive Predictive Engine (PPE) to quantitatively estimate future resource requirements and a Recommendation Engine consisting of the scoring and flavor engines. This framework receives the application’s actual and predicted consumption of CPU and memory, cost fluctuations, and CSPs’ options and then the selection of various flavors is performed in the runtime. Metrics are gathered, stored, and analyzed in real-time through Telegraf, InfluxDB, and Apache Libcloud for current resource allocation. Experimental results of the system on AWS and OpenStack show the benefit of using the proposed framework, which reduced the number of EBS and VMs by 19% and the cost saving by up to 17% compared with traditional and reactive approaches. This solution turns static resource allocation into a real-time predictive accuracy of how resources are best utilized as well as the expense at the hybrid cloud environment.

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Agnes Osagie mail -

Sandra Terazic mail -

Barbara Charchekhandra mail

link https://doi.org/10.54216/JCHCI.090207

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

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Galoitica: Journal of Mathematical Structures and Applications | 2025

The Characterization of 4-Cyclic Refined Vector Spaces

This paper is dedicated to study 4-cyclic refined vector spaces, where we classify these spaces by using semi-module isomorphisms as direct product of classical complex vector spaces. In addition, we study the inner products defined over these structures and we present sufficient conditions for 4-cyclic refined orthogonality.

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Mohammad Abobala mail -

Hasan Sankari mail

link https://doi.org/10.54216/GJMSA.0120101

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2025

Turiyam Set Based Four Way Mathematical Characterization of Retracted Paper

Recent time retraction analysis is considered as one of the major issues. The retraction is a regular process where some time it is true, some time happen due to conflict, some time due to indescribable parameters or last via self retraction by authors or Editor. It is happening due to pressure on academia and its quality measurement by quantitative publications and citation rather than qualitative. It forces researchers to add the co-authors for increment, promotion, or citation rather than focusing on true research. Some time the retraction happens due to self correction, genuine mistake or conflict of interest with editor. These types of genuine or self retraction happended using Turiyam awareness of authors or editor which can be considered as positive retraction. It is also noted that negative results are also part of the research as equal to positive or noble research. It is difficult to characterize these types of retraction. In this paper author has introduced a mathematical model for precise analysis of retracted paper and its characterization in true (t), false(f), indeterminant (i) and liberal (l) region for intellectual measurement. Same time the extension of work for undefined or unknown paramerters of retraction or unretraction is also discussed using the complement of Turiyam operator with an example.

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Prem Kumar Singh mail

link https://doi.org/10.54216/GJMSA.0120102

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

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Journal of Artificial Intelligence and Metaheuristics | 2026

An Enhanced LSTM Framework for Solar Radiation Forecasting Using Hybrid Grey Wolf–Waterwheel Plant Optimization

Accurate solar radiation prediction is critical for optimizing renewable energy utilization, yet traditional machinelearning models struggle with suboptimal forecasting accuracy. To address this challenge, we propose a novel hybrid optimization algorithm, the Grey Wolf and Water Whale Plant Optimizer (GWWWPA), to enhance Long Short-Term Memory (LSTM) networks for improved solar radiation forecasting. The proposed method leverages the exploration-exploitation synergy of the Grey Wolf Optimizer (GWO) and Water Whale Plant Algorithm (WWPA) to optimize LSTM hyperparameters effectively. Experimental results on the NASA Space Apps Moscow dataset demonstrate that GWWWPA-LSTM outperforms existing optimization techniques, achieving the lowest Mean Squared Error (MSE) of 0.00019392 and the highest R2 score of 0.917262, surpassing standalone GWO, WWPA, PSO, and WOA. These findings highlight the potential of hybrid metaheuristic approaches in enhancing predictive accuracy, facilitating more reliable solar energy forecasting, and supporting sustainable energy management systems.

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Alaa Mohamed Abdel-Moati mail -

Abdullah Muhammad Ibrahim mail

link https://doi.org/10.54216/JAIM.110106

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2026

An Intelligent DGGO-Based Machine Learning Framework for Predicting Student Adaptability in Online Education

The recent shift to online education worldwide has highlighted the urgent need to evaluate and improve students’ flexibility in the online learning environment. The paper solves that problem by creating a predictive framework that enables measuring students’ adaptability using modern techniques for advanced perception of adaptability and advanced machine learning (ML) and metaheuristic optimization methods. The research proposes an improved variant of geese’s cooperative flight behavior, called the Dynamic Greylag Goose Optimization (DGGO) algorithm, to optimize the hyperparameters of a base model, a Decision Tree (DT). A comparison of the baseline DT showed an accuracy of 93.81% and a sensitivity (True Positive Rate, TPR) of 93.14%. The DGGO optimization further improved the DT model’s performance to 97.15% accuracy and 96.86% sensitivity, with improved convergence and tuning. To be flexible to changes, the optimized model reduces classification errors and improves predictive consistency. The findings demonstrate the effectiveness of metaheuristic-based optimization for educational data mining and provide preliminary evidence that DGGO can serve as a robust, scalable, and intelligent framework to enhance predictive analytics and decision-support capabilities in the modern e-learning environment.

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Osama Alabedallat mail

link https://doi.org/10.54216/JAIM.110107

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new