Financial Sector-Ready Framework for USD–PKR Exchange Rate Forecasting Using Ninja Optimization

El-Sayed M. El-Kenawy

doi:https://doi.org/10.54216/AJBOR.130103

International Journal of Neutrosophic Science | 2026

An Intelligent Semantic Orientation Identification Framework on Economic Text Using Q-Neutrosophic Soft Matrix under Interval-Valued for Financial Sentiment Analysis

Neutrosophic Logic is a neonate field of research in which all propositions are considered to have the percentage of truth in a subset I, F, and T. Neutrosophic set (NS) has been positively utilized for indeterminate data processing, and proven benefits for addressing the indeterminacy data information and is still a method nominated for classification application and data analysis. Soft set (SS) is a powerful device for handling the uncertainty of information in a parametric situation. On the other hand, the concept of interval-valued neutrosophic soft sets (IVNSSs) is a novel generality of the neutrosophic soft sets (NSSs) to the NSs once the authors incorporate the important features of IVNS and soft sets (SSs) in one method. Therefore, this method operated to offer decision-makers with flexibility in the procedure of understanding unclear information. From the scientific viewpoint, the procedure of estimating this higher performance IVNSS vanishes. Q-neutrosophic SSs are fundamentally NSSs considered by 3 independent 2D membership functions that represents indeterminacy, falsity and uncertainty. Therefore, it is used to 2D inconsistent, imprecise and indeterminate data, which seem in most real world challenges.  The usage of robo-readers for analyzing news texts is the advanced technology trend in financial technology. A considerable effort has been invested to develop refined financial orientation that is applied to inspect how financial sentiments related to future performance of the company. Recently, the financial sentiment analysis (SA) has become a more and more related subfield within text analytics that addresses the computational analysis of subjectivity and opinion in texts. Most of the methods have concentrated on particular fields, utilizing type-based corpora as training data for machine learning (ML) methods that classify the input text as both negative and positive. In this manuscript, we develop a Semantic Orientation Identification Framework in Economic Text Using Q-neutrosophic soft matrix under Interval-valued (SOIFET-IVQNSM) model for financial SA. The aim of the paper is to propose an innovative approach for identifying semantic orientation in economic texts to enhance financial sentiment and prediction accuracy. Primarily, the input text data is preprocessed utilizing diverse preprocessing levels like removal of stop words, tokenization, stemming, spelling correction, and lemmatization to make it suitable for further processing. Besides, the word embedding process is mainly executed by the term frequency-inverse document frequency (TF-IDF) model to transform economic text into meaningful vector representation. For classification purpose, the proposed SOIFET-IVQNSM model designs a Q-neutrosophic soft matrix under Interval-valued (IV-Q-NSM) model. The simulation validation of the SOIFET-IVQNSM algorithm is tested on a benchmark database, and the results are measured under several metrics. The simulation result highlighted the improvement of the SOIFET-IVQNSM system in semantic orientation identification.

groups

Zokir Mamadiyarov mail -

Ziyodulla Khakimov mail -

Dilmurad Bekjanov mail -

Hafis Hajiyev mail -

Natalia Falina mail

link https://doi.org/10.54216/IJNS.270230

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

A Predictive Analytics for Customer Lifetime Value Estimation in Digital Banking using Interval-Valued Neutrosophic Set with Fine Tuning Approach

As a generality of fuzzy sets (FS) and intuitionistic FS (IFS), neutrosophic sets (NS) was progressed by F. Smarandache for signifying incomplete, inaccurate, and uneven data present in the real world. Neutrosophic Logic (NL) is a neonate research field in which every proposition was projected to have the proportion of truth in a sub-set T, I, and F. Neutrosophic sets (NS) have been well employed for indeterminate information handling, and determine benefits to tackle indeterminate data. A NS is categorized by indeterminacy-, truth-, and a falsity- membership functions. Atanassov as a major simplification of FS presented the notion of IFS. IFS are very beneficial in conditions when problem description by linguistic variables, assumed with only a membership function, appears to be difficult. In recent times, IFS have been employed to numerous areas like medical diagnosis, logic programming, decision-making issues, etc. An interval NS (INS) is an example of NS, which is employed in real engineering and scientific applications. Owing to the competition in the banking industry and the importance, access to customer information is vital to establish a successful relationship that benefits both parties. Representing longer-term customer relationships and building brand equity are essential in modern banking, and therefore increasing relationship quality plays a significant part in the development of new services and customer lifetime value (CLV) approximation.  CLV is an estimated profit that can be achieved by the organization from a customer for some time. Presently, the development of Machine Learning (ML) methods has resulted in better precision and effectiveness. Therefore, by utilizing ML methods of real-time customer data, predictions of a more precise future value of the customer are gained by businesses, which helps in establishing a more personal marketing approach. In this manuscript, we propose a Customer Lifetime Value Estimation using Interval-Valued Neutrosophic Set and Parameter Optimization Algorithms (CLVE-IVNSPOA). The foremost main of this paper is to progress a predictive analytics model for estimating customer lifetime value in digital banking utilizing advanced optimization methods. Initially, the data pre-processing phase was employed by using the Z-score method. Moreover, the pelican optimization algorithm (POA) is mainly executed by the feature subset selection in order to select the most optimal features from a dataset. For CLV prediction, the Interval-Valued Neutrosophic Set (IVNS) technique is exploited. At last, the model parameter adjustment process is performed through improved shark optimization (ISHO) algorithm for improving the prediction performance. The experimental evaluation of the CLVE-IVNSPOA occurs using benchmark database. The experimental outcomes indicated out an improved performance of CLVE-IVNSPOA compared to existing systems.

groups

Alisher Sherov mail -

Ziyodulla Khakimov mail -

Yurii Vorobev mail -

Emil Hajiyev mail -

Tatyana Khorolskaya mail

link https://doi.org/10.54216/IJNS.270118

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2025

Graded HyperRough Set and Linguistic HyperRough Set

Numerous mathematical frameworks have been developed to handle uncertainty, including Fuzzy Sets,1 Intuitionistic Fuzzy Sets,2 Hyperfuzzy Sets,3 Picture Fuzzy Sets,4 Hesitant Fuzzy Sets,5, 6 Neutrosophic Sets,7 Plithogenic Sets,8 and Soft Sets,9 and research in this area continues to evolve rapidly. Rough set theory provides a foundational method for approximating subsets using lower and upper bounds based on equivalence relations, offering an effective approach to modeling uncertainty in classification and data analysis.10, 11 Building upon these foundations, extended models such as HyperRough Sets and SuperHyperRough Sets have been proposed.12 In this paper, we present novel definitions that further generalize Graded Rough Sets and Linguistic Rough Sets—specifically, the Graded HyperRough Set and the Linguistic HyperRough Set. These new frameworks are expected to contribute to the advancement of research in fields such as decision-making, language theory, and artificial intelligence.

groups

Takaaki Fujita mail -

Arif Mehmood mail

link https://doi.org/10.54216/GJMSA.120201

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2026

Digital Forensic Investigation of an iOS Mobile Phone Using iTunes and iCloud Backup

The growing popularity of iOS devices and the increasing complexities of forensic investigation of these devices requires more research attention. Due to the complex encryption and closed nature of iPhones, it is inherently complicated to perform digital forensic investigations. While there are many extraction and analysis methods for iphone, the most comprehensive (but most complex) is the full physical acquisition. However, the likelihood of acquiring physical extraction of an iPhone is becoming more challenging as Apple improves on its mobile technology, with more emphasis on privacy and security. Factors such as the adoption of full file and disk encryption, and secure enclave technology poses serious challenge to forensic investigators. This paper explored alternatives, by extracting and analyzing valuable evidential artifacts using iTunes and iCloud, unique to the iOS environment. This research involved the forensic examination of an iPhone XR running on iOS 17.5, using Oxygen Forensic Device Extractor v2.13.1, with each step documented. The study uncovered several artifact locations and provided a brief description of each, and their usefulness in a forensics analysis. Some of these include user-generated content, system artifacts, application data, and cloud interactions, such as contacts, SMS data, call history, media files, database, browser data, application data and others, that could prove vital in solving a case. This study made valuable contribution to the body of knowledge by highlighting specific challenges faced in iOS forensics and recommending a methodical approach to examining and analyzing evidential artifacts using iTunes and iCloud. The paper also addressed the gap in available literature in iOS forensics.

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Robinson Tombari Sibe mail -

Adewale Alayegun mail

link https://doi.org/10.54216/JCIM.170212

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2026

An Automated Detection and Classification of Retinopathy of Prematurity Stages Using SWIN Transformer

Retinopathy of prematurity (ROP) remains the leading cause of blindness in children. The detection and treatment of this disease mainly depend on subjective evaluation of the features of retinal blood vessels. This method is not only time-consuming but also prone to errors. The increasing number of such cases demands an urgent need for automated models to improve the accuracy and efficiency of diagnosis and treatment. This paper presents a method for early detection of ROP using the Swin Transformer, a hierarchical vision transformer architecture. This work focuses solely on the screening stages for ROP, as documented between 2015 and 2020, based on a dataset composed of 3720 retinal images from preterm infants, kindly made available by the Al-Amal Eye Center located in Baghdad, Iraq. The proposed model achieved a classification accuracy of 98.67% on a clinical ROP dataset. The results highlight the importance of the most recent in-depth learning methods in enhancing early detection techniques, ultimately leading to improved clinical outcomes for at-risk infants.

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Nazar Salih Absulhussein mail -

Bashar I. Hameed mail -

Humam K. Yaseen mail -

Nebras H. Ghaeb mail -

Mohamed Ksantini mail

link https://doi.org/10.54216/FPA.210215

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

Enforcement of q-Rung Orthopair Fuzzy Subsets to Q-Ideals

This paper presents an innovative generalization of intuitionistic fuzzy Q-subalgebras (IF-Q-S) by incorporating the structure of q-Rung Orthopair fuzzy sets (q-ROFS), which are distinguished by their independen membership and non-membership functions. It inserts and investigates q-Rung Orthopair fuzzy Q-subalgebras (q-ROFQ-S), demonstrating that this model is equivalent to a combination of a fuzzy Q-subalgebra (F-Q-S) and an anti-fuzzy Q-subalgebra (AF-Q-S). The study’s notable contributions include the definition of the nil radical and an exploration of its properties under homomorphisms. Additionally, it establishes that the union of q-ROFQ-subalgebras can itself form such a subalgebra under particular commutative conditions. Expanding the concept to the realm of ideals, the paper defines q-Rung Orthopair fuzzy Q-ideals (q-ROFQ-I) and proves that every q-regular q-ROFQ-S is inherently a q-ROFQ-I. This work offers a robust and versatile algebraic framework for addressing approximation in complex nonlinear systems.

groups

Mohammad Hamidi mail -

Sirous Jahanpanah mail -

Florentin Smarandache mail

link https://doi.org/10.54216/IJNS.270231

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2026

Age and Language Learning a Comparative Study of Young and Adult Learners with Data Fusion Perspectives

This study investigates the influence of age on second language acquisition by comparing language learning outcomes between young learners (aged 8–12) and adult learners (aged 25–40). Drawing on both cognitive and sociolinguistic perspectives, and leveraging data fusion techniques that integrate test results, classroom observations, and learner interviews, the research examines differences in pronunciation, grammar acquisition, vocabulary retention, and communicative competence. The fusion of multiple data modalities ensures a more holistic view of learner performance. Findings indicate that young learners exhibit greater native-like pronunciation and long-term retention, while adult learners outperform in grammatical accuracy and metalinguistic awareness. Motivational factors and learning environments also played significant roles. The study concludes that while age affects specific aspects of language learning, no age group holds a universal advantage. Data fusion-based insights highlight the need for age-sensitive instructional strategies that cater to the cognitive and emotional needs of learners at different stages.

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Shahab Ahmad Al Maaytah mail

link https://doi.org/10.54216/FPA.210214

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new

American Journal of Business and Operations Research | 2025

Boosting Financial and Strategic Forecasting of Sustainable Development Goals with Human-Inspired Metaheuristic Optimization and GRU-Based Deep Learning

Achieving the United Nations Sustainable Development Goals (SDGs) requires robust forecasting tools capable of capturing complex temporal and multi-dimensional patterns in global sustainability data. Traditional statistical models often struggle with the high dimensionality and nonlinear dynamics of such datasets, motivating the adoption of advanced Deep Learning (DL) methods combined with metaheuristic optimization techniques. This paper proposes a novel forecasting framework leveraging Gated Recurrent Units (GRUs), Long Short-Term Memory networks (LSTMs), Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs), optimized using the Human-Inspired Metaheuristic Optimization Algorithm (iHOW) and its binary variant (biHOW) for feature selection. The key contribution lies in integrating metaheuristic-driven feature selection and hyperparameter tuning to significantly enhance predictive performance and computational efficiency in SDG forecasting. Results highlight substantial improvements over baseline models: the GRU baseline achieved an R2 of 0.8037 with a Mean Squared Error (MSE) of 0.0772; application of biHOW for feature selection improved the GRU’s performance to an R2 of 0.9251 and MSE of 0.0011; and further hyperparameter tuning with iHOW elevated performance to an R2 of 0.9671 with MSE maintained at 0.0011. These results demonstrate the effectiveness of iHOW in balancing exploration and exploitation, providing high-accuracy forecasts with reduced error, thereby supporting more informed decision-making. The implications extend beyond sustainability analytics, presenting transferable forecasting frameworks for data-driven, real-time decision support in business sectors such as finance, energy, healthcare, and climate risk management. This alignment of predictive analytics with strategic financial and operational planning underscores the commercial value of integrating AI-driven forecasting into sustainability-focused investment and policy frameworks.

groups

Doaa Sami Khafaga mail

link https://doi.org/10.54216/AJBOR.130101

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

Details open_in_new

American Journal of Business and Operations Research | 2025

Financial Sector-Ready Framework for Corporate Performance Forecasting Using Football Optimization

In today’s interconnected global economy, accurate financial forecasting is critical for strengthening corporate decision-making, mitigating investment risks, and maintaining competitive advantage over the long term. Traditional forecasting models often struggle with the complexities of high-dimensional and nonlinear financial data. To address this challenge, we present a hybrid forecasting framework that integrates advanced machine learning techniques with an intelligent optimization algorithm. Specifically, the model combines Long Short- Term Memory (LSTM) networks with the Football Optimization Algorithm (FbOA) to optimize key features and tuning parameters. This approach yields more stable, efficient, and accurate financial predictions using a compact set of influential variables. The proposed framework offers a cost-effective solution for corporate finance applications, enhancing investor confidence and supporting strategic economic development. By bridging cutting-edge AI methodologies and practical financial analytics, this study highlights the transformative potential of hybrid models in reshaping financial forecasting in dynamic markets.

groups

Marwa M. Eid mail -

Asifa Iqbal mail -

Shahid Mahmood mail -

S. K. Towfek mail

link https://doi.org/10.54216/AJBOR.130102

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

Details open_in_new

American Journal of Business and Operations Research | 2025

Financial Sector-Ready Framework for USD–PKR Exchange Rate Forecasting Using Ninja Optimization

Accurate exchange rate prediction is a critical challenge in financial forecasting, as fluctuations in exchange rates directly impact trade balances, investment strategies, and monetary policy decisions. Motivated by the need for robust and precise forecasting models, this study presents a novel framework that integrates deep learning (DL) methodologies with advanced metaheuristic optimization. At the core of this framework is the Continuous-Time Sequence Model (CTSM), complemented by the binary Ninja Optimization Algorithm (bNiOA) for feature selection and the Ninja Optimization Algorithm (NiOA) for hyperparameter tuning. Experimental results demonstrate substantial improvements in predictive performance. The baseline CTSM model achieved an accuracy of 0.8168 with a mean squared error (MSE) of 0.0718. After applying the bNiOA-driven feature selection, accuracy increased markedly to 0.9576, while the MSE was reduced to0.00067. Further optimization of hyperparameters through NiOA elevated the model’s accuracy to 0.9963, with an MSE of 0.00088. These results validate that the proposed optimization-enhanced deep learning pipeline effectively reduces feature redundancy and dimensionality, while finely tuning model parameters to achieve superior accuracy and generalization. The implications of this study are significant, providing policymakers, investors, and businesses with a powerful tool for risk management, strategic planning, and informed decision-making in volatile currency markets.

groups

El-Sayed M. El-Kenawy mail

link https://doi.org/10.54216/AJBOR.130103

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

Details open_in_new