Over-Under Sampling Approach with Adaptive Synthetic and Tomek Links Methods to Handle Data Imbalance in Sentence Classification on Halal Assurance Certificate Documents

Dadang; Rahmat; R. Rizal

doi:https://doi.org/10.54216/FPA.190215

Fusion: Practice and Applications | 2025

Fusion of Economic and Financial Factors Affecting Household Deposits in Banks: An Econometric Analysis

The examination of commercial bank deposits together with their influencing factors relies on econometric analyses in this paper. The econometric model for commercial bank deposit base factors used a multiple linear regression (LS) method because the data came from time series that included multiple variables. The research used 74 economic indicators spanning an eight-year period and collected those indicators in monthly intervals. The dependent variable was the deposit volume (y), while the independent variables were the inflation rate (x1), the minimum wage (x2), the number of individuals using digital banking services (x3), the average interest rate on term deposits (x4), and the per capita GDP (x5). Our analysis, based on data from the Central Bank of the Republic of Uzbekistan, indicates that the selected independent variables are significantly related to the growth of the deposit base. The implementation of multiple linear regression (LS) answered Gauss-Markov assumption tests successfully while the Durbin-Watson test and Shapiro-Wilk test along with the Breusch-Pagan test evaluated the statistical import of the obtained results. The key findings indicate that a 1% increase in the inflation rate leads to a 1.06% decrease in the deposit volume; a 1% increase in the minimum wage results in a 0.32% increase in the deposit volume; a 1% increase in the number of individuals using digital banking services leads to a 0.59% increase in the deposit volume; a 1% increase in the average interest rate on term deposits results in a 0.81% increase in the deposit volume; and a 1% increase in per capita GDP causes a 0.79% increase in the deposit volume. Banks should concentrate their efforts on fighting inflation while developing their digital systems because these strategies build a better deposit base, which boosts interbank rivalry and supports economic stability.

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Zokir Mamadiyarov mail -

Sаmаriddin Mаkhmudov mail -

Bunyod Utanov mail -

Dilorom Kasimova mail -

Guzal Bekmurodova mail -

Zohid Hakimov mail

link https://doi.org/10.54216/FPA.190206

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

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Fusion: Practice and Applications | 2025

A Novel Deep Learning Approach for Automated Melanoma Classification using Hybrid CNN and Vision Transformer Model

Melanoma Skin cancer is a serious type of cancer affecting people globally in order to improve survival rates, it is crucial to detect the infection at an early stage. Old Traditional methods for cancer detection make use of biopsies, which were time-consuming and involved complex procedures, which delayed diagnosis. However, accurate diagnosis is challenging due its complex imaging techniques. With the advancements in technology, particularly in deep learning techniques like CNN, have significantly improved the accuracy and efficiency of melanoma skin cancer detection. This research paper presents a Novel Hybrid deep learning architecture that combines Convolution Neural Networks (CNNs) and Vision Transformers (ViT) for automated classification of skin lesions into binary categories: Malignant (cancerous) and Benign (Non-cancerous). The proposed model influences CNN's superior ability to extract local features alongside ViT's capability to extract global features. This hybrid architecture was trained and evaluated on ISIC 2020 challenging Dataset of dermatological images representing excellent performance with an accuracy of 94%, with a precision of 91%, recall (sensitivity) of 90%, and an F1 score of 91% after 25 epochs.  The model's robustness is further authorized through confusion matrix analysis, which forms a strong classification capability across various melanoma presentations. The proposed hybrid approach offers a more efficient and less complex approach in the automatic detection and identification of melanoma skin cancer, thus increasing the chances of successful early intervention and improving patient outcomes, thus making it suitable for Clinical use and sets a foundation for future developments in automated skin cancer detection systems. In comparison to other advanced networks, this model displays superior performance.

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Hamsalekha R. mail -

Glan Devadhas George mail -

T. Y. Satheesha mail

link https://doi.org/10.54216/FPA.190207

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

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Fusion: Practice and Applications | 2025

Fusion of Information in University Quality Assessment: Determining Factors in Self-Assessment and External Evaluation in Ecuadorian Higher Education

This study aimed to identify the most relevant factors influencing the effectiveness of self-assessment and external evaluation processes in higher education in Ecuador. Through an analytical approach, the DEMATEL method integrated with neutrosophic logic was employed to evaluate interactions, prioritize these factors, and enhance information fusion in decision-making. The methodology allowed for the incorporation of inherent uncertainty and subjectivity in evaluation, generating a more adaptive and robust model for integrating multiple sources of information. The results revealed that key factors included the clarity of quality indicators, institutional commitment to continuous improvement, training of evaluators, and institutional infrastructure. Furthermore, the study highlighted that the fusion of internal and external evaluation data is crucial for a comprehensive quality assessment. The most influential factors within the system were identified as the impact of evaluation results on decision-making and infrastructure quality. Findings indicate that improving educational quality in Ecuador requires strengthening data integration mechanisms, ensuring coherence between self-assessment and external evaluation, and optimizing the interaction between different quality assurance processes. It is recommended to enhance information fusion strategies in quality assurance policies to improve the efficiency and accuracy of evaluation processes in higher education.

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Cecilia Santana mail -

Carlos Ortiz mail

link https://doi.org/10.54216/FPA.190208

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

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Fusion: Practice and Applications | 2025

Improved Deep Learning model for Ancient Cuneiform Symbols Classification

Cuneiform script, among the earliest writing systems, poses a distinct challenge for classification because of its complex symbols and varied linguistic contexts. This study investigates the use of Convolutional Neural Network (CNN) architectures for the classification of cuneiform symbols. The preprocessing includes resizing the cuneiform images to a uniform dimension and categorizing them into training, validation, and testing sets. A modified CNN model has been introduced. The CNN model demonstrates a lower parameter count in comparison to other deep learning models, which frequently necessitate significant storage capacity. The results from the CLI dataset indicate that the proposed CNN model reached an impressive accuracy of 99.55%, This study enhances computational approaches for the analysis of ancient scripts and underscores the significance of utilizing deep learning techniques within the fields of historical linguistics and digital humanities.

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Raed Majeed mail -

Hiyam Hatem mail -

Wael Abd-Alaziz mail

link https://doi.org/10.54216/FPA.190209

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Fusion analysis of factors determining sustainable development of automobile enterprises in Uzbekistan

The sustainable development of automobile enterprises in Uzbekistan is a topic that deserves our attention and analysis. In this study, we focus on utilizing fusion analysis to identify and understand the factors that play a crucial role in the sustainable growth of these enterprises. By examining and fusion, multiple dimensions, such as economic, environmental, social and technological factors, we aim to provide valuable insights and recommendations for promoting sustainable within the automobile industry in Uzbekistan. Through fusion analysis, we examine how economic factors, such as market demand, production efficiency, and financial viability, influence the sustainable development of the country and automobile enterprises.

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Nazarova Ra’no Rustamovna mail

link https://doi.org/10.54216/FPA.190210

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2025

Dynamics of Predator-Prey Interactions, Analyzing the Effects of Time Delays and Neymark-Saker Bifurcation

The study examines the dynamics of a predator-prey model that includes temporal delays, concentrating on the impact of these delays on system stability and behavior.It delineates criteria for the global stability of the positive equilibrium using a generalized Lyapunov function and the Razumkin-type theorem, emphasizing the significance of temporal delays in biological systems. The research highlights the Neymark-Saker (NS) bifurcation, examining the impact of fractional configurations on this bifurcation and the system’s overall dynamic stability. The research utilizes the Lyapunov-Razumihin approach to identify bifurcation points and forecast the system’s progression in intricate ecological settings. The research examines the presence of periodic solutions and local stability criteria related to the two delays in predator-prey interactions. Numerical simulations are used to substantiate the theoretical results, specifically for the periodic bifurcation solutions associated with the Neymark-Saker bifurcation.

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Thwiba A. Khalid mail

link https://doi.org/10.54216/IJNS.260224

Volume & Issue

Vol. Volume 26 / Iss. Issue 2

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Fusion: Practice and Applications | 2025

Extracting the Trustworthy Glaucoma Features using WGMO based EvoTransform: Advanced Vision Transformer from Fundus Images

Glaucoma is a dangerous eye illness that greatly reduces the sharpness of a person's vision. If not caught early enough, this retinal disorder can damage the optic nerve head (ONH) and cause permanent blindness. Automated glaucoma diagnosis now has tool support thanks to recent advances in deep learning besides the convenience of computing resources. The low reliability of generic convolutional neural networks has prevented their widespread usage in medical procedures, even if deep learning has improved illness diagnosis using medical pictures. While there has been a rise in the use of deep learning for glaucoma classification, very few studies have tested whether or not the models are easy to understand and interpret, which bodes well for their future use. Medical picture feature extraction using Vision Transformers is showcased in this study utilising an EvoTransform: Advanced Evolutionary Algorithm Integration in Transformer Networks named as (EvoTAEA). Combining the powers of Convolutional Neural Networks with Vision Transformers, the suggested EAT Former architecture takes advantage of their data pattern recognition in addition adaptability capabilities. The classification accuracy is enhanced by using the Wild Geese Migration Optimizer (WGMO) to fine-tune the parameters of the proposed feature extraction. The design makes use of new parts, such as the Multi-Scale Region Aggregation, Global and Local Interaction, and Enhanced EA based Transformer blocks with Feed-Forward networks. For dynamically simulating non-standard places, it also presents the Modulated Deformable MSA module. Important components of the Vision Transformer (ViT) model are covered in the study, including patch-based processing, Multi-Head Attention mechanism, and positional context inclusion. In order to give an inductive bias, it presents the Multi-Scale Region Aggregation module, which combines data from several receptive fields. The MSA-based global module is improved by the Global and Local Interaction module, which adds a local path for extracting discriminative local info. An approach to glaucoma diagnosis that integrates ResNet-50, DenseNet-201, and Xception is suggested in the study. Two publicly available datasets, ORIGA and ACRIMA, are used to evaluate the trials. This model can help with the automated diagnosis of glaucoma using fundus pictures.

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Archana E. mail -

Geetha S. mail -

Victo Sudha George G. mail

link https://doi.org/10.54216/FPA.190212

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

A Novel Binary Swordfish Movement Optimization Algorithm (BSMOA) for Efficient Feature Selection

As optimization tasks become increasingly complex, particularly in feature selection, there is a growing need for algorithms capable of robustly balancing exploration and exploitation. In this work, we propose the Binary Swordfish Movement Optimization Algorithm (BSMOA), inspired by the synchronized and agile movements of swordfish. BSMOA employs adaptive parameters to navigate high-dimensional search spaces through dynamic exploration, exploitation, and elimination stages. Extensive experiments on benchmark datasets demonstrate that BSMOA outperforms state-of-the-art algorithms, including bHHO, bGWO, and bPSO, regarding average error, feature reduction, and computational efficiency. Key contributions of BSMOA include its improved balance between global and local search and its ability to achieve stable and accurate feature selection. This work has broad implications for applications in machine learning, engineering design, and other optimization domains, providing a reliable tool for tackling challenging binary optimization problems.

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El-Sayed M. El-kenawy mail -

Amel Ali Alhussan mail -

Doaa Sami Khafaga mail -

Amal H. Alharbi mail -

Sarah A. Alzakari mail -

Abdelaziz A. Abdelhamid mail -

Abdelhameed Ibrahim mail -

Marwa M. Eid mail

link https://doi.org/10.54216/FPA.190213

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Enhancing IoT Intrusion Detection with a Hybrid Deep Learning-Evolutionary Algorithm: An Ensemble Strategy Approach

In the context of dynamic and highly diverse IoT (Internet of Things), the nature of threats and the amount of data that needs to be processed by IDSs (Intrusion Detection System) have become much greater and represent considerable problems for modern security systems. This work presents a new method called a Hybrid Deep Learning-Evolutionary Algorithm with an Ensemble Strategy (HDLE-EASE) for improving intrusion detection in IoT systems. Our method combines the spatial feature extraction capability of CNN (Convolutional Neural Networks) and temporal feature extraction of LSTM (Long Short-Term Memory) networks with the optimization feature of GA to optimize model parameters. We further incorporate a composite of boosting-bagging hybrid to enhance the stability and reliability of the intrusion detection mechanism. As privacy and scalability are critical issues in IoT networks, we propose a federated learning approach, allowing for model training on IoT networks while preserving data privacy. Furthermore, the presented approach includes a reinforcement-learning module for the capability of adapting to newly emerge and changing security threats. Initial tests show that the detection accuracy and model optimization capabilities of HDLE-EASE significantly outperform other methods, while its adaptability makes the tool a promising one for developing a holistic solution to protect IoT systems against a wide range of attacks.

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Basil Xavier mail -

Jaspher Willsie Kathrine mail -

Priyadharsini mail -

Gladwin Rufus mail -

R. Venkatesan mail

link https://doi.org/10.54216/FPA.190214

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Over-Under Sampling Approach with Adaptive Synthetic and Tomek Links Methods to Handle Data Imbalance in Sentence Classification on Halal Assurance Certificate Documents

Data imbalance is a common problem in machine learning, specifically in classification, in which examples in a dominant class outnumber examples in a minority class many times over. Besides, such a problem keeps a model unable to discover meaningful patterns for a minority class —hence, such a problem reduces model performance specifically in terms of Recall and F1-Score.  In current work, activity is performed in overcoming data imbalance problem in sentence classification model of documents of assurance certificate for halal with a combination of over-sampling and under-sampling techniques, namely Adaptive Synthetic (ADASYN) and Tomek Links. Text Classification technique is adopted in classifying sentences regarding assurance of halal in documents of assurance certificate for halal Text Classification; since incorrect classification of such sentences is not preferable, therefore, it is important to make sure no information about halal product is missed out. Over-sampling techniques considered include the SMOTE, Borderline SMOTE, ADASYN, and SMOTENC, and under-sampling techniques include the Random Under-Sampler, Near Miss, and Tomek Links. As comparative result, best performance gain in terms of Accuracy (0.759), F1-Score (0.748), Recall (0.759), and Precision (0.768) is generated with ADASYN. In our use case, ADASYN + Tomek Links is effective; recall is important in case of classification of documents for assurance certificate for halal and therefore, we cannot miss any relevant sentences. The proposed approach remarkably enhances the accuracy level for halal-related sentence identification and can be adopted in the halal product checking systems in industries with a halal feature.

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Dadang Heksaputra mail -

Rahmat Gernowo mail -

R. Rizal Isnanto mail

link https://doi.org/10.54216/FPA.190215

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new