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Fusion: Practice and Applications | 2025

Hybrid chaotic bat artificial bee colony algorithm assisted hybrid machine learning based intrusion detection system

Network intrusions are becoming more common, resulting in significant privacy violations, financial losses, and the illegal transfer of sensitive information. Numerous intrusion strategies pose a threat to data, computer resources, and networks. While hackers may focus on obtaining trade secrets, private information, or confidential data that can then be disclosed for illegal purposes, each type of intrusion aims to achieve a distinct objective. False attack detection by security systems and changing threat environments create challenges such as delayed identification of true attacks and long-term financial harm. This paper presents a novel hybrid optimization algorithm-assisted deep learning model for accurately identifying intrusion types and enhancing network security. Initially, input information is composed from openly obtainable datasets. The input data is cleaned, normalized, and standardized to produce accurate results. An improved synthetic minority oversampling technique (ISMOTE) for data balance reduces the method's overfitting problem. Then, the Chaotic Bat Artificial Bee Colony optimization algorithm (CBABCOA) is used to identify critical features and reduce feature dimensionality issues. HSVM-XGBoost (Hybrid Kernel Support Vector Machine-Extreme Gradient Boosting) is used for intrusion detection and classification. The Chaotic Binary Horse Optimization Algorithm (CBHOA) is used for hyper parameter tuning. This method makes use of the CIC UNSW-NB15 Augmented dataset, the CICIDS 2019 data set, and the NSL-KDD information set. The proposed method achieves better than the other method.

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Vasanth Nayak mail -
Sumathi Pawar mail -
Sunil Kumar B. L. mail
link https://doi.org/10.54216/FPA.190204

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new
Fusion: Practice and Applications | 2025

Machine Learning Models with Statistical Analysis Techniques for ForecastingWind Turbines Scada Systems Measurement

Wind energy is one of the fastest-growing sustainable, clean, and renewable sources, attracting significant attention and investment from many countries. However, given the substantial capital investment required for wind power plants, understanding the proposed plants’ performance becomes critical before implementation. This assessment is most effectively conducted using refined wind power predictability models and precise wind velocity data. Accurate wind forecasts are essential for informed decision-making and effective wind energy utilization. In this study, three advanced Machine Learning (ML) regression methods were applied to the TNWind dataset to predict the power output of wind turbines. The dataset variables included date and time (measured at 10-minute intervals), low-voltage active power (in kW), wind speed (in m/s), the theoretical wind power curve (in kWh), and wind direction. To predict wind power output, six supervised ML models were trained, including Random Forest Regressor (RF), Extreme Gradient Boosting Regressor (XGB), Gradient Boosting Regressor (GB), Support Vector Machine Regressor (SVR), K-Neighbors Regressor (KN), and Linear Regressor. The analysis revealed that the Random Forest model outperformed the others, achieving exceptional performance metrics: an R2 value of 0.97, an MAE of 0.17 and an MSE of 0.07. The analysis to identify the outcomes for wind power generation from machine learning proves that renewable energies are more capable and are a lucrative investment.

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Mona Ahmed Yassen mail -
El-Sayed M. El-Kenawy mail -
Mohamed Gamal Abdel-Fattah mail -
Islam Ismael mail -
Hossam El.Deen Salah Mostafa mail
link https://doi.org/10.54216/FPA.190205

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new
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

Details open_in_new
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

Details open_in_new
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

Details open_in_new
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

Details open_in_new
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

Details open_in_new
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