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

Nazarova Ra’no

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

Fusion: Practice and Applications | 2025

A Novel Smart Cities Framework for GCC Countries

There is a need to create and develop smart cities that could help improve the quality of life in global countries. The goal of this paper is to develop a novel smart city framework for the GCC countries. This study presents a comprehensive analysis of smart city features across multiple cities worldwide, leveraging data from a reliable world cities database. Through exploratory data analysis and visualization techniques, we examined various aspects of smart city development, including mobility, environment, government, economy, people, and living standards. It turned out from the literature that globally, there is a focus on some of the dimensions of smart cities while others did not receive much attention. Smart economy and smart environment were not receiving much attention globally. A framework was developed for the GCC countries that focuses on all the dimensions of the smart cities, but most of the attention is on smart governance and smart economy since these two dimensions help improve the quality of life and diversify the sources of the economy in the country. This framework is useful for GCC countries as it would have great implications on the desired outcomes of smart cities and link with the strategic development goals that most GCC countries have, whether it is the 2030, 2035, or even the 2040 vision.

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Khawla Alhasan mail

link https://doi.org/10.54216/FPA.190120

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Natural Language Processing Driven Applied Linguistics for Sarcasm Detection Using Artificial Hummingbird Algorithm with Deep Learning

Natural Language Processing (NLP)-driven applied linguistics for sarcasm detection includes computational models to understand and identify sarcastic expressions within text. This interdisciplinary method integrates linguistics principles with advanced NLP techniques to identify subtle and nuanced cues indicative of sarcasm correctly. It includes computational approaches like linguistic feature extraction, machine learning models, and sentiment analysis. Furthermore, deep learning (DL) algorithms, including transformers and recurrent neural networks (RNNs), hold significant potential in capturing complex linguistic nuances inherent in sarcastic expression. These approaches can learn the hierarchical representation of text, which enables capturing context dependency, which is crucial for accurately detecting sarcasm. The applications of NLP-driven applied linguistics for sarcasm detection show great potential in various domains namely social media analysis, online content moderation, and customer feedback interpretation. By automating sarcasm detection, this system can enhance communication understanding, improve sentiment analysis accuracy, and contribute to better decision-making processes in various contexts. This study develops automated Sarcasm Detection using the Artificial Hummingbird Algorithm with Deep Learning (ASD-AHADL) technique. The ASD-AHADL technique applies the optimal DL model for detecting sarcastic content. To achieve this, the ASD-AHADL technique undergoes data preprocessing and the BERT-based word embedding process at the initial stage. Followed by the ASD-AHADL technique uses attention-gated recurrent unit long short-term memory (AGRU-LSTM) for the sarcasm detection process. At last, the AHA-based parameter tuning process is involved to fine-tune the parameters based on the DL algorithm. The experimental study of the ASD-AHADL technique has been tested under a social media dataset. The outcomes indicated that the solution of the ASD-AHADL technique was significant compared to others.

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Maryam Alsolami mail

link https://doi.org/10.54216/JISIoT.160120

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Innovations in Health Anomaly Detection: A Comparative Review of Machine Learning and Statistical Approaches

One of the significant challenges in modern healthcare is the early and accurate detection of health anomalies, especially in the case of life-threatening diseases such as breast cancer. This paper investigates the comparative efficacy of ML models and statistical methods for the classification of breast tumors as benign or malignant using the Breast Cancer Wisconsin (Diagnostic) Dataset. The dataset, comprising various tumor cell attributes, was preprocessed with Principal Component Analysis (PCA) to enhance model training efficiency. The first 11 principal components retained 95% of the total variance, ensuring minimal information loss while reducing dimensionality. We compared the performance of several machine learning algorithms, including Logistic Regression, Support Vector Machines (SVM), Artificial Neural Networks (ANN), Decision Trees (DT), Random Forests (RF), Naïve Bayes (NB), and K-Nearest Neighbors (KNN). Among them, Logistic Regression, SVM, and ANN achieved near-perfect classification accuracy with balanced precision-recall metrics, where the accuracy rates were all more than 98%. XGBoost and Random Forest were also very impressive as advanced models, while simple models like Decision Trees and Naïve Bayes proved to be less potent and were unable to manage class imbalances and complex data patterns. Our main findings are essentially reflective of the transformative role machine learning would play in healthcare; for instance, enhancing the accuracy of diagnosis, optimizing clinical workflow, and promoting decision-making. These insights are made actionable for practitioners in healthcare to promote the adoption of reliable ML solutions for breast cancer detection. In the future, real-time data integration, external validation, and hybrid modeling approaches must be considered to further enhance the practical utility of these findings.

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Nada M. Sallam mail -

Eman Ben Salah mail

link https://doi.org/10.54216/FPA.190203

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new

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.

groups

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.

groups

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