Utilizing Machine Learning Techniques Research Articles

Implementation Paper on Dermatological Proriasis and other Skin Diseases Diagnosis Using Machine Learning

Abstract: This paper "Dermatological Psoriasis and Other Skin Diseases Diagnosis Using Machine Learning" aims to develop an efficient and accurate system for identifying psoriasis, a chronic skin condition, through the utilization of machine learning techniques. Psoriasis affects millions worldwide, presenting challenges in its diagnosis and treatment. Leveraging advanced algorithms, this project seeks to analyse various skin images and clinical data to create a robust model capable of distinguishing psoriatic lesions from other dermatological conditions with high precision. By employing machine learning algorithms such as convolutional neural networks (CNNs) and support vector machines (SVMs), alongside feature extraction methods, the system aims to automate and streamline the diagnostic process, potentially reducing both time and burdens on healthcare professionals. Ultimately, the implementation of such a tool could significantly enhance early detection rates, improve patient outcomes, and facilitate more targeted treatment strategies for individuals affected by psoriasis

Car Price Prediction Using Machine Learning

Abstract: The objective of the "Car Price Prediction Using Machine Learning" project is to anticipate car prices based on pertinent variables by utilizing predictive modelling and advanced data analytics. The rising need for precise and active pricing systems in the automobile sector is addressed by this initiative. The system will evaluate past vehicle data, including brand, model, manufacturing year, the mileage, type of fuel, and other details, by utilizing machine learning techniques. In order to produce specific predictions, the suggested model will be trained on an extensive dataset, noticing patterns and parallels within the data. In order to create a reliable prediction model, the research focuses on using regression techniques, such as ensemble approaches or linear regression. System of measurement like as absolute mean error and R-squared coefficient will be used to evaluate the predicted accuracy of the system in order to regulate its usefulness. If this resourcefulness is employed successfully, it will have a big impact on the automobile business for clients and venders alike by giving them a tool to evaluate fair market prices and helping them make decisions. This study enhances to the body of knowledge in price analysis using machine learning and lays the footing for future improvements in the prognostication of dynamic market trends in the automobile sector.

A Real Time Facial Recognition and Tracking System for Personnel Presence

Abstract: Our project aims to revolutionize the conventional attendance system by implementing a facial recognition-based solution. The existing manual methods are prone to human error and require significant maintenance. By leveraging facial recognition technology, our system will offer improved precision and efficiency, reducing the need for manual work. The system will maintain a database of students' images, matching them during class to mark attendance accurately. Utilizing machine learning techniques, specially the Haar-Cascade classifier and local binary pattern histogram method for face detection and recognition, respectively, our system will ensure reliable attendance tracking. The attendance data will be stored in a MySQL database and Microsoft Excel file, providing a streamlined and secure alternative to the traditional attendance process.

Skin Disease Detection Model

Abstract: The human skin is a remarkable organ susceptible to a myriad of know and unknown diseases. Many of these ailment are widespread, with some ranking among common worldwide. The complexity of diagnosing these diseases is compounded by challenges such as variations in skin texture, the presence of hair, and diverse skin colors. In some areas have limited access to medical facilities, individuals often neglect early symptoms, leading to exacerbated conditions over time. Furthermore, traditional diagnostic methods for skin diseases are time consuming. To address these challenges, there is a critical need to develop advanced diagnostic methods utilizing machine learning techniques to enhance accuracy cross various skin diseases. Machine learning algorithms have proven valuable in medical applications, leveraging image feature values to facilitate decision making. The diagnostic process involves three key stages: feature extraction, training, and testing. By employing machine learning technology, these algorithms learn from a diverse set of skin images o enhance their diagnostic capabilities. The primary goal is to significantly improved the accuracy of skin disease detection. This study focuses on utilizing color and texture features for the classification of skin diseases. The distinctive color of healthy skin differs from that affected by disease, while texture features effectively discern smoothness, coarseness, and regularity in images. Key features such as texture, color, and shape phyla pivotal role in image classification. The incorporation of convolution neural networks (CNN) further augments the capabilities of image classification in the realm of skin disease diagnosis

Skin Disease Detection

The human skin is a remarkable organ susceptible to a myriad of know and unknown diseases. Many of these ailment are widespread, with some ranking among common worldwide. The complexity of diagnosing these diseases is compounded by challenges such as variations in skin texture, the presence of hair, and diverse skin colors. In some areas have limited access to medical facilities, individuals often neglect early symptoms, leading to exacerbated conditions over time. Furthermore, traditional diagnostic methods for skin diseases are time-consuming. To address these challenges, there is a critical need to develop advanced diagnostic methods utilizing machine learning techniques to enhance accuracy cross various skin diseases. Machine learning algorithms have proven valuable in medical applications, leveraging image feature values to facilitate decision-making. The diagnostic process involves three key stages: feature extraction, training, and testing. By employing machine learning technology, these algorithms learn from a diverse set of skin images o enhance their diagnostic capabilities. The primary goal is to significantly improved the accuracy of skin disease detection. This study focuses on utilizing color and texture features for the classification of skin diseases. The distinctive color of healthy skin differs from that affected by disease, while texture features effectively discern smoothness, coarseness, and regularity in images. Key features such as texture, color, and shape phyla pivotal role in image classification. The incorporation of convolution neural networks (CNN) further augments the capabilities of image classification in the realm of skin disease diagnosis

Machine Learning for Intrusion Detection: A Reproducible Baseline is All You Need

Ensuring Responsible AI practices is paramount in the advancement of systems founded upon machine learning (ML) principles, particularly in sensitive domains like intrusion detection within cybersecurity. A fundamental aspect of Responsible AI is reproducibility, which guarantees the reliability and transparency of research outcomes. In this paper, we address the critical challenge of establishing reproducible for intrusion detection utilizing ML techniques. Leveraging the NSL-KDD dataset and the Edge-IIoTset, we carry out extensive experiments to evaluate the efficacy of our approach. Our study prioritizes meticulous experiment design and careful implementation setups, aligning with the principles of Responsible AI. Through rigorous experimentation and insightful discussions, we underscore the importance of reproducibility as a cornerstone in ensuring the resilience and reliability of intrusion detection systems. Our findings offer valuable insights for researchers and practitioners striving to develop Responsible AI solutions in cybersecurity and beyond. The source code is publicly accessible at https://github.com/Salma-00/Machine-Learning-for-Intrusion-Detection.

Monkey Pox Detection using Deep learning

Monkeypox is a rare zoonotic disease caused by the Monkeypox virus, which manifests in humans with symptoms similar to those of smallpox. Early detection and accurate diagnosis are crucial for effective management and containment of outbreaks. This research proposes a novel Monkeypox detection system utilizing machine learning techniques to enhance the speed and accuracy of diagnosis. The proposed system integrates various data sources, including clinical records, medical imaging, and demographic information, to develop a comprehensive dataset for model training. Machine learning algorithms, such as convolutional neural networks (CNNs) for image analysis and ensemble methods for combining diverse data modalities, are employed to identify patterns indicative of Monkeypox infection

Prostate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer Learning.

Prostate cancer is a common cancer with significant implications for global health. Prompt and precise identification is crucial for efficient treatment strategizing and enhanced patient results. This research study investigates the utilization of machine learning techniques to diagnose prostate cancer. It emphasizes utilizing deep learning models, namely VGG16, VGG19, ResNet50, and ResNet50V2, to extract relevant features. The random forest approach then uses these features for classification. The study begins by doing a thorough comparison examination of the deep learning architectures outlined above to evaluate their effectiveness in extracting significant characteristics from prostate cancer imaging data. Key metrics such as sensitivity, specificity, and accuracy are used to assess the models' efficacy. With an accuracy of 99.64%, ResNet50 outperformed other tested models when it came to identifying important features in images of prostate cancer. Furthermore, the analysis of understanding factors aims to offer valuable insights into the decision-making process, thereby addressing a critical problem for clinical practice acceptance. The random forest classifier, a powerful ensemble learning method renowned for its adaptability and ability to handle intricate datasets, then uses the collected characteristics as input. The random forest model seeks to identify patterns in the feature space and produce precise predictions on the presence or absence of prostate cancer. In addition, the study tackles the restricted availability of datasets by utilizing transfer learning methods to refine the deep learning models using a small amount of annotated prostate cancer data. The objective of this method is to improve the ability of the models to generalize across different patient populations and clinical situations. This study's results are useful because they show how well VGG16, VGG19, ResNet50, and ResNet50V2 work for extracting features in the field of diagnosing prostate cancer, when used with random forest's classification abilities. The results of this work provide a basis for creating reliable and easily understandable machine learning-based diagnostic tools for detecting prostate cancer. This will enhance the possibility of an early and precise diagnosis in clinical settings such as index terms deep learning, machine learning, prostate cancer, cancer identification, and cancer classification.

A Machine Learning-Based Mortality Prediction Model for Patients with Chronic Hepatitis C Infection: An Exploratory Study.

Background: Chronic hepatitis C (HCV) infection presents global health challenges with significant morbidity and mortality implications. Successfully treating patients with cirrhosis may lead to mortality rates comparable to the general population. This study aims to utilize machine learning techniques to create predictive mortality models for individuals with chronic HCV infections. Methods: Data from chronic HCV patients at Sultan Qaboos University Hospital (2009-2017) underwent analysis. Data pre-processing handled missing values and scaled features using Python via Anaconda. Model training involved SelectKBest feature selection and algorithms such as logistic regression, random forest, gradient boosting, and SVM. The evaluation included diverse metrics, with 5-fold cross-validation, ensuring consistent performance assessment. Results: A cohort of 702 patients meeting the eligibility criteria, predominantly male, with a median age of 47, was analyzed across a follow-up period of 97.4 months. Survival probabilities at 12, 36, and 120 months were 90.0%, 84.0%, and 73.0%, respectively. Ten key features selected for mortality prediction included hemoglobin levels, alanine aminotransferase, comorbidities, HCV genotype, coinfections, follow-up duration, and treatment response. Machine learning models, including the logistic regression, random forest, gradient boosting, and support vector machine models, showed high discriminatory power, with logistic regression consistently achieving an AUC value of 0.929. Factors associated with increased mortality risk included cardiovascular diseases, coinfections, and failure to achieve a SVR, while lower ALT levels and specific HCV genotypes were linked to better survival outcomes. Conclusions: This study presents the use of machine learning models to predict mortality in chronic HCV patients, providing crucial insights for risk assessment and tailored treatments. Further validation and refinement of these models are essential to enhance their clinical utility, optimize patient care, and improve outcomes for individuals with chronic HCV infections.

Machine Learning in Credit Risk Forecasting —— A Survey on Credit Risk Exposure

Credit risk is one of the most important elements in risk management area. Traditional regression types of credit risk models are straightforward to implement and model outputs are easy to interpret. However, the model accuracy can always be suboptimal to fit the real credit risk data series. Especially, the model performance even deteriorates under extreme economic scenarios. In contrast, the modern machine learning models can handle different drawbacks of regression types of models. In this paper, we survey the recent literatures on applying the machine learning or deep learning methods in credit risk forecast with special focus on study the superiorities of these techniques. Besides of delivering better prediction accuracies, we uncover other four advantages for machine learning type of default forecast which have been shown in few literatures. We also survey the less studied machine learning or deep learning type of prepayment forecast. By reviewing past literatures from both default and prepayment risk aspects, we can gain comprehensive overview of utilizing machine learning techniques in credit risk forecasting and valuable insights for future risk management research.

Detection of Various Botnet Attacks Using Machine Learning Techniques

With the rapid growth in the quantity of Internet of Things (IoT) devices linked with the network, there exists a concurrent rise in network attacks, including overwhelming and service disruption incidents. The increasing prevalence of network attacks, such as overwhelming and service denial, poses a threat to IoT devices, leading to network disruptions and service disruption. Detecting these attacks is challenging due to the diverse array of heterogeneous devices in the IoT environment, making traditional rule-based security solutions less effective. Developing optimal security models for diverse device types is challenging. Machine learning (ML) offers an alternative approach, enabling the creation of effective security models by leveraging empirical data specific to each device. We utilize machine learning techniques for the detection of Internet of Things (IoT) attacks. Our focus is on botnet attacks directed at variety of IoT devices. We undertake the development of machine learning-based models tailored to each specific category of device for enhanced security. We utilize the N-BaIoT dataset, which incorporates injected botnet attacks (specifically Gafgyt and Mirai) across diverse IoT device types, including Doorbell, Baby Monitor, Security Camera, and Webcam. We develop models for detecting botnets for each IoT device by utilizing diverse machine learning algorithms. Following model development, we assess the utility of the models with a strong detection F1-score through classification analysis. The novelty of this work lies in crafting a Machine Learning-based framework designed to identify IoT botnet attacks, followed by successful detection of such attacks across diverse IoT devices utilizing this framework. Among the most widely used machine learning algorithms on the NBaIoT dataset, Decision Trees, Random Forests, and K-Nearest Neighbors (KNN) demonstrate superior performance.

Prediction of shear stress distribution in compound channel with smooth converging floodplains

Abstract Climate change can have a profound impact on river flooding, leading to increased frequency and severity of floods. To mitigate these effects, it is crucial to focus on enhancing early warning systems and bolstering infrastructure resilience through improved forecasting. This proactive approach enables communities to better plan for and respond to flood events, thereby minimizing the adverse consequences of climate change on river floods. During river flooding, the channels often take on a compound nature, with varying geometries along the flow length. This complexity arises from construction and agricultural activities along the floodplains, resulting in converging, diverging, or skewed compound channels. Modelling the flow in these channels requires consideration of additional momentum transfer factors. In this study, machine learning techniques, including Gene Expression Programming (GEP), Artificial Neural Networks (ANN), and Support Vector Machines (SVM), were employed. The focus was on a compound channel with converging floodplains, predicting the shear force carried by the floodplains in terms of non-dimensional flow and hydraulic parameters. The findings indicate that the proposed ANN model outperformed GEP, SVM, and other established approaches in accurately predicting floodplain shear force. This research underscores the efficacy of utilizing machine learning techniques in the examination of river hydraulics.

Segmentation of mature human oocytes provides interpretable and improved blastocyst outcome predictions by a machine learning model

Within the medical field of human assisted reproductive technology, a method for interpretable, non-invasive, and objective oocyte evaluation is lacking. To address this clinical gap, a workflow utilizing machine learning techniques has been developed involving automatic multi-class segmentation of two-dimensional images, morphometric analysis, and prediction of developmental outcomes of mature denuded oocytes based on feature extraction and clinical variables. Two separate models have been developed for this purpose—a model to perform multiclass segmentation, and a classifier model to classify oocytes as likely or unlikely to develop into a blastocyst (Day 5–7 embryo). The segmentation model is highly accurate at segmenting the oocyte, ensuring high-quality segmented images (masks) are utilized as inputs for the classifier model (mask model). The mask model displayed an area under the curve (AUC) of 0.63, a sensitivity of 0.51, and a specificity of 0.66 on the test set. The AUC underwent a reduction to 0.57 when features extracted from the ooplasm were removed, suggesting the ooplasm holds the information most pertinent to oocyte developmental competence. The mask model was further compared to a deep learning model, which also utilized the segmented images as inputs. The performance of both models combined in an ensemble model was evaluated, showing an improvement (AUC 0.67) compared to either model alone. The results of this study indicate that direct assessments of the oocyte are warranted, providing the first objective insights into key features for developmental competence, a step above the current standard of care—solely utilizing oocyte age as a proxy for quality.

A comprehensive study on machine learning models combining with oversampling for bronchopulmonary dysplasia-associated pulmonary hypertension in very preterm infants

BackgroundBronchopulmonary dysplasia-associated pulmonary hypertension (BPD-PH) remains a devastating clinical complication seriously affecting the therapeutic outcome of preterm infants. Hence, early prevention and timely diagnosis prior to pathological change is the key to reducing morbidity and improving prognosis. Our primary objective is to utilize machine learning techniques to build predictive models that could accurately identify BPD infants at risk of developing PH.MethodsThe data utilized in this study were collected from neonatology departments of four tertiary-level hospitals in China. To address the issue of imbalanced data, oversampling algorithms synthetic minority over-sampling technique (SMOTE) was applied to improve the model.ResultsSeven hundred sixty one clinical records were collected in our study. Following data pre-processing and feature selection, 5 of the 46 features were used to build models, including duration of invasive respiratory support (day), the severity of BPD, ventilator-associated pneumonia, pulmonary hemorrhage, and early-onset PH. Four machine learning models were applied to predictive learning, and after comprehensive selection a model was ultimately selected. The model achieved 93.8% sensitivity, 85.0% accuracy, and 0.933 AUC. A score of the logistic regression formula greater than 0 was identified as a warning sign of BPD-PH.ConclusionsWe comprehensively compared different machine learning models and ultimately obtained a good prognosis model which was sufficient to support pediatric clinicians to make early diagnosis and formulate a better treatment plan for pediatric patients with BPD-PH.

Plasma Metabolic Profiles-Based Prediction of Induction Chemotherapy Efficacy in Nasopharyngeal Carcinoma: Results of a Bidirectional Clinical Trial.

The efficacy of induction chemotherapy (IC) as a primary treatment for advanced nasopharyngeal carcinoma (NPC) remains a topic of debate, with a lack of dependable biomarkers for predicting its efficacy. This study seeks to establish a predictive classifier utilizing plasma metabolomics profiling. A total of 166 NPC patients enrolled in the clinical trial NCT05682703 and undergoing IC were included in the study. Plasma lipoprotein profiles were obtained using 1H-NMR before and after IC treatment. An AI-assisted radiomics method was developed to effectively evaluate the efficacy. Metabolic biomarkers were identified through a machine learning approach based on a discovery cohort and subsequently validated in a validation cohort that mimicked the most unfavorable scenario in real-world. Our research findings indicate that the effectiveness of IC varies among individual patients, with a correlation observed between efficacy and changes in metabolite profiles. Utilizing machine learning techniques, it was determined that the XGB model exhibited notable efficacy, attaining an Area Under the Curve (AUC) value of 0.792 (95% CI, 0.668-0.913). In the validation cohort, the model exhibited strong stability and generalizability with an AUC of 0.786 (95%CI, 0.533-0.922). In this study, we found that dysregulation of plasma lipoprotein may result in resistance to IC in NPC patients. The prediction model constructed based on the plasma metabolites' profile as good predictive capabilities and potential for real-world generalization. This discovery has implications for the development of treatment strategies and may offer insight into potential targets for enhancing the effectiveness of IC.

Data Mining Strategies for Gold Price Prediction Using Multi-Factorial Influences

The prediction of gold prices is a critical endeavor in the realm of financial markets, given the precious metal's significance as a store of value and its susceptibility to various economic, geopolitical, and market influences. This research focuses on the application of machine learning (ML) models to predict gold rates by incorporating factors such as the dollar exchange rate, crude oil prices, and element prices. The objective is to utilize advanced ML algorithms, including regression, decision trees, and ensemble methods, to analyze and interpret the complex relationships between these key factors and gold prices. Through the use of robust ML models and historical data, the study aims to improve the accuracy of gold rate predictions. Additionally, the research seeks to explore the importance of features and interactions among variables to develop comprehensive predictive models. The utilization of ML techniques in this context is expected to provide a deeper understanding of the dynamic influences of the dollar exchange rate, crude oil prices, and element prices on gold rates, offering valuable insights for investment and risk management strategies in financial markets.

Art Design and Interior Color Selection for New Energy Vehicles using sustainable AI algorithm

New energy vehicles (NEVs) are becoming increasingly popular in the automotive industry as a sustainable solution to reduce carbon emissions. In order to attract and retain customers, NEVS needs to have visually appealing exterior designs and interior color schemes. The traditional process of selecting art design and interior colors for vehicles can take time and effort. To address this challenge, our team has developed a sustainable AI algorithm for art design and interior color selection for NEVs. This algorithm utilizes machine learning techniques to analyze market trends, customer preferences, and sustainable color choices. The algorithm uses a database of color options and design elements to generate customizable options for NEV manufacturers. It reduces the need for physical prototypes and manual color matching, leading to cost and time savings. The algorithm also takes into consideration the sustainable aspect of NEVs by suggesting color options that are environmentally friendly and promote eco-consciousness.

Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine

Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat island (SUHI) phenomena. This research focuses on the nexus between LULC alterations and variations in LST and air temperature (Tair), with a specific emphasis on the intensified SUHI effect in Kharkiv, Ukraine. Employing an integrated approach, this study analyzes time-series data from Landsat and MODIS satellites, alongside Tair climate records, utilizing machine learning techniques and linear regression analysis. Key findings indicate a statistically significant upward trend in Tair and LST during the summer months from 1984 to 2023, with a notable positive correlation between Tair and LST across both datasets. MODIS data exhibit a stronger correlation (R2 = 0.879) compared to Landsat (R2 = 0.663). The application of a supervised classification through Random Forest algorithms and vegetation indices on LULC data reveals significant alterations: a 70.3% increase in urban land and a decrement in vegetative cover comprising a 15.5% reduction in dense vegetation and a 62.9% decrease in sparse vegetation. Change detection analysis elucidates a 24.6% conversion of sparse vegetation into urban land, underscoring a pronounced trajectory towards urbanization. Temporal and seasonal LST variations across different LULC classes were analyzed using kernel density estimation (KDE) and boxplot analysis. Urban areas and sparse vegetation had the smallest average LST fluctuations, at 2.09 °C and 2.16 °C, respectively, but recorded the most extreme LST values. Water and dense vegetation classes exhibited slightly larger fluctuations of 2.30 °C and 2.24 °C, with the bare land class showing the highest fluctuation 2.46 °C, but fewer extremes. Quantitative analysis with the application of Kolmogorov-Smirnov tests across various LULC classes substantiated the normality of LST distributions p > 0.05 for both monthly and annual datasets. Conversely, the Shapiro-Wilk test validated the normal distribution hypothesis exclusively for monthly data, indicating deviations from normality in the annual data. Thresholded LST classifies urban and bare lands as the warmest classes at 39.51 °C and 38.20 °C, respectively, and classifies water at 35.96 °C, dense vegetation at 35.52 °C, and sparse vegetation 37.71 °C as the coldest, which is a trend that is consistent annually and monthly. The analysis of SUHI effects demonstrates an increasing trend in UHI intensity, with statistical trends indicating a growth in average SUHI values over time. This comprehensive study underscores the critical role of remote sensing in understanding and addressing the impacts of climate change and urbanization on local and global climates, emphasizing the need for sustainable urban planning and green infrastructure to mitigate UHI effects.

Detection of Botnet in IOT Using Machine Learning

The proliferation of Internet of Things (IoT) devices has introduced unprecedented connectivity and convenience but also heightened the vulnerability to botnet attacks. There are an increasing number of Internet of Things (IoT) devices connected to the network these days, and due to the advancement in technology, the security threads and cyberattacks, such as botnets, are emerging and evolving rapidly with high-risk attacks. These attacks disrupt IoT transition by disrupting networks and services for IoT devices. Many recent studies have proposed ML and DL techniques for detecting and classifying botnet attacks in the IoT environment. This project presents a straightforward approach to detect botnet activity within IoT networks through the utilization of machine learning techniques. By analyzing network traffic patterns and employing unsupervised learning algorithms, we demonstrate an effective method to identify and mitigate botnet threats in IoT environments. By this project we intend to offer a valuable contribution in enhancing the security of IoT ecosystem. Key Word: Internet of Things(IoT),cybersecurity, botnet attacks, machine learning(ML),UNSW-NB15 dataset, exploratory data analysis, XgBoost

Application of artificial intelligence in mine ventilation: a brief review.

In recent years, there has been a notable integration of artificial intelligence (AI) technologies into mine ventilation systems. A mine ventilation network presents a complex system with numerous interconnected processes, some of which pose challenges for deterministic simulation methods. The utilization of machine learning techniques and evolutionary algorithms offers a promising avenue to address these complexities, resulting in enhanced monitoring and control of air parameter distribution within the ventilation network. These methods facilitate the timely identification of resistance faults and enable prompt calculation of ventilation parameters during emergency scenarios, such as underground explosions and fires. Furthermore, evolutionary algorithms play a crucial role in the advancement of methods for visual analysis of ventilation systems. However, it is essential to acknowledge that the current utilization of AI technologies in mine ventilation is limited and does not encompass the full spectrum of challenging-to-formalize problems. Promising areas for AI application include analyzing changes in air distribution caused by unaccounted thermal draft and gas pressure, as well as developing novel approaches for calculating shock losses. Moreover, the application of AI technologies in optimizing large-scale mine ventilation networks remains an unresolved issue. Addressing these challenges holds significant potential for enhancing safety and efficiency in mine ventilation systems.