Obesity continues to pose a major global health concern, highlighting the urgent need for effective early risk assessment strategies. This study presents a comprehensive machine learning framework aimed at classifying obesity risk while ensuring transparency and interpretability in decision-making. By utilizing detailed data on individuals' physical attributes and lifestyle behaviors, the proposed system identifies patterns associated with varying obesity levels. A key focus of the project is to enhance the interpretability of predictions, allowing users and healthcare professionals to understand the reasoning behind the classification outcomes. This contributes not only to improved trust in the model's results but also supports the development of targeted and personalized preventive measures. Ultimately, the approach bridges the gap between advanced predictive capabilities and practical, human-understandable insights for better health management.

Heart failure remains a leading cause of mortality worldwide, necessitating robust predictive models to facilitate timely medical interventions. This study presents a machine learning framework for heart failure survival prediction, leveraging an optimized XGBoost model integrated with the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. Utilizing a dataset of 5000 clinical records, including features such as age, ejection fraction, and serum creatinine, we applied SelectKBest with Chi-square for feature selection to identify the most impactful predictors. The XGBoost model, selected after evaluating multiple algorithms (including Logistic Regression, Decision Tree, KNN, SVM, and Random Forest), was fine-tuned to optimize hyperparameters, achieving a test accuracy of 99.70%, with precision, recall, and F1-scores near 1.00, and an AUC-ROC of 0.9998. SMOTE effectively balanced the dataset, enhancing the model’s ability to predict minority class outcomes. Model performance was rigorously assessed using metrics like accuracy, confusion matrices, and learning curves to detect overfitting, with results indicating minimal overfitting in XGBoost. Compared to the baseline Gradient Boosting Machine (GBM) with Adaptive Inertia Weight Particle Swarm Optimization (AIW-PSO) from prior work, which achieved 94% accuracy on a smaller dataset (299 patients), our approach demonstrates superior performance, likely due to the larger dataset and advanced preprocessing. This study highlights the efficacy of XGBoost combined with SMOTE for clinical predictive tasks and offers a scalable, high-accuracy tool for heart failure prognosis, with potential to improve patient outcomes through precise and timely clinical decision-making.

Artificial intelligence (AI) continues to drive transformative changes across various domains, with medical science emerging as one of its most impactful beneficiaries. In particular, lung cancer—being one of the most fatal forms of cancer—demands advanced tools for early and reliable detection. This study introduces a comprehensive approach to lung cancer classification by incorporating synthetic data generation to address class imbalances and enhance model performance. The methodology involves augmenting medical datasets to improve data diversity and applying advanced machine learning techniques for predictive analysis. Extensive evaluations were carried out using multiple data preprocessing strategies and comparative models to ensure robustness and reliability. This framework demonstrates the potential of combining synthetic data augmentation with predictive modeling in aiding early diagnosis, supporting clinicians in making informed decisions, and ultimately contributing to more effective and personalized treatment strategies.

Crude oil is a globally significant energy resource whose price fluctuations have far-reaching economic and industrial impacts. Accurate forecasting of crude oil prices is crucial for strategic decision-making in sectors such as finance, energy, and transportation. This project presents a machine learning-based approach to predict monthly crude oil prices using historical market data and engineered time-series features. The model is developed using the CatBoost Regressor, a high-performance gradient boosting algorithm known for its efficiency, accuracy, and ability to handle complex non-linear data. The predictive features include lagged prices from previous months, rolling statistical indicators (mean and standard deviation), temporal features such as month and year (encoded using sine and cosine transformations to preserve seasonality), and both percentage and absolute monthly price changes. The dataset spans over four decades (1983–2025), ensuring that the model captures long-term patterns and short-term fluctuations. The performance of the model is evaluated using standard regression metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), demonstrating strong predictive accuracy and generalization capability. This project showcases the effectiveness of feature engineering combined with gradient boosting techniques for time-series forecasting and provides a reliable, interpretable, and scalable solution for crude oil price prediction.

Sports like basketball and baseball have seen significant advancements through the effective use of sports analytics. In contrast, machine learning applications in football have largely concentrated on outcome prediction rather than player evaluation. This study aims to bridge that gap by presenting a descriptive analysis of football player performance using a football-specific dataset. Traditionally, player performance assessments rely on expert panels, though the criteria they use remain undisclosed. In this research, the Support Vector Classifier (SVC) algorithm is employed to analyze and classify player performance data, identifying key functional attributes relevant to different playing positions. By tuning kernel functions and hyperparameters, the model effectively highlights the most impactful performance metrics, offering objective insights that align with expert evaluations. The dataset used comprises detailed performance data from football matches, making the analysis specific and relevant to the sport. The application of SVC allowed the development of highly accurate classifications with minimal error, thus validating the algorithm’s effectiveness in rating prediction tasks. The results indicate that SVC can serve as a powerful tool in football analytics, enabling data-driven decision-making for coaches, analysts, and scouts. This approach not only enhances transparency in player assessment but also supports more strategic planning based on performance-driven evidence.

Ransomware attacks are becoming increasingly frequent and sophisticated, posing serious challenges to cybersecurity defenses worldwide. Traditional detection methods often fall short when faced with the evolving nature of ransomware, which frequently employs obfuscation and evasion techniques to bypass static signature-based systems. In this study, a machine learning-based approach is proposed to detect ransomware through the analysis of API call data, which captures the dynamic behavior of programs during execution. The temporal dynamics, intricate sequential patterns, and high dimensionality inherent in ransomware behavior are key factors considered in this research. A Random Forest classifier is employed due to its robustness and ability to handle complex datasets, delivering high predictive accuracy.The model is trained using features derived from temporal intervals, API call frequencies, and sequential patterns, allowing it to effectively distinguish between ransomware and benign software. With an accuracy exceeding 95%, the system demonstrates strong predictive performance and practical applicability. This approach is further integrated into a Flask-based web application, enabling real-time detection in an interactive and user-friendly environment. The proposed method provides a scalable and efficient architecture for ransomware classification, offering security professionals a powerful tool for early threat identification and mitigation. By leveraging ensemble learning techniques, the system exemplifies the potential of behavioral analysis in advancing automated malware detection and strengthening overall cybersecurity resilience.

Rolling bearing faults frequently cause rotating equipment failure, leading to costly downtime and maintenance expenses. As a result, researchers have focused on developing effective methods for diagnosing these faults. In this paper, we explore the potential of Machine Learning (ML) techniques for classifying the health status of bearings. Our approach involves decomposing the signal, extracting statistical features, and using feature selection employing Binary Grey Wolf Optimization. We propose an ensemble method using voting classifiers to diagnose faults based on the reduced set of features. To evaluate the performance of our methods, we utilize several performance indicators. Our results demonstrate that the proposed voting classifiers method achieves superior fault classification, highlighting its potential for use in predictive maintenance applications.

Efficient agricultural planning is essential for ensuring global food security amid increasing population pressures and limited cultivable land. This study introduces a crop recommendation system that leverages machine learning techniques to analyze diverse parameters, including physical and chemical soil properties and environmental factors. The model is trained using the Random Forest Classifier, which achieved an accuracy of 99% on the test dataset. The system supports the recommendation of 22 crops, ranging from cereals and legumes to fruits and commercial crops. By employing a data-driven approach, the model assists farmers in selecting the most suitable crop based on current conditions, thereby promoting sustainable agricultural practices and maximizing yield. This work contributes to the advancement of intelligent agriculture and sets a foundation for future expansions such as seasonal crop rotation and precision farming solutions.

With the rapid expansion of the internet and digital services, ensuring network security has become a critical challenge. Traditional rule-based intrusion detection systems often fail to adapt to evolving cyber-attacks, making machine learning (ML) a promising alternative for anomaly detection in network traffic. This project focuses on developing an intelligent anomaly detection framework using advanced ML techniques. The KDD Cup 1999 dataset is used as the benchmark dataset, consisting of both normal and malicious network connections. Data pre-processing, feature selection, and model training are performed to enhance detection accuracy and efficiency. Several algorithms are evaluated, including Decision Trees, Random Forest, and Gradient Boosting methods. Among these, CatBoost, an advanced gradient boosting algorithm, demonstrates superior performance with an accuracy of over 99% on test data, outperforming the approaches mentioned in the base paper. The system effectively classifies traffic into normal and attack categories, offering a scalable and accurate solution for intrusion detection. Additionally, a Flask-based web application is developed with modules for user authentication, anomaly prediction, and visualization of model performance, making the solution practical for real-world deployment.

This study addresses the growing concern of mental health challenges faced by college students engaged in entrepreneurship. It proposes a novel evaluation tool designed to assess psychological well-being by capturing complex relationships within mental health data. The approach integrates advanced feature processing and a dual-structure network to enhance the recognition and evaluation of psychological characteristics. The model’s effectiveness is demonstrated through comprehensive testing on multiple public datasets from different countries, highlighting its strong predictive capabilities. The findings offer valuable insights for developing targeted mental health interventions, supporting entrepreneurship education, and providing practical guidance to improve the success rates of student entrepreneurs. This work holds significant implications for educational institutions and policy makers aiming to foster healthier and more supportive environments for young entrepreneurs.

Life Expectancy prediction models play a critical role in shaping the social, economic, and healthcare structures of countries worldwide. Accurate forecasting of life expectancy has profound implications for public health planning, resource allocation, insurance modeling, and the development of long-term care strategies. While traditional models have primarily relied on mortality rates and demographic statistics of target populations, these approaches often lack the complexity needed to capture the multifaceted nature of human longevity. Recent research emphasizes the integration of broader determinants such as education levels, healthcare access, economic indicators, and social welfare metrics to enhance prediction accuracy. In response to this growing need for more robust forecasting methods, this study explores the application of advanced machine learning algorithms to predict life expectancy across both developed and developing regions. Grid Search Cross-Validation (Grid Search CV) was utilized to fine-tune hyperparameters and prevent overfitting, thereby enhancing the generalization capability of the models. Among the ensemble methods, Random Forest and XGBoost emerged as top performers due to their robustness in handling complex, nonlinear relationships and high-dimensional data. Additionally, AdaBoost contributed significantly by focusing on correcting errors made by weaker models, leading to better convergence and stability in prediction. The use of Grid Search CV ensured that the optimal configuration of each algorithm was selected based on cross-validated performance metrics such as Mean Absolute Error (MAE) and R² score. This data-driven approach demonstrated substantial improvements over conventional statistical methods, highlighting the potential of machine learning in building dynamic and highly accurate life expectancy prediction systems.

Parkinson’s Disease (PD) is a chronic and progressive neurological disorder that affects the body's nervous system, leading to difficulties in movement, coordination, and speech. One of the early symptoms of PD is dysphonia—a voice disorder characterized by altered vocal quality, pitch, and loudness. Since seventy to ninety percent of individuals with Parkinson’s exhibit speech impairments, analyzing vocal features becomes a valuable approach for early diagnosis. Existing studies have applied various machine learning models to detect PD using speech signals; however, challenges such as class imbalance, optimal feature selection, and limited interpretability remain prevalent. To overcome these limitations, this study introduces a predictive framework utilizing the K-Nearest Neighbors (KNN) algorithm for the detection of Parkinson’s disease based on speech data. KNN, a non-parametric and instance-based learning algorithm, classifies patients by comparing their vocal feature patterns with those of the nearest neighbors in the dataset. Its simplicity, flexibility in handling non-linear data distributions, and effectiveness with small to medium-sized datasets make it suitable for medical diagnostic tasks. By optimizing the value of k and using distance metrics such as Euclidean or Manhattan distance, the model can achieve high accuracy in distinguishing PD patients from healthy individuals. Moreover, feature normalization and dimensionality reduction techniques are applied to improve the performance and reliability of KNN. This approach aims to enhance the precision of early Parkinson’s detection while maintaining interpretability, offering a clinically relevant and data-driven solution compared to traditional diagnostic methods.

The phenomenon of cancellations in hotel bookings is a significant challenge in the hospitality sector as it distorts demand forecasting and can lead to substantial revenue losses. Forecasting booking cancellations remains relatively underexplored, particularly in understanding the behavioral factors driving cancellations. This paper proposes a novel approach to predicting hotel booking cancellations using a Multi-Layer Perceptron (MLP) Classifier, a type of deep learning model capable of capturing non-linear relationships in structured datasets. The MLP Classifier strengthens the prediction process by analyzing various customer and booking-related attributes such as lead time, room type, location, and customer segment. By adjusting hyperparameters including the number of hidden layers, neurons, activation functions, and learning rate, the model effectively learns patterns associated with cancellations, leading to highly accurate predictions. This approach provides hotel managers with a reliable forecasting tool to anticipate cancellation risks, enabling more effective demand planning, resource allocation, and revenue management strategies.

Port congestion and prolonged vessel waiting times present significant obstacles to the efficiency of global maritime logistics, resulting in elevated operational costs and logistical inefficiencies. In response to these challenges, this study introduces a classification-based predictive framework leveraging the Random Forest Classifier to categorize vessel arrivals into different delay risk levels. By classifying expected waiting times into discrete categories, this approach enables port authorities to make informed, real-time decisions regarding resource allocation and scheduling priorities. Unlike traditional regression models that predict continuous delay durations, the proposed classification model focuses on identifying critical thresholds that signify congestion risk. This shift allows for more actionable insights, particularly in time-sensitive port operations. The use of the Random Forest Classifier as the core predictive model enhances accuracy and robustness by combining multiple decision trees into an ensemble, reducing overfitting and improving generalization. Its ability to handle heterogeneous maritime data ensures reliable performance across diverse operational scenarios. Furthermore, Random Forest provides measures of feature importance, enabling the identification of key contributing factors—such as voyage characteristics, berth availability, and historical delay patterns—that influence vessel waiting times. This framework offers a scalable and practical solution for intelligent transportation systems, aligning with the growing need for smart port management and optimized logistics planning.

Efficient and reliable operation of photovoltaic (PV) systems is crucial for sustainable energy generation. However, faults such as partial shading and dirt accumulation significantly reduce the power output of PV modules. To address this issue, this project presents a machine learning-based fault detection framework for classifying and identifying faults in PV systems using only electrical and environmental data. The proposed system utilizes supervised machine learning algorithms including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and Artificial Neural Network (ANN). The dataset comprises key features such as voltage, current, ambient temperature, and irradiance, collected under three operating conditions: normal operation, partial shading, and dirt accumulation. Data preprocessing techniques such as normalization, label encoding, and data splitting were applied to prepare the dataset for training and testing. The performance of each model was evaluated using standard classification metrics: accuracy, precision, recall, F1-score, and confusion matrix. Experimental results demonstrate that all models performed well in detecting faults within the same system used for training, with the Artificial Neural Network achieving the highest accuracy, exceeding 98% precision. However, when models were tested on data from a different PV system with varying characteristics, performance degraded, highlighting the need for system-specific model training. This study concludes that machine learning models, particularly ANN, are effective for fault detection in PV systems when trained on relevant system data. The approach offers a cost-effective and automated solution for enhancing the reliability and performance of solar energy systems.

Electrodermal activity (EDA) has emerged as a valuable physiological indicator for assessing pain levels in individuals. This study focuses on identifying key features within EDA signals that are influential in classifying pain responses. The methodology involves decomposing EDA signals, extracting meaningful features based on signal characteristics such as domain amplitude and frequency, and applying classification techniques to distinguish between painful and non-painful stimuli. Feature selection methods are also employed to enhance model performance by identifying the most relevant attributes. The results confirm that EDA signals provide reliable insights for pain level classification, highlighting their potential application in healthcare for more accurate and automated pain assessment.

People can use credit cards for online transactions as it provides an efficient and easy-to-use facility. With the increase in usage of credit cards, the capacity of credit card misuse has also enhanced. Credit card frauds cause significant financial losses for both credit card holders and financial companies. In this research study, the main aim is to detect such frauds, including the accessibility of public data, high-class imbalance data, the changes in fraud nature, and high rates of false alarm. The relevant literature presents many machine learning based approaches for credit card detection, such as Extreme Learning Method, Decision Tree, Random Forest, Support Vector Machine, Logistic Regression and XG Boost. However, due to low accuracy, there is still a need to apply state of the art deep learning algorithms to reduce fraud losses. The main focus has been to apply the recent development of deep learning algorithms for this purpose. Comparative analysis of both machine learning and deep learning algorithms was performed to find efficient outcomes. The detailed empirical analysis is carried out using the European card benchmark dataset for fraud detection. A machine learning algorithm was first applied to the dataset, which improved the accuracy of detection of the frauds to some extent. Later, three architectures based on a convolutional neural network are applied to improve fraud detection performance. Further addition of layers further increased the accuracy of detection. A comprehensive empirical analysis has been carried out by applying variations in the number of hidden layers, epochs and applying the latest models. The evaluation of research work shows the improved results achieved, such as accuracy, f1-score, precision and AUC Curves having optimized values of 99.9%,85.71%,93%, and 98%, respectively. The proposed model outperforms the state-of-the-art machine learning and deep learning algorithms for credit card detection problems. In addition, we have performed experiments by balancing the data and applying deep learning algorithms to minimize the false negative rate. The proposed approaches can be implemented effectively for the real-world detection of credit card fraud.

Insurance fraud, particularly within the automobile insurance sector, is a significant challenge faced by insurers, leading to financial losses and influencing pricing strategies. Fraud detection models are often impacted by class imbalance, where fraudulent claims are much rarer than legitimate claims, and missing data further complicates the process. This research tackles these issues by utilizing two car insurance datasets—an Egyptian real-life dataset and a standard dataset. The proposed methodology includes addressing missing data and class imbalance, and it incorporates the AdaBoost Classifier to enhance the model’s accuracy and predictive power. The results demonstrate that addressing class imbalance plays a crucial role in improving model performance, while handling missing data also contributes to more reliable predictions. The AdaBoost Classifier significantly outperforms existing techniques, improving prediction accuracy and reducing overfitting, which is often a challenge in fraud detection models. This study presents valuable insights into how improving data quality and using advanced algorithms like AdaBoost can enhance fraud detection systems, ultimately leading to more effective identification of fraudulent claims. These enhancements can significantly aid insurance companies in reducing financial losses, improving decision-making, and refining pricing models.

Breast cancer continues to be one of the leading causes of cancer-related deaths among women worldwide. Early and accurate diagnosis is crucial to improving treatment outcomes and survival rates. This study develops a robust machine learning-based breast cancer classification system utilizing the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Emphasizing the importance of feature selection, the study identifies the most influential tumor characteristics that significantly contribute to malignancy prediction. Multiple classification algorithms—including Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbors (k-NN)—were implemented and rigorously evaluated to determine their predictive performance. After applying feature scaling and selecting optimal features, the SVM model achieved the highest classification accuracy of 96.49%, demonstrating its effectiveness in distinguishing between benign and malignant tumors. To facilitate practical clinical application, the model was deployed via a web-based interface built using Flask, allowing healthcare professionals to input tumor measurements and receive immediate diagnostic predictions. This deployment bridges the gap between complex machine learning models and real-world usability, supporting early detection efforts in clinical settings. The project underscores the potential of combining advanced computational techniques with intuitive interfaces to improve diagnostic workflows. Future work aims to integrate explainable artificial intelligence (XAI) methods to enhance transparency in prediction outcomes and to extend the model’s applicability by incorporating diverse and larger clinical datasets, thereby increasing its robustness and generalizability.

Electric Vehicle (EV) adoption is rapidly increasing, necessitating efficient and reliable battery management systems, especially in battery swapping infrastructures. This project presents a Machine Learning-driven web application for real-time battery health estimation, aimed at enhancing the efficiency of EV battery swapping systems. Built using Flask as the backend web framework and Python for data processing and machine learning, the system predicts two critical parameters of battery condition: State of Health (SoH) and remaining charge cycles.To achieve accurate predictions, the application leverages Random Forest Regression and XGBoost, two powerful ensemble learning algorithms, trained on historical battery usage data including charge/discharge current, voltage, temperature, and cycle counts. The system processes user input in real time and displays the battery’s health status via a user-friendly interface, enabling swift decision-making at battery swapping stations. This solution not only promotes proactive maintenance and optimal utilization of EV batteries but also supports sustainable energy practices by reducing the chances of premature battery disposal. The combination of ML with a lightweight Flask-based deployment makes the application scalable, efficient, and suitable for integration into real-world EV infrastructure.

Pregnancy complications remain a major concern in maternal and fetal healthcare, where early and accurate detection is essential for effective intervention. Conventional manual interpretation of Cardiotocography (CTG) data is time-consuming and often subjective, leading to inconsistencies in fetal health assessment. To address this, the present study proposes an efficient machine learning-based system utilizing the XGBoost algorithm, known for its superior performance in handling structured data. Leveraging a publicly available CTG dataset, the proposed model achieves a notable accuracy of 96%, significantly outperforming earlier approaches. This demonstrates XGBoost's capability to model complex patterns in fetal monitoring data with high reliability. The system enhances diagnostic precision, reduces clinician workload, and supports timely clinical decision-making. This work highlights the potential of integrating robust ML models into prenatal care workflows, thereby advancing automated fetal health evaluation and improving maternal and neonatal outcomes.

Electricity demand forecasting plays a pivotal role in power grid management, enabling optimal resource allocation, load balancing, and energy trading decisions. Traditional statistical models like ARIMA or linear regression struggle to handle high-dimensional, non-linear relationships commonly found in modern datasets that include weather variations, seasonal shifts, and holiday influences. To address these limitations, this study proposes a deep learning-based framework using a Multi-Layer Perceptron (MLP) architecture tailored for short-term national power load prediction. The model is trained on a real-world dataset comprising 15 continuous and categorical features, such as surface temperature (T2M), humidity (QV2M), wind speed (W2M), and atmospheric liquid water content (TQL), collected from three different geographical zones — TOC, SAN, and DAV — along with holiday and school calendar data. Each input is normalized using Min-Max Scaling to stabilize the learning process and accelerate convergence. The model architecture features multiple fully connected layers with ReLU activation functions to effectively model non-linear dependencies. Through extensive experimentation and hyperparameter tuning, the proposed MLP model achieved a Mean Absolute Percentage Error (MAPE) of 4.81%, showcasing significant accuracy and reliability in predicting power demand. Compared to traditional approaches, this deep learning method offers better generalization, is less prone to feature correlation pitfalls, and adapts well to changing patterns in weather and user behavior. Such a solution is vital for energy utilities seeking a scalable, data-driven strategy to anticipate load fluctuations and prevent outages in real time.

Healthcare fraud detection is a critical task that faces significant challenges due to imbalanced datasets, which often result in suboptimal model performance. Previous studies have primarily relied on traditional machine learning (ML) techniques, which struggle with issues like overfitting caused by Random Oversampling (ROS), noise introduced by the Synthetic Minority Oversampling Technique (SMOTE), and crucial information loss due to Random Undersampling (RUS). In this study, we propose a novel approach to address the imbalanced data problem in healthcare fraud detection, with a focus on the Medicare Part B dataset. Our approach begins with the careful extraction of the categorical feature "Provider Type," which allows for the generation of new, synthetic instances by replicating existing types to enhance diversity within the minority class. To further balance the dataset, we employ a hybrid resampling technique, SMOTE-ENN, which integrates the Synthetic Minority Oversampling Technique (SMOTE) with Edited Nearest Neighbors (ENN) to generate synthetic data points while removing noisy, irrelevant instances. This combined technique not only balances the dataset but also helps in mitigating the potential adverse effects of imbalanced data. We evaluate the performance of the logistic regression model on the resampled dataset using common evaluation metrics such as accuracy, F1 score, recall, precision, and the AUC-ROC curve. Additionally, we emphasize the importance of the Area Under the Precision-Recall Curve (AUPRC) as a critical metric for evaluating model performance in imbalanced scenarios. The experimental results demonstrate that logistic regression achieves an impressive 98% accuracy, outperforming other methods and validating the efficacy of our proposed approach for detecting healthcare fraud in imbalanced datasets.

Sleep apnea is a prevalent and potentially life-threatening sleep disorder characterized by frequent interruptions in breathing during sleep. These episodes can lead to serious health complications including cardiovascular diseases, fatigue, and impaired cognitive function. While recent deep learning models have shown strong performance in apnea detection, they often require high computational resources and lack transparency, making them less suitable for real-time or edge-based healthcare applications. In this study, we propose a machine learning-based approach using Extreme Gradient Boosting (XGBoost) to detect sleep apnea events efficiently and accurately from physiological signals.The proposed system involves a robust feature engineering pipeline that extracts statistical, temporal, and frequency-domain features from biosignals such as ECG, airflow, and oxygen saturation (SpO₂) data. These features are selected and refined using correlation analysis and feature importance techniques to eliminate redundancy and enhance classification performance. The extracted features are then input to an XGBoost classifier, which is optimized using cross-validation and hyperparameter tuning to address class imbalance and improve generalization. Our method is evaluated on the Sleep Heart Health Study (SHHS) dataset and demonstrates competitive accuracy while significantly reducing computational overhead compared to deep learning models. Moreover, the model provides interpretable outputs through feature importance analysis, allowing clinical professionals to better understand decision factors. This makes the approach highly suitable for real-time monitoring, portable device deployment, and explainable AI-driven diagnostics in the context of sleep apnea and related disorders.

Early and accurate detection of rice leaf diseases is essential for maintaining crop health, preventing yield loss, and supporting sustainable agriculture. Traditional identification methods rely heavily on expert inspection, which is time-consuming, subjective, and unsuitable for large-scale implementation. This study proposes a scalable image-based framework for detecting and classifying rice leaf diseases using advanced deep learning techniques. A DenseNet121 transfer learning model was employed, combined with extensive data augmentation and progressive fine-tuning, enabling the system to extract robust features directly from rice leaf images without requiring manual segmentation or handcrafted features. The framework was trained and validated on a dataset comprising six major rice leaf disease categories and achieved a 97.35% test accuracy, significantly outperforming conventional approaches. The results demonstrate that the proposed system is highly effective for automated rice disease detection and offers a reliable foundation for future integration into decision-support tools for precision agriculture.

Handwritten English text often varies greatly in style, shape, and clarity, making it difficult for machines to accurately recognize and convert such text into readable digital form. This project focuses on building an intelligent system capable of recognizing unclear or complex handwritten words and converting them into clear, machine-readable English text. The proposed approach employs a Convolutional Recurrent Neural Network (CRNN) architecture that integrates a pretrained ResNet18 for spatial feature extraction and a Bidirectional Long Short-Term Memory (BiLSTM) network for sequence learning. The use of Connectionist Temporal Classification (CTC) loss enables end-to-end training without the need for explicit character segmentation, allowing the model to handle varying word lengths and handwriting styles. The system effectively learns to identify patterns and structures in handwritten text, achieving high accuracy in both character-level and word-level recognition. Experimental evaluation demonstrates that the proposed model provides robust and efficient performance, outperforming traditional CNN-based classifiers. This work contributes toward the development of a reliable deep learning framework for general-purpose handwritten English word recognition.

Kidney diseases such as cysts, tumors, and stones are life-threatening conditions that require early and accurate detection for effective treatment. Traditional diagnostic methods using CT scans often depend on manual interpretation by radiologists, which can be time-consuming and prone to error. To address this challenge, we propose a deep learning–based automated system for multi-class kidney disease classification using CT images. The system utilizes EfficientNetV2B0, a state-of-the-art convolutional neural network, to extract deep features from CT scans. A custom classification head with Global Average Pooling, Dropout, and Dense layers is employed to classify images into four categories: Normal, Cyst, Tumor, and Stone. Data augmentation and class weighting are applied to handle dataset imbalance and improve generalization. The model achieves high accuracy and robustness, outperforming conventional CNN approaches. Furthermore, the trained model is integrated into a Flask web application, providing a user-friendly interface with functionality for image upload, real-time prediction with confidence scores, and visualization of training results through charts. This approach demonstrates the potential of advanced deep learning models combined with web deployment to support radiologists in fast, reliable, and scalable kidney disease diagnosis.

Artificial intelligence has significantly transformed the landscape of fraud detection in the banking sector. This study investigates the integration of advanced AI techniques to strengthen credit card fraud prevention systems. By leveraging the structural relationships within transaction data and applying data compression and anomaly detection strategies, the approach enhances the identification of suspicious activities. Case studies from two banking institutions are used to validate the methodology, demonstrating its ability to detect fraudulent behavior effectively. The system emphasizes adaptability to evolving fraud patterns and the capacity to analyze complex financial data efficiently. Overall, the research highlights the importance of intelligent data-driven models in improving the reliability and responsiveness of banking fraud detection frameworks.

Detecting anomalies in industrial machine sounds is essential for maintaining operational efficiency, preventing costly equipment failures, and ensuring workplace safety. However, this task is challenging due to the complex and variable nature of industrial environments, including background noise and changing operating conditions. This study presents a comprehensive approach that utilizes advanced feature extraction methods to capture the important acoustic characteristics of machinery sounds. Various machine learning and deep learning techniques are applied to explore effective strategies for anomaly detection. The research employs multiple datasets to evaluate the proposed methods under different experimental conditions. Results from extensive testing demonstrate the effectiveness of the approach in real-world industrial settings, highlighting its potential for enhancing predictive maintenance through improved sound analysis. This work contributes to advancing the capabilities of industrial monitoring systems by providing a reliable means to detect abnormalities in machine operations.

Melanoma remains one of the deadliest forms of skin cancer, responsible for the majority of skin cancer-related deaths worldwide. Early and accurate detection is crucial for improving patient survival, yet visual diagnosis through dermoscopy is often difficult and prone to variability, even among expert dermatologists. In this study, we propose an advanced deep learning–based framework for melanoma classification using dermoscopic images, aimed at improving diagnostic accuracy and confidence compared to traditional convolutional neural networks (CNNs) explored in prior work. Unlike the base study that employed semi-supervised multi-teacher ensemble learning, our approach leverages a supervised training pipeline with a state-of-the-art ConvNeXt architecture, focal loss to address class imbalance, and progressive fine-tuning to enhance feature extraction. The model was trained and validated on a stratified dataset split into training, validation, and test sets, achieving 92% classification accuracy and an AUC of 0.974 on the independent test set—outperforming baseline CNN ensembles. Comprehensive evaluation using confusion matrices, ROC analysis, and Grad-CAM visualizations further demonstrated the model’s robustness and interpretability. These results highlight the potential of ConvNeXt-based architectures for reliable and explainable melanoma diagnosis, offering clinicians an effective decision-support tool for early detection and management of skin cancer

Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, emphasizing the need for early and accurate diagnosis. Histopathological examination of colorectal tissue is the clinical gold standard but involves time-consuming manual processes that are susceptible to human error. To address this, the study introduces an advanced dual-track deep learning architecture aimed at automating the classification of CRC histopathology images. The model incorporates a mechanism for capturing both global and local features from tissue samples and enhances focus on diagnostically significant regions through attention refinement. By integrating multiple layers of feature extraction and attention strategies, the proposed system improves the quality and precision of the analysis. This approach demonstrates potential for aiding pathologists, reducing diagnostic workload, and increasing the reliability of colorectal cancer detection.

Clinically, the proportion and classification of white blood cells (WBCs) are traditionally performed through manual microscopic analysis, which is subjective and time-consuming. To address this, automated methods using machine learning (ML) and deep learning (DL) have been explored. In this study, we propose an advanced WBC classification system based on the MobileNetV2 architecture, which offers a lightweight yet highly efficient solution for medical image analysis. Open datasets such as Raabin-WBC and private clinical data were used to validate the model. WBC images were preprocessed using U-Net for effective segmentation of the nucleus, cytoplasm, and whole cell regions. The MobileNetV2 model was then applied for feature extraction and classification, leveraging its inverted residual structure and depthwise separable convolutions to enhance accuracy while reducing computational cost. Experimental results demonstrated that the proposed model achieved a classification accuracy of outperforming traditional ML-based approaches and providing results comparable to state-of-the-art deep learning architectures. The system was further integrated into a user-friendly graphical interface for real-time clinical use, completing the segmentation and classification tasks . The application of MobileNetV2 in this context significantly improves diagnostic efficiency, scalability, and consistency in peripheral blood smear (PBS) testing, making it an effective tool for assisting hematologists in routine diagnostics.

Bean rust and angular leaf spot are major threats to bean cultivation, significantly affecting crop health and reducing yields. Timely and accurate disease detection is essential for maintaining agricultural productivity, yet conventional diagnostic methods often rely on expert intervention and are not scalable for large farming operations.This research introduces an explainable deep learning-based framework tailored for bean leaf disease classification. The model is designed to effectively recognize visual disease patterns, both broad and subtle, across diverse leaf samples. To ensure transparency in decision-making, the framework integrates visual interpretability features that highlight the specific regions of the leaf image influencing the classification outcomes. The system was trained and validated using a curated dataset of bean leaf images covering multiple disease conditions and healthy samples. It demonstrated robust performance in recognizing and distinguishing between disease types, emphasizing its potential as a valuable tool for real-time, automated crop monitoring. The model's lightweight design and interpretability make it suitable for deployment in field environments, contributing to smarter, data-driven agricultural practices.

In contemporary society, many health challenges are being effectively addressed through the application of computer science and artificial intelligence. Cardiac arrhythmia, a critical cardiovascular disorder, poses serious health risks and requires timely detection to prevent fatalities. This study explores the use of advanced AI techniques to improve early diagnosis of high-risk cardiac arrhythmia cases. We propose a novel methodology based on the NasNetMobile architecture, a lightweight and efficient convolutional neural network designed to extract meaningful features from ECG signals for accurate classification. The proposed NasNetMobile-based framework integrates deep learning capabilities to enhance the detection of cardiac arrhythmias by leveraging its optimized network structure for mobile and resource-constrained environments. This approach aims to provide a more accessible and effective solution for continuous cardiac monitoring and early warning, potentially supporting healthcare providers in delivering timely interventions and improving patient outcomes.

Heart disease remains one of the leading causes of death globally. Echocardiography is a widely used technique for diagnosing cardiovascular conditions, yet accurately interpreting echocardiogram images requires specialized medical expertise. To address this challenge, this study introduces a deep learning-based approach utilizing the EfficientNetB0 architecture for the automatic classification of heart diseases from echocardiogram data. EfficientNetB0, known for its compound scaling method that balances network depth, width, and resolution, provides a lightweight yet powerful solution for medical image analysis. The model is trained to automatically extract complex and discriminative features from echocardiographic images, reducing reliance on manual interpretation. By leveraging its efficiency and strong generalization capability, EfficientNetB0 ensures high accuracy while maintaining low computational cost, making it particularly suitable for real-time clinical use. This approach aims to support medical professionals in improving diagnostic speed, consistency, and accessibility. The proposed system holds promise for enhancing early detection and prognosis of cardiovascular diseases, ultimately making advanced diagnostic capabilities more scalable across diverse healthcare settings.

This study introduces a deep learning model for remote sensing lithology classification that leverages the efficiency of EfficientNetB0, a lightweight yet powerful convolutional neural network architecture. The model automates the identification and classification of various rock types in remote sensing images, effectively addressing the challenges of multi-class classification. EfficientNetB0 is employed as the primary feature extractor due to its compound scaling strategy, which uniformly balances network depth, width, and resolution. This design enables accurate recognition of complex geological patterns while maintaining low computational cost and memory usage. The proposed approach preprocesses the input images, extracts hierarchical spatial features using EfficientNetB0, and employs a classification head to predict the corresponding lithology classes. By integrating EfficientNetB0, the framework combines the advantages of automated feature learning with high scalability and practical applicability for large-scale geospatial datasets. Optimization techniques such as backpropagation, dropout, and regularization are applied to improve generalization and minimize overfitting. This enhances the robustness of the model in handling diverse lithological features across varying terrain conditions. The proposed system offers a reliable and efficient solution for automatic rock type classification in remote sensing, contributing to the advancement of geoscientific research and supporting real-time applications in mineral exploration, environmental monitoring, and geological mapping.

We research how deep learning convolutional neural networks (CNN) can be used to automatically classify the unique data of naval ships images from the dataset collection. We investigate the impact of data preprocessing and externally obtained images on model performance and propose the Xception algorithm as an enhancement to our existing CNN approach. Additionally, we explore how the models can be made transparent using visually appealing interpretability techniques. Our findings demonstrate that the Xception algorithm significantly improves classification performance compared to the traditional CNN approach. The results highlight the importance of appropriate image preprocessing, with image combined with soft augmentation contributing notably to model performance. This research is original in several aspects, notably the uniqueness of the acquired dataset and the analytical modeling pipeline, which includes comprehensive data preprocessing steps and the use of deep learning techniques. Furthermore, the research employs explanatory tools like Xception to enhance model interpretability and usability. We believe the proposed methodology offers significant potential for documenting historic image collections.

After the coronavirus disease 2019 (COVID-19) outbreak, the viral infection known as monkeypox gained significant attention, and the World Health Organization (WHO) classified it as a global public health emergency. Due to the similarities between monkeypox and other pox viruses, traditional classification methods face challenges in accurately identifying the disease. Moreover, the sharing of sensitive medical data raises concerns about privacy and security. Integrating deep neural networks with federated learning (FL) offers a promising approach to overcome these challenges in medical data categorization. In this context, we propose an FL-based framework leveraging the Xception deep learning model to securely classify monkeypox and other pox viruses. The proposed framework utilizes the Xception model for classification and a federated learning environment to ensure data security. This approach allows the model to be trained on distributed data sources without transferring sensitive data, thus enhancing privacy protection. The federated learning environment also enables collaboration across institutions while maintaining the confidentiality of patient data. The experiments are conducted using publicly available datasets, demonstrating the effectiveness of the proposed framework in providing secure and accurate classification of monkeypox disease. Additionally, the framework shows promise in other medical classification tasks, highlighting its potential for widespread application in the healthcare sector.

Mango leaf diseases pose a significant threat to crop yield and quality, directly impacting agricultural productivity and farmer livelihoods. Accurate and timely detection of these diseases is essential for effective disease management and sustainable farming practices. This project proposes a deep learning-based approach for automated mango leaf disease classification using the Xception (Extreme Inception) model, a state-of-the-art convolutional neural network (CNN) architecture. Xception leverages depthwise separable convolutions to efficiently capture fine-grained patterns such as texture, color variations, and lesion shapes in leaf images, enabling high accuracy in disease classification. To ensure data privacy and scalability, the system incorporates federated learning, allowing multiple agricultural institutions or farmer cooperatives to collaboratively train the model without sharing raw data. This decentralized approach prevents unauthorized access to sensitive datasets while continuously improving model performance across diverse environments. The proposed system thus combines advanced deep learning techniques with privacy-preserving collaborative learning, offering a robust, secure, and scalable solution for early detection and classification of mango leaf diseases. Its implementation can contribute significantly to precision agriculture, reducing economic losses and supporting sustainable crop management.

Osteoporosis is a skeletal disease that is difficult to identify in advance of symptoms. Existing skeletal disease screening methods, such as dual-energy X-ray absorptiometry, are only used for specific purpose due to cost and safety reasons once symptoms develop. Early detection of osteopenia and osteoporosis using other modalities for relatively frequent examinations is helpful in terms of early treatment and cost. Recently, many studies have proposed deep learning-based osteoporosis diagnosis methods for various modalities and achieved outstanding results. However, these studies have limitations in clinical use because they require tedious processes, such as manually cropping a region of interest or diagnosing osteoporosis rather than osteopenia. In this study, we present a classification task for diagnosing osteopenia and osteoporosis using computed tomography (CT). Additionally, we propose a multi-view CT network (MVCTNet) that automatically classifies osteopenia and osteoporosis using two images from the original CT image. Unlike previous methods that use a single CT image as input, the MVCTNet captures various features from the images generated by our multi-view settings. The MVCTNet comprises two feature extractors and three task layers. Two feature extractors use the images as separate inputs and learn different features through dissimilarity loss. The target layers learn the target task through the features of the two feature extractors and then aggregate them. For the experiments, we use a dataset containing 2,883 patients’ CT images labeled as normal, osteopenia, and osteoporosis. Additionally, we observe that the proposed method improves the performance of all experiments based on the quantitative and qualitative evaluations

The automated classification and quality grading of fruits are critical components in advancing agricultural efficiency, yet remain underutilized in current computer vision applications. This study presents a dual-stage deep learning approach leveraging same-domain transfer learning with the NASNetMobile architecture for simultaneous fruit type recognition and quality assessment. Initially, the model is trained to classify six distinct fruits—banana, apple, orange, pomegranate, lime, and guava—using the FruitNet dataset. The learned parameters from this classification task are then transferred to a secondary grading model to evaluate the quality of the identified fruits. To overcome dataset imbalance and enhance generalization, a fusion of data augmentation strategies including AugMix, CutMix, and MixUp is employed. Experimental results confirm that this methodology improves both classification and grading performance, highlighting the effectiveness of intra-domain transfer learning. The proposed framework offers a scalable and efficient solution for real-time fruit inspection systems, contributing significantly to the development of intelligent agricultural automation technologies.

In this work, we extend the recently proposed Quantum Vision (QV) theory in deep learning for object recognition by integrating it with the Xception architecture, forming a novel Heavy QV-Xception model. The QV theory, inspired by the particle-wave duality in quantum physics, treats objects as information waves rather than static images, enabling deep neural networks to capture richer representations. Building on this concept, our Heavy QV-Xception model leverages a robust QV block to transform conventional images into wave-function representations and processes them through the depthwise separable convolutional layers of Xception for enhanced feature extraction. This hybrid approach benefits from both the quantum-inspired information representation and the efficient, high-performance architecture of Xception. Extensive experiments on multiple benchmark datasets demonstrate that the Heavy QV-Xception model consistently outperforms standard Xception and other conventional CNNs, highlighting the effectiveness of combining QV theory with advanced deep learning architectures for improved object recognition accuracy.

Early detection of ovarian cancer remains a major challenge due to subtle symptoms and poor survival rates. This study presents a comparative analysis of machine learning (ML) and deep learning (DL) models for predicting ovarian cancer using clinical and biomarker data. The dataset undergoes comprehensive preprocessing, including handling missing values, outlier removal, normalization, and dimensionality reduction via PCA. Feature selection techniques such as Feature Importance, Recursive Feature Elimination (RFE), and autoencoder-based methods are employed to enhance model performance. Various classifiers—including KNN, SVM, Logistic Regression, Random Forest, and deep networks like ANN, FNN, CNN, RNN, and Xception—are evaluated. Our results indicate that the Xception model, combined with autoencoder-based feature selection, achieved the highest accuracy, demonstrating its capability to capture complex feature interactions. This study highlights the significance of integrating optimized preprocessing, feature engineering, and deep learning for effective early diagnosis of ovarian cancer.

Early and accurate diagnosis of Alzheimer’s disease (AD), particularly the transition from Cognitively Normal (CN) to Mild Cognitive Impairment (MCI), is crucial for timely intervention and improved patient outcomes. Building upon existing deep learning approaches that utilize convolutional neural networks (CNNs) with channel attention mechanisms, this research develops an enhanced model based on the Xception architecture. The proposed system employs advanced feature extraction techniques to capture both local and global neuroimaging features, integrated through a learned fusion mechanism. Our model significantly outperforms previous methods, achieving an accuracy of 99% in binary classification of CN versus MCI subjects. This improvement underscores the potential of the Xception network in capturing subtle imaging biomarkers associated with early cognitive decline. The system aims to provide a reliable, accessible diagnostic tool to support clinicians in early Alzheimer’s detection, facilitating timely and targeted therapeutic strategies.

The increasing prevalence of thyroid cancer underscores the critical need for efficient classification and early detection of thyroid nodules. Automated systems can significantly aid physicians by expediting diagnostic processes. However, achieving this goal remains challenging due to limited medical image datasets and the complexity of feature extraction. This study addresses these challenges by emphasizing the extraction of meaningful features essential for tumor detection. The proposed approach integrates advanced techniques for feature extraction, enhancing the capability to classify thyroid nodules in ultrasound images. The classification framework includes distinguishing between benign and malignant nodules, as well as identifying specific suspicious classifications. The combined classifiers provide a comprehensive characterization of thyroid nodules, demonstrating promising accuracy in preliminary evaluations. These results mark a significant advancement in thyroid nodule classification methodologies. This research represents an innovative approach that could potentially offer valuable support in clinical settings, facilitating more rapid and accurate diagnosis of thyroid cancer.

Fingerprint Liveness Detection (FLD) is a critical component of biometric authentication systems, protecting them from presentation attacks using artificial fingerprints fabricated from materials such as silicone, gelatine, and latex. While existing methods based on Convolutional Neural Networks (CNNs) or multimodal biometric traits provide promising results, they often increase system complexity, computational cost, or hardware requirements. To overcome these limitations, this paper introduces a lightweight deep learning framework for robust fingerprint liveness detection. The proposed system employs an efficient object detection model with an enhanced backbone and decoupled detection head, enabling the extraction of fine ridge-level features such as pore distribution and distortions, as well as global liveness cues like perspiration dynamics and texture irregularities. Unlike multimodal approaches that require auxiliary biometric data, the framework operates solely on fingerprint images, ensuring hardware simplicity while retaining high discriminative power. The model is trained end-to-end on benchmark datasets, incorporating advanced regularization and a cosine-annealed Adam optimizer to improve generalization and reduce overfitting. Experimental evaluations confirm that the proposed framework achieves superior spoof detection accuracy, strong resistance to novel attack materials, and fast inference speed compared to state-of-the-art approaches. With its lightweight design and adaptability, the system offers a practical and scalable solution for enhancing the reliability of biometric authentication in real-world scenarios.

Predicting adolescent concern over unhealthy food advertisements is critical for promoting health awareness and guiding public policy. This study utilizes XGBoost, a gradient boosting machine learning model, to predict concern levels among adolescents based on demographic and behavioral features. Survey data from 1030 adolescents were collected, including age, parental education, and advertisement exposure types, such as celebrity endorsements and free toys. The model is trained with hyperparameter tuning and synthetic oversampling to handle imbalanced classes. Explainable AI techniques (LIME and SHAP) are applied to interpret feature importance, providing insights into which factors most influence adolescent concern. Results demonstrate that XGBoost achieves high predictive accuracy, offering an effective and interpretable solution for understanding and mitigating the impact of unhealthy food advertisements.