Sleep disorders, such as Insomnia, Sleep Apnea, and other conditions, significantly impact individuals' health and well-being. Accurate and efficient classification of these disorders can aid in early diagnosis and effective treatment, enhancing the quality of life for affected individuals. The existing systems predominantly rely on Artificial Neural Networks (ANN) for classification, which, while effective, can be computationally intensive and less interpretable. This study proposes a Random Forest-based approach for classifying sleep disorders, utilizing a dataset consisting of 400 samples with 13 relevant features. Random Forest model was selected for its robustness, interpretability, and superior ability to handle complex, non-linear relationships within the data. By employing this algorithm, the study aims to classify sleep disorders into three classes: Insomnia, None, and Sleep Apnea, demonstrating improved performance compared to traditional ANN-based systems. The evaluation of the Random Forest model is conducted using standard performance metrics, including accuracy, precision, recall, and F1-score, which show that the proposed approach outperforms existing models, offering enhanced accuracy and reliability in the classification of sleep disorders.

Cardiovascular diseases (CVD) are a leading cause of death worldwide, highlighting the need for effective early detection methods. This study presents a machine learning-based approach for detecting cardiovascular diseases using optimal feature selection techniques and the Random Forest algorithm. The model is designed to enhance predictive accuracy by identifying the most relevant features from patient data, such as age, gender, chest pain type, and other critical health indicators. The Random Forest algorithm was chosen due to its robustness and ability to handle complex data interactions. Through extensive testing, the model demonstrated high accuracy in predicting CVD, outperforming traditional methods. This approach can potentially serve as a reliable tool for early diagnosis and preventive care in clinical settings, ultimately aiding in the reduction of CVD-related fatalities.

Cervical cancer, the second most prevalent cancer among women worldwide, is predominantly caused by the human papillomavirus (HPV). Despite advancements in medical technology, cervical cancer remains a significant contributor to female mortality, especially in low-resource regions. Early detection is crucial, as survival rates exceed 50% when the disease is identified at an early stage. To address this critical need, we present WFC2DS (Web Framework for Cervical Cancer Detection System), an innovative expert web system designed to transform cervical cancer diagnosis. WFC2DS leverages a powerful ensemble of machine learning algorithms, including the highly efficient XGBoost classifier, to analyses a large dataset of 858 patients with 36 attributes, using the Biopsy attribute as the primary target variable. The implementation of the XGBoost algorithm within WFC2DS achieves outstanding performance, including an accuracy rate of 99.9% and superior results in F1 score, precision, and sensitivity, surpassing existing diagnostic systems. This groundbreaking approach demonstrates the potential of advanced machine learning techniques to significantly reduce the global burden of cervical cancer, offering a transformative advancement in women’s healthcare. WFC2DS not only enhances the accuracy and reliability of cervical cancer detection but also represents a critical step forward in the development of web-based diagnostic tools that are accessible, efficient, and capable of improving healthcare outcomes for women worldwide.

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.

Cyber-attacks are growing with the rapid development and widespread use of internet technology, with botnet attacks emerging as one of the most harmful threats. Identifying these botnets is increasingly challenging due to numerous attack vectors and the ongoing evolution of malware. As Internet of Things (IoT) technology advances, many network devices have become vulnerable to botnet attacks, leading to substantial losses across various sectors. These botnets pose serious risks to network security, and deep learning models have shown promise in efficiently identifying botnet activity from network traffic data. In this research, we propose a botnet identification system based on the stacking of artificial neural networks (ANN), convolutional neural networks (CNN), long short-term memory (LSTM), and recurrent neural networks (RNN), referred to as the ACLR model. Additionally, we introduce an XGBoost algorithm, achieving an accuracy of 81%, as a complementary approach to enhance classification performance. Experiments were conducted using both individual models and the proposed ACLR model for performance comparison. Utilizing the UNSW-NB15 dataset, which includes nine different attack types such as ‘Normal’, ‘Generic’, ‘Exploits’, ‘Fuzzers’, ‘DoS’, ‘Reconnaissance’, ‘Analysis’, ‘Backdoor’, ‘Shell Code’, and ‘Worms’, we found that the ACLR model achieved a testing accuracy of 0.9698, effectively capturing the intricate patterns and characteristics of botnet attacks. The model's robustness and generalizability were further demonstrated through k-fold cross-validation, with k values of 3, 5, 7, and 10 yielding a k = 5 accuracy score of 0.9749. Moreover, the proposed model detects botnets with a high receiver operating characteristic area under the curve (ROC-AUC) of 0.9934 and a precision-recall area under the curve (PR-AUC) of 0.9950. Performance comparisons with existing state-of-the-art models further corroborate the superior effectiveness of the proposed approach, highlighting the potential of these findings to significantly aid in combating evolving threats and enhancing cybersecurity procedures.

Early detection of stroke warning symptoms can significantly reduce the severity of ischemic stroke, which is the leading cause of mortality and disability worldwide. This study aims to develop a predictive model for ischemic stroke by leveraging advanced machine learning techniques. In this approach, an artificial neural network (ANN) was employed as a feature extractor to capture the complex relationships within the healthcare dataset comprising 5110 patient health profiles. To enhance the predictive capability, a stacking ensemble model was constructed by integrating three powerful classifiers: Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). The synthetic minority oversampling technique (SMOTE) was utilized to address class imbalance in the dataset, ensuring that the model effectively learns from underrepresented classes. The models were evaluated using performance metrics such as accuracy, precision, recall, F1-score, area under the curve (AUC), and confusion matrix. The stacking ensemble model demonstrated superior performance, achieving a 95.9% accuracy, outperforming individual classifiers and prior studies on the same dataset and providing interpretability to the predictions and facilitating personalized decision-making in resource-constrained settings. This proposed model offers a robust framework for early stroke detection, with potential applications in clinical decision support systems.

The persistence of SMS spam continues to pose significant challenges, necessitating the development of effective detection systems that can handle increasingly sophisticated evasion techniques employed by spammers. This study addresses these challenges by presenting a comprehensive SMS spam filtering system utilizing machine learning models, specifically focusing on Long Short-Term Memory (LSTM) networks. We introduce a new SMS dataset message, comprising 61% legitimate (ham) messages and 39% spam, which represents the largest publicly available SMS spam dataset to date. A longitudinal analysis of spam evolution was performed, followed by the extraction of semantic and syntactic features for evaluation. We then conducted a comparative analysis of various machine learning approaches, ranging from shallow models to advanced deep neural networks. Our findings reveal that shallow models and traditional anti-spam services are vulnerable to evasion techniques, resulting in poor performance. In contrast, the LSTM model demonstrated superior performance, achieving 98% accuracy in classifying SMS messages. Despite this high accuracy, some evasion strategies still challenge the detection process, highlighting areas for further research. This study advocates for continued development in robust SMS spam filtering systems and provides valuable insights into the effectiveness of deep learning models in combating SMS spam.

Employee turnover presents a significant challenge for organizations worldwide. While advanced machine learning algorithms hold promise for predicting turnover, their real-world application often falls short due to the limitations in fully leveraging the relational structure within employee tabular data. To bridge this gap, this study proposes a novel framework that transforms traditional tabular employee data into a knowledge graph structure, enabling the use of Graph Convolutional Networks (GCNs) for more refined feature extraction. This approach goes beyond mere prediction, incorporating explainable artificial intelligence (XAI) techniques to uncover the key factors that influence an employee's decision to stay with or leave an organization. Our empirical analysis, conducted on a comprehensive dataset of 1,470 IBM employees, demonstrated the effectiveness of our methodology. When benchmarked against five widely-used machine learning models, our enhanced Linear Support Vector Machine (L-SVM) model, augmented with knowledge-graph-based features, achieved remarkable accuracy. The use of knowledge graphs further enriches this analysis by modeling the complex relationships within an organization, offering a holistic view of the dynamics that influence turnover. Additionally, the focus on explainable AI has grown, allowing HR professionals to understand the key factors driving turnover predictions, enabling proactive interventions.

Pregnancy complications pose significant risks to maternal and fetal health, necessitating early detection for timely interventions. Manual analysis of cardiotocography (CTG) tests, the conventional practice among obstetricians, is labor-intensive and prone to variability. This study addresses the critical need for accurate fetal health classification using advanced machine learning (ML) techniques, focusing on the application of XGBoost, a powerful gradient boosting algorithm. Utilizing a publicly available dataset, despite its size, this research leverages its rich features to develop and analyze ML models. The objective is to explore and demonstrate the efficacy of ML models in classifying fetal health based on data. Our proposed system applies the XGBoost algorithm and achieves an exceptional accuracy of 96%, surpassing previous methods. This highlights the algorithm's robustness in enhancing diagnostic precision and facilitating timely interventions. The study underscores the potential of integrating ML models into routine clinical practices to streamline fetal health assessments. By optimizing resource allocation and improving time efficiency, these models contribute to early complication detection and enhanced prenatal care. Further research is encouraged to refine ML applications, promising continued advancements in fetal health assessment and maternal care.

Numerous research studies underscore the importance of accurately detecting head impacts and implementing effective safety measures. This study addresses this critical need by leveraging machine learning algorithms applied to data obtained from piezoelectric sensors mounted on a simulated head model. Using a systematic approach, we employ Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models to analyze normalized sensor data, with the goal of precisely identifying impact locations. Through rigorous k-fold cross-validation and comprehensive performance evaluation, we demonstrate that the XGBoost model slightly outperforms the RF model, achieving a Root Mean Square Error (RMSE) of 0.4764 and a coefficient of determination (R²) of 0.9085. Feature importance assessments indicate an optimal sensor placement strategy, which may facilitate a reduction in model complexity while maintaining predictive accuracy. The superior performance of the XGBoost model, along with strategic sensor placement, underscores the study's contribution to enhancing head impact safety measures in both sports and industrial contexts. These findings pave the way for future research aimed at deploying intelligent safety systems that harness the synergy between wearable technology and machine learning.

Heart disease (HD), including heart attacks, is a leading cause of death worldwide, making accurate determination of a patient's risk a significant challenge in medical data analysis. Early detection and continuous monitoring by physicians can significantly reduce mortality rates, but heart disease is not always easily detectable, and physicians cannot monitor patients around the clock. Machine learning (ML) offers a promising solution to enhance diagnostics through more accurate predictions based on data from healthcare sectors globally. This study aims to employ various feature selection methods to develop an effective ML technique for early-stage heart disease prediction. The feature selection process utilized three distinct methods: chi-square, analysis of variance (ANOVA), and mutual information (MI), leading to three selected feature groups designated as SF-1, SF-2, and SF-3. We then evaluated ten different ML classifiers, including Naive Bayes, support vector machine (SVM), voting, XGBoost, AdaBoost, bagging, decision tree (DT), K-nearest neighbor (KNN), random forest (RF), and logistic regression (LR), to identify the best approach and feature subset. The proposed prediction method was validated using a private dataset, a publicly available dataset, and multiple cross-validation techniques. To address the challenge of unbalanced data, the Synthetic Minority Oversampling Technique (SMOTE) was applied. Experimental results showed that the AdaBoost classifier achieved optimal performance with the combined datasets and the SF-2 feature subset, yielding rates of 96.84% for accuracy, 95.32% for sensitivity, 91.12% for specificity, 94.67% for precision, 92.36% for F1 score, and 98.50% for AUC. Additionally, an explainable artificial intelligence approach utilizing SHAP methodologies is being developed to provide insights into the system's prediction process. The proposed technique demonstrates significant promise for the healthcare sector, facilitating early-stage heart disease prediction with reduced costs and minimal time. Ultimately, the best-performing ML method has been integrated into a mobile app, enabling users to input HD symptoms and receive rapid heart disease predictions.

Addressing the intricate challenge of fake news detection, traditionally reliant on the expertise of professional fact-checkers due to the inherent uncertainty in fact-checking processes, this research leverages advancements in language models to propose a novel Long Short-Term Memory (LSTM)-based network. The proposed model is specifically tailored to navigate the uncertainty inherent in the fake news detection task, utilizing LSTM's capability to capture long-range dependencies in textual data. The evaluation is conducted on the well-established LIAR dataset, a prominent benchmark for fake news detection research, yielding an impressive accuracy of 99%. Moreover, recognizing the limitations of the LIAR dataset, we introduce LIAR2 as a new benchmark, incorporating valuable insights from the academic community. Our study presents detailed comparisons and ablation experiments on both LIAR and LIAR2 datasets, establishing our results as the baseline for LIAR2. The proposed approach aims to enhance our understanding of dataset characteristics, contributing to refining and improving fake news detection methodologies by effectively leveraging the strengths of LSTM architecture.

This study investigates the prevalence and impact of social anxiety among high school students at Little Scholars Matriculation Hr. Sec. School in Thanjavur, Tamil Nadu, India. A dataset was created by surveying students with a 17-item Social Phobia Inventory (SPIN) questionnaire, which includes questions related to their experiences with social interactions, fear of judgment, and discomfort in various social situations. Using this dataset, the research applies a Random Forest machine learning approach to analyze student responses and assess the severity of social anxiety. The model aims to predict the levels of social anxiety by identifying significant features that contribute to higher distress levels. Through feature selection and correlation analysis, the study uncovers complex relationships between various aspects of social interactions that influence social anxiety. The performance of the Random Forest model is evaluated based on its accuracy and predictive power, demonstrating its ability to predict social anxiety in high school students effectively. The study highlights the potential of Random Forest for accurately identifying key factors associated with social phobia and recommends further research to refine predictive models, offering valuable insights for enhancing mental health support systems for high school students.

Efficient bed management is essential for minimizing hospital costs, improving efficiency, and enhancing patient outcomes. This study introduces a predictive framework for forecasting hospital-ICU length of stay (LOS) at admission, leveraging hospital EHR data. Unlike prior work, which heavily relied on advanced tree-based models, this research proposes a K-Nearest Neighbors (KNN) model with hyperparameter optimization using GridSearchCV for predicting ICU patients’ LOS. The KNN model effectively classifies patients into short and long LOS categories by learning patterns in clinical information systems (CIS). To ensure robustness, we evaluated the model using various performance metrics, including Accuracy, AUC, Sensitivity, Specificity, F1-score, Precision, and Recall. The optimized KNN model demonstrated competitive predictive performance with improved interpretability compared to traditional complex models. Additionally, explainable artificial intelligence (xAI) techniques were incorporated to provide transparent insights into the decision-making process, further enhancing the trustworthiness of the predictions. This work highlights the potential of using machine learning models like KNN for reliable, interpretable, and efficient ICU LOS prediction, aiding hospitals in improving resource allocation and patient care outcomes.

Dyslexia is a specific learning disability that makes reading and writing difficult, often leading to academic struggles if not detected early. Identifying dyslexia, particularly in languages with complex orthographies, remains a significant challenge. Traditional methods for diagnosis are typically reliant on manual feature extraction and expert intervention, which can be time-consuming and error-prone. To address these limitations, we propose an optimal ensemble learning model for dyslexia prediction, incorporating machine learning techniques with an adaptive genetic algorithm for weight optimization. Our approach first involves extracting relevant features associated with dyslexia. These features are then processed using a hybrid ensemble model, combining three powerful machine learning algorithms: Gradient Boosting, Support Vector Classifier (SVC), and AdaBoost, through a soft voting mechanism. The ensemble model is further enhanced by employing an adaptive genetic algorithm to fine-tune the weights assigned to each individual model, optimizing their contributions to the final prediction. We demonstrate the effectiveness of this method by conducting experiments on a dyslexia dataset, where our proposed model significantly outperforms traditional approaches in terms of classification accuracy and robustness. This results in a more precise and scalable solution for dyslexia detection, with the potential to aid in early diagnosis and intervention.

Breast cancer is one of the leading causes of cancer-related deaths among women, making early and precise detection a critical challenge. Traditional methods for breast cancer classification often rely on static models that may not fully capture the complexities of the data. In this study, we propose an enhanced approach for breast cancer detection using a Support Vector Classifier (SVC), coupled with a GridSearchCV procedure for hyperparameter optimization. The goal of this model is to achieve better classification performance by systematically exploring different parameter configurations, including the regularization parameter (C), kernel type, and gamma. The model was trained and tested on the Wisconsin Breast Cancer Diagnostic (WBCD) dataset, with GridSearchCV employed to select the optimal hyperparameters through a 5-fold cross-validation process. The resulting SVC model achieved significant improvements in accuracy, precision, recall, F1 score, and AUC when compared to other standard machine learning models. This approach demonstrates the potential of SVC with grid search as a robust solution for breast cancer classification, offering better diagnostic capabilities and aiding in more accurate predictions of malignant and benign tumors. Our findings highlight the importance of hyperparameter optimization in machine learning for healthcare applications and suggest that the proposed model could play a vital role in the early diagnosis and treatment of breast cancer.

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.

The agricultural sector in many South Asian countries, including Bangladesh and India, plays a pivotal role in the economy, with a significant portion of the population relying on it for their livelihood. However, farmers face challenges like unpredictable weather, soil variability, and natural disasters such as floods and erosion, leading to crop losses and financial difficulties. This often results in a decline in interest in agriculture despite government support. Our study focuses on predicting the classification of various crops, such as rice, jute, and maize, using a combination of soil and weather features. The predictive model leverages soil parameters like Nitrogen, Phosphorus, Potassium, and pH levels, alongside weather variables such as Temperature, Humidity, and Rainfall. We propose a hybrid approach that integrates machine learning with genetic algorithms, where a Random Forest Classifier is used for crop classification across 22 different crop types. The Genetic Algorithm is utilized to optimize hyperparameters, enhancing model performance and robustness. Additionally, we applied Explainable AI (XAI) techniques, including Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), to interpret and validate the model’s predictions. By improving feature selection and model parameters, our approach addresses limitations associated with existing models, providing more reliable and accurate predictions. This system has the potential to reduce crop losses, improve agricultural productivity, and contribute to the sustainability and prosperity of the agricultural sector.

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.

This study delves into the application of machine learning techniques for predicting rice production in Indonesia, a country where rice is not just a staple food but also a key component of the agricultural sector. Utilizing data from 2018 to 2023, sourced from the Central Bureau of Statistics of Indonesia and the Meteorology, Climatology, and Geophysics Agency of Indonesia, this research presents a comprehensive approach to agricultural forecasting. The study begins with an Exploratory Data Analysis (EDA) to understand the variability and distribution of variables such as harvested area, production, rainfall, humidity, and temperature. Significant regional disparities in rice production are identified, highlighting the complexity of agricultural forecasting in Indonesia. Five machine learning models—Random Forest, Gradient Boosting, Decision Tree, Support Vector Machine, and XGBRegressor—are trained and tested. The XGBRegressor model stands out for its superior performance, demonstrating its high predictive accuracy and reliability. Hyperparameter tuning using the GridSearchCV technique was conducted on all five models, resulting in performance improvements across the board. This research not only underscores the effectiveness of machine learning in improving rice production predictions in Indonesia but also sets the stage for future research. It highlights the potential of advanced analytical techniques in enhancing agricultural productivity and decision-making, paving the way for further explorations into more sophisticated models and a broader range of data, ultimately contributing to the resilience and sustainability of Indonesia’s agricultural sector.

Brain hemorrhage refers to a potentially fatal medical disorder that affects millions of individuals. The percentage of patients who survive can be significantly raised with the prompt identification of brain hemorrhages, due to image-guided radiography, which has emerged as the predominant treatment modality in clinical practice. A Computed Tomography Image has frequently been employed for the purpose of identifying and diagnosing neurological disorders. The manual identification of anomalies in the brain region from the Computed Tomography Image demands the radiologist to devote a greater amount of time and dedication. In the most recent studies, a variety of techniques rooted in Deep learning and traditional Machine Learning have been introduced with the purpose of promptly and reliably detecting and classifying brain hemorrhage. This overview provides a comprehensive analysis of the surveys that have been conducted by utilizing Machine Learning and Deep Learning. This research focuses on the main stages of brain hemorrhage, which involve preprocessing, feature extraction, and classification, as well as their findings and limitations. Moreover, this in-depth analysis provides a description of the existing benchmark datasets that are utilized for the analysis of the detection process. A detailed comparison of performances is analyzed. Moreover, this paper addresses some aspects of the above-mentioned technique and provides insights into prospective possibilities for future research.

Impaired renal function poses a risk across all age groups, with kidney diseases often progressing without noticeable symptoms until they reach an advanced stage. Given the global shortage of nephrologists and the growing public health concern over renal failure, there is an urgent need for an AI-driven system capable of autonomously detecting kidney abnormalities. This project addresses that need by developing a Convolutional Neural Network (CNN)-based model to detect kidney diseases, specifically targeting cysts, stones, and tumours—common causes of renal failure. A comprehensive dataset of 12,446 CT whole abdomen and program images was collected and annotated, covering four categories: cyst, tumor, stone, and normal. The CNN model achieved a notable accuracy of 97%, outperforming previous YOLOv8-based models. Key evaluation metrics, including precision, recall, F1 score, and specificity, demonstrate the model’s ability to reliably differentiate among kidney abnormalities. This system provides a customizable and effective platform for the clinical diagnosis of renal conditions, potentially enhancing diagnostic accuracy and accessibility in healthcare settings, particularly in regions with limited access to specialists. The results underscore the potential of deep learning in medical imaging, offering a promising solution to aid in early detection and treatment of kidney diseases.

Developing a robust and efficient system for identifying medicinal plants using a combination of deep learning (DL) and traditional machine learning (ML) techniques. Medicinal plants have been integral to healthcare for centuries, providing essential ingredients for drug development and medical treatments. While over 25% of medicines in developed countries are derived from these plants, approximately 80% of individuals in developing countries rely on them for primary healthcare. Traditionally, the identification of these plants is performed manually by experts, a process that is tedious, time-consuming, and often subjective, heavily reliant on the availability of trained personnel. Incorrect identification can lead to severe health consequences, emphasizing the need for a more reliable and efficient identification method. In light of this challenge, your project presents an innovative solution that automates the identification of medicinal plants using images captured by smartphones in their natural environments. The proposed system leverages a cascaded architecture combining deep learning and traditional machine learning techniques. The core of the system utilizes a pre-trained Xception model for feature extraction, capturing intricate features from plant images and benefiting from knowledge gained from extensive training on large datasets. These extracted features are then optimized using, enhancing their quality for classification. A (random forest) is employed to classify the medicinal plants based on the optimized features, excelling in high-dimensional data scenarios. This rapid identification capability, combined with high accuracy and robustness, underscores the system’s practical applicability for users who need reliable plant identification on the go. By integrating deep learning for feature extraction and traditional machine learning for classification, your project addresses the critical need for efficient and accurate medicinal plant identification, enhancing the reliability of plant identification while providing a practical solution that can be easily used in real-world scenarios, particularly in settings where expert knowledge is limited.

Deep learning has become one of remote sensing scientists’ most efficient computer vision tools in recent years. However, the lack of training labels for the remote sensing datasets means that scientists need to solve the domain adaptation (DA) problem to narrow the discrepancy between satellite image datasets. As a result, image segmentation models that are then trained, could better generalize and use an existing set of labels instead of requiring new ones. This work proposes an unsupervised DA model that preserves semantic consistency and per-pixel quality for the images during the style-transferring phase. This article’s major contribution is proposing the improved architecture of the SemI2I model, which significantly boosts the proposed model’s performance and makes it competitive with the state-of-the-art CyCADA model. A second contribution is testing the CyCADA model on the remote sensing multiband datasets, such as WorldView-2 and SPOT-6. The proposed model preserves semantic consistency and per-pixel quality for the images during the style-transferring phase. Thus, the semantic segmentation model, trained on the adapted images, shows substantial performance gain compared to the SemI2I model and reaches similar results as the state-of-the-art CyCADA model. The future development of the proposed method could include ecological domain transfer, a priori evaluation of dataset quality in terms of data distribution, or exploration of the inner architecture of the DA model.

Cerebrovascular diseases such as stroke are among the most common causes of death and disability worldwide and are preventable and treatable. Early detection of strokes and their rapid intervention play an important role in reducing the burden of disease and improving clinical outcomes. In recent years, machine learning methods have attracted a lot of attention as they can be used to detect strokes. The aim of this study is to identify reliable methods, algorithms, and features that help medical professionals make informed decisions about stroke treatment and prevention. To achieve this goal, we have developed an early stroke detection system based on CT images of the brain, utilizing a ResNet (Residual Network) model to detect strokes at a very early stage. For image classification, the ResNet model is employed to extract the most relevant features for classification. Cross-validation was used to evaluate the system's effectiveness, employing metrics such as precision, recall, F1 score, ROC (Receiver Operating Characteristic Curve), and AUC (Area Under the Curve). The proposed diagnostic system allows physicians to make informed decisions about stroke treatment.

We aim to advance deep learning-based smart agriculture by implementing semantic segmentation on crop images captured in real field environments. Our primary objective is to accurately detect diseases, thereby facilitating the automation of agricultural management processes. A significant challenge in this task is the small size of disease regions, which serve as the Regions of Interest (RoI) and make precise prediction difficult. Previously, the RoI-Attention Network (RA-Net) was used to address this challenge by utilizing an RoI-attentive image, focusing on regions predicted as diseased and their surroundings to enhance the network's ability to detect these small regions. However, we now propose using the U-Net architecture as an improvement over RA-Net. U-Net, with its symmetric design and skip connections, is expected to better capture the context and details needed for accurately segmenting small disease regions. By leveraging U-Net's ability to integrate both high-level and low-level features, we aim to refine the precision of disease detection, enhancing the overall effectiveness of automated agricultural management systems.

Down syndrome is a chromosomal condition resulting from an additional copy of chromosome 21, leading to various developmental challenges and distinctive physical characteristics. Children with Down syndrome typically exhibit unique craniofacial features, including a shorter midface, broader facial width, flat nasal bridge, almond-shaped eyes, and a smaller, somewhat flattened head. These identifiable traits can significantly aid in early diagnosis and intervention. This study focuses on the early diagnosis of Down syndrome using an advanced neural network approach based on ResNet50. We utilized a dataset of 3,009 facial images of children aged 0 to 15, comprising both those with Down syndrome and healthy children, to conduct our experiments. Our proposed method, ResNet50-DNSNet, leverages the deep residual learning framework of ResNet50 for robust feature extraction, capturing intricate spatial features from the input images. By fine-tuning the pre-trained ResNet50 model, we extracted high-level features that enhance the model's ability to distinguish between facial characteristics of children with Down syndrome and those without. We evaluated the performance of our approach using several artificial intelligence techniques, including logistic regression, support vector machines, and gradient boosting methods. Extensive experimental results demonstrated that our ResNet50-DNSNet achieved an impressive accuracy of 0.99, surpassing state-of-the-art methods. The model's performance was rigorously validated through k-fold cross-validation, ensuring reliability and robustness. Additionally, we assessed the runtime computational complexity of our proposed approach. This innovative research has the potential to transform the early diagnosis of Down syndrome in children through the analysis of facial images, facilitating timely intervention and support.

"A Large Dataset to Enhance Skin Cancer Classification with Transformer-Based Deep Neural Networks" reflects a research approach aimed at improving the accuracy of skin cancer diagnosis by utilizing cutting-edge deep learning techniques, specifically Transformer models, on a large dataset. Using a substantial dataset related to skin cancer plays a crucial role in this research, as larger datasets provide more diverse examples that help the model generalize better to unseen data. This enhances the model’s ability to recognize patterns across various types of skin conditions, including Melanoma, Melanocytic Nevi, Basal Cell Carcinoma, Actinic Keratoses, Benign Keratosis-like Lesions, Dermatofibroma, and Vascular Lesions. Transformer-based deep neural networks are applied here, leveraging self-attention mechanisms that allow the model to analyse and capture complex relationships within image data. This self-attention mechanism works by weighing the importance of each part of an image in relation to others, effectively capturing spatial dependencies across image regions. This allows the model to focus on relevant features in diverse and detailed skin images, making Transformer-based architectures a promising technique for medical image analysis.

With the rapid growth of urbanization and industrialization, the challenge of managing increasing volumes of waste has become critical. Effective waste sorting and recycling are essential to reducing environmental impact and promoting sustainability. Deep learning has proven to be an effective tool in automating complex tasks such as image classification, making it ideal for waste categorization applications. This study introduces a deep learning model based on the NASNet architecture for the classification of recyclable waste into six distinct categories: cardboard, glass, metal, paper, plastic, and litter. By utilizing the NASNetLarge base model, the proposed model leverages a pre-trained, highly efficient architecture capable of extracting complex and hierarchical features from waste images. Custom layers, including global average pooling, dropout for regularization, and fully connected dense layers, are added to enhance the model’s ability to learn discriminative features and prevent overfitting. The model’s performance is optimized using the Adam optimizer with categorical cross-entropy loss, ensuring reliable and robust classification even in challenging conditions such as varying image quality and background noise. To improve model interpretability, we employ Score-CAM saliency maps, which provide visual explanations for the model’s decision-making process, allowing users to understand which parts of the image influenced its predictions. This interpretability aspect is vital for building trust in automated systems, particularly in real-world applications like waste sorting. The proposed NASNet-based model demonstrates superior performance compared to existing waste classification methods and offers a promising solution for automating waste management processes. It has the potential to significantly enhance the efficiency of recycling systems, reduce human error, and contribute to sustainable environmental practices.

Recent advances in synthetic image generation, particularly through artificial intelligence, have led to the creation of images so realistic that they are virtually indistinguishable from real photographs. This presents significant challenges for data authenticity and reliability, especially in areas such as journalism, social media, and scientific research, where the integrity of images is critical. This study proposes an approach to effectively distinguish between real and AI-generated images using a deep learning model based on ResNet50. The classification task is framed as a binary problem, where images are categorized as either "real" or "AI-generated." While synthetic images can replicate complex visual details such as lighting, reflections, and textures, subtle visual imperfections often differentiate them from genuine photographs. The study investigates these differences, focusing on minor artifacts and inconsistencies that are typically present in AI-generated content, such as background distortions, lighting anomalies, and unnatural textures. These artifacts are not always perceptible to the human eye, but can be reliably detected by machine learning models. The ResNet50 model is employed to learn and classify these visual cues, enabling the system to achieve high accuracy in distinguishing real images from synthetic ones. By training on a large dataset of both real and AI-generated images, the model identifies key image features that serve as indicators of authenticity. The study also explores the interpretability of the model's decisions, shedding light on which aspects of the images are most informative for classification.

Paddy leaf diseases are a significant concern for rice farmers globally, as they can lead to severe reductions in crop yield and quality, threatening food security and economic stability in rice-dependent regions. Traditional methods of disease detection rely on manual inspection by experts, which is not only time-consuming and costly but also infeasible for large-scale farms. Moreover, such methods are prone to human error and inconsistencies, particularly in early stages when visual symptoms may be subtle. Addressing this critical issue, our study proposes an automated, high-accuracy classification approach for paddy leaf disease detection using an Xception-based deep learning model. The Xception model, known for its depthwise separable convolution architecture, is fine-tuned and adapted to capture the nuanced patterns of various paddy diseases, such as bacterial blight, brown spot, and leaf smut. Initialized with ImageNet weights, the model is further refined with custom layers to enhance its specificity and robustness in recognizing complex disease features specific to paddy leaves. This customization ensures that the model is highly sensitive to slight variations in leaf texture, color, and shape caused by different pathogens, allowing it to classify multiple disease types effectively. To optimize model performance, we employ the Adam optimizer and categorical cross-entropy loss function, which helps achieve smooth and reliable convergence. The proposed model not only serves as a tool for accurate disease classification but also supports early detection, which is crucial for timely intervention and preventing disease spread. By enabling real-time disease monitoring, this approach aids farmers in applying precise treatment, minimizing the excessive use of pesticides, and reducing environmental impact. Furthermore, this method can be integrated into mobile or IoT-based solutions, making it accessible for rural and resource-limited settings. Ultimately, our work contributes to sustainable agriculture by empowering farmers with a scalable, low-cost solution for disease management, safeguarding crop yields, and promoting food security.

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.

The demand for high-quality tomatoes to meet consumer and market standards, combined with large-scale production, has necessitated the development of an inline quality grading system. Manual grading is time-consuming, costly, and labor-intensive. This study introduces a novel approach for tomato quality sorting and grading, leveraging pre-trained convolutional neural networks (CNNs) for feature extraction and traditional machine learning algorithms for classification in a hybrid model. Image preprocessing and fine-tuning techniques were applied to enable deep layers to learn and concentrate on complex and significant features. In our existing approach, features extracted by CNNs were classified using support vector machines (SVM), achieving notable accuracy rates. Specifically, the CNN-SVM model attained the best accuracy in the binary classification of tomatoes as healthy or rejected, and in the multiclass classification of tomatoes as ripe, unripe, or rejected. In this study, we propose an enhanced algorithm using the Xception model for feature extraction. The Xception-based approach aims to further improve classification accuracy and performance metrics. The performance of the proposed Xception model was evaluated using metrics such as accuracy, recall, precision, specificity, and F1-score, demonstrating its potential to outperform the existing CNN-SVM ensemble model. This methodology offers significant advancements in the automated grading and sorting of tomatoes, ensuring higher efficiency and consistency in quality assessment.

Accurately classifying white blood cell subtypes is essential for diagnosing various blood diseases. Traditional methods in computer vision often require manually engineered features, which are time-consuming and can limit performance. In contrast, machine learning approaches offer improved accuracy but typically demand extensive labeled datasets, which are challenging and costly to obtain. This study introduces a semi-supervised learning approach tailored for white blood cell classification. By leveraging a combination of a small amount of labeled data and a larger set of unlabeled data, the model learns to identify and categorize different white blood cell subtypes directly from microscopic images. This methodology capitalizes on the inherent structure and patterns present in the data, enhancing classification performance without relying solely on predefined features. The proposed approach was evaluated using a dataset comprising synthetic images representing various white blood cell subtypes. Results demonstrate promising accuracy in distinguishing between different cell types, showcasing potential applications in clinical diagnostics. By minimizing the reliance on manually labeled data while maintaining high classification accuracy, this approach offers a scalable solution for automating and improving the efficiency of white blood cell analysis in medical settings.

Otoscopy is a diagnostic procedure to visualize the external ear canal and eardrum, facilitating the detection of various ear pathologies and conditions. Timely otoscopy image classification offers significant advantages, including early detection, reduced patient anxiety, and personalized treatment plans. This paper introduces a novel framework specifically tailored for otoscopy image classification. It leverages octave 3D convolution and a combination of feature and region-focus modules to create an accurate and robust classification system capable of distinguishing between various otoscopic conditions. This architecture is designed to efficiently capture and process the spatial and feature information present in otoscopy images. Using a public otoscopy dataset, reached a classification and an F1 score across 11 classes of ear conditions. A comparative analysis demonstrates that surpasses other established machine learning model, including Xception Model, across various evaluation metrics. The research’s contribution to improved diagnostic accuracy reduced human error, expedited diagnostics, and its potential for telemedicine applications.

In recent years, jute has become a crucial natural fiber crop, facing increasing threats from insect pests that can significantly undermine agricultural productivity. The challenge of accurately identifying these pests is further complicated by factors such as complex backgrounds, fuzzy features, and the presence of multiple small targets, which make detection difficult for conventional methods. A notable barrier has been the scarcity of datasets specifically tailored for jute pests, which limits the effectiveness and generalization capabilities of traditional pest identification models. To combat this issue, we undertook the construction of a comprehensive image dataset that encompasses nine distinct types of jute pests, meticulously designed to enhance model training and evaluation. In this study, we developed a robust deep convolutional neural network (CNN) specifically for jute pest detection, employing OpenCV for effective image preprocessing and augmentation techniques to improve data diversity. Our model achieved an impressive accuracy of 98%, demonstrating its capability to enhance pest recognition even in challenging environmental conditions. The experimental results reflect significant improvements across various performance metrics, including Precision, Recall, and F1 score, alongside a noteworthy increase in mean Average Precision (mAP). This research not only addresses the critical need for effective pest detection solutions in jute cultivation but also offers a high-performance, lightweight model that optimizes both recognition accuracy and computational efficiency, paving the way for more sustainable agricultural practices and effective pest management strategies. Through these advancements, we aim to contribute to the resilience and productivity of jute farming, ultimately supporting the livelihoods of farmers reliant on this vital crop.

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.

The simultaneous classification and grading of fruits are essential yet underexplored facets of computer vision in agricultural automation. This study proposes the application of same-domain transfer learning using the NASNetMobile architecture to facilitate multi-fruit classification and grading. Our dual-model framework initially employs NASNetMobile to distinguish between six fruit types—bananas, apples, oranges, pomegranates, limes, and guavas—within the FruitNet dataset. Subsequently, the learned parameters are transferred to a second model, which focuses on grading the quality of the fruits. To address the class imbalance in the dataset, we incorporate a combination of AugMix, CutMix, and MixUp, significantly improving model generalization. These findings affirm the utility of same-domain transfer learning in enhancing grading accuracy using knowledge gained from classification tasks. The study demonstrates the potential for integrating this approach into machine vision systems to advance agricultural automation. Moving forward, this approach could be scaled to address broader cultivation challenges through the continued development of fine-grained visual analysis capabilities.

Cancer remains one of the leading causes of death globally, with nearly 10 million deaths reported in 2020. Among all types, oral cancer ranks as the sixth most prevalent worldwide. Its lethality is mainly attributed to late-stage diagnoses, where treatment becomes more challenging. However, early detection—particularly during pre-cancerous stages—can significantly reduce mortality rates. Early screening and treatment are essential to improving survival rates, highlighting the need for efficient diagnostic methods. In this study, a method is proposed to distinguish between benign and malignant oral cavity lesions while classifying their pre-cancerous stages. This approach explores five different color spaces to extract color and texture-based features from oral cavity images, which are critical for identifying varying lesion stages. These features are then classified using the MobileNetV2 architecture of Convolutional Neural Networks (CNN), chosen for its efficiency in resource-constrained environments. MobileNetV2 offers a lightweight design, which makes it particularly suitable for mobile and real-time applications, where computational resources may be limited. The proposed method stands out due to its combination of handcrafted feature extraction and deep learning classification, utilizing MobileNetV2 for improved accuracy and speed. By capturing complex patterns in both color and texture from the images, the model offers a powerful tool for oral cancer detection, outperforming traditional methods in both time and computational efficiency. The model's ability to work with limited resources makes it an excellent choice for low-cost, mobile-based diagnostic tools. The method demonstrates promising results in both binary and multi-class classifications, successfully differentiating benign, malignant, and pre-cancerous lesions. This technique could greatly enhance early oral cancer detection, especially in settings with limited access to advanced medical facilities. By using a resource-efficient architecture, the method is accessible, scalable, and effective for widespread use in real-world applications, contributing to the reduction of oral cancer mortality through early and accurate diagnosis.

Yoga, a holistic practice blending physical and mental well-being, has gained significant global recognition in recent years. Accurate classification of yogic postures, however, remains a complex challenge due to variations in posture alignment, occluded body parts, and diverse background conditions. This study introduces YogaPoseNet, an advanced approach leveraging the power of the NASNet architecture to overcome these challenges in yogic posture classification. NASNet’s adaptive feature extraction capabilities enable it to effectively capture intricate details of yoga postures, eliminating the need for manual feature fusion from multiple architectures. The proposed model focuses on extracting high-level semantic features to handle posture complexities while maintaining robustness across varied scenarios. By employing this technique, YogaPoseNet offers a precise and efficient solution, paving the way for innovative applications in yoga analysis, personal fitness, and health monitoring systems. This work highlights the potential of cutting-edge neural architectures in redefining traditional practices through technology.