Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global *** study addresses the pressing issue of brain tumor classification using Magnetic reson...
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Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global *** study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging(MRI).It focuses on distinguishing between Low-Grade Gliomas(LGG)and High-Grade Gliomas(HGG).LGGs are benign and typically manageable with surgical resection,while HGGs are malignant and more *** research introduces an innovative custom convolutional neural network(CNN)model,*** stands out as a lightweight CNN model compared to its *** research utilized the BraTS 2020 dataset for its *** with the gradient-boosting algorithm,GliomaCNN has achieved an impressive accuracy of 99.1569%.The model’s interpretability is ensured through SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM++).They provide insights into critical decision-making regions for classification *** challenges in identifying tumors in images without visible signs,the model demonstrates remarkable performance in this critical medical application,offering a promising tool for accurate brain tumor diagnosis which paves the way for enhanced early detection and treatment of brain tumors.
Parkinson's disease (PD) is a neurodegenerative disorder of progressive degeneration, with motor and speech impairments. In this study, we investigate the potential of vowel-based acoustic biomarkers for early det...
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The rapid growth of medical text presents a significant challenge for healthcare professionals to stay updated with current research and clinical guidelines. Effective text summarization is essential to facilitate acc...
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The major risk of gas leakages to naval ships and vessels is to both personnel and machinery. Traditional types of gas detectors use fixed wired sensors, which tend to be extremely challenging for maintenance in compl...
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Cardiovascular diseases (CVDs) pose a serious threat to world health. This work uses a large dataset with clinical and demographic information to predict CVD risk using deep learning techniques. In order to enhance ge...
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Data mining is a technique used in the healthcare industry to analyze vast amounts of information for early illness diagnosis and prognosis. When it comes to its early prediction stages of breast cancer, many differen...
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In recent years, the edge computing paradigm enables the movement of processing units and storage nearer to the data available locations. The mechanism completes the computation in a short span of time in minimum band...
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Neural Style Transfer (NST) has emerged as a powerful technique for artistic image synthesis by fusing the base image with style source. In this study, we present a comparative analysis of NST using popular convolutio...
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Glaucoma is one of the leading causes of visual impairment worldwide. If diagnosed too late, the disease can irreversibly cause severe damage to the optic nerve, resulting in permanent loss of central vision and blind...
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Glaucoma is one of the leading causes of visual impairment worldwide. If diagnosed too late, the disease can irreversibly cause severe damage to the optic nerve, resulting in permanent loss of central vision and blindness. Therefore, early diagnosis of the disease is critical. Recent advancements in machine learning techniques have greatly aided ophthalmologists in timely and efficient diagnosis through the use of automated systems. Training the machine learning models with the most informative features can significantly enhance their performance. However, selecting the most informative feature subset is a real challenge because there are 2n potential feature subsets for a dataset with n features, and the conventional feature selection techniques are also not very efficient. Thus, extracting relevant features from medical images and selecting the most informative is a challenging task. Additionally, a considerable field of study has evolved around the discovery and selection of highly influential features (characteristics) from a large number of features. Through the inclusion of the most informative features, this method has the potential to improve machine learning classifiers by enhancing their classification performance, reducing training and testing time, and lowering system diagnostic costs by incorporating the most informative features. This work aims in the same direction to propose a unique, novel, and highly efficient feature selection (FS) approach using the Whale Optimization Algorithm (WOA), the Grey Wolf Optimization Algorithm (GWO), and a hybridized version of these two metaheuristics. To the best of our knowledge, the use of these two algorithms and their amalgamated version for FS in human disease prediction, particularly glaucoma prediction, has been rare in the past. The objective is to create a highly influential subset of characteristics using this approach. The suggested FS strategy seeks to maximize classification accuracy while reducing the t
The creation of a Real-Time Task Manager with a user-friendly interface and effective system monitoring is presented in this work utilising Python. The Psutil library was used to retrieve comprehensive system informat...
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