Lung cancer remains a critical global health challenge, necessitating advanced predictive models to enhance diagnostic and prognostic accuracy. This study employs natural language processing (NLP) and deep learning to...
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The current study focuses on how various machinelearning (ML) algorithms can be used in waste management and emphasizes the significance of intelligent technologies in addressing contemporary issues. By leading a bro...
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Obtaining high-quality explanations of a model’s output enables developers to identify and correct biases, align the system’s behavior with human values, and ensure ethical compliance. Explainable Artificial Intelli...
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Chatbots: Chatbots, as robots, are the most recent and intelligent live chat communication devices. Furthermore, these days, it is commonly one of the customer service methods as it is also perfect for providing an ar...
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This paper delves into the transformative impact of modern technology on human health, exploring the shift towards utilizing mobile applications as integral tools for health monitoring, control, and management. As soc...
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An Artificial Intelligence (AI) powered hybrid smart parking system optimizes parking allocation across various applications, including smart hospitals, colleges, offices, and shopping malls. The system uses AI and Io...
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An Artificial Intelligence (AI) powered hybrid smart parking system optimizes parking allocation across various applications, including smart hospitals, colleges, offices, and shopping malls. The system uses AI and IoT technologies to enhance the user experience, streamline operations, and improve efficiency. It dynamically allocates parking spaces based on real-time demand, user preferences, and contextual factors. The system accurately predicts parking demand, optimizes space allocation and provides personalized recommendations, reducing congestion and waiting times. The hybrid smart parking algorithm combines machinelearning techniques with domain-specific insights to prioritize parking allocation in diverse environments. The study emphasizes the importance of leveraging advanced technologies to address complex urban challenges, such as parking management, and aims to pave the way for sustainable, efficient, and user-centric parking solutions in smart cities. and Random Forest with an overall average score of 0.9400. machinelearning has become crucial for optimizing parking management systems, especially in densely populated cities. To address this challenge, advanced predictive models have been developed to anticipate parking duration based on slot availability, peak hours, and traffic conditions. The Random Forest model outperforms Logistic Regression, Random Forest, and K-Nearest Neighbors in predicting parking length, achieving high accuracy and performance metrics. It maintains user satisfaction and low operational costs, making it a recommended system for further implementation in parking management tasks. The classification report shows a respectable performance with 50% accuracy and a good recall for open slots. The optimization procedure was effective and did not reveal any notable areas for improvement. The optimized route (Slot2, Slot1, Slot3) provides an efficient parking sequence, with high accuracy (95%), good user satisfaction (90%) and suitabili
This study focuses on predictive analytics and healthcare, specifically the prediction of Glucose Intolerance, a chronic metabolic disease with significant global health implications. The research aims to develop effi...
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This study focuses on predictive analytics and healthcare, specifically the prediction of Glucose Intolerance, a chronic metabolic disease with significant global health implications. The research aims to develop efficient tools for risk assessment and early identification, offering medical practitioners reliable instruments for identifying high-risk patients and implementing preventive measures in a timely manner. A significant challenge in managing Glucose Intolerance is the absence of effective and precise prediction models. Traditional risk assessment techniques often fail to capture the multifaceted nature of Glucose Intolerance development, leading to delayed treatments and suboptimal patient outcomes. To address this issue, we conducted a comprehensive study utilizing various machinelearning algorithms, including Decision Trees, Random Forest, Gradient Boosting, CatBoost, K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Logistic Regression, and a Voting Classifier, to predict Glucose Intolerance. The primary objective was to identify the most effective combination of features and models for accurate predictions. Key factors considered included patient demographics, lifestyle characteristics, medical history, and genetic susceptibility, which were used to build robust and personalized prediction models. We conducted a comparative analysis of the machinelearning models' performance based on cross-validated accuracy with test-train splits and folds: 0.2, 6;0.05, 4;0.1, 2;0.2, 6;and 0.05, 2. The results showed that Random Forest achieved test accuracies of 74%, 76%, 82%, 75%, and 79%;KNN achieved 70%, 72%, 74%, 75%, and 72%;SVC achieved 74%, 72%, 77%, 77%, and 72%;Logistic Regression achieved 73%, 76%, 79%, 73%, and 76%;Gradient Boosting achieved 72%, 79%, 75%, 73%, and 79%;XGBoost achieved 72%, 79%, 77%, 74%, and 79%;CatBoost achieved 74%, 76%, 79%, 75%, and 76%;Decision Tree achieved 60%, 83%, 74%, 70%, and 79%;and Voting Classifier achieved 74%, 7
Elderly people are more vulnerable to falls, which can result in serious injuries, a lower quality of life, and higher medical expenses. Traditional fall detection methods, such as wearable sensors or vision-based sys...
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Federated learning (FL) is an enabling technology for supporting distributed machinelearning across several de-vices on decentralized data. A critical challenge when FL in practice is the system resource heterogeneit...
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The SkyScript dataset was developed by integrating large-scale remote sensing images from Google Earth Engine with geo-tagged semantic data from OpenStreetMap. This open-access dataset, consisting of 2.6 million image...
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