Urban sanitation and drainage maintenance are critical for sustainable city infrastructure. Sustainable municipal infrastructure requires maintaining urban drainage and sanitation. Traditional cleaning methods are ine...
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The use of smart search autocomplete for accommodation discovery within Online Travel Agent (OTA) platforms is essential, as it significantly influences the user experience when utilizing these services. This paper pr...
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We prove bounds for the approximation and estimation of certain binary classification functions using ReLU neural networks. Our estimation bounds provide a priori performance guarantees for empirical risk minimization...
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We prove bounds for the approximation and estimation of certain binary classification functions using ReLU neural networks. Our estimation bounds provide a priori performance guarantees for empirical risk minimization us-ing networks of a suitable size, depending on the number of training samples available. The obtained approximation and estimation rates are independent of the dimension of the input, showing that the curse of dimensionality can be overcome in this setting;in fact, the input dimension only enters in the form of a polynomial factor. Regarding the regularity of the target classification function, we assume the interfaces between the different classes to be locally of Barron-type. We complement our results by studying the relations between various Barron-type spaces that have been proposed in the literature. These spaces differ substantially more from each other than the current literature suggests.
The greatest cause of blindness in the world and a frequent cause of diabetes melitus is Diabetic-Retinopathy (DR). To stop vision loss in afflicted people, early identification and decision making are essential. Diab...
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Diabetic retinopathy (DR) is a serious eye condition affecting diabetic patients, stemming from microvascular damage to retinal blood vessels. If not diagnosed and treated early, DR can lead to blindness. Traditional ...
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In clinical research, adequate use of gathered information to provide an intelligent framework to assist the doctors is a great challenge for the current biomedical research community. This study proposed a probabilis...
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In clinical research, adequate use of gathered information to provide an intelligent framework to assist the doctors is a great challenge for the current biomedical research community. This study proposed a probabilistic deep neural network (PDNN) to select wart treatment method, where the layered structure of artificial neurons plays a crucial role in generating the optimal feature space. However, the probabilistic and thresholding technique is used to minimize the false negative and false positive instances. In the existing approaches, prediction accuracy and biasedness are major concerns in identifying the best wart treatment method. The benchmark dataset consists of 180 patients toward the selection of immunotherapy and cryotherapy treatment methods. Based on the feature descriptors about the wart, the baseline classifiers such as Naive Bayes (NB), logistic regression and ensemble (LR), support vector machine (SVM), decision tree (DT), bagging, random forest (RF), and eXtreme Gradient Boosting (XGB) along with the developed PDNN was constructed by taking splitting ratio criteria into account. The standard statistical measures such as the measure of accuracy (MoA), error rate, sensitivity, specificity, and area under the curve (AUC) were considered to evaluate the predictive behavior. The proposed PDNN approach obtained promising results: moA, error rate, sensitivity, specificity, and measure of AUC as 0.9778, 0.0222, 0.9762, 0.9792, and 0.9818 while selecting immunotherapy and 0.9889, 0.0111, 1.0000, 0.9796, and 0.9970 in case of cryotherapy. The developed PDNN outperforms baseline classifiers and existing state-of-the-art wart treatment expert systems. The proposed model will improve the success rate and saves the diagnosing time. PDNN-based wart treatment identification system can be implemented in real time after consulting with a domain specialist.
The goal of this project is to disrupt the way businesses monitor their operations by integrating IoT and machinelearning to gain a deeper understanding of customer behavior and improve operational efficiency. Tradit...
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Using SSD and YOLO to efficiently detect potholes and speed bumps is the main goal of this project, which aims to improve road safety and construction quality. Hazard priority is emphasized and the limitations of the ...
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Chronic liver disease is a significant health concern, necessitating a comprehensive understanding of its underlying causes. The work aims to identify the key factors contributing to chronic liver disease using Factor...
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Image captioning is a challenging task in artificial intelligence that involves generating descriptive captions for images automatically. In this project, we propose a novel approach leveraging advanced technologies s...
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