Although thermography has been proposed over the past decade as an effective method for breast cancer diagnosis, the complexity of thermograms presents a significant obstacle, making their interpretation challenging. ...
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Evaluation of Electronic Medical Certification (EMC) is integral to today's healthcare systems since they are a consolidated database of individual patients' medical histories. Security measures and privacy is...
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Image data transfer has increased rigorously in the present times in social networking sites, mobile apps and live streaming video applications. This phenomenon puts enormous effect on internet bandwidth and speed of ...
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In the last decade,there has been remarkable progress in the areas of object detection and recognition due to high-quality color images along with their depth maps provided by RGB-D *** enable artificially intelligent...
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In the last decade,there has been remarkable progress in the areas of object detection and recognition due to high-quality color images along with their depth maps provided by RGB-D *** enable artificially intelligent machines to easily detect and recognize objects and make real-time decisions according to the given *** cues can improve the quality of object detection and *** main purpose of this research study to find an optimized way of object detection and identification we propose techniques of object detection using two RGB-D *** proposed methodology extracts image normally from depth maps and then performs clustering using the Modified Watson Mixture Model(mWMM).mWMM is challenging to handle when the quality of the image is not ***,the proposed RGB-D-based system uses depth cues for segmentation with the help of *** it extracts multiple features from the segmented *** selected features are fed to the Artificial Neural Network(ANN)and Convolutional Neural Network(CNN)for detecting *** achieved 92.13%of mean accuracy over NYUv1 dataset and 90.00%of mean accuracy for the Redweb_v1 ***,their results are compared and the proposed model with CNN outperforms other state-of-the-art *** proposed architecture can be used in autonomous cars,traffic monitoring,and sports scenes.
Accurate and specific segmentation of brain tumour is essential in medical imaging for accurate diagnosis and planning of treatments. In this research paper, we suggest the use of a Densenet121 model for tumor recogni...
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Deep learning is the process of determining parameters that reduce the cost function derived from the *** optimization in neural networks at the time is known as the optimal *** solve optimization,it initialize the pa...
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Deep learning is the process of determining parameters that reduce the cost function derived from the *** optimization in neural networks at the time is known as the optimal *** solve optimization,it initialize the parameters during the optimization *** should be no variation in the cost function parameters at the global *** momentum technique is a parameters optimization approach;however,it has difficulties stopping the parameter when the cost function value fulfills the global minimum(non-stop problem).Moreover,existing approaches use techniques;the learning rate is reduced during the iteration *** techniques are monotonically reducing at a steady rate over time;our goal is to make the learning rate *** present a method for determining the best parameters that adjust the learning rate in response to the cost function *** a result,after the cost function has been optimized,the process of the rate Schedule is *** approach is shown to ensure convergence to the optimal *** indicates that our strategy minimizes the cost function(or effective learning).The momentum approach is used in the proposed *** solve the Momentum approach non-stop problem,we use the cost function of the parameter in our proposed *** a result,this learning technique reduces the quantity of the parameter due to the impact of the cost function *** verify that the learning works to test the strategy,we employed proof of convergence and empirical tests using current methods and the results are obtained using Python.
Image segmentation is very important when detecting the presence of tumor in the human brain. Despite classifying Magnetic Resonance Imaging (MRI) images into the presence or absence of a brain tumor, it is also neces...
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Artificial intelligence heavily relies on neural networks, which enable machines to acquire knowledge and make informed choices by processing data inputs. Time series analysis plays a crucial role in enhancing the cap...
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In order to address the challenges faced in detecting objects in UAV aerial images, we propose an enhanced YOLOv8-CSD object detection algorithm. Our aim is to overcome issues such as low accuracy, complex model struc...
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The imminent rise of Autonomous Vehicles (AVs) is revolutionizing the future of transport. The Vehicular Fog Computing (VFC) paradigm has emerged to alleviate the load of compute-intensive and delay-sensitive AV progr...
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