Essays about: "Deep neural networks"
Showing result 11 - 15 of 862 essays containing the words Deep neural networks.
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11. Combining Cell Painting, Gene Expression and Structure-Activity Data for Mechanism of Action Prediction
University essay from Uppsala universitet/Nationellt resurscentrum för biologi och bioteknikAbstract : The rapid progress in high-throughput omics methods and high-resolution morphological profiling, coupled with the significant advances in machine learning (ML) and deep learning (DL), has opened new avenues for tackling the notoriously difficult problem of predicting the Mechanism of Action (MoA) for a drug of clinical interest. Understanding a drug's MoA can enrich our knowledge of its biological activity, shed light on potential side effects, and serve as a predictor of clinical success. READ MORE
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12. Object Recognition in Satellite imagesusing improved ConvolutionalRecurrent Neural Network
University essay from Blekinge Tekniska Högskola/Institutionen för datavetenskapAbstract : Background:The background of this research lies in detecting the images from satellites. The recognition of images from satellites has become increasingly importantdue to the vast amount of data that can be obtained from satellites. This thesisaims to develop a method for the recognition of images from satellites using machinelearning techniques. READ MORE
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13. Sales forecasting for supply chain using Artificial Intelligence
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Supply chain management and logistics are two sectors currently experiencing a transformation thanks to the advent of AI(Artificial Intelligence) technologies. Leveraging predictive analytics powered by AI presents businesses with novel opportunities to streamline their operations effectively. READ MORE
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14. Restaurant Daily Revenue Prediction : Utilizing Synthetic Time Series Data for Improved Model Performance
University essay from Uppsala universitet/Avdelningen för beräkningsvetenskapAbstract : This study aims to enhance the accuracy of a demand forecasting model, XGBoost, by incorporating synthetic multivariate restaurant time series data during the training process. The research addresses the limited availability of training data by generating synthetic data using TimeGAN, a generative adversarial deep neural network tailored for time series data. READ MORE
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15. Few-Shot Learning for Quality Inspection
University essay from Högskolan i Halmstad/Akademin för informationsteknologiAbstract : The goal of this project is to find a suitable Few-Shot Learning (FSL) model that can be used in a fault detection system for use in an industrial setting. A dataset of Printed Circuit Board (PCB) images has been created to train different FSL models. READ MORE