Essays about: "Deep Neural Network"

Showing result 21 - 25 of 885 essays containing the words Deep Neural Network.

1. 21. Combining Cell Painting, Gene Expression and Structure-Activity Data for Mechanism of Action Prediction

   University essay from Uppsala universitet/Nationellt resurscentrum för biologi och bioteknik

   Author : Erik Everett Palm; [2023]
   Keywords : bioinformatics; deep learning; machine learning; joint model; tabular data; image data;

   Abstract : 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

3. 22. Object Recognition in Satellite imagesusing improved ConvolutionalRecurrent Neural Network

   University essay from Blekinge Tekniska Högskola/Institutionen för datavetenskap

   Author : TARUN NATTALA; [2023]
   Keywords : CRNN; CNN; RNN; Machine Learning and Satellite Image Recognition.;

   Abstract : 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

4. 23. EMONAS : Evolutionary Multi-objective Neuron Architecture Search of Deep Neural Network

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Jiayi Feng; [2023]
   Keywords : DNN Deep Neural Network ; NAS Neural Architecture Search ; EA Evolutionary Algorithm ; Multi-Objective Optimization; Binary One Optimization; Embedded Systems; DNN Deep Neural Network ; NAS Neural Architecture Search ; EA Evolutionary Algorithm ; Multi-Objective Optimization; Binary One Optimization; Inbyggda system;

   Abstract : Customized Deep Neural Network (DNN) accelerators have been increasingly popular in various applications, from autonomous driving and natural language processing to healthcare and finance, etc. However, deploying them directly on embedded system peripherals within real-time operating systems (RTOS) is not easy due to the paradox of the complexity of DNNs and the simplicity of embedded system devices. READ MORE

5. 24. Restaurant Daily Revenue Prediction : Utilizing Synthetic Time Series Data for Improved Model Performance

   University essay from Uppsala universitet/Avdelningen för beräkningsvetenskap

   Author : Stella Jarlöv; Anton Svensson Dahl; [2023]
   Keywords : demand forecasting; data augmentation; time series data; machine learning; restaurant industry; generative adversarial networks; TimeGAN; XGBoost;

   Abstract : 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

6. 25. Deep Learning Model Deployment for Spaceborne Reconfigurable Hardware : A flexible acceleration approach

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Javier Ferre Martin; [2023]
   Keywords : Space Situational Awareness; Deep Learning; Convolutional Neural Networks; FieldProgrammable Gate Arrays; System-On-Chip; Computer Vision; Dynamic Partial Reconfiguration; High-Level Synthesis; Rymdsituationstänksamhet; Djupinlärning; Konvolutionsnätverk; Omkonfigurerbara Field-Programmable Gate Arrays FPGAs ; System-On-Chip SoC ; Datorseende; Dynamisk partiell omkonfigurering; Högnivåsyntes.;

   Abstract : Space debris and space situational awareness (SSA) have become growing concerns for national security and the sustainability of space operations, where timely detection and tracking of space objects is critical in preventing collision events. Traditional computer-vision algorithms have been used extensively to solve detection and tracking problems in flight, but recently deep learning approaches have seen widespread adoption in non-space related applications for their high accuracy. READ MORE