Physical Sciences and Mathematics Commons^™

Predicting Indoor Crowd Density Using Column-Structured Deep Neural Network, Akihito Sudo, Teck Hou (Deng Dehao) Teng, Hoong Chuin Lau, Yoshihide Sekimoto

Research Collection School Of Computing and Information Systems

This work proposes a deep neural network approach known as the column-structured deep neural network (COL-DNN-R) for predicting crowd density in an indoor environment using historical Wi-Fi traces of individual visitors. With a structure designed to minimize feature engineering, COL-DNN accepts raw features such as crowd density, opening and closing hours and peak visitor counts for extracting features. The extracted features are used by a regression model R for predicting the crowd densities. Standard regression models such as MLP, RF and SVM can be used as R. Experiments are performed to investigate the effect of feature representation and model structure …

Signet: A Neural Network Architecture For Predicting Protein-Protein Interactions, Muhammad S. Ahmed

Electronic Thesis and Dissertation Repository

The study of protein-protein interactions (PPI) is critically important within the field of Molecular Biology, as proteins facilitate key organismal functions including the maintenance of both cellular structure and function. Current experimental methods for elucidating PPIs are greatly hindered by large operating costs, lengthy wait times, as well as low accuracy. The recent development of computational PPI predicting techniques has worked to address many of these issues. Despite this, many of these methods utilize over-engineered features and naive learning algorithms. With the recent advances in Machine Learning and Artificial Intelligence, we attempt to view this problem through a novel, deep …

Estimation Of Human Poses Categories And Physical Object Properties From Motion Trajectories, Mona Fathollahi Ghezelghieh

USF Tampa Graduate Theses and Dissertations

Despite the impressive advancements in people detection and tracking, safety is still a key barrier to the deployment of autonomous vehicles in urban environments [1]. For example, in non-autonomous technology, there is an implicit communication between the people crossing the street and the driver to make sure they have communicated their intent to the driver. Therefore, it is crucial for the autonomous car to infer the future intent of the pedestrian quickly. We believe that human body orientation with respect to the camera can help the intelligent unit of the car to anticipate the future movement of the pedestrians. To …

Investigate Genomic 3d Structure Using Deep Neural Network, Yan Zhang

Theses and Dissertations

The 3D structures of the chromosomes play fundamental roles in essential cellular functions, e.g., gene regulation, gene expression, evolution and Hi-C technique provides the interaction density between loci on chromosomes. In this dissertation, we developed multiple algorithms, focusing the deep learning approach, to study the Hi-C datasets and the genomic 3D structures.

Building 3D structure of the genome one of the most critical purpose of the Hi-C technique. Recently, several approaches have been developed to reconstruct the 3D model of the chromosomes from HiC data. However, all of the methods are based on a particular mathematical model and lack of …