Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 5 of 5
Full-Text Articles in Entire DC Network
Semi-Supervised Learning Using Triple-Siamese Network, Debapriya Banerjee
Semi-Supervised Learning Using Triple-Siamese Network, Debapriya Banerjee
Computer Science and Engineering Theses
Missing data problem is inevitable in mostly all research areas including Artificial Intelligence, Machine Learning and Computer Vision where we have modicum knowledge about the complete dataset. One of the key reasons of missing data in AI is insufficiency of accurately labeled data. To solve a classification problem using ML or training a Deep Neural Network model, we need a huge amount of labeled data. It is difficult to get labeled data but unlabeled data is inexpensive and available easily. It is usual that we get no more than a single element per class to train our models due to …
Deepsign: A Deep-Learning Architecture For Sign Language, Jai Amrish Shah
Deepsign: A Deep-Learning Architecture For Sign Language, Jai Amrish Shah
Computer Science and Engineering Theses
Sign languages are used by deaf people for communication. In sign languages, humans use hand gestures, body, facial expressions and movements to convey meaning. Humans can easily learn and understand sign languages, but automatic sign language recognition for machines is a challenging task. Using recent advances in the field of deep-learning, we introduce a fully automated deep-learning architecture for isolated sign language recognition. Our architecture tries to address three problems: 1) Satisfactory accuracy with limited data samples 2) Reducing chances of over-fitting when the data is limited 3) Automating recognition of isolated signs. Our architecture uses deep convolutional encoder-decoder architecture …
Classification Of Clinical Narratives Using Convolutional Neural Network, Nikit Rajiv Lonari
Classification Of Clinical Narratives Using Convolutional Neural Network, Nikit Rajiv Lonari
Computer Science and Engineering Theses
Patient safety is a key aspect for good consumer care. When an individual is hospitalized or receives medication the family wants the patient safety to be above all factors. For instance, a drug can do both either cure the disease or perhaps, give rise to an adverse event. A drug administered for an indicated condition has substantial power to reduce or cure a disease and further to prevent it from happening again in the future but at the risk of side effects. At present, there are several methods in patient safety and in particular in the area of signal detection …
Convolutional And Recurrent Neural Networks For Pedestrian Detection, Vivek Arvind Balaji
Convolutional And Recurrent Neural Networks For Pedestrian Detection, Vivek Arvind Balaji
Computer Science and Engineering Theses
Pedestrian Detection in real time has become an interesting and a challenging problem lately. With the advent of autonomous vehicles and intelligent traffic monitoring systems, more time and money are being invested into detecting and locating pedestrians for their safety and towards achieving complete autonomy in vehicles. For the task of pedestrian detection, Convolutional Neural Networks (ConvNets) have been very promising over the past decade. ConvNets have a typical feed-forward structure and they share many properties with the visual system of the human brain. On the other hand, Recurrent Neural Networks (RNNs) are emerging as an important technique for image …
Cell Segmentation In Cancer Histopathology Images Using Convolutional Neural Networks, Viswanathan Kavassery Rajalingam
Cell Segmentation In Cancer Histopathology Images Using Convolutional Neural Networks, Viswanathan Kavassery Rajalingam
Computer Science and Engineering Theses
Cancer, the second most dreadful disease causing large scale deaths in humans is characterized by uncontrolled growth of cells in the human body and the ability of those cells to migrate from the original site and spread to distant sites. The major proportion of deaths in cancer is due to improper primary diagnosis that raises the need for Computer Aided Diagnosis (CAD). Digital Pathology is a technique that acts as second set of eyes to radiologists in delivering expert level preliminary diagnosis for cancer patients. Cell segmentation is a challenging step in digital pathology that identifies cell regions from micro-slide …