Browsing by Subject "Deep Learning"
Now showing items 1-15 of 15
-
Active Neural Learners for Text with Dual Supervision
Dual supervision for text classification and information retrieval, which involves training the machine with class labels augmented with text annotations that are indicative of the class, has been shown to provide significant ... -
Classification and Analysis of a Large MEG Dataset using Convolutional Neural Networks
Convolutional neural networks were used to classify and analyse a large magnetoencephalography (MEG) dataset. Networks were trained to classify between active and baseline intervals recorded during cued button pressing. ... -
Concept Embedding for Deep Neural Functional Analysis of Genes and Deep Neural Word Sense Disambiguation of Biomedical Text
As far as Gene Ontology (GO) is concerned, most of the existing gene functional similarity measures combine information content-based semantic similarity scores of single GO-term pairs to estimate gene functional similarity, ... -
Convolutional Neural Networks for Real-Time Weed Identification in Wild Blueberry Production
Yield-limiting weeds in wild blueberry fields, including hair fescue and sheep sorrel, are traditionally managed with uniform applications of herbicides. Spot applications of herbicides reduce the volume required for ... -
DDOS DETECTION MODELS USING MACHINE AND DEEP LEARNING ALGORITHMS AND DISTRIBUTED SYSTEMS
Distributed Denial-of-Service (DDoS) attacks are considered to be a major security threat to online servers and cloud providers. Intrusion detection systems have utilized machine learning as one of the solutions to the ... -
A DEEP IMAGE CLASSIFICATION APPROACH FOR FISHING ACTIVITY DETECTION FROM AIS DATA
Maritime transport and vessel activities on the ocean have a significant impact on marine life, which consequently affects human life. Therefore analyzing and monitoring the fishing activities using the vast amount of ... -
DEVELOPMENT OF AN AUTOMATED DEBRIS DETECTION SYSTEM FOR WILD BLUEBERRY HARVESTERS USING A CONVOLUTIONAL NEURAL NETWORK TO IMPROVE FRUIT QUALITY
Improving wild blueberry fruit quality has become increasingly important to producers due to the tightening profit margin facing the industry. The continuous development of field management practices (i.e., application of ... -
Discriminative Shape Feature Pooling in Deep Convolutional Networks for Visual Classification
Unlike conventional handcrafted feature extractors, deep learning approach can extract generic image features without relying on explicit domain knowledge. Recently, there is a trend of combining handcrafted features with ... -
An Empirical Analysis of Cross-entropy Based and Metric-based Methods on North Atlantic Right Whale Acoustic Data
With increasing concern for marine species extinction, a massive effort has been made to conserve, prevent, and search for a sustainable solution. However, data labeling is a labor-heavy and time-consuming work, resulting ... -
Learning Embeddings for Text and Images from Structure of the Data
In the big data era, most of the data generated every day are high dimensional such as text and image data. Learning compact representations from the input data can help in dealing with the high dimensionality and visualization ... -
A Machine Learning Framework for Host Based Intrusion Detection using System Call Abstraction
The number of cyber threats is increasing faster than the number of defensive strategies deployed to tackle those threats. An automated Intrusion Detection System (IDS) has the capability to detect, classify, and predict ... -
Modelling Human Target Reaching using A novel predictive deep reinforcement learning technique
It is hypothesized that the brain builds an internal representation of the world and its body. Moreover, it is well established that human decision making and instrumental control uses multiple systems, some which are ... -
Multi-path Convolutional Neural Networks for Image Classification
Convolutional Neural Networks have demonstrated high performance in the ImageNet Large-Scale Visual Recognition Challenges contest. Nevertheless, the published results only show the overall performance for all image classes. ... -
Stock Movement Prediction with Deep Learning, Finance Tweets Sentiment, Technical Indicators, and Candlestick Charting
Stock prediction has been a popular research topic. Due to its stochastic nature, predicting the future stock market remains a difficult problem. This thesis studies the application of Deep Neural Networks (DNNS) in ... -
UNSUPERVISED PARAPHRASE GENERATION FROM HIERARCHICAL LANGUAGE MODELS
Paraphrase generation is a challenging problem that requires a semantic representation of language. Language models implemented with deep neural networks (DNN) have the ability to transform text to a real valued vector ...
