Browsing by Subject "machine learning"
Now showing items 1-20 of 24
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ADAPTIVE LONG TERM TRACKING AND AUTONOMOUS FOLLOWING USING STEREO-CAMERA OF AN UNMANNED AERIAL VEHICLE WITH COLLISION AVOIDANCE
Unmanned aerial vehicles commercially called quadcopters or drones have become increasingly popular over recent years, delving into wide range of fields from medicine for providing immediate health care or in agriculture ... -
Author Style Analysis in Text Documents Based on Character and Word N-Grams
We describe our research on text analytics methods for detecting differences and similarities in the style of authors of text documents. Automatic methods for analyzing the written style of authors have applications in ... -
Authorship Attribution using Written and Read Documents
In Authorship Attribution (AA), a task of identifying the author on an unseen document, it is often hard to obtain large amounts of training text written by an author. In our research, we analyze the influence of the size ... -
Biologically Informed Feature Selection in Large Scale Genomics
Predictive genetics is a promising field of research, particularly in medical science where the ability to identify disease or treatment response could provide novel methods of mitigating their negative effects. Machine ... -
A Comprehensive Study On One-way Backscatter Traffic Analysis
(2015-04-27)Since the occurrence and variety of Distributed Denial of Service (DDoS) has dramatically increased, the discovery of DDoS signatures (rules) become very difficult for current intrusion detection mechanisms. Darknets, which ... -
Data Mining in Social Media for Stock Market Prediction
(2012-09-05)In this thesis, machine learning algorithms are used in NLP to get the public sentiment on individual stocks from social media in order to study its relationship with the stock price change. The NLP approach of sentiment ... -
Diversity and Novelty as Objectives in Poker
Evolutionary algorithms are capable to lead to efficient solutions without a predefined design and few human bias. However, they can be prone to early convergence and may be deceived by a non-informative or deceptive fitness ... -
EXPLORING A MACHINE LEARNING BASED APPROACH FOR ANALYZING ANONYMIZED DATA
Information found in log files is often stored in human readable plain text. Research study participants do not want sensitive information recorded, and when data is made publicly available, participants ask that they ... -
Exploring data leakage via supervised learning
Data security includes but not limited to, data encryption, tokenization, and key management practices that protect data across all applications and platforms. In this thesis, I aim to explore whether any data leakage takes ... -
Hybrid Tag Recommendation in Collaborative Tagging Systems
(2012-04-20)The simplicity and flexibility of tagging allows users to collaboratively create large, loosely structured repositories of Web resources. One of its main drawbacks is the need for manual formulation of tags for each posted ... -
An Investigation of Using Machine Learning with Distribution Based Flow Features for Classifying SSL Encrypted Network Traffic
(2012-08-20)Encrypted protocols, such as Secure Socket Layer (SSL), are becoming more prevalent because of the growing use of e-commerce, anonymity services, gaming and Peer-to-Peer (P2P) applications such as Skype and Gtalk. The ... -
Learning in Non-Stationary Environments
(2013-08-26)Real-world decision making is challenging due, in part, to changes in the underlying reward structure: the best option last week may be less rewarding today. Determining the best response is even more challenging when ... -
Measuring Inter-Pulse Intervals in Sperm Whale Clicks: Development of an Automatic Method and its Potential for Identifying Eastern Caribbean Social Units
Sperm whale clicks have a unique multi-pulsed structure, where the inter-pulse interval (IPI) is related to body length. This feature makes passive acoustic monitoring especially informative for sperm whales. I investigated ... -
NLP AND MACHINE LEARNING TECHNIQUES TO DETECT ONLINE HARASSMENT ON SOCIAL NETWORKING PLATFORMS
Social media has become an unavoidable part of our daily lives. It attracts di erent users with di erent mindsets. In particular, Twitter is a platform with a diverse audience who engage in di erent topics and interact ... -
Predicting Political Donations Using Data Driven Lifestyle Profiles Generated from Character N-Gram Analysis of Heterogeneous Online Sources
This paper describes an approach for generating multi-dimensional Activities, Interests, and Opinions (AIO) insights from disparate web sources. The method involves identifying psychographic profiles using text analysis ... -
PREDICTING THE OUTCOME OF KIDNEY TRANSPLANTS USING MACHINE LEARNING METHODS
The prediction of the survival of kidney grafts is based on the procedure of matching kidney donors and recipients. Machine learning can be effectively used to analyze the appropriate donor-recipient attributes from a ... -
Providing Real-Valued Actions for Tangled Program Graphs Under the CartPole Benchmark
The Tangled Program Graph framework (TPG) is a genetic programming approach to reinforcement learning. Canonical TPG is limited to performing discrete actions. This thesis investigates mechanisms by which TPG might perform ... -
Spatiotemporal Pattern Detection in Multi-cell Recordings Using Unsupervised Learning
Detection of spatiotemporal patterns have many applications in areas such as computer vision and data mining. Specifically, the analysis and mining of biological data with high dimensionality (e.g. multi-cell recordings, ... -
SUPERVISED MACHINE/DEEP LEARNING TECHNIQUES – A CASE STUDY OF POWDERY MILDEW DETECTION ON THE STRAWBERRY LEAF
This research proposed the algorithm, that can detect powdery mildew and give the highest classification accuracy (CA). Three image processing and two machine learning algorithms (artificial neural network; ANN and support ... -
Towards Coevolutionary Genetic Programming with Pareto Archiving Under Streaming Data
(2013-08-16)Classification under streaming data constraints implies that training must be performed continuously, can only access individual exemplars for a short time after they arrive, must adapt to dynamic behaviour over time, and ...
