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Deep Learning is appealing for learning from large amounts of unlabeled/unsupervised data, making it attractive for extracting meaningful representations and patterns from big data. Deep learning, by its simplest definition, is expressed as the application of machine learning methods to the big data. In this study, it was investigated how to apply hierarchical deep learning models for the problems...
The present work includes the temporal modeling of the oviposition activity of the Aedes aegypti mosquito, a vector of viral diseases such as Dengue, Chicungunya and Zika, based on time series of data extracted from earth observation satellite images. Unlike previous works, Machine Learning techniques that are capable of capturing nonlinear relationships between variables, such as artificial neural...
State of the art network intrusion detection systems are heavily influenced by signature based techniques for detecting threats which are extracted from raw packet captures and firewall logs. With the recent emergence of cloud computing and big data analytics, supervised machine learning is also being used to detect deviations of the network traffic patterns from already-known normal patterns. Subsequently,...
Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications...
Human-level control through deep learning and deep reinforcement learning have revealed the unique and powerful potentials through a very complex Go game. The AlphaGo, developed by Google DeepMind, has beat the top Go game player early this year. The scientific and technological advancement behind the success of AlphaGo attracted researchers from multiple areas, including machine learning, artificial...
The purpose of this paper is to understand various problems related to Computer Science and find solutions from optimized machine learning algorithm by realizing machine learning API and API server. The representative machine-learning algorithm, TensorFlow, need to express algorithm from the stage of nodes and edges while IBM Watson only uses functions in completed form. Those are the problems to...
In order to achieve autonomy and intelligence of autonomous underwater vehicle (AUV), dynamic target following in the unknown environment is one of the important problems to solve. Reinforcement learning (RL) offers the possibility of learning a policy to solve a particular task without manual intervention and previous experience. However, RL algorithms are not competent for continuous space problems...
This article consists of a collection of slides from the author's conference presentation. Some of the topics covered include: Machine learning 101: Neural nets, backprop, RNNs; Applications; Structured prediction; Unsupervised learning; "Neural Programs"; Architecture exploration; Towards hardware-friendlier DL; and Software.
Incorporating knowledge from domain expert to a classifier is one of the techniques which require to be considered in solving imbalanced dataset problems. In this study, the proposed technique is a development to extend the process for imbalanced dataset where the individual classification system has already been designed for balanced data set. This paper introduces a methodology and preliminary results...
Assessing the quality of sensor data in environmental monitoring applications is important, as erroneous readings produced by malfunctioning sensors, calibration drift, and problematic climatic conditions such as icing or dust, are common. Traditional data quality checking and correction is a painstaking manual process, so the development of automatic systems for this task is highly desirable. This...
Due to the rise and rapid growth of E-Commerce, use of credit cards for online purchases has dramatically increased and it caused an explosion in the credit card fraud. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In real life, fraudulent transactions are scattered with genuine transactions and...
An Intrusion detection system is designed to classify the system activities into normal and abnormal. We use a combination of machine learning approaches as to detect the system attacks. The experimental results of the study show that increasing the number of classifiers has a threshold limit and the system accuracy will remain constant if the number of classifiers goes beyond this limit. The determination...
In the domain of agricultural robotics, one major application is crop scouting, e.g., for the task of weed control. For this task a key enabler is a robust detection and classification of the plant and species. Automatically distinguishing between plant species is a challenging task, because some species look very similar. It is also difficult to translate the symbolic high level description of the...
To extract implicit knowledge and data relationships from the audio and audio similarity measure, this paper uses the audio mining techniques. A model for audio clustering and classification technique is proposed. Neural networks are used for classifying the data. The working prototype of the Music classification system has been developed and tested in MATLAB 6.5 using the signal Processing Toolbox...
Although the term design quality of digital systems can be assessed from many aspects, the distribution and density of bugs are two decisive factors. This paper presents the application of machine learning techniques to model the relationship between specified metrics of high-level design and its associated bug information. By employing the project repository (i.e., high level design and bug repository),...
MicroRNAs are one type of noncoding RNA that regulate their target mRNAs before mRNAs are translated into proteins. Although it has been demonstrated that the regulation is through partial binding of the seed region of a miRNA and its targets, the mechanism of this process is not fully discovered. Some biological experiments have shown that even perfect base pairing in the seed region does not always...
Traditionally e-learning systems are emphasized on the online content generation and most of them fail in considering the requirements and learning styles of end user, while representing it. Therefore, appears the need for adaptation to the user's learning behavior. Adaptive e-learning refers to an educational system that understands the learning content and the user interface according to pedagogical...
Transmission tower occupies an important position in the event of transmission of electricity. The failure of transmission tower would cause serious economic losses. As a damage identification parameter, variation ratio of curvature mode has a great ability to damage location. In the field of damage location identification on transmission tower, variation ratio of curvature mode achieved good results...
Reducing power consumption has become a priority in microprocessor design as more devices become mobile and as the density and speed of components lead to power dissipation issues. Power allocation strategies for individual components within a chip are being researched to determine optimal configurations to balance power and performance. Modelling and estimation tools are necessary in order to understand...
Classification is a major problem of study that involves formulation of decision boundaries based on the training data samples. The limitations of the single neural network approaches motivate the use of multiple neural networks for solving the problem in the form of ensembles and modular neural networks. While the ensembles solve the problem redundantly, the modular neural networks divide the computation...
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