Search results for "machine learning."
showing 10 items of 1455 documents
Accurate Wound and Lice Detection in Atlantic Salmon Fish Using a Convolutional Neural Network
2022
The population living in the coastal region relies heavily on fish as a food source due to their vast availability and low cost. This need has given rise to fish farming. Fish farmers and the fishing industry face serious challenges such as lice in the aquaculture ecosystem, wounds due to injuries, early fish maturity, etc. causing millions of fish deaths in the fish aquaculture ecosystem. Several measures, such as cleaner fish and anti-parasite drugs, are utilized to reduce sea lice, but getting rid of them entirely is challenging. This study proposed an image-based machine-learning technique to detect wounds and the presence of lice in the live salmon fish farm ecosystem. A new equally di…
Regularized extreme learning machine for regression problems
2011
Extreme learning machine (ELM) is a new learning algorithm for single-hidden layer feedforward networks (SLFNs) proposed by Huang et al. [1]. Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This paper proposes an algorithm for pruning ELM networks by using regularized regression methods, thus obtaining a suitable number of the hidden nodes in the network architecture. Beginning from an initial large number of hidden nodes, irrelevant nodes are then pruned using ridge regression, elastic net and lasso methods; hence, the architectural design of ELM network can be automated. Empirical studies…
An entropy-based machine learning algorithm for combining macroeconomic forecasts
2019
This paper applies a Machine Learning approach with the aim of providing a single aggregated prediction from a set of individual predictions. Departing from the well-known maximum-entropy inference methodology, a new factor capturing the distance between the true and the estimated aggregated predictions presents a new problem. Algorithms such as ridge, lasso or elastic net help in finding a new methodology to tackle this issue. We carry out a simulation study to evaluate the performance of such a procedure and apply it in order to forecast and measure predictive ability using a dataset of predictions on Spanish gross domestic product.
A machine learning application to predict early lung involvement in scleroderma: A feasibility evaluation
2021
Introduction: Systemic sclerosis (SSc) is a systemic immune-mediated disease, featuring fibrosis of the skin and organs, and has the greatest mortality among rheumatic diseases. The nervous system involvement has recently been demonstrated, although actual lung involvement is considered the leading cause of death in SSc and, therefore, should be diagnosed early. Pulmonary function tests are not sensitive enough to be used for screening purposes, thus they should be flanked by other clinical examinations
Strategic cyber threat intelligence : Building the situational picture with emerging technologies
2020
In 2019, e-criminals adopted new tactics to demand enormous ransoms from large organizations by using ransomware, a phenomenon known as “big game hunting.” Big game hunting is an excellent example of a sophisticated and coordinated modern cyber-attack that has a significant impact on the target. Cyber threat intelligence (CTI) increases the possibilities to detect and prevent cyber-attacks and gives defenders more time to act. CTI is a combination of incident response and traditional intelligence. Intelligence modifies raw data into information for decision-making and action. CTI consists of strategic, operational, or tactical intelligence on cyber threats. Security event monitoring, event-…
Boosting Hankel matrices for face emotion recognition and pain detection
2017
HighligthsDynamics of face expression descriptors are modeled for emotion recognition.A set of Hankel matrices is built upon several multi-scale face representations.Boosting and random subspace projection are used for dynamics selection.Dynamics of Haar-like features and Gabor Energies are compared.Fine-grained dynamics of subtle expressions can be modeled at small spatial scales. Studies in psychology have shown that the dynamics of emotional expressions play an important role in face emotion recognition in humans. Motivated by these studies, in this paper the dynamics of face expressions are modeled and used for automatic emotion recognition and pain detection.Given a temporal sequence o…
Adaptive Sequential Interpolator Using Active Learning for Efficient Emulation of Complex Systems
2020
Many fields of science and engineering require the use of complex and computationally expensive models to understand the involved processes in the system of interest. Nevertheless, due to the high cost involved, the required study becomes a cumbersome process. This paper introduces an interpolation procedure which belongs to the family of active learning algorithms, in order to construct cheap surrogate models of such costly complex systems. The proposed technique is sequential and adaptive, and is based on the optimization of a suitable acquisition function. We illustrate its efficiency in a toy example and for the construction of an emulator of an atmosphere modeling system.
Approximating Empirical Surface Reflectance Data through Emulation: Opportunities for Synthetic Scene Generation
2019
Collection of spectroradiometric measurements with associated biophysical variables is an essential part of the development and validation of optical remote sensing vegetation products. However, their quality can only be assessed in the subsequent analysis, and often there is a need for collecting extra data, e.g., to fill in gaps. To generate empirical-like surface reflectance data of vegetated surfaces, we propose to exploit emulation, i.e., reconstruction of spectral measurements through statistical learning. We evaluated emulation against classical interpolation methods using an empirical field dataset with associated hyperspectral spaceborne CHRIS and airborne HyMap reflectance spectra…
Event Reconstruction
2014
Event reconstruction is one of the most important step in digital forensic investigations. It allows investigators to have a clear view of the events that have occurred over time. Event reconstruction is a complex task which requires exploration of a large amount of events due to the pervasiveness of new technologies nowadays. Any evidence produced at the end of the investigative process must also meet the requirements of the courts, such as reproducibility, verifiability, validation, etc. After defining the most important concepts of event reconstruction, a survey of the challenges of this field and solutions proposed so far is given in this chapter. Irish Research Council Science Foundati…
Mismatches between objective parameters and measured perception assessment in room acoustics: a holistic approach
2014
Psychoacoustic research in the field of concert halls has revealed that many aspects concerning listening perception have yet to be totally understood. On the one hand, the objective room acoustics of performance spaces are reflected in parameters, some standardized and some not, but these are related to a limited number of perceptual attributes of human response. In general, these objective parameters cannot accurately describe the acoustic details due to their inherent simplification. Under these premises, impulse responses (576 receivers) are measured in 16 concert halls, according to standard procedures, and the perception and satisfaction of the occupants of the rooms are evaluated by …