All Outputs (10)

Fast and Scalable Approaches to Accelerate the Fuzzy k Nearest Neighbors Classifier for Big Data (2019)
Journal Article

One of the best-known and most effective methods in supervised classification is the k nearest neighbors algorithm (kNN). Several approaches have been proposed to improve its accuracy, where fuzzy approaches prove to be among the most successful, hig... Read More about Fast and Scalable Approaches to Accelerate the Fuzzy k Nearest Neighbors Classifier for Big Data.

Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study (2019)
Journal Article

Detecting anomalies in time series data is becoming mainstream in a wide variety of industrial applications in which sensors monitor expensive machinery. The complexity of this task increases when multiple heterogeneous sensors provide information of... Read More about Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study.

A Preliminary Approach for the Exploitation of Citizen Science Data for Fast and Robust Fuzzy k-Nearest Neighbour Classification (2019)
Conference Proceeding

Citizen science is becoming mainstream in a wide variety of real-world applications in astronomy or bioinformatics, in which, for example, classification tasks by experts are very time consuming. These projects engage amateur volunteers that are task... Read More about A Preliminary Approach for the Exploitation of Citizen Science Data for Fast and Robust Fuzzy k-Nearest Neighbour Classification.

Evolving Deep CNN-LSTMs for Inventory Time Series Prediction (2019)
Conference Proceeding

Inventory forecasting is a key component of effective inventory management. In this work, we utilise hybrid deep learning models for inventory forecasting. According to the highly nonlinear and non-stationary characteristics of inventory data, the mo... Read More about Evolving Deep CNN-LSTMs for Inventory Time Series Prediction.

Fuzzy Hot Spot Identification for Big Data: An Initial Approach (2019)
Conference Proceeding

Hot spot identification problems are present across a wide range of areas, such as transportation, health care and energy. Hot spots are locations where a certain type of event occurs with high frequency. A recent big data approach is capable of iden... Read More about Fuzzy Hot Spot Identification for Big Data: An Initial Approach.

A Taxonomy of Traffic Forecasting Regression Problems From a Supervised Learning Perspective (2019)
Journal Article

One contemporary policy to deal with traffic congestion is the design and implementation of forecasting methods that allow users to plan ahead of time and decision makers to improve traffic management. Current data availability and growing computatio... Read More about A Taxonomy of Traffic Forecasting Regression Problems From a Supervised Learning Perspective.

Virtual porous materials to predict the air void topology and hydraulic conductivity of asphalt roads (2019)
Journal Article

This paper investigates the effects of air void topology on hydraulic conductivity in asphalt mixtures with porosity in the range 14%–31%. Virtual asphalt pore networks were generated using the Intersected Stacked Air voids (ISA) method, with its par... Read More about Virtual porous materials to predict the air void topology and hydraulic conductivity of asphalt roads.

A review on the self and dual interactions between machine learning and optimisation (2019)
Journal Article

Machine learning and optimisation are two growing fields of artificial intelligence with an enormous number of computer science applications. The techniques in the former area aim to learn knowledge from data or experience, while the techniques from... Read More about A review on the self and dual interactions between machine learning and optimisation.

PAS3-HSID: a Dynamic Bio-Inspired Approach for Real-Time Hot Spot Identification in Data Streams (2019)
Journal Article

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Hot spot identification is a very relevant problem in a wide variety of areas such as health care, energy or transportation. A hot spot is defined as a region of high likelihood o... Read More about PAS3-HSID: a Dynamic Bio-Inspired Approach for Real-Time Hot Spot Identification in Data Streams.

A Simulation-based Optimisation Approach for Inventory Management of Highly Perishable Food (2019)
Conference Proceeding

The taste and freshness of perishable foods decrease dramatically with time. Effective inventory management requires understanding of market demand as well as balancing customers needs and references with products’ shelf life. The objective is to av... Read More about A Simulation-based Optimisation Approach for Inventory Management of Highly Perishable Food.