Enrique Onieva

• PhD in Computer Science
• Lecturer at University of Deusto

Accurate estimation of the future state of the traffic is an attracting area for researchers in the field of Intelligent Transportation Systems (ITS). This kind of predictions can lead to traffic managers and drivers to act in consequence, reducing the economic and social impact of a possible congestion. Due to the inter-urban traffic information n...

A real-world newspaper distribution problem with recycling policy is tackled in this work. To meet all the complex restrictions contained in such a problem, it has been modeled as a rich vehicle routing problem, which can be more specifically considered as an asymmetric and clustered vehicle routing problem with simultaneous pickup and deliveries,...

Metaheuristics have proven to get a good performance solving difficult optimization problems in practice. Despite its success, metaheuristics still suffers from several problems that remains open as the variability of their performance depending on the problem or instance being solved. One of the approaches to deal with these problems is the hybrid...

This paper presents a method of optimizing the elements of a hierarchy of fuzzy-rule-based systems (FRBSs). It is a hybridization of a genetic algorithm (GA) and the cross-entropy (CE) method, which is here called GACE. It is used to predict congestion in a 9-km-long stretch of the I5 freeway in California, with time horizons of 5, 15, and 30 min....

An improved solution for drug distribution is presented in this paper. It is divided into two parts: i) a multi-operator evolutionary algorithm in charge of calculating the initial delivery routes and ii) an ambient intelligence-based support system able to tracing the merchandise along the distribution route. The first one establishes the routes t...

While machine learning's role in financial trading has advanced considerably, algorithmic transparency and explainability challenges still exist. This research enriches prior studies focused on high‐frequency financial data prediction by introducing an explainable reinforcement learning model for portfolio management. This model transcends basic as...

The amount of information that is produced on a daily basis in the financial markets is vast and complex; consequently, the development of systems that simplify decision-making is an essential endeavor. In this article, several intelligent systems are proposed and tested to predict the closing price of the IBEX 35 index using more than ten years of...

For companies involved in the supply chain, proper warehousing management is crucial. Warehouse layout arrangement and operation play a critical role in a company’s ability to maintain and improve its competitiveness. Reducing costs and increasing efficiency are two of the most crucial warehousing goals. Deciding on the best warehouse layout is a r...

Due to rising consumer demand and traffic congestion, last-mile logistics is becoming more challenging. To optimize urban distribution networks, digital image processing plays a key role in addressing these challenges through efficient traffic monitoring systems, an essential component of intelligent transportation systems. This paper introduces th...

Concept drift occurs when the statistical properties of a data distribution change over time, causing the performance of machine learning models trained on prior data to degrade. This is a prevalent issue in many real-world applications where the data distribution can shift due to factors such as user behaviour alterations, environmental changes, o...

The amount of data generated daily in the financial markets is diverse and extensive; hence, creating systems that facilitate decision-making is crucial. In this paper, different intelligent systems are proposed and tested to predict the closing price of the IBEX 35 using ten years of historical data with four different neural networks architecture...

Electric vehicles (EVs) have been adopted in urban areas to reduce environmental pollution and global warming as a result of the increasing number of freight vehicles. However, there are still deficiencies in routing the trajectories of last-mile logistics that continue to impact social and economic sustainability. For that reason, in this paper, a...

Classifier ensemble pruning is a strategy through which a subensemble can be identified via optimizing a predefined performance criterion. Choosing the optimum or suboptimum subensemble decreases the initial ensemble size and increases its predictive performance. In this article, a set of heuristic metrics will be analyzed to guide the pruning proc...

Classifier ensembles are characterized by the high quality of classification, thanks to their generalizing ability. Most existing ensemble algorithms use all learning samples to learn the base classifiers that may negatively impact the ensemble’s diversity. Also, the existing ensemble pruning algorithms often return suboptimal solutions that are bi...

Warehousing management is essential for many companies involved in the supply chain. The optimal arrangement and operation of warehouses play important role in companies, allowing them to maintain and increase their competitiveness. One of the main goals of warehousing is the reduction of costs and improvement of efficiency. Determination of the id...

With the increase in the global population and rising demand for food and other materials, there has been a rise in the amount of waste being generated daily by each house and locality. Most of the waste that is generated is thrown into the garbage containers, from where it is collected by the area municipalities. Improper management of waste in ci...

This work presents an application of different deep learning related paradigms to the diagnosis of multiple chest pathologies. Within the article, the application of a well-known deep Convolutional Neural Network (DenseNet) is used and fine-tuned for different chest X-Ray medical diagnosis tasks. Different image augmentation methods are applied ove...

Background Machine Learning (ML) has experienced an increasing use given the possibilities to expand the scientific knowledge of different disciplines, such as nanotechnology. This has allowed the creation of Cheminformatic models, capable of predicting biological activity and physicochemical characteristics of new components with high success rate...

This paper introduces a novel hybridisation technique combining the Backtracking Search (BS) and Differential Evolution (DE) algorithms. The proposed hybridisation executes diversity loss and stagnation detection mechanisms to maintain the diversity of the populations, in addition, modifications are done over the mutation operators of the component...

Multiple classifier systems (MCSs) constitute one of the most competitive paradigms for obtaining more accurate predictions in the field of machine learning. Systems of this type should be designed efficiently in all of their stages, from data preprocessing to multioutput decision fusion. In this article, we present a framework for utilizing the po...

Nanoparticles (NPs), decorated with coating agents (polymers, gels, proteins, etc.), form Nanoparticle Drug Delivery Systems (DDNS) of high interest in Nanotechnology and Biomaterials science. There is an increasing publication of...

Nanosystems are gaining momentum in pharmaceutical sciences because of the wide variety of possibilities for designing these systems to have specific functions. Specifically, studies of new cancer cotherapy drug-vitamin release nanosystems (DVRNs) including anticancer compounds and vitamins or vitamin derivatives have revealed encouraging results....

The current growing demand for low-cost edge devices to bridge the physical–digital divide has triggered the growing scope of Radio Frequency Identification (RFID) technology research. Besides object identification, researchers have also examined the possibility of using RFID tags for low-power wireless sensing, localisation and activity inference....

Traffic forecasting is an important research area in Intelligent Transportation Systems that is focused on anticipating traffic in order to mitigate congestion. In this work we propose a deep neural network that simultaneously extracts the spatial features of traffic, using graph convolution, and its temporal features by means of Long Short Term Me...

Determining the biological activity of vitamin derivatives is needed given that organic synthesis of analogs of vitamins is an active field of interest for medicinal chemistry, pharmaceuticals, and food additives. Accordingly, scientists from different disciplines perform preclinical assays (n ij ) with a considerable combination of assay condition...

Multiple classifier systems have proven superiority over individual ones to solve classification tasks. One of the main issues in those solution relies in data size, when the amount of data to be analyzed becomes huge. In this paper, the performance of ensemble system to succeed by using only portions of the available data is analyzed. For this, ex...

Within the framework of the work carried out by the University of Deusto (http://www.deusto.es/) on the social impact of research, a series of research projects with high potential for social impact are selected annually, and from these, the so-called Deusto Social Impact Briefings (DSIB) are prepared and published as short monographs. They are aim...

Within the framework of the work carried out by the University of Deusto (http://www.deusto.es/) on the social impact of research, a series of research projects with high potential for social impact are selected annually, and from these, the so-called Deusto Social Impact Briefings (DSIB) are prepared and published as short monographs. They are aim...

Aims: Given the current gaps of scientific knowledge and the need of efficient application of food law, this paper makes an analysis of principles of European food law for the appropriateness of applying biological activity Machine Learning prediction models to guarantee public safety. Background: Cheminformatic methods are able to design and cr...

Nano-systems for cancer co-therapy including vitamins or vitamins derivatives have showed adequate results to continue with further researches to better understand them. However, the number of different combinations of drugs,...

One of the most challenging issues when facing a classification problem is to deal with imbalanced datasets. Recently, ensemble classification techniques have proven to be very successful in addressing this problem. We present an ensemble classification approach based on feature space partitioning for imbalanced classification. A hybrid metaheurist...

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 different nature, scales and frequencies from the same machine. Traditionally, machine learning tec...

The emergence of Industry 4.0 and the Internet of Things (IoT) has meant that the manufacturing industry has evolved from embedded systems to cyber-physical systems (CPSs). This transformation has provided manufacturers with the ability to measure the performance of industrial equipment by means of data gathered from on-board sensors. This allows t...

Vehicular Ad-Hoc Networks (VANETs) have attracted a high interest in recent years due to the huge number of innovative applications that they can enable. Some of these applications can have a high impact on reducing Greenhouse Gas emissions produced by vehicles, especially those related to traffic management and driver assistance. Many of these ser...

Radio Frequency Identification (RFID) technology is one of the most popular systems to uniquely identify items by attaching a tag to them. The growing number of tagged items that need to be identified in one reader interrogation area leads to high tag collision rates. Therefore, fast anti-collision protocols are required to minimize the total tags...

Class imbalance is among the most persistent complications which may confront the traditional supervised learning task in real-world applications. Among the different kind of classification problems that have been studied in the literature, the imbalanced ones, particularly those that represents real-world problems, have attracted the interest of m...

Intelligent Transportation Systems emerged to meet the increasing demand for more efficient, reliable and safer transportation systems. These systems combine electronic, communication and information technologies with traffic engineering to respond to the former challenges. The benefits of Intelligent Transportation Systems have been extensively pr...

The thirteen papers included in this special issue represent a selection of extended contributions presented at the 10th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2015 held in Bilbao, Spain, March 22nd–24th 2015, and organized by the BISITE and the DeustoTech research groups. The International Conference on Hybrid Arti...

This work presents the evolution of a solution for predictive maintenance to a Big Data environment. The proposed adaptation aims for predicting failures on wind turbines using a data-driven solution deployed in the cloud and which is composed by three main modules. (i) A predictive model generator which generates predictive models for each monitor...

Researchers who investigate in any area related to computational algorithms (both defining new algorithms or improving existing ones) usually find large difficulties to test their work. Comparisons among different researches in this field are often a hard task, due to the ambiguity or lack of detail in the presentation of the work and its results....

The authors wish to make the following corrections to this paper: [...]

This work presents the evolution of a solution for predictive maintenance to a Big Data environment. The proposed adaptation aims for predicting failures on wind turbines using a data-driven solution deployed in the cloud and which is composed by three main modules. (i) A predictive model generator which generates predictive models for each monitor...

The knowledge of the number of tags is critical in many Radio Frequency Identification (RFID) applications. This paper is concerned with the problem of estimating an RFID tag population size when the number of tags is much higher than the frame size. A novel estimation scheme called ‘Scalable Minimum Mean Square Error’ (sMMSE) is proposed. The prop...

In this paper, a comparative study between a hybrid technique that combines a Genetic Algorithm with a Cross Entropy method to optimize Fuzzy Rule-Based Systems, and literature techniques is presented. These techniques are applied to traffic congestion datasets in order to determine their performance in this area. Different types of datasets have b...

This study is focused on a decentralised intelligent transportation systems with distributed intelligence based on classification techniques. The rationale behind this architecture is to offer a fully distributed, flexible and scalable system. The architecture encompasses the entire process of capture and management of available road data, enabling...

In this paper, we present an offline map matching technique designed for indoor localization systems based on conditional random fields (CRF). The proposed algorithm can refine the results of existing indoor localization systems and match them with the map, using loose coupling between the existing localization system and the proposed map matching...

This paper describes a new cooperative Intelligent Transportation System architecture that aims to enable collaborative sensing services. The main goal of this architecture is to improve transportation efficiency and performance. The system, which has been proven within the participation in the ICSI (Intelligent Cooperative Sensing for Improved tra...

TIMON is an EU research project under the programme Horizon 2020 that aims at creating a cooperative ecosystem integrating traffic information, transport management, ubiquitous data and system self-management. The objective of TIMON is to provide real-time services through a web based platform and a mobile APP for drivers, Vulnerable Road Users (VR...

Up to date energy-aware radio frequency identification (RFID) anticollision protocols are mainly focused on RFID systems using active tags. The introduction of RFID in portable devices, and the increasing popularity of passive tags, demands low energy-consumption anticollision protocols in environments with passive tags. Memoryless protocols presen...

The paper presents an approach to train combined classifiers based on feature space splitting and selection of the best classifier ensemble to each subspace of feature space. The learning method uses a hybrid algorithm that combines a Genetic Algorithm and Cross Entropy Method. The proposed approach was evaluated on the basis of the comprehensive c...

A real-world newspaper distribution problem with recycling policy is tackled in this work. In order to meet all the complex restrictions contained in such a problem, it has been modeled as a rich vehicle routing problem, which can be more specifically considered as an asymmetric and clustered vehicle routing problem with simultaneous pickup and del...

A new methodology which integrates fuzzy logic with RFID anti-collision protocols is proposed. The resulting FuzzyQ protocol significantly decreases the identification time by updating the transmission frame size in a dynamic and adaptive way. Simulation results show the performance of FuzzyQ compared with earlier protocols based on the standard EP...

This paper describes a new cooperative Intelligent Transportation System (ITS) architecture which aim is to enable collaborative sensing services targeting to improve transportation efficiency and performance. This objective will be carried out by applying a combination of cooperative applications and methods for data sensing, acquisition, processi...

Nowadays, parallel genetic algorithms are one of the most used meta-heuristics for solving combinatorial optimization problems. One of the challenges that arise when implementing these kind of algorithms is the communication between subpopulations. This communication, called migration, is a determining factor for a good performance of the algorithm...

This chapter proposes a novel approach, based on fuzzy logic and genetic algorithms, to build traffic congestion prediction systems from a high number of input variables. For the purpose of achieving more accurate and robust traffic prediction, it proposes a genetic hierarchical fuzzy rule-based system (GHFRBS) capable of predicting traffic congest...

Dynamic Optimization Problems (DOPs) have attracted a growing interest in recent years. This interest is mainly due to two reasons: their closeness to practical real conditions and their high complexity. The majority of the approaches proposed so far to solve DOPs are population-based methods, because it is usually believed that their higher divers...

The complexity of deployment and high implementation costs impede proliferation of real time location systems, depriving society of the benefits in terms of safety that these systems are capable of providing. The people tracking system presented in this article prioritizes ease of installation and adaptability to new low cost devices in emerging ma...

In this paper, a metaheuristic that combines a Genetic Algorithm and a Cross Entropy Algorithm is presented. The aim of this work is to achieve a synergy between the capabilities of the algorithms using different population sizes in order to obtain the closest value to the optimal of the function. The proposal is applied to 12 benchmark functions w...

Transportation is an essential area in the nowadays society. Due to the rapid technological progress, it has gained a great importance, both for business sector and citizenry. Among the different types of transport, one that has gained notoriety recently is the transportation on-demand, because it can affect very positively the people quality of li...

This paper describes a custom-built foot-mounted pedestrian indoor localization system based on commercially available low-cost inertial sensors connected wirelessly (via Bluetooth) to a smartphone. Foot-mounted inertial measurement units (IMUs) are becoming the basis for many pedestrian positioning systems as a component of accurate indoor navigat...

This paper focuses on the application of Multi-Objective Evolutionary Algorithms (MOEAs) to develop a Fuzzy Rule-Based System (FRBS) dedicated to manage the speed of an autonomous vehicle in an intersection scenario. Compared to other intersection scenarios, the main point here is that the autonomous vehicle is approaching an intersection that is b...

The effectiveness of Intelligent Transportation Systems depends largely on the ability to integrate information from diverse sources and the suitability of this information for the specific user. This paper describes a new approach for the management and exchange of this information, related to multimodal transportation. A novel software architectu...

Resumen— El incremento de vehículos en las ciudades y autopistas conlleva una serie de problemas. Entre ellos, las aglomeraciones de tráfico es uno de los más importantes. Predecir la congestión a corto plazo en un punto o una sección de carretera se ha convertido en uno de los puntos esenciales de los Sistemas Inteligentes de Transporte. Este trab...

The constant growth of the number of vehicles in today’s world demands improvements in the safety and efficiency of roads and road use. This can be in part satisfied by the implementation of autonomous driving systems because of their greater precision than human drivers in controlling a vehicle. As result, the capacity of the roads would be increa...

Nowadays, public transportation has become an essential area for the actual society, which directly affects the quality of life. There are different sort of public transportation systems. One type that receives much attention these days because of its great social interest is the transportation on-demand. Some of the most well-known on-demand trans...

The growth of Location Based Services and Location Aware Services in indoor environments has focused the attention of the research community on indoor location systems, especially on those based on WLAN networks and Received Signal Strength (RSS). Despite the advances reached, the development of reliable, accurate and low-cost indoor location syste...

This paper describes a new approach for the management and exchange of information related to multimodal transportation. The publication of transport information as semantic information is established through the development of an ontology for multimodal transport and the design of a distributed architecture which allows the integration of the tran...

Foot-mounted inertial measurement units (IMUs) are becoming the basis for many pedestrian positioning systems as a component of accurate indoor navigation. However, most of solutions that implement low-cost IMUs are often connected to a laptop by a wired connection which interferes with the pedestrian movements. Moreover, nobody walks carrying a la...

Researchers who investigate in fields relate with optimization problems in Supply Chain (SC), in special those that involve the process of inventory and its distribution, find difficulties to relate the knowledge areas such as operation research and computer science, organizing the procedure and evaluating the solutions obtained. After analyzed thi...

Combinatorial optimization is a field that receives much attention in artificial intelligence. Many problems of this type can be found in the literature, and a large number of techniques have been developed to be applied to them. Nowadays, population algorithms have become one of the most successful metaheuristics for solving this kind of problems....

Since their first formulation, genetic algorithms (GA) have been one of the most widely used techniques to solve combinatorial optimization problems. The basic structure of the GA is known by the scientific community, and thanks to their easy application and good performance, GAs are the focus of a lot of research works annually. Although throughou...

Nowadays, the development of new meta-heuristics for solving optimization problems is a topic of interest in the scientific community. In the literature, a large number of techniques of this kind can be found. Several of them are classical algorithms, as the tabu search, simulated annealing and genetic algorithm. Anyway, there are many recently pro...

This paper examines the influence of neutral crossover operators in a genetic algorithm (GA) applied to the one-dimensional bin packing problem. In the experimentation 16 benchmark instances have been used and the results obtained by three different GAs are compared with the ones obtained by an evolutionary algorithm (EA). The aim of this work is t...

In this paper the influence of using heuristic functions to initialize the population of a classic genetic algorithm (GA) applied to the N-Queens Problem (NQP) is analyzed. The aim of this work is to evaluate the impact of the heuristic initialization phase on the results of the classic GA. In order to probe this, several experiments using two diff...

Many applications of optimization techniques, such as classification and regression problems, require long simulations to evaluate the performance of their solutions. Problems where the fitness function can be divided into smaller pieces---problem partitioning---demand techniques that approximate the overall fitness from that obtained in a small re...

The integration of the different process that conform the supply chain (SC) is fundamental to obtain a better coordination level. The inventory control and its distribution, are the processes that researches have found as the keys in the loss of efficiency and effectiveness in the field of logistics, affecting so the synchronization in the SC manag...

Researchers who investigate in any field related to computational algorithms (defining new algorithms or improving existing ones) find large difficulties when evaluating their work. Comparisons among different scientific works in this area is often difficult, due to the ambiguity or lack of detail in the presentation of the work or its results. In...

Combinatorial optimization is a widely studied field within artificial intelligence. There are many problems of this type, and many techniques applied to them can be found in the literature. Especially, population techniques have received much attention in this area, being genetic algorithms (GA) the most famous ones. Although throughout history ma...

Taking practical and effective traffic prediction and control measures to ease highway traffic congestion is a significant issue in the research field of Intelligent Transportation Systems (ITS). This paper develops a Hierarchical Fuzzy Rule-Based System (HFRBS) optimized by Genetic Algorithms (GAs) to develop an accurate and robust traffic congest...