Enrique Onieva
Universidad de Deusto, Mobility, Post-Doc
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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... more
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 rates in training and test partitions. Given the current gaps of scientific knowledge and the need for efficient application of medicines products law, this paper analyzes the position of regulators for marketing medicinal nanoproducts in the European Union and the role of ML in the authorization process. Methods: In terms of methodology, a dogmatic study of the European regulation and the guidance of the European Medicine Agency on the use of predictive models for nanomaterials was carried out. The study has, as the framework of reference, the European Regulation 726/2004 and has focused on the analysis of how ML processes are contemplated i...
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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... more
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 services require disseminating information from a central server to a set of vehicles located in a particular region. This task presents important challenges in VANETs, especially when it is made at big scale. In this work, we present a new approach for information dissemination in VANETs where the structure of the communications is configured using a model based on Covering Location Problems that it is optimized by means of a Genetic Algorithm. The results obtained over a realistic scenario show that the new approach can provide good solutions for very demanding response times and that obtains competitive results with respect to reference algorithms proposed in literature.
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The number of possible designs of nano-systems is elevated. The design depends on the function we need to develop. Among these systems we highlight Nanoparticle Drug Delivery Systems (DDNS) of high interest not only for Nanotechnology but... more
The number of possible designs of nano-systems is elevated. The design depends on the function we need to develop. Among these systems we highlight Nanoparticle Drug Delivery Systems (DDNS) of high interest not only for Nanotechnology but also for Biomaterials science.1–3 In this work we fusion the following information: 1) Drug-vitamin release nano-systems (DVRNs). This data set was collected from literature. 2) Vitamin derivatives data set extracted from ChEMBL database. Both data sets contain different assay conditions and molecular descriptors. Once we fusion the information, we apply Perturbation Theory Machine Learning (PTML) method in order to build the model. Once built with Perturbation Theory Operators (PT Operators), it presents both Specificity and Sensibility higher than 80%. Until the best of our knowledge, we developed the first multi-label PTML model useful to design DVRNs for optimal biological activity.
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Persistent problems related to traffic congestion, road safety and environmental challenges could be solved if people, vehicles, infrastructure and businesses were connected in a cooperative ecosystem. The creation of such an ecosystem... more
Persistent problems related to traffic congestion, road safety and environmental challenges could be solved if people, vehicles, infrastructure and businesses were connected in a cooperative ecosystem. The creation of such an ecosystem has been key in the Horizon 2020 TIMON project, where it is the baseline for delivering information services related to traffic and multimodal transport to road users and administration drivers. The main objective of TIMON is to increase the safety, sustainability, flexibility and efficiency of road transport systems by taking advantage of cooperative communication and by processing open data related to mobility through a cooperative, open web-based platform and mobile app developed to deliver information and services to drivers, businesses and Vulnerable Road Users (VRU) in real time. TIMON has built up a large, strong community of more than 100 users (citizens) in the city of Ljubljana and has directly benefited their daily mobility and transport in...
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We combine Perturbation Theory and Machine Learning (PTML algorithm) to train a model able to predicting the best components for Nanoparticle Drug Delivery Systems (DDNS).
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Research Interests: Cognitive Science, Mathematics, Applied Mathematics, Computer Science, Evolutionary algorithms, and 11 moreHeuristics, Soft Computing, Simulated Annealing, Optimization techniques, Vehicle Routing Problems, Firefly Algorithm, Vehicle Routing Problem, Mathematical Optimization, Nature Inspired Computing, Bio and Nature Inspired Algorithms, and Pickup
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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... more
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 this problem, a simple framework has been developed to use in the searching of near-optimal solutions in the field of Inventory Routing Problems (IRP). In this paper this framework is described in detail, and all the phases to follow are introduced step by step. Although some of these phases can be extended for other type of optimization problem in the SC, the literature of this study is focused in IRP. This field has been chosen due to its importance in the real world, and its great relevance in the literature. The use of benchmark instances for evaluating of results is highlighted and these instances are organized according the concrete pro...
sium on Computational Intelligence and Games (CIG). First, we describe the competition regulations and the software framework. Then, the five best teams describe the methods of computational intelligence they used to develop their drivers... more
sium on Computational Intelligence and Games (CIG). First, we describe the competition regulations and the software framework. Then, the five best teams describe the methods of computational intelligence they used to develop their drivers and the lessons they learned from the participation in the championship. The orga-nizers provide short summaries of the other competitors. Finally, we summarize the championship results, followed by a discussion about what the organizers learned about 1) the development of high-performing car racing controllers and 2) the organization of scientific competitions.
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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... more
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 deliveries, variable costs and forbidden paths (AC-VRP-SPDVCFP). This is the first study of such a problem in the literature. For this reason, a benchmark composed by 15 instances has been also proposed. In the design of this benchmark, real geographical positions have been used, located in the province of Bizkaia, Spain. For the proper treatment of this AC-VRP-SPDVCFP, a discrete firefly algorithm (DFA) has been developed. This application is the first application of a firefly algorithm to any rich vehicle routing problem. To prove that the proposed DFA is a promising technique, its performance has been compared with two other well-known tec...
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... more
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 throughout history there have been many studies analyzing various concepts of GAs, in the literature there are few studies that analyze objectively the influence of using blind crossover operators for combinatorial optimization problems. For this reason, in this paper a deep study on the influence of using them is conducted. The study is based on a comparison of nine techniques applied to four well-known combinatorial optimization problems. Six of the techniques are GAs with different configurations, and the remaining three are evolutionary algorithms that focus exclusively on the mutation process. Finally, to perform a reliable comparison of these...
La velocidad a seguir por el vehiculo autonomo es calculada por un sistema basado en reglas difusas (SBRD); que sera optimizado por medio de un algoritmo genetico. La funcion de coste sera calculada mediante la simulacion incremental de... more
La velocidad a seguir por el vehiculo autonomo es calculada por un sistema basado en reglas difusas (SBRD); que sera optimizado por medio de un algoritmo genetico. La funcion de coste sera calculada mediante la simulacion incremental de escenarios de cruce, y evaluando si las decisiones tomadas son adecuadas para el escenario. Una vez calculada la velocidad a seguir, esta se manda a una capa de bajo nivel, encargada de accionar los pedales del vehiculo.
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... more
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. This paper focuses on passive UHF RFID sensing. An RFID system consists of a reader and various numbers of tags, which can incorporate different kinds of sensors. These sensor tags require fast anti-collision protocols to minimise the number of collisions with the other tags sharing the reader’s interrogation zone. Therefore, RFID application developers must be mindful of anti-collision protocols. Dynamic Frame Slotted Aloha (DFSA) anti-collision protocols have been used extensively in the literature because EPCglobal Class 1 Generation 2 (EPC C1G2), which is the current communication protocol standard in RFID, employs this strategy. Prot...
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Accurate early-stage medical diagnosis of breast cancer can improve the survival rates and fuzzy rule-base system (FRBS) has been a promising classification system to detect breast cancer. However, the existing classification systems... more
Accurate early-stage medical diagnosis of breast cancer can improve the survival rates and fuzzy rule-base system (FRBS) has been a promising classification system to detect breast cancer. However, the existing classification systems involves large number of input variables for training and produces a large number of fuzzy rules, which lead to high complexity and barely acceptable accuracy. In this paper, we present a genetic optimised serial hierarchical FRBS, which incorporates lateral tuning of membership functions and optimisation of the rule base. The serial hierarchical structure of FRBS allows selecting and ranking the input variables, which reduces the system complexity and distinguish the importance of attributes in datasets. We conduct an experimental study on Original Wisconsin Breast Cancer Database and Wisconsin Breast Cancer Diagnostic Database from UCI Machine Learning Repository, and show that the proposed system can classify breast cancer accurately and efficiently.
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Abstract 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... more
Abstract 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 Memory (LSTM) cells to make both short-term and long-term predictions. The model is trained and tested using sparse trajectory (GPS) data coming from the ride-hailing service of DiDi in the cities of Xi'an and Chengdu in China. Besides, presenting the deep neural network, we also propose a data-reduction technique based on temporal correlation to select the most relevant road links to be used as input. Combining the suggested approaches, our model obtains better results compared to high-performance algorithms for traffic forecasting, such as LSTM or the algorithms presented in the TRANSFOR19 forecasting competition. The model is capable of maintaining its performance over different time-horizons from 5 min to up to 4 h with multi-step predictions.
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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... more
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 technique. The purpose of this research is to investigate the efficiency of using the CRF technique in offline map matching problems for different scenarios and parameters. The algorithm was applied to several real and simulated trajectories of different lengths. The results were then refined and matched with the map using the CRF algorithm.