This page has only limited features, please log in for full access.
Deep Learning methods have been proven to be flexible to model complex phenomena. This has also been the case of Intelligent Transportation Systems, in which several areas such as vehicular perception and traffic analysis have widely embraced Deep Learning as a core modeling technology. Particularly in short-term traffic forecasting, the capability of Deep Learning to deliver good results has generated a prevalent inertia towards using Deep Learning models, without examining in depth their benefits and downsides. This paper focuses on critically analyzing the state of the art in what refers to the use of Deep Learning for this particular Intelligent Transportation Systems research area. To this end, we elaborate on the findings distilled from a review of publications from recent years, based on two taxonomic criteria. A posterior critical analysis is held to formulate questions and trigger a necessary debate about the issues of Deep Learning for traffic forecasting. The study is completed with a benchmark of diverse short-term traffic forecasting methods over traffic datasets of different nature, aimed to cover a wide spectrum of possible scenarios. Our experimentation reveals that Deep Learning could not be the best modeling technique for every case, which unveils some caveats unconsidered to date that should be addressed by the community in prospective studies. These insights reveal new challenges and research opportunities in road traffic forecasting, which are enumerated and discussed thoroughly, with the intention of inspiring and guiding future research efforts in this field.
Eric L. Manibardo; Ibai Lana; Javier Del Ser. Deep Learning for Road Traffic Forecasting: Does It Make a Difference? IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -25.
AMA StyleEric L. Manibardo, Ibai Lana, Javier Del Ser. Deep Learning for Road Traffic Forecasting: Does It Make a Difference? IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-25.
Chicago/Turabian StyleEric L. Manibardo; Ibai Lana; Javier Del Ser. 2021. "Deep Learning for Road Traffic Forecasting: Does It Make a Difference?" IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-25.
Fire disaster throughout the globe causes social, environmental, and economical damage, making its early detection and instant reporting essential for saving human lives and properties. Smoke detection plays a key role in early fire detection but majority of the existing methods are limited to either indoor or outdoor surveillance environments, with poor performance for hazy scenarios. In this paper, we present a Convolutional Neural Network (CNN)-based smoke detection and segmentation framework for both clear and hazy environments. Unlike existing methods, we employ an efficient CNN architecture, termed EfficientNet, for smoke detection with better accuracy. We also segment the smoke regions using DeepLabv3+, which is supported by effective encoders and decoders along with a pixel-wise classifier for optimum localization. Our smoke detection results evince a noticeable gain up to 3% in accuracy and a decrease of 0.46% in False Alarm Rate (FAR), while segmentation reports a significant increase of 2% and 1% in global accuracy and mean Intersection over Union (IoU) scores, respectively. This makes our method a best fit for smoke detection and segmentation in real-world surveillance settings.
Salman Khan; Khan Muhammad; Tanveer Hussain; Javier Del Ser; Fabio Cuzzolin; Siddhartha Bhattacharyya; Zahid Akhtar; Victor Hugo C. de Albuquerque. DeepSmoke: Deep Learning Model for Smoke Detection and Segmentation in Outdoor Environments. Expert Systems with Applications 2021, 115125 .
AMA StyleSalman Khan, Khan Muhammad, Tanveer Hussain, Javier Del Ser, Fabio Cuzzolin, Siddhartha Bhattacharyya, Zahid Akhtar, Victor Hugo C. de Albuquerque. DeepSmoke: Deep Learning Model for Smoke Detection and Segmentation in Outdoor Environments. Expert Systems with Applications. 2021; ():115125.
Chicago/Turabian StyleSalman Khan; Khan Muhammad; Tanveer Hussain; Javier Del Ser; Fabio Cuzzolin; Siddhartha Bhattacharyya; Zahid Akhtar; Victor Hugo C. de Albuquerque. 2021. "DeepSmoke: Deep Learning Model for Smoke Detection and Segmentation in Outdoor Environments." Expert Systems with Applications , no. : 115125.
Unsupervised anomaly discovery in stream data is a research topic with many practical applications. However, in many cases, it is not easy to collect enough training data with labeled anomalies for supervised learning of an anomaly detector in order to deploy it later for identification of real anomalies in streaming data. It is thus important to design anomalies detectors that can correctly detect anomalies without access to labeled training data. Our idea is to adapt the Online evolving Spiking Neural Network (OeSNN) classifier to the anomaly detection task. As a result, we offer an Online evolving Spiking Neural Network for Unsupervised Anomaly Detection algorithm (OeSNN-UAD), which, unlike OeSNN, works in an unsupervised way and does not separate output neurons into disjoint decision classes. OeSNN-UAD uses our proposed new two-step anomaly detection method. Also, we derive new theoretical properties of neuronal model and input layer encoding of OeSNN, which enable more effective and efficient detection of anomalies in our OeSNN-UAD approach. The proposed OeSNN-UAD detector was experimentally compared with state-of-the-art unsupervised and semi-supervised detectors of anomalies in stream data from the Numenta Anomaly Benchmark and Yahoo Anomaly Datasets repositories. Our approach outperforms the other solutions provided in the literature in the case of data streams from the Numenta Anomaly Benchmark repository. Also, in the case of real data files of the Yahoo Anomaly Benchmark repository, OeSNN-UAD outperforms other selected algorithms, whereas in the case of Yahoo Anomaly Benchmark synthetic data files, it provides competitive results to the results recently reported in the literature.
Piotr S. Maciąg; Marzena Kryszkiewicz; Robert Bembenik; Jesus L. Lobo; Javier Del Ser. Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks. Neural Networks 2021, 139, 118 -139.
AMA StylePiotr S. Maciąg, Marzena Kryszkiewicz, Robert Bembenik, Jesus L. Lobo, Javier Del Ser. Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks. Neural Networks. 2021; 139 ():118-139.
Chicago/Turabian StylePiotr S. Maciąg; Marzena Kryszkiewicz; Robert Bembenik; Jesus L. Lobo; Javier Del Ser. 2021. "Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks." Neural Networks 139, no. : 118-139.
In general, artery-specific calcification analysis comprises the simultaneous calcification segmentation and quantification tasks. It can help provide a thorough assessment for calcification of different coronary arteries, and further allow for an efficient and rapid diagnosis of cardiovascular diseases (CVD). However, as a high-dimensional multi-type estimation problem, artery-specific calcification analysis has not been profoundly investigated due to the intractability of obtaining discriminative feature representations. In this work, we propose a Multi-task learning network with Multi-view Weighted Fusion Attention (MMWFAnet) to solve this challenging problem. The MMWFAnet first employs a Multi-view Weighted Fusion Attention (MWFA) module to extract discriminative feature representations by enhancing the collaboration of multiple views. Specifically, MWFA weights these views to improve multi-view learning for calcification features. Based on the fusion of these multiple views, the proposed approach takes advantage of multi-task learning to obtain accurate segmentation and quantification of artery-specific calcification simultaneously. We perform experimental studies on 676 non-contrast Computed Tomography scans, achieving state-of-the-art performance in terms of multiple evaluation metrics. These compelling results evince that the proposed MMWFAnet is capable of improving the effectivity and efficiency of clinical CVD diagnosis.
Weiwei Zhang; Guang Yang; Nan Zhang; Lei Xu; Xiaoqing Wang; Yanping Zhang; Heye Zhang; Javier Del Ser; Victor Hugo C. de Albuquerque. Multi-task learning with Multi-view Weighted Fusion Attention for artery-specific calcification analysis. Information Fusion 2021, 71, 64 -76.
AMA StyleWeiwei Zhang, Guang Yang, Nan Zhang, Lei Xu, Xiaoqing Wang, Yanping Zhang, Heye Zhang, Javier Del Ser, Victor Hugo C. de Albuquerque. Multi-task learning with Multi-view Weighted Fusion Attention for artery-specific calcification analysis. Information Fusion. 2021; 71 ():64-76.
Chicago/Turabian StyleWeiwei Zhang; Guang Yang; Nan Zhang; Lei Xu; Xiaoqing Wang; Yanping Zhang; Heye Zhang; Javier Del Ser; Victor Hugo C. de Albuquerque. 2021. "Multi-task learning with Multi-view Weighted Fusion Attention for artery-specific calcification analysis." Information Fusion 71, no. : 64-76.
Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers’ personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.
Ibai Laña; Javier Sanchez-Medina; Eleni Vlahogianni; Javier Del Ser. From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability. Sensors 2021, 21, 1121 .
AMA StyleIbai Laña, Javier Sanchez-Medina, Eleni Vlahogianni, Javier Del Ser. From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability. Sensors. 2021; 21 (4):1121.
Chicago/Turabian StyleIbai Laña; Javier Sanchez-Medina; Eleni Vlahogianni; Javier Del Ser. 2021. "From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability." Sensors 21, no. 4: 1121.
Advances in information and signal processing technologies have a significant impact on autonomous driving (AD), improving driving safety while minimizing the efforts of human drivers with the help of advanced artificial intelligence (AI) techniques. Recently, deep learning (DL) approaches have solved several real-world problems of complex nature. However, their strengths in terms of control processes for AD have not been deeply investigated and highlighted yet. This survey highlights the power of DL architectures in terms of reliability and efficient real-time performance and overviews state-of-the-art strategies for safe AD, with their major achievements and limitations. Furthermore, it covers major embodiments of DL along the AD pipeline including measurement, analysis, and execution, with a focus on road, lane, vehicle, pedestrian, drowsiness detection, collision avoidance, and traffic sign detection through sensing and vision-based DL methods. In addition, we discuss on the performance of several reviewed methods by using different evaluation metrics, with critics on their pros and cons. Finally, this survey highlights the current issues of safe DL-based AD with a prospect of recommendations for future research, rounding up a reference material for newcomers and researchers willing to join this vibrant area of Intelligent Transportation Systems.
Khan Muhammad; Amin Ullah; Jaime Lloret; Javier Del Ser; Victor Hugo C. de Albuquerque. Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions. IEEE Transactions on Intelligent Transportation Systems 2020, 22, 4316 -4336.
AMA StyleKhan Muhammad, Amin Ullah, Jaime Lloret, Javier Del Ser, Victor Hugo C. de Albuquerque. Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions. IEEE Transactions on Intelligent Transportation Systems. 2020; 22 (7):4316-4336.
Chicago/Turabian StyleKhan Muhammad; Amin Ullah; Jaime Lloret; Javier Del Ser; Victor Hugo C. de Albuquerque. 2020. "Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions." IEEE Transactions on Intelligent Transportation Systems 22, no. 7: 4316-4336.
In the smart city context, Big Data analytics plays an important role in processing the data collected through IoT devices. The analysis of the information gathered by sensors favors the generation of specific services and systems that not only improve the quality of life of the citizens, but also optimize the city resources. However, the difficulties of implementing this entire process in real scenarios are manifold, including the huge amount and heterogeneity of the devices, their geographical distribution, and the complexity of the necessary IT infrastructures. For this reason, the main contribution of this paper is the PADL description language, which has been specifically tailored to assist in the definition and operationalization phases of the machine learning life cycle. It provides annotations that serve as an abstraction layer from the underlying infrastructure and technologies, hence facilitating the work of data scientists and engineers. Due to its proficiency in the operationalization of distributed pipelines over edge, fog, and cloud layers, it is particularly useful in the complex and heterogeneous environments of smart cities. For this purpose, PADL contains functionalities for the specification of monitoring, notifications, and actuation capabilities. In addition, we provide tools that facilitate its adoption in production environments. Finally, we showcase the usefulness of the language by showing the definition of PADL-compliant analytical pipelines over two uses cases in a smart city context (flood control and waste management), demonstrating that its adoption is simple and beneficial for the definition of information and process flows in such environments.
Josu Díaz-De-Arcaya; Raúl Miñón; Ana I. Torre-Bastida; Javier Del Ser; Aitor Almeida. PADL: A Modeling and Deployment Language for Advanced Analytical Services. Sensors 2020, 20, 6712 .
AMA StyleJosu Díaz-De-Arcaya, Raúl Miñón, Ana I. Torre-Bastida, Javier Del Ser, Aitor Almeida. PADL: A Modeling and Deployment Language for Advanced Analytical Services. Sensors. 2020; 20 (23):6712.
Chicago/Turabian StyleJosu Díaz-De-Arcaya; Raúl Miñón; Ana I. Torre-Bastida; Javier Del Ser; Aitor Almeida. 2020. "PADL: A Modeling and Deployment Language for Advanced Analytical Services." Sensors 20, no. 23: 6712.
The concern of the industrial sector about the increase of energy costs has stimulated the development of new strategies for the effective management of energy consumption in industrial setups. Along with this growth, the irruption and continuous development of digital technologies have generated increasingly complex industrial ecosystems. These ecosystems are supported by a large number of variables and procedures for the operation and control of industrial processes and assets. This heterogeneous technological scenario has made industries difficult to manage by traditional means. In this context, the disruptive potential of cyber physical systems is beginning to be considered in the automation and improvement of industrial services. Particularly, intelligent data-driven approaches relying on the combination of Energy Management Systems (EMS), Manufacturing Execution Systems (MES), Internet of Things (IoT) and Data Analytics provide the intelligence needed to optimally operate these complex industrial environments. The work presented in this manuscript contributes to the definition of the aforementioned intelligent data-driven approaches, defining a systematic, intelligent procedure for the energy efficiency diagnosis and improvement of industrial plants. This data-based diagnostic procedure hinges on the analysis of data collected from industrial plants, aimed at minimizing energy costs through the continuous assessment of the production-consumption ratio of the plant (i.e. energy per piece or kg produced). The proposed methodology aims to support managers and energy-efficiency technicians to minimize the plant’s energy consumption without affecting the production and therefore, increase its competitiveness. The data used in the design of this methodology are real data from a company dedicated to the design and manufacture of automotive components and one of the main manufacturers in the automotive sector worldwide. The present methodology is under the pending patent application EU19382002.4-120.
Izaskun Mendia; Sergio Gil-Lopez; Javier Del Ser; Iñaki Grau; Adelaida Lejarazu; Erik Maqueda; Eugenio Perea. An Intelligent Procedure for the Methodology of Energy Consumption in Industrial Environments. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 92 -103.
AMA StyleIzaskun Mendia, Sergio Gil-Lopez, Javier Del Ser, Iñaki Grau, Adelaida Lejarazu, Erik Maqueda, Eugenio Perea. An Intelligent Procedure for the Methodology of Energy Consumption in Industrial Environments. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():92-103.
Chicago/Turabian StyleIzaskun Mendia; Sergio Gil-Lopez; Javier Del Ser; Iñaki Grau; Adelaida Lejarazu; Erik Maqueda; Eugenio Perea. 2020. "An Intelligent Procedure for the Methodology of Energy Consumption in Industrial Environments." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 92-103.
Multi-view video summarization (MVS) has not received much attention from the research community due to inter-view correlations and views’ overlapping, etc. The majority of previous MVS works offline, relying on only summary, and require additional communication bandwidth and transmission time, with no focus on foggy environments. We propose an edge intelligence-based MVS and activity recognition framework that combines artificial intelligence with Internet of Things (IoT) devices. In our framework, resource-constrained devices with cameras use a lightweight CNN-based object detection model to segment multi-view videos into shots, followed by mutual information computation that helps in summary generation. Our system does not rely solely on a summary, but encodes and transmits it to a master device using a neural computing stick for inter-view correlations computation and efficient activity recognition, an approach which saves computation resources, communication bandwidth, and transmission time. Experiments show an increase of 0.4 unit in F-measure on an MVS Office dataset and 0.2% and 2% improved accuracy for UCF-50 and YouTube 11 datasets, respectively, with lower storage and transmission times. The processing time is reduced from 1.23 to 0.45 seconds for a single frame and optimally 0.75 seconds faster MVS. A new dataset is constructed by synthetically adding fog to an MVS dataset to show the adaptability of our system for both certain and uncertain IoT surveillance environments.
Tanveer Hussain; Khan Muhammad; Amin Ullah; Javier Del Ser; Amir H. Gandomi; Muhammad Sajjad; Sung Wook Baik; Victor Hugo C. de Albuquerque. Multiview Summarization and Activity Recognition Meet Edge Computing in IoT Environments. IEEE Internet of Things Journal 2020, 8, 9634 -9644.
AMA StyleTanveer Hussain, Khan Muhammad, Amin Ullah, Javier Del Ser, Amir H. Gandomi, Muhammad Sajjad, Sung Wook Baik, Victor Hugo C. de Albuquerque. Multiview Summarization and Activity Recognition Meet Edge Computing in IoT Environments. IEEE Internet of Things Journal. 2020; 8 (12):9634-9644.
Chicago/Turabian StyleTanveer Hussain; Khan Muhammad; Amin Ullah; Javier Del Ser; Amir H. Gandomi; Muhammad Sajjad; Sung Wook Baik; Victor Hugo C. de Albuquerque. 2020. "Multiview Summarization and Activity Recognition Meet Edge Computing in IoT Environments." IEEE Internet of Things Journal 8, no. 12: 9634-9644.
Concernings related to image security have increased in the last years. One of the main reasons relies on the replacement of conventional photography to digital images, once the development of new technologies for image processing, as much as it has helped in the evolution of many new techniques in forensic studies, it also provided tools for image tampering. In this context, many companies and researchers devoted many efforts towards methods for detecting such tampered images, mostly aided by autonomous intelligent systems. Therefore, this work focuses on introducing a rigorous survey contemplating the state-of-the-art literature on computer-aided tampered image detection using machine learning techniques, as well as evolutionary computation, neural networks, fuzzy logic, Bayesian reasoning, among others. Besides, it also contemplates anomaly detection methods in the context of images due to the intrinsic relation between anomalies and tampering. Moreover, it aims at recent and in-depth researches relevant to the context of image tampering detection, performing a survey over more than 100 works related to the subject, spanning across different themes related to image tampering detection. Finally, a critical analysis is performed over this comprehensive compilation of literature, yielding some research opportunities and discussing some challenges in an attempt to align future efforts of the community with the niches and gaps remarked in this exciting field.
Kelton A.P. da Costa; João P. Papa; Leandro A. Passos; Danilo Colombo; Javier Del Ser; Khan Muhammad; Victor Hugo C. de Albuquerque. A critical literature survey and prospects on tampering and anomaly detection in image data. Applied Soft Computing 2020, 97, 106727 .
AMA StyleKelton A.P. da Costa, João P. Papa, Leandro A. Passos, Danilo Colombo, Javier Del Ser, Khan Muhammad, Victor Hugo C. de Albuquerque. A critical literature survey and prospects on tampering and anomaly detection in image data. Applied Soft Computing. 2020; 97 ():106727.
Chicago/Turabian StyleKelton A.P. da Costa; João P. Papa; Leandro A. Passos; Danilo Colombo; Javier Del Ser; Khan Muhammad; Victor Hugo C. de Albuquerque. 2020. "A critical literature survey and prospects on tampering and anomaly detection in image data." Applied Soft Computing 97, no. : 106727.
With the advent of fast data streams, real-time machine learning has become a challenging task, demanding many processing resources. In addition, they can be affected by the concept drift effect, by which learning methods have to detect changes in the data distribution and adapt to these evolving conditions. Several emerging paradigms such as the so-called Smart Dust, Utility Fog, or Swarm Robotics are in need for efficient and scalable solutions in real-time scenarios, and where usually computing resources are constrained. Cellular automata, as low-bias and robust-to-noise pattern recognition methods with competitive classification performance, meet the requirements imposed by the aforementioned paradigms mainly due to their simplicity and parallel nature. In this work we propose LUNAR, a streamified version of cellular automata devised to successfully meet the aforementioned requirements. LUNAR is able to act as a real incremental learner while adapting to drifting conditions. Furthermore, LUNAR is highly interpretable, as its cellular structure represents directly the mapping between the feature space and the labels to be predicted. Extensive simulations with synthetic and real data will provide evidence of its competitive behavior in terms of classification performance when compared to long-established and successful online learning methods.
Jesus L. Lobo; Javier Del Ser; Francisco Herrera. LUNAR: Cellular automata for drifting data streams. Information Sciences 2020, 543, 467 -487.
AMA StyleJesus L. Lobo, Javier Del Ser, Francisco Herrera. LUNAR: Cellular automata for drifting data streams. Information Sciences. 2020; 543 ():467-487.
Chicago/Turabian StyleJesus L. Lobo; Javier Del Ser; Francisco Herrera. 2020. "LUNAR: Cellular automata for drifting data streams." Information Sciences 543, no. : 467-487.
Smart grids are a type of complex cyber–physical system (CPS) that integrates the communication capabilities of smart devices into the grid to facilitate remote operation and control of power systems. However, this integration exposes many existing vulnerabilities of conventional supervisory control and data acquisition (SCADA) systems, resulting in severe cyber threats to the smart grid and potential violation of security objectives. Stealing sensitive information, modifying firmware, or injecting function codes through compromised devices are examples of possible attacks on the smart grid. Therefore, early detection of cyberattacks on the grid is crucial to protect it from sabotage. Machine learning (ML) methods are conventional approaches for detecting cyberattacks that use features of smart grid networks. However, developing an effective, highly accurate detection method with reduced computational overload, is still a challenging research problem. In this work, an efficient and effective security control approach is proposed to detect cyberattacks on the smart grid. The proposed approach combines both feature reduction and detection techniques to reduce the extremely large number of features and achieve an improved detection rate. A correlation-based feature selection (CFS) method is used to remove irrelevant features, improving detection efficiency. An instance-based learning (IBL) algorithm classifies normal and cyberattack events using the selected optimal features. This study describes a set of experiments conducted on public datasets from a SCADA power system based on a 10-fold cross-validation technique. Experimental results show that the proposed approach achieves a high detection rate based on a small number of features drawn from SCADA power system measurements.
Abdu Gumaei; Mohammad Mehedi Hassan; Shamsul Huda; Rafiul Hassan; David Camacho; Javier Del Ser; Giancarlo Fortino. A robust cyberattack detection approach using optimal features of SCADA power systems in smart grids. Applied Soft Computing 2020, 96, 106658 .
AMA StyleAbdu Gumaei, Mohammad Mehedi Hassan, Shamsul Huda, Rafiul Hassan, David Camacho, Javier Del Ser, Giancarlo Fortino. A robust cyberattack detection approach using optimal features of SCADA power systems in smart grids. Applied Soft Computing. 2020; 96 ():106658.
Chicago/Turabian StyleAbdu Gumaei; Mohammad Mehedi Hassan; Shamsul Huda; Rafiul Hassan; David Camacho; Javier Del Ser; Giancarlo Fortino. 2020. "A robust cyberattack detection approach using optimal features of SCADA power systems in smart grids." Applied Soft Computing 96, no. : 106658.
Ensembles, especially ensembles of decision trees, are one of the most popular and successful techniques in machine learning. Recently, the number of ensemble-based proposals has grown steadily. Therefore, it is necessary to identify which are the appropriate algorithms for a certain problem. In this paper, we aim to help practitioners to choose the best ensemble technique according to their problem characteristics and their workflow. To do so, we revise the most renowned bagging and boosting algorithms and their software tools. These ensembles are described in detail within their variants and improvements available in the literature. Their online-available software tools are reviewed attending to the implemented versions and features. They are categorized according to their supported programming languages and computing paradigms. The performance of 14 different bagging and boosting based ensembles, including XGBoost, LightGBM and Random Forest, is empirically analyzed in terms of predictive capability and efficiency. This comparison is done under the same software environment with 76 different classification tasks. Their predictive capabilities are evaluated with a wide variety of scenarios, such as standard multi-class problems, scenarios with categorical features and big size data. The efficiency of these methods is analyzed with considerably large data-sets. Several practical perspectives and opportunities are also exposed for ensemble learning.
Sergio González; Salvador García; Javier Del Ser; Lior Rokach; Francisco Herrera. A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities. Information Fusion 2020, 64, 205 -237.
AMA StyleSergio González, Salvador García, Javier Del Ser, Lior Rokach, Francisco Herrera. A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities. Information Fusion. 2020; 64 ():205-237.
Chicago/Turabian StyleSergio González; Salvador García; Javier Del Ser; Lior Rokach; Francisco Herrera. 2020. "A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities." Information Fusion 64, no. : 205-237.
In recent algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.
Daniel Molina; Javier Poyatos; Javier Del Ser; Salvador García; Amir Hussain; Francisco Herrera. Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations. Cognitive Computation 2020, 12, 897 -939.
AMA StyleDaniel Molina, Javier Poyatos, Javier Del Ser, Salvador García, Amir Hussain, Francisco Herrera. Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations. Cognitive Computation. 2020; 12 (5):897-939.
Chicago/Turabian StyleDaniel Molina; Javier Poyatos; Javier Del Ser; Salvador García; Amir Hussain; Francisco Herrera. 2020. "Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations." Cognitive Computation 12, no. 5: 897-939.
Brain tumor is one of the most dangerous cancers in people of all ages, and its grade recognition is a challenging problem for radiologists in health monitoring and automated diagnosis. Recently, numerous methods based on deep learning have been presented in the literature for brain tumor classification (BTC) in order to assist radiologists for a better diagnostic analysis. In this overview, we present an in-depth review of the surveys published so far and recent deep learning-based methods for BTC. Our survey covers the main steps of deep learning-based BTC methods, including preprocessing, features extraction, and classification, along with their achievements and limitations. We also investigate the state-of-the-art convolutional neural network models for BTC by performing extensive experiments using transfer learning with and without data augmentation. Furthermore, this overview describes available benchmark data sets used for the evaluation of BTC. Finally, this survey does not only look into the past literature on the topic but also steps on it to delve into the future of this area and enumerates some research directions that should be followed in the future, especially for personalized and smart healthcare.
Khan Muhammad; Salman Khan; Javier Del Ser; Victor Hugo C. de Albuquerque. Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey. IEEE Transactions on Neural Networks and Learning Systems 2020, 32, 507 -522.
AMA StyleKhan Muhammad, Salman Khan, Javier Del Ser, Victor Hugo C. de Albuquerque. Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey. IEEE Transactions on Neural Networks and Learning Systems. 2020; 32 (2):507-522.
Chicago/Turabian StyleKhan Muhammad; Salman Khan; Javier Del Ser; Victor Hugo C. de Albuquerque. 2020. "Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey." IEEE Transactions on Neural Networks and Learning Systems 32, no. 2: 507-522.
Functional networks are a powerful extension of neural networks where the scalar weights are replaced by neural functions. This paper concerns the problem of parametric learning of the associative model, a functional network that represents the associativity operator. This problem can be formulated as a nonlinear continuous least-squares minimization problem, solved by applying a swarm intelligence approach based on a modified memetic self-adaptive version of the firefly algorithm. The performance of our approach is discussed through an illustrative example. It shows that our method can be successfully applied to solve the parametric learning of functional networks with unknown functions.
Akemi Gálvez; Andrés Iglesias; Eneko Osaba; Javier Del Ser. Parametric Learning of Associative Functional Networks Through a Modified Memetic Self-adaptive Firefly Algorithm. Lecture Notes in Computer Science 2020, 566 -579.
AMA StyleAkemi Gálvez, Andrés Iglesias, Eneko Osaba, Javier Del Ser. Parametric Learning of Associative Functional Networks Through a Modified Memetic Self-adaptive Firefly Algorithm. Lecture Notes in Computer Science. 2020; ():566-579.
Chicago/Turabian StyleAkemi Gálvez; Andrés Iglesias; Eneko Osaba; Javier Del Ser. 2020. "Parametric Learning of Associative Functional Networks Through a Modified Memetic Self-adaptive Firefly Algorithm." Lecture Notes in Computer Science , no. : 566-579.
Autonomous vehicles rely on sophisticated hardware and software technologies for acquiring holistic awareness of their immediate surroundings. Deep learning methods have effectively equipped modern self-driving cars with high levels of such awareness. However, their application requires high-end computational hardware, which makes utilization infeasible for the legacy vehicles that constitute most of today's automotive industry. Hence, it becomes inherently challenging to achieve high performance while at the same time maintaining adequate computational complexity. In this paper, a monocular vision and scalar sensor-based model car is designed and implemented to accomplish autonomous driving on a specified track by employing a lightweight deep learning model. It can identify various traffic signs based on a vision sensor as well as avoid obstacles by using an ultrasonic sensor. The developed car utilizes a single Raspberry Pi as its computational unit. In addition, our work investigates the behavior of economical hardware used to deploy deep learning models. In particular, we herein propose a novel, computationally efficient, and cost-effective approach. The designed system can serve as a platform to facilitate the development of economical technologies for autonomous vehicles that can be used as part of intelligent transportation or advanced driver assistance systems. The experimental results indicate that this model can achieve real-time response on a resource-constrained device without significant overheads, thus making it a suitable candidate for autonomous driving in current intelligent transportation systems.
Muhammad Sajjad; Muhammad Irfan; Khan Muhammad; Javier Del Ser; Javier Sanchez-Medina; Sergey Andreev; Weiping Ding; Jong Weon Lee. An Efficient and Scalable Simulation Model for Autonomous Vehicles With Economical Hardware. IEEE Transactions on Intelligent Transportation Systems 2020, 22, 1718 -1732.
AMA StyleMuhammad Sajjad, Muhammad Irfan, Khan Muhammad, Javier Del Ser, Javier Sanchez-Medina, Sergey Andreev, Weiping Ding, Jong Weon Lee. An Efficient and Scalable Simulation Model for Autonomous Vehicles With Economical Hardware. IEEE Transactions on Intelligent Transportation Systems. 2020; 22 (3):1718-1732.
Chicago/Turabian StyleMuhammad Sajjad; Muhammad Irfan; Khan Muhammad; Javier Del Ser; Javier Sanchez-Medina; Sergey Andreev; Weiping Ding; Jong Weon Lee. 2020. "An Efficient and Scalable Simulation Model for Autonomous Vehicles With Economical Hardware." IEEE Transactions on Intelligent Transportation Systems 22, no. 3: 1718-1732.
Due to their unprecedented capacity to learn patterns from raw data, deep neural networks have become the de facto modeling choice to address complex machine learning tasks. However, recent works have emphasized the vulnerability of deep neural networks when being fed with intelligently manipulated adversarial data instances tailored to confuse the model. In order to overcome this issue, a major effort has been made to find methods capable of making deep learning models robust against adversarial inputs. This work presents a new perspective for improving the robustness of deep neural networks in image classification. In computer vision scenarios, adversarial images are crafted by manipulating legitimate inputs so that the target classifier is eventually fooled, but the manipulation is not visually distinguishable by an external observer. The reason for the imperceptibility of the attack is that the human visual system fails to detect minor variations in color space, but excels at detecting anomalies in geometric shapes. We capitalize on this fact by extracting color gradient features from input images at multiple sensitivity levels to detect possible manipulations. We resort to a deep neural classifier to predict the category of unseen images, whereas a discrimination model analyzes the extracted color gradient features with time series techniques to determine the legitimacy of input images. The performance of our method is assessed over experiments comprising state-of-the-art techniques for crafting adversarial attacks. Results corroborate the increased robustness of the classifier when using our discrimination module, yielding drastically reduced success rates of adversarial attacks that operate on the whole image rather than on localized regions or around the existing shapes of the image. Future research is outlined towards improving the detection accuracy of the proposed method for more general attack strategies.
Izaskun Oregi; Javier Del Ser; Aritz Pérez; José A. Lozano. Robust image classification against adversarial attacks using elastic similarity measures between edge count sequences. Neural Networks 2020, 128, 61 -72.
AMA StyleIzaskun Oregi, Javier Del Ser, Aritz Pérez, José A. Lozano. Robust image classification against adversarial attacks using elastic similarity measures between edge count sequences. Neural Networks. 2020; 128 ():61-72.
Chicago/Turabian StyleIzaskun Oregi; Javier Del Ser; Aritz Pérez; José A. Lozano. 2020. "Robust image classification against adversarial attacks using elastic similarity measures between edge count sequences." Neural Networks 128, no. : 61-72.
Borehole resistivity measurements are routinely employed to measure the electrical properties of rocks penetrated by a well and to quantify the hydrocarbon pore volume of a reservoir. Depending on the degree of geometrical complexity, inversion techniques are often used to estimate layer-by-layer electrical properties from measurements. When used for well geosteering purposes, it becomes essential to invert the measurements into layer-by-layer values of electrical resistivity in real time. We explore the possibility of using deep neural networks (DNNs) to perform rapid inversion of borehole resistivity measurements. Accordingly, we construct a DNN that approximates the following inverse problem: given a set of borehole resistivity measurements, the DNN is designed to deliver a physically reliable and data-consistent piecewise one-dimensional layered model of the surrounding subsurface. Once the DNN is constructed, we can invert borehole measurements in real time. We illustrate the performance of the DNN for inverting logging-while-drilling (LWD) measurements acquired in high-angle wells via synthetic examples. Numerical results are promising, although further work is needed to achieve the accuracy and reliability required by petrophysicists and drillers.
M. Shahriari; D. Pardo; Artzai Picon; A. Galdran; J. Del Ser; C. Torres-Verdín. A deep learning approach to the inversion of borehole resistivity measurements. Computational Geosciences 2020, 24, 971 -994.
AMA StyleM. Shahriari, D. Pardo, Artzai Picon, A. Galdran, J. Del Ser, C. Torres-Verdín. A deep learning approach to the inversion of borehole resistivity measurements. Computational Geosciences. 2020; 24 (3):971-994.
Chicago/Turabian StyleM. Shahriari; D. Pardo; Artzai Picon; A. Galdran; J. Del Ser; C. Torres-Verdín. 2020. "A deep learning approach to the inversion of borehole resistivity measurements." Computational Geosciences 24, no. 3: 971-994.
Stream data processing has lately gained momentum with the arrival of new Big Data scenarios and applications dealing with continuously produced information flows. Unfortunately, traditional machine learning algorithms are not prepared to tackle the specific challenges imposed by data stream processing, such as the need for learning incrementally, limited memory and processing time requirements, and adaptation to non-stationary data, among others. To face these paradigms, Spiking Neural Networks have emerged as one of the most promising stream learning techniques, with variants such as Evolving Spiking Neural Networks capable of efficiently addressing many of these challenges. Interestingly, these networks resort to a particular population encoding scheme - Gaussian Receptive Fields - to transform the incoming stimuli into temporal spikes. The study presented in this manuscript sheds light on the predictive potential of this encoding scheme, focusing on how it can be applied as a computationally lightweight, model-agnostic preprocessing step for data stream learning. We provide informed intuition to unveil under which circumstances the aforementioned population encoding method yields effective prediction gains in data stream classification with respect to the case where no preprocessing is performed. Results obtained for a variety of stream learning models and both synthetic and real stream datasets are discussed to empirically buttress the capability of Gaussian Receptive Fields to boost the predictive performance of stream learning methods, spanning further research towards extrapolating our findings to other machine learning problems.
Jesus L. Lobo; Izaskun Oregi; Albert Bifet; Javier Del Ser. Exploiting the stimuli encoding scheme of evolving Spiking Neural Networks for stream learning. Neural Networks 2020, 123, 118 -133.
AMA StyleJesus L. Lobo, Izaskun Oregi, Albert Bifet, Javier Del Ser. Exploiting the stimuli encoding scheme of evolving Spiking Neural Networks for stream learning. Neural Networks. 2020; 123 ():118-133.
Chicago/Turabian StyleJesus L. Lobo; Izaskun Oregi; Albert Bifet; Javier Del Ser. 2020. "Exploiting the stimuli encoding scheme of evolving Spiking Neural Networks for stream learning." Neural Networks 123, no. : 118-133.