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共 111 条记录，以下是71-80 订阅

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A Semi-automatic Object Identification Technique Combining Computer Vision and Deep learning for the Crosswalk Detection Problem 21st

A Semi-automatic Object Identification Technique Combining C...

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21st international conference on intelligent data engineering and automated learning

作者： Rubio, thiago R. P. M. Cruz, Jose Aleixo Jacob, Joao Garrido, Daniel Cardoso, Henrique Lopes Silva, Daniel Rodrigues, Rui Univ Porto Fac Engn Porto Portugal Artificial Intelligence & Comp Sci Lab LIACC Porto Portugal

ISBN: (纸本)9783030623647;9783030623654

Object detection in the traffic domain has faced growing relevance through the years in developing autonomous driving mechanisms. As with vehicles, pedestrians face a very dynamic context, and identifying relevant objects from a pedestrian perspective presents many challenges. Improving the detection of some objects, such as crosswalks, is very relevant in this regard. this paper presents a technique that applies a computer vision approach to automatically generate datasets for training YOLO-based deep learning algorithms. An initial precision of 0.82 achieved with the generated dataset, which is increased to 0.84 after manually removing incorrect annotations. Results show that our approach leverages the dataset building process by reducing the manual workload needed. the approach could be used for training other object detection models used in traffic scenarios.

关键词： Crosswalk identification Autonomous pedestrian Computer vision Deep learning Traffic

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data Generation Using Gene Expression Generator 21st

Data Generation Using Gene Expression Generator

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21st international conference on intelligent data engineering and automated learning

作者： Farou, Zakarya Mouhoub, Noureddine Horvath, Tomas Eotvos Lorand Univ Fac Informat Telekom Innovat Labs Dept Data Sci & Engn Pazmany Peter Setany 1-C H-1117 Budapest Hungary Univ Bordeaux Bordeaux Comp Sci Lab LaBRI Sci & Technol Campus351 Cours Liberat F-33400 Talence France Pavol Jozef Safarik Univ Inst Comp Sci Jesenna 5 Kosice 04001 Slovakia

ISBN: (纸本)9783030623647;9783030623654

Generative adversarial networks (GANs) could be used efficiently for image and video generation when labeled training data is available in bulk. In general, building a good machine learning model requires a reasonable amount of labeled training data. However, there are areas such as the biomedical field where the creation of such a dataset is time-consuming and requires expert knowledge. thus, the aim is to use data augmentation techniques as an alternative to data collection to improve data classification. this paper presents the use of a modified version of a GAN called Gene Expression Generator (GEG) to augment the available data samples. the proposed approach was used to generate synthetic data for binary biomedical datasets to train existing supervised machine learning approaches. Experimental results show that the use of GEG for data augmentation with amodified version of leave one out cross-validation (LOOCV) increases the performance of classification accuracy.

关键词： data generation Generative adversarial networks Gene expression data Cancer classification

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21st international conference on intelligent data engineering and automated learning

作者： Pereira, Danilo Santos, Flavio Matos, Leonardo N. Novais, Paulo Zanchettin, Cleber Ludermir, Teresa B. Univ Fed Pernambuco Recife PE Brazil Univ Fed Sergipe Sao Cristovao SE Brazil Univ Minho Braga Portugal

ISBN: (纸本)9783030623647;9783030623654

Knowledge transfer is the task of transferring the knowledge learned by a model A to a new model B. this task is essential in Deep learning, since there are complex models with excellent results, but computationally costly to be executed. the Similarity Knowledge Transfer (SKT) method proposes an approach to transfer the knowledge layer-by-layer between a donor model and a receiver model. this transfer is carried out through the representations learned by the layers from the teacher model. Despite presenting good results, the SKT method proposes just a way to transfer knowledge between two models. therefore, this work presents the Similarity Knowledge Transfer Ensemble (SKTE) method, a generic form of SKT that allows the transfer from several teachers to a single student model. We carried out experiments with the CIFAR10 benchmark, where the results obtained showed promising results in this activity.

关键词： Knowledge transfer Ensembles Deep learning

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One-Shot Only Real-Time Video Classification: A Case Study in Facial Emotion Recognition 1

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21st international conference on intelligent data engineering and automated learning

作者： Basbrain, Arwa Gan, John Q. Univ Essex Sch Comp Sci & Elect Engn Colchester Essex England King Abdulaziz Univ Fac Comp & Informat Technol Jeddah Saudi Arabia

ISBN: (数字)9783030623623

ISBN: (纸本)9783030623616;9783030623623

Video classification is an important research field due to its applications ranging from human action recognition for video surveillance to emotion recognition for human-computer interaction. this paper proposes a new method called One-Shot Only (OSO) for real-time video classification with a case study in facial emotion recognition. Instead of using 3D convolutional neural networks (CNN) or multiple 2D CNNs with decision fusion as in the previous studies, the OSO method tackles video classification as a single image classification problem by spatially rearranging video frames using frame selection or clustering strategies to form a simple representative storyboard for spatio-temporal video information fusion. It uses a single 2D CNN for video classification and thus can be optimised end-to-end directly in terms of the classification accuracy. Experimental results show that the OSO method proposed in this paper outperformed multiple 2D CNNs with decision fusion by a large margin in terms of classification accuracy (by up to 13%) on the AFEW 7.0 dataset for video classification. It is also very fast, up to ten times faster than the commonly used 2D CNN architectures for video classification.

关键词： Video-based facial emotion recognition Convolutional neural network Spatial-temporal data fusion

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Cloud Type Identification Using data Fusion and Ensemble learning 21st

Cloud Type Identification Using Data Fusion and Ensemble Lea...

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21st international conference on intelligent data engineering and automated learning

作者： Huertas-Tato, Javier Martin, Alejandro Camacho, David Univ Europea Madrid Madrid Spain Univ Politecn Madrid Madrid Spain

ISBN: (纸本)9783030623647;9783030623654

Cloud type classification is a complex multi-class problem where total sky images are analysed to determine their category such as Stratus or Cirrus, among others. However, many properties of this domain make high classification accuracy difficult to achieve. In this paper, we design a novel fusion approach, showing that recent image classification architectures based on deep learning, such as Convolutional Neural Networks, can be improved using statistical features directly calculated from images. In this research, three powerful CNNs have been trained on a comprehensive dataset: VGG-19, Inception-ResNet V2 and Inception V3. Simultaneously, a pool of standard machine learning classifiers have been trained on 14 different statistical characteristics on each colour channel. the results evidence that a fusion approach of the predictions of an image-trained CNN and a feature-trained Random Forest classifier improves the classification ability of both methods individually, reaching 95.05% macro average weighted precision over 12 classes in a complex highly imbalanced dataset with noisy examples.

关键词： Cloud type classification data fusion Ensemble learning Convolutional Neural Network

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Deep learning in Aeronautics: Air Traffic Trajectory Classification Based on Weather Reports 21st

Deep Learning in Aeronautics: Air Traffic Trajectory Classif...

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21st international conference on intelligent data engineering and automated learning

作者： Jimenez-Campfens, Nestor Colomer, Adrian Nunez, Javier Mogollon, Juan M. Rodriguez, Antonio L. Naranjo, Valery Univ Politecn Valencia Inst Res & Innovat Bioengn Valencia Spain Skylife Engn SL Parque Cient & Tecnol Cartuja Seville Spain

ISBN: (纸本)9783030623647;9783030623654

New paradigms in aviation, as the expected shortage of qualified pilots and the increasing number of flights worldwide, present big challenges to aeronautic enterprises and regulators. In this sense, a concept known as Single Pilot Operations arises in the task of dealing with these challenges, for which, automation becomes necessary, especially in Air Traffic Management. In this regard, this paper presents a deep learning-based approach to leveraging the job of both ground controllers and pilots. Making use of Meteorological Terminal Air Reports, obtained regularly from every aerodrome worldwide, we created a model based on a multi-layer perceptron capable of determining the approach trajectory of an aircraft thirty minutes prior to the expected landing time. Experiments on aircraft trajectories from Toulouse to Seville, show an accuracy, recall and F1-score higher than 0.9 for the resultant predictive model.

关键词： Air traffic management Weather reports METAR Trajectory prediction Deep learning

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Predictive vaccinology: Optimisation of predictions using support vector machine classifiers

Predictive vaccinology: Optimisation of predictions using su...

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6th international conference on intelligent data engineering and automated learning - ideal 2005

作者： Bozic, Ivana Zhang, Guang Lan Brusic, Vladimir Faculty of Mathematics University of Belgrade Belgrade Serbia Institute for Infocomm Research 21 Heng Mui Keng Terrace Singapore 119613 Serbia School of Computer Engineering Nanyang Technological University Nanyang Avenue Singapore 639798 Serbia School of Land and Food Sciences Institute for Molecular Bioscience University of Queensland Brisbane Australia

Promiscuous human leukocyte antigen (HLA) binding peptides are ideal targets for vaccine development. Existing computational models for prediction of promiscuous peptides used hidden Markov models and artificial neural networks as prediction algorithms. We report a system based on support vector machines that outperforms previously published methods. Preliminary testing showed that it can predict peptides binding to HLA-A2 and -A3 supertype molecules with excellent accuracy, even for molecules where no binding data are currently available. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Optimization

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An Analysis of Protein Patterns Present in the Saliva of Diabetic Patients Using Pairwise Relationship and Hierarchical Clustering 1

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21st international conference on intelligent data engineering and automated learning

作者： Soares, Airton Esteves, Eduardo Rosa, Nuno Esteves, Ana Cristina Lins, Anthony Bastos-Filho, Carmelo J. A. Univ Pernambuco Recife PE Brazil Univ Catolica Portuguesa Fac Dent Med Ctr Interdisciplinary Res Hlth CIIS Viseu Portugal Univ Beira Interior Fac Ciencias Saude UBI Covilha Portugal Univ Catolica Pernambuco Recife PE Brazil

ISBN: (数字)9783030623623

ISBN: (纸本)9783030623616;9783030623623

Molecular diagnosis is based on the quantification of RNA, proteins, or metabolites whose concentration can be correlated to clinical situations. Usually, these molecules are not suitable for early diagnosis or to follow clinical evolution. Large-scale diagnosis using these types of molecules depends on cheap and preferably noninvasive strategies for screening. Saliva has been studied as a noninvasive, easily obtainable diagnosis fluid, and the presence of serum proteins in it enhances its use as a systemic health status monitoring tool. With a recently described automated capillary electrophoresis-based strategy that allows us to obtain a salivary total protein profile, it is possible to quantify and analyze patterns that may indicate disease presence or absence. the data of 19 persons with diabetes and 58 healthy donors obtained by capillary electrophoresis were transformed, treated, and grouped so that the structured values could be used to study individuals' health state. After Pairwise Relationships and Hierarchical Clustering analysis were observed that amplitudes of protein peaks present in the saliva of these individuals could be used as differentiating parameters between healthy and unhealthy people. It indicates that these characteristics can serve as input for a future computational intelligence algorithm that will aid in the stratification of individuals that manifest changes in salivary proteins.

关键词： data mining Clustering algorithms Saliva Capillary electrophoresis Diagnosis

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Unified Performance Measure for Binary Classification Problems 21st

Unified Performance Measure for Binary Classification Proble...

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21st international conference on intelligent data engineering and automated learning

作者： Redondo, Ana R. Navarro, Jorge Fernandez, Ruben R. de Diego, Isaac Martin Moguerza, Javier M. Fernandez-Munoz, Juan Jose Rey Juan Carlos Univ C Tulipan S-N Mostoles 28933 Spain

ISBN: (纸本)9783030623647;9783030623654

Different performance measures are used to inspect, compare and evaluate the behaviour of classifiers in Machine learning (ML). ML researchers employ one or several of these performance measures in their classification studies to report their success. However, no widespread consensus has been reached on a unified chosen measure. In this work, we introduce a reliable and informative measure, the Unified Performance Measure (UPM), by modifying the F1-score to avoid its undesired behaviour in imbalanced classification problems. UPM is compared with alternative performance measures, like the F1-score or Accuracy, in both simulated confusion matrices and real datasets. the proposed measure outperforms the alternatives, providing a promising new research line.

关键词： Classification models Confusion matrices F1-score Accuracy Matthews correlation coefficient Imbalanced data Binary classification

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Bridging the Gap of Neuroscience, Philosophy, and Evolutionary Biology to Propose an Approach to Machine learning of Human-Like Ethics 21st

Bridging the Gap of Neuroscience, Philosophy, and Evolutiona...

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21st international conference on intelligent data engineering and automated learning

作者： Lori, Nicolas Ferreira, Diana Alves, Victor Machado, Jose Univ Minho Ctr Algoritmi P-4710057 Braga Portugal

ISBN: (纸本)9783030623647;9783030623654

the growing explosion of ideas such as Artificial Intelligence (AI), smart environments and ubiquitous computing has led to the creation of the Ambient Intelligence (AmI) paradigm. As AmI begins to take place, moving from a futuristic idea to a reality, we are gradually witnessing the creation of an omnipresent, responsive, and intelligent atmosphere in which thousands of tiny sensors and natural user interfaces will be embedded in our natural movements and in our social and physical interactions. Hence, a key challenge in this multi-disciplinary approach is to get machines to act according to ethical priorities that make sense to human beings. In this study, we improve the capacity for machine ethics to approach human ethics by assessing the computation of transaction values and we argue that it is possible to perform such a computation using recent work that describes the effects of human decision-making using an axiomatic framework. this paper clarifies the relationship between the brain's 3-axes of neuroscience, the 3 Plato's Transcendentals of philosophy and the biological evolution's 3-components, as well as the top-down vs. bottom-up approaches to machine ethics.

关键词： Artificial Intelligence Ambient Intelligence Machine ethics Transaction value Aesthetics evolution Plato's Transcendentals Axiomatic systems

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