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Proceedings of the 5th International Conference on Advances in Artificial Intelligence

作者： Desmond Bala Bisandu Mohammed Salih Homaid irene Moulitsas Salvatore Filippone Department of Computational Engineering Sciences Cranfield University UK and Machine Learning and Data Analytics Laboratory Digital Aviation Research and Technology Centre (DARTeC) Cranfield University UK Department of Computational Engineering Sciences Cranfield University UK and Machine Learning and Data Analytics Laboratory Digital Aviation Research and Technology Centre(DARTeC) Cranfield University UK Department of Computational Engineering Sciences Cranfield University UK and Machine learning and data analytics laboratory digital aviation research and technology centre(DARTeC) Cranfield University UK Department of Civil and Computer Engineering Universita di Roma Italy and Department of Computational Engineering Sciences Cranfield Univeristy United Kingdom

ISBN: (纸本)9781450390699

Flight delays have negatively impacted the socio-economics state of passengers, airlines and airports, resulting in huge economic losses. Hence, it has become necessary to correctly predict their occurrences in decision-making because it is important for the effective management of the aviation industry. Developing accurate flight delays classification models depends mostly on the air transportation system complexity and the infrastructure available in airports, which may be a region-specific issue. However, no specific prediction or classification model can handle the individual characteristics of all airlines and airports at the same time. Hence, the need to further develop and compare predictive models for the aviation decision system of the future cannot be over-emphasised. In this research, flight on-time data records from the United State Bureau of Transportation Statistics was employed to evaluate the performances of Deep Feedforward Neural Network, Neural Network, and Support Vector machine models on a binary classification problem. The research revealed that the models achieved different accuracies of flight delay classifications. The Support Vector machine had the worst average accuracy than Neural Network and Deep Feedforward Neural Network in the initial experiment. The Deep Feedforward Neural Network outperformed Support Vector machines and Neural Network with the best average percentage accuracies. Going further to investigate the Deep Feedforward Neural Network architecture on different parameters against itself suggest that training a Deep Feedforward Neural Network algorithm, regardless of data training size, the classification accuracy peaks. We examine which number of epochs works best in our flight delay classification settings for the Deep Feedforward Neural Network. Our experiment results demonstrate that having many epochs affects the convergence rate of the model; unlike when hidden layers are increased, it does not ensure better or higher ac

关键词： deep learning Support Vector machine flight delays Classification deep neural network

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An accurate detection is not all you need to combat label noise in web-noisy datasets

arXiv

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arXiv 2024年

作者： Albert, Paul Valmadre, Jack Arazo, Eric Krishna, Tarun O’Connor, Noel E. McGuinness, Kevin Australian Institute for Machine Learning University of Adelaide Australia CeADAR Ireland’s Centre for Applied Artificial Intelligence Ireland Insight Centre for Data Analytics Dublin City University Ireland

Training a classifier on web-crawled data demands learning algorithms that are robust to annotation errors and irrelevant examples. This paper builds upon the recent empirical observation that applying unsupervised contrastive learning to noisy, web-crawled datasets yields a feature representation under which the in-distribution (ID) and out-of-distribution (OOD) samples are linearly separable [3]. We show that direct estimation of the separating hyperplane can indeed offer an accurate detection of OOD samples, and yet, surprisingly, this detection does not translate into gains in classification accuracy. Digging deeper into this phenomenon, we discover that the near-perfect detection misses a type of clean examples that are valuable for supervised learning. These examples often represent visually simple images, which are relatively easy to identify as clean examples using standard loss- or distance-based methods despite being poorly separated from the OOD distribution using unsupervised learning. Because we further observe a low correlation with SOTA metrics, this urges us to propose a hybrid solution that alternates between noise detection using linear separation and a state-of-the-art (SOTA) small-loss approach. When combined with the SOTA algorithm PLS, we substantially improve SOTA results for real-world image classification in the presence of web noise https://***/PaulAlbert31/LSA. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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System Architecture for Reading and Interpreting Physical Printouts of Medical Forms

System Architecture for Reading and Interpreting Physical Pr...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Ekaterina Snegireva Grigory R. Khazankin Igor Mikheenko Stream Data Analytics and Machine Learning laboratory Novosibirsk State University Novosibirsk Russia Novosibirsk State University Novosibirsk Russia Meshalkin National Medical Research Center Novosibirsk Russia

This article describes the developed architecture of the system module for processing and interpreting analog medical data. Patients often undergo examinations in various medical institutions, and since their results are often handed out to the patient in printed form, the receiving institution transfers them to its database manually. There is also a tendency to completely refuse analog media and use only digital ones. But in this case, another problem appears - either loss or conversion of the accumulated analog base into digital format. These days, automatic document management systems for medical institutions - Health information systems (HIS) - are actively developing. The software module developed in accordance with the architecture described in the article can be used by developers of various HIS to automate the work with analog data. If it is necessary, it can also be freely expanded by adding new modules for working with various analog data. In this article, we take ECG scans and medical test results as examples of such data. As a result of the work undertaken the prototype of the designed system was developed and tested.

关键词： Waste materials databases Prototypes Systems architecture Computer architecture Electrocardiography Medical tests

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Achieving Efficient and Realistic Full-Radar Simulations and Automatic data Annotation by Exploiting Ray Meta data from a Radar Ray Tracing Simulator

Achieving Efficient and Realistic Full-Radar Simulations and...

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IEEE National Conference on Radar

作者： Christian Schüβler Marcel Hoffmann Vanessa Wirth Björn Eskofier Tim Weyrich Marc Stamminger Martin Vossiek Institute of Microwaves and Photonics Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Chair of Visual Computing Machine Learning and Data Analytics Lab Chair of Digital Reality

In this work, a novel radar simulation concept for efficiently simulating realistic radar data for range, Doppler, and arbitrary antenna positions is introduced. With the concept, the simulated radar signal can also be automatically annotated by splitting it into multiple parts. Annotations that are almost perfect - including the annotation of exotic effects, such as multi-path - can be produced with this approach. Signal parts originating from different parts of an object can be labelled with it as well. To this end, the computation process used in a Monte Carlo shooting and bouncing rays (SBR) simulator is adapted. By considering the hits of each simulated ray, various meta data can be stored, such as hit position, mesh pointer, object IDs, and many more. This collected meta data can then be utilized to predict path-length changes caused by object motion to obtain Doppler information or to apply specific ray filter rules to obtain radar signals that only fulfil specific conditions, such as multiple bounces, or signals that contain specific object IDs. Using this approach, perfect, and otherwise almost impossible, annotation schemes can be realized.

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Beyond the Known: Adversarial Autoencoders in Novelty Detection

arXiv

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arXiv 2024年

作者： Asad, Muhammad Ullah, Ihsan Sistu, Ganesh Madden, Michael G. Machine Learning Research Group School of Computer Science University of Galway Ireland Insight SFI Research Centre for Data Analytics University of Galway Ireland Valeo Vision Systems Tuam Ireland

In novelty detection, the goal is to decide if a new data point should be categorized as an inlier or an outlier, given a training dataset that primarily captures the inlier distribution. Recent approaches typically use deep encoder and decoder network frameworks to derive a reconstruction error, and employ this error either to determine a novelty score, or as the basis for a one-class classifier. In this research, we use a similar framework but with a lightweight deep network, and we adopt a probabilistic score with reconstruction error. Our methodology calculates the probability of whether the sample comes from the inlier distribution or not. This work makes two key contributions. The first is that we compute the novelty probability by linearizing the manifold that holds the structure of the inlier distribution. This allows us to interpret how the probability is distributed and can be determined in relation to the local coordinates of the manifold tangent space. The second contribution is that we improve the training protocol for the network. Our results indicate that our approach is effective at learning the target class, and it outperforms recent state-of-the-art methods on several benchmark datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Depth Estimation using Weighted-loss and Transfer learning

arXiv

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arXiv 2024年

作者： Hafeez, Muhammad Adeel Madden, Michael G. Sistu, Ganesh Ullah, Ihsan Machine Learning Research Group School of Computer Science University of Galway Ireland Insight SFI Research Centre for Data Analytics University of Galway Ireland Valeo Vision Systems Tuam Ireland

Depth estimation from 2D images is a common computer vision task that has applications in many fields including autonomous vehicles, scene understanding and robotics. The accuracy of a supervised depth estimation method mainly relies on the chosen loss function, the model architecture, quality of data and performance metrics. In this study, we propose a simplified and adaptable approach to improve depth estimation accuracy using transfer learning and an optimized loss function. The optimized loss function is a combination of weighted losses to which enhance robustness and generalization: Mean Absolute Error (MAE), Edge Loss and Structural Similarity Index (SSIM). We use a grid search and a random search method to find optimized weights for the losses, which leads to an improved model. We explore multiple encoder-decoder-based models including DenseNet121, DenseNet169, DenseNet201, and EfficientNet for the supervised depth estimation model on NYU Depth dataset v2. We observe that the EfficientNet model, pre-trained on ImageNet for classification when used as an encoder, with a simple upsampling decoder, gives the best results in terms of RSME, REL and log10: 0.386, 0.113 and 0.049, respectively. We also perform a qualitative analysis which illustrates that our model produces depth maps that closely resemble ground truth, even in cases where the ground truth is flawed. The results indicate significant improvements in accuracy and robustness, with EfficientNet being the most successful architecture. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal encoding

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Estimating the robustness of classification models by the structure of the learned feature-space

arXiv

引用

arXiv 2021年

作者： Ho, Kalun Pfreundt, Franz-Josef Keuper, Janis Keuper, Margret CC-HPC Fraunhofer ITWM Fraunhofer-Platz 1 Kaiserslautern67663 Germany Institute for Machine Learning and Analytics Offenburg University Germany Data and Web Science Group University of Mannheim Germany

Over the last decade, the development of deep image classification networks has mostly been driven by the search for the best performance in terms of classification accuracy on standardized benchmarks like ImageNet. More recently, this focus has been expanded by the notion of model robustness, i.e. the generalization abilities of models towards previously unseen changes in the data distribution. While new benchmarks, like ImageNet-C, have been introduced to measure robustness properties, we argue that fixed testsets are only able to capture a small portion of possible data variations and are thus limited and prone to generate new overfitted solutions. To overcome these drawbacks, we suggest to estimate the robustness of a model directly from the structure of its learned feature-space. We introduce robustness indicators which are obtained via unsupervised clustering of latent representations from a trained classifier and show very high correlations to the model performance on corrupted test data. © 2021, CC BY.

关键词： Classification (of information)

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learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches

Learning Embeddings for Image Clustering: An Empirical Study...

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International Conference on Pattern Recognition

作者： Kalun Ho Janis Keuper Franz-Josef Pfreundt Margret Keuper Data and Web Science Group University of Mannheim Germany Institute for Machine Learning and Analytics (IMLA) Offenburg University Germany Competence Center High Performance Computing Fraunhofer ITWM Kaiserslautern Germany

In this work, we evaluate two different image clustering objectives, k-means clustering and correlation clustering, in the context of Triplet Loss induced feature space embeddings. Specifically, we train a convolutional neural network to learn discriminative features by optimizing two popular versions of the Triplet Loss in order to study their clustering properties under the assumption of noisy labels. Additionally, we propose a new, simple Triplet Loss formulation, which shows desirable properties with respect to formal clustering objectives and outperforms the existing methods. We evaluate all three Triplet loss formulations for K-means and correlation clustering on the CIFAR-10 image classification dataset.

关键词： Correlation Pattern recognition Noise measurement Convolutional neural networks Image classification

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Facial Expression Recognition Using Multi-Block Deep CNN 5

Facial Expression Recognition Using Multi-Block Deep CNN

引用

5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Priyadarshini, V. Srinivasulu Reddy, U. Reddy Chirra, Venkata Rami Mrudula, M. Suneetha, M. National Institute of Technology Dept. of Computer Science & Engineering Andhra Pradesh India Machine Learning & Data Analytics Lab National Institute of Technology Dept. of Computer Applications Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Malla Reddy College of Engineering Department of CSE Telangana India Independent Researcher Amaravati India

ISBN: (纸本)9798331528539

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Semi-Supervised Siamese Network for Identifying Bad data in Medical Imaging datasets

arXiv

引用

arXiv 2021年

作者： Belton, Niamh Lawlor, Aonghus Curran, Kathleen M. Science Foundation Ireland Centre for Research Training in Machine Learning School of Medicine School of Computer Science University College Dublin Insight Centre for Data Analytics University College Dublin Dublin Ireland

Noisy data present in medical imaging datasets can often aid the development of robust models that are equipped to handle real-world data. However, if the bad data contains insufficient anatomical information, it can have a severe negative effect on the model’s performance. We propose a novel methodology using a semi-supervised Siamese network to identify bad data. This method requires only a small pool of ‘reference’ medical images to be reviewed by a non-expert human to ensure the major anatomical structures are present in the Field of View. The model trains on this reference set and identifies bad data by using the Siamese network to compute the distance between the reference set and all other medical images in the dataset. This methodology achieves an Area Under the Curve (AUC) of 0.989 for identifying bad data. Code will be available at https://***/JYFuV. © 2021, CC BY.

关键词： Medical imaging

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