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Beyond the Known: Adversarial Autoencoders in Novelty Detection

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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

Depth Estimation using Weighted-loss and Transfer learning

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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

Stroke Home Rehabilitation Approach Using Mobile Application Based on PostNet machine learning Model 23

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Stroke Home Rehabilitation Approach Using Mobile Application...

7th International Conference on Medical and Health Informatics, ICMHI 2023

作者： Das, Utpal Chandra Le, Ngoc Thien Benjapolakul, Watit Vitoonpong, Timporn Pluempitiwiriyawej, Charnchai Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Department of Electrical Engineering Chulalongkorn University Bangkok10330 Thailand Department of Rehabilitation Medicine Faculty of Medicine Chulalongkorn University Bangkok10330 Thailand Multimedia Data Analytics and Processing Research Unit Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand

ISBN: (纸本)9798400700712

Stroke is a significant cause of mortality and disability globally, with its occurrence in the human brain and motor function being linked to various parts of the human body. Stroke victims often experience disabilities or mobility problems in affected body parts, either on one or both sides of the body. Physiotherapy exercises are the primary treatment and medication for stroke patients, necessitating daily monitoring by a physiotherapist. However, this approach is expensive, and rehabilitation centers and physicians are scarce. Numerous research studies have been conducted to address this issue from various perspectives. This study proposes a Convolutional Neural Network (CNN) based pose net machine learning (ML) model for stroke home rehabilitation using pose detection and classification with a skeleton base model and human pose estimation drawing. We trained our ML model using different human pose images for this model. The tested accuracy of our CNN model is 100% for our exercise pose image test case. Later we build and mobile application for remote rehabilitation. Tested the application with ten different subjects, achieving a 98% for elbow extension, and in the case of elbow flexion and Normal position of balancing both sides, all the persons achieved a 100 % accuracy rate under a laboratory environment. © 2023 ACM.

关键词： Patient treatment

A Deep Feedforward Neural Network and Shallow Architectures Effectiveness Comparison: Flight Delays Classification Perspective 21

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A Deep Feedforward Neural Network and Shallow Architectures ...

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

CONVLORA AND ADABN BASED DOMAIN ADAPTATION VIA SELF-TRAINING

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arXiv 2024年

作者： Aleem, Sidra Dietlmeier, Julia Arazo, Eric Little, Suzanne SFI Research Centre for Machine Learning Dublin City University Ireland Insight SFI Research Centre for Data Analytics Dublin City University Ireland School of Computing Dublin City University Ireland

Existing domain adaptation (DA) methods often involve pre-training on the source domain and fine-tuning on the target domain. For multi-target domain adaptation, having a dedicated/separate fine-tuned network for each target domain, that retain all the pre-trained model parameters, is prohibitively expensive. To address this limitation, we propose Convolutional Low-Rank Adaptation (ConvLoRA). ConvLoRA freezes pre-trained model weights, adds trainable low-rank decomposition matrices to convolutional layers, and backpropagates the gradient through these matrices thus greatly reducing the number of trainable parameters. To further boost adaptation, we utilize Adaptive Batch Normalization (AdaBN) which computes target-specific running statistics and use it along with ConvLoRA. Our method has fewer trainable parameters and performs better or on-par with large independent fine-tuned networks (with less than 0.9% trainable parameters of the total base model) when tested on the segmentation of Calgary-Campinas dataset containing brain MRI images. Our approach is simple, yet effective and can be applied to any deep learning-based architecture which uses convolutional and batch normalization layers. Code is available at: ConvLoRA. © 2024, CC BY.

关键词： Convolution

System Architecture for Reading and Interpreting Physical Printouts of Medical Forms

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System Architecture for Reading and Interpreting Physical Pr...

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

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

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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

The 32nd AIAI Irish Conference on Artificial Intelligence and Cognitive Science 32

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The 32nd AIAI Irish Conference on Artificial Intelligence an...

32nd Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2024

作者： Pakrashi, Arjun Rushe, Ellen Namee, Brian Mac Brennan, Rob Yaman, Bezya School of Computer Science University College Dublin BelfieldDublin 4 Ireland School of Computer Science Dublin City University Collins Ave Ext Whitehall Dublin 9 Ireland The Insight Centre for Data Analytics Ireland SFI Centre for Research Training in Machine Learning Ireland ADAPT Centre Ireland School of Computer Science Trinity College Dublin College Green Dublin 2 Ireland

The 32nd Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2024), hosted by University College Dublin (UCD) in collaboration with Dublin City University (DCU), featured high-quality research in AI and cognitive science, with a focus on trustworthy AI, fairness, and bias mitigation. Of 82 submissions, 44 papers were accepted, covering applications in healthcare, smart mobility, financial forecasting, anomaly detection, and sports analytics, alongside fundamental machine learning advancements. Keynote speakers Dr. Susan Leavy (UCD) discussed AI’s societal impact, while Prof. Eamonn Keogh (UC Riverside) critiqued research in time series anomaly detection. AICS 2024 reaffirmed its role as Ireland’s leading AI research forum, fostering discussions on cutting-edge advancements and their societal implications. © 2024 Copyright for this paper by its authors.

关键词： Adversarial machine learning

An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited data

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arXiv 2024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 Ireland Insight Centre for Data Analytics University College Dublin Dublin Ireland

The diagnostic accuracy and subjectivity of existing Knee Osteoarthritis (OA) ordinal grading systems has been a subject of ongoing debate and concern. Existing automated solutions are trained to emulate these imperfect systems, whilst also being reliant on large annotated databases for fully-supervised training. This work proposes a three stage approach for automated continuous grading of knee OA that is built upon the principles of Anomaly Detection (AD);learning a robust representation of healthy knee X-rays and grading disease severity based on its distance to the centre of normality. In the first stage, SS-FewSOME is proposed, a self-supervised AD technique that learns the 'normal' representation, requiring only examples of healthy subjects and Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection