ORCID Profile
0000-0002-6478-8699
Current Organisations
University of Augsburg
,
Imperial College London
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Information Systems | Pattern Recognition and Data Mining | Computer-Human Interaction
Expanding Knowledge in the Information and Computing Sciences | Mental Health |
Publisher: IEEE
Date: 05-2019
Publisher: ISCA
Date: 20-08-2017
Publisher: IEEE
Date: 07-2018
Publisher: IEEE
Date: 06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2022
Publisher: ISCA
Date: 20-08-2017
Publisher: Hindawi Limited
Date: 30-03-2020
DOI: 10.1155/2020/8975078
Abstract: The classification process of lung nodule detection in a traditional computer-aided detection (CAD) system is complex, and the classification result is heavily dependent on the performance of each step in lung nodule detection, causing low classification accuracy and high false positive rate. In order to alleviate these issues, a lung nodule classification method based on a deep residual network is proposed. Abandoning traditional image processing methods and taking the 50-layer ResNet network structure as the initial model, the deep residual network is constructed by combining residual learning and migration learning. The proposed approach is verified by conducting experiments on the lung computed tomography (CT) images from the publicly available LIDC-IDRI database. An average accuracy of 98.23% and a false positive rate of 1.65% are obtained based on the ten-fold cross-validation method. Compared with the conventional support vector machine (SVM)-based CAD system, the accuracy of our method improved by 9.96% and the false positive rate decreased by 6.95%, while the accuracy improved by 1.75% and 2.42%, respectively, and the false positive rate decreased by 2.07% and 2.22%, respectively, in contrast to the VGG19 model and InceptionV3 convolutional neural networks. The experimental results demonstrate the effectiveness of our proposed method in lung nodule classification for CT images.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 30-09-2021
DOI: 10.36227/TECHRXIV.16689484.V1
Abstract: Traditionally, speech emotion recognition (SER) research has relied on manually handcrafted acoustic features using feature engineering. However, the design of handcrafted features for complex SER tasks requires significant manual effort, which impedes generalisability and slows the pace of innovation. This has motivated the adoption of representation learning techniques that can automatically learn an intermediate representation of the input signal without any manual feature engineering. Representation learning has led to improved SER performance and enabled rapid innovation. Its effectiveness has further increased with advances in deep learning (DL), which has facilitated deep representation learning where hierarchical representations are automatically learned in a data-driven manner. This paper presents the first comprehensive survey on the important topic of deep representation learning for SER. We highlight various techniques, related challenges and identify important future areas of research. Our survey bridges the gap in the literature since existing surveys either focus on SER with hand-engineered features or representation learning in the general setting without focusing on SER. /
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: IEEE
Date: 10-2017
Publisher: Elsevier BV
Date: 11-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: ISCA
Date: 20-08-2017
Publisher: Elsevier BV
Date: 03-2022
Publisher: ISCA
Date: 15-09-2019
Publisher: Springer Science and Business Media LLC
Date: 16-01-2022
Publisher: ISCA
Date: 20-08-2017
Publisher: ISCA
Date: 15-09-2019
Publisher: Springer International Publishing
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 30-09-2021
DOI: 10.36227/TECHRXIV.16689484
Abstract: Traditionally, speech emotion recognition (SER) research has relied on manually handcrafted acoustic features using feature engineering. However, the design of handcrafted features for complex SER tasks requires significant manual effort, which impedes generalisability and slows the pace of innovation. This has motivated the adoption of representation learning techniques that can automatically learn an intermediate representation of the input signal without any manual feature engineering. Representation learning has led to improved SER performance and enabled rapid innovation. Its effectiveness has further increased with advances in deep learning (DL), which has facilitated deep representation learning where hierarchical representations are automatically learned in a data-driven manner. This paper presents the first comprehensive survey on the important topic of deep representation learning for SER. We highlight various techniques, related challenges and identify important future areas of research. Our survey bridges the gap in the literature since existing surveys either focus on SER with hand-engineered features or representation learning in the general setting without focusing on SER. /
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 08-04-2017
Publisher: ACM
Date: 02-10-2018
Publisher: Elsevier BV
Date: 2015
Publisher: ACM
Date: 28-05-2018
Publisher: ISCA
Date: 20-08-2017
Publisher: ACM
Date: 20-11-2019
Publisher: MDPI AG
Date: 06-06-2021
DOI: 10.3390/MET11060922
Abstract: High-entropy alloys (HEAs) with multiple constituent elements have been extensively studied in the past 20 years, due to their promising engineering application. Previous experimental and computational studies of HEAs focused mainly on equiatomic or near equiatomic HEAs. However, there is probably far more treasure in those non-equiatomic HEAs with carefully designed composition. In this study, the molecular dynamics (MD) simulation combined with machine learning (ML) methods was used to predict the mechanical properties of non-equiatomic CuFeNiCrCo HEAs. A database was established based on a tensile test of 900 HEA single-crystal s les by MD simulation. Eight ML models were investigated and compared for the binary classification learning tasks, ranging from shallow models to deep models. It was found that the kernel-based extreme learning machine (KELM) model outperformed others for the prediction of yield stress and Young’s modulus. The accuracy of the KELM model was further verified by the large-sized polycrystal HEA s les. The results show that computational simulation combined with ML methods is an efficient way to predict the mechanical performance of HEAs, which provides new ideas for accelerating the development of novel alloy materials for engineering applications.
Publisher: JMIR Publications Inc.
Date: 02-02-2023
DOI: 10.2196/42965
Abstract: Despite efforts, the UK death rate from asthma is the highest in Europe, and 65% of people with asthma in the United Kingdom do not receive the professional care they are entitled to. Experts have recommended the use of digital innovations to help address the issues of poor outcomes and lack of care access. An automated SMS text messaging–based conversational agent (ie, chatbot) created to provide access to asthma support in a familiar format via a mobile phone has the potential to help people with asthma across demographics and at scale. Such a chatbot could help improve the accuracy of self-assessed risk, improve asthma self-management, increase access to professional care, and ultimately reduce asthma attacks and emergencies. The aims of this study are to determine the feasibility and usability of a text-based conversational agent that processes a patient’s text responses and short s le voice recordings to calculate an estimate of their risk for an asthma exacerbation and then offers follow-up information for lowering risk and improving asthma control assess the levels of engagement for different groups of users, particularly those who do not access professional services and those with poor asthma control and assess the extent to which users of the chatbot perceive it as helpful for improving their understanding and self-management of their condition. We will recruit 300 adults through four channels for broad reach: Facebook, YouGov, Asthma + Lung UK social media, and the website Healthily (a health self-management app). Participants will be screened, and those who meet inclusion criteria (adults diagnosed with asthma and who use WhatsApp) will be provided with a link to access the conversational agent through WhatsApp on their mobile phones. Participants will be sent scheduled and randomly timed messages to invite them to engage in dialogue about their asthma risk during the period of study. After a data collection period (28 days), participants will respond to questionnaire items related to the quality of the interaction. A pre- and postquestionnaire will measure asthma control before and after the intervention. This study was funded in March 2021 and started in January 2022. We developed a prototype conversational agent, which was iteratively improved with feedback from people with asthma, asthma nurses, and specialist doctors. Fortnightly reviews of iterations by the clinical team began in September 2022 and are ongoing. This feasibility study will start recruitment in January 2023. The anticipated completion of the study is July 2023. A future randomized controlled trial will depend on the outcomes of this study and funding. This feasibility study will inform a follow-up pilot and larger randomized controlled trial to assess the impact of a conversational agent on asthma outcomes, self-management, behavior change, and access to care. PRR1-10.2196/42965
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2017
Publisher: ISCA
Date: 20-08-2017
Publisher: ISCA
Date: 15-09-2019
Publisher: ISCA
Date: 02-09-2018
Publisher: Elsevier
Date: 2020
Publisher: ISCA
Date: 15-09-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2020
Publisher: Elsevier BV
Date: 12-2018
DOI: 10.1016/J.YMETH.2018.07.007
Abstract: Due to the complex and intricate nature associated with their production, the acoustic-prosodic properties of a speech signal are modulated with a range of health related effects. There is an active and growing area of machine learning research in this speech and health domain, focusing on developing paradigms to objectively extract and measure such effects. Concurrently, deep learning is transforming intelligent signal analysis, such that machines are now reaching near human capabilities in a range of recognition and analysis tasks. Herein, we review current state-of-the-art approaches with speech-based health detection, placing a particular focus on the impact of deep learning within this domain. Based on this overview, it is evident while that deep learning based solutions be become more present in the literature, it has not had the same overall dominating effect seen in other related fields. In this regard, we suggest some possible research directions aimed at fully leveraging the advantages that deep learning can offer speech-based health detection.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Publisher: IEEE
Date: 07-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2020
Publisher: Wiley
Date: 25-12-2018
DOI: 10.1111/PCN.12799
Abstract: Emotional expressions are one of the most widely studied topics in neuroscience, from both clinical and non-clinical perspectives. Atypical emotional expressions are seen in various psychiatric conditions, including schizophrenia, depression, and autism spectrum conditions. Understanding the basics of emotional expressions and recognition can be crucial for diagnostic and therapeutic procedures. Emotions can be expressed in the face, gesture, posture, voice, and behavior and affect physiological parameters, such as the heart rate or body temperature. With modern technology, clinicians can use a variety of tools ranging from sophisticated laboratory equipment to smartphones and web cameras. The aim of this paper is to review the currently used tools using modern technology and discuss their usefulness as well as possible future directions in emotional expression research and treatment strategies. The authors conducted a literature review in the PubMed, EBSCO, and SCOPUS databases, using the following key words: 'emotions,' 'emotional expression,' 'affective computing,' and 'autism.' The most relevant and up-to-date publications were identified and discussed. Search results were supplemented by the authors' own research in the field of emotional expression. We present a critical review of the currently available technical diagnostic and therapeutic methods. The most important studies are summarized in a table. Most of the currently available methods have not been adequately validated in clinical settings. They may be a great help in everyday practice however, they need further testing. Future directions in this field include more virtual-reality-based and interactive interventions, as well as development and improvement of humanoid robots.
Publisher: Frontiers Media SA
Date: 12-08-2021
Publisher: ISCA
Date: 02-09-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: ISCA
Date: 15-09-2019
Publisher: ACM
Date: 15-10-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2018
Publisher: Springer International Publishing
Date: 2017
Publisher: ACM
Date: 02-07-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Springer Science and Business Media LLC
Date: 11-01-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2023
Publisher: ACM
Date: 15-10-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 09-2017
Publisher: IEEE
Date: 05-2019
Location: United Kingdom of Great Britain and Northern Ireland
Start Date: 10-2019
End Date: 12-2024
Amount: $380,000.00
Funder: Australian Research Council
View Funded Activity