76 research outputs found
Unsupervised Adversarial Domain Adaptation for Cross-Lingual Speech Emotion Recognition
Cross-lingual speech emotion recognition (SER) is a crucial task for many
real-world applications. The performance of SER systems is often degraded by
the differences in the distributions of training and test data. These
differences become more apparent when training and test data belong to
different languages, which cause a significant performance gap between the
validation and test scores. It is imperative to build more robust models that
can fit in practical applications of SER systems. Therefore, in this paper, we
propose a Generative Adversarial Network (GAN)-based model for multilingual
SER. Our choice of using GAN is motivated by their great success in learning
the underlying data distribution. The proposed model is designed in such a way
that can learn language invariant representations without requiring
target-language data labels. We evaluate our proposed model on four different
language emotional datasets, including an Urdu-language dataset to also
incorporate alternative languages for which labelled data is difficult to find
and which have not been studied much by the mainstream community. Our results
show that our proposed model can significantly improve the baseline
cross-lingual SER performance for all the considered datasets including the
non-mainstream Urdu language data without requiring any labels.Comment: Accepted in Affective Computing & Intelligent Interaction (ACII 2019
Secure and robust machine learning for healthcare: A survey
Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images. Notwithstanding the impressive performance of ML/DL, there are still lingering doubts regarding the robustness of ML/DL in healthcare settings (which is traditionally considered quite challenging due to the myriad security and privacy issues involved), especially in light of recent results that have shown that ML/DL are vulnerable to adversarial attacks. In this paper, we present an overview of various application areas in healthcare that leverage such techniques from security and privacy point of view and present associated challenges. In addition, we present potential methods to ensure secure and privacy-preserving ML for healthcare applications. Finally, we provide insight into the current research challenges and promising directions for future research
Communication and Control in Collaborative UAVs: Recent Advances and Future Trends
The recent progress in unmanned aerial vehicles (UAV) technology has
significantly advanced UAV-based applications for military, civil, and
commercial domains. Nevertheless, the challenges of establishing high-speed
communication links, flexible control strategies, and developing efficient
collaborative decision-making algorithms for a swarm of UAVs limit their
autonomy, robustness, and reliability. Thus, a growing focus has been witnessed
on collaborative communication to allow a swarm of UAVs to coordinate and
communicate autonomously for the cooperative completion of tasks in a short
time with improved efficiency and reliability. This work presents a
comprehensive review of collaborative communication in a multi-UAV system. We
thoroughly discuss the characteristics of intelligent UAVs and their
communication and control requirements for autonomous collaboration and
coordination. Moreover, we review various UAV collaboration tasks, summarize
the applications of UAV swarm networks for dense urban environments and present
the use case scenarios to highlight the current developments of UAV-based
applications in various domains. Finally, we identify several exciting future
research direction that needs attention for advancing the research in
collaborative UAVs
Prevalence of work related musculoskeletal disorders among physiotherapists of Multan
Background: Work related musculoskeletal disorders are increasing day by day in hospitals and clinics due to high demand activities. These disorders decline the efficiency of therapists and result in social as well as economic losses. These injuries mostly occur due to abnormal postures and poor ergonomics. Aim and objectives: The purpose of this study was to rule out pain which disturbs the daily living of physiotherapists due to work related musculoskeletal disorder. This study also helps physiotherapists to improve their skills, technique, posture, ergonomics and all the other factors that are related to these disorders. Method: Survey was performed among physiotherapists that are performing duties in government hospitals, private clinics and all the other settings in Multan. This study included 100 physiotherapists of Multan. Self-design questionnaire as well as a Nordic questionnaire was used to collect data about WRMSK disorders. Data was gathered and was observed statistically. Results: 89% of physiotherapist were having had work related musculoskeletal disorder & 11% had no complaint, 75% female and 25% male & 23 to 50 years age population data was taken & the study observed that novice practitioner are at the increased risk of developing work-related musculoskeletal disorder & 45% were non specialized and 55% were specialized. The most commonly affected regions were upper back, low back and hip joint respectively. Conclusion: Upper back pain, lower back pain and hip pain were the most common complications among physiotherapists. Manual therapy shows association with the prevalence of MSK disorder
Privacy-preserving artificial intelligence in healthcare: Techniques and applications
There has been an increasing interest in translating artificial intelligence (AI) research into clinically-validated applications to improve the performance, capacity, and efficacy of healthcare services. Despite substantial research worldwide, very few AI-based applications have successfully made it to clinics. Key barriers to the widespread adoption of clinically validated AI applications include non-standardized medical records, limited availability of curated datasets, and stringent legal/ethical requirements to preserve patients' privacy. Therefore, there is a pressing need to improvise new data-sharing methods in the age of AI that preserve patient privacy while developing AI-based healthcare applications. In the literature, significant attention has been devoted to developing privacy-preserving techniques and overcoming the issues hampering AI adoption in an actual clinical environment. To this end, this study summarizes the state-of-the-art approaches for preserving privacy in AI-based healthcare applications. Prominent privacy-preserving techniques such as Federated Learning and Hybrid Techniques are elaborated along with potential privacy attacks, security challenges, and future directions. [Abstract copyright: Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.
In vitro antibacterial activity and phytochemical analysis of hexane extract of Vicia sativa
Vicia sativa is traditionally used medicinal plant in skin infections, asthma, bronchitis, urinary diseases and also used as antiseptic, anti-poison, aphrodisiac, anti rheumatic and antipyretic. In the present study n-hexane extract of V. sativa was evaluated for the antibacterial activity against pathogenic bacteria Staphylococcus aureus, Bacillus atrophaeus, Escherichia coli and S. epidermidis by disc diffusion method. Minimum inhibitory concentration of the n-hexane extract against all bacteria was determined by broth dilution method. Preliminary phytochemical analysis and HPLC analysis showed the presence of a number of bioactive constituents which exhibits antibacterial activity. So the current study showed that V. sativa possesses the significant antibacterial activity
Robust Surgical Tools Detection in Endoscopic Videos with Noisy Data
Over the past few years, surgical data science has attracted substantial
interest from the machine learning (ML) community. Various studies have
demonstrated the efficacy of emerging ML techniques in analysing surgical data,
particularly recordings of procedures, for digitizing clinical and non-clinical
functions like preoperative planning, context-aware decision-making, and
operating skill assessment. However, this field is still in its infancy and
lacks representative, well-annotated datasets for training robust models in
intermediate ML tasks. Also, existing datasets suffer from inaccurate labels,
hindering the development of reliable models. In this paper, we propose a
systematic methodology for developing robust models for surgical tool detection
using noisy data. Our methodology introduces two key innovations: (1) an
intelligent active learning strategy for minimal dataset identification and
label correction by human experts; and (2) an assembling strategy for a
student-teacher model-based self-training framework to achieve the robust
classification of 14 surgical tools in a semi-supervised fashion. Furthermore,
we employ weighted data loaders to handle difficult class labels and address
class imbalance issues. The proposed methodology achieves an average F1-score
of 85.88\% for the ensemble model-based self-training with class weights, and
80.88\% without class weights for noisy labels. Also, our proposed method
significantly outperforms existing approaches, which effectively demonstrates
its effectiveness
Socially-aware congestion control in ad-hoc networks: Current status and the way forward
Ad-hoc social networks (ASNETs) represent a special type of traditional ad-hoc network in whicha user’s social properties (such as the social connections and communications metadata as wellas application data) are leveraged for offering enhanced services in a distributed infrastructurelessenvironments. However, the wireless medium, due to limited bandwidth, can easily suffer from theproblem of congestion when social metadata and application data are exchanged among nodes—a problem that is compounded by the fact that some nodes may act selfishly and not share itsresources. While a number of congestion control schemes have been proposed for the traditional ad-hoc networks, there has been limited focus on incorporating social awareness into congestion controlschemes. We revisit the existing traditional ad-hoc congestion control and data distribution protocolsand motivate the need for embedding social awareness into these protocols to improve performance.We report that although some work is available in opportunistic network that uses socially-awaretechniques to control the congestion issue, this area is largely unexplored and warrants more researchattention. In this regards, we highlight the current research progress and identify multiple futuredirections of research
Multivessel Coronary Artery Segmentation and Stenosis Localisation using Ensemble Learning
Coronary angiography analysis is a common clinical task performed by
cardiologists to diagnose coronary artery disease (CAD) through an assessment
of atherosclerotic plaque's accumulation. This study introduces an end-to-end
machine learning solution developed as part of our solution for the MICCAI 2023
Automatic Region-based Coronary Artery Disease diagnostics using x-ray
angiography imagEs (ARCADE) challenge, which aims to benchmark solutions for
multivessel coronary artery segmentation and potential stenotic lesion
localisation from X-ray coronary angiograms. We adopted a robust baseline model
training strategy to progressively improve performance, comprising five
successive stages of binary class pretraining, multivessel segmentation,
fine-tuning using class frequency weighted dataloaders, fine-tuning using
F1-based curriculum learning strategy (F1-CLS), and finally multi-target
angiogram view classifier-based collective adaptation. Unlike many other
medical imaging procedures, this task exhibits a notable degree of
interobserver variability. %, making it particularly amenable to automated
analysis. Our ensemble model combines the outputs from six baseline models
using the weighted ensembling approach, which our analysis shows is found to
double the predictive accuracy of the proposed solution. The final prediction
was further refined, targeting the correction of misclassified blobs. Our
solution achieved a mean F1 score of for coronary artery
segmentation, and for stenosis localisation, positioning our team in
the 5th position on both leaderboards. This work demonstrates the potential of
automated tools to aid CAD diagnosis, guide interventions, and improve the
accuracy of stent injections in clinical settings.Comment: Submission report for ARCADE challenge hosted at MICCAI202
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