22 research outputs found
Transfer Learning for Real-time Deployment of a Screening Tool for Depression Detection Using Actigraphy
Automated depression screening and diagnosis is a highly relevant problem
today. There are a number of limitations of the traditional depression
detection methods, namely, high dependence on clinicians and biased
self-reporting. In recent years, research has suggested strong potential in
machine learning (ML) based methods that make use of the user's passive data
collected via wearable devices. However, ML is data hungry. Especially in the
healthcare domain primary data collection is challenging. In this work, we
present an approach based on transfer learning, from a model trained on a
secondary dataset, for the real time deployment of the depression screening
tool based on the actigraphy data of users. This approach enables machine
learning modelling even with limited primary data samples. A modified version
of leave one out cross validation approach performed on the primary set
resulted in mean accuracy of 0.96, where in each iteration one subject's data
from the primary set was set aside for testing.Comment: 5 pages, 4 figures, conference, to be published in UKSIM2
In Rain or Shine: Understanding and Overcoming Dataset Bias for Improving Robustness Against Weather Corruptions for Autonomous Vehicles
Several popular computer vision (CV) datasets, specifically employed for
Object Detection (OD) in autonomous driving tasks exhibit biases due to a range
of factors including weather and lighting conditions. These biases may impair a
model's generalizability, rendering it ineffective for OD in novel and unseen
datasets. Especially, in autonomous driving, it may prove extremely high risk
and unsafe for the vehicle and its surroundings. This work focuses on
understanding these datasets better by identifying such "good-weather" bias.
Methods to mitigate such bias which allows the OD models to perform better and
improve the robustness are also demonstrated. A simple yet effective OD
framework for studying bias mitigation is proposed. Using this framework, the
performance on popular datasets is analyzed and a significant difference in
model performance is observed. Additionally, a knowledge transfer technique and
a synthetic image corruption technique are proposed to mitigate the identified
bias. Finally, using the DAWN dataset, the findings are validated on the OD
task, demonstrating the effectiveness of our techniques in mitigating
real-world "good-weather" bias. The experiments show that the proposed
techniques outperform baseline methods by averaged fourfold improvement.Comment: Under revie
Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion
With the rapid industrialization and technological advancements, innovative
engineering technologies which are cost effective, faster and easier to
implement are essential. One such area of concern is the rising number of
accidents happening due to gas leaks at coal mines, chemical industries, home
appliances etc. In this paper we propose a novel approach to detect and
identify the gaseous emissions using the multimodal AI fusion techniques. Most
of the gases and their fumes are colorless, odorless, and tasteless, thereby
challenging our normal human senses. Sensing based on a single sensor may not
be accurate, and sensor fusion is essential for robust and reliable detection
in several real-world applications. We manually collected 6400 gas samples
(1600 samples per class for four classes) using two specific sensors: the
7-semiconductor gas sensors array, and a thermal camera. The early fusion
method of multimodal AI, is applied The network architecture consists of a
feature extraction module for individual modality, which is then fused using a
merged layer followed by a dense layer, which provides a single output for
identifying the gas. We obtained the testing accuracy of 96% (for fused model)
as opposed to individual model accuracies of 82% (based on Gas Sensor data
using LSTM) and 93% (based on thermal images data using CNN model). Results
demonstrate that the fusion of multiple sensors and modalities outperforms the
outcome of a single sensor.Comment: 14 Pages, 9 Figure
Novel Redundant Sensor Fault Detection and Accommodation Algorithm for an Air-breathing Combustion System and its Real-time Implementation
Failure of sensors used to provide a feedback signal in control system can cause serious deterioration in performance of system, and even instability may be observed. Based on knowledge of aircraft engine systems, the main cause of fault in such air-breathing combustion systems (ACS) with no rotating parts is due to the pressure sensors. Fast online detection of faults before the error grows very large and accommodation is critical to the success of the mission. However, at the same time, it is necessary to avoid false alarms. Hence, early detection of small magnitude faults with acceptable reliability is very challenging, especially in the presence of sensor noise, unknown engine-to-engine variation and deterioration and modeling uncertainty. This paper discusses the novel fault detection and accommodation (FDA) algorithm based on analytical redundancy based technique for ACS.Defence Science Journal, 2010, 60(1), pp.61-75, DOI:http://dx.doi.org/10.14429/dsj.60.10
Explainable Misinformation Detection Across Multiple Social Media Platforms
In this work, the integration of two machine learning approaches, namely
domain adaptation and explainable AI, is proposed to address these two issues
of generalized detection and explainability. Firstly the Domain Adversarial
Neural Network (DANN) develops a generalized misinformation detector across
multiple social media platforms DANN is employed to generate the classification
results for test domains with relevant but unseen data. The DANN-based model, a
traditional black-box model, cannot justify its outcome, i.e., the labels for
the target domain. Hence a Local Interpretable Model-Agnostic Explanations
(LIME) explainable AI model is applied to explain the outcome of the DANN mode.
To demonstrate these two approaches and their integration for effective
explainable generalized detection, COVID-19 misinformation is considered a case
study. We experimented with two datasets, namely CoAID and MiSoVac, and
compared results with and without DANN implementation. DANN significantly
improves the accuracy measure F1 classification score and increases the
accuracy and AUC performance. The results obtained show that the proposed
framework performs well in the case of domain shift and can learn
domain-invariant features while explaining the target labels with LIME
implementation enabling trustworthy information processing and extraction to
combat misinformation effectively.Comment: 28 pages,4 figure
Safe and Effective Autonomous Decision Making In Intelligent Robtic Systems
EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Multiscale Object Detection from Drone Imagery Using Ensemble Transfer Learning
Object detection in uncrewed aerial vehicle (UAV) images has been a longstanding challenge in the field of computer vision. Specifically, object detection in drone images is a complex task due to objects of various scales such as humans, buildings, water bodies, and hills. In this paper, we present an implementation of ensemble transfer learning to enhance the performance of the base models for multiscale object detection in drone imagery. Combined with a test-time augmentation pipeline, the algorithm combines different models and applies voting strategies to detect objects of various scales in UAV images. The data augmentation also presents a solution to the deficiency of drone image datasets. We experimented with two specific datasets in the open domain: the VisDrone dataset and the AU-AIR Dataset. Our approach is more practical and efficient due to the use of transfer learning and two-level voting strategy ensemble instead of training custom models on entire datasets. The experimentation shows significant improvement in the mAP for both VisDrone and AU-AIR datasets by employing the ensemble transfer learning method. Furthermore, the utilization of voting strategies further increases the 3reliability of the ensemble as the end-user can select and trace the effects of the mechanism for bounding box predictions
NikshayChain: A Blockchain-Based Proposal for Tuberculosis Data Management in India
A recent development in the Internet of Things (IoT) has accelerated the application of IoT-based solutions in healthcare. Next-Gen networks and IoT, supported by the development of technologies such as Artificial Intelligence (AI) and blockchain, have propelled the growth of e-health applications. However, there are some unique challenges in the widespread acceptance of IoT in healthcare. Safe storage, transfer, authorized access control, and the privacy and security aspects of patient data management are crucial barriers to the widespread adoption of IoT in healthcare. This makes it necessary to identify current issues in the various health data management systems to develop novel healthcare solutions. As a case study, this work considers a scheme launched by the Government of India for tuberculosis care called Nikshay Poshan Yojana (NPY). It is a web-based Direct Benefit Transfer scheme to provide a nutritional incentive of INR 500/- per month to all tuberculosis patients. The main objective of this work is to identify the current implementation challenges of the NPY scheme from patient and healthcare stakeholder perspectives and proposes a blockchain-based architecture called NikshayChain for sharing patient medical reports and bank details among several healthcare stakeholders within or across Indian cities. The proposed architecture accelerates healthcare stakeholder productivity by reducing workload and overall costs while ensuring effective data management. This architecture can significantly improve medical care, incentive transfer, and data verification, propelling the use of e-health applications