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