Two-stage automated diagnosis framework for urogenital schistosomiasis in microscopy images from low-resource settings

Purpose: Automated diagnosis of urogenital schistosomiasis using digital microscopy images of urine slides is an essential step toward the elimination of schistosomiasis as a disease of public health concern in Sub-Saharan African countries. We create a robust image dataset of urine samples obtained from field settings and develop a two-stage...

Toward automatic generation of control structures for process flow diagrams with large language models

Developing Piping and Instrumentation Diagrams (P&IDs) is a crucial step during process development. We propose a data-driven method for the prediction of control structures. Our methodology is inspired by end-to-end transformer-based human language translation models. We cast the control structure prediction as a translation task where...

Using Explainable Artificial Intelligence to Improve Transparency of Reinforcement Learning for Online Adaptive Flight Control: Breaking Open the Black Box

Deep Reinforcement Learning (DRL) shows great potential for flight control, due to its adaptability, fault-tolerance, and as it does not require an accurate system model. However, these techniques, like many machine learning applications, are considered black-box as their inner workings are hidden. This paper aims to break open the black box of...

Prognostics for Electromagnetic Relays using Deep Learning

Electromagnetic Relays (Electromagnetic Relay (EMR)s) are omnipresent in electrical systems, ranging from mass-produced consumer products to highly specialised, safety-critical industrial systems. Our detailed literature review focused on EMR reliability highlighting the methods used to estimate the State of Health or the Remaining Useful...

Coevolution of machine learning and process-based modelling to revolutionize Earth and environmental sciences: A perspective

Machine learning (ML) applications in Earth and environmental sciences (EES) have gained incredible momentum in recent years. However, these ML applications have largely evolved in ‘isolation’ from the mechanistic, process-based modelling (PBM) paradigms, which have historically been the cornerstone of scientific discovery and policy support....

Classifying Human Pilot Skill Level Using Deep Artificial Neural Networks

To fully optimize the synergy between human operators and machines in modern day’s highly automated vehicle control tasks, a real-time quantitative feedback of skill level is required. Direct feedback of skill level could be used to enable scalable levels of autonomy of the controlled system, or to provide a warning when sudden skill level...

Artificial intelligence in breast-specific gamma imaging: Exploring the possibilities of automation of breast cancer detection and classification

Introduction Breast cancer is the most frequently diagnosed type of cancer in women in 2020. Treatment and prognosis of breast cancer is highly dependent on early and accurate diagnosis. In recent years, many studies have evaluated the use of artificial intelligence for the detection of breast cancer in mammographic images. Another imaging...

Image Search Engine for Digital History: A deep learning approach

This research investigates and describes an image search engine for digital history using deep learning technologies. It is part of the Engineering Historical Memory research, contributing to a multilingual and transcultural approach to decode-encode the treasure of human experience and transmit it to the next generation of world citizens. The...

Evaluating the performance of the LIME and Grad-CAM explanation methods on a LEGO multi-label image classification task

In this paper, we run two methods of explanation, namely LIME and Grad-CAM, on a convolutional neural network trained to label images with the LEGO bricks that are visible in them. We evaluate them on two criteria, the improvement of the network's core performance and the trust they are able to generate for users of the system. We nd that in...

Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning

Modeling dynamic geophysical phenomena is at the core of Earth and environmental studies. The geoscientific community relying mainly on physical representations may want to consider much deeper adoption of artificial intelligence (AI) instruments in the context of AI's global success and emergence of big Earth data. A new perspective of using...

Deep Reinforcement Learning for Goal-directed Visual Navigation

Safe navigation in a cluttered environment is a key capability for the autonomous operation of Micro Aerial Vehicles (MAVs). This work explores a (deep) Reinforcement Learning (RL) based approach for monocular vision based obstacle avoidance and goal directed navigation for MAVs in cluttered environments. We investigated this problem in the...