To summarize existing research in knowledge driven machine learning (KDML) in PHM and identify trends and gaps, we provide a hierarchical structure to organize the advancement of KDML research. Rather than the current literature search methodology to group KDML mainly based on embedding approaches (in...
ml pipelines deep learning expert foundations of deep learning in python foundations of deep learning in python 2 applied deep learning with pytorch detecting defects in steel sheets with computer-vision project text generation using language models with lstm project classifying sentiment of reviews using...
One of the current trends regarding the evolution of SMT processors is the type and amount of functional units deployed to adapt to the workload evolution. Among emerging workloads, machine learning (ML) workloads have increased in popularity in recent years in the scientific community [4,5]. M...
After that in 2010, RPA-based software robots were implemented in the recognition of handwritten and printed text recognition. In the year 2020 onwards the use of AI-based OCR evolved with the help of machine learning and deep learning techniques in this area. Fig. 3 Evolution of mixed text ...
learning model leveraging deep learning techniques such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), and Bi-directional LSTM (Bi-LSTM) to detect thyroid cancer mutations early. The model is trained on a dataset sourced from asia.ensembl.org and IntOGen.org, consisting of ...
Benign paroxysmal positional vertigo (BPPV) is a prevalent form of vertigo that necessitates a skilled physician to diagnose by observing the nystagmus and vertigo resulting from specific changes in the patient’s position. In this study, we aim to explo
of biomedical literature. Biomedical relation classification has seen significant improvement from the application of advanced machine learning techniques on free-form text from large collections of scholarly publications. Despite this improvement, the reliance on large chunks of raw text makes these ...
In contrast to traditional machine learning methods, our DL-based method takes sequence data in the form of windows directly as an input, reducing the need for hand-crafted feature extraction. A pre-requisite for this approach is that the sequence data must be encoded in a form that is reada...
We formulate physics-informed neural networks (PINNs) for full-field reconstruction of rotational flow beneath nonlinear periodic water waves using a small
We applied pre-trained word embedding to a deep learning-based NER model using large unlabeled colonoscopy reports. We compared variants of the long short-term memory (LSTM) and BioBERT [53] model to select the one with the best performance for colonoscopy reports, which was the bidirectional ...