Genre determination is achieved through a bi-LSTM attention model. Studies [41, 42] presents a probabilistic method, considering the importance of each background scene within different video categories, and recommends measuring shot length for varying genres. Yadav and Vishwakarma [43] developed a ...
Subsequently, a global context vector can be computed based on the code weights, and all medical code representations undergo weighted aggregation to form the final visit representation in the form of an embedding matrix. During model training, we also append the lab values to the visit ...
To solve aforementioned issues, trustworthy AI has emerged as a thoroughly important scientific research direction [9]. Over the past decades, numerous investigators have concentrated their efforts on optimizing model structures or enhancing learning algorithms in order to enhance the credibility of AI [...
Automating the monitoring of COVID-19 patients using ICTs can bring benefits such as efficiency, real-time monitoring, remote access, scalability, timely intervention, data analysis, resource optimization, and contribution to research and surveillance efforts [1,2,3,4,5,6,9,10]. These motivations...
A smart decision support system to diagnose arrhythymia using ensembled ConvNet and ConvNet-LSTM model. Exp Syst Appl. 2023;213:118933. Article Google Scholar Nie X, Wang L, Ding H, Xu M. Strawberry verticillium wilt detection network based on multi-task learning and attention. IEEE ...
However, the current limitations include: insufficient attention to the incompleteness of medical data for constructing BA; Lack of machine learning-based BA (ML-BA) on the Chinese population; Neglect of the influence of model overfitting degree on the stability of the association results. Methods ...
To avoid the contingency of negative sample selection, we repeated the above processes 5 times to form 5 datasets. We trained and tested the model with each dataset separately that all the experimental results in this paper were the average value of 5 experiments. Statistical analysis We analyzed...
Similarly, the effectiveness of a convolutional neural network (CNN), a CNN with long short-term memory (CNN-LSTM), and a bidirectional LSTM (BiLSTM)-CNN was explored in terms of automatically detecting hateful content on social media using the Arabic Hate Speech (ArHS) dataset, which consists...
In Recurrent Neural Networks or LSTMs, the importance of the past elements can vanish with distance. Using transformers, instead of sequentially applying the same network, the idea is to connect the current token to all the elements, preceding and posterior, where each element has a positional ...
Cui2vec[23] is a deep learning model that represents medical concepts in a vector form. It works by mapping all the medical concepts into a common concept unique identifier space using the thesaurus from the Unified Medical Language System (UMLS). Differing with med2vec, cui2vec embeds medica...