AIMC Topic: Deep Learning

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Exploration of the Application Effect of the Darongtong Course Model Based on Deep Learning Enhancement in Nursing.

Contrast media & molecular imaging
In order to explore the application effect of the Darongtong course model based on deep learning enhancement in nursing, a total of 500 students in the school are investigated. The students in the contrast set are given the traditional teaching mode,...

Clinical and Biological Significances of a Ferroptosis-Related Gene Signature in Lung Cancer Based on Deep Learning.

Computational and mathematical methods in medicine
Acyl-CoA synthetase long-chain family member 4 (ACSL4) has been linked to the occurrence of tumors and is implicated in the ferroptosis process. Deep learning has been applied to many areas in health care, including imaging diagnosis, digital patholo...

Research on Named Entity Recognition Based on Multi-Task Learning and Biaffine Mechanism.

Computational intelligence and neuroscience
Commonly used nested entity recognition methods are span-based entity recognition methods, which focus on learning the head and tail representations of entities. This method lacks obvious boundary supervision, which leads to the failure of the correc...

Deep Learning-Based Correlation Analysis between the Evaluation Score of English Teaching Quality and the Knowledge Points.

Computational intelligence and neuroscience
As one of the three main courses from primary school to senior high school, improving the quality of English teaching in and out of class has become the top priority of colleges and universities. English knowledge points are complex, and domestic sch...

Regional Language Speech Recognition from Bone-Conducted Speech Signals through Different Deep Learning Architectures.

Computational intelligence and neuroscience
Bone-conducted microphone (BCM) senses vibrations from bones in the skull during speech to electrical audio signal. When transmitting speech signals, bone-conduction microphones (BCMs) capture speech signals based on the vibrations of the speaker's s...

An Assessment of Lexical, Network, and Content-Based Features for Detecting Malicious URLs Using Machine Learning and Deep Learning Models.

Computational intelligence and neuroscience
The World Wide Web services are essential in our daily lives and are available to communities through Uniform Resource Locator (URL). Attackers utilize such means of communication and create malicious URLs to conduct fraudulent activities and deceive...

Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique.

Journal of digital imaging
Since radiology reports needed for clinical practice and research are written and stored in free-text narrations, extraction of relative information for further analysis is difficult. In these circumstances, natural language processing (NLP) techniqu...

Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study.

European radiology
OBJECTIVES: To develop and validate a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme (GBM), and to investigate the biological pathways and genetic alterations underlying the DLIS.

Comparisons of deep learning and machine learning while using text mining methods to identify suicide attempts of patients with mood disorders.

Journal of affective disorders
BACKGROUND: Suicide attempt is one of the most severe consequences for patients with mood disorders. This study aimed to perform deep learning and machine learning while using text mining to identify patients with suicide attempts and to compare thei...

Domain generalization in deep learning based mass detection in mammography: A large-scale multi-center study.

Artificial intelligence in medicine
Computer-aided detection systems based on deep learning have shown great potential in breast cancer detection. However, the lack of domain generalization of artificial neural networks is an important obstacle to their deployment in changing clinical ...