AIMC Topic: Deep Learning

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Deep Learning-Based Prediction of Enzyme Optimal pH and Design of Point Mutations to Improve Acid Resistance.

ACS synthetic biology
An accurate deep learning predictor of enzyme optimal pH is essential to quantitatively describe how pH influences the enzyme catalytic activity. CatOpt, developed in this study, outperformed existing predictors of enzyme optimal pH (RMSE = 0.833 and...

Deep Learning Model for Fast Determination of Equilibrium Dissociation Constants Using Biolayer Interferometry Sensorgrams.

Analytical chemistry
This paper explores the fusion model of deep learning with Bio-Layer Interferometry (BLI), a key detection method for biomolecular interactions. We constructed a convolutional neural network model capable of quickly predicting the value of the equili...

Deep learning reveals how cells pull, buckle, and navigate fibrous environments.

Proceedings of the National Academy of Sciences of the United States of America
Cells in tissues navigate fibrous environments fundamentally differently than they do on flat substrates, but the establishment of cell forces in physiological fibrous settings remains poorly understood. Although factors such as the stiffness of the ...

Enhancing AI-based diabetic retinopathy diagnosis through universal cross-camera image adaptation.

BMJ open ophthalmology
OBJECTIVE: To evaluate the effectiveness of a deep learning-based style adaptation strategy in improving the diagnostic accuracy and cross-camera generalisability of artificial intelligence (AI) for detecting diabetic retinopathy (DR).

OCRNet a robust deep learning framework for alphanumeric character recognition to assist the visually impaired.

Scientific reports
Optical Character Recognition (OCR) is a part of transformative Artificial Intelligence (AI) technology which translates printed or handwritten texts into digital, machine-readable form. These OCR systems act as an assistive tool for visually impaire...

An intelligent fusion-based transfer learning model with artificial protozoa optimiser for enhancing gesture recognition to aid visually impaired people.

Scientific reports
Generally, the interaction of gestures presents a set of benefits to persons with disabilities, from improving motor, social, and cognitive skills to delivering a secure and controlled atmosphere for engaging in real-world scenarios. Automatic detect...

Hybrid deep learning framework for cardiovascular disease diagnosis and prognosis using GAN, LSTM, GRU, VARMA, and deep DynaQ network.

Scientific reports
Cardiovascular diseases (CVDs) are a major cause of morbidity and mortality worldwide. Effective CVD treatment requires early and accurate diagnosis. CVD diagnosis and prognosis can be done using medical image analysis. In this paper, we propose a no...

Using convolutional neural networks with late fusion to predict heart disease.

Scientific reports
Cardiovascular diseases are responsible for one-third of all deaths that occur globally. Machine learning and data mining have made it easier and quicker for physicians to diagnose or identify patients. This article presents a novel late fusion metho...

Textual emotion recognition to improve real-time communication of disabled people in sustainable environments using an ensemble deep learning approach.

Scientific reports
Social media platforms are prevalently used to express and share opinions on a wide range of topics, which has amplified interest in textual emotion detection. However, accurately detecting emotions in individuals, especially those with communication...

Enhancing cardiac disease prediction with explainable bidirectional LSTM.

Scientific reports
Cardiovascular disorders (heart diseases) are the most prevalent cause of death on a global scale. So early detection and classification increase the likelihood of survival. In the context of machine learning techniques, there is always a need for an...