AIMC Topic: Deep Learning

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Optimization of house price evaluation model based on multi-source geographic big data and deep neural network.

PloS one
The real estate market requires effective and precise house price prediction, as conventional models often face difficulties in generalization, computational efficiency, and interpretability. The research problem is addressed by introducing the House...

Football sports automatic judgment model based on improved YOLOv7 and RNN.

PloS one
The extraction, classification, and judgment of sports video scenes can improve work efficiency and accuracy. To understand sports videos in dynamic scenes, this study applies deep learning technology, firstly introducing clustering algorithm and att...

A knowledge-data fusion framework accelerates deep reinforcement learning for real-time control of urban drainage systems.

Water research
Deep reinforcement learning (DRL) has been applied to real-time control (RTC) of urban drainage systems (UDSs), with impressive performance and efficiency in reducing urban flooding and combined sewer overflows (CSO). However, for complex UDSs, learn...

Efficient Generation of Protein and Protein-Protein Complex Dynamics via SE(3)-Parameterized Diffusion Models.

Journal of chemical information and modeling
Protein and protein-protein complex conformations play a critical role in biological functions, while exploring these via traditional molecular dynamics (MD) simulation is computationally expensive. Enhanced sampling methods offer improvements but re...

Deep Learning-Enabled Real-Time Single-Shot Refocusing of Microwell Array for Digital Melting Curve Analysis.

Analytical chemistry
Digital melting curve analysis (dMCA) represents a breakthrough technology for multiplexed nucleic acid detection within limited fluorescence channels, utilizing thermal melting imaging postdigital PCR. However, conventional dMCA suffers from accurac...

Optimizing myocardial infarction detection: a hybrid CNN-GRU deep learning approach.

BMC medical informatics and decision making
BACKGROUND: Myocardial infarction (MI) is a life-threatening condition caused by sudden interruption of blood supply to the heart. Electrocardiogram (ECG) is the primary tool for MI diagnosis, but interpretation challenges exist. This study aimed to ...

Training convolutional neural networks with the Forward-Forward Algorithm.

Scientific reports
Recent successes in image analysis with deep neural networks are achieved almost exclusively with Convolutional Neural Networks (CNNs), typically trained using the backpropagation (BP) algorithm. In a 2022 preprint, Geoffrey Hinton proposed the Forwa...

Empowering people with intellectual disabilities using integrated deep learning architecture driven enhanced text-based emotion classification.

Scientific reports
Emotion recognition is an important research field including psychology, healthcare, and human-computer interaction (HCI). However, conventional techniques mainly rely on textual analysis and facial expressions, and they also have potential flaws, ma...

An interpretable crop leaf disease and pest identification model based on prototypical part network and contrastive learning.

Scientific reports
The disease and pest recognition algorithms based on computer vision can automatically process and analyze a large amount of disease and pest images, thereby achieving rapid and accurate identification of disease and pest categories on crop leaves. C...

Advanced phenotyping features utilizing deep learning techniques for automated analysis of stomatal guard cell orientation.

Scientific reports
Stomata are vital for controlling gas exchange and water vapor release, which significantly affect photosynthesis and transpiration. Characterizing stomatal traits such as size, density, and distribution is essential for adaptation to the environment...