Deep Learning Framework Research Articles

TransAnno-Net: A Deep Learning Framework for Accurate Cell Type Annotation of Mouse Lung Tissue Using Self-supervised Pretraining.

TransAnno-Net: A Deep Learning Framework for Accurate Cell Type Annotation of Mouse Lung Tissue Using Self-supervised Pretraining.

Multimodal depression recognition and analysis: Facial expression and body posture changes via emotional stimuli.

Multimodal depression recognition and analysis: Facial expression and body posture changes via emotional stimuli.

Design of a novel multiband terahertz metamaterial absorber using a deep learning framework

Design of a novel multiband terahertz metamaterial absorber using a deep learning framework

Forecasting the output performance of PEMFCs via a novel deep learning framework considering varying operating conditions and time scales

Forecasting the output performance of PEMFCs via a novel deep learning framework considering varying operating conditions and time scales

Advancing urban solar assessment: A deep learning and atmospheric modelling framework for quantifying PV yield and carbon reduction

Advancing urban solar assessment: A deep learning and atmospheric modelling framework for quantifying PV yield and carbon reduction

Multi-task learning for joint prediction of breast cancer histological indicators in dynamic contrast-enhanced magnetic resonance imaging.

Multi-task learning for joint prediction of breast cancer histological indicators in dynamic contrast-enhanced magnetic resonance imaging.

Semi-supervised deep learning framework based on modified pyramid scene parsing network for multi-label fine-grained classification and diagnosis of apple leaf diseases

Semi-supervised deep learning framework based on modified pyramid scene parsing network for multi-label fine-grained classification and diagnosis of apple leaf diseases

Labor-Efficient Pathological Auxiliary Diagnostic Model for Primary and Metastatic Tumor Tissue Detection in Pancreatic Ductal Adenocarcinoma.

Labor-Efficient Pathological Auxiliary Diagnostic Model for Primary and Metastatic Tumor Tissue Detection in Pancreatic Ductal Adenocarcinoma.

A multimodal deep learning framework for automated major adverse cardiovascular events prediction in patients with end-stage renal disease integrating clinical and cardiac MRI data

A multimodal deep learning framework for automated major adverse cardiovascular events prediction in patients with end-stage renal disease integrating clinical and cardiac MRI data

TasteNet: A novel deep learning approach for EEG-based basic taste perception recognition using CEEMDAN domain entropy features.

TasteNet: A novel deep learning approach for EEG-based basic taste perception recognition using CEEMDAN domain entropy features.

PursuitNet: A deep learning model for predicting competitive pursuit-like behavior in mice.

PursuitNet: A deep learning model for predicting competitive pursuit-like behavior in mice.

A physics-constrained deep learning framework enhanced with signal decomposition for accurate short-term photovoltaic power generation forecasting

A physics-constrained deep learning framework enhanced with signal decomposition for accurate short-term photovoltaic power generation forecasting

Cascade learning in multi-task encoder-decoder networks for concurrent bone segmentation and glenohumeral joint clinical assessment in shoulder CT scans.

Cascade learning in multi-task encoder-decoder networks for concurrent bone segmentation and glenohumeral joint clinical assessment in shoulder CT scans.

CGKN: A deep learning framework for modeling complex dynamical systems and efficient data assimilation

CGKN: A deep learning framework for modeling complex dynamical systems and efficient data assimilation

AI-driven deep learning framework for enhanced neurodegenerative disease diagnosis: A novel CNN with attention mechanisms and data balancing

AI-driven deep learning framework for enhanced neurodegenerative disease diagnosis: A novel CNN with attention mechanisms and data balancing

Univariate deep learning framework for short-term SST forecasting at high spatio-temporal scales

Abstract We present a univariate hybrid machine learning framework to predict daily high resolution Sea Surface Temperature (SST) near the Gulf of Kutch region at a resolution of ~ 5.5 km. The hybrid model integrates Intrinsic Mode Functions (IMF) derived from variational mode decomposition with a Long Short-Term Memory (LSTM) network to augment predictive skill. The predicted SST demonstrates impressive performance up to lead times of 7 days. Using statistical metrics like Kullback-Leibler divergence and mutual information, we show that the SST predicted from the hybrid model with a lead time of 3 days decisively outperforms the high-resolution GLORYS SST reanalysis let alone forecast skills of a data-assimilated dynamical model. Using conditional probability, we show that the SST forecasts from the hybrid model are quite reliable over the entire range of SST observations in the study domain. In contrast, the reliability of GLORYS falters in the lower range of SST observations. Also, the hybrid model excels in capturing fine-scale SST features, such as SST fronts, and detecting Marine Heatwaves (MHWs) up to 3 days in advance. These capabilities hold significant applications for Potential Fishing Zone identification and coral bleaching alerts. The hybrid model framework is also adept at forecasting location specific high frequency (3 hourly) SST with a lead time of a day.

GenRepAI: Utilizing Artificial Intelligence to Identify Repeats in Genomic Suffix Trees

Background: The human genome is densely populated with repetitive DNA sequences that play crucial roles in genomic functions and structures but are also implicated in over 40 human diseases. The computational challenge of identifying and characterizing these repeats is significant due to the complexity and size of the genome, which are overwhelming traditional algorithms. Methods: To address these challenges, we propose GenRepAI, a deep learning framework to navigate and analyze genomic suffix trees. GenRepAI employs supervised machine learning classifiers trained on labeled datasets of repeat annotations and unsupervised anomaly detection to identify novel repeat sequences. The models are trained using convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and vision transformers to classify and annotate repeats within the human genome. Results: GenRepAI is designed to comprehensively profile repeats that underlie various neurological diseases, allowing researchers to identify pathogenic expansions. The framework will integrate into existing genomic analysis pipelines, with the capability to screen patient genomes and highlight potential causal variants for further validation. Conclusion: GenRepAI is set to become a foundational tool in genomics, leveraging artificial intelligence to enhance the characterization of repetitive sequences. It promises significant advancements in the molecular diagnosis of repeat expansion disorders and contributes to a deeper understanding of genomic structure and function, with broad applications in personalized medicine.

Deep learning framework incorporating simultaneous optimization and training for concurrent estimation and prediction of battery state of health

Deep learning framework incorporating simultaneous optimization and training for concurrent estimation and prediction of battery state of health

SPARK and SAD: Leading-edge deep learning frameworks for robust and effective intrusion detection in SCADA systems

SPARK and SAD: Leading-edge deep learning frameworks for robust and effective intrusion detection in SCADA systems

A deep learning framework for reconstructing Breast Amide Proton Transfer weighted imaging sequences from sparse frequency offsets to dense frequency offsets.

A deep learning framework for reconstructing Breast Amide Proton Transfer weighted imaging sequences from sparse frequency offsets to dense frequency offsets.