AIMC Topic: Deep Learning

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An intelligent deep augmented model for detection of banana leaves diseases.

Microscopy research and technique
One of the most popular fruits worldwide is the banana. Accurate identification and categorization of banana diseases is essential for maintaining global fruits security and stakeholder profitability. Four different types of banana leaves exist Healt...

The Artificial Neural Twin - Process optimization and continual learning in distributed process chains.

Neural networks : the official journal of the International Neural Network Society
Industrial process optimization and control is crucial to increase economic and ecologic efficiency. However, data sovereignty, differing goals, or the required expert knowledge for implementation impede holistic implementation. Further, the increasi...

Elucidating and forecasting the organochlorine pesticides in suspended particulate matter by a two-stage decomposition based interpretable deep learning approach.

Water research
Accurately predicting the concentration of organochlorine pesticides (OCPs) presents a challenge due to their complex sources and environmental behaviors. In this study, we introduced a novel and advanced model that combined the power of three distin...

Efficient EEG Feature Learning Model Combining Random Convolutional Kernel with Wavelet Scattering for Seizure Detection.

International journal of neural systems
Automatic seizure detection has significant value in epilepsy diagnosis and treatment. Although a variety of deep learning models have been proposed to automatically learn electroencephalography (EEG) features for seizure detection, the generalizatio...

Metadata information and fundus image fusion neural network for hyperuricemia classification in diabetes.

Computer methods and programs in biomedicine
OBJECTIVE: In diabetes mellitus patients, hyperuricemia may lead to the development of diabetic complications, including macrovascular and microvascular dysfunction. However, the level of blood uric acid in diabetic patients is obtained by sampling p...

Artificial intelligence-based model for the recurrence of hepatocellular carcinoma after liver transplantation.

Surgery
BACKGROUND: Artificial intelligence-based models might improve patient selection for liver transplantation in hepatocellular carcinoma. The objective of the current study was to develop artificial intelligence-based deep learning models and determine...

Deep Learning With Ultrasound Images Enhance the Diagnosis of Nonalcoholic Fatty Liver.

Ultrasound in medicine & biology
OBJECTIVE: This research aimed to improve diagnosis of non-alcoholic fatty liver disease (NAFLD) by deep learning with ultrasound Images and reduce the impact of the professional competence and personal bias of the diagnostician.

Deep reinforcement learning control of combined chemotherapy and anti-angiogenic drug delivery for cancerous tumor treatment.

Computers in biology and medicine
By virtue of the chronic and dangerous nature of cancer, researchers have explored various approaches to managing the abnormal cell growth associated with this disease using novel treatment methods. This study introduces a control system based on nor...

Research on low-power driving fatigue monitoring method based on spiking neural network.

Experimental brain research
Fatigue driving is one of the leading causes of traffic accidents, and the rapid and accurate detection of driver fatigue is of paramount importance for enhancing road safety. However, the application of deep learning models in fatigue driving detect...

SmartCADD: AI-QM Empowered Drug Discovery Platform with Explainability.

Journal of chemical information and modeling
Artificial intelligence (AI) has emerged as a pivotal force in enhancing productivity across various sectors, with its impact being profoundly felt within the pharmaceutical and biotechnology domains. Despite AI's rapid adoption, its integration into...