AIMC Topic: Algorithms

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Application of Multiple Deep Learning Architectures for Emotion Classification Based on Facial Expressions.

Sensors (Basel, Switzerland)
Facial expression recognition (FER) is essential for discerning human emotions and is applied extensively in big data analytics, healthcare, security, and user experience enhancement. This study presents a comprehensive evaluation of ten state-of-the...

Gesture Recognition Achieved by Utilizing LoRa Signals and Deep Learning.

Sensors (Basel, Switzerland)
This study proposes a novel gesture recognition system based on LoRa technology, integrating advanced signal preprocessing, adaptive segmentation algorithms, and an improved SS-ResNet50 deep learning model. Through the combination of residual learnin...

The EU project Real4Reg: unlocking real-world data with AI.

Health research policy and systems
BACKGROUND: The use of real-world data is established in post-authorization regulatory processes such as pharmacovigilance of drugs and medical devices, but is still frequently challenged in the pre-authorization phase of medicinal products. In addit...

Auxiliary meta-learning strategy for cancer recognition: leveraging external data and optimized feature mapping.

BMC cancer
As reported by the International Agency for Research on Cancer (IARC), the global incidence of cancer reached nearly 20 million new cases in recent years, with cancer-related fatalities amounting to around 9.7 million. This underscores the profound i...

A hybrid deep learning model approach for automated detection and classification of cassava leaf diseases.

Scientific reports
Detecting cassava leaf disease is challenging because it is hard to identify diseases accurately through visual inspection. Even trained agricultural experts may struggle to diagnose the disease correctly which leads to potential misjudgements. Tradi...

Deeply supervised two stage generative adversarial network for stain normalization.

Scientific reports
The color variations present in histopathological images pose a significant challenge to computational pathology and, consequently, negatively affect the performance of certain pathological image analysis methods, especially those based on deep learn...

Using machine learning to predict deterioration of symptoms in COPD patients within a telemonitoring program.

Scientific reports
COPD exacerbations have a profound clinical impact on patients. Accurately predicting these events could help healthcare professionals take proactive measures to mitigate their impact. For over a decade, telEPOC, a telehealthcare program, has collect...

Detection of human activities using multi-layer convolutional neural network.

Scientific reports
Human Activity Recognition (HAR) plays a critical role in fields such as healthcare, sports, and human-computer interaction. However, achieving high accuracy and robustness remains a challenge, particularly when dealing with noisy sensor data from ac...

Enhancing e-waste management: a novel light gradient AdaBoost support vector classification approach.

Environmental monitoring and assessment
The global consequences of electronic waste significantly affect the environment and human health. Accurate classification is essential for effective recycling and management to mitigate serious environmental harm caused by improper disposal. However...

Machine Learning-Based Mortality Prediction for Acute Gastrointestinal Bleeding Patients Admitted to Intensive Care Unit.

Current medical science
OBJECTIVE: The study aimed to develop machine learning (ML) models to predict the mortality of patients with acute gastrointestinal bleeding (AGIB) in the intensive care unit (ICU) and compared their prognostic performance with that of Acute Physiolo...