AIMC Topic: Algorithms

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Blind Recognition of Forward Error Correction Codes Based on Recurrent Neural Network.

Sensors (Basel, Switzerland)
Forward error correction coding is the most common way of channel coding and the key point of error correction coding. Therefore, the recognition of which coding type is an important issue in non-cooperative communication. At present, the recognition...

Modeling and optimizing callus growth and development in Cannabis sativa using random forest and support vector machine in combination with a genetic algorithm.

Applied microbiology and biotechnology
Plant callus is generally considered to be a mass of undifferentiated cells and can be used for secondary metabolite production, physiological studies, and plant transformation/regeneration. However, there are several types of callus with different m...

Classification of Mental Stress Using CNN-LSTM Algorithms with Electrocardiogram Signals.

Journal of healthcare engineering
The mental stress faced by many people in modern society is a factor that causes various chronic diseases, such as depression, cancer, and cardiovascular disease, according to stress accumulation. Therefore, it is very important to regularly manage a...

An Overview of Deep Learning Techniques on Chest X-Ray and CT Scan Identification of COVID-19.

Computational and mathematical methods in medicine
Pneumonia is an infamous life-threatening lung bacterial or viral infection. The latest viral infection endangering the lives of many people worldwide is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. This pa...

Deep learning-based prediction of future growth potential of technologies.

PloS one
Research papers are a repository of information on the various elements that make up science and technology R&D activities. Generating knowledge maps based on research papers enables identification of specific areas of scientific and technical resear...

How to replace a physiotherapist: artificial intelligence and the redistribution of expertise.

Physiotherapy theory and practice
The convergence of large datasets, increased computational power, and enhanced algorithm design has led to the increased success of machine learning (ML) and artificial intelligence (AI) across a wide variety of healthcare professions but which, so f...

Depression Diagnosis Modeling With Advanced Computational Methods: Frequency-Domain eMVAR and Deep Learning.

Clinical EEG and neuroscience
Electroencephalogram (EEG)-based automated depression diagnosis systems have been suggested for early and accurate detection of mood disorders. EEG signals are highly irregular, nonlinear, and nonstationary in nature and are traditionally studied fro...

Performance evaluation of nanotubular halloysites from weathered pegmatites in removing heavy metals from water through novel artificial intelligence-based models and human-based optimization algorithm.

Chemosphere
The efforts of this study aimed to evaluate the feasibility of the nanotubular halloysites in weathered pegmatites (NaHWP) for removing heavy metals (i.e., Cd, Pb) from water. Furthermore, two novel intelligent models, such as teaching-learning-based...

Modeling of aquifer vulnerability index using deep learning neural networks coupling with optimization algorithms.

Environmental science and pollution research international
A reliable assessment of the aquifer contamination vulnerability is essential for the conservation and management of groundwater resources. In this study, a recent technique in artificial intelligence modeling and computational optimization algorithm...

SSMD: Semi-Supervised medical image detection with adaptive consistency and heterogeneous perturbation.

Medical image analysis
Semi-Supervised classification and segmentation methods have been widely investigated in medical image analysis. Both approaches can improve the performance of fully-supervised methods with additional unlabeled data. However, as a fundamental task, s...