AIMC Topic: Neural Networks, Computer

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Feature selection for classification based on machine learning algorithms for prostate cancer.

Biomedical physics & engineering express
Microarray technology has transformed the biotechnological research to next level in the recent years. It provides the expression levels of various genes involved in a particular disease. Prostate cancer disease turned into life threatening cancer. T...

Modeling land use and land cover dynamics of Bale Mountains National Park using Google Earth Engine and cellular automata-artificial neural network (CA-ANN) model.

PloS one
This research aimed to assess the observed land use and land cover (LULC) changes of Bale Mountains National Park (BMNP) from 1993 to 2023 and its future projections for the years (2033 and 2053). The study utilized multi-date Landsat imagery from 19...

Exploring deep learning in phage discovery and characterization.

Virology
Bacteriophages, or bacterial viruses, play diverse ecological roles by shaping bacterial populations and also hold significant biotechnological and medical potential, including the treatment of infections caused by multidrug-resistant bacteria. The d...

Machine learning and clinical EEG data for multiple sclerosis: A systematic review.

Artificial intelligence in medicine
Multiple Sclerosis (MS) is a chronic neuroinflammatory disease of the Central Nervous System (CNS) in which the body's immune system attacks and destroys the myelin sheath that protects nerve fibers, leading to a wide range of debilitating symptoms a...

Learn to explain transformer via interpretation path by reinforcement learning.

Neural networks : the official journal of the International Neural Network Society
In recent years, the Transformer model has become a key part of many AI systems, making it important to understand how it works. The large parameter size and complex structure of the Transformer make interpretation more difficult and less efficient. ...

Interpretable unsupervised neural network structure for data clustering via differentiable reconstruction of ONMF and sparse autoencoder.

Neural networks : the official journal of the International Neural Network Society
Neural networks, while powerful, often face significant challenges in terms of interpretability, particularly in clustering tasks. Traditional methods typically rely on post-hoc explanations or supervised learning, which limit their ability to provid...

Testing on continuous production of mefenamic acids-Design of experiment through simulation and process optimisation.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
In the pharmaceutical manufacturing industry, continuous production methods have been recognised as providing several benefits compared to traditional batch production. These benefits include increased flexibility, higher product output, enhanced qua...

Contrastive learning and mixture of experts enables precise vector embeddings in biological databases.

Scientific reports
The advancement of transformer neural networks has significantly enhanced the performance of sentence similarity models. However, these models often struggle with highly discriminative tasks and generate sub-optimal representations of complex documen...

A swin transformer and CNN fusion framework for accurate Parkinson disease classification in MRI.

Scientific reports
Parkinson's disease ranks as the second most prevalent neurological disorder after Alzheimer's disease. Convolutional neural networks (CNNs) have been extensively employed in Parkinson's disease (PD) detection using MR images. However, CNN models gen...

A neural network model for the evolution of reconstructive social learning.

Scientific reports
Learning from others is an important adaptation. However, the evolution of social learning and its role in the spread of socially transmitted information are not well understood. Few models of social learning account for the fact that socially transm...