AIMC Topic: Neural Networks, Computer

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Recent Advances in Food Drying Modeling: Empirical to Multiscale Physics-Informed Neural Networks.

Comprehensive reviews in food science and food safety
Food insecurity is a major global challenge. Food preservation, particularly through drying, presents a promising solution to enhance food security and minimize waste. Fruits and vegetables contain 80%-90% water, and much of this is removed during dr...

NNKcat: deep neural network to predict catalytic constants (Kcat) by integrating protein sequence and substrate structure with enhanced data imbalance handling.

Briefings in bioinformatics
Catalytic constant (Kcat) is to describe the efficiency of catalyzing reactions. The Kcat value of an enzyme-substrate pair indicates the rate an enzyme converts saturated substrates into product during the catalytic process. However, it is challengi...

MMsurv: a multimodal multi-instance multi-cancer survival prediction model integrating pathological images, clinical information, and sequencing data.

Briefings in bioinformatics
Accurate prediction of patient survival rates in cancer treatment is essential for effective therapeutic planning. Unfortunately, current models often underutilize the extensive multimodal data available, affecting confidence in predictions. This stu...

Decoupled GNNs based on multi-view contrastive learning for scRNA-seq data clustering.

Briefings in bioinformatics
Clustering is pivotal in deciphering cellular heterogeneity in single-cell RNA sequencing (scRNA-seq) data. However, it suffers from several challenges in handling the high dimensionality and complexity of scRNA-seq data. Especially when employing gr...

Prediction of total phosphorus removal in hybrid constructed wetlands: a machine learning approach for rice mill wastewater treatment.

Water environment research : a research publication of the Water Environment Federation
Efficient prediction of pollutant concentrations in constructed wetlands is critical for optimizing treatment performance, yet existing methodologies often fail to account for the influence of meteorological conditions and flow rate variations in rea...

The effects of the post-delay epochs on working memory error reduction.

PLoS computational biology
Accurate retrieval of the maintained information is crucial for working memory. This process primarily occurs during post-delay epochs, when subjects receive cues and generate responses. However, the computational and neural mechanisms that underlie ...

Early detection of Alzheimer's disease using deep learning methods.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Alzheimer's disease (AD), a leading cause of dementia, requires early detection for effective intervention. This study employs AI to analyze multimodal datasets, including clinical, biomarker, and neuroimaging data, using hybrid deep le...

Fusion-Brain-Net: A Novel Deep Fusion Model for Brain Tumor Classification.

Brain and behavior
PROBLEM: Brain tumors are among the most prevalent and lethal diseases. Early diagnosis and precise treatment are crucial. However, the manual classification of brain tumors is a laborious and complex task.

The long journey of artificial intelligence in medicine: an overview.

Clinical and experimental rheumatology
Artificial intelligence (AI) has its roots in the history of philosophy and of applied mathematics of the 17th, 18th and 19th centuries. Throughout the 20th century, significant advancements in mathematics and computer science laid the groundwork for...

GKNnet: an relational graph convolutional network-based method with knowledge-augmented activation layer for microbial structural variation detection.

Briefings in bioinformatics
Structural variants (SVs) in microbial genomes play a critical role in phenotypic changes, environmental adaptation, and species evolution, with deletion variations particularly closely linked to phenotypic traits. Therefore, accurate and comprehensi...