AIMC Topic: Neural Networks, Computer

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Predicting the Seasonal Dynamics of Fruit Fly Anastrepha fraterculus Populations in Apple Orchards Using Artificial Neural Networks.

Neotropical entomology
The fruit fly Anastrepha fraterculus (Wiedemann) (Diptera: Tephritidae) is one of the main pests in apple orchards. Artificial neural networks (ANNs) are tools with good ability to predict phenomena such as the seasonal dynamics of pest populations. ...

Decoding binocular color differences via EEG signals: linking ERP dynamics to chromatic disparity in CIELAB space.

Experimental brain research
This study explores how differences in colors presented separately to each eye (binocular color differences) can be identified through EEG signals, a method of recording electrical activity from the brain. Four distinct levels of green-red color diff...

Utilizing CNNs for classification and uncertainty quantification for 15 families of European fly pollinators.

PloS one
Pollination is essential for maintaining biodiversity and ensuring food security, and in Europe it is primarily mediated by four insect orders (Coleoptera, Diptera, Hymenoptera, Lepidoptera). However, traditional monitoring methods are costly and tim...

Performance enhancement of a wind driven PMSG using an artificial neural network based nonlinear backstepping controller.

PloS one
With the increasing demand for wind energy in the electric power generation industry, optimizing robust and efficient control strategies is essential for a wind energy conversion system (WECS). In this regard, this study proposes a novel hybrid contr...

Optimization design of interior space based on the two-stage deep learning network and Single sample-driven method.

PloS one
This study addresses the limitations of traditional interior space design, particularly the timeliness and uniqueness of solutions, by proposing an optimized design framework that integrates a two-stage deep learning network with a single-sample-driv...

YOLOv11-WBD: A wavelet-bidirectional network with dilated perception for robust metal surface defect detection.

PloS one
In the field of quality control, metal surface defect detection is an important yet challenging task. Although YOLO models perform well in most object detection scenarios, metal surface images under operational conditions often exhibit coexisting hig...

Predicting the future risk and outcomes of severe heart failure and coronary artery disease with machine learning in the UK Biobank Cohort.

PloS one
BACKGROUND: In order to seriously impact the global burden of heart failure (HF) and coronary artery disease (CAD), identifying at-risk individuals as early as possible is vital. Risk calculator tools in wide clinical use today are informed by tradit...

Nondestructive detection of biogenic amines in muscle of Chinese mitten crab (Eriocheir sinensis): A basis for quality assessment using infrared spectroscopy and deep learning.

Food chemistry
Biogenic amines (BAs) are critical indicators of spoilage in aquatic products, but conventional detection methods are destructive and inefficient. This study proposes a nondestructive approach combining near-infrared (NIR) spectroscopy with deep lear...

Machine Learning-Aided Screening and Design Rule Discovery for LWIR-Transparent Optical Materials.

Journal of chemical information and modeling
The development of low-cost, high-performance materials with enhanced transparency in the long-wavelength infrared (LWIR) region (800-1250 cm/8-12.5 μm) is essential for advancing thermal imaging and sensing technologies. Traditional LWIR optics rely...

GSH and Halides Directed Controllable Synthesis of Chiral Gold Nanostars for Sensitive Diagnosis of Nephritis Types Using SERS and Transformer Neural Network.

Analytical chemistry
Surface-enhanced Raman spectroscopy (SERS) has shown potential for early disease diagnosis via urinary metabolomics, but still faces challenges in achieving stable hot spots and processing complex clinical data. In this study, the preparation of chir...