AIMC Topic: Neural Networks, Computer

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A prediction approach to COVID-19 time series with LSTM integrated attention mechanism and transfer learning.

BMC medical research methodology
BACKGROUND: The prediction of coronavirus disease in 2019 (COVID-19) in broader regions has been widely researched, but for specific areas such as urban areas the predictive models were rarely studied. It may be inaccurate to apply predictive models ...

Predictive performance of count regression models versus machine learning techniques: A comparative analysis using an automobile insurance claims frequency dataset.

PloS one
Accurate forecasting of claim frequency in automobile insurance is essential for insurers to assess risks effectively and establish appropriate pricing policies. Traditional methods typically rely on a Poisson distribution for modeling claim counts; ...

Biologically plausible gated recurrent neural networks for working memory and learning-to-learn.

PloS one
The acquisition of knowledge and skills does not occur in isolation but learning experiences amalgamate within and across domains. The process through which learning can accelerate over time is referred to as learning-to-learn or meta-learning. While...

Development and evaluation of a deep learning segmentation model for assessing non-surgical endodontic treatment outcomes on periapical radiographs: A retrospective study.

PloS one
This study aimed to evaluate the performance of a deep learning-based segmentation model for predicting outcomes of non-surgical endodontic treatment. Preoperative and 3-year postoperative periapical radiographic images of each tooth from routine roo...

Different pixel sizes of topographic data for prediction of soil salinity.

PloS one
Modeling techniques can be powerful predictors of soil salinity across various scales, ranging from local landscapes to global territories. This study was aimed to examine the accuracy of soil salinity prediction model integrating ANNs (artificial ne...

Computing nasalance with MFCCs and Convolutional Neural Networks.

PloS one
Nasalance is a valuable clinical biomarker for hypernasality. It is computed as the ratio of acoustic energy emitted through the nose to the total energy emitted through the mouth and nose (eNasalance). A new approach is proposed to compute nasalance...

Enhancing bowel sound recognition with self-attention and self-supervised pre-training.

PloS one
Bowel sounds, a reflection of the gastrointestinal tract's peristalsis, are essential for diagnosing and monitoring gastrointestinal conditions. However, the absence of an effective, non-invasive method for assessing digestion through auscultation ha...

DICCR: Double-gated intervention and confounder causal reasoning for vision-language navigation.

Neural networks : the official journal of the International Neural Network Society
Vision-language navigation (VLN) is a challenging task that requires agents to capture the correlation between different modalities from redundant information according to instructions, and then make sequential decisions on visual scenes and text ins...

Synth-CLIP: Synthetic data make CLIP generalize better in data-limited scenarios.

Neural networks : the official journal of the International Neural Network Society
Prompt learning is a powerful technique that enables the transfer of Vision-Language Models (VLMs) like CLIP to downstream tasks. However, when the prompt-based methods are fine-tuned solely on base classes, they often struggle to generalize to novel...

MPIC: Exploring alternative approach to standard convolution in deep neural networks.

Neural networks : the official journal of the International Neural Network Society
In the rapidly evolving field of deep learning, Convolutional Neural Networks (CNNs) retain their unique strengths and applicability in processing grid-structured data such as images, despite the surge of Transformer architectures. This paper explore...