AIMC Topic: Neural Networks, Computer

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Operant Conditioning Neuromorphic Circuit With Addictiveness and Time Memory for Automatic Learning.

IEEE transactions on biomedical circuits and systems
Most operant conditioning circuits predominantly focus on simple feedback process, few studies consider the intricacies of feedback outcomes and the uncertainty of feedback time. This paper proposes a neuromorphic circuit based on operant conditionin...

SSCI: Self-Supervised Deep Learning Improves Network Structure for Cancer Driver Gene Identification.

International journal of molecular sciences
The pathogenesis of cancer is complex, involving abnormalities in some genes in organisms. Accurately identifying cancer genes is crucial for the early detection of cancer and personalized treatment, among other applications. Recent studies have used...

Deep residual 2D convolutional neural network for cardiovascular disease classification.

Scientific reports
Cardiovascular disease (CVD) continues to be a major global health concern, underscoring the need for advancements in medical care. The use of electrocardiograms (ECGs) is crucial for diagnosing cardiac conditions. However, the reliance on profession...

Energy expenditure prediction in preschool children: a machine learning approach using accelerometry and external validation.

Physiological measurement
This study aimed to develop convolutional neural networks (CNNs) models to predict the energy expenditure (EE) of children from raw accelerometer data. Additionally, this study sought to external validation of the CNN models in addition to the linear...

Inversion model of soil salinity in alfalfa covered farmland based on sensitive variable selection and machine learning algorithms.

PeerJ
PURPOSE: Timely and accurate monitoring of soil salinity content (SSC) is essential for precise irrigation management of large-scale farmland. Uncrewed aerial vehicle (UAV) low-altitude remote sensing with high spatial and temporal resolution provide...

Parametric seasonal-trend autoregressive neural network for long-term crop price forecasting.

PloS one
Crop price forecasting is difficult in that supply is not as elastic as demand, therefore, supply and demand should be stabilized through long-term forecasting and pre-response to the price. In this study, we propose a Parametric Seasonal-Trend Autor...

Guide for the application of the data augmentation approach on sets of texts in Spanish for sentiment and emotion analysis.

PloS one
Over the last ten years, social media has become a crucial data source for businesses and researchers, providing a space where people can express their opinions and emotions. To analyze this data and classify emotions and their polarity in texts, nat...

BSNEU-net: Block Feature Map Distortion and Switchable Normalization-Based Enhanced Union-net for Acute Leukemia Detection on Heterogeneous Dataset.

Journal of imaging informatics in medicine
Acute leukemia is characterized by the swift proliferation of immature white blood cells (WBC) in the blood and bone marrow. It is categorized into acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), depending on whether the cell-lin...

A novel approach for heart disease prediction using hybridized AITHO algorithm and SANFIS classifier.

Network (Bristol, England)
In today's world, heart disease threatens human life owing to higher mortality and morbidity across the globe. The earlier prediction of heart disease engenders interoperability for the treatment of patients and offers better diagnostic recommendatio...

Deep learning enabled in vitro predicting biological tissue thickness using force measurement device.

Computers in biology and medicine
Accurate perception of biological tissues (BT) thickness is essential for preliminary evaluation of medical diagnosis and animal nutrition. However, traditional thickness measuring approaches of BT require complex operation, high-cost, and trigger bi...