AIMC Topic: Support Vector Machine

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Small Low-Contrast Target Detection: Data-Driven Spatiotemporal Feature Fusion and Implementation.

IEEE transactions on cybernetics
Detecting small low-contrast targets in the airspace is an essential and challenging task. This article proposes a simple and effective data-driven support vector machine (SVM)-based spatiotemporal feature fusion detection method for small low-contra...

A Machine Learning Applied Diagnosis Method for Subcutaneous Cyst by Ultrasonography.

Oxidative medicine and cellular longevity
For decades, ultrasound images have been widely used in the detection of various diseases due to their high security and efficiency. However, reading ultrasound images requires years of experience and training. In order to support the diagnosis of cl...

Inverse free reduced universum twin support vector machine for imbalanced data classification.

Neural networks : the official journal of the International Neural Network Society
Imbalanced datasets are prominent in real-world problems. In such problems, the data samples in one class are significantly higher than in the other classes, even though the other classes might be more important. The standard classification algorithm...

Tropical support vector machines: Evaluations and extension to function spaces.

Neural networks : the official journal of the International Neural Network Society
Support Vector Machines (SVMs) are one of the most popular supervised learning models to classify using a hyperplane in an Euclidean space. Similar to SVMs, tropical SVMs classify data points using a tropical hyperplane under the tropical metric with...

Inversion of Soil Organic Matter Content Based on Improved Convolutional Neural Network.

Sensors (Basel, Switzerland)
Soil organic matter (SOM) is an important source of nutrients required during crop growth and is an important component of cultivated soil. In this paper, we studied the possibility of using deep learning methods to establish a multi-feature model to...

Deep residual neural-network-based robot joint fault diagnosis method.

Scientific reports
A data driven method-based robot joint fault diagnosis method using deep residual neural network (DRNN) is proposed, where Resnet-based fault diagnosis method is introduced. The proposed method mainly deals with kinds of fault types, such as gain err...

A Highly Energy-Efficient Hyperdimensional Computing Processor for Biosignal Classification.

IEEE transactions on biomedical circuits and systems
Hyperdimensional computing (HDC) is a brain-inspired computing paradigm that operates on pseudo-random hypervectors to perform high-accuracy classifications for biomedical applications. The energy efficiency of prior HDC processors for this computati...

New Algorithm of Traditional Chinese Medicine and Protection of Intangible Cultural Heritage Based on Big Data Deep Learning.

BioMed research international
Traditional Chinese medicine (TCM) is a summary of the diagnosis and treatment experience formed by the working people in the long-term struggle against diseases, so it is very important to protect the intangible cultural heritage of TCM. How to extr...

A Crop Growth Prediction Model Using Energy Data Based on Machine Learning in Smart Farms.

Computational intelligence and neuroscience
In the recent past, the agricultural industry has rapidly digitalized in the form of smart farms through the broad usage of data analysis and artificial intelligence. Commonly, high operating costs in a smart farm are primarily due to inefficient ene...

An Artificial Intelligence-Based Bio-Medical Stroke Prediction and Analytical System Using a Machine Learning Approach.

Computational intelligence and neuroscience
Stroke-related disabilities can have a major negative effect on the economic well-being of the person. When left untreated, a stroke can be fatal. According to the findings of this study, people who have had strokes generally have abnormal biosignals...