AIMC Topic: Support Vector Machine

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A stacking ensemble deep learning approach to cancer type classification based on TCGA data.

Scientific reports
Cancer tumor classification based on morphological characteristics alone has been shown to have serious limitations. Breast, lung, colorectal, thyroid, and ovarian are the most commonly diagnosed cancers among women. Precise classification of cancers...

A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia.

Sensors (Basel, Switzerland)
Atrial fibrillation (AF) is the most common cardiovascular disease (CVD), and most existing algorithms are usually designed for the diagnosis (i.e., feature classification) or prediction of AF. Artificial intelligence (AI) algorithms integrate the di...

Machine learning in medicine: a practical introduction to natural language processing.

BMC medical research methodology
BACKGROUND: Unstructured text, including medical records, patient feedback, and social media comments, can be a rich source of data for clinical research. Natural language processing (NLP) describes a set of techniques used to convert passages of wri...

Intelligent Disease Prediagnosis Only Based on Symptoms.

Journal of healthcare engineering
People often concern the relationships between symptoms and diseases when seeking medical advices. In this paper, medical data are divided into three copies, records related to main disease categories, records related to subclass disease types, and r...

A hybrid novel SVM model for predicting CO emissions using Multiobjective Seagull Optimization.

Environmental science and pollution research international
The agricultural sector is one of the most important sources of CO emissions. Thus, the current study predicted CO emissions based on data from the agricultural sectors of 25 provinces in Iran. The gross domestic product (GDP), the square of the GDP ...

FEA and Machine Learning Techniques for Hidden Structure Analysis.

Sensors (Basel, Switzerland)
This study focuses on investigating and predicting two hidden structures: plant root system architecture and non-visible bubbles in plexiglass. Current approaches are damaging, expensive, or time-consuming. Infrared imaging was used to study the root...

Developing a boosted decision tree regression prediction model as a sustainable tool for compressive strength of environmentally friendly concrete.

Environmental science and pollution research international
One of the most significant parameters in concrete design is compressive strength. Time and money could be saved if the compressive strength of concrete is accurately measured. In this study, two machine learning models, namely, boosted decision tree...

Affective Computing on Machine Learning-Based Emotion Recognition Using a Self-Made EEG Device.

Sensors (Basel, Switzerland)
In this research, we develop an affective computing method based on machine learning for emotion recognition using a wireless protocol and a wearable electroencephalography (EEG) custom-designed device. The system collects EEG signals using an eight-...

Improving Measures of Chemical Structural Similarity Using Machine Learning on Chemical-Genetic Interactions.

Journal of chemical information and modeling
A common strategy for identifying molecules likely to possess a desired biological activity is to search large databases of compounds for high structural similarity to a query molecule that demonstrates this activity, under the assumption that struct...

A Two-Level Speaker Identification System via Fusion of Heterogeneous Classifiers and Complementary Feature Cooperation.

Sensors (Basel, Switzerland)
We present a new architecture to address the challenges of speaker identification that arise in interaction of humans with social robots. Though deep learning systems have led to impressive performance in many speech applications, limited speech data...