AIMC Topic: Neural Networks, Computer

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Machine Learning-Based Software Defect Prediction for Mobile Applications: A Systematic Literature Review.

Sensors (Basel, Switzerland)
Software defect prediction studies aim to predict defect-prone components before the testing stage of the software development process. The main benefit of these prediction models is that more testing resources can be allocated to fault-prone modules...

Premature Ventricular Contraction Recognition Based on a Deep Learning Approach.

Journal of healthcare engineering
Electrocardiogram signal (ECG) is considered a significant biological signal employed to diagnose heart diseases. An ECG signal allows the demonstration of the cyclical contraction and relaxation of human heart muscles. This signal is a primary and n...

Effective CBMIR System Using Hybrid Features-Based Independent Condensed Nearest Neighbor Model.

Journal of healthcare engineering
In recent times, a large number of medical images are generated, due to the evolution of digital imaging modalities and computer vision application. Due to variation in the shape and size of the images, the retrieval task becomes more tedious in the ...

Models of Artificial Intelligence-Assisted Diagnosis of Lung Cancer Pathology Based on Deep Learning Algorithms.

Journal of healthcare engineering
In this article, in order to explore the application of a diagnosis system for lung cancer, we use an auxiliary diagnostic system to predict and diagnose the good and evil attributes of chest CT pulmonary nodules. This research improves the new diagn...

Detection of Types of Mental Illness through the Social Network Using Ensembled Deep Learning Model.

Computational intelligence and neuroscience
In today's era, social networking platforms are widely used to share emotions. These types of emotions are often analyzed to predict the user's behavior. In this paper, these types of sentiments are classified to predict the mental illness of the use...

A Prediction Model Analysis of Behavior Recognition Based on Genetic Algorithm and Neural Network.

Computational intelligence and neuroscience
With the extensive application of virtual technology and simulation algorithm, motion behavior recognition is widely used in various fields. The original neural network algorithm cannot solve the problem of data redundancy in behavior recognition, an...

Application of Lightweight Deep Learning Model in Vocal Music Education in Higher Institutions.

Computational intelligence and neuroscience
The aim is to improve the teaching quality of music majors and cultivate their innovative ability. This article takes Vocal Music Education (VME) method as the research object to explore the teaching reform of Music Major courses. Firstly, this artic...

China's Economic Forecast Based on Machine Learning and Quantitative Easing.

Computational intelligence and neuroscience
In this paper, six variables, including export value, real exchange rate, Chinese GDP, and US IPI, and their seasonal variables, are used as determinants to model and forecast China's export value to the US using three methods: BP neural network, ARI...

Diffeomorphic transforms for data augmentation of highly variable shape and texture objects.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Training a deep convolutional neural network (CNN) for automatic image classification requires a large database with images of labeled samples. However, in some applications such as biology and medicine only a few experts ca...

A data-driven approach to characterizing nonlinear elastic behavior of soft materials.

Journal of the mechanical behavior of biomedical materials
The Autoprogressive (AutoP) method is a data-driven inverse method that leverages finite element analysis (FEA) and machine learning (ML) techniques to build constitutive relationships from measured force and displacement data. Previous applications ...