AIMC Topic: Machine Learning

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A pretraining domain decomposition method using artificial neural networks to solve elliptic PDE boundary value problems.

Scientific reports
Developing methods of domain decomposition (DDM) has been widely studied in the field of numerical computation to estimate solutions of partial differential equations (PDEs). Several case studies have also reported that it is feasible to use the doma...

Convolution neural network with batch normalization and inception-residual modules for Android malware classification.

Scientific reports
Deep learning technology is changing the landscape of cybersecurity research, especially the study of large amounts of data. With the rapid growth in the number of malware, developing of an efficient and reliable method for classifying malware has be...

Predicting social anxiety in young adults with machine learning of resting-state brain functional radiomic features.

Scientific reports
Social anxiety is a symptom widely prevalent among young adults, and when present in excess, can lead to maladaptive patterns of social behavior. Recent approaches that incorporate brain functional radiomic features and machine learning have shown po...

Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues.

Experimental biology and medicine (Maywood, N.J.)
Auscultation plays an important role in the clinic, and the research community has been exploring machine learning (ML) to enable remote and automatic auscultation for respiratory condition screening via sounds. To give the big picture of what is goi...

Machine Learning Model Drift: Predicting Diagnostic Imaging Follow-Up as a Case Example.

Journal of the American College of Radiology : JACR
OBJECTIVE: Address model drift in a machine learning (ML) model for predicting diagnostic imaging follow-up using data augmentation with more recent data versus retraining new predictive models.

Investigation of a Data Split Strategy Involving the Time Axis in Adverse Event Prediction Using Machine Learning.

Journal of chemical information and modeling
Adverse events are a serious issue in drug development, and many prediction methods using machine learning have been developed. The random split cross-validation is the de facto standard for model building and evaluation in machine learning, but care...

GLD-Net: Deep Learning to Detect DDoS Attack via Topological and Traffic Feature Fusion.

Computational intelligence and neuroscience
Distributed denial of service (DDoS) attacks are the most common means of cyberattacks against infrastructure, and detection is the first step in combating them. The current DDoS detection mainly uses the improvement or fusion of machine learning and...

An Efficient Outlier Detection with Deep Learning-Based Financial Crisis Prediction Model in Big Data Environment.

Computational intelligence and neuroscience
As Big Data, Internet of Things (IoT), cloud computing (CC), and other ideas and technologies are combined for social interactions. Big data technologies improve the treatment of financial data for businesses. At present, an effective tool can be use...

Artificial Intelligence in Allergy and Immunology: Comparing Risk Prediction Models to Help Screen Inborn Errors of Immunity.

International archives of allergy and immunology
BACKGROUND: Inborn errors of immunity (IEI) are underdiagnosed disorders, leading to increased morbimortality and expenses for healthcare system.

Can Machine Learning classifiers be used to regulate nutrients using small training datasets for aquaponic irrigation?: A comparative analysis.

PloS one
With the recent advances in the field of alternate agriculture, there has been an ever-growing demand for aquaponics as a potential substitute for traditional agricultural techniques for improving sustainable food production. However, the lack of dat...