AIMC Topic: Machine Learning

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Artificial Intelligence Based Customer Churn Prediction Model for Business Markets.

Computational intelligence and neuroscience
The introduction of artificial intelligence (AI) and machine learning (ML) technologies in recent years has resulted in improved company performance. Customer churn forecast is a difficult problem in many corporate sectors, particularly the telecommu...

Detecting High-Risk Factors and Early Diagnosis of Diabetes Using Machine Learning Methods.

Computational intelligence and neuroscience
Diabetes is a chronic disease that can cause several forms of chronic damage to the human body, including heart problems, kidney failure, depression, eye damage, and nerve damage. There are several risk factors involved in causing this disease, with ...

Improved Manual Annotation of EEG Signals through Convolutional Neural Network Guidance.

eNeuro
The development of validated algorithms for automated handling of artifacts is essential for reliable and fast processing of EEG signals. Recently, there have been methodological advances in designing machine-learning algorithms to improve artifact d...

Artificial Intelligence in Oncological Hybrid Imaging.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin
BACKGROUND: Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications a...

A radiomics feature-based machine learning models to detect brainstem infarction (RMEBI) may enable early diagnosis in non-contrast enhanced CT.

European radiology
OBJECTIVES: Magnetic resonance imaging has high sensitivity in detecting early brainstem infarction (EBI). However, MRI is not practical for all patients who present with possible stroke and would lead to delayed treatment. The detection rate of EBI ...

Dysgraphia disorder forecasting and classification technique using intelligent deep learning approaches.

Progress in neuro-psychopharmacology & biological psychiatry
Writing abilities are impacted by dysgraphia, a condition of learning disability. It might be challenging to diagnose dysgraphia at an initial point of a child's upbringing. Problematic abilities linked to Dysgraphia difficulties that is utilized in ...

Adaptive graph convolutional clustering network with optimal probabilistic graph.

Neural networks : the official journal of the International Neural Network Society
The graph convolutional network (GCN)-based clustering approaches have achieved the impressive performance due to strong ability of exploiting the topological structure. The adjacency graph seriously affects the clustering performance, especially for...

Surface similarity parameter: A new machine learning loss metric for oscillatory spatio-temporal data.

Neural networks : the official journal of the International Neural Network Society
Supervised machine learning approaches require the formulation of a loss functional to be minimized in the training phase. Sequential data are ubiquitous across many fields of research, and are often treated with Euclidean distance-based loss functio...

Three simple steps to improve the interpretability of EEG-SVM studies.

Journal of neurophysiology
Machine-learning systems that classify electroencephalography (EEG) data offer important perspectives for the diagnosis and prognosis of a wide variety of neurological and psychiatric conditions, but their clinical adoption remains low. We propose he...

AIMIC: Deep Learning for Microscopic Image Classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Deep learning techniques are powerful tools for image analysis. However, the lack of programming experience makes it difficult for novice users to apply this technology. This project aims to lower the barrier for clinical us...