2020 journal article

Bimodal affect recognition based on autoregressive hidden Markov models from physiological signals


By: F. Akbulut n, H. Perros n & M. Shahzad n

author keywords: Affect recognition; Autoregressive hidden Markov models; Machine learning; Biosignals; Heart rate variability
MeSH headings : Electrocardiography; Emotions; Female; Happiness; Heart Rate; Humans; Male
TL;DR: A method to accurately recognize six emotions using ECG and EDA signals and applying autoregressive hidden Markov models (AR-HMMs) and heart rate variability analysis on these signals is presented. (via Semantic Scholar)
Source: Web Of Science
Added: October 12, 2020

2019 journal article

Performance Analysis of Microservice Design Patterns


By: A. Akbulut n & H. Perros n

author keywords: Time factors; Logic gates; Computer architecture; Random access memory; Measurement; Internet; Message systems; Microservices; Design Patterns; Microservices Architecture; Performance Analysis; Software Architecture
TL;DR: In this article, performance results related to query response time, efficient hardware usage, hosting costs, and packet-loss rate are obtained, for three microservice design patterns practiced in the software industry. (via Semantic Scholar)
UN Sustainable Development Goal Categories
9. Industry, Innovation and Infrastructure (Web of Science; OpenAlex)
Source: Web Of Science
Added: March 10, 2020

2018 journal article

Fetal health status prediction based on maternal clinical history using machine learning techniques


By: A. Akbulut n, E. Ertugrul* & V. Topcu*

author keywords: Machine learning; Medical diagnosis; Risk prediction; Pregnancy; Fetal health; Prognosis; m-Health
MeSH headings : Algorithms; Area Under Curve; Bayes Theorem; Congenital Abnormalities / diagnosis; Decision Trees; Diagnosis, Computer-Assisted / methods; Female; Fetus / physiology; Health Status; Humans; Internet; Logistic Models; Machine Learning; Mobile Applications; Perception; Pregnancy; ROC Curve; Regression Analysis; Reproducibility of Results; Support Vector Machine; Telemedicine; Ultrasonography, Prenatal
TL;DR: A prediction system with assistive e-Health applications to help clinicians and families to better predict fetal congenital anomalies besides the traditional pregnancy tests using machine learning techniques and e- health applications is developed. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: October 19, 2018

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