@article{akbulut_perros_shahzad_2020, title={Bimodal affect recognition based on autoregressive hidden Markov models from physiological signals}, volume={195}, ISSN={["1872-7565"]}, DOI={10.1016/j.cmpb.2020.105571}, abstractNote={Background and objective: Affect provides contextual information about the emotional state of a person as he/she communicates in both verbal and/or non-verbal forms. While human’s are great at determining the emotional state of people while they communicate in person, it is challenging and still largely an unsolved problem to computationally determine the emotional state of a person. Methods: Emotional states of a person manifest in the physiological biosignals such as electrocardiogram (ECG) and electrodermal activity (EDA) because these signals are impacted by the peripheral nervous system of the body, and the peripheral nervous system is strongly coupled with the mental state of the person. In this paper, we present 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. The six emotions include happiness, sadness, surprise, fear, anger, and disgust. Results: We evaluated our method on a comprehensive new dataset collected from 30 participants. Our results show that our proposed method achieves an average accuracy of 88.6% in distinguishing across the 6 emotions. Conclusions: The key technical depth of the paper is in the use of the AR-HMMs to model the EDA signal and the use of LDA to enable accurate emotion recognition without requiring a large number of training samples. Unlike other studies, we have taken a hierarchical approach to classify emotions, where we first categorize the emotion as either positive or negative and then identify the exact emotion.}, journal={COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE}, author={Akbulut, Fatma Patlar and Perros, Harry G. and Shahzad, Muhammad}, year={2020}, month={Oct} }
 @article{akbulut_perros_2019, title={Performance Analysis of Microservice Design Patterns}, volume={23}, ISSN={["1941-0131"]}, DOI={10.1109/MIC.2019.2951094}, abstractNote={Microservice-based solutions are currently gaining momentum because they do not have the disadvantages of traditional monolithic architectures. Business interest in microservices is increasing since the microservice architecture brings a lightweight, independent, reuse-oriented, and fast service deployment approach that minimizes infrastructural risks. This approach is at an early stage of its development, and in view of this, it is important to understand the performance of its design patterns. In this article, we obtained performance results related to query response time, efficient hardware usage, hosting costs, and packet-loss rate, for three microservice design patterns practiced in the software industry.}, number={6}, journal={IEEE INTERNET COMPUTING}, author={Akbulut, Akhan and Perros, Harry G.}, year={2019}, pages={19–27} }
 @article{akbulut_ertugrul_topcu_2018, title={Fetal health status prediction based on maternal clinical history using machine learning techniques}, volume={163}, ISSN={["1872-7565"]}, DOI={10.1016/j.cmpb.2018.06.010}, abstractNote={Congenital anomalies are seen at 1-3% of the population, probabilities of which are tried to be found out primarily through double, triple and quad tests during pregnancy. Also, ultrasonographical evaluations of fetuses enhance detecting and defining these abnormalities. About 60-70% of the anomalies can be diagnosed via ultrasonography, while the remaining 30-40% can be diagnosed after childbirth. Medical diagnosis and prediction is a topic that is closely related with e-Health and machine learning. e-Health applications are critically important especially for the patients unable to see a doctor or any health professional. Our objective is to help clinicians and families to better predict fetal congenital anomalies besides the traditional pregnancy tests using machine learning techniques and e-Health applications.In this work, we developed a prediction system with assistive e-Health applications which both the pregnant women and practitioners can make use of. A performance comparison (considering Accuracy, F1-Score, AUC measures) was made between 9 binary classification models (Averaged Perceptron, Boosted Decision Tree, Bayes Point Machine, Decision Forest, Decision Jungle, Locally-Deep Support Vector Machine, Logistic Regression, Neural Network, Support Vector Machine) which were trained with the clinical dataset of 96 pregnant women and used to process data to predict fetal anomaly status based on the maternal and clinical data. The dataset was obtained through maternal questionnaire and detailed evaluations of 3 clinicians from RadyoEmar radiodiagnostics center in Istanbul, Turkey. Our e-Health applications are used to get pregnant women's health status and clinical history parameters as inputs, recommend them physical activities to perform during pregnancy, and inform the practitioners and finally the patients about possible risks of fetal anomalies as the output.In this paper, the highest accuracy of prediction was displayed as 89.5% during the development tests with Decision Forest model. In real life testing with 16 users, the performance was 87.5%. This estimate is sufficient to give an idea of fetal health before the patient visits the physician.The proposed work aims to provide assistive services to pregnant women and clinicians via an online system consisting of a mobile side for the patients, a web application side for their clinicians and a prediction system. In addition, we showed the impact of certain clinical data parameters of pregnant on the fetal health status, statistically correlated the parameters with the existence of fetal anomalies and showed guidelines for future researches.}, journal={COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE}, author={Akbulut, Akhan and Ertugrul, Egemen and Topcu, Varol}, year={2018}, month={Sep}, pages={87–100} }