Sina Khanmohammadi

Sina Khanmohammadi
University of Oklahoma | ou · School of Computer Science

Doctor of Philosophy

About

27
Publications
7,354
Reads
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328
Citations
Introduction
Sina Khanmohammadi currently works at the Department of Electrical and Systems Engineering, Washington University in St. Louis. Sina's research interest include data science, complex systems, and computational neurosciences.
Additional affiliations
November 2016 - present
Washington University in St. Louis
Position
  • PostDoc Position
May 2016 - July 2016
Binghamton University
Position
  • Instructor
Description
  • - ISE 261 (Probabilistic Systems I) [Online Delivery]
January 2016 - May 2016
Binghamton University
Position
  • Research Assistant
Description
  • - SSIE 641X (Advance Topics in Network Science) - SSIE 617 (Fuzzy Sets, Fuzzy Logic & Fuzzy Systems) - SSIE 500 (Computational Tools)
Education
September 2012 - October 2016
Binghamton University
Field of study
  • Systems Science
January 2011 - May 2012
University of Hertfordshire
Field of study
  • Manufacturing Management
September 2006 - September 2010
University of Tabriz
Field of study
  • Computer Science

Publications

Publications (27)
Conference Paper
Full-text available
In this paper a hybrid fuzzy inference and transfer function modeling is used to predict the irregular human behavior during hard and stressful tasks such as dangerous military missions. A set of affecting factors such as missioner's experience, fatigue, sunshine intensity, hungriness, thirstiness, psychological characteristics, affright, etc. may...
Article
Objective We analyze a slow electrographic pattern, Macroperiodic Oscillations (MOs), in the EEG from a cohort of young critical care patients (n=43) with continuous EEG monitoring. We construct novel quantitative methods to quantify and understand MOs. Methods We applied a nonparametric bilevel spectral analysis to identify MOs, a millihertz (0.0...
Article
In this study, we consider the problem of localizing focal brain injuries from surface electroencephalogram (EEG) recordings. To this end, we introduce a new analysis technique termed frequency-based intrinsic network dynamic reactivity (FINDR), which quantifies the extent to which different brain regions (defined in EEG channel space) are responsi...
Article
Purpose: Seizures occur in 10% to 40% of critically ill children. We describe a phenomenon seen on color density spectral array but not raw EEG associated with seizures and acquired brain injury in pediatric patients. Methods: We reviewed EEGs of 541 children admitted to an intensive care unit between October 2015 and August 2018. We identified...
Article
Objective: We devise a data-driven framework to assess the level of consciousness in etiologically heterogeneous comatose patients using intrinsic dynamical changes of resting-state Electroencephalogram (EEG) signals. Methods: EEG signals were collected from 54 comatose patients (GCS≤8) and 20 control patients (GCS>8). We analyzed the EEG signals...
Article
Indirect quantification of the synchronization between two dynamical systems from measured experimental data has gained much attention in recent years, especially in the computational neuroscience community where the exact model of the neuronal dynamics is unknown. In this regard, one of the most promising methods for quantifying the interrelations...
Conference Paper
Recent studies suggest that disruptions in resting state functional connectivity - a measure of stationary statistical association between brain regions - can be used as an objective marker of brain injury. However, fewer characterizations have examined the disruption of intrinsic brain dynamics after brain injury. Here, we examine this issue using...
Article
Epilepsy is one of the most common neurological disorders in the world. Prompt detection of seizure onset from electroencephalogram (EEG) signals can improve the treatment of epileptic patients. This paper presents a new adaptive patientspecific seizure onset detection framework that dynamically selects a feature from enhanced EEG signals to discri...
Article
Full-text available
One of the biggest problems for major airline is predicting flight delay. Airlines try to reduce delays to gain the loyalty of their customers. Hence, a prediction model that airliners can use to forecast possible delays is of significant importance. In this regards, artificial neural network (ANN) techniques can be beneficial for this application....
Conference Paper
Full-text available
Existing seizure onset detection methods usually rely on a large number of extracted features regardless of computational efficiency, which reduces their applicability for real-time seizure detection. In this study, a simple distance based seizure onset detection algorithm is proposed to distinguish seizure and non-seizure EEG signals. The proposed...
Article
Data clustering has been proven to be an effective method for discovering structure in medical datasets. The majority of clustering algorithms produce exclusive clusters meaning that each sample can belong to one cluster only. However, most real-world medical datasets have inherently overlapping information, which could be best explained by overlap...
Data
Seminar: You can watch it on https://vimeo.com/163555445 and https://sites.google.com/binghamton.edu/salihtutun/teaching-and-talks
Conference Paper
Full-text available
Understanding the behavior of a terrorist group is a complex phenomenon because of the uncertainty in strategies and tactics used by terrorists. Current literature suggests that terrorism has an evolutionary nature and terrorist groups change behavior according to a government’s counter-terrorism policies. The goal of this research is to model how...
Conference Paper
Full-text available
Terrorists are increasingly using suicide attacks to attack different targets. The government finds it challenging to track these attacks since the terrorists have learned from experience to avoid unsecured communications such as social media. Therefore, we propose a new approach that will predict the characteristics of future suicide attacks by an...
Conference Paper
Full-text available
Hospital readmission prediction continues to be a highly-encouraged area of investigation mainly because of the readmissions reduction program by the Centers for Medicare and Medicaid services (CMS). The overall goal is to reduce the number of early hospital readmissions by identifying the key risk factors that cause hospital readmissions. This is...
Conference Paper
Full-text available
In recent years, terrorist attacks around the world have begun to develop more complex strategies and tactics that are not easily recognizable. Furthermore, in uncertain situations, agencies need to know whether the perpetrator was a terrorist or someone motivated by other factors (e.g. criminal activity) so that they can develop appropriate strate...
Article
Knowledge-based systems such as expert systems are of particular interest in medical applications as extracted if-then rules can provide interpretable results. Various rule induction algorithms have been proposed to effectively extract knowledge from data, and they can be combined with classification methods to form rule-based classifiers. However,...
Conference Paper
The present study aims to build a classification model that discriminates between chronological ages of subjects based on resting-state electroencephalography (EEG) data collected from a community sample of 269 children aged 7 to 11. Specifically, spectral power densities in four classical frequency bands: Delta (0.5–3 Hz), Theta (4–7 Hz), Alpha (8...
Conference Paper
Full-text available
Supervised classification algorithms have become very popular because of their potential application in developing intelligent data analytic software. These algorithms are known to be sensitive to the characteristic and structure of input datasets, therefore, researchers use different algorithm selection methods to select the most suitable classifi...
Article
Full-text available
Today, there is a significant demand for fast, accurate, and automated methods for the discrimination of latent patterns in neuroelectric waveforms. One of the main challenges is the development of efficient feature extraction tools to utilize the rich spatio-temporal information inherent in large scale human electrocortical activity. In this paper...
Conference Paper
The aircraft landings scheduling problem at an airport has become very challenging due to the increase of air traffic. Traditionally, this problem has been widely studied by formulating it as an optimization model solved by various operation research approaches. However, these approaches are not able to capture the dynamic nature of the aircraft la...
Conference Paper
Full-text available
Association rule based classification is one of the popular data mining techniques applied in medical domain. The major advantage is its interpretable results that medical doctors can easily adopt for diagnostic decision-making. The classification framework consists of data discretization, association rule generation, and classification. The discre...
Conference Paper
Full-text available
Human resources are essential in manufacturing and service industries, and one of the main issues regarding human resources is how to predict the risk of human errors in different circumstances. Human errors play a significant role in the overall performance of manufacturing and service industries. For example, according to the Institute of Medicin...
Conference Paper
Full-text available
Cardiac surgery is an important medical treatment for coronary vessel patients. Different models have been introduced to determine the risk factors related to side effects of this operation. The goal of this research is to study EuroSCORE (European System for Cardiac Operative Risk Evaluation) as a useful method for predicting the risk of mortality...
Chapter
Full-text available
A new fuzzy approach is developed for defining the general criticality of activities where some other features such as probability of finishing on time zone, probability of impact, impact threat and ability to retaliate are considered as criticality factors of activities in project management process. In this way the risky situation (vulnerability)...
Conference Paper
In this paper a new fuzzy approach is developed for defining the general criticality of activities where some other features such as probability of finishing on time zone, probability of impact, impact threat and ability to retaliate are considered as criticality factors of activities in project management process. In this way the risky situation (...

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