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S Berlin Brahnam - Missouri State University. Springfield, MO, US

S Berlin Brahnam S Berlin Brahnam

Professor, Computer Information Systems | Missouri State University

Springfield, MO, UNITED STATES

Dr. Brahnam's research includes decision support systems, AI and computer vision, and rhetoric and conversational agents.

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Biography

Dr. S Berlin Brahnam is a Professor of Computer Information Systems at Missouri State University. Her research interests include decision support systems, artificial intelligence and computer vision, modeling and simulation, cultural and ethical aspects of technology, and rhetoric and conversational agents.

Her teaching interests are in the areas of management information systems, decision support systems, distance education, programming languages and Internet for business.

Industry Expertise (3)

Computer Software Research Education/Learning

Areas of Expertise (4)

Computer Programming Articifical Intelligence Cultural aspects of computing Agent abuse and misuse

Accomplishments (9)

Daisy Portenier Loukes Research Professorship (professional)

2015-2018

Best Paper Award for HCI Thematic Area (professional)

2015
HCI International
“Design of a bullying detection/alert system for school-wide intervention.”

Daisy Portenier Loukes Research Fellow (professional)

2013-2015

Foundation Award for Research (professional)

2013
Missouri State University

Best Contribution to Theory Award (professional)

2013
Northeast Decision Sciences Institute
"Heterogeneous ensembles for the missing feature problem."

Summer Faculty Fellowship (professional)

2011
Missouri State University

Faculty College Recognition Award for Excellence in Research (professional)

2006
College of Business, Missouri State University

Faculty College Recognition Award for Excellence in Teaching (professional)

2005
College of Business, Missouri State University

Science Fellowship (professional)

1997-1999
The Graduate Center of the City University of New York

Education (3)

The City University of New York: Ph.D., Computer Science 2002

The City College of New York: M.S., Computer Science 1997

The City College of New York: M.F.A., Intermedia 1992

Affiliations (4)

  • Association for the Advancement of Artificial Intelligence
  • Association for Computing Machinery
  • Association for Information Systems
  • Association of Information Technology Professionals

Languages (1)

  • English

Media Appearances (5)

Here’s Why You Should Stop Swearing at Siri Right Now

Fortune  online

2016-09-29

Do you yell at Alexa? Swear at Siri? Be honest—you've been there. And so have many of us. But why yell at what is essentially software? Gartner research vice president Frank Buytendijk thinks many have and wondered why in a recent blog post. Buytendijk cited some numbers from research conducted by Dr. Sheryl Brahnam, professor of computer information systems at Missouri State University.

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Calling Siri Names? You’re Not Alone – A Closer Look at Misuse of AI Agents

Techemergence  radio

2015-11-01

Brahnam's research yields questions in relative new territory: Are AI prone to receiving misuse? Why do people misuse these agents in ways that they would not treat a human? What types of regulations will we need as AI improves and becomes more intelligent?

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Easing the Pain: Helping Infants Suffering from Pain

CBS  online

2008-02-21

Doctors monitor some of the physical signs of pain, like blood pressure and heart rate, and the obvious behavioral signs like crying and facial expressions. But how do you know the difference between a cry of pain or a cry for hunger?

"You just can't stand over an infant 24-7 and watch them. And that's why I thought a machine system would be pretty good at handling some of these problems," said computer scientist Sheryl Brahnam.

Brahnam works with facial recognition technology to identify key spots in a baby's face that signal pain.

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The New Face of Emoticons

MIT Technology Review  online

2007-03-27

“Already, people use avatars on message boards and in other settings,” says Sheryl Brahnam, an assistant professor of computer information systems at Missouri State University, in Springfield. In many respects, she says, this system bridges the gap between emoticons and avatars.

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Computing: Nobody Enjoys Telephoning a Call Centre. Could “Chatbot” Technology Make the Experience Less Painful?

The Economist Technology Quarterly  print

2007-03-10

There is more to handling call-centre queries than simply understanding language and looking things up in databases. Sheryl Brahnam, a researcher at Missouri State University in Springfield, suggests that it will also be necessary to program chatbots to deal with verbal abuse.

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Event Appearances (3)

Robust ensemble learning for data mining

IEEE SOFA (2014)  Timisoara, Romania

Re/Framing Gender Identifications in (Inter)actions with Virtual Conversational Partners

Rhetoric Society of America Conference (2012)  Philadelphia, Pennsylvania

Ethical considerations in the customer abuse of artificial conversational agents

18th Annual International Business Ethics Conference (2011)  New York, New York

Research Grants (3)

Summer Fellowship Research Grant

Missouri State University 

2011

Provost's Futures Grant

Missouri State University 

2007

Faculty Grant

Missouri State University 

2006
Customer Verbal Abuse of Online Embodied Conversational Agents

Minds-Eye (1)

A mother – and a computer – can differentiate a baby’s cry

As you ask a hologram in an airport for directions, chat with a bot on a computer, or even ask Siri for your schedule, there’s no denying technology is changing the world. Dr. Brahnam is at the forefront of some of these developments.

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Articles (19)

Handcrafted vs. non-handcrafted features for computer vision classification Pattern Recognition

2017

This work presents a generic computer vision system designed for exploiting trained deep Convolutional Neural Networks (CNN) as a generic feature extractor and mixing these features with more traditional hand-crafted features.

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Ensemble of texture descriptors and classifiers for face recognition Applied Computing and Informatics

2017

Presented in this paper is a novel system for face recognition that works well in the wild and that is based on ensembles of descriptors that utilize different preprocessing techniques.

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Multi-label classifier based on histogram of gradients for predicting the anatomical therapeutic chemical class/classes of a given compound Bioinformatics

2017

Given an unknown compound, is it possible to predict its ATC (Anatomical Therapeutic Chemical) class/classes? This is a challenging yet important problem since such a prediction could be used to deduce not only a compound's possible active ingredients but also its therapeutic, pharmacological, and chemical properties, thereby substantially expediting the pace of drug development.

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An ensemble of visual features for gaussians of local descriptors and non-binary coding for texture descriptors Expert Systems with Applications

2017

This paper presents an improved version of a recent state-of-the-art texture descriptor called Gaussians of Local Descriptors (GOLD), which is based on a multivariate Gaussian that models the local feature distribution that describes the original image.

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Comparison of in-person and screen-based analysis using communica-tion models: A first step towards the psychoanalysis of telecommunications and its noise Journal Psychoanalytic Perspectives

2017

As the popularity of computer-mediated psychoanalysis rises, it is important that analysts and researchers undertake a more comprehensive investigation of the parameters involved in mediation and their effects on the psychoanalytic setting, the analytic field, and the unconscious of the analytic couple. The primary aim of this paper is to offer a series of communication models that visually lay out for comparison purposes key aspects involved in both in-person and mediated psychoanalytic communication.

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Combining visual and acoustic features for audio classification tasks Pattern Recognition Letters

2017

In this paper a novel and effective approach for automated audio classification is presented that is based on the fusion of different sets of features, both visual and acoustic. A number of different acoustic and visual features of sounds are evaluated and compared. These features are then fused in an ensemble that produces better classification accuracy than other state-of-the-art approaches.

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Weighted reward–punishment Editing Pattern Recognition Letters

2016

The Nearest Neighbor classifier is a popular nonparametric classification method that has been successfully applied to many pattern recognition problems. Its usefulness has been limited, however, because of its computational complexity and sensitivity to outliers in the training set.

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Ensemble of different approaches for a reliable person re-identification system Applied Computing and Informatics

2016

An ensemble of approaches for reliable person re-identification is proposed in this paper. The proposed ensemble is built combining widely used person re-identification systems using different color spaces and some variants of state-of-the-art approaches that are proposed in this paper.

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Multilayer descriptors for medical image classification Computers in Biology and Medicine

2016

In this paper, we propose a new method for improving the performance of 2D descriptors by building an n-layer image using different preprocessing approaches from which multilayer descriptors are extracted and used as feature vectors for training a Support Vector Machine.

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Improving the descriptors extracted from the co-occurrence matrix using preprocessing approaches Expert Systems with Applications

2015

In this paper, we investigate the effects that different preprocessing techniques have on the performance of features extracted from Haralick's co-occurrence matrix, one of the best known methods for analyzing image texture.

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Computer vision for virus image classification Biosystems Engineering

2015

In this paper we present a new ensemble of descriptors for the classification of transmission electron microscopy images of viruses that is based on texture analysis.

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Toward a general-purpose heterogeneous ensemble for pattern classification Computational Intelligence and Neuroscience

2015

We perform an extensive study of the performance of different classification approaches on twenty-five datasets (fourteen image datasets and eleven UCI data mining datasets). The aim is to find General-Purpose (GP) heterogeneous ensembles (requiring little to no parameter tuning) that perform competitively across multiple datasets.

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Ensemble of face/eye detectors for accurate automatic face detection International Journal of Latest Research in Science and Technology

2015

In this work, we propose a simple yet effective face detector that combines several face/eye detectors that possess different characteristics. Specifically, we report an extensive study for combining face/eye detectors that results in a final system we call FED that combines three face detectors that extract regions of candidate faces from an image with two approaches for eye detection: the enhanced Pictorial Structure (PS) model for coarse eye localization and a new approach proposed here (called PEC) that provides precise eye localization.

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Region-based approaches and descriptors extracted from the co-occurrence matrix International Journal of Latest Research in Science and Technology

2014

Recently proposed texture descriptors extracted from the co-occurrence matrix across several datasets is surveyed and validated in this paper; moreover, two new methods for extracting features from the Gray Level Co-occurrence Matrix (GLCM) are proposed.

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A set of descriptors for identifying the protein-drug interaction in cellular networking Journal of Theoretical Biology

2014

The study of protein–drug interactions is a significant issue for drug development. Unfortunately, it is both expensive and time-consuming to perform physical experiments to determine whether a drug and a protein are interacting with each other. Some previous attempts to design an automated system to perform this task were based on the knowledge of the 3D structure of a protein, which is not always available in practice. With the availability of protein sequences generated in the post-genomic age, however, a sequence-based solution to deal with this problem is necessary.

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An empirical study of different approaches for protein classification The Scientific World Journal

2014

Many domains would benefit from reliable and efficient systems for automatic protein classification. An area of particular interest in recent studies on automatic protein classification is the exploration of new methods for extracting features from a protein that work well for specific problems. These methods, however, are not generalizable and have proven useful in only a few domains. Our goal is to evaluate several feature extraction approaches for representing proteins by testing them across multiple datasets.

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'Ensemble of shape descriptors for shape retrieval and classification International Journal of Advanced Intelligence Paradigms

2014

Shape classification has long been a field of study in computer vision. In this work, we propose an ensemble of approaches using the weighted sum rule that is based on a set of widely used shape descriptors inner-distance shape context, shape context, and height functions.

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Ensemble of different local descriptors, codebook generation methods and subwindow configurations for building a reliable computer vision system Journal of King Saud University - Science

2014

In the last few years, several ensemble approaches have been proposed for building high performance systems for computer vision. In this paper we propose a system that incorporates several perturbation approaches and descriptors for a generic computer vision system.

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Indirect immunofluorescence image classification using texture descriptors Expert Systems with Applications

2014

In this work, we propose an ensemble of texture descriptors for HEp-2 cell classification. Our system is based on a “pyramidal application” of local binary patterns coupled with a method for handling nonuniform bins.

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