Bonny Banerjee
University of Memphis
Diagrammatic reasoningHuman–computer interactionMachine learningData miningBenchmark (computing)Artificial intelligenceSet (psychology)Domain (software engineering)Pattern recognitionConstraint satisfaction problemGestureInferenceSpeech recognitionComputer visionMathematicsComputer scienceProcess (engineering)Constraint satisfactionArtificial neural networkFeature extractionFeature learningCluster analysisTheoretical computer science
Publications 60
#1Ananya Dutta (U of M: University of Memphis)H-Index: 1
#2Bonny Banerjee (U of M: University of Memphis)H-Index: 11
Last. Subhash C. Chauhan (University of Texas at Austin)H-Index: 5
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Background: Pancreatic cancer (PC) is a disease with poor prognosis and survival rate. There is a pertinent need to identify the risk factors of this disease. The purpose of this study is to use machine learning methods to identify a subset of factors (a.k.a. features) from the PLCO dataset as predictors of PC. The Prostate, Lung, Colorectal and Ovarian (PLCO) cancer dataset is collected by the National Cancer Institute from 155,000 participants (49.5% male). Each participant responded to three ...
Round-the-clock monitoring of human behavior and emotions is required in many healthcare applications which is very expensive but can be automated using machine learning (ML) and sensor technologies. Unfortunately, the lack of infrastructure for collection and sharing of such data is a bottleneck for ML research applied to healthcare. Our goal is to circumvent this bottleneck by simulating a human body in virtual environment. This will allow generation of potentially infinite amounts of shareabl...
The phenomenon of self-organization has been of special interest to the neural network community for decades. In this paper, we study a variant of the Self-Organizing Map (SOM) that models the phenomenon of self-organization of the particles forming a string when the string is tightened from one or both ends. The proposed variant, called the String Tightening Self-Organizing Neural Network (STON), can be used to solve certain practical problems, such as computation of shortest homotopic paths, s...
Monitoring using sensors is ubiquitous in our environment. In this paper, a state estimation model is proposed for continuous activity monitoring from multimodal and heterogenous sensor data. Each sensor is modeled as an independent agent in the predictive coding framework. It can sample its environment, communicate with other agents, and adapt its internal model to its environment in an unsupervised manner. Using controlled experiments, we show that limitations of each sensor, such as inference...
Jun 1, 2020 in CVPR (Computer Vision and Pattern Recognition)
#1Murchana BaruahH-Index: 1
#2Bonny Banerjee (U of M: University of Memphis)H-Index: 11
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Apr 3, 2020 in AAAI (National Conference on Artificial Intelligence)
#1Masoumeh Heidari Kapourchali (U of M: University of Memphis)H-Index: 5
#2Bonny Banerjee (U of M: University of Memphis)H-Index: 11
We propose an agent model capable of actively and selectively communicating with other agents to predict its environmental state efficiently. Selecting whom to communicate with is a challenge when the internal model of other agents is unobservable. Our agent learns a communication policy as a mapping from its belief state to with whom to communicate in an online and unsupervised manner, without any reinforcement. Human activity recognition from multimodal, multisource and heterogeneous sensor da...
1 Citations
#1Zahid Akhtar (U of M: University of Memphis)H-Index: 19
#2Dipankar Dasgupta (U of M: University of Memphis)H-Index: 55
Last. Bonny Banerjee (U of M: University of Memphis)H-Index: 11
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In recent years, there has been an exponential increase in photo and video manipulation by easy-to-use editing tools (e.g., Photoshop). Especially, ‘face digital manipulations’ (e.g., face swapping) is a critical issue for automated face recognition systems (AFRSs) as it detrimentally effects the AFRS’ performance. Also, the advent of powerful deep learning methods has led to realistic face sample generation and manipulation. Despite recent advances in face manipulation detection techniques, man...
8 CitationsSource
#1Jayanta K. Dutta (Bosch)H-Index: 7
#2Bonny Banerjee (U of M: University of Memphis)H-Index: 11
Abstract Outlier detection is an active area of research in data mining and a large number of algorithms exist. Our goal is to come up with a guideline on how to choose the most appropriate outlier detection algorithm for a given dataset without exploiting any domain- or application-specific information. Extensive experimentations with a number of state-of-the-art algorithms on thousands of benchmark datasets revealed a clear trend. For datasets with low dimensionality and low difficulty level, ...
2 CitationsSource