James Keller

• University of Missouri

In this article, we introduce a novel framework for learning spatial concepts within a human-in-the-loop (HITL) context, highlighting the critical role of explainability in AI systems. By incorporating human feedback, the approach enhances the learning process, making it particularly suitable for applications where user trust and interpretability a...

Longitudinal monitoring of heart rate (HR) and heart rate variability (HRV) can aid in tracking cardiovascular diseases (CVDs), sleep quality, sleep disorders, and reflect autonomic nervous system activity, stress levels, and overall well-being. These metrics are valuable in both clinical and everyday settings. In this paper, we present a transform...

Purpose To use neural network machine learning (ML) models to identify the most relevant ocular biomarkers for the diagnosis of primary open-angle glaucoma (POAG). Methods Neural network models, also known as multi-layer perceptrons (MLPs), were trained on a prospectively collected observational dataset comprised of 93 glaucoma patients confirmed...

In this paper, the idea of a fuzzy color and a fuzzy color space is shown in an interactive way. It is proposed several interactive elements, where readers can understand the different steps to build them. Furthermore, these elements allow the user to test with his/her own images via the behavior of the fuzzy colors.

When dealing with unbounded streaming data, such as network packets or frames from a continuous live video feed, it is not feasible to apply iterative algorithms over the full dataset. The streaming soft neural gas (StreamSoNG) algorithm proposed by Wu et al. is particularly appealing given its ability to model arbitrary topologies in the data, how...

Deep learning has become increasingly common in aerial imagery analysis. As its use continues to grow, it is crucial that we understand and can explain its behavior. One eXplainable AI (XAI) approach is to generate linguistic summarizations of data and/or models. However, the number of summaries can increase significantly with the number of data at...

In-home, sensor technologies can promote chronic disease self-management and independence among older adults. However, the translation of these technologies from assistive living/long-term care to community-dwelling older adults is lacking. This study aimed to tailor such technologies for private use by older adults, and gather feedback about adopt...

Recent developments in the use of artificial intelligence in the diagnosis and monitoring of glaucoma are discussed. To set the context and fix terminology, a brief historic overview of artificial intelligence is provided, along with some fundamentals of statistical modeling. Next, recent applications of artificial intelligence techniques in glauco...

Synthetic aperture sonar (SAS) imagery is crucial for several applications, including target recognition and environmental segmentation. Deep learning models have led to much success in SAS analysis; however, the features extracted by these approaches may not be suitable for capturing certain textural information. To address this problem, we presen...

Training a surgeon to be skilled and competent to perform a given surgical procedure is essential in providing a high quality of care and reducing the risk of complications. However, existing training techniques limit us from conducting in-depth analyses of surgical motions to evaluate these skills accurately. We develop a method to identify the ge...

Older adults aged 65 and above are at higher risk of falls. Predicting fall risk early can provide caregivers time to provide interventions, which could reduce the risk, potentially avoiding a possible fall. In this paper, we present an analysis of 6-month fall risk prediction in older adults using geriatric assessments, GAITRite measurements, and...

Older adults have experienced greater isolation and mental health concerns during the COVID-19 pandemic. In long-term care (LTC) settings, residents have been particularly impacted due to strict lockdown policies. Little is known about how these policies have impacted older adults. This study leveraged existing research with embedded sensors instal...

Older adults age 65 and above are at higher risk of falls. In this research, we explored an analysis of 6-month fall risk prediction in older adults using geriatric assessments, GAITRite measurements, and fall history. We used the SHAP (SHapley Additive exPlanations) approach to explain our model predictions to understand which predictor variables...

Left ventricular (LV) catheterization provides LV pressure-volume (P-V) loops and it represents the gold standard for cardiac function monitoring. This technique, however, is invasive and this limits its applicability in clinical and in-home settings. Ballistocardiography (BCG) is a good candidate for non-invasive cardiac monitoring, as it is based...

Purpose: This study proposes a novel approach to obtain personalized estimates of cardiovascular parameters by combining (i) electrocardiography and ballistocardiography for noninvasive cardiovascular monitoring, (ii) a physiology-based mathematical model for predicting personalized cardiovascular variables, and (iii) an evolutionary algorithm (EA)...

Feature representation is an important aspect of remote-sensing-based image classification. While deep convolutional neural networks (DCNNs) are able to effectively amalgamate information, large numbers of parameters often make learned features inscrutable and difficult to transfer to alternative models. In order to better represent statistical tex...

Older adults have experienced greater isolation and mental health concerns during the COVID-19 pandemic. In long-term care (LTC) settings, residents have been particularly impacted due to strict lockdown policies. Little is known about how these policies have impacted older adults. This study leveraged existing research with embedded sensors instal...

The time interval between the peaks in the electroccardiogram (ECG) and ballistocardiogram (BCG) waveforms, TEB, has been associated with the pre-ejection period (PEP), which is an important marker of ventricular contractility. However, the applicability of BCG-related markers in clinical practice is limited by the difficulty to obtain a replicable...

The Possibilistic Fuzzy Local Information C-Means (PFLICM) method is presented as a technique to segment side-look synthetic aperture sonar (SAS) imagery into distinct regions of the sea-floor. In this work, we investigate and present the results of an automated feature selection approach for SAS image segmentation. The chosen features and resultin...

Examining most streaming clustering algorithms leads to the understanding that they are actually incremental classification models. They model existing and newly discovered structures via summary information that we call footprints. Incoming data is normally assigned a crisp label (into one of the structures) and that structure's footprint is incre...

The rapid aging of the population worldwide requires increased attention from healthcare providers and the entire society. For the elderly to live independently, many health issues related to old age, such as frailty and risk of falling, need increased attention and monitoring. When monitoring daily routines for older adults, it is desirable to det...

Synthetic Aperture Sonar (SAS) surveys produce imagery with large regions of transition between seabed types. Due to these regions, it is difficult to label and segment the imagery and, furthermore, challenging to score the image segmentations appropriately. While there are many approaches to quantify performance in standard crisp segmentation sche...

The purpose of this paper is two-fold. First, and foremost, it fixes an error that somehow made it through all the reviewing, both by the authors and the referees. Second, it provides insights into the meaning and variation of the main PCM parameters in this approach to transfer clustering.

Time-series analysis has been an active area of research for years, with important applications in forecasting or discovery of hidden information such as patterns or anomalies in observed data. In recent years, the use of time-series analysis techniques for the generation of descriptions and summaries in natural language of any variable, such as te...

In this work, we present an in-depth and systematic analysis using tools such as local interpretable model-agnostic explanations (LIME) (arXiv:1602.04938) and divergence measures to analyze what changes lead to improvement in performance in fine tuned models for synthetic aperture sonar (SAS) data. We examine the sensitivity to factors in the fine...

In this paper, we investigate performing joint dimensionality reduction and classification using a novel histogram neural network. Motivated by a popular dimensionality reduction approach, t-Distributed Stochastic Neighbor Embedding (t-SNE), our proposed method incorporates a classification loss computed on samples in a low-dimensional embedding sp...

This article is a position paper about models and algorithms that are generally called "stream clustering." Semantics and methods used in this field are often co-opted from static clustering, but they do not serve well for streaming data analysis. Most "state-of-the-art" methods, such as sequential k-means, Birch, CluStream, DenStream, etc., acknow...

In-home monitoring has the potential to track health changes for older adults with chronic health conditions, thereby making early treatment possible when exacerbations arise. A recliner chair is often used by older adults for sleeping at night, especially by those with breathing difficulty, neck and back problems. Here, we present a sensor system...

While most deep learning architectures are built on convolution, alternative foundations such as morphology are being explored for purposes such as interpretability and its connection to the analysis and processing of geometric structures. The morphological hit-or-miss operation has the advantage that it considers both foreground information and ba...

Presents a missing funding acknowledgment for Dr. Mihail Popescu in the above named paper.

Examining most streaming clustering algorithms leads to the understanding that they are actually incremental classification models. They model existing and newly discovered structures via summary information that we call footprints. Incoming data is normally assigned crisp labels (into one of the structures) and that structure's footprints are incr...

The modern era of machine learning is focused on data-driven solutions. While this has resulted in astonishing leaps in numerous applications, explainability has not witnessed the same growth. The reality is, most machine learning solutions are black boxes. Herein, we focus on data/information fusion in machine learning. Specifically, we explore fo...

Traditional machine learning and data mining have made tremendous progress in many knowledge-based areas, such as clustering, classification, and regression. However, the primary assumption in all of these areas is that the training and testing data should be in the same domain and have the same distribution. This assumption is difficult to achieve...

Presents information on the 2019 IEEE International Conference on Fuzzy Systems.

Neural networks have demonstrated breakthrough results in numerous application domains. While most architectures are built on the premise of convolution, alternative foundations like morphology are being explored for reasons like interpretability and its connection to the analysis and processing of geometric structures. Herein, we investigate new d...