Machine Learning

Elsevier, International Journal of Critical Infrastructure Protection, Volume 31, December 2020
Early and accurate anomaly detection in critical infrastructure (CI), such as water treatment plants and electric power grid, is necessary to avoid plant damage and service disruption. Several machine learning techniques have been employed for the design of an effective anomaly detector in such systems. However, threats such as from insiders and state actors, introduce challenges in the design of an effective anomaly detector. This work presents a multi-layer perceptron (MLP) based anomaly detector that uses an unsupervised approach to safeguard CI from the adverse impacts of cyber-attacks.
Elsevier, EClinicalMedicine, Volume 28, November 2020
Background: The aim of this study is to use classification methods to predict future onset of Alzheimer's disease in cognitively normal subjects through automated linguistic analysis. Methods: To study linguistic performance as an early biomarker of AD, we performed predictive modeling of future diagnosis of AD from a cognitively normal baseline of Framingham Heart Study participants. The linguistic variables were derived from written responses to the cookie-theft picture-description task.
Climate change requires joint actions between government and local actors. Understanding the perception of people and communities is critical for designing climate change adaptation strategies. Those most affected by climate change are populations in coastal regions that face extreme weather events and sea-level increases. In this article, geospatial perception of climate change is identified, and the research parameters are quantified.
Objective imaging-based biomarker discovery for psychiatric conditions is critical for accurate diagnosis and treatment. Using a machine learning framework, this work investigated the utility of brain's functional network topology (complex network features) extracted from functional magnetic resonance imaging (fMRI) functional connectivity (FC) as viable biomarker of autism spectrum disorder (ASD). To this end, we utilized resting-state fMRI data from the publicly available ABIDE dataset consisting of 432 ASD patients and 556 matched healthy controls.
Alzheimer's disease is the most common form of dementia and is a serious health problem. The disease is expected to increase further in the upcoming years with the increase of the elderly population. Developing new treatments and diagnostic methods is getting more important. In this study, we focused on the early diagnosis of dementia in Alzheimer's disease via analysis of neuroimages. We analyzed the data diagnosed by the Alzheimer's Disease Neuroimaging Initiative (ADNI) protocol.
The use of advanced technological solutions (“neurotechnologies”) can improve the clinical outcomes of neurorehabilitation after stroke. Here, Micera et al. propose a paradigm shift that is based on a deep understanding of the basic mechanisms of natural stroke recovery and technology-assisted neurorehabilitation to improve the clinical effectiveness of neurotechnology.
The cost-effectiveness and reliability of waste collection services in informal settlements can be difficult to optimize given the geospatial and temporal variability of latrine use. Daily servicing to avoid overflow events is inefficient, but dynamic scheduling of latrine servicing could reduce costs by providing just-in-time servicing for latrines. This study used cellular-connected motion sensors and machine learning to dynamically predict when daily latrine servicing could be skipped with a low risk of overflow.
This work intends to develop an intelligent, four-dimensional (namely X-Y-Z plus somatosensory), partial control, and virtual-reality-enabled Tai-Chi System (VTCS). Tai-Chi is a traditional mind-body wellness and healing art, and its clinical benefits have been well documented. VTCS integrates Tai-Chi with a series of cutting-edge computer technologies including 4D sensor technology, big-data, signal processing and analysis, human body kinematics, deep learning, virtual reality, and 4D-reconstruction, etc.
The 2011 RAD-AID Conference on International Radiology for Developing Countries discussed data, experiences, and models pertaining to radiology in the developing world, where widespread shortages of imaging services significantly reduce health care quality and increase health care disparities. This white paper from the 2011 RAD-AID conference represents consensus advocacy of multidisciplinary strategies to improve the planning, accessibility, and quality of imaging services in the developing world.

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