scholarly journals Using big data from long-form recordings to study development and optimize societal impact

2021 ◽  
Author(s):  
Meg Cychosz ◽  
Alejandrina Cristia
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Applied Research ◽  
Language Documentation ◽  
Long Form ◽  
Child Centered ◽  
Observational Bias ◽  
Basic And Applied Research ◽  
Equitable Treatment ◽  
Study Development

Big data are everywhere. In this chapter, we focus on one source: long-form, child-centered recordings collected using wearable technologies. Because these recordings are simultaneously unobtrusive and encompassing, they may be a break-through technology for clinicians and researchers from several diverse fields. We demonstrate this possibility by outlining three applications for the recordings---clinical treatment, large-scale interventions, and language documentation---where we see the greatest potential. We argue that incorporating these recordings into basic and applied research will result in more equitable treatment of patients, more reliable measurements of the effects of interventions on real-world behavior, and deeper scientific insights with less observational bias. We conclude by outlining a proposal for a semi-structured online platform where vast numbers of long-form recordings could be hosted and more representative, less biased algorithms could be trained.

Applied Research in Membrane Fouling in the Membrane Bioreactor Systems with Large-scale Equipment

2019 ◽  
Vol 1 (1) ◽  
pp. 1-10
Author(s):  
Kong Linghan ◽  
Zhao Weidian ◽  
Ran Deqin ◽  
Hui Bing ◽  
Lu Linguo ◽  
...  
Keyword(s):  
Membrane Fouling ◽  
Large Scale ◽  
Membrane Bioreactor ◽  
Applied Research

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

Nonsuicidal Self-Injury Among Youth

2018 ◽  
Author(s):  
Adam Bryant Miller ◽  
Maya Massing-Schaffer ◽  
Sarah Owens ◽  
Mitchell J. Prinstein
Keyword(s):  
Risk Factors ◽  
Applied Research ◽  
Evidence Based ◽  
Prevalence Rates ◽  
Evidence Based Treatment ◽  
The Past ◽  
Self Injury ◽  
Assessment Techniques ◽  
Basic And Applied Research ◽  
Nonsuicidal Self Injury

Nonsuicidal self-injury (NSSI) is direct, intentional harm to one’s own body performed without the intent to die. NSSI has a marked developmental onset reaching peak prevalence in adolescence. NSSI is present in the context of multiple psychological disorders and stands alone as a separate phenomenon. Research has accumulated over the past several decades regarding the course of NSSI. While great advances have been made, there remains a distinct need for basic and applied research in the area of NSSI. This chapter reviews prevalence rates, correlates and risk factors, and leading theories of NSSI. Further, it reviews assessment techniques and provides recommendations. Then, it presents the latest evidence-based treatment recommendations and provides a case example. Finally, cutting edge research and the next frontier of research in this area are outlined.

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