scholarly journals A machine learning framework to quantify and assess the impact of COVID-19 on the power sector: An Indian context

2021 ◽  
pp. 100078
Author(s):  
Manu Suvarna ◽  
Apoorva Katragadda ◽  
Ziying Sun ◽  
Yun Bin Choh ◽  
Qianyu Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Sector ◽  
Learning Framework ◽  
Indian Context ◽  
The Impact

scholarly journals Machine learning based classification of cells into chronological stages using single-cell transcriptomics

2018 ◽  
Author(s):  
Sumeet Pal Singh ◽  
Sharan Janjuha ◽  
Samata Chaudhuri ◽  
Susanne Reinhardt ◽  
Sevina Dietz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Predictive Power ◽  
Single Cells ◽  
Premature Aging ◽  
Calorie Intake ◽  
Learning Framework ◽  
Pancreatic Cells ◽  
Age Related ◽  
The Impact

ABSTRACTAge-associated deterioration of cellular physiology leads to pathological conditions. The ability to detect premature aging could provide a window for preventive therapies against age-related diseases. However, the techniques for determining cellular age are limited, as they rely on a limited set of histological markers and lack predictive power. Here, we implement GERAS (GEnetic Reference for Age of Single-cell), a machine learning based framework capable of assigning individual cells to chronological stages based on their trans criptomes. GERAS displays greater than 90% accuracy in classifying the chronological stage of zebrafish and human pancreatic cells. The framework demonstrates robustness against biological and technical noise, as evaluated by its performance on independent samplings of single-cells. Additionally, GERAS determines the impact of differences in calorie intake and BMI on the aging of zebrafish and human pancreatic cells, respectively. We further harness the predictive power of GERAS to identify genome-wide molecular factors that correlate with aging. We show that one of these factors, junb, is necessary to maintain the proliferative state of juvenile beta-cells. Our results showcase the applicability of a machine learning framework to classify the chronological stage of heterogeneous cell populations, while enabling to detect pro-aging factors and candidate genes associated with aging.

scholarly journals An interpretable machine-learning framework for dark matter halo formation

2019 ◽  
Vol 490 (1) ◽  
pp. 331-342 ◽  
Author(s):  
Luisa Lucie-Smith ◽  
Hiranya V Peiris ◽  
Andrew Pontzen
Keyword(s):  
Machine Learning ◽  
Dark Matter ◽  
Initial Density ◽  
Dark Matter Halo ◽  
Halo Formation ◽  
Learning Framework ◽  
Interpretable Machine Learning ◽  
Leibler Divergence ◽  
The Difference ◽  
The Impact

ABSTRACT We present a generalization of our recently proposed machine-learning framework, aiming to provide new physical insights into dark matter halo formation. We investigate the impact of the initial density and tidal shear fields on the formation of haloes over the mass range 11.4 ≤ log (M/M⊙) ≤ 13.4. The algorithm is trained on an N-body simulation to infer the final mass of the halo to which each dark matter particle will later belong. We then quantify the difference in the predictive accuracy between machine-learning models using a metric based on the Kullback–Leibler divergence. We first train the algorithm with information about the density contrast in the particles’ local environment. The addition of tidal shear information does not yield an improved halo collapse model over one based on density information alone; the difference in their predictive performance is consistent with the statistical uncertainty of the density-only based model. This result is confirmed as we verify the ability of the initial conditions-to-halo mass mapping learnt from one simulation to generalize to independent simulations. Our work illustrates the broader potential of developing interpretable machine-learning frameworks to gain physical understanding of non-linear large-scale structure formation.

Creational and Structural Patterns in a Flexible Machine Learning Framework for Medical Ultrasound Diagnostics

10.29007/p1wk ◽  
2019 ◽  
Author(s):  
Corey Thibeault
Keyword(s):  
Machine Learning ◽  
Design Patterns ◽  
Clinical Applications ◽  
Machine Learning Algorithms ◽  
The Novel ◽  
Learning Framework ◽  
Flexible Machine ◽  
Learning From Data ◽  
Ultrasound Diagnostics ◽  
The Impact

The impact of machine learning in medicine has arguably lagged behind its commercial counterparts. This may be attributable to the generally slower pace and higher costs associated with clinical applications, but also present are the conflicting constraints and requirements of learning from data in a highly regulated industry that introduce levels of complexity unique to the medical space. Because of this, the balance between innovation and controlled development is challenging. Adding to this are the multiple modalities found in most clinical applications where applying traditional machine learning preprocessing and cross-validation techniques can be precarious. This work presents the novel use of creational and structural design patterns in a generalized software framework intended to alleviate some of those difficulties. Designed to be a configurable pipeline to not only support the experimentation and development of diagnostic machine learning algorithms, but also to support the transition of those algorithms into production level systems in a composed manner. The resulting framework provides the foundation for developing unique tools by both novice and expert data scientists.

scholarly journals DEVELOPING A MACHINE LEARNING FRAMEWORK FOR ESTIMATING SOIL MOISTURE WITH VNIR HYPERSPECTRAL DATA

2018 ◽  
Vol IV-1 ◽  
pp. 101-108 ◽  
Author(s):  
S. Keller ◽  
F. M. Riese ◽  
J. Stötzer ◽  
P. M. Maier ◽  
S. Hinz
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Regression Models ◽  
Hyperspectral Data ◽  
Supervised Machine Learning ◽  
Self Organizing Maps ◽  
Learning Framework ◽  
Sensor Field ◽  
Regression Framework ◽  
The Impact

<p><strong>Abstract.</strong> In this paper, we investigate the potential of estimating the soil-moisture content based on VNIR hyperspectral data combined with LWIR data. Measurements from a multi-sensor field campaign represent the benchmark dataset which contains measured hyperspectral, LWIR, and soil-moisture data conducted on grassland site. We introduce a regression framework with three steps consisting of feature selection, preprocessing, and well-chosen regression models. The latter are mainly supervised machine learning models. An exception are the self-organizing maps which combine unsupervised and supervised learning. We analyze the impact of the distinct preprocessing methods on the regression results. Of all regression models, the extremely randomized trees model without preprocessing provides the best estimation performance. Our results reveal the potential of the respective regression framework combined with the VNIR hyperspectral data to estimate soil moisture measured under real-world conditions. In conclusion, the results of this paper provide a basis for further improvements in different research directions.</p>

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Role of Nutraceuticals in Health Promotion and Disease Prevention: A Review in the Indian Context

2020 ◽  
Vol 19 (2) ◽  
pp. 139-145
Author(s):  
Sheena Chhabra ◽  
Apurva Bakshi ◽  
Ravineet Kaur
Keyword(s):  
Health Promotion ◽  
Disease Prevention ◽  
Human Health ◽  
Health Issues ◽  
Scholarly Literature ◽  
Indian Context ◽  
The Impact ◽  
Immense Potential ◽  
Specific Health

Nutraceuticals have been around for quite some time. As the nomenclature suggests, they are placed somewhere between food (nutra-) and medicine (-ceuticals) in terms of their impact on human health. Researches have focused on the impact of various types of nutraceuticals on health, their efficacy in health promotion and disease prevention, and often on suitable uses of certain categories of nutraceuticals for specific health issues. However, we are still far from utilizing the immense potential of nutraceuticals for benefiting human health in a substantial manner. We review the available scholarly literature regarding the role of nutraceuticals in health promotion, their efficacy in disease prevention and the perception of nutraceuticals' health benefits by consumers. Thereafter we analyze the need for regulation of nutraceuticals and various provisions regarding the same.

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