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Human Behavior Prediction and Analysis Using Machine Learning-A Review
2021
Turkish Journal of Computer and Mathematics Education
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The research in this paper contributes the review of the human behavior analysis through different body parameters, food habits and social media influences with social behavior of the person. ...
Hence After a detailed study on different human health disease classification techniques it is found that machine learning techniques are reliable for the feature extraction and analysis of the different ...
They have proposed Social Influence Deep Learning or SIDL framework in order to do so. ...
doi:10.17762/turcomat.v12i5.1499
fatcat:2spmtos53rfs3g5d2qhfycmska
Advances in Processing, Mining, and Learning Complex Data: From Foundations to Real-World Applications
2018
Complexity
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Social Network Data Social influence analysis is important for many social network applications, including recommendation and cyber security analysis. ...
integrated the negative ε dragging technique and the kernel method into linear regression for robust pattern classification under the condition that the consistency and compatibility between the test ...
Acknowledgments The Guest Editorial Team would like to express their gratitude to all the authors for their interest in selecting this special issue as a venue for their scholarly work dissemination. ...
doi:10.1155/2018/7861860
fatcat:6mc7cqtqzjcjlghqbmlhb5hifa
Preserving differential privacy in convolutional deep belief networks
2017
Machine Learning
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We applied our model in a health social network, i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for human behavior prediction, human behavior classification, and handwriting ...
In this paper, we focus on developing a private convolutional deep belief network (pCDBN), which essentially is a convolutional deep belief network (CDBN) under differential privacy. ...
We thank Xiao Xiao and Rebeca Sacks for their contributions. ...
doi:10.1007/s10994-017-5656-2
pmid:30867620
pmcid:PMC6411072
fatcat:zep7fjkizffonjfscoh6obmtne
Preserving Differential Privacy in Convolutional Deep Belief Networks
[article]
2018
arXiv
pre-print
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We applied our model in a health social network, i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for human behavior prediction, human behavior classification, and handwriting ...
In this paper, we focus on developing a private convolutional deep belief network (pCDBN), which essentially is a convolutional deep belief network (CDBN) under differential privacy. ...
We thank Xiao Xiao and Rebeca Sacks for their contributions. ...
arXiv:1706.08839v2
fatcat:oll7ugptbjaihb4vfohdrtwnmm
Machine Learning Algorithms for Depression: Diagnosis, Insights, and Research Directions
2022
Electronics
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ML methodologies are being utilized in mental health to predict the probabilities of mental disorders and, therefore, execute potential treatment outcomes. ...
The global technological development in healthcare digitizes the scopious data, enabling the map of the various forms of human biology more accurately than traditional measuring techniques. ...
The algorithms under review are SVM (linear kernel), SVM (nonlinear kernel), and relevance vector regression. Only one mental health domain (MHD) is used to analyze in this survey. ...
doi:10.3390/electronics11071111
fatcat:bx5z4vbqgrd67htkaz6rmt65ou
A Systematic Review of Applications of Machine Learning Techniques for Wildfire Management Decision Support
2022
Inventions
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Wildfire management decision support models are thus important for avoiding or mitigating the effects of these events. ...
In this context, this paper aims at providing a review of recent applications of machine learning methods for wildfire management decision support. ...
spatial [90] B prediction model for forest fire Convolutional Neural Network Yunnan Province, China Area under curve = 0.86 susceptibility Boosted Regression Tree, [91] B Assessing forest fire susceptibility ...
doi:10.3390/inventions7010015
fatcat:cce4w6uuv5gwlk4ladocmw6cvu
2020 Index IEEE Transactions on Affective Computing Vol. 11
2021
IEEE Transactions on Affective Computing
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
Note that the item title is found only under the primary entry in the Author Index. ...
Rank Regression Framework for Depression Score Prediction from Speech. ...
doi:10.1109/taffc.2021.3055662
fatcat:het65admgnbbvn4fdzdgmftuqu
TSViz: Demystification of Deep Learning Models for Time-Series Analysis
2019
IEEE Access
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This paper presents a novel framework for demystification of convolutional deep learning models for time-series analysis. ...
This is extremely important in domains like finance, industry 4.0, self-driving cars, health-care, counter-terrorism etc., where reasons for reaching a particular prediction are equally important as the ...
FIGURE 6 . 6 Application of the inverse optimization framework for the regression use-case. ...
doi:10.1109/access.2019.2912823
fatcat:yiaqmn425bdtbhnnt6h4z65baa
Grand Challenges in Pedometrics-AI Research
2021
Frontiers in Soil Science
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in the social, cultural, economic, and political domains (e.g., land management, carbon credit markets, economic incentives and programs, human resource capital). ...
The conceptual frameworks underlying pedometrics and digital soil mapping (DSM) have been rooted in factorial models that relate soils and factors that influence soil formation, socalled soil-environmental ...
ACKNOWLEDGMENTS I would like to thank the critical discussion within the Pedometrics Commission of the International Union of Soil Science for the inspiration to write this short article. ...
doi:10.3389/fsoil.2021.714323
fatcat:esp4js65lbfetciby3ltd7o3zm
VaxEquity: A Data-Driven Risk Assessment and Optimization Framework for Equitable Vaccine Distribution
[article]
2022
arXiv
pre-print
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The machine learning-based risk prediction model characterizes how the risk is influenced by the underlying essential factors, e.g., the vaccination level among the population in each COVAX country. ...
This paper aims to address this challenge by first proposing a data-driven risk assessment and prediction model and then developing a decision-making framework to support the strategic vaccine distribution ...
Convolutional Neural Network We further investigate a CNN model with a total of 5 layers and 256 nodes.
C. Linear Regression This section studies the LR model for risk prediction. ...
arXiv:2201.07321v1
fatcat:u2mvefwcfbeybihmfq2sp5mj2i
Noise Annoyance Prediction of Urban Substation Based on Transfer Learning and Convolutional Neural Network
2022
Energies
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In a fixed learning rate and epoch setting, the influence of different mini-batch size values on the prediction accuracy of the model was compared and analyzed. ...
To accurately assess the degree of noise annoyance caused by substations to surrounding residents, we established a noise annoyance prediction model based on transfer learning and a convolution neural ...
Based on transfer learning, we developed a convolutional neural network model for the degree of noise annoyance under small data sets. ...
doi:10.3390/en15030749
doaj:9b34b1dfbb1a461ba274b26b63d6d877
fatcat:o63z663trbdkvjiqetlhbn44dm
Artificial Intelligence and Big Data in Public Health
2018
International Journal of Environmental Research and Public Health
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In this paper, we describe fundamental concepts underlying AI and Big Data and their significance to public health. ...
The important issues of ethics and the need for an overarching regulatory framework for AI and precision medicine have not been sufficiently addressed in public health, and need further attention. ...
Common approaches include multivariate regression analysis and neural networks to construct predictive models. ...
doi:10.3390/ijerph15122796
fatcat:kt4mqavofvg6jgvzee3p4ntsqq
IEEE Access Special Section Editorial: Scalable Deep Learning for Big Data
2020
IEEE Access
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The article ''Integration of Google Play content and frost prediction using CNN: Scalable IoT framework for big data,'' by Latif et al., introduces a CNN model approach for frost event prediction based ...
The article ''Deep forest regression for short-term load forecasting of power systems,'' by Yin et al., proposes deep forest regression for the short-term load forecasting of power systems. ...
doi:10.1109/access.2020.3041166
fatcat:zkzdnzk22jge3l5mwju3j42mcu
Deep Learning-Based Text Emotion Analysis for Legal Anomie
2022
Frontiers in Psychology
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Text emotion analysis is an effective way for analyzing the emotion of the subjects' anomie behaviors. ...
Bi-direction Convolutional Word Embedding Classification Framework (BCDF) can express the word vector in the text and embed the part of speech tagging information as a feature of sentence representation ...
In social media and mental health, the deep learning-based classification model can be used to identify and predict various psychological disorders and calculate personality. ...
doi:10.3389/fpsyg.2022.909157
fatcat:cbzostrigbaythigq6upjjfitq
Estimation of BMI from Facial Images using Semantic Segmentation based Region-Aware Pooling
[article]
2021
arXiv
pre-print
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The recent works have either employed hand-crafted geometrical face features or face-level deep convolutional neural network features for face to BMI prediction. ...
Although useful, these methods missed the detailed local information which is essential for exact BMI prediction. ...
Other than health, BMI values have also been used to estimate and predict the social behaviors of societies. ...
arXiv:2104.04733v1
fatcat:j2au5ndfgzctfe5bxv47iewf4m
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