Envy Prediction from Users' Photos using Convolutional Neural Networks

Mohaimenul Azam Khan Raiaan; Abdullah Al Mamun; Md Adnanul Islam; Mohammed Eunus Ali; Md Saddam Hossain Mukta

doi:10.1109/ICCECE51049.2023.10085092

Envy Prediction from Users' Photos using Convolutional Neural Networks

Mohaimenul Azam Khan Raiaan
, Abdullah Al Mamun
, Md Adnanul Islam
, Mohammed Eunus Ali
, Md Saddam Hossain Mukta

Department of Human Centred Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

7 Citations (Scopus)

Abstract

Envy is often considered a negative trait in human behavior. However, envy also has a positive insight that can motivate a person to accomplish her desired goals. In this paper, we propose a novel method to identify a user's state of envy (i.e., benign or malicious) based on features from her photos. Specifically, we build a fine-Tuned Convolutional Neural Network (CNN) model that takes the user's photo as input and predicts whether the user has benign or malicious envy characteristics in the given photo. For this study, we create a new dataset containing photos of 255 users of different gender and age group. We conduct ablation studies to build an optimal CNN model to obtain a commendable test accuracy of 97.9%.

Original language	English
Title of host publication	2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE 2023)
Editors	Chandrama Shaw, C.M. Basak, Tanya Das, M. Bandyopadhyay
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Number of pages	7
ISBN (Electronic)	9781665452519
ISBN (Print)	9781665452526
DOIs	https://doi.org/10.1109/ICCECE51049.2023.10085092
Publication status	Published - 2023
Event	International Conference on Computer, Electrical and Communication Engineering 2023 - Kolkata, India Duration: 20 Jan 2023 → 21 Jan 2023 https://ieeexplore.ieee.org/xpl/conhome/10084914/proceeding (Proceedings) https://www.iccece.com/iccece23/index.php (Website)

Conference

Conference	International Conference on Computer, Electrical and Communication Engineering 2023
Abbreviated title	ICCECE 2023
Country/Territory	India
City	Kolkata
Period	20/01/23 → 21/01/23
Internet address	https://ieeexplore.ieee.org/xpl/conhome/10084914/proceeding (Proceedings) https://www.iccece.com/iccece23/index.php (Website)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

BeMaS
Convolutional Neural Network
Deep Learning
Envy

Access to Document

10.1109/ICCECE51049.2023.10085092

Cite this

Raiaan, M. A. K., Al Mamun, A., Islam, M. A., Ali, M. E., & Mukta, M. S. H. (2023). Envy Prediction from Users' Photos using Convolutional Neural Networks. In C. Shaw, C. M. Basak, T. Das, & M. Bandyopadhyay (Eds.), 2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE 2023) IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICCECE51049.2023.10085092

Raiaan, Mohaimenul Azam Khan ; Al Mamun, Abdullah ; Islam, Md Adnanul et al. / Envy Prediction from Users' Photos using Convolutional Neural Networks. 2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE 2023). editor / Chandrama Shaw ; C.M. Basak ; Tanya Das ; M. Bandyopadhyay. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2023.

@inproceedings{789b854c09a44834abc34194c3550dd0,

title = "Envy Prediction from Users' Photos using Convolutional Neural Networks",

abstract = "Envy is often considered a negative trait in human behavior. However, envy also has a positive insight that can motivate a person to accomplish her desired goals. In this paper, we propose a novel method to identify a user's state of envy (i.e., benign or malicious) based on features from her photos. Specifically, we build a fine-Tuned Convolutional Neural Network (CNN) model that takes the user's photo as input and predicts whether the user has benign or malicious envy characteristics in the given photo. For this study, we create a new dataset containing photos of 255 users of different gender and age group. We conduct ablation studies to build an optimal CNN model to obtain a commendable test accuracy of 97.9\%.",

keywords = "BeMaS, Convolutional Neural Network, Deep Learning, Envy",

author = "Raiaan, \{Mohaimenul Azam Khan\} and \{Al Mamun\}, Abdullah and Islam, \{Md Adnanul\} and Ali, \{Mohammed Eunus\} and Mukta, \{Md Saddam Hossain\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; International Conference on Computer, Electrical and Communication Engineering 2023, ICCECE 2023 ; Conference date: 20-01-2023 Through 21-01-2023",

year = "2023",

doi = "10.1109/ICCECE51049.2023.10085092",

language = "English",

isbn = "9781665452526",

editor = "Chandrama Shaw and C.M. Basak and Tanya Das and M. Bandyopadhyay",

booktitle = "2023 International Conference on Computer, Electrical \& Communication Engineering (ICCECE 2023)",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

url = "https://ieeexplore.ieee.org/xpl/conhome/10084914/proceeding, https://www.iccece.com/iccece23/index.php",

}

Raiaan, MAK, Al Mamun, A, Islam, MA , Ali, ME & Mukta, MSH 2023, Envy Prediction from Users' Photos using Convolutional Neural Networks. in C Shaw, CM Basak, T Das & M Bandyopadhyay (eds), 2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE 2023). IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, International Conference on Computer, Electrical and Communication Engineering 2023, Kolkata, India, 20/01/23. https://doi.org/10.1109/ICCECE51049.2023.10085092

Envy Prediction from Users' Photos using Convolutional Neural Networks. / Raiaan, Mohaimenul Azam Khan; Al Mamun, Abdullah; Islam, Md Adnanul et al.
2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE 2023). ed. / Chandrama Shaw; C.M. Basak; Tanya Das; M. Bandyopadhyay. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2023.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

TY - GEN

T1 - Envy Prediction from Users' Photos using Convolutional Neural Networks

AU - Raiaan, Mohaimenul Azam Khan

AU - Al Mamun, Abdullah

AU - Islam, Md Adnanul

AU - Ali, Mohammed Eunus

AU - Mukta, Md Saddam Hossain

PY - 2023

Y1 - 2023

N2 - Envy is often considered a negative trait in human behavior. However, envy also has a positive insight that can motivate a person to accomplish her desired goals. In this paper, we propose a novel method to identify a user's state of envy (i.e., benign or malicious) based on features from her photos. Specifically, we build a fine-Tuned Convolutional Neural Network (CNN) model that takes the user's photo as input and predicts whether the user has benign or malicious envy characteristics in the given photo. For this study, we create a new dataset containing photos of 255 users of different gender and age group. We conduct ablation studies to build an optimal CNN model to obtain a commendable test accuracy of 97.9%.

AB - Envy is often considered a negative trait in human behavior. However, envy also has a positive insight that can motivate a person to accomplish her desired goals. In this paper, we propose a novel method to identify a user's state of envy (i.e., benign or malicious) based on features from her photos. Specifically, we build a fine-Tuned Convolutional Neural Network (CNN) model that takes the user's photo as input and predicts whether the user has benign or malicious envy characteristics in the given photo. For this study, we create a new dataset containing photos of 255 users of different gender and age group. We conduct ablation studies to build an optimal CNN model to obtain a commendable test accuracy of 97.9%.

KW - BeMaS

KW - Convolutional Neural Network

KW - Deep Learning

KW - Envy

UR - https://www.scopus.com/pages/publications/85153409053

U2 - 10.1109/ICCECE51049.2023.10085092

DO - 10.1109/ICCECE51049.2023.10085092

M3 - Conference Paper

AN - SCOPUS:85153409053

SN - 9781665452526

BT - 2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE 2023)

A2 - Shaw, Chandrama

A2 - Basak, C.M.

A2 - Das, Tanya

A2 - Bandyopadhyay, M.

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

T2 - International Conference on Computer, Electrical and Communication Engineering 2023

Y2 - 20 January 2023 through 21 January 2023

ER -

Raiaan MAK, Al Mamun A, Islam MA , Ali ME, Mukta MSH. Envy Prediction from Users' Photos using Convolutional Neural Networks. In Shaw C, Basak CM, Das T, Bandyopadhyay M, editors, 2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE 2023). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2023 doi: 10.1109/ICCECE51049.2023.10085092

Envy Prediction from Users' Photos using Convolutional Neural Networks

Abstract

Conference

UN SDGs

Keywords

Access to Document

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