2. CONTENTS
Introduction
Literature Review
About Research work
Motivation
Problem statement
Objectives
Course work
References
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3. INTRODUCTION
• Mental health issues can lead to serious consequences like depression, self-mutilation, and worse, especially
for university students who are not physically and mentally mature
• As society becomes increasingly more informatized, humans require a higher level of computer intelligence.
Human-computer interaction (HCI) is not limited to the original hardware-based interaction.
• Some relatively smarter interaction methods are gradually appearing in people's lives, such as a series of more
intelligent methods related to face recognition, gesture recognition, and voice recognition.
• Intelligent systems based on ML and DL can help to establish communication between humans and
computers.
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4. Introduction to Machine Learning:
Machine learning is an application of artificial intelligence.
It describes all about computers being able to think and act with less human intervention and also
perform tasks on their own by previous experiences.
The difference between normal computer software and machine learning is that a human developer
hasn’t given codes that instructs the system how to react to situation, instead it is being trained by a
large number of data.
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5. Introduction to Deep Learning:
Deep Learning is an evolution of Machine Learning. It describes all about computers
learning to think using structures modeled on the human brain.
In deep learning, a computer model learns to perform classification tasks directly from
images, text, or sound.
The term “deep” usually refers to the number of hidden layers in the neural network.
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6. Application areas of ML & DL in healthcare:
Diagnosis and disease identification
Health records improvement
Finding the best cure
Making diagnosis via image analysis.
Personalizing treatment.
Adjusting behaviour
Medical research and clinical trial improvement.
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7. Importance of ML & DL in health-care:
Machine Learning for healthcare technologies provides algorithms with self-learning neural networks that
are able to increase the quality of treatment by analyzing external data on a patient’s condition, their X-
rays, CT scans, various tests, and screenings.
Also, worth mentioning, deep learning is now largely used for detecting Problematic places in human body
like cancer cells ,etc..
The early disease detection is one of the most prevalent tasks in machine learning and deep learning, and it
plays an important role in modern medical diagnosis and pre-treatment systems.
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8. LITERATURE REVIEW
S.No
Author &
Year
Article Title Result
1
Wenhong Zhao
(Jun-2022)
Multi-modal Educational Data Fusion for Students’Mental Health Detection
The CASTLE framework and
MOON algorithm, but also to explore the
detectability of students with mental health
problems.
2
Andrew Danowitz
(Jun-2022)
Mental Health in Engineering Education : Identifying Population and
Intersectional Variation
Confirm that engineering students face
higher rates of anxiety and depressive
disorders than the general U.S. population.
3
Ziyu Li
(Mar-2022)
The Recognition of Multiple Anxiety-Levels Based on
Electroencephalograph
Correlation Analysis Between EEG
Features and Predetermined Anxiety
Levels, Classification Performance.
4
Muhammad Awais
(Dec-2021)
LSTM-Based Emotion Detecting using psychological signals: IOT frame
work for healthcare and distance learning in Covid-19
Emotion Recognition Through Physiological
Sensors, the average communication delay
in proposed protocols, TS-MAC and R-
MAC, along with state of the art is evaluated
8
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9. Cont..
S.No
Author &
Year
Article Title Result
5
Jiang Bian
(Aug-2021)
CRLEDD: Regularized Causalities Learning for Early
Detection of Diseases using Electronic Health Record (EHR)
Data
CRLEDD is designed to lower the expected
error rate of LDA model for high-dimensional EHR data,
through regularizing the precision matrix with Graphical
Lasso
6 Xingeng Liu
(May-2021)
Analysis of the Architecture of the Mental Health
Education System for College Students Based on the
Internet of Things and Privacy Security
To achieve the protection of private data. Dataset
experiments prove that compared with existing algorithms,
the algorithm and model proposed in this can better
balance the level of privacy protection and classification
Accuracy
7
Suzanne Lischer
(Jan-2021)
Remote learning and students’mental health
during the Covid-19pandemic:A mixed- method enquiry
The majority of respondents were female, and the mean
age was 27 (median, 25). The response rate from the
different faculties varied considerably
8
Jie Zhang
(Sep-2020)
Anxiety Recognition of College Students Using a Takagi-
Sugeno -Kang Fuzzy System Modeling Method and Deep
Features
Verify the superiority of the anxiety identification method
The experimental results further demonstrate that the depth
features have richer information than traditional features.
9
Sep 2022
10. Cont..
S. No
Author &
Year
Article Title Result
9
Yasuhiro Kotera,
et al (May-2020)
Mental health of Malaysian university students: UK
comparison, and relationship between negative mental
health attitudes, self compassion, and resilience
To evaluate and explore
mental health of Malaysian students, in relation to
negative mental health attitudes, self-compassion, and
resilience.
10
Xuemei Chen
(Apr-2020)
A Depression Recognition Method for College Students Using
Deep Integrated SVM Algorithm
Feature extraction and dimensionality reduction for
depression-prone user identification were performed,
and input data suitable for the classifier was constructed.
11
Mohammed A. Mamun
(Oct-2019)
Mental Health Problems and Associated Predictors
Among Bangladeshi Students
That 52.2% of the participants moderate to extremely
severe depression, 58.1% had moderate to extremely
severe anxiety, and 24.9% had moderate to extremely
severe stress
12
Kaili Zhao
(Aug-2016)
Joint Patch and Multi-label Learning for Facial
Action Unit and Holistic Expression Recognition
Presented a Joint Patch and Multi-label Learning (JPML)
framework for facial AU and holistic expression
recognition.
10
Sep 2022
11. Introduction to the RESEARCH WORK with CASTLE(frame work):
An educational data fusion detection framework CASTLE for achieving an accurate detection through fusing multi-
modal data generated from campus life
Firstly, utilize representation learning to fuse data on social life, academic performance, and physical appearance.
An algorithm, named MOON (multi-view social network embedding), is proposed to represent students’ social
life in a comprehensive way by fusing students’ heterogeneous social relations effectively.
Second, a synthetic minority oversampling technique algorithm (SMOTE) is applied to the label imbalance issue.
Finally, a DNN (deep neural network) model is utilized for the final detection.
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12. The extensive results demonstrate the promising performance of the proposed methods in comparison to an
extensive range of state-of-the-art baselines
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13. Introduction of Anxiety Recognition:
At present anxiety focused on the associations between EEG features and anxiety changes observed that anxiety
level changes were proportional to changes in alpha waves in high anxiety subjects.
Furthermore, extensive literature has revealed that EEG asymmetry is related to changes in emotion-related traits
and states.
Previous researches has successfully differentiated anxiety states using a machine learning method designed a
riding task with data recording via Photoplethysmogram.
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14. The description of the EEG data preprocessing and EEG data segmentation, and the key techniques,
including calculation of various EEG features, feature selection and classification are,
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15. MOTIVATION
In this study, by eliciting different anxiety levels and collecting corresponding EEG data, then enhanced the
accuracy of multi-level anxiety recognition and simultaneously revealed the impact of different features on
anxiety recognition
These findings might provide new insights into anxiety study, lay the foundation for the detection of
continuous anxiety changes and help people better understand anxiety .
Will develop subsequent versions of the CASTLE framework to fuse more features, like students’
Internet access patterns and life orderliness, to achieve better detection performance.
15
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16. PROBLEM STATEMENT
The accurate detections are hard to achieve due to the inherent complexity and heterogeneity of unstructured
multi-modal data generated by campus life. It is a complex emotional state that has a great impact on people’s
physical and mental health by inducing various anxiety states of college students with ElectroEncephaloGraph
(EEG) recording, comprehensive EEG features, including not only commonly used frequency domain features
but also the time domain, statistical and nonlinear features were extracted from different EEG bands and brain
locations
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17. OBJECTIVES
To Address the issues and also intend to integrate the CASTLE framework into the modern educational
management system to assist with educational decision making using Machine Learning and Deep Learning-
Based Models.
To attempt and develop the data-driven methods for capturing students’ social patterns based on groupwork
records, or discussion records on LMS, to replace the questionnaire-based data collection.
To introduce the causal learning related techniques to analyze the experimental results.
17
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18. Data Set description:
The data was collected from the Data World repository & Kaggle
data sets.
1. Real world Educational Data Set for Student data
2. EHR Data Set (Electronic Health Record data)
3. EEG Recording type Data Set of college students from various States.
18
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19. COURSE WORK(mandatory subjects for research work)
S.No Name of the subject Credits Type of Study
1 Research Methodology 4
Institutional
course
2 Research and Publication Ethics 2
Institutional
course
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20. COURSE WORK(based on research area interested subjects)
S.No Name of the subject Credits Type of Study
1 Machine Learning 3 Self study
2 Deep Learning 3 Self study
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21. COURSE WORK(optional interested subjects based on research
area)
S.No Name of the subject Credits Type of Study
1 Image Processing 3 Self study
2 Data Analytics 3 Self study
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22. REFERENCES
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[2] P. Y. Collins and S. Saxena, ‘‘Action on mental health needs global cooperation,’’ Nature, vol. 532, no. 7597, pp. 25–27, Apr. 2016.
[3] A. D. Bergin, E. P. Vallejos, E. B. Davies, D. Daley, T. Ford, G. Harold,S. Hetrick, M. Kidner, Y. Long, S. Merry, R. Morriss, K. Sayal,E. Sonuga-
Barke, J. Robinson, J. Torous, and C. Hollis, ‘‘Preventive digital mental health interventions for children and young people: A review of the
design and reporting of research,’’ NPJ Digit. Med., vol. 3, no. 1, pp. 1–9, Dec. 2020.
[4] T. M. Evans, L. Bira, J. B. Gastelum, L. T. Weiss, and N. L. Vanderford, ‘‘Evidence for a mental health crisis in graduate education,’’ Nature
Biotechnol., vol. 36, no. 3, p. 282, 2018.
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Conf. Comput. Sci. Cham Switzerland: Springer, 2021, pp.570–583.
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[7] K. E. Vytal, et al., “The complex interaction between anxiety and cognition: Insight from spatial and verbal working memory,”Frontiers
Hum. Neurosci,, vol. 7, 2013, Art. no. 93.
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23. Cont..
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[13] J. Atkinson, et al., “Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers,” Expert
Syst. Appl., vol. 47, pp. 35–41, 2016.
[14] X. W. Wang, et al., “Emotional state classification from EEG data using machine learning approach,” Neurocomputing, vol. 129,
pp. 94–106, 2014. VIT-AP University
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24. Cont..
[15] F. Liu, et al., “Multivariate classification of social anxiety disorder using whole brain functional connectivity,” Brain Struct.
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[16] R. Xiao and X. Liu, ‘‘Analysis of the architecture of the mental health education system for college students based on the
Internet of Things and privacy security,’’ IEEE Access, vol. 9, pp. 81089–81096, 2021.
[17] F. Amin, A. Ahmad, and G. S. Choi, ‘‘Towards trust and friendliness approaches in the social Internet of Things,’’ Appl. Sci., vol. 9,
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