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1. 1
2025-2026 – Mini Project I
“Development of Fusion Search”
Type of Project: Application
Expected Project Outcome : Publication
Presented by
KULDEEP – (71812405050)
HARSHAN D J- ( 71812405028)
ABISHEK K R - ( 71812405004)
Batch No:2IT01
Review No : I
Date : 23.08.2025
Reaccredited by NAAC with A+ grade]
Panel No:26 Group No:01
Supervised by
GUIDE :Dr.M.Kalaiarasu
Professor(Information Technology)

2. Contents
2
S.No Description Slide Number
1 Introduction 3
2 Problem identification, scope and objective 4-5
3 Literature Survey 6-10
4 Existing technology of the project 11-12
5 Methodology 13
6 Timeline & Proposed Outcome 14-15
7 References 16-18

3. Introduction
This is a web development project designed for college students to enhance
their academic performance and career opportunities.
• Calculates and predicts students’ upcoming academic performance for
better planning.Provides curated study resources from YouTube, online
certifications, and more.
• Delivers updated internship news and application alerts for career growth.
• Offers department-wise tech news tailored to students’ academic interests.
• Assists placement coordinators by mapping company requirements
to student CGPA.
3

4. Problem Definition
College students often struggle with tracking their academic progress,
accessing quality study resources, and staying informed about internships and
relevant tech news. Placement coordinators face challenges in matching
students with suitable companies based on eligibility criteria. The lack of a
centralized system creates confusion and inefficiency for both students and
coordinators. There is a pressing need for a unified platform that addresses
these issues.
Scope
This project will serve as a central academic and career hub for college students
of all departments. It will integrate performance analytics, study resources,
internship updates, tech news, and placement mapping. The platform will
streamline student support and placement processes within institutions.
4
Problem identification, scope and
objectives

5. Problem identification, scope and
objectives
5
Objectives
• Predict and track students’ academic performance.
• Curate and deliver diverse study and career resources.
• Facilitate effective placement coordination by matching companies to
eligible students.

6. Literature Survey - 01
• Source: Kumar, M., & Singh, A. (2022). “Predicting student academic
performance using machine learning techniques”, IEEE Access, Vol.
10, pp. 34567–34575.
6
Experiment Inference Problem gap
A machine learning–based
model was applied on
student academic datasets
(grades, attendance,
demographic details) to
predict performance
outcomes
Decision tree and
random forest models
achieved higher
accuracy in predicting
students at risk of poor
performance compared
to traditional statistical
methods
. Further improvement is
needed by integrating
non-academic factors
such as socio-economic
background, mental
health, and
extracurricular activities.

7. Literature Survey - 02
• Source: Patel, R., & Sharma, K. (2021). “Personalized educational
resource recommendation using hybrid recommender systems”,
Education and Information Technologies, Vol. 26(5), pp. 5671–5686.
7
Experiment Inference Problem gap
A recommender system
was developed to curate
personalized study
resources for students
using collaborative
filtering and content-
based filtering
The system improved
students’ engagement by
18% and enhanced
learning outcomes
compared to generic
study material
distribution.
Does not yet incorporate
adaptive learning paths
based on student
feedback or changing
skill demands in the job
market.

8. Literature Survey - 03
• Source: Almarzouqi, R. et al. (2023). “Early prediction of student
dropout using deep learning techniques”, Computers & Education:
Artificial Intelligence, Vol. 4, 100121.
8
Experiment Inference Problem gap
Deep learning models were
tested to forecast student
dropout risk by analyzing
longitudinal academic data
and behavioral logs.
LSTM (Long Short-
Term Memory) networks
provided superior
accuracy in early
prediction of dropout
compared to logistic
regression.
Future work should
integrate real-time
monitoring systems and
explore intervention
strategies once at-risk
students are detected.

9. Literature Survey - 04
• Source: Choudhury, P., & Iyer, S. (2020). “Smart placement management
using machine learning and data analytics”, International Journal of
Computer Applications, Vol. 176(24), pp. 45–51.
9
Experiment Inference Problem gap
An intelligent placement
system was designed to
match students with
companies based on skill
sets, academic records, and
job profiles
The system increased
placement match
satisfaction by 25%
compared to traditional
manual shortlisting.
System does not yet
account for soft skills,
real-time industry
demands, or evolving
career paths like gig
economy roles.

10. Literature Survey - 05
• Source: Wang, L., & Thomas, J. (2022). “Integrated student performance
analytics and career support systems”, Journal of Educational Technology
Systems, Vol. 51(2), pp. 203–222.
10
Experiment Inference Problem gap
A hybrid platform was
developed integrating
academic performance
analytics, career guidance,
and placement readiness
assessment
Students using the
platform showed higher
employability test scores
and improved internship
placement rates
compared to control
groups
Current frameworks lack
scalability for large
universities and do not
incorporate AI-driven
career path forecasting

11. Existing technology of the project
Thinkfic:
• Machine learning models for predicting student academic performance.
• Platforms aggregating study materials from YouTube and online courses.
• Placement portals matching students to companies based on CGPA.
Source: "Machine learning-based academic performance prediction: A review
and implications“
Publication Year: 2025
Repository: PubMed Central (PMC)
11

12. Existing technology of the project
Photographs
12

13. Methodology to be adopted
13
Flow Chart

14. Timeline of the Project and Budget
14
Week Activities Notes
Week 1
Study of research paper Get know about the
various studies on
students growth get to
know about existing
tech’s
Week 2
Implementation of 50%
Web development
Free resources and
internship program
added to web
Week 3
Discussion with guide Get to know about the
problems and adding
suggested things by
guide

15. Proposed Outcome of the Project
15

16. References
1. Predict Student Academic Performance using an Interval
Belief Rule Base (2025)
• Reference: Zhou, W., Li, Y., Li, J., Zhang, T., Duan, X., Ma,
N., & Wang, Y. (2025). A student academic performance
prediction model based on the interval belief rule base.
Scientific Reports, 15, 30607.
• What it covers: Combines expert knowledge and random
forest attribute selection to build a hybrid model (IBRB-C)
with parameter optimization. Achieves very low MSEs (0.0024
and 0.1014) in predicting graduate applications and GPA,
demonstrating superior accuracy.
16

17. References
2. Web-Based Prediction System for Academic Performance
(2022)
• Reference: Alboaneen, D., Almelihi, M., Alsubaie, R., Alghamdi,
R., Alshehri, L., & Alharthi, R. (2022). Development of a web-
based prediction system for students’ academic performance.
Data, 7(2), 21.
• What it covers: Developed a real-time, online system using ML
algorithms like SVM, Random Forest, ANN, KNN, and Linear
Regression. The model, trained on academic and demographic
data, achieved a mean absolute percentage error (MAPE) of
6.34%.
17

18. References
3. Ensemble Learning for Performance Prediction (2025)
• Reference: Ashraf, I., & Alfarhood, S. (2025). Performance
prediction of students in higher education using multi-model
ensemble approach. [Journal/Publisher Unknown].
• What it covers: Compares ensemble-based classifiers
(bagging, boosting, stacking, voting) across algorithms like
Naïve Bayes, J48, Adaboost, logistic regression, and MLP.
Finds boosted trees and stacking outperform others depending
on dataset size. Useful for risk identification and policy
guidance.
18