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This document describes a proposed intelligent laboratory management system based on Internet of Things (IoT) and machine learning. The system uses an STM32 microcontroller, RFID reader, and Raspberry Pi to automate student attendance tracking and course information display. It also analyzes student performance data using machine learning algorithms like XGBoost to predict academic performance and help educators evaluate student progress and identify areas for improvement. The system aims to standardize and optimize laboratory management with an intelligent, automated approach.
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 12 | Dec 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 518
The figure above shows the proposed IoT based laboratory
management system. The attendance system can be divided
into three parts: STM32 Microcontroller, RaspberryPi3with
display module and the clouddatabase.STM32isresponsible
for the storage and the transmission of most of the data
which it generates whenever an ID card is scanned.
Raspberry Pi 3 is mainly responsible for displaying
information of the student on board. The cloud database is
responsible for storing of large amounts of data uploaded
which is mainly signing the attendance of the students and
retrieving the course information.
Fig. 2. Performance Prediction
Our aim was to predict the academic performance of
students and analyze the impact factor of various attributes
that influence this academic performance. This helps the
educator to analyze the progress of student and suggest the
necessary measure to improveandevolvethelearnerto new
heights. The student database consists of academic
information such as Roll Number, Name, Academic Progress
and Attendance.
Database consist of following attribute which are taken
consider consideration in analysis phase
Dataset Attribute Description
Student StudID
Name
Progress
Attendance
Unique ID for Student
Name of Student
Academic Progress of
Individual
Attendance
Progress Assignment
Due Date
Academic Test
Marks Scored
Assignment assigned to
Student
Due Date for
Assignment
Test on Designated
Topic
Marks obtained in Test
Table -1: Table of Student Database
A. SUPERVISED LEARNING
In this system, we are implementing supervised machine
learning algorithms. Supervised learning is where you have
input variables (x) and an output variable (Y)andyouuse an
algorithm to learn the mapping function from the input to
the output.
Y=f(x)
The main goal is to have a mapping function so well that
when you have new input x it can predictappropriateoutput
Y. It is called supervised learning because the process of an
algorithm learning from the training dataset can be thought
of as a teacher supervising the learning process. We know
the correct answers, the algorithm iteratively makes
predictions on the training data and is corrected by the
teacher. Learning stops when the algorithm achieves an
acceptable level of performance.
XGBoost Algorithm: XGBoost is a classification technique,
based on the original framework called Extreme Gradient
Boosting. XGBoost can be used for tree ensemble learning or
linear gradient learning and be used to perform various
objective functions such as binary classification, regression,
and multi-class classification. The algorithm was developed
to efficiently reduce computing time and allocate an optimal
usage of memory resources. Important features of
implementation include handling of missing value.
Fig. 3. XGBoost [9]
III. RELATED WORK
A. Medha Sagar, Arushi Gupta, Rishabh Kaushal: Performance
Prediction and Behavioral Analysis of Student Programming
Ability
Performance prediction of students is done by creating a
machine learning model based on the hackerearth datasets.
B. Yichen Ma, Fuyao Wang, Zhuo Zheng Wang: Intelligent
Laboratory Management System Based on Internet of Things,
the 12th InternationalConferenceforInternetTechnologyand
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