1. HKBK COLLEGE of ENGINEERING
(Approved by AICTE & Affiliated to VTU)
22/1, Nagawara, Arabic College Post, Bangalore-45, Karnataka
Email: info@hkbk.edu.in URL: www.hkbk.edu.in
Department of Information Sciences &
Engineering
0th Review
on
“Final Year Project”
Presented by:
1HK20IS034 - Ihthishamulla Mohammed Yaser Arafath
1HK20IS055 - Mohammed Faizan
1HK20IS080 - Rasiya Nishath
1HK19IS112 – Syeda Hiba Jasmeen
3. Domain
• The Internet of Things (IoT) refers to the network of
interconnected physical devices equipped with sensors,
software, and communication technologies, enabling them to
gather and exchange data. IoT finds applications in diverse
areas such as smart homes, healthcare, agriculture, and
industrial automation. Key components include sensor
devices, connectivity protocols, cloud computing, and edge
computing. On the other hand, Data Science involves
extracting valuable insights and knowledge from structured
and unstructured data through scientific methods, algorithms,
and systems. This field plays a pivotal role in various sectors,
including business analytics, healthcare, and finance,
facilitating data-driven decision-making.
4. Problem Statement 1
Project Lifeline: Smart Technologies for Swift Well
Rescues.
The Intelligent Well Rescue System stands as a pioneering project
at the intersection of Internet of Things (IoT) technology and
machine learning algorithms, revolutionizing the efficiency and
safety of well rescue operations. In the unfortunate event of an
individual falling into a well, this innovative system deploys a
sophisticated array of sensors and cutting-edge technologies to
swiftly and effectively coordinate rescue efforts. Depth sensors
and accelerometers play a pivotal role in determining the precise
position and condition of the trapped individual.
5. Problem Statement 2
Driver Drowsiness Detection System
• In today's high-paced world, ensuring road safety is crucial, with
driver drowsiness being a major cause of accidents. This project
aims to address this issue by developing a Driver Drowsiness
Detection System using Python, utilizing computer vision and
machine learning. The system analyzes real-time data from an in-
vehicle camera, identifying facial cues like eye closure duration and
head movements to gauge driver alertness. Through a multi-stage
process, including face detection and landmark identification, the
system activates alerts upon detecting signs of drowsiness,
employing visual, auditory, or haptic feedback. This implementation
contributes significantly to reducing accidents caused by human
error, enhancing overall road safety.
6. Problem Statement 3
Bone Fracture Detection Using Machine Learning
• The project seeks to revolutionize the diagnosis of bone fractures
in medical imaging, specifically X-ray images, by employing
artificial intelligence and machine learning. By training computer
programs on extensive datasets of normal and fractured bone
images, the technology learns to discern subtle differences. This
enables rapid and accurate fracture detection, significantly
reducing diagnosis time, expediting crucial treatments, and
ensuring consistent results. The collaboration between machine
learning and human expertise provides a valuable tool for
challenging cases, potentially transforming the field of radiology
and optimizing patient care through advanced and efficient
diagnostic processes.
