MUHAMMAD NAMEER AKHTER
SDE-
-
github.com/nameerakhter
ROORKEE, UTTRAKHAND
linkedin.com/in/nameerakhter
Education
Skills
B.TECH CSE, UPES, DEHRADUN
2020 – 2024
CGPA: 8.14
Languages:
PYTHON, JAVASCRIPT, TYPESCRIPT
10TH AND 12TH, DELHI PUBLIC SCHOOL, ROORKEE
12TH PERCENTAGE: 90.2
10TH CGPA: 10.0
Professional Experience
VR DEVELOPER | VIRTUAL LABS, ROORKEE
06/2024 – 07/2024
Developed a VR lab simulator to virtually perform
experiments of electrical engineering.
Created a virtual Wheatstone bridge simulation
with user- adjustable components.
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RESEARCH INTERN | IIT ROORKEE
01/2024 – 06/2024
Implemented Deep Learning to accurately predict
faults in Induction motors using Vibration signals.
Built and improved models including 1D CNN,
LSTM, GRU, Manual feature extraction with ANN,
Residual networks, SVM, Decision tree, XGBoost.
Increased Accuracy of existing models by 30%.
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FRAMEWORKS & DATABASE:
TENSORFLOW, EXPRESSJS NEXTJS, MONGODB
LIBRARIES:
NUMPY, PANDAS, SCI-KIT LEARN, REACT, NODE.JS
Projects
ANONYMOUS FEEDBACK
A web application that allows users to provide
feedback anonymously.
Message suggestions with AI using Open AI API
and a custom user dashboard and authorization
with credentials.
Tech stack: Next.Js, NextAuth, Zod, ShadcnUI,
typescript, MongoDB, Tailwind CSS.
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FACIAL RECOGNITION BASED ATTENDANCE
SYSTEM
Streamlined college attendance system by
designing a facial recognition system, replacing
inefficient manual processes.
Implemented a daily auto-generated Excel sheet
feature to keep track of attendance.
Tech stack: Python, Tensorflow, facial recognition,
openCV.
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FRONT-END WEB DEVELOPER | PLUTOSONE
06/2023 – 08/2023
Built and optimized web interface used by the
company making the design more responsive and
cleaner.
Built secure and responsive payment forms using
ReactJS, ensuring a seamless checkout experience
for users.
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HEALTH MONITORING OF INDUCTION MOTORS
Improved accuracy of 1D CNN to 97.6%,1D
CNN+GRU to 100%, Feature extraction + SVM to
97.60%, residual networks to 99.89%.
Tech stack: Python, TensorFlow, Scikit-learn,
Neural Networks
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PROJECT INTERN| PHEMESOFT
04/2023 – 06/2023
Collected a robust dataset of plant leaf imagery
consisting of 21,367 images of plant leaves.
Designed and implemented a CNN model for plant
disease detection and classification using a
curated image dataset.
Achieved an accuracy of 93% on the trained
model.
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CarePlus
Built a new way for hospitals to manage their
patient appointments.
Mobile notification to User and Doctors for
appointment confirmation.
Tech Stack: NextJs, Zod, ShadcnUI, Typescript,
Appwrite, Tailwind CSS, Twilio.
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