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Schedule Interview NowMy name is Md Motiur R. and I have over 4 years of experience in the tech industry. I specialize in the following technologies: Natural Language Processing, Computer Vision, Data Science, Data Visualization, Python, etc.. I hold a degree in Bachelor of Science (BS), Master of Science (MS), Doctor of Philosophy (PhD). Some of the notable projects I've worked on include: Advanced CNN Models for Cancer Detection, NeuroNet for Alzheimer’s Disease Diagnosis, Test Assisted VLM for Medical Image Segmentation, Medical Image Segmentation Transformer With CAM Decoder, A deep learning approach for detecting diabetes, etc.. I am based in Chattogram, Bangladesh. I've successfully completed 10 projects while developing at Softaims.
I am a business-driven professional; my technical decisions are consistently guided by the principle of maximizing business value and achieving measurable ROI for the client. I view technical expertise as a tool for creating competitive advantages and solving commercial problems, not just as a technical exercise.
I actively participate in defining key performance indicators (KPIs) and ensuring that the features I build directly contribute to improving those metrics. My commitment to Softaims is to deliver solutions that are not only technically excellent but also strategically impactful.
I maintain a strong focus on the end-goal: delivering a product that solves a genuine market need. I am committed to a development cycle that is fast, focused, and aligned with the ultimate success of the client's business.
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4 years
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Purdue University
This project evaluates 12 deep learning (CNN and Transformer) models for classifying cancer from medical images. The models tested include ResNet50, VGG16, DenseNet121, EfficientNet-B0, MobileNetV2, X
NeuroNet is a multimodal deep learning framework for multiclass Alzheimer’s Disease (AD) diagnosis, integrating MRI images with clinical metadata (e.g., MMSE, age, genetic info). It enhances feature e
We propose TAV, a text-assisted vision model for medical image segmentation that integrates a novel Tri-Guided Attention Module (TGAM). TGAM computes visual-visual, language-language, and language-vis
MIST is a transformer-based medical image segmentation model that enhances local context capture using a novel Convolutional Attention Mixing (CAM) decoder. It combines a MaxViT encoder with attention
This study introduces a Conv-LSTM model for diabetes classification, evaluated against CNN, LSTM, and CNN-LSTM on the Pima Indians Diabetes Database. Using Boruta for feature selection and Grid Search
Bachelor of Science (BS) in Computer engineering
2012-01-01-2016-01-01
Master of Science (MS) in Computer engineering
2016-01-01-2019-01-01
Doctor of Philosophy (PhD) in Computer science
2022-01-01-2026-01-01