Iftekhar

Hello,

I'm Iftekhar Ahmed

a computer science graduate with a focus on computer vision research. I enjoy building cool projects and adapting to new technologies.

Résumé

Skills

Language

Web Technology

Database

ML/DL Technology

Publications

Iftekhar Ahmed, et al. (2024). Multi-Model Attentional Fusion Ensemble for Accurate Skin Cancer Classification. IEEE Access, Vol. 12, pp. 181009-181024. DOI: 10.1109/ACCESS.2024.3510224

Iftekhar Ahmed, et al. (2024). Heart Disease Prediction through Enhanced Machine Learning and Diverse Feature Selection Approaches. In IEEE 10th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), pp. 119-124. IEEE. July 30-31, 2024, Bandung, Indonesia. DOI:10.1109/ICSIMA62563.2024.10675564

Iftekhar Ahmed, et al. (2022). Performance Analysis of Machine Learning Algorithms in Chronic Kidney Disease Prediction. In IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 0417-0423. IEEE. October 12-15, 2022, Vancouver, BC, Canada. DOI:10.1109/IEMCON56893.2022.9946591

Featured Projects

Imsearch

Imsearch is a powerful tool designed to help you find and compare similar images within a directory. Simply upload an image, select a directory, and let imsearch do the rest. It will analyze and sort all images in the specified directory based on their similarity to the uploaded image.

LU CP Archive

LU-CP-Archive is a MERN stack application designed to assist students who are doing competitive programming.

Wrap Up School

Wrap-up-school is an Ed-Tech startup, that offers short, it offers short-term live courses tailored to cover one chapter each

Featured Blogs

Practical Machine Learning : Part - 1The series "Practical Machine Learning" begins with this article. It covers the essentials needed to get started with machine learning, including the types of ML and basic principles.

Designing an Image Classifier Model ArchitectureThis blog takes you on a journey into building image classification models with deep learning, using the well-known CIFAR-10 dataset as an example. We’ll start with some basic background to get you up to speed, then go step by step through creating different types of neural networks. You’ll learn how a simple Multilayer Perceptron can be improved by adding Convolutional Neural Networks, how pooling layers make the data easier to handle, and how techniques like dropout and batch normalization help prevent overfitting and keep training stable. In the end, you’ll see that building a good AI model isn’t about following a fixed recipe it’s about experimenting, tweaking, and finding what works best for your data and goals.

Deep Learning Essentials : Key Concepts Before Diving DeepThis blog post serves as a comprehensive introduction to the fundamental concepts of Deep Learning. It delves into the building blocks of neural networks, explains the inner workings of forward pass and backpropagation, and explores various techniques for optimizing and fine-tuning your models.