mohammad95labbaf,Mohammad Ali Labbaf Khaniki,github

👋 Hi, I’m @mohammad95labbaf (Mohammad Ali Labbaf Khaniki)

👀 I’m interested in Deep Learning.

🌱 I’m currently learning new techniques in Computer Science and Python Development.

📫 You can find me on GitHub and Google Scholar.

Mohammad Ali Labbaf Khaniki's Projects

brain-tumor-transferlearning

The Brain Tumor MRI Dataset from Kaggle is employed for automated brain tumor detection and classification research. Investigated methods include using pre-trained models (VGG16, ResNet50, and ViT). 🧠🔍

energyprediction-arima-transformer

This report investigates the energy consumption prediction using time series analysis and deep learning methodologies. The extracted features and implemented transformer model demonstrate a high predictive accuracy.

eurusd_lstm_attention

This project is dedicated to forecasting 1-hour EURUSD exchange rates through the strategic amalgamation of advanced deep learning techniques. The incorporation of key technical indicators—RSI, MA, EMA, and VWAP—enhances the model's grasp of market dynamics

lung-segmentation

The preparation for the Lung X-Ray Mask Segmentation project included the use of augmentation methods like flipping to improve the dataset, along with measures to ensure data uniformity and quality. The model architecture was explored with two types of ResNets: the traditional CNN layers and Depthwise Separable.

mohammad95labbaf

Config files for my GitHub profile.

outlier-imbalanced-fraud-detection

The Credit Card Fraud Detection project uses statistical techniques and machine learning for identifying fraudulent transactions. It includes data preprocessing, outlier detection using Boxplots and Z-scores, and a decision tree model. Evaluation goes beyond accuracy, considering precision, recall, F1-score, and ROC AUC.

pso-ga-strategy-optimization

This repository focuses on optimizing a trend-based trading strategy for the EURUSD currency pair. By combining PSO and GA, the goal is to maximize returns while minimizing risk. The strategy considers buy and sell signals based on Supertrend and EMA conditions, with a fixed commission of 3 pips per trade.

umap_breast_cancer

This repository explores the interplay between dimensionality reduction techniques and classification algorithms in the realm of breast cancer diagnosis. Leveraging the Breast Cancer Wisconsin dataset, it assesses the impact of various methods, including PCA, Kernel PCA, LLE, UMAP, and Supervised UMAP, on the performance of a Decision Tree.

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