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adhdeepnet

ADHDeepNet is a model that integrates temporal and spatial characterization, attention modules, and explainability techniques, optimized for EEG data ADAD diagnosis. Neural Architecture Search (NAS), Hyper-parameter optimization, and data augmentation are also incorporated to enhance the model's performance and accuracy.

all-purpose-clustering-tool

Developed a Windows tool using PyQt5, integrating K-means clustering for data analysis. The application recommends optimal cluster numbers, identifies cluster members, and allows exporting results to Excel.

automated-circuit-analysis-system

Developed an AI-driven project for Printed Circuit Board (PCB) analysis, incorporating computer vision for image registration, IC detection, and recognition, along with web scraping for data extraction. Also created a GUI to automate and streamline the entire PCB evaluation process.

bank-marketing-campaign-classification

cnn-lstm-action-recognizer

Implemented a CNN-LSTM Action Recognizer for dynamic motion analysis, integrating convolutional and recurrent neural networks to efficiently recognize and classify actions in video data of UCF101 dataset.

data-distribution-finder

Developed a Windows-based app for analyzing data distributions and identifying the best-fitted distribution using the Maximum Likelihood Estimation algorithm. The app features histogram analysis, error ranking, and allows users to directly save results along with distribution charts.

fault-detection-in-dc-microgrids

Using DIgSILENT, a smart-grid case study was designed for data collection, followed by feature extraction using FFT and DWT. Post-extraction, feature selection. CNN-based and extensive machine learning techniques were then applied for fault detection.

gnn-mnist

Implementing a Graph Neural Network in PyTorch for edge prediction on MNIST images, showcasing a novel approach in connectivity analysis.

home-automation-server

hyper-parameter-optimization

image-reconstruction-ae

Autoencoders (AEs) were developed for high-resolution image reconstruction from TinyImageNet and pair recovery from averaged CIFAR-10 composites, demonstrating proficiency in neural network design and image processing.

metaheuristic-optimization

Projects with Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO)

mri-ms-plaques-segmentation

A 3D Attention U-Net model is developed, aimed at segmenting and tracking Multiple Sclerosis lesions in MRI images.

nasa-website-queries-prediction

A hybrid approach was developed to predict NASA website queries using neural networks and metaheuristic optimization algorithms. The weights of the model was optimized using GWO, PSO, and ICA, harnessing the strengths of these algorithms to achieve remarkable results.

neuro-vision-transformer

Developed the Neuro Vision Transformer, an innovative machine learning model utilizing Vision Transformer architecture for EEG signal classification, focusing on distinguishing ADHD from healthy participants.

post-services-forecasting

In 2021, a precise forecast of Iran Post's 2021-2022 income was achieved using ARIMA, with only a 1.5\% error. This approach was subsequently extended to estimate the income and traffic for 2022-2023.

robot-path-planning

An algorithm for robot navigation was designed, accounting for random obstacles and determining optimal paths. It leverages a genetic algorithm to pinpoint the shortest route from start to end.

sam-breast-tumor-segmentation

Developed a custom application of the Segment Anything Model (SAM) for breast cancer tissue segmentation, utilizing Hugging Face's Transformers and fine-tuning the decoder to predict segmentation masks in medical imaging.

tactilenet

Developed TactileNet, the first deep-learning model designed for surface roughness recognition using EEG data. This project leverages CNNs to classify surface textures encountered through a robotic device in tactile trials.

telecom-churn-analysis

Developed a churn prediction model using XGBoost, with comprehensive data preprocessing and hyperparameter tuning. Applied SHAP for feature importance analysis, leading to actionable business insights for targeted customer retention.

telecomsent

Developed BERT and ML models for targeted sentiment analysis on social media posts, extracting insights on telecom customer sentiments and service aspects.

vivit-medical-video-classification

Developed the ViViT model for medical video classification, enhancing 3D organ image analysis using transformer-based architectures.

wsn-routing

The optimization of Wireless Sensor Networks (WSNs) using low-power nodes focused on energy efficiency, introducing a routing strategy for stable nodes based on Ant Colony Optimization (ACO).

wsn-scheduling-with-reinforcement-learning

Developed a reinforcement learning framework using Deep Q-Networks (DQN) to optimize scheduling in Wireless Sensor Networks (WSN), enhancing energy efficiency and state estimation through a custom simulation environment.