Mohammadali Charoosaei
Machine Learning, Deep Learning, and Computer Vision Researcher
I am a machine learning, deep learning, and computer vision researcher.
I am studying my MSc in Artificial Intelligence at Manchester Met University.
Prior to that, I received my B.Sc. degree in Computer Engineering in 2021.
I have authored or co-authored top-tier peer-reviewed research articles in leading academic journals and conferences.
I reviewed 100+ research articles for IEEE Access journal. (Certificate)
To be submitted soon
Review activity for IEEE access. (100+) Official record
Publisher: IEEE - Journal: IEEE Access - ISSN: 2169-3536
Collected metal objects datasets.
Applied feature extraction with HOG and LBP.
Extracted and saved the desired features for labeled images in the form of a feature matrix.
Classified with k-NN and SVM and Naïve Bayes.
Examined the resulting confusion matrix, the performance of HOG and LBP were compared in the classes.
Trained and tested several models with DL techniques including YOLOv5, Retina Net, Detectron2, Faster R-CNNVGG16, VGG19, and AlexNet were implemented on the dataset.
Collected handwitten logic circuits datasets.
Augmented the pictures.
Compared the performance of different deep learning methods: YOLO, Faster R-CNN, RetinaNet, Detectron2 on the dataset.
I trained a YOLO model on a video footage capturing vehicular movement.
The video contains various types of vehicles traversing a road scene.
Utilizing YOLO and perspective transformation techniques, the code identifies and tracks vehicles in each frame.
Vehicle positions are extracted and connected, allowing for the estimation of their speeds.
The final output is a video showing annotated vehicle speeds, providing insights into the dynamics of traffic flow.
This project utilizes a VGG16 model to classify chest X-ray images into COVID, Normal, and Viral Pneumonia categories.
Preprocesses the COVID-19 Image Dataset obtained from Kaggle to create training and testing sets.
Adapts the VGG16 architecture with modifications for classification and compiles it using Adam optimizer and categorical crossentropy loss.
Applies data preprocessing techniques including rescaling, rotation, and zoom using Keras ImageDataGenerator.
Trains the model for 40 epochs and evaluates its performance on the test set, analyzing accuracy and loss metrics for validation.
This project employs a Convolutional Neural Network (CNN) to detect breast cancer using the Breast Ultrasound Images Dataset, which comprises benign, malignant, and normal breast ultrasound images.
Utilizes the Breast Ultrasound Images Dataset downloaded from Kaggle, preprocessing it to exclude normal images for segmentation and classification.
Designs a CNN architecture for image segmentation with encoder and decoder blocks, compiled using Adam optimizer and soft dice loss function.
Evaluates the trained model on the test set, computing metrics such as sensitivity, specificity, accuracy, PPV, and NPV for individual images and overall performance.
Plots the Receiver Operating Characteristic (ROC) curve to assess the model's discrimination ability between classes.
I trained MediaPipe and OpenPose models on a YOUTUBE video.
This video is about dancing along to 34 MINUTES of KIDZ BOP dance.
The included codes, which is in form of a IPython notebook, gets the video and performs preproccessing.
Then, applying different layes, the key points of the human body will be extracted in each frame of the video.
After that, the key points will be connected together, creating the estimated pose of the person.
This project implements deep learning models to classify brain tumors from MRI images.
Preprocesses the dataset to remove redundant backgrounds and restructures the dataset.
Configures model architecture and training parameters in config.py.
Trains the models using the specified configurations.
Evaluates the trained models on test data to assess classification performance.
I trained yolov5s on the WIDER face dataset.
The WIDER dataset comprises of 390k+ face pictures, with bouding boxes and label formats.
The faces with area of less than 2 percent of the whole image are considered.
Finally, the model is trained on the dataset; the final accuracy on the validaion dataset is 93.6%
Face detection in this project is done in real-time using Haar cascades.
Sentiments are analyzed based on the VGG-Face model and cosine similarity used in Facebook's DeepFace framework.
DeepFace is originally designed to handle the dominant face expression in the frame, but some modifications have been made to cope with multiple faces.
Created a database and populated each telegram message in the DB.
Look up for the keyword in the search bar and show the messages including the content.
Augmented the dataset pictures.
Compared the performance of different deep learning methods: YOLO, Faster R-CNN, RetinaNet, Detectron2.
