Analyzing a dataset consisting of different types of Persian rice and the goal of the project was to build an artificial deep neural network which can detect different kinds of rice
Internship
Jul.2019 - Sep.2019
IT Department ,
Moein Hotel ,
Fuman , Iran.
openCVpython
This program was an educational plan based on openCv (Open Source Computer Vision Library)
Which can be used to detect and recognize faces, track camera movements…
The purpose of this thesis was to set the groundwork for future research on developing
a machine-learning based anomaly detection system for hospitalized patients. The thesis began with an analysis of the National Early Warning Score and identified the need for an updated system. Nordlandssykehuset presented us with two datasets to explore the possibility of creating an anomaly detection system using unsupervised learning on patient physiological registrations. A non-temporal dimension and the viability of training clustering models on physiological data are examined throughout the thesis. With 29000 records trained using various algorithms and four distance metrics, we evaluated each model’s viability and practicality, then selected the most effective one and discussed its results.
Implemented a Supervision Website for a Plate Detector
System which is
currently used in many universities in Guilan Province including
College of Technical and Vocational Chamran
