Hashem Qaryouti is a Computer Engineering student at German Jordanian University, Amman, Jordan, with experience in big data engineering, software development, and machine learning projects. He has worked with technologi…
Hashem Qaryouti is a Computer Engineering student at German Jordanian University, Amman, Jordan, with experience in big data engineering, software development, and machine learning projects. He has worked with technologies including Python, Java Spring Boot, Apache Spark, Hadoop, Scala, Oracle Database, Docker, and cloud deployment, and has completed internships at Orange, Amman, Jordan.
A fully funded project by the German Academic Exchange Service (DAAD), Trier, Germany.
Mobile Application Development.
Skills: Java Spring Boot, Oracle Database, RESTFUL APIS, Docker and Cloud Deployment.
A Deep Learning Approach for Speech Recognition Using Channel State Information Signals.
Skills: Deep Learning, CNN, Python, PyTorch, MATLAB, Signal Processing Algorithms, Data Collection, C Programming, Linux, Data Labelling, Linux 802.11n CSI Tool, Time-Frequency Spatial Representation, Prototyping.
Description: This project aims to create a system that can analyze the Channel State Information (CSI) signals to accurately recognize spoken words.
A full-time Paid Internship, Second Phase of Dual Studies Program
Deploy scripts on AWS using EC2 and S3 or on a local cluster.
Parsing multiple file formats like JSON, CSV, XML into Data frames using Apache Spark.
Processing and Analyzing Data using RDDs, Datasets and Data frames.
Analyzing large datasets using SparkML to support business intelligence.
A full time Paid Internship, First Phase of Dual Studies Program
Working with SQL, PLSQL for data manipulation, query optimization and database management.
Developing complex SQL queries to extract, transform, and load (ETL) data from various sources.
Designing database schemas, tables, views, and stored procedures.
Reading files from external resources using external tables to process external data.
A Dataset that is provided from the University of Southern California in which a set of videos are captured from different positions of cameras on Trucks.
Skills: Data Annotation, Computer Vision Annotation Tool (CVAT), Python Programming, YoloV5, Image Processing Algorithms.
Description: This project aims to create a system that is able to classify street issues like cracks, potholes, illegal dumping.
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