I'm a Big Data Engineer with a strong background in data systems, backend development, and artificial intelligence. I’m passionate about building intelligent solutions that create real impact and improve how people interact with technology.
My work bridges data engineering and AI, with a special interest in Natural Language Processing and Generative AI. I enjoy combining technical depth with creative problem-solving, and I'm always looking for new challenges that push me to grow and innovate.
I believe in continuous learning, collaboration, and using technology to drive meaningful change.
Built a system to analyze and enhance Friday sermons using AI and Big Data.
Collected fatwas from various trusted sources and applied data manipulation techniques to extract the most asked-about topics.
The system enables real-time discovery of trending religious concerns, helping sermon givers focus on relevant and timely subjects.
Applied machine learning to predict future trending topics and recommend related sermons from official sermon platforms, offering ready-to-use, audience-aligned content.
Used MongoDB for data storage, Apache Kafka for real-time data streaming, and Apache Spark for large-scale data processing and analysis
Building a system that estimates the price of an apartment in Nablus using Machine learning (ML).
The system depends on receiving information about the apartment, such as its location, the number of rooms, its area, the number of bathrooms, the number of salons, the view of the apartment and other important information, and the price is estimated based on the information entered.
Build a new system to discover the diseases spread in the Arab countries from people tweets on Twitter.
Our system classifies the words from unstructured text (tweets) into proper type which include symptoms, disease or nothing.
Used machine learning algorithms and NLP tools. We produced a new dataset for this purpose. The system produced F-measure with 72%.
Used Angular 8 to build the front-end for the website built to make our project available for free using or for testing purposes and we used the Django framework in the back-end side. Used React-native to create mobile applications to run the system on it.
Built a new Named entity recognition (NER) system for the Arabic language.
NER involves the process of locating proper nouns from unstructured text and classifying them into proper type: person name, location name, organization name without having seen them previously.
Used machine learning algorithms and NLP tools.
It produced F-measure with 91.31% which is the state of the art.
Used Angular 8 to build the front-end for the website built to make our project available for free using or for testing purposes and we used the Django framework in the back-end side.
Build a compiler for a new programming language which has specific rules.
We used C++ to build the compiler.
The user can enter a statement, and the program will show any lexical error.
The user can perform calculations and store the results inside the variables.
Create an Android application such as the sites of universities.
The student who registers the school site will have a special schedule for him automatically.
Publish the marks of the student.
Publish the presence and absence and all information related to the student.,
Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021) · Mar 1, 2021
The extraction of named entities from unstructured text is a crucial component in numerous Natural Language Processing (NLP) applications such as information retrieval, question answering, machine translation, to name but a few. Named-entity Recognition (NER) aims at locating proper nouns from unstructured text and classifying them into a predefined set of types, such as persons, locations, and organizations. There has been extensive research on improving the accuracy of NER in English text. For other languages such as Arabic, extracting Named-entities is quite challenging due to its morphological structure. In this paper, we introduce ArabiaNer, a system employing Conditional Random Field (CRF) learning algorithm with extensive feature engineering steps to effectively extract Arabic named Entities. ArabiaNer produced state-of-the-art results with f1-score of 91.31% when applied on the ANERcrop dataset.
Authors: Mohammad Hudhud, DR.Hamed Abdelhaq, DR.Fadi Mohsen