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HISHAM KHALAF ALLAHEM

Assistant Professor

HISHAM KHALAF ALLAHEM
allahem@ju.edu.sa
Information Systems - Computer and Information Sciences College
College
Computer and Information Sciences College
كلية علوم الحاسب والمعلومات
Department
Information Systems
نظم المعلومات
Researches
7
7
Page Visits
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Current courses

Recent courses

Code Year Course Name
IS 251 Systems Analysis and Design (I)
IS 211 Foundations of Information Systems
IS 211 Foundations of Information Systems
IS 323 Database Management Systems
IS 323 Database Management Systems

Fm researches

Last Researches

Innovative Tailored Semantic Embedding and Machine Learning for Precise Prediction of Drug-Drug Interaction Seriousness
HISHAM KHALAF ZAYED ALLAHEM
Comprehensive Network Analysis of Lung Cancer Biomarkers Identifying Key Genes Through RNA-Seq Data and PPI Networks
HISHAM KHALAF ZAYED ALLAHEM
Enhancing Breast Cancer Detection in Ultrasound Images: An Innovative Approach Using Progressive Fine-Tuning of Vision Transformer Models
HISHAM KHALAF ZAYED ALLAHEM

fm info

Researches

Innovative Tailored Semantic Embedding and Machine Learning for Precise Prediction of Drug-Drug Interaction Seriousness
HISHAM KHALAF ZAYED ALLAHEM
Enhancing Breast Cancer Detection in Ultrasound Images: An Innovative Approach Using Progressive Fine-Tuning of Vision Transformer Models
HISHAM KHALAF ZAYED ALLAHEM
Comprehensive Network Analysis of Lung Cancer Biomarkers Identifying Key Genes Through RNA-Seq Data and PPI Networks
HISHAM KHALAF ZAYED ALLAHEM
Layer-Weighted Attention and Ascending Feature Selection: An Approach for Seriousness Level Prediction Using the FDA Adverse Event Reporting System
HISHAM KHALAF ZAYED ALLAHEM
Automated labour detection framework to monitor pregnant women with a high risk of premature labour using machine learning and deep learning
HISHAM KHALAF ZAYED ALLAHEM
Automated uterine contractions pattern detection framework to monitor pregnant women with a high risk of premature labour
HISHAM KHALAF ZAYED ALLAHEM
Framework to monitor pregnant women with a high risk of premature labour using sensor networks
HISHAM KHALAF ZAYED ALLAHEM

Education

Ph.D.
Dalhousie University - Faculty of Computer Science
2022 - 2014

Machine learning techniques have recently been used to predict and detect premature labour. Recent studies have used machine learning classifiers such as Random Forest and Decision Tree to categorize and recognize electrohysterography contractions with a high accuracy rate. In addition, deep learning models such as artificial neural networks, similar to machine learning techniques, have been designed to mimic the human brain to analyze and extract complex relationships between data.

BA
Jouf University - College of Computer and Information Science
2008 - 2004
M.A.
Dalhousie University - Faculty of Computer Science
2013 - 2010

Radio Frequency Identification (RFID) systems are rapidly becoming popular in a variety of applications such as supply chain management, storage management and healthcare. Such a system consists of a tag with a unique identifier, a tag reader and a backend server. Due to the system's limited computational resources, it can be subject to various types of attacks. This can exacerbate when the reader itself is mobile. The objective of this thesis is to propose a mutual authentication scheme for mobile RFID systems.

Certifications

Certificate in University Teaching and Learning
2019 - 2020
CNLT 5000
2025 - 2019
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