M
Muhammad Fazal Ijaz
Researcher at Sejong University
Publications - 71
Citations - 2200
Muhammad Fazal Ijaz is an academic researcher from Sejong University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 12, co-authored 35 publications receiving 436 citations. Previous affiliations of Muhammad Fazal Ijaz include Dongguk University.
Papers
More filters
Journal ArticleDOI
Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM
Parvathaneni Naga Srinivasu,Jalluri Gnana SivaSai,Muhammad Fazal Ijaz,Akash Kumar Bhoi,Wonjoon Kim,James Jin Kang +5 more
TL;DR: In this article, the authors proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM), which proved to be efficient with better accuracy that can work on lightweight computational devices.
Journal ArticleDOI
Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods.
TL;DR: The present work proposes a cervical cancer prediction model (CCPM) that offers early prediction of cervical cancer using risk factors as inputs and employs random forest (RF) as a classifier.
Journal ArticleDOI
A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing.
Ganjar Alfian,Muhammad Syafrudin,Muhammad Fazal Ijaz,M. Alex Syaekhoni,Norma Latif Fitriyani,Jongtae Rhee +5 more
TL;DR: The results show that commercial versions of the BLE-based sensors and the proposed real-time data processing are sufficiently efficient to monitor the vital signs data of diabetic patients and that Long Short-Term Memory can accurately predict the future BG level based on the current sensor data.
Journal ArticleDOI
Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest
TL;DR: The result showed that by integrating DBSCAN-based outlier detection, SMOTE, and RF, diabetes and hypertension could be successfully predicted and the proposed HPM provided the best performance result as compared to other models for predicting diabetes as well as hypertension.
Journal ArticleDOI
Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda
TL;DR: In this article , a comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease is presented.