Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal The Indonesian Journal of Computer Science
Yousif Mohammed Ismail
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Integration of Deep Learning Applications and IoT for Smart Healthcare Diana Hayder Hussein; Yousif Mohammed Ismail; Shavan Askar; Media Ali Ibrahim
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4611

Abstract

The integration of deep learning (DL) applications with the Internet of Things (IoT) has emerged as a transformative approach for advancing smart healthcare systems. This review synthesizes findings from seven research studies, each exploring the intersection of these technologies in improving healthcare delivery, patient monitoring, and medical decision-making. The paper highlights how IoT devices, including sensors and wearables, generate vast amounts of real-time health data, which DL models leverage for predictive analytics, diagnosis, and personalized treatment recommendations. Key areas explored include: Data Acquisition and Processing: IoT-enabled sensors play a critical role in collecting physiological data, such as heart rate, blood pressure, and glucose levels, which are then processed by DL algorithms to identify patterns and anomalies, Remote Patient Monitoring: The combination of IoT and DL facilitates continuous monitoring of chronic conditions and allows for real-time intervention, reducing hospital readmissions and enhancing patient independence.
Deep Learning Techniques for Network Security Yousif Mohammed Ismail; Diana Hayder Hussein; Shavan Askar; Media Ali Ibrahim
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4737

Abstract

This article explores the seven outstanding deep-learning techniques used to enhance network security. It provides a comprehensive analysis of how these techniques address various cybersecurity challenges, including intrusion detection, malware classification, and anomaly detection. This review highlights the effectiveness of deep learning models such as Convolutional Neural Networks (Recurrent neural networks (RNNs) and automatic encoders used in processing large datasets and identifying complex patterns representing security threats. The article also discusses the advantages and limitations of each technique, emphasizing the importance of feature extraction, model training, and real-time processing capabilities. By combining the findings of the current research, this review aims to guide future research and practical implementation of deep learning in securing network infrastructure against evolving cyber threats. The review provided a comprehensive summary of the deep learning techniques used in network security, highlighting their strengths and limitations. The findings showed that deep learning has significant potential to improve detection and response to network threats, although challenges related to model interpretability, data quality, and computational efficiency should be addressed.
Co-Authors Diana Hayder Hussein Media Ali Ibrahim Shavan Askar