Academic Profile

Mahjoubeh Nazari holds a Master’s degree in Computer Engineering with a specialization in Computer Architecture at University of Isfahan, Isfahan, Iran. Her Master’s thesis focused on an artificial intelligence project aimed at object recognition to assist blind or visually impaired individuals, utilizing state-of-the-art AI and image processing techniques. She is passionate about research and continuously updating her knowledge in this field. Her recent investigations have explored the impact of artificial intelligence in medical tools and healthcare applications within smart cities. She is currently working as a data researcher and programs primarily in Python.

Editorial Roles

No Editorial Roles

This user currently does not serve as an editor for any ICCK journals.

ICCK Publications

Total Publications: 1
Open Access | Research Article | 12 January 2026
Hybrid XGBoost-CNN Model for Anomaly Detection: A New Approach for IoT Wireless Sensor Networks
by
Khongorzul Dashdondov
,
Mahjoobe Nazari Chamazkoti
,
Akmalbek Abdusalomov
,
Habib Ullah
,
Muhammad Zubair Khan
,
Bakht Sher Ali
ICCK Transactions on Advanced Computing and Systems | Volume 2, Issue 1: 42-52, 2026 | DOI: 10.62762/TACS.2025.354651
Abstract
The Internet of Things (IoT) continues to expand rapidly, resulting in increasingly heterogeneous and complex wireless sensor networks (WSNs). Traditional anomaly detection approaches cannot cope with dynamic traffic patterns, high data volumes, and strict resource constraints. This study presents a hybrid XGBoost–CNN model that integrates XGBoost-based feature selection with a lightweight Convolutional Neural Network optimized for IoT environments. The proposed model was evaluated using real-world IoT traffic data and benchmarked against XGBoost, KNN, and SVM. Experimental results show that the hybrid approach improves detection accuracy by over 1%, increases throughput by 22–40%, and r... More >
Graphical Abstract
Hybrid XGBoost-CNN Model for Anomaly Detection: A New Approach for IoT Wireless Sensor Networks