News – Intelligent Data Visual Computing Research (IDVCR) 
Abstract: The rapid growth in technology and several IoT devices make cyberspace unsecure and eventually lead to Significant Cyber Incidents (SCI). Cyber Security is a technique that protects systems over the internet from SCI. Data Mining and Machine Learning (DM-ML) play an important role in Cyber Security in the prediction, prevention, and detection of SCI. This study sheds light on the importance of Cyber Security as well as the impact of COVID-19 on cyber security. The dataset (SCI as per the report of the Center for Strategic and International Studies (CSIS)) is divided into two subsets (pre-pandemic SCI and post-pandemic SCI).
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Authors: S. Khawaja | M. Naeemullah | S. Iqbal | et al. | Published: PLOS ONE, 2025 (Date is an approximation)
Abstract: This study proposes novel deep learning models (customized CNN and optimized ResNet) for the highly accurate multi-classification of brain tumor types (gliomas, pituitary, meningiomas, and no tumor) from MRI images.
Authors: S. ul Amin | S. Khawaja | M. Naeemullah | et al. | Published: PLOS ONE, 2020
Abstract: This research focuses on utilizing a Convolutional Neural Network (CNN) for accurate and automatic detection and classification of different types of white blood cells from blood smear images.
Authors: S. Iqbal | S. Khawaja | M. A. S. R. Ali | et al. | Published: IEEE Xplore, 2021
Abstract: This paper introduces a novel hybrid deep learning architecture designed for high-accuracy and efficient detection and classification of brain tumors from MRI scans.
Authors: S. Khawaja | S. Iqbal | J. Javed | et al. | Published: PLOS ONE, 2023
Abstract: Development of a robust hybrid deep learning model for the accurate identification and categorization of various rice plant diseases from images to aid in early detection and treatment.
Authors: S. Khawaja | M. Naeemullah | S. Iqbal | et al. | Published: Sensors (MDPI), 2022
Abstract: A study proposing a hybrid deep learning approach for the accurate and reliable automated classification of skin lesions, focusing on improving the detection of malignant melanoma.
Authors: S. Khawaja | M. Naeemullah | S. Iqbal | M.A. Khan | et al. | Published: Scientific Reports, 2023
Abstract: This research presents a method for classifying retinal images into different grades of Diabetic Retinopathy (DR) using a hybrid deep learning model for early and accurate diagnosis.
Authors: S. Khawaja | M. Naeemullah | H. Tariq | et al. | Published: MDPI, 2025 (Year is an approximation)
Abstract: A comprehensive study on various deep learning architectures (EfficientNet, ResNet, VGG, etc.) for improved and reliable classification of different subtypes of leukemia from blood cell images.
Authors: S. Khawaja | M. Naeemullah | J. Javed | et al. | Published: Computer Systems Science and Engineering, 2021
Abstract: Proposal of an automated novel detection model (DeepCovNet/Hybrid Model) for COVID-19 cases using chest X-ray images, achieving high accuracy in real-time screening.
Authors: S. Khawaja | M. Naeemullah | S. Iqbal | S. ul Amin | et al. | Published: Applied Sciences (MDPI), 2021
Abstract: This work introduces a deep learning model for the highly accurate classification and recognition of human faces under varying conditions, contributing to improved security and identification systems.
Authors: S. Khawaja | M. Naeemullah | M. Sharif | H. Tariq | et al. | Published: Applied Sciences (MDPI), 2022
Abstract: Utilization of advanced transfer learning techniques on pre-trained models to achieve superior accuracy in classifying various types of brain tumors from medical images.
Authors: S. Khawaja | M. Naeemullah | M. Sadiq | J. Javed | et al. | Published: Agronomy (MDPI), 2023
Abstract: Development of a robust and scalable deep learning framework for the precise and early detection of various diseases affecting plants, crucial for agricultural health and yield.
Authors: M. Naeemullah | S. Khawaja | J. Javed | Z. Manzoor | et al. | Published: Journal of Healthcare Engineering, 2021
Abstract: Implementation of a deep learning model for the quick and effective classification of COVID-19 from medical images (like X-rays or CT scans), assisting diagnostic efforts.
Authors: S. Khawaja | M. Naeemullah | S. Iqbal | J. Javed | et al. | Published: Applied Sciences (MDPI), 2020
Abstract: An investigation into using transfer learning techniques with various pre-trained convolutional neural networks to enhance the accuracy and efficiency of brain tumor classification.
Authors: S. Khawaja | M. Naeemullah | S. Iqbal | et al. | Published: International Journal of Advanced Computer Science, 2020
Abstract: This research focuses on developing an efficient computational method for the early and accurate detection of brain tumors from medical images, minimizing diagnostic time.
