Author
Contributions by role
Author 3
Sundas Iqbal
Nanjing University of Information Science and Technology (NUIST)
Summary
Edited Journals
IECE Contributions

Free Access | Review Article | 27 March 2025
Navigating Ethical Challenges in 6G-Enabled Smart Cities: Privacy, Equity, and Governance
IECE Transactions on Sensing, Communication, and Control | Volume 2, Issue 1: 48-65, 2025 | DOI:10.62762/TSCC.2025.291581
Abstract
The rapid urbanization and technological advancements have driven the development of smart cities, envisioned as sustainable, efficient, and interconnected urban spaces. The integration of sixth-generation (6G) wireless technology in smart cities promises unprecedented opportunities in connectivity, low-latency communication, and data management, which transforms urban living. However, this evolution raises critical ethical concerns related to privacy, inclusion, transparency, accountability, and environmental sustainability. This paper explores the ethical considerations inherent in designing smart cities with 6G, emphasizing data governance, equity, and human-centric approaches. It delves... More >

Graphical Abstract
Navigating Ethical Challenges in 6G-Enabled Smart Cities: Privacy, Equity, and Governance

Free Access | Research Article | 25 March 2025
Comparative Analysis of Automated Knee Osteoarthritis Severity Classification from X-Ray Images Using CNNs and VGG16 Architecture
IECE Transactions on Sensing, Communication, and Control | Volume 2, Issue 1: 36-47, 2025 | DOI:10.62762/TSCC.2025.378503
Abstract
Osteoarthritis (OA) is a degenerative joint disease that primarily affects the knee, causing cartilage deterioration and discomfort. Early diagnosis is crucial for effective management, as it can slow disease progression and improve the quality of life. This study proposes a deep learning approach to automatically classify knee OA severity from X-ray images using Convolutional Neural Networks (CNNs) and the VGG16 model. The models were trained on a dataset of knee X-ray images, and performance was evaluated using accuracy, precision, recall, and F1-score. The proposed CNNs model achieved 99% training accuracy and 80% testing accuracy after 50 epochs, while the VGG16 model, after fine-tuning... More >

Graphical Abstract
Comparative Analysis of Automated Knee Osteoarthritis Severity Classification from X-Ray Images Using CNNs and VGG16 Architecture

Free Access | Research Article | 12 December 2024
Enhancing Fake News Detection with a Hybrid NLP-Machine Learning Framework
IECE Transactions on Intelligent Systematics | Volume 1, Issue 3: 203-214, 2024 | DOI:10.62762/TIS.2024.461943
Abstract
The increasing prevalence of fake news on social media has become a significant challenge in today’s digital landscape. This paper proposes a hybrid framework for fake news detection, combining Natural Language Processing (NLP) techniques and machine learning algorithms. Using Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction, and classifiers such as Logistic Regression (LR), Naïve Bayes (NB), and Support Vector Machines (SVM), the model integrates Maximum Likelihood Estimation (MLE) with Logistic Regression to achieve 95% accuracy and 93% precision on a Kaggle dataset. The results highlight the potential of combining statistical and NLP approaches to improve fake... More >

Graphical Abstract
Enhancing Fake News Detection with a Hybrid NLP-Machine Learning Framework