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 >

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 >

Sentiment analysis is a crucial component of intelligent information processing systems, enabling machines to understand and categorize human opinions expressed in text. While extensively studied for high-resource languages such as English and Chinese, it remains underexplored for low-resource languages like Urdu. This paper presents an intelligent multilingual sentiment analysis framework for Urdu text by integrating supervised machine learning techniques with a transformer-based model. We manually annotated and preprocessed a dataset collected from various Urdu blog websites, categorizing sentiments into positive, neutral, and negative classes. Four machine learning classifiers—Support V... More >