JOURNAL OF ENGINEERING AND COMPUTER SCIENCES

Autonomous vehicles rely heavily on image processing techniques for traffic sign recognition, yet these approaches face significant challenges due to environmental conditions, occlusions, and high computational demands. To address these limitations, this paper introduces a novel acoustic-based approach named Noise Pattern Recognition (NPR) system that utilizes sound-based detection to enhance road awareness for autonomous vehicles. Instead of relying on visual inputs, our approach encodes traffic sign information through specially designed road bumps that generate distinct noise patterns when vehicles pass over them. These acoustic patterns, structured similarly to Morse code, are captured by onboard microphones, processed using signal analysis techniques, and converted into binary sequences that correspond to specific traffic signs. The proposed system consists of three key components: a sound recording module, a signal processing module, and a transmission module that relays detected traffic sign information to the vehicle’s control system. Simulation results show the feasibility of this method by demonstrating its robustness against environmental interference and its ability to operate efficiently with minimal computational resources.

Agriculture is vibrant for the economy of many countries worldwide. Optimal performance in terms of reliability, efficiency, and adequacy of the irrigation system plays an important role in the economic uplift of a country. Sediment accumulation in a canal significantly affects its morphology as well as the water supply for agriculture. This study comprises the application of a numerical model to study this highly important and challenging issue of the sediment transport process in Upper Chenab Canal (UCC), Pakistan. The SETRIC model has been applied to investigate the sediment transport in UCC. Manning’s formula is used to deal with the roughness in governing equations. The Ackers and White equation is adopted for estimating the sediment load. Data regarding the discharge, sediment concentration, gradation curve for sediment, and geometry of the canal were collected from Marala Head Office, Sialkot. The condition of the canal, for which the data has been collected, was investigated, and found that its performance is not optimal. Outlets of the upper reach of the canal draw more water, whereas the delivery performance ratio (DPR) of the tail outlets is not up to the mark. The model has been applied to determine the non-silting and non-scouring concentration of sediment flows through a hit-and-miss approach until the DPR values of the outlets at the upper and tail reaches of the canal converge, indicating uniform supply. Canal efficiency can further be improved by allowing non-silting and non-scouring sediment. The results of this study will be highly useful for canal operation and management.

The rapid development and popularity of social media have allowed people to express their ideas and views, and feelings through social networks such as Twitter and Meta. This research focused on sentiment analysis of social media content about electronic learning (E-learning) since public opinions can help organizations and individuals. In this area, the social media content is abundant and unregulated. Therefore, sentiment analysis has become mainly an area of research interest. This study explores the machine learning approach for sentiment analysis of Twitter content to analyze people's opinions about E-learning. Using the Twitter API, the number of tweets collected was 42368. Tweets that involved E-learning were selected by using a programmable code written in Python. Then, a Support Vector Machine classifier was trained on the pre-processed data for analysing the tweets. The result of this study showed that the classifier's accuracy applied to the dataset was 92%. Generally, the people's opinions were positive toward E-learning.

Model-Driven Engineering (MDE) has become a key approach for addressing the complexity and heterogeneity of smart city systems. However, the state-of-the-art in MDE applications for smart cities remains underexplored. This study systematically reviews 42 primary studies, published between January 2019 and August 2024, to examine how MDE techniques are applied in smart cities, focusing on tools, techniques, and challenges. Six key themes emerged: MDE tools and techniques, security and privacy, scalability and interoperability, digital twins and emerging technologies, strategic alignment and enterprise architecture, and testing and verification. These themes highlight MDE’s potential to enable high-level design, rapid prototyping, and integration of diverse technologies. While tools like SI4IoT and InterSCity demonstrate MDE’s adaptability, challenges such as scalability, real-world validation, and lack of standardization persist. This study provides a comprehensive understanding of the state-of-the-art, identifies emerging trends, and proposes future research directions to address existing gaps, paving the way for more robust and scalable MDE solutions in smart cities.