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Concrete is a cost-effective construction material widely used in various building infrastructure projects. High-performance concrete, characterized by strength and durability, is crucial for structures that must withstand heavy loads and extreme weather conditions. Accurate prediction of concrete strength under different mixtures and loading conditions is essential for optimizing performance, reducing costs, and enhancing safety. Recent advancements in machine learning offer solutions to challenges in structural engineering, including concrete strength prediction. This paper evaluated the performance of eight popular machine learning models, encompassing regression methods such as Linear, Ridge, and LASSO, as well as tree-based models like Decision Trees, Random Forests, XGBoost, SVM, and ANN. The assessment was conducted using a standard dataset comprising 1030 concrete samples. Our experimental results demonstrated that ensemble learning techniques, notably XGBoost, outperformed other algorithms with an R-Square (R 2 ) of 0.91 and a Root Mean Squared Error (RMSE) of 4.37. Additionally, we employed the SHAP (SHapley Additive exPlanations) technique to analyze the XGBoost model, providing civil engineers with insights to make informed decisions regarding concrete mix design and construction practices.

Owing to the increasing number of IoT gadgets and the growth of big data, we are now facing massive amounts of diverse data that require proper preprocessing before they can be analyzed. Conventional methods involve sending data directly to the cloud, where it is cleaned and sorted, resulting in a more crowded network, increased latency, and a potential threat to users’ privacy. This paper presents an enhanced version of the BlueEdge framework—a neural network solution designed for the automated classification of data types on edge devices. We achieve this by utilizing a feed-forward neural network and optimized features to identify the presence of 14 distinct data types. Because of this, input data can be preprocessed near its source, and not in the cloud. We utilized a comprehensive dataset comprising 1400 samples, encompassing various data formats from around the world. Compared with rule-based methods, experimental assessment achieves better performance, and results in reduced data transmission (reduced by 62%) and processing latency (78 times faster than cloud-based systems), with resource efficiency comparable to low-end mobile devices. Additionally, our strategy demonstrates strong performance under various data conditions, achieving accuracy levels of over 85% on datasets that may include variations and a noise level as high as 20%. The approach used here is capable of processing data for IoT devices used in education, which can lead to more efficient connections with the cloud and better privacy preservation.

Vehicular ad-hoc networks (VANETs) address a steadily expanding demand, particularly for public emergency applications. Real-time localization of destination vehicles is important for determining the route to deliver messages. Existing location administration services in VANETs are classified as flooding-based, flat-based, and geographic-based location services. Existing localization techniques suffer from network disconnection and overloading because of 5G VANET topology changes. 5G VANETs have low delay and support time-sensitive applications. A traffic light-inspired location service (TLILS) is proposed to manage localization inspired by traffic lights. The proposed optimized localization service uses roadside units (RSUs) as location servers. RSUs with the maximum traffic weight metrics were chosen. Traffic weight metrics are based on speed of vehicles, connection time and density of neighboring vehicles. The proposed TLILS outperforms both Name-ID Hybrid Routing (NIHR) and Zoom-Out Geographic Location Service (ZGLS) for packet delivery ratio (PDR) and delay. TLILSs guarantee the highest PDR (0.96) and the shortest end-to-end delay (0.001 s) over NIHR and ZGLS.

With the rapid growth of the Internet of Things (IoT) and the emergence of big data, handling massive amounts of data has become a major challenge. Traditional approaches involve sending raw data to cloud data centers for cleaning, processing, and interpretation using data warehouse tools. However, this study introduces BlueEdge, a fog edge mobile application that aims to shift the cleaning and preprocessing tasks from the cloud to the edge. We compare BlueEdge with four popular data cleaning tools (WinPure, DoubleTake, WizSame, and DQGlobal) that operate within data warehouse architectures, such as Hadoop servers. The comparison considers criteria such as time consumption, resource utilization (memory and CPU), and tool performance. BlueEdge utilizes Natural Language Processing (NLP) techniques, including those from the Natural Language Toolkit (NLTK) and Python packages, to connect with a real-time database. As shown in our results, the accuracy values that BlueEdge showed ranged between 72 and 95% across 6 categories of name-based duplicate detection tasks, proving its competitive performance in mobile edge environments. The validation of the framework was done using a larger dataset of 146 error cases with statistically significant values having confidence interval of between 3.4% to 5.8. Statistical comparison indicates consistently significant changes ( p < 0.05) compared to baseline settings of four commercial tools with large effect sizes ( Cohen d: 0.89- 1.34). BlueEdge takes care of data duplication elimination services such as using different spelling and pronunciation (78.4%, CI: 73.1–83.7%), misspellings (72.0%, CI: 66.2–77.8%), name abbreviations (90.5%, CI: 86.1–94.9%), honorific prefixes (95.2%, CI: 91.8–98.6%), common nicknames (76.2%, C The reliable performance of edge-based data cleaning is verified through cross-validation analysis (81.7% ± 2.3%), the results of which prove the consistency of its activity. Additionally, BlueEdge utilizes a minimal bandwidth of only 5000 bytes per edge on mobile phones, unlike data warehouses that require 10,000–60,000 bytes on Hadoop machines. Additionally, BlueEdge is designed to reduce the time taken for data cleaning to 1 s at the data edge, unlike the standard 4–30 s it normally takes for data warehouses. The blue edge is easy to use without authorization of the mobile devices, where the application is conducted free of charge. The framework was validated through controlled experimental testing and real-world deployment at an IT services company, achieving an overall ITSQM quality score of 8.9/10 and demonstrating practical effectiveness in organizational settings. This foundation has been further enhanced with neural network-based classification approaches, which are currently under peer review.

Car congestion is a pressing issue for everyone on the planet. Car congestion can be caused by accidents, traffic lights, rapid accelerations, deceleration, and hesitation of drivers, as well as a small low-carrying capacity road without bridges. Increasing road width and constructing roundabouts and bridges are solutions to car congestion, but the cost is significant. TLR (traffic light recognition) reduces accidents and traffic congestion caused by traffic lights (TLs). Image processing with convolutional neural network (CNN) lakes dealing with harsh weather. A semi-automatic annotation for traffic light detection employs a global navigation satellite system, raising the cost of automobiles. Data was not collected in harsh conditions, and tracking was not supported. Integrated channel feature tracking (ICFT) combines detection and tracking, but it does not support sharing information with neighbors. This study used vehicular ad-hoc networks (VANETs) for VANET traffic light recognition (VTLR). Information exchange as well as monitoring of the TL status, time remaining before a change, and recommended speeds are supported. Based on testing, it has been determined that VTLR performs better than semi-automatic annotation, image processing with CNN, and ICFT in terms of delay, success ratio, and the number of detections per second.

Vehicular ad-hoc networks (VANETs) address a steadily expanding demand, particularly for public emergency applications. Real-time localization of destination vehicles is important for determining the route to deliver messages. Existing location administration services in VANETs are classified as flooding-based, flat-based, and geographic-based location services. Existing localization techniques suffer from network disconnection and overloading because of 5G VANET topology changes. 5G VANETs have low delay and support time-sensitive applications. A traffic light-inspired location service (TLILS) is proposed to manage localization inspired by traffic lights. The proposed optimized localization service uses roadside units (RSUs) as location servers. RSUs with the maximum traffic weight metrics were chosen. Traffic weight metrics are based on speed of vehicles, connection time and density of neighboring vehicles. The proposed TLILS outperforms both Name-ID Hybrid Routing (NIHR) and Zoom-Out Geographic Location Service (ZGLS) for packet delivery ratio (PDR) and delay. TLILSs guarantee the highest PDR (0.96) and the shortest end-to-end delay (0.001 s) over NIHR and ZGLS.