Editorial Summary of Selected Articles

The rapid growth of urban populations and the increasing demand for smart, efficient infrastructure have necessitated the development of innovative solutions to tackle the challenges faced by the modern world. ¹ This special section compiles significant contributions from scholars focusing on technological advancements and pioneering methods in the context of Big Data and the Internet of Things (IoT) in Complex Information Systems. The articles highlighted below present state-of-the-art research addressing issues related to urban mobility, healthcare, disaster management, carbon emissions, and resource optimization. After the vigorous review process, we have selected the following five articles.

GUB: User Behavior Similarity-Based Recommendation

Using the Entity Interaction Knowledge Graph, this study introduces a novel recommendation system that takes user behavior similarities into account. ² The suggested model incorporates many behavior types to better accurately anticipate user preferences than conventional methods relying on user-item interactions. When compared to current methods, the method performed better, exhibiting more user satisfaction and better recommendation quality. This method is unique in that it considers a wide range of user behavior, resulting in a more sophisticated recommendation system.

Pneumonia Detection Using Enhanced CNN Model

An enhanced Convolutional Neural Network (CNN) was created to detect pneumonia in chest X-rays. ³ Its efficacy was compared to well-known models such as ResNet-50 and VGG-19. The accuracy of the Enhanced CNN was 92.4%, which was substantially higher than that of other models. This research demonstrates the utility of model optimization and transfer learning in enhancing diagnostic accuracy, providing healthcare providers with a more dependable and accessible instrument to identify pneumonia early and mitigate severe complications.

Extraction and Analysis of GTFS Datasets

In order to facilitate better research of urban transportation systems, this article presents a novel method for extracting abstract transit networks from General Transit Feed Specification (GTFS) datasets. ⁴ The technology created in this study allows for deeper insights into the quality and structure of public transportation networks by integrating virtual stations to adjust for data inconsistencies. This tool may be used to improve smart city planning and infrastructure.

Data-Driven Analysis of Power Carbon Emissions

This study offers a data-driven approach for examining carbon emissions using empirical mode decomposition to address the problem of carbon neutrality in urban power systems. ⁵ The authors create a dynamic framework for tracking and lowering carbon footprints by fusing big data and macro-energy ideas. The suggested method contributes to wider environmental objectives by providing policy recommendations to promote sustainable power usage in cities, apart from being advantageous for precise carbon tracking.

Cloud Resource Scheduling with Improved Honey Badger Algorithm

An improved Honey Badger Algorithm (IHBA) designed to maximize cloud resource scheduling is presented in the article. ⁶ IHBA considerably increases task convergence rates and efficiently distributes load by including novel fitness functions and local search techniques. IHBA showed greater efficiency and flexibility in handling large-scale activities compared to other meta-heuristic algorithms, offering a viable option for cloud-based applications in smart city services.

Scholarly Contributions and Future Directions

The selected articles demonstrate a wide variety of creative methods to improve living conditions in Big Data and the IoT in Complex Information Systems. These studies demonstrate the promise of integrating technologies such as AI, IoT, Big Data, and cloud computing to address difficult challenges, ranging from health diagnostics and catastrophe management to urban transit and resource scheduling.

Led by Professor Victor Chang, the IoTBDS (IoT, Big Data, and Security) and COMPLEXIS (Complex Information Systems) conferences have been instrumental in fostering academic endeavors that support these developments. By organizing these conferences, the team has promoted a worldwide research community that works together to solve practical issues with state-of-the-art technologies. The team’s efforts have produced several noteworthy achievements, such as improvements in cloud computing, smart healthcare, and urban infrastructure, which have had a major impact on the development of smart cities globally.

We have also produced a video to illustrate the research contributions and impact of these five research projects and the contributions and community services led by Prof Chang and the team. Please refer to our YouTube website for further details: https://www.youtube.com/watch?v=LH6Jp-E5DL4.