Natural language processing (NLP), which is known as an emerging technology creating considerable value in multiple areas, has recently shown its great potential in government operations and public administration applications. However, while the number of publications on NLP is increasing steadily, there is no comprehensive review for a holistic understanding of how NLP is being adopted by governments. In this regard, we present a systematic literature review on NLP applications in governments by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. The review shows that the current literature comprises three levels of contribution: automation, extension, and transformation. The most-used NLP techniques reported in government-related research are sentiment analysis, machine learning, deep learning, classification, data extraction, data mining, topic modelling, opinion mining, chatbots, and question answering. Data classification, management, and decision-making are the most frequently reported reasons for using NLP. The salient research topics being discussed in the literature can be grouped into four categories: (1) governance and policy, (2) citizens and public opinion, (3) medical and healthcare, and (4) economy and environment. Future research directions should focus on (1) the potential of chatbots, (2) NLP applications in the post-pandemic era, and (3) empirical research for government work.

The 3D bin packing problem is a challenging combinatorial optimization problem with numerous real-world applications. In this paper, we present a novel approach for solving this problem by integrating a generative adversarial network (GAN) with a genetic algorithm (GA). Our proposed GAN-based GA utilizes the GAN to generate high-quality solutions and improve the exploration and exploitation capabilities of the GA. We evaluate the performance of the proposed algorithm on a set of benchmark instances and compare it with two existing algorithms. The simulation studies demonstrate that our proposed algorithm outperforms both existing algorithms in terms of the number of used bins while achieving comparable computation times. Our proposed algorithm also performs well in terms of solution quality and runtime on instances of different sizes and shapes. We conduct sensitivity analysis and parameter tuning simulations to determine the optimal values for the key parameters of the proposed algorithm. Our results indicate that the proposed algorithm is robust and effective in solving the 3D bin packing problem. The proposed GAN-based GA algorithm and its modifications can be applied to other optimization problems. Our research contributes to the development of efficient and effective algorithms for solving complex optimization problems, particularly in the context of logistics and manufacturing. In summary, the proposed algorithm represents a promising solution to the challenging 3D bin packing problem and has the potential to advance the state-of-the-art in combinatorial optimization.

The concept of the “smart city” has emerged with the advancement of technology, but some facilities are not sufficiently intelligent, such as parking lots. Hence, this paper proposes an inexpensive and plug-to-play camera-based smart parking system for airports. The system utilizes inverse perspective mapping (IPM) to provide an aerial view image of the parking lot, which is then processed to extract parking space information. The system also includes a guidance system to assist drivers in finding available parking spaces. The system is simulated on a 3D scene based on the parking lot of Macao International Airport. In the experiment, our system achieved an accuracy rate of 97.03% and a mean distance error of 8.59 pixels. This research study shows the potential of enhancing parking lots using only cameras as data collectors, and the results show that the system is capable of providing accurate and useful information. It performs well in parking lots with open space, in particular. Moreover, it is an economical solution for implementing a smart parking lot.

In the era of educational digital transformation, integrating information and communication technology (ICT) into school administration aligns with the goals of promoting personalized learning, equity, and teaching quality. This study examines how ICT reshapes management practices, addresses challenges, and achieves educational objectives. To explore ICT’s impact on school administration (2009–2024), we conducted a systematic scoping review of four databases (Web of Science, Scopus, ScienceDirect, and IEEE Xplore) following the PRISMA-ScR guidelines. Retrieved studies were screened, analyzed, and synthesized to identify key trends and challenges. The results show that ICT significantly improves administrative efficiency. Automated systems streamline routine tasks, allowing administrators to allocate more time to strategic planning. It enables data-driven decision-making. By analyzing large datasets, ICT helps identify trends in student performance and resource utilization, facilitating accurate forecasting and better resource allocation. Moreover, ICT strengthens stakeholder communication. Online platforms enable instant interaction among teachers, students, and parents, increasing the transparency and responsiveness of school administration. However, there are challenges. Data privacy concerns can erode trust, as student and staff data collection and use may lead to breaches. Infrastructure deficiencies, such as unreliable internet and outdated equipment, impede implementation. The digital divide exacerbates inequality, with under-resourced schools struggling to utilize ICT fully. ICT is vital in educational administration. Its integration requires a strategic approach. This study offers insights for optimizing educational management via ICT and highlights the need for equitable technological advancement to create an inclusive, high-quality educational system.

Based on text analysis, public big data management is studied. The public data management of Mount Wutai tourism network travel notes is discussed. The positive, neutral, and negative effects of the naive Bayesian classification model and decision tree classification model on the tourism sentiment attitude of Mount Wutai are compared. The relationship between tourism resources, tourism facilities, tourism services, tourism environment, and tourism sentiment and attitude of Wutai Mountain is analyzed. The results show that the true positive rate, true negative rate, and F-measure of the Bayesian decision tree classifier to classify positive text are 86.64%, 81.27%, and 84.62%, respectively. The true positive rate for neutral text is 82.05%, the true negative rate is 78.89%, and the F-measure is 77.11%. The true positive rate for negative text is 83.67%, the true negative rate is 98.29%, and the F-measure is 82.83%. The Bayesian decision tree classifier can evaluate positive and negative texts better than neutral texts. The true positive rate of the C4.5 decision tree classifier for positive text is 91.44%, the true negative rate is 86.57%, and the F-measure is 89.45%. The true positive rate for neutral text is 90.17%, the true negative rate is 83.28%, and the F-measure is 84.06%. The true positive rate for negative text is 91.84%, the true negative rate is 99.05%, and the F-measure is 90.91%. The decision tree classifier has a better evaluation effect on positive and negative texts than on neutral texts. The ROC curve of the evaluation effect of the two classifiers shows that the evaluation effect of the two classifiers has a better evaluation effect on positive text than that of the neutral and negative texts, and the evaluation effect of the C4.5 decision tree classifier is better than that of the Bayesian classifier. The promotion degree of tourism resources and facilities in forwarding online travel notes is obviously higher, and there is a high correlation between tourism resources and facilities and forward online travel notes. In negative online travel notes, the promotion degree of tourism service and tourism environment is high, and the correlation between tourism service and tourism environment and negative online travel notes is high. In summary, improving the quality of tourism services and the tourism environment of Mount Wutai scenic spots can better enhance the recognition and satisfaction of tourists with Mount Wutai tourism.

In order to investigate the effect of various natural language processing models on different data processing, this paper adopted the consumer reviews of two well-known Internet retailing websites: Yelp and Zappos, and used four text embedding methods: word2vec, Glove, BERT, and GPT-2 and two text classification methods: SVM and Neural Network (NN) for text classification, in order to compare the performance of the combinations of these text mining techniques. The result shows that BERT is the best-performing text embedding method overall in both datasets when used with both SVM and NN. It is also found that NN is better than SVM for overall text classification. As an exploratory experiment, we aim to provide a three-dimensional comparison to find the most suitable algorithm for consumer review data, and the implication is that BERT and NN can achieve satisfactory results in most of the scenarios.