Smart cities are an innovative concept for managing metropolitan areas to increase their residents’ sustainability and quality of life. This article examines the management and evolution of energy generation, various storage systems and the applications they serve, and infrastructure technology’s current condition and future prospects. Additionally, the study also examines energy-related construction and transportation systems and technologies. The Smart Cities Energy Prediction Task Force predicts electrical usage using STLF, SVM, and e-learning machines. To keep a system working well throughout the year, fossil fuels must be utilised as a backup energy source. Technologies can only benefit if integrated into the city’s infrastructure. By 2050, it is anticipated that the global population will surpass 10 billion, with most people settling in metropolitan regions. Between 2020 and 2027, the global market for smart energy is anticipated to expand by 27.1% annually, from USD 122.2 billion in 2020 to USD 652 billion in 2026. In 2020, Europe will account for 31.8 per cent of total smart energy product sales. China’s GDP is projected to grow by 33.0 per cent annually, reaching USD 176.1 billion by the conclusion of the analysis period. Consequently, smart cities are expanding and blooming worldwide, yet there are no permanent standards.

/pdf/energy-and-sustainable-development-in-smart-cities-an-2eyeddgw.pdf

Energy and Sustainable Development in Smart Cities: An Overview

Smart cities have revolutionized urban living by incorporating sophisticated technologies to optimize various aspects of urban infrastructure, such as transportation systems. Effective traffic management is a crucial component of smart cities, as it has a direct impact on the quality of life of residents and tourists. Utilizing deep radial basis function (RBF) networks, this paper describes a novel strategy for enhancing traffic intelligence in smart cities. Traditional methods of traffic analysis frequently rely on simplistic models that are incapable of capturing the intricate patterns and dynamics of urban traffic systems. Deep learning techniques, such as deep RBF networks, have the potential to extract valuable insights from traffic data and enable more precise predictions and decisions. In this paper, we propose an RBF-based method for enhancing smart city traffic intelligence. Deep RBF networks combine the adaptability and generalization capabilities of deep learning with the discriminative capability of radial basis functions. The proposed method can effectively learn intricate relationships and nonlinear patterns in traffic data by leveraging the hierarchical structure of deep neural networks. The deep RBF model can learn to predict traffic conditions, identify congestion patterns, and make informed recommendations for optimizing traffic management strategies by incorporating these rich and diverse data. To evaluate the efficacy of our proposed method, extensive experiments and comparisons with real-world traffic datasets from a smart city environment were conducted. In terms of prediction accuracy and efficiency, the results demonstrate that the deep RBF-based approach outperforms conventional traffic analysis methods. Smart city traffic intelligence is enhanced by the model capacity to capture nonlinear relationships and manage large-scale data sets.

pdf/enhancing-traffic-intelligence-in-smart-cities-using-2h7dn23y48.pdf

Enhancing Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function

In the quest to meet the escalating demands of citizens, future smart cities emerge as crucial entities. Their role becomes even more vital given the current challenges posed by rapid urbanization and the need for sustainable and inclusive living spaces. At the heart of these future smart cities are advancements in information and communication technologies, with Industry 5.0 playing an increasingly significant role. This paper endeavors to conduct an exhaustive survey to analyze future technologies, including the potential of Industry 5.0 and their implications for smart cities. The crux of the paper is an exploration of technological advancements across various domains that are set to shape the future of urban environments. The discussion spans diverse areas including but not limited to cyber–physical systems, fog computing, unmanned aerial vehicles, renewable energy, machine learning, deep learning, cybersecurity, and digital forensics. Additionally, the paper sheds light on the specific role of Industry 5.0 in the smart city context, illuminating its impact on enabling advanced cybersecurity measures, fostering human–machine collaboration, driving intelligent automation in urban services, and refining data management and decision making. The paper also offers an in-depth review of the existing frameworks that are shaping smart city applications, evaluating how Industry 5.0 technologies could augment these frameworks. In particular, the paper delves into the various technological challenges that smart cities face, bringing potential Industry 5.0-enabled solutions to the fore.

Unlocking the Future: Fostering Human–Machine Collaboration and Driving Intelligent Automation through Industry 5.0 in Smart Cities

Research trends, themes, and insights on artificial neural networks for smart cities towards SDG-11

The strength of carbon nanotubes (CNTs) and cement composites is dependent on multiple variables. In addition, CNTs added to a cement-based matrix can boost its strength. However, the information related to CNTs characteristics is limited and scarce. Their incorporation may substantially enhance the mechanical and durability properties of cementitious mixtures. Despite challenges such as high cost and workability problems. Therefore, proper consumption of these materials must be used to attain desired qualities. The principal plan of this investigation is to create predictive framework by utilizing machine-learning algorithms. Gene expression programming (GEP), and the random forest algorithm (RFA) is employed to estimate the compressive strength of concrete mixed with CNTs. GEP is used as an individual approach, and RFA is used as an ensemble method to depict the most influential model. The outcomes of the two models are assessed by employing external K-fold cross-validation, and a comparison is done. A comprehensive database is established comprising 282 data points for the CS with blended CNTs. The model is then calibrated using six inputs, including curing time (CT), water-to-cement ratio (W/C), fine aggregate (FA), carbon nanotube content (CNTs), cement content (CC), and coarse aggregate (CA). In addition, the predicted results are validated using k-fold cross-validation, and performance metrics, such as mean absolute error (MAE), root squared error (RSE), correlation coefficient (R2), root mean square error (RMSE), and relative root mean square error (RRMSE). The result shows that RF regression with the nth estimator shows robust accuracy by showing minimal errors as analyzed to individual RF and GEP models. Likewise, the nth model depicts higher R2 = 0.96, and validation results demonstrate low errors. Moreover, the GEP model excels in terms of prediction through the empirical equation. In addition, Shapley analysis (SHAP) is performed to check the distribution of parameters to output. The result reveals that curing time, cement, and water to binder have substantial influence of CNT based concrete composite. 

Compressive strength prediction of concrete blended with carbon nanotubes using gene expression programming and random forest: Hyper-tuning and optimization

The manuscript represents a comeprehensive and systematic literature review on the machine learning methods in the emerging applications of smart city. Application domains include the essential aspect of the smart cities including the energy, healthcare, transportation, security, and pollution. The methodology presents the state-of-the-art, taxonomy, evaluation and model performance. The study concludes that the hybrid models and ensembles are the best performers since they exhibit both high accuracy and not-costly complexity. On the other hand, the deep learning (DL) techniques had higher accuracy than the hybrid models and ensembles, but they demanded relatively higher computation power. Moreover, all these advanced ML methods had a slower processing speed than the single methods. Likewise, the support vector machine (SVM) and decision tree (DT) generally outperformed the artificial neural network (ANN) for accuracy and other metrics. However, since the difference is negligible, it can be concluded that using either of them is appropriate. The study’s findings identify the pros and cons of the methods in each application for future researchers, practitioners, and policy-makers for the right problem within the context of smart cities.

https://doi.org/10.1109/access.2022.3181718

When Smart Cities Get Smarter via Machine Learning: An In-depth Literature Review

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When Smart Cities Get Smarter via Machine Learning: An In-depth Literature Review