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As the most active research branch in the field of artificial intelligence, deep learning has gradually become a research hotspot in the medical field in recent years, and has achieved some success in medical image and signal processing, biomedical and healthcare, medical information mining and retrieval, showing great application prospects. Aiming at the problem of the uneven distribution of data in biomedical and healthcare area under smart city, this paper proposes a novel supervised network by a dual-task deep learning scheme. First, we optimize the early distribution by supervising the features of the early stage, which helps the shallow network to determine the distribution of data, and avoids the gradient disappearance and slow convergence. Then, a novel supervised network framework is proposed to focus on the prediction accuracy and generate crowd response maps that reflect the importance of different features. The final distribution revising module uses high-frequency semantic information to further supervise the predicted distribution. The noise data suppression module is proposed to suppress the misjudgment on fake biomedical data. Experimental results on four commonly healthcare used datasets show that our proposed deep learning model performs better in the biomedical data analysis and density distribution, and outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

With the increasing reliance and demand for artificial intelligence in the construction of smart cities, AI has been embedded in various applications and infrastructures. However, uncertainties, concerns, and even fears about AI are constantly emerging. This study based on actor‐network theory (ANT) showcases the application practices of artificial intelligence in smart city, including smart transportation, smart water management, smart healthcare, smart grids, and smart city evaluation standards, and analyzes the process of constructing an actor‐network involving governments, technology development companies, and urban residents. It demonstrates the interactive network processes such as problem statement, interest alignment, recruitment, and mobilization. It explores the relationships among humans, humans and technology, and the self‐challenges faced by technology, such as digital ethics, machine ethics, and relational alienation. In the future, it is necessary to establish explainable, transparent, safe, and responsible AI in the development of smart cities, grant certain rights, and respect to nonhuman actors such as data, AI, and machines, and formulate a series of laws and ethical guidelines to ensure the sustainable development of smart city. [ABSTRACT FROM AUTHOR]

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Smart cities and information and communications technology is a rapidly growing field in both research and real-world implementation, but it is one that is still new and with many different ideas. Unfortunately, there is less cooperation and knowledge sharing across the field, and research often fails to move into real-world applications, which holds it back from becoming fully realized. This paper aims to provide an overview of the current state of smart cities, its definitions, technologies, and technical dimensions, from architectural design to standards and data handling, and how they are handled in the real world and its impact on society. Additionally, it examines important smart city projects, their applications, and ranking systems. This text aims to forecast the future of the field, its impact, the challenges it faces, and what should be addressed to help it reach its full potential. [ABSTRACT FROM AUTHOR]

In-cabin occupancy detection has become increasingly important due to incidents involving children left in vehicles under extreme temperature conditions. Frequency modulated continuous wave (FMCW) radars are widely used for non-contact monitoring and sensing applications, particularly for occupancy detection. However, the confined and metallic environment inside vehicle cabins presents significant challenges due to multipath reflections. This paper introduces a novel approach that detects the occupied space in each seat to determine occupancy, using the variance of detected points as an indicator of volume occupancy. In an experimental study involving 70 different scenarios with single and multiple subjects, we classify occupants in each seat into one of three categories: adult, baby, or empty. The proposed method achieves an overall accuracy of 96.7% using an Adaboost classifier and a miss-detection rate of 1.8% for detecting babies. This approach demonstrates superior robustness to multipath interference compared to traditional energy-based methods, offering a significant advancement in in-cabin occupancy detection technology. [ABSTRACT FROM AUTHOR]

Computer Vision (CV) has become increasingly important for Single-Board Computers (SBCs) due to their widespread deployment in addressing real-world problems. Specifically, in the context of smart cities, there is an emerging trend of developing end-to-end video analytics solutions designed to address urban challenges such as traffic management, disaster response, and waste management. However, deploying CV solutions on SBCs presents several pressing challenges (e.g., limited computation power, inefficient energy management, and real-time processing needs) hindering their use at scale. Graphical Processing Units (GPUs) and software-level developments have emerged recently in addressing these challenges to enable the elevated performance of SBCs; however, it is still an active area of research. There is a gap in the literature for a comprehensive review of such recent and rapidly evolving advancements on both software and hardware fronts. The presented review provides a detailed overview of the existing GPU-accelerated edge-computing SBCs and software advancements including algorithm optimization techniques, packages, development frameworks, and hardware deployment specific packages. This review provides a subjective comparative analysis based on critical factors to help applied Artificial Intelligence (AI) researchers in demonstrating the existing state of the art and selecting the best suited combinations for their specific use-case. At the end, the paper also discusses potential limitations of the existing SBCs and highlights the future research directions in this domain. [ABSTRACT FROM AUTHOR]

The development of smart cities will require the construction of smart buildings. Smart buildings will demand the incorporation of elements for efficient monitoring and control of electrical consumption. The development of efficient AI algorithms is needed to generate more accurate electricity consumption predictions; therefore; anomaly detection in electricity consumption predictions has become an important research topic. This work focuses on the study of the detection of anomalies in domestic electrical consumption in Mexico. A predictive machine learning model of future electricity consumption was generated to evaluate various anomaly-detection techniques. Their effectiveness in identifying outliers was determined, and their performance was documented. A 30-day forecast of electrical consumption and an anomaly-detection model have been developed using isolation forest. Isolation forest successfully captured up to 75% of the anomalies. Finally, the Shapley values have been used to generate an explanation of the results of a model capable of detecting anomalous data for the Mexican context. [ABSTRACT FROM AUTHOR]

The urban land-use allocation problem is a spatial optimization problem that allocates optimum land-uses to specific land units in urban areas. This problem is an NP (nondeterministic polynomial time)-hard problem because of involving many objective functions, many constraints, and complex search space. Moreover, this subject is an important issue in smart cities and newly developed areas of cities to achieve a sustainable arrangement of land-uses. Different types ofMulti-Objective Optimization Algorithms (MOOAs) based on Artificial Intelligence (AI) have been frequently employed, but their ability and performance have not been evaluated and compared properly. This paper aims to employ and compare three commonly used MOOAs i.e. NSGA-II, MOPSO, and MOEA/D in urban land-use allocation problems. Selected algorithms belong to different categories of MOOAs family to investigate their advantage and disadvantages. The objective functions of this study are compatibility, dependency, suitability, and compactness of land-uses and the constraint is compensating of Per-Capita demand in the urban environment. Evaluation of results is based on the dispersion of the solutions, diversity of the solutions' space, and comparing the number of dominant solutions in Pareto-Fronts. The results showed that all three algorithms improved the objective functions related to the current arrangement of the land-uses. However, the run time of NSGA-II is the worst, related to the Diversity Metric (DM) which represents the regularity of the distance between solutions at the highest degree. Moreover, MOPSO provides the best Scattering Diversity Metric (SDM) which shows the diversity of solutions in the solution space. Furthermore, In terms of algorithm execution time, MOEA/D performed better than the other two. So, Decision-makers should consider different aspects in choosing the appropriate MOOA for land-use management problems. [ABSTRACT FROM AUTHOR]

Although walking methodologies (WMs) and machine learning (ML) have been objects of interest for urban scholars, it is difficult to find research that integrates both. We propose a 'cyborg walk' method and apply it to studying litter in public spaces. Walking routes are created based on an unsupervised learning algorithm (k-means) to classify public spaces. Then, a deep learning model (YOLOv5) is used to collect data from geotagged photos taken by an automatic Insta360 X3 camera worn by human walkers. Results from image recognition have an accuracy between 83.7% and 95%, which is similar to what is validated by the literature. The data collected by the machine are automatically georeferenced thanks to the metadata generated by a GPS attached to the camera. WMs could benefit from the introduction of ML for informative route optimisation and georeferenced visual data quantification. The links between these findings and the existing WM literature are discussed, reflecting on the parallels between this 'cyborg walk' experiment and the seminal cyborg metaphor proposed by Donna Haraway. [ABSTRACT FROM AUTHOR]

Living in a smart city has many advantages, such as improved waste and water management, access to quality healthcare facilities, effective and safe transportation systems, and personal protection. Explainable AI (XAI) is called a system that is capable of providing explanations for its judgments or predictions. This term describes a model, its expected impacts, and any potential biases that may be present. XAI tools and frameworks can aid in comprehending and trusting the output and outcomes generated by machine-learning algorithms. This study used XAI methods to classify cities based on smart city metrics. The logistic regression method with LIME achieved perfect accuracy, precision, recall, and F1-score, predicting correctly all cases. [ABSTRACT FROM AUTHOR]

This research explores the efficacy of the YOLOR (You Only Learn One Representation) algorithm integrated with the Deep Sort algorithm for real-time vehicle detection, classification, and counting in Morelia, Mexico. The study aims to enhance traffic monitoring and management by leveraging advanced deep learning techniques. The methodology involves deploying the YOLOR model at six key monitoring stations, with varying confidence levels and pre-trained weights, to evaluate its performance across diverse traffic conditions. The results demonstrate that the model is effective compared to other approaches in classifying multiple vehicle types. The combination of YOLOR and Deep Sort proves effective in tracking vehicles and distinguishing between different types, providing valuable data for optimizing traffic flow and infrastructure planning. This innovative approach offers a scalable and precise solution for intelligent traffic management, setting new methodologies for urban traffic monitoring systems.

The advent of sixth-generation (6G) networks promises revolutionary advancements in wireless communication, marked by unprecedented speeds, ultra-low latency, and ubiquitous connectivity. This research paper delves into the integration of Artificial Intelligence (AI) in 6G network applications, exploring the challenges and outlining future directions for this transformative synergy. The study investigates the key AI technologies for 6G: the potential of AI to optimize network performance, enhance user experience, and enable novel applications in diverse domains and AI-enabled applications. Analyzing the current landscape, the paper identifies key challenges such as scalability, security, and ethical considerations in deploying AI-enabled 6G networks. Moreover, it explores the dynamic interplay between AI and 6G technologies, shedding light on the intricate relationships that underpin their successful integration. The research contributes valuable insights to the ongoing discourse surrounding the convergence of AI and 6G networks, laying the groundwork for a robust and intelligent future communication infrastructure.

The urban land-use allocation problem is a spatial optimization problem that allocates optimum land-uses to specific land units in urban areas. This problem is an NP (nondeterministic polynomial time)-hard problem because of involving many objective functions, many constraints, and complex search space. Moreover, this subject is an important issue in smart cities and newly developed areas of cities to achieve a sustainable arrangement of land-uses. Different types ofMulti-Objective Optimization Algorithms (MOOAs) based on Artificial Intelligence (AI) have been frequently employed, but their ability and performance have not been evaluated and compared properly. This paper aims to employ and compare three commonly used MOOAs i.e. NSGA-II, MOPSO, and MOEA/D in urban land-use allocation problems. Selected algorithms belong to different categories of MOOAs family to investigate their advantage and disadvantages. The objective functions of this study are compatibility, dependency, suitability, and compactness of land-uses and the constraint is compensating of Per-Capita demand in the urban environment. Evaluation of results is based on the dispersion of the solutions, diversity of the solutions’ space, and comparing the number of dominant solutions in Pareto-Fronts. The results showed that all three algorithms improved the objective functions related to the current arrangement of the land-uses. However, the run time of NSGA-II is the worst, related to the Diversity Metric (DM) which represents the regularity of the distance between solutions at the highest degree. Moreover, MOPSO provides the best Scattering Diversity Metric (SDM) which shows the diversity of solutions in the solution space. Furthermore, In terms of algorithm execution time, MOEA/D performed better than the other two. So, Decision-makers should consider different aspects in choosing the appropriate MOOA for land-use management problems.

As urbanization continues to pose new challenges for cities around the world, the concept of smart cities is a promising solution, with artificial intelligence (AI) playing a central role in this transformation. This paper presents a literature review of AI solutions applied in smart cities, focusing on its six main areas: smart mobility, smart environment, smart governance, smart living, smart economy, and smart people. The analysis covers publications from 2021 to 2024 available on Scopus. This paper examines the application of AI in each area and identifies barriers, advances, and future directions. The authors set the following goals of the analysis: (1) to identify solutions and applications using artificial intelligence in smart cities; (2) to identify the barriers to implementation of artificial intelligence in smart cities; and (3) to explore directions of the usage of artificial intelligence in smart cities.

Abstract Numerous strategies have been proposed for the detection and prevention of non‐technical electricity losses due to fraudulent activities. Among these, machine learning algorithms and data‐driven techniques have gained prominence over traditional methodologies due to their superior performance, leading to a trend of increasing adoption in recent years. A novel two‐step process is presented for detecting fraudulent Non‐technical losses (NTLs) in smart grids. The first step involves transforming the time‐series data with additional extracted features derived from the publicly available State Grid Corporation of China (SGCC) dataset. The features are extracted after identifying abrupt changes in electricity consumption patterns using the sum of finite differences, the Auto‐Regressive Integrated Moving Average model, and the Holt‐Winters model. Following this, five distinct classification models are used to train and evaluate a fraud detection model using the SGCC dataset. The evaluation results indicate that the most effective model among the five is the Gradient Boosting Machine. This two‐step approach enables the classification models to surpass previously reported high‐performing methods in terms of accuracy, F1‐score, and other relevant metrics for non‐technical loss detection.

One essential component of the futuristic way of living in "smart cities" is the installation of surveillance cameras. There are a wide variety of applications for surveillance cameras, including but not limited to: investigating and preventing crimes, identifying sick individuals (coronavirus), locating missing persons, and many more. In this research, we provided a system for smart city outdoor item recognition using visual data collected by security cameras. The object identification model used by the proposed outdoor system was an enhanced version of RetinaNet. A state of the art object identification model, RetinaNet boasts lightning-fast processing and pinpoint accuracy. Its primary purpose was to rectify the focal loss-based training dataset's inherent class imbalance. To make the RetinaNet better at identifying tiny objects, we increased its receptive field with custom-made convolution blocks. In addition, we adjusted the number of anchors by decreasing their scale and increasing their ratio. Using a mix of open-source datasets including BDD100K, MS COCO, and Pascal Vocab, the suggested outdoor object identification system was trained and tested. While maintaining real-time operation, the suggested system's performance has been markedly enhanced in terms of accuracy.

The field of the Internet of Things (IoT) is dominating various areas of technology. As the number of devices has increased, there is a need for efficient communication with low resource consumption and energy efficiency. Low Power Wide Area Networks (LPWANs) have emerged as a transformative technology for the IoT as they provide long-range communication capabilities with low power consumption. Among the various LPWAN technologies, Long Range Wide Area Networks (LoRaWAN) are widely adopted due to their open standard architecture, which supports secure, bi-directional communication and is particularly effective in outdoor and complex urban environments. This technology is helpful in enabling a variety of IoT applications that require wide coverage and long battery life, such as smart cities, industrial IoT, and environmental monitoring. The integration of Machine Leaning (ML) and Artificial Intelligence (AI) into LoRaWAN operations has further enhanced its capability and particularly optimized resource allocation and energy efficiency. This systematic literature review provides a comprehensive examination of the integration of ML and AI technologies in the optimization of LPWANs, with a specific focus on LoRaWAN. This review follows the PRISMA model and systematically synthesizes current research to highlight how ML and AI enhance operational efficiency, particularly in terms of energy consumption, resource management, and network stability. The SLR aims to review the key methods and techniques that are used in state-of-the-art LoRaWAN to enhance the overall network performance. We identified 25 relevant primary studies. The study provides an analysis of key findings based on research questions on how various LoRaWAN parameters are optimized through advanced ML, DL, and RL techniques to achieve optimized performance. [ABSTRACT FROM AUTHOR]

Advancements in cloud computing, flying ad-hoc networks, wireless sensor networks, artificial intelligence, big data, 5th generation mobile network and internet of things have led to the development of smart cities. Owing to their massive interconnectedness, high volumes of data are collected and exchanged over the public internet. Therefore, the exchanged messages are susceptible to numerous security and privacy threats across these open public channels. Although many security techniques have been designed to address this issue, most of them are still vulnerable to attacks while some deploy computationally extensive cryptographic operations such as bilinear pairings and blockchain. In this paper, we leverage on biometrics, error correction codes and fuzzy commitment schemes to develop a secure and energy efficient authentication scheme for the smart cities. This is informed by the fact that biometric data is cumbersome to reproduce and hence attacks such as side-channeling are thwarted. We formally analyze the security of our protocol using the Burrows–Abadi–Needham logic logic, which shows that our scheme achieves strong mutual authentication among the communicating entities. The semantic analysis of our protocol shows that it mitigates attacks such as de-synchronization, eavesdropping, session hijacking, forgery and side-channeling. In addition, its formal security analysis demonstrates that it is secure under the Canetti and Krawczyk attack model. In terms of performance, our scheme is shown to reduce the computation overheads by 20.7% and hence is the most efficient among the state-of-the-art protocols. [ABSTRACT FROM AUTHOR]

This study follows a novel path toward alarming the dangerous conditions of the buildings by means of piezoelectricity. piezoelectric materials have the ability to translate electrical voltage and displacement to each other interchangeably. This feature is employed in this research to define a critical threshold for the displacements. The case study of this research is a graphene-platelets reinforced functionally-graded piezoelectric (GPLR-FGP) rectangular wall with one free edged at its top. The voltage would be gradually increased until the natural frequency approaches to zero. This circumstance puts the wall in the direct danger of failing its stability. Accordingly, displacements of the wall at this condition can be considered as the critical deflections of the system. The wall's governing equations are established in the background of higher-order shear deformation theory (HOSDT). Differential quadrature approach (DQA) is employed to obtain the natural frequency of the wall. Deep-learning is utilized to accelerate the computation process by means of training a deep network of neurons through the instances determined by the explained numerical approach. Efficiency of the innovative implemented solution in this research is verified through a comparative analysis with the results of the published articles. This research can be considered as the main stone of developing innovative solutions for alarming safety issues in the smart cities of near future. [ABSTRACT FROM AUTHOR]

Smart Cities rely on the Internet of Things (IoT) which is characterised by its diverse, distributed and complex infrastructure. These complexities present a range of cybersecurity challenges, making IoT security an area of great importance. The existing vulnerabilities in IoT leaves Smart Cities susceptible to various types of malicious attacks. These vulnerabilities can be exploited, ranging from opportunistic monetary gains to acts of terrorism that disrupt the systems of rival nations. The fact that conventional cybersecurity protection systems cannot be applied to IoT environments in Smart Cities is an additional challenge. The relationship between cybersecurity and IoT security of Smart Cities has become a crucial gap that must be thoroughly investigated. Hence, this paper extensively reviewing the existing literature concerning IoT applications in Smart Cities, with emphasis on cybersecurity and IoT security concerns. This study concludes that real-time threat detection and mitigation by leveraging fog computing and artificial intelligence models represent a significant advancement in securing IoT infrastructure against cyberattacks. The research outcomes contribute to the critical understanding of the challenges regarding IoT security and paves the way for advancing the research of cybersecurity within the Smart Cities ecosystem. [ABSTRACT FROM AUTHOR]

Smart cities collaborate with various technological systems, including Internet of Things (IoT), artificial intelligence (AI), and drones, to fulfil the expectations of stakeholders and the needs of individuals and society. The use of such advanced technological systems may impose a burden on the natural environment, posing a risk to the sustainability of nature. We have already proposed the concept of biophilia as a service (BaaS) that is a service contributing to human well‐being and the sustainability of nature by emphasizing the relationship between humans and nature, promoting actions that safeguard nature, and collaborating with various organizations. In this paper, a smart, sustainable, and resilient city in harmony with nature is referred to as a nature‐based smart city (NBSC) and BaaS is introduced to NBSC. We define the architecture of NBSC introducing BaaS using the Unified Architecture Framework (UAF) and extract drivers, opportunities, and challenges of the NBSC. Requirements of BaaS for NBSC are derived from the goals and the capabilities. Also, the iterating operational processes of BaaS in the context of NBSC are described and defined. Furthermore, we discuss some findings from description of the architecture of NBSC introducing BaaS to promote the nature safeguard activity, and necessity to make a balance between human well‐being and sustainable natural environment. [ABSTRACT FROM AUTHOR]

In the global monitoring of smart cities, the demands of global object detection systems based on cloud and fog computing in intelligent systems can be satisfied by photographs with globally recognized properties. Nevertheless, conventional techniques are constrained by the imaging depth of field and can produce artefacts or indistinct borders, which can be disastrous for accurately detecting the object. In light of this, this paper proposes an artificial intelligence‐based gradient learning network that gathers and enhances domain information at different sizes in order to produce globally focused fusion results. Gradient features, which provide a lot of boundary information, can eliminate the problem of border artefacts and blur in multi‐focus fusion. The multiple‐receptive module (MRM) facilitates effective information sharing and enables the capture of object properties at different scales. In addition, with the assistance of the global enhancement module (GEM), the network can effectively combine the scale features and gradient data from various receptive fields and reinforce the features to provide precise decision maps. Numerous experiments have demonstrated that our approach outperforms the seven most sophisticated algorithms currently in use. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, Internet of Things, artificial intelligence, cloud computing, and other revolutionary technologies (e.g., edge and fog computing) have become the pillar of smart cities. These latter make users' lives easier, thanks to a wide variety of smart services offered in different dimensions (e.g., smart living, smart mobility, smart economy, smart governance). However, the rapid adoption of smart services by users and the full servicelization of several cities around the world is faced with two major issues: the lack of knowledge regarding smart services' capacities (e.g., features, contextual requirements, pricing models, privacy policies, provisioning terms, etc.), and the lack of unified rating and quantification of smart services' QoS behavior. Indeed, interested users often exploit traditional search tools (e.g., Web search engines, social networks) to find and rate the needed services. This behavior has scattered the smart services' usage data (e.g., users contexts, ratings) across multiple providers platforms, which makes the search task beyond the capacity of users and, even, other service providers. Although recommender systems are a natural solution to exempt users from exploring the huge space of the offered smart services, current recommendation approaches for smart city environments are unable to deliver correct recommendations. In fact, they have been initially designed to single‐network settings (a single service repository), while smart services' consumers often are involved in multiple provider platforms. To the best of our knowledge, there exists no approach that treated smart service recommendation across multiple information networks. Therefore, the goal of this paper is to propose a cross‐network recommender system for smart cities. We first model the multiplex network of smart services' providers as a multirelational fuzzy lattice family thanks to fuzzy relational concept analysis (fuzzy RCA), which is a powerful mathematical method for data analysis and clustering. We also use the concept of anchor users to connect providers networks via the users involved in more than one provider platform. Guided by anchors' cross‐network relations, we compute the similarity between users and we define algorithms for exploring the smart services' information network, i.e. lattice family. Extensive experiments have proved the effectiveness of cross‐network recommendation and the quality of produced recommendations, compared to state‐of‐the‐art single‐network recommendation. [ABSTRACT FROM AUTHOR]

This paper considers a host of definitions and labels attached to the concept of smart cities to identify four dimensions that ground a review of ethical concerns emerging from the current debate. These are: (1) network infrastructure, with the corresponding concerns of control, surveillance, and data privacy and ownership; (2) post-political governance, embodied in the tensions between public and private decision-making and cities as post-political entities; (3) social inclusion, expressed in the aspects of citizen participation and inclusion, and inequality and discrimination; and (4) sustainability, with a specific focus on the environment as an element to protect but also as a strategic element for the future. Given the persisting disagreements around the definition of a smart city, the article identifies in these four dimensions a more stable reference framework within which ethical concerns can be clustered and discussed. Identifying these dimensions makes possible a review of the ethical implications of smart cities that is transversal to their different types and resilient towards the unsettled debate over their definition. [ABSTRACT FROM AUTHOR]

Currently, technology-based methods are widely used in the solutions developed for smart cities and sustainable transportation. Owing to the rapid advances in technology, the traditional structure of transportation networks is undergoing a paradigm shift. Artificial Intelligence (AI), that has started to have disruptive effects in many sectors in the future, is expected to be one of the most influential factors in the paradigm shift in transportation. In this paper, the dynamics of the paradigmatic shift in transportation to promote sustainable transportation in Türkiye are evaluated through conducting a review of the existing literature. Scenarios exhibiting the use of disruptive and innovative technologies in transport systems, specifically, AI applications in intelligent transportation systems (ITS), are examined. Additionally, the economic, environmental, and social impacts of AI applications are discussed by emphasizing the need to identify priority areas for the effective use of AI in the field of intelligent transport. Thus, this paper, by summarizing the use of AI-based technologies for intelligent transport in Türkiye, contributes to the literature by providing an overview of the existing knowledge. [ABSTRACT FROM AUTHOR]

One essential component of the futuristic way of living in "smart cities" is the installation of surveillance cameras. There are a wide variety of applications for surveillance cameras, including but not limited to: investigating and preventing crimes, identifying sick individuals (coronavirus), locating missing persons, and many more. In this research, we provided a system for smart city outdoor item recognition using visual data collected by security cameras. The object identification model used by the proposed outdoor system was an enhanced version of RetinaNet. A state of the art object identification model, RetinaNet boasts lightning-fast processing and pinpoint accuracy. Its primary purpose was to rectify the focal loss-based training dataset's inherent class imbalance. To make the RetinaNet better at identifying tiny objects, we increased its receptive field with custom-made convolution blocks. In addition, we adjusted the number of anchors by decreasing their scale and increasing their ratio. Using a mix of open-source datasets including BDD100K, MS COCO, and Pascal Vocab, the suggested outdoor object identification system was trained and tested. While maintaining real-time operation, the suggested system's performance has been markedly enhanced in terms of accuracy. [ABSTRACT FROM AUTHOR]

Big Data is steadily expanding beyond the boundaries of its foundational constructs of three primary Vs, Volume, Velocity and Variety, and two secondary Vs, Veracity and Value. The advent of 5G networks, Edge computing and IoT technologies has transformed Big Data into this modern context. With these new manifestations of Big Data, the focus is not only on the data itself but on the context that it applies to its immediate environment as well as the human and societal perception of this context. It is increasingly challenging for conventional AI algorithms to process and transform this data, analyse and visualise a broad spectrum of insights, and then formulate the explainability of such insights in terms of bias, transparency, safety, ethics, and causality. Self-structuring Artificial Intelligence (SSAI) addresses the limitations of conventional AI by adapting to the inherent structure of the data, incrementally learning and abstracting from this structure. SSAI has not been investigated in a cloud-based setting for generating explainable insights from these new types of Big Data. In this paper we propose a cloud-based architecture for explainable Big Data analytics using SSAI in highly-connected 5G and Edge computing environments. The proposed architecture is empirically evaluated on a commercial scale Big Data use case of Smart Grid for Smart Cities. The results of these experiments confirm the functionality and effectiveness of the proposed architecture. [ABSTRACT FROM AUTHOR]

China is the largest foreign information and communications technology (ICT) investor in Africa, and Chinese telecommunication companies including Huawei, CloudWalk, and Transsion have brought a range of artificial intelligence (AI) technology to Africa, from face recognition to smart cities. Existing analysis of China's AI impact in Africa, however, habitually falls into a Cold War-style narrative of 'authoritarianism' against the backdrop of 'liberal democracy.' This article calls for going beyond the oversimplified paradigm and proposes to examine the new phenomenon through a more sophisticated and critical lens. First, I show how (English language) media's investigations and academia's analysis of China's AI policy and its global impact (particularly in Africa) usually concentrate on a deep concern about the exportation of authoritarianism from Beijing. The article then demonstrates that the 'authoritarianism vs liberal democracy' model is problematic, if not outdated, and particularly insufficient to capture the multi-layered and overlapping realities of authoritarian and democratic moves by states around the world and the distinguishing properties of AI technologies. Based on a critical appraisal of the notions of surveillance capitalism and data colonialism and the recent empirical evidence from Africa in the third and fourth sections, I propose that these two concepts are more productive analytical frameworks for understanding what is going on in Africa related to the presence of China's AI, as well as for updating and moving forward the debates about whether China is colonizing Africa. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is emerging as an impactful feature of the life, planning and governance of 21st-century cities. Once confined to the realm of science fiction and small-scale technological experiments, AI is now all around us, in the shape of urban artificial intelligences including autonomous cars, robots, city brains and urban software agents. The aim of this article is to critically examine the nature of urbanism in the emergent age of AI. More specifically, we shed light on how urban AI is impacting the development of cities, and argue that an urbanism influenced by AI, which we term AI urbanism, differs in theory and practice from smart urbanism. In the future, the rise of a post-smart urbanism driven by AI has the potential to form autonomous cities that transcend, theoretically and empirically, traditional smart cities. The article compares common practices and understandings of smart urbanism with emerging forms of urban living, urban governance and urban planning influenced by AI. It critically discusses the limitations and potential pitfalls of AI urbanism and offers conceptual tools and a vocabulary to understand the urbanity of AI and its impact on present and future cities. [ABSTRACT FROM AUTHOR]

The article analyzes the evolution of Smart Cities from a legal perspective and highlights the importance of social, environmental, and economic integration in municipal management. The importance of Urban Agendas as a strategic planning tool is mentioned, and a methodology is proposed to measure the effectiveness of actions developed by Smart Cities. The book also addresses aspects such as environmental and energy challenges, joint management of energy supply through energy communities, environmental sustainability, and the digitalization of cities. Additionally, it reflects on digital rights and artificial intelligence in the public sector. In summary, the book offers a comprehensive and updated vision of Smart Cities and their impact on local administration. [Extracted from the article]

This article explores the intertwining connections among artificial intelligence, machine learning, digital transformation, and computational sustainability, detailing how these elements jointly empower citizens within a smart city framework. As technological advancement accelerates, smart cities harness these innovations to improve residents' quality of life. Artificial intelligence and machine learning act as data analysis powerhouses, making urban living more personalized, efficient, and automated, and are pivotal in managing complex urban infrastructures, anticipating societal requirements, and averting potential crises. Digital transformation transforms city operations by weaving digital technology into every facet of urban life, enhancing value delivery to citizens. Computational sustainability, a fundamental goal for smart cities, harnesses artificial intelligence, machine learning, and digital resources to forge more environmentally responsible cities, minimize ecological impact, and nurture sustainable development. The synergy of these technologies empowers residents to make well-informed choices, actively engage in their communities, and adopt sustainable lifestyles. This discussion illuminates the mechanisms and implications of these interconnections for future urban existence, ultimately focusing on empowering citizens in smart cities. [ABSTRACT FROM AUTHOR]

The internet is the greatest innovation of this era. The innovation of the internet has revolutionized the planet entirely by bringing the people and machines closer to each other than ever. The recent development in computing and networking technologies brings the next generation of internet which is called as Internet of Things (IoT). As the industrialization and the population are increasing the movement of people towards urbanization is also increasing. Therefore, cities are converting up into smart cities that can be accomplished with the service of the IoT. Water is one of the crucial resources for the existence of human life, so efficient supervision of water systems plays a significant role in smart cities. This paper uses three different modules of smart water management in context to smart cities. In this work three different sensors such as water flow sensor, water temperature sensor, and ultrasonic sensors are used to monitor water consumption. These sensors are interfaced with microcontrollers such as Raspberry Pi and NodeMCU. The sensors data can be observed on the Thing speak cloud server. This system helps in monitoring the leakage of water as well as to control the unnecessary use of water. Also, this module provides data in real-time for each user and that data can be used for monthly billing of water usage. Hence, the consumer will have to pay for the amount of water he/she can use only. Therefore, this model will allow the users to manage the water consumption efficiently. [ABSTRACT FROM AUTHOR]

Effective monitoring of road fixtures is essential for urban safety and functionality. However, traditional inspections are time-consuming, costly, and error prone, while current automated solutions struggle with high initial setup costs, limited flexibility preventing wide adaptation, and reliance on centralized processing that can delay response times. This study introduces an edge AI-based remote road fixture monitoring system which automatically and continuously updates the information of the road digital twin (DT). The main component is a small-sized edge device consisting of a camera, GPS, and IMU sensors designed to be installed in typical cars. The device captures images, detects the fixture, and estimates their location by employing deep learning and feature matching. This information is transmitted to a dedicated cloud server and represented on a user-friendly user interface. Experiments were conducted to test the system's performance. The results showed that the device could successfully detect the fixture and estimate their global coordinates. Outputs were marked and shown on the road DT, proving the integrated and smooth operation of the whole system. The proposed Edge AI device demonstrated that it could significantly reduce the data size by 80–84% compared to traditional methods. With a satisfactory object detection accuracy of 65%, the system effectively identifies traffic poles, stop signs, and streetlights, integrating these findings into a digital twin for real-time monitoring. The proposed system improves road monitoring by cutting down on maintenance and emergency response times, increasing the ease of data use, and offering a foundation for an overview of urban road fixtures' current state. However, the system's reliance on the quality of data collected under varying environmental conditions suggests potential improvements for consistent performance across diverse scenarios. [ABSTRACT FROM AUTHOR]

The article focuses on the technological advancements in Point of Interest (POI) recommendation systems for smart and sustainable cities. Topics discussed include the integration of Artificial Intelligence (AI), blockchain, and big data to enhance urban planning, the role of POI systems in promoting sustainable mobility and local businesses, and various studies exploring the challenges and innovations in implementing these systems across different regions.