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Generative Pre-Trained Transformer (GPT) models excel in text generation, text compilation, and language-related tasks. ChatGPT is based on the GPT model that responds to queries and has human-like conversational capabilities. This includes writing posts for social media, software codes, emails and essays, etc. We tried to use ChatGPT for creating a research paper summary. During our experimentation using ChatGPT, we found that ChatGPT cannot directly read links or URLs, as it is trained on a large amount of text data. Research paper summarization is essential in academics. Hence to solve this problem of research paper summarization using ChatGPT, a software system was designed. In this paper, we present how the capability of ChatGPT could be enhanced to summarize pdfs when a URL is provided. Furthermore, the generated summary can be modified in different styles of writing such as creative, expanded, shortened, and professional.

Manuscript writing support services using AI technology have become increasingly available in recent years. In keeping with this trend, we need to sort out issues related to authorship in academic writing. Authorship is attached to the contribution of researchers who report innovative research, the originality of which forms the core of their identity. The most important originality is demonstrated in the discussion of study findings. In the discussion section of this paper, we argue that if a researcher uses AI-based manuscript writing support to draft the discussion section, this does not necessarily diminish the researcher’s originality. Rather, AI support may allow the researcher to perform creative work in a more refined fashion. Presumably, selecting which AI support to use or evaluating and properly adjusting AI would still remain an important aspect of research for researchers. It is thus reasonable to view a researcher as a cooperative existence realized through a network of cooperative work that includes the use of AI. Discussions on this topic will be scientifically and socially important as AI technology advances in the future.

This review analyzes the most influential artificial intelligence (AI) studies in health and life sciences from the past three years, delineating the evolving role of AI in these fields. We identified and analyzed the top 50 cited articles on AI in biomedicine, revealing significant trends and thematic categorizations, including Drug Development, Real-World Clinical Implementation, and Ethical and Regulatory Aspects, among others. Our findings highlight a predominant focus on AIs application in clinical settings, particularly in diagnostics, telemedicine, and medical education, accelerated by the COVID-19 pandemic. The emergence of AlphaFold marked a pivotal moment in protein structure prediction, catalyzing a cascade of related research and signifying a broader shift towards AI-driven approaches in biological research. The review underscores AIs pivotal role in disease subtyping and patient stratification, facilitating a transition towards more personalized medicine strategies. Furthermore, it illustrates AIs impact on biology, particularly in parsing complex genomic and proteomic data, enhancing our capabilities to disentangle complex, interconnected molecular processes. As AI continues to permeate the health and life sciences, balancing its rapid technological advancements with ethical stewardship and regulatory vigilance will be crucial for its sustainable and effective integration into healthcare and research.

Hydroponics is a soilless farming technique that has emerged as a sustainable alternative. However, new technologies such as Industry 4.0, the internet of things (IoT), and artificial intelligence are needed to keep up with issues related to economics, automation, and social challenges in hydroponics farming. One significant issue is optimizing growth parameters to identify the best conditions for growing fruits and vegetables. These parameters include pH, total dissolved solids (TDS), electrical conductivity (EC), light intensity, daily light integral (DLI), and nutrient solution/ambient temperature and humidity. To address these challenges, a systematic literature review was conducted aiming to answer research questions regarding the optimal growth parameters for leafy green vegetables and herbs and spices grown in hydroponic systems. The review selected a total of 131 papers related to indoor farming, hydroponics, and aquaponics. The review selected a total of 123 papers related to indoor farming, hydroponics, and aquaponics. The majority of the articles focused on technology description (38.5%), artificial illumination (26.2%), and nutrient solution composition/parameters (13.8%). Additionally, remaining 10.7% articles focused on the application of sensors, slope, environment and economy. This comprehensive review provides valuable information on optimized growth parameters for smart hydroponic systems and explores future prospects and the application of digital technologies in this field.

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.

This review explores the transformative integration of artificial intelligence (AI) and healthcare through conversational AI leveraging Natural Language Processing (NLP). Focusing on Large Language Models (LLMs), this paper navigates through various sections, commencing with an overview of AI’s significance in healthcare and the role of conversational AI. It delves into fundamental NLP techniques, emphasizing their facilitation of seamless healthcare conversations. Examining the evolution of LLMs within NLP frameworks, the paper discusses key models used in healthcare, exploring their advantages and implementation challenges. Practical applications in healthcare conversations, from patient-centric utilities like diagnosis and treatment suggestions to healthcare provider support systems, are detailed. Ethical and legal considerations, including patient privacy, ethical implications, and regulatory compliance, are addressed. The review concludes by spotlighting current challenges, envisaging future trends, and highlighting the transformative potential of LLMs and NLP in reshaping healthcare interactions.

This study explores the applications and implications of blockchain technology in the Australian higher education system, focusing on its integration with artificial intelligence (AI). By addressing critical challenges in credential verification, administrative efficiency, and academic integrity, this integration aims to enhance the global competitiveness of Australian higher education institutions. A comprehensive review of 25 recent research papers quantifies the benefits, challenges, and prospects of blockchain adoption in educational settings. Our findings reveal that 52% of the reviewed papers focus on systematic reviews, 28% focus on application-based studies, and 20% combine both approaches. The keyword analysis identified 287 total words, with “blockchain” and “education” as the most prominent themes. This study highlights blockchain’s potential to improve credential management, academic integrity, administrative efficiency, and funding mechanisms in education. However, challenges such as technical implementation (24%), regulatory compliance (32%), environmental concerns (28%), and data security risks (40%) must be addressed to achieve widespread adoption. This study also discusses critical prerequisites for successful blockchain integration, including infrastructure development, staff training, regulatory harmonisation, and the incorporation of AI for personalised learning. Our research concludes that blockchain, when strategically implemented and combined with AI, has the potential to transform the Australian higher education system, significantly enhancing its integrity, efficiency, and global competitiveness.

This paper reviews the digital transformation of grocery shopping, focusing on the technological innovations that have redefined consumer experiences over the past decades. By analyzing both academic literature and up-to-date information from websites, the study provides a review of the evolution of grocery shopping from traditional methods to modern, technology-driven approaches. The review categorizes developments into two primary areas: in-store and online grocery shopping. In-store shopping has progressed from traditional interactions to the implementation of self-service checkouts, handheld scanners, mobile apps, and AI-based solutions, including augmented reality (AR) and facial recognition. This paper reviews the first area which are in-store technological solutions. This study aims to highlight the revolution in grocery shopping from a technological perspective, present the most significant digital achievements, and outline the future possibilities for further advancements in this field.

This paper analyzes the ways that the widespread use of generative AIs (GAIs) in education and, more broadly, in contributing to and reflecting the collective intelligence of our species, can and will change us. Methodologically, the paper applies a theoretical model and grounded argument to present a case that GAIs are different in kind from all previous technologies. The model extends Brian Arthur’s insights into the nature of technologies as the orchestration of phenomena to our use by explaining the nature of humans’ participation in their enactment, whether as part of the orchestration (hard technique, where our roles must be performed correctly) or as orchestrators of phenomena (soft technique, performed creatively or idiosyncratically). Education may be seen as a technological process for developing these soft and hard techniques in humans to participate in the technologies, and thus the collective intelligence, of our cultures. Unlike all earlier technologies, by embodying that collective intelligence themselves, GAIs can closely emulate and implement not only the hard technique but also the soft that, until now, was humanity’s sole domain; the very things that technologies enabled us to do can now be done by the technologies themselves. Because they replace things that learners have to do in order to learn and that teachers must do in order to teach, the consequences for what, how, and even whether learning occurs are profound. The paper explores some of these consequences and concludes with theoretically informed approaches that may help us to avert some dangers while benefiting from the strengths of generative AIs. Its distinctive contributions include a novel means of understanding the distinctive differences between GAIs and all other technologies, a characterization of the nature of generative AIs as collectives (forms of collective intelligence), reasons to avoid the use of GAIs to replace teachers, and a theoretically grounded framework to guide adoption of generative AIs in education.

The falling cost of IoT cameras, the advancement of AI-based computer vision algorithms, and powerful hardware accelerators for deep learning have enabled the widespread deployment of surveillance cameras with the ability to automatically analyze streaming video feeds to detect events of interest. While streaming video analytics is currently largely performed in the cloud, edge computing has emerged as a pivotal component due to its advantages of low latency, reduced bandwidth, and enhanced privacy. However, a distinct gap persists between state-of-the-art computer vision algorithms and the successful practical implementation of edge-based streaming video analytics systems. This paper presents a comprehensive review of more than 30 research papers published over the last 6 years on IoT edge streaming video analytics (IE-SVA) systems. The papers are analyzed across 17 distinct dimensions. Unlike prior reviews, we examine each system holistically, identifying their strengths and weaknesses in diverse implementations. Our findings suggest that certain critical topics necessary for the practical realization of IE-SVA systems are not sufficiently addressed in current research. Based on these observations, we propose research trajectories across short-, medium-, and long-term horizons. Additionally, we explore trending topics in other computing areas that can significantly impact the evolution of IE-SVA systems.

The integration of the Internet of Things (IoT), big data, and machine learning (ML) has pioneered a transformation across several fields. Equipping electrical engineering students to remain abreast of the dynamic technological landscape is vital. This underscores the necessity for an educational tool that can be integrated into electrical engineering curricula to offer a practical way of learning the concepts and the integration of IoT, big data, and ML. Thus, this paper offers the IoT-Edu-ML-Stream open-source platform, a graphical user interface (GUI)-based emulation software tool to help electrical engineering students design and emulate IoT-based use cases with big data analytics. The tool supports the emulation or the actual connectivity of a large number of IoT devices. The emulated devices can generate realistic correlated IoT data and stream it via the message queuing telemetry transport (MQTT) protocol to a big data platform. The tool allows students to design ML models with different algorithms for their chosen use cases and train them for decision-making based on the streamed data. Moreover, the paper proposes learning outcomes to be targeted when integrating the tool into an electrical engineering curriculum. The tool is evaluated using a comprehensive survey. The survey results show that the students gained significant knowledge about IoT concepts after using the tool, even though many of them already had prior knowledge of IoT. The results also indicate that the tool noticeably improved the students’ practical skills in designing real-world use cases and helped them understand fundamental machine learning analytics with an intuitive user interface.

Digital twins are a relatively new form of digital modeling that has been gaining popularity in recent years. This is in large part due to their ability to update in real time to their physical counterparts and connect across multiple devices. As a result, much interest has been directed towards using digital twins in the healthcare industry. Recent advancements in smart wearable technologies have allowed for the utilization of human digital twins in healthcare. Human digital twins can be generated using biometric data from the patient gathered from wearables. These data can then be used to enhance patient care through a variety of means, such as simulated clinical trials, disease prediction, and monitoring treatment progression remotely. This revolutionary method of patient care is still in its infancy, and as such, there is limited research on using wearables to generate human digital twins for healthcare applications. This paper reviews the literature pertaining to human digital twins, including methods, applications, and challenges. The paper also presents a conceptual method for creating human body digital twins using wearable sensors.

Since its release in November 2022, ChatGPT and the related AI technology have disrupted multiple fields of society where people anticipate its pathways with a mixture of hope and fear. Among the affected fields, education, in particular, may incur one of the largest impacts in the future partly due to its nature of learning and teaching knowledge, an element that is more or less questioned by the rise of these technologies. As education can be seen as a component that determines the future of every other field of society, tools such as ChatGPT must be optimally regulated to enhance its gain or mitigate its loss. To contribute to this goal, this paper approaches the state of ChatGPT and its expected impacts on higher education through the lens of two major educational theories—John Dewey’s Reflective-Thought-and-Action model and revised Bloom’s taxonomy—aiming to propose possible evaluative criteria for the optimal usage of ChatGPT in academia. As ChatGPT is a relatively new topic of research yet a topic that requires an immediate focus due to its capabilities, this paper also aims to provide these criteria as one of the concrete starting points of future research in this field.

This paper presents an in-depth exploration of the transformative impact of integrating the Internet of Things (IoT), cloud computing, and artificial intelligence (AI) within the domain of aviation maintenance. It articulates the transition from conventional health monitoring practices to a more advanced, comprehensive health management approach, leveraging these modern technologies. This paper emphasizes the pivotal shift from reactive maintenance strategies to proactive and predictive maintenance paradigms, facilitated by the real-time data collection capabilities of IoT devices and the analytical prowess of AI. This transition not only enhances the safety and reliability of flight operations but also optimizes maintenance procedures, thereby reducing operational costs and improving efficiency. This paper meticulously outlines the implementation challenges, including technological integration, regulatory compliance, and security concerns, while proposing a future research agenda to address these issues and further harness the potential of these technologies in revolutionizing aviation maintenance.

This paper advocates for a proactive approach to traffic safety by introducing a collaborative Misbehaviour Response System (MBR) designed to preemptively address hazardous driving behaviours such as wrong-way driving and distracted driving. The system integrates with electric vehicles (EVs), leveraging advanced technologies like ADAS, edge computing, and cloud services to enhance road safety. Upon detection of misbehaviour, the MBR system utilizes data from interconnected parking facilities to identify the nearest safe location and provides navigation guidance to authorities and nearby vehicles. The paper presents a prototype of the MBR system, demonstrating its efficiency in detecting misbehaviours and coordinating swift responses. It also discusses the system’s limitations and societal implications, outlining future research directions, including integration with autonomous vehicle systems and variable speed limit technologies, to further improve road safety through proactive and context-aware response mechanisms.

Purpose: The advancements in deep learning and AI technologies have led to the development of such language models, in 2022, as OpenAI’s ChatGPT. The primary objective of this paper is to thoroughly examine the capabilities of ChatGPT within the realm of business-process management (BPM). This exploration entails analyzing its practical application, particularly through process-mining techniques, within the context of automotive after-sales processes. Originality: this article highlights the issue of possible ChatGPT application in selected stages of after-sales processes in the automotive sector. Methods: to achieve the main aim of this paper, methods such as a literature review, participant observation, unstructured interviews, CRISP-DM methodology, and process mining were used. Findings: This study emphasizes the promising impact of implementing the ChatGPT OpenAI tool to enhance processes in the automotive after-sales sector. Conducted in 2023, shortly after the tool’s introduction, the research highlights its potential to contribute to heightened customer satisfaction within the after-sales domain. The investigation focuses on the process-execution time. A key premise is that waiting time represents an additional cost for customers seeking these services. Employing process-mining methodologies, the study identifies stages characterized by unnecessary delays. Collaborative efforts with domain experts are employed to establish benchmark durations for researched processes’ stages. The study proposes the integration of ChatGPT to improve and expedite stages, including service reception, reception check-out, repair and maintenance, and claim repair. This holistic approach aligns with the current imperatives of business-process improvement and optimalization, aiming to enhance operational efficiency and customer-centric service delivery in the automotive after-sales sector.

In this paper, we have developed the SEMAR (Smart Environmental Monitoring and Analytics in Real-Time) IoT application server platform for fast deployments of IoT application systems. It provides various integration capabilities for the collection, display, and analysis of sensor data on a single platform. Recently, Artificial Intelligence (AI) has become very popular and widely used in various applications including IoT. To support this growth, the integration of AI into SEMAR is essential to enhance its capabilities after identifying the current trends of applicable AI technologies in IoT applications. In this paper, we first provide a comprehensive review of IoT applications using AI techniques in the literature. They cover predictive analytics, image classification, object detection, text spotting, auditory perception, Natural Language Processing (NLP), and collaborative AI. Next, we identify the characteristics of each technique by considering the key parameters, such as software requirements, input/output (I/O) data types, processing methods, and computations. Third, we design the integration of AI techniques into SEMAR based on the findings. Finally, we discuss use cases of SEMAR for IoT applications with AI techniques. The implementation of the proposed design in SEMAR and its use to IoT applications will be in future works.

Many educators and professionals in different industries may need to become more familiar with the basic concepts of artificial intelligence (AI) and generative artificial intelligence (Gen-AI). Therefore, this paper aims to introduce some of the basic concepts of AI and Gen-AI. The approach of this explanatory paper is first to introduce some of the underlying concepts, such as artificial intelligence, machine learning, deep learning, artificial neural networks, and large language models (LLMs), that would allow the reader to better understand generative AI. The paper also discusses some of the applications and implications of generative AI on businesses and education, followed by the current challenges associated with generative AI.

In the era of generative AI and specifically large language models (LLMs), exemplified by ChatGPT, the intersection of artificial intelligence and human reasoning has become a focal point of global attention. Unlike conventional search engines, LLMs go beyond mere information retrieval, entering into the realm of discourse culture. Their outputs mimic well-considered, independent opinions or statements of facts, presenting a pretense of wisdom. This paper explores the potential transformative impact of LLMs on democratic societies. It delves into the concerns regarding the difficulty in distinguishing ChatGPT-generated texts from human output. The discussion emphasizes the essence of authorship, rooted in the unique human capacity for reason—a quality indispensable for democratic discourse and successful collaboration within free societies. Highlighting the potential threats to democracy, this paper presents three arguments: the Substitution argument, the Authenticity argument, and the Facts argument. These arguments highlight the potential risks that are associated with an overreliance on LLMs. The central thesis posits that widespread deployment of LLMs may adversely affect the fabric of a democracy if not comprehended and addressed proactively and properly. In proposing a solution, we advocate for an emphasis on education as a means to mitigate risks. We suggest cultivating thinking skills in children, fostering coherent thought formulation, and distinguishing between machine-generated output and genuine, i.e., human, reasoning. The focus should be on the responsible development and usage of LLMs, with the goal of augmenting human capacities in thinking, deliberating and decision-making rather than substituting them.

Humanistic Buddhism is one of the mainstreams of modern Buddhism, with special emphasis on the humanistic dimension. With the development of artificial intelligence (AI) technology, Humanistic Buddhism is also at an important stage of modernization and transformation, thus facing a continuous negotiation between religious values and technological innovations. This paper first argues that AI is technically beneficial to the propagation of Buddhism by citing several cases in which AI technology has been used in Buddhism. Then, by comparing Master Hsing Yun’s Buddhist ethics to “Posthuman” ethics, it points out that the theories of Humanistic Buddhism share similarities with AI and Posthuman ethics. Among them, Master Hsing Yun’s theory of “the nature of insentient beings” provides an important theoretical reference for the question of “whether AI can become a Buddha”. From the technical and ethical dimensions, it points out that the interaction between Humanistic Buddhism and AI can promote original uses or implementations of AI technology. However, it should also be noted that compared to the cases of “Artificial Narrow Intelligence”discussed in the paper, the “Strong AI” could lead to much more ethical crises. It is also likely to cause the cult of science and technology, and thus subvert the humanistic tradition of Buddhism with a new instrumental rationality. In addition, there are some potential pitfalls that Humanistic Buddhism may encounter when using AI. Hence, while it is necessary to encourage the use of technologies such as AI in contemporary Buddhism, it is also important for Buddhism to keep a critical distance from digital technologies.

This paper provides a comprehensive review of solutions based on artificial intelligence (AI) in the urban energy sector, with a focus on their applications and impacts. The study employed a literature review methodology to analyze recent research on AI’s role in energy-related solutions, covering the years 2019 to 2023. The authors classified publications according to their main focus, resulting in two key areas of AI implementation: residential and individual user applications, and urban infrastructure integration for society. The objectives of this review of the literature are the following: O1: to identify trends, emerging technologies, and applications using AI in the energy field; O2: to provide up-to-date insights into the use of AI in energy-related applications; O3: to gain a comprehensive understanding of the current state of AI-driven urban energy solutions; O4: to explore future directions, emerging trends, and challenges in the field of AI-driven energy solutions. This paper contributes to a deeper understanding of the transformative potential of AI in urban energy management, providing valuable insights and directions for researchers and practitioners in the field. Based on the results, it can be claimed that AI connected to energy at homes is used in the following areas: heating and cooling, lighting, windows and blinds, home devices, and energy management systems. AI is integrating into urban infrastructure through the following solutions: enhancement of electric vehicle charging infrastructure, reduction in vehicle emissions, development of smart grids, and efficient energy storage. What is more, the latest challenges associated with the implementation of AI-driven energy solutions include the need to balance resident comfort with energy efficiency in smart homes, ensuring compatibility and cooperation among various devices, preventing unintended energy consumption increases due to constant connectivity, the management of renewable energy sources, and the coordination of energy consumption.

Scaled agile development approaches are now used widely in modern software engineering, allowing businesses to improve teamwork, productivity, and product quality. The incorporation of artificial intelligence (AI) into scaled agile development methods (SADMs) has emerged as a potential strategy in response to the ongoing demand for simplified procedures and the increasing complexity of software projects. This paper explores the intersection of AI-driven assistants within the context of the scaled agile framework (SAFe) for large-scale software development, as it stands out as the most widely adopted framework. Our paper pursues three principal objectives: (1) an evaluation of the challenges and impediments encountered by organizations during the implementation of SADMs, (2) an assessment of the potential advantages stemming from the incorporation of AI in large-scale contexts, and (3) the compilation of aspects of SADMs that AI-driven assistants enhance. Through a comprehensive systematic literature review, we identified and described 18 distinct challenges that organizations confront. In the course of our research, we pinpointed seven benefits and five challenges associated with the implementation of AI in SADMs. These findings were systematically categorized based on their occurrence either within the development phase or the phases encompassing planning and control. Furthermore, we compiled a list of 15 different AI-driven assistants and tools, subjecting them to a more detailed examination, and employing them to address the challenges we uncovered during our research. One of the key takeaways from this paper is the exceptional versatility and effectiveness of AI-driven assistants, demonstrating their capability to tackle a broader spectrum of problems. In conclusion, this paper not only sheds light on the transformative potential of AI, but also provides invaluable insights for organizations aiming to enhance their agility and management capabilities.

This research paper provides a comprehensive review of the emerging trends in AI-based stock market prediction. The paper highlights the key concepts, approaches, and techniques employed in AI-based stock market prediction and discusses their strengths and limitations. Key topics covered include deep learning, natural language processing, sentiment analysis, and reinforcement learning. This paper also presents case studies and evaluates the performance of different AI-based models in predicting stock market trends. Overall, this research paper provides valuable insights into the latest advancements in AI-based stock market prediction and their potential implications for investors, financial analysts, and policy makers.

This paper reviews the diverse applications of drone technologies in the built environment and their role in climate change research. Drones, or unmanned aerial vehicles (UAVs), have emerged as valuable tools for environmental scientists, offering new possibilities for data collection, monitoring, and analysis in the urban environment. The paper begins by providing an overview of the different types of drones used in the built environment, including quadcopters, fixed-wing drones, and hybrid models. It explores their capabilities and features, such as high-resolution cameras, LiDAR sensors, and thermal imaging, which enable detailed data acquisition for studying climate change impacts in urban areas. The paper then examines the specific applications of drones in the built environment and their contribution to climate change research. These applications include mapping urban heat islands, assessing the energy efficiency of buildings, monitoring air quality, and identifying sources of greenhouse gas emissions. UAVs enable researchers to collect spatially and temporally rich data, allowing for a detailed analysis and identifying trends and patterns. Furthermore, the paper discusses integrating UAVs with artificial intelligence (AI) to derive insights and develop predictive models for climate change mitigation and adaptation in urban environments. Finally, the paper addresses drone technologies’ challenges and the future directions in the built environment. These challenges encompass regulatory frameworks, privacy concerns, data management, and the need for an interdisciplinary collaboration. By harnessing the potential of drones, environmental scientists can enhance their understanding of climate change impacts in urban areas and contribute to developing sustainable strategies for resilient cities.

While artificial intelligence has successful and innovative applications in common medicine, could its application facilitate research on rare diseases? This study explores the application of artificial intelligence (AI) in orphan drug research, focusing on how AI can address three major barriers: high financial risk, development complexity, and low trialability. This paper begins with an overview of orphan drug development and AI applications, defining key concepts and providing a background on the regulatory framework of and AI’s role in medical research. Next, it examines how AI can lower financial risks by streamlining drug discovery and development processes, analyzing complex data, and predicting outcomes to improve our understanding of rare diseases. This study then explores how AI can enhance clinical trials through simulations and virtual trials, compensating for the limited patient populations available for rare disease research. Finally, it discusses the broader implications of integrating AI in orphan drug development, emphasizing the potential for AI to accelerate drug discovery and improve treatment success rates, and highlights the need for ongoing innovation and regulatory support to maximize the benefits of AI-driven research in healthcare. Based on those results, we discuss the implications for traditional and AI-powered business in the drug industry.

As artificial intelligence becomes ubiquitous in our lives, so do the opportunities to combine machine and human intelligence to obtain more accurate and more resilient prediction models across a wide range of domains. Hybrid intelligence can be designed in many ways, depending on the role of the human and the algorithm in the hybrid system. This paper offers a brief taxonomy of hybrid intelligence, which describes possible relationships between human and machine intelligence for robust forecasting. In this taxonomy, biological intelligence represents one axis of variation, going from individual intelligence (one individual in isolation) to collective intelligence (several connected individuals). The second axis of variation represents increasingly sophisticated algorithms that can take into account more aspects of the forecasting system, from information to task to human problem-solvers. The novelty of the paper lies in the interpretation of recent studies in hybrid intelligence as precursors of a set of algorithms that are expected to be more prominent in the future. These algorithms promise to increase hybrid system’s resilience across a wide range of human errors and biases thanks to greater human-machine understanding. This work ends with a short overview for future research in this field.

Gravitational wave research presents a range of intriguing challenges, each of which has driven significant progress in the field. Key research problems include glitch classification, glitch cancellation, gravitational wave denoising, binary black hole signal detection, gravitational wave bursts, and minor issues that contribute to the overall understanding of gravitational wave phenomena. This paper explores the applications of artificial intelligence, deep learning, and machine learning techniques in addressing these challenges. The main goal of the paper is to provide an effective view of AI and deep learning usage for gravitational wave analysis. Thanks to the advancements in artificial intelligence and machine learning techniques, aided by GPUs and specialized software frameworks, these techniques have played a key role over the last decade in the identification, classification, and cancellation of gravitational wave signals, as presented in our results. This paper provides a comprehensive exploration of the adoption rate of these techniques, with reference to the software and hardware involved, their effectiveness, and potential limitations, offering insights into the advancements in the analysis of gravitational wave data.

The use of innovative technology in the field of Speech and Language Therapy (SLT) has gained significant attention nowadays. Despite being a promising research area, Socially Assistive Robots (SARs) have not been thoroughly studied and used in SLT. This paper makes two main contributions: firstly, providing a comprehensive review of existing research on the use of SARs to enhance communication skills in children and adolescents. Secondly, organizing the information into tables that categorize the interactive play scenarios described in the surveyed papers. The inclusion criteria for play scenarios in the tables are based only on their effectiveness for SLT proven by experimental findings. The data, systematically presented in a table format, allow readers to easily find relevant information based on various factors, such as disorder type, age, treatment technique, robot type, etc. The study concludes that the despite limited research on the use of social robots for children and adolescents with communication disorders (CD), promising outcomes have been reported. The authors discuss the methodological, technical, and ethical limitations related to the use of SARs for SLT in clinical or home environments, as well as the huge potential of conversational Artificial Intelligence (AI) as a secondary assistive technology to facilitate speech and language interventions.

Artificial intelligence (AI) and machine learning (ML) have revolutionized the way health organizations approach social media. The sheer volume of data generated through social media can be overwhelming, but AI and ML can help organizations effectively manage this information to improve telehealth, remote patient monitoring, and the well-being of individuals and communities. Previous research has revealed several trends in AI–ML adoption: First, AI can be used to enhance social media marketing. Drawing on sentiment analysis and related tools, social media is an effective way to increase brand awareness and customer engagement. Second, social media can become a very useful data collection tool when integrated with new AI–ML technologies. Using this function well requires researchers and practitioners to protect users’ privacy carefully, such as through the deployment of privacy-enhancing technologies (PETs). Third, AI–ML enables organizations to maintain a long-term relationship with stakeholders. Chatbots and related tools can increase users’ ability to receive personalized content. The review in this paper identifies research gaps in the literature. In view of these gaps, the paper proposes a conceptual framework that highlights essential components for better utilizing AI and ML. Additionally, it enables researchers and practitioners to better design social media platforms that minimize the spread of misinformation and address ethical concerns more readily. It also provides insights into the adoption of AI and ML in the context of remote patient monitoring and telehealth within social media platforms.

This paper evaluates the status quo of modern society and identifies the neglect of humanism as the root cause of many of today’s global challenges. Note that “smart cities” are not excluded from this indictment. The “Ptolemaic Universe” offers a means to restore our symbiosis with the environment. The ReSeT model is proposed as a tool to analyze the Ptolemaic Universe. Using ReSeT: Homo Sapiens becomes dependent on AI resulting in Homo Digitalis, with further evolution in concert with AI resulting in Homo Digitus. All of these stages are then analyzed in the context of global trusted dependability (GTD). The wellness domain, provides the design specification framework for Homo Digitus’ human-centered and resilient “smart city” ecosystem. This ultimately leads to a better world of increased wellness for Homo Hominus, with better smart cities emphasizing education and science, promoting wisdom and common sense, and rejecting violence. In summary, humanity has generated diverse social structures with erratic outcomes. On the other hand, technology provides a successful foundation for modern society especially in the Pandemic Era. However, technology’s contributions are generally not publicly acknowledged. The paper concludes with several initiatives designed to establish a trusted and resilient society.

In recent times, large language models (LLMs) have made significant strides in generating computer code, blurring the lines between code created by humans and code produced by artificial intelligence (AI). As these technologies evolve rapidly, it is crucial to explore how they influence code generation, especially given the risk of misuse in areas such as higher education. The present paper explores this issue by using advanced classification techniques to differentiate between code written by humans and code generated by ChatGPT, a type of LLM. We employ a new approach that combines powerful embedding features (black-box) with supervised learning algorithms including Deep Neural Networks, Random Forests, and Extreme Gradient Boosting to achieve this differentiation with an impressive accuracy of 98%. For the successful combinations, we also examine their model calibration, showing that some of the models are extremely well calibrated. Additionally, we present white-box features and an interpretable Bayes classifier to elucidate critical differences between the code sources, enhancing the explainability and transparency of our approach. Both approaches work well, but provide at most 85–88% accuracy. Tests on a small sample of untrained humans suggest that humans do not solve the task much better than random guessing. This study is crucial in understanding and mitigating the potential risks associated with using AI in code generation, particularly in the context of higher education, software development, and competitive programming.

International students face unique challenges in pursuing higher education in a foreign country. To address these challenges and enhance their academic experience, higher education institutions are increasingly exploring the use of artificial intelligence (AI) applications. This research essay aims to investigate the impact of AI on the education of international students. Instead of a traditional literature review, it employs a research approach to examine the potential applications of AI and discuss associated concerns. The research paper explores various AI applications, such as personalized learning experiences, adaptive testing, predictive analytics, and chatbots for learning and research. By analyzing the role of AI in education for international students, this research paper sheds light on how AI can improve learning efficiency and provide customized educational support. Additionally, it identifies significant risks and limitations, including privacy concerns, cultural differences, language proficiency, and ethical implications, which must be effectively addressed. The findings contribute to a better understanding of the potential impact of AI on international students’ educational experiences and offer insights into the integration of AI into educational administration and learning processes.

This paper explores the impact of Artificial Intelligence (AI) on the role of dental assistants and nurses in orthodontic practices, as there is a gap in understanding the currently evolving impact on orthodontic treatment workflows. The introduction of AI-language models such as ChatGPT 4 is changing patient-office communication and transforming the role of orthodontic nurses. Teledentistry is now heavily reliant on AI implementation in orthodontics. This paper presents the proof of a novel concept: an AI-powered orthodontic workflow that provides new responsibilities for an orthodontic nurse. It also provides a report of an assessment of such a workflow in an orthodontic practice that uses an AI solution called Dental Monitoring over a period of three years. The paper evaluates the benefits and drawbacks of daily automated assessments of orthodontic treatment progress, the impact of AI on personalized care, and the new role of a dental assistant. The paper concludes that AI will improve dental practice through more precise and personalized treatment, bringing new roles and responsibilities for trained medical professionals but raising new ethical and legal issues for dental practices.

Multitudinous health data are continually being produced as our activities, including medicine, evolve into the digital age where data plays a decisive role. Challenges come along as well, concerning the collection, secure storage, verification and secure access to the continuously growing data at such a broad scale before valuable information can be extracted to contribute to medical advancement nowadays. With the decentralization feature, huge successes of blockchain technology in overcoming similar challenges in the finance and cryptocurrency sector brought us the confidence to investigate and reveal its immeasurable potential for the health sector, specifically in dentistry. Dentistry is an important area of healthcare, but there is relatively little research focusing on its interactions with blockchain technology. Given the limited amount of existing research on this specific subject, this paper focuses on blockchain in dentistry and aims to provide a conceptual framework for the possible applications of blockchain in dentistry. The framework is organised by different areas of dentistry operations so that dental professionals can easily refer to and identify areas of interest. This contributes to increasing the awareness of blockchain technology among dental professionals and promoting blockchain-empowered revolutions in dentistry. This paper also discusses how blockchain fits alongside other emerging technologies, the challenges that have to be overcome to maximise the functionality and efficiency of this technology, as well as future research directions concerning blockchain implementations in the dental industry.

Technology has played a vital part in improving quality of life, especially in healthcare. Artificial intelligence (AI) and the Internet of Things (IoT) are extensively employed to link accessible medical resources and deliver dependable and effective intelligent healthcare. Body wearable devices have garnered attention as powerful devices for healthcare applications, leading to various commercially available devices for multiple purposes, including individual healthcare, activity alerts, and fitness. The paper aims to cover all the advancements made in the wearable Medical Internet of Things (IoMT) for healthcare systems, which have been scrutinized from the perceptions of their efficacy in detecting, preventing, and monitoring diseases in healthcare. The latest healthcare issues are also included, such as COVID-19 and monkeypox. This paper thoroughly discusses all the directions proposed by the researchers to improve healthcare through wearable devices and artificial intelligence. The approaches adopted by the researchers to improve the overall accuracy, efficiency, and security of the healthcare system are discussed in detail. This paper also highlights all the constraints and opportunities of developing AI enabled IoT-based healthcare systems.

In recent decades, epidemic and pandemic illnesses have grown prevalent and are a regular source of concern throughout the world. The extent to which the globe has been affected by the COVID-19 epidemic is well documented. Smart technology is now widely used in medical applications, with the automated detection of status and feelings becoming a significant study area. As a result, a variety of studies have begun to focus on the automated detection of symptoms in individuals infected with a pandemic or epidemic disease by studying their body language. The recognition and interpretation of arm and leg motions, facial recognition, and body postures is still a developing field, and there is a dearth of comprehensive studies that might aid in illness diagnosis utilizing artificial intelligence techniques and technologies. This literature review is a meta review of past papers that utilized AI for body language classification through full-body tracking or facial expressions detection for various tasks such as fall detection and COVID-19 detection, it looks at different methods proposed by each paper, their significance and their results.

The value of artificial intelligence and machine learning applications for use in heritage research is increasingly appreciated. In specific areas, notably remote sensing, datasets have increased in extent and resolution to the point that manual interpretation is problematic and the availability of skilled interpreters to undertake such work is limited. Interpretation of the geophysical datasets associated with prehistoric submerged landscapes is particularly challenging. Following the Last Glacial Maximum, sea levels rose by 120 m globally, and vast, habitable landscapes were lost to the sea. These landscapes were inaccessible until extensive remote sensing datasets were provided by the offshore energy sector. In this paper, we provide the results of a research programme centred on AI applications using data from the southern North Sea. Here, an area of c. 188,000 km2 of habitable terrestrial land was inundated between c. 20,000 BP and 7000 BP, along with the cultural heritage it contained. As part of this project, machine learning tools were applied to detect and interpret features with potential archaeological significance from shallow seismic data. The output provides a proof-of-concept model demonstrating verifiable results and the potential for a further, more complex, leveraging of AI interpretation for the study of submarine palaeolandscapes.

In recent decades, artificial intelligence has undergone transformative advancements, reshaping diverse sectors such as healthcare, transport, agriculture, energy, and the media. Despite the enthusiasm surrounding AI’s potential, concerns persist about its potential negative impacts, including substantial energy consumption and ethical challenges. This paper critically reviews the evolving landscape of AI sustainability, addressing economic, social, and environmental dimensions. The literature is systematically categorized into “Sustainability of AI” and “AI for Sustainability”, revealing a balanced perspective between the two. The study also identifies a notable trend towards holistic approaches, with a surge in publications and empirical studies since 2019, signaling the field’s maturity. Future research directions emphasize delving into the relatively under-explored economic dimension, aligning with the United Nations’ Sustainable Development Goals (SDGs), and addressing stakeholders’ influence.

Diminished reality (DR) is a technology in which a background image is overwritten on a real object to make it appear as if the object has been removed from real space. This paper presents a real-time DR application that employs deep learning. A DR application can remove objects inside a 3D region defined by a user in images captured using a smartphone. By specifying the 3D region containing the target object to be removed, DR can be realized for targets with various shapes and sizes, and the specified target can be removed even if the viewpoint changes. To achieve fast and accurate DR, a suitable network was employed based on the experimental results. Additionally, the loss function during the training process was improved to enhance completion accuracy. Then, the operation of the DR application at 10 fps was verified using a smartphone and a laptop computer.

Nowadays, one of the important aspects of research on call/contact centre (CC) systems is how to automate their operations. Process automation is influenced by the continuous development in the implementation of virtual assistants. The effectiveness of virtual assistants depends on numerous factors. One of the most important is correctly recognizing the intent of clients conversing with the machine. Recognizing intentions is not an easy process, as often the client’s actual intentions can only be correctly identified after considering the client’s emotional state. When it comes to human–machine communication, the ability of a virtual assistant to recognize the client’s emotional state would greatly improve its effectiveness. This paper proposes a new method for recognizing interlocutors’ emotions dedicated directly to contact centre systems. The developed method provides opportunities to determine emotional states in text and voice channels. It provides opportunities to explore both the client’s and the agent’s emotional states. Information about agents’ emotions can be used to build their behavioural profiles, which is also applicable in contact centres. In addition, the paper explored the possibility of emotion assessment based on automatic transcriptions of recordings, which also positively affected emotion recognition performance in the voice channel. The research used actual conversations that took place during the operation of a large, commercial contact centre. The proposed solution makes it possible to recognize the emotions of customers contacting the hotline and agents handling these calls. Using this information in practical applications can increase the efficiency of agents’ work, efficiency of bots used in CC and increase customer satisfaction.

Bitcoin (BTC)—the first cryptocurrency—is a decentralized network used to make private, anonymous, peer-to-peer transactions worldwide, yet there are numerous issues in its pricing due to its arbitrary nature, thus limiting its use due to skepticism among businesses and households. However, there is a vast scope of machine learning approaches to predict future prices precisely. One of the major problems with previous research on BTC price predictions is that they are primarily empirical research lacking sufficient analytical support to back up the claims. Therefore, this study aims to solve the BTC price prediction problem in the context of both macroeconomic and microeconomic theories by applying new machine learning methods. Previous work, however, shows mixed evidence of the superiority of machine learning over statistical analysis and vice versa, so more research is needed. This paper applies comparative approaches, including ordinary least squares (OLS), Ensemble learning, support vector regression (SVR), and multilayer perceptron (MLP), to investigate whether the macroeconomic, microeconomic, technical, and blockchain indicators based on economic theories predict the BTC price or not. The findings point out that some technical indicators are significant short-run BTC price predictors, thus confirming the validity of technical analysis. Moreover, macroeconomic and blockchain indicators are found to be significant long-term predictors, implying that supply, demand, and cost-based pricing theories are the underlying theories of BTC price prediction. Likewise, SVR is found to be superior to other machine learning and traditional models. This research’s innovation is looking at BTC price prediction through theoretical aspects. The overall findings show that SVR is superior to other machine learning models and traditional models. This paper has several contributions. It can contribute to international finance to be used as a reference for setting asset pricing and improved investment decision-making. It also contributes to the economics of BTC price prediction by introducing its theoretical background. Moreover, as the authors still doubt whether machine learning can beat the traditional methods in BTC price prediction, this research contributes to machine learning configuration and helping developers use it as a benchmark.

Road condition monitoring (RCM) has been a demanding strategic research area in maintaining a large network of transport infrastructures. With advancements in computer vision and data mining techniques along with high computing resources, several innovative pavement distress evaluation systems have been developed in recent years. The majority of these technologies employ next-generation distributed sensors and vision-based artificial intelligence (AI) methodologies to evaluate, classify and localize pavement distresses using the measured data. This paper presents an exhaustive and systematic literature review of these technologies in RCM that have been published from 2017–2022 by utilizing next-generation sensors, including contact and noncontact measurements. The various methodologies and innovative contributions of the existing literature reviewed in this paper, together with their limitations, promise a futuristic insight for researchers and transport infrastructure owners. The decisive role played by smart sensors and data acquisition platforms, such as smartphones, drones, vehicles integrated with non-intrusive sensors, such as RGB, and thermal cameras, lasers and GPR sensors in the performance of the system are also highlighted. In addition to sensing, a discussion on the prevalent challenges in the development of AI technologies as well as potential areas for further exploration paves the way for an all-inclusive and well-directed futuristic research on RCM.

Building Information Modelling (BIM) has been increasingly integrated with Artificial Intelligence (AI) solutions to automate building construction processes. However, the methods for effectively transforming data from BIM formats, such as Industry Foundation Classes (IFC), into formats suitable for AI applications still need to be explored. This paper conducts a Systematic Literature Review (SLR) following the PRISMA guidelines to analyse current data preparation approaches in BIM applications. The goal is to identify the most suitable methods for AI integration by reviewing current data preparation practices in BIM applications. The review included a total of 93 articles from SCOPUS and WoS. The results include eight common data types, two data management frameworks, and four primary data conversion methods. Further analysis identified three barriers: first, the IFC format’s lack of support for time-series data; second, limitations in extracting geometric information from BIM models; and third, the absence of established toolchains to convert IFC files into usable formats. Based on the evidence, the data readiness is at an intermediate level. This research may serve as a guideline for future studies to address the limitations in data preparation within BIM for AI integration.

Photovoltaic power generation is influenced not only by variable environmental factors, such as solar radiation, temperature, and humidity, but also by the condition of equipment, including solar modules and inverters. In order to preserve energy production, it is essential to maintain and operate the equipment in optimal condition, which makes it crucial to determine the condition of the equipment in advance. This paper proposes a method of determining a degradation of efficiency by focusing on photovoltaic equipment, especially inverters, using LSTM (Long Short-Term Memory) for maintenance. The deterioration in the efficiency of the inverter is set based on the power generation predicted through the LSTM model. To this end, a correlation analysis and a linear analysis were performed between the power generation data collected at the power plant to learn the power generation prediction model and the data collected by the environmental sensor. With this analysis, a model was trained using solar radiation data and power data that are highly correlated with power generation. The results of the evaluation of the model’s performance show that it achieves a MAPE of 7.36, an RMSE of 27.91, a MAE of 18.43, and an R2 of 0.97. The verified model is applied to the power generation data of the selected inverters for the years 2020, 2021, and 2022. Through statistical analysis, it was determined that the error rate in 2022, the third year of its operation, increased by 159.55W on average from the error rate of the power generation forecast in 2020, the first year of operation. This indicates a 0.75% decrease in the inverter’s efficiency compared to the inverter’s power generation capacity. Therefore, it is judged that it can be applied effectively to analyses of inverter efficiency in the operation of photovoltaic plants.

In the dynamic realm of golf, where every swing can make the difference between victory and defeat, the strategic selection of golf clubs has become a crucial factor in determining the outcome of a game. Advancements in artificial intelligence have opened new avenues for enhancing the decision-making process, empowering golfers to achieve optimal performance on the course. In this paper, we introduce an AI-based game planning system that assists players in selecting the best club for a given scenario. The system considers factors such as distance, terrain, wind strength and direction, and quality of lie. A rule-based model provides the four best club options based on the player’s maximum shot data for each club. The player picks a club, shot, and target and a probabilistic classification model identifies whether the shot represents a birdie opportunity, par zone, bogey zone, or worse. The results of our model show that taking into account factors such as terrain and atmospheric features increases the likelihood of a better shot outcome.

Surveys on explainable artificial intelligence (XAI) are related to biology, clinical trials, fintech management, medicine, neurorobotics, and psychology, among others. Prognostics and health management (PHM) is the discipline that links the studies of failure mechanisms to system lifecycle management. There is a need, which is still absent, to produce an analytical compilation of PHM-XAI works. In this paper, we use preferred reporting items for systematic reviews and meta-analyses (PRISMA) to present a state of the art on XAI applied to PHM of industrial assets. This work provides an overview of the trend of XAI in PHM and answers the question of accuracy versus explainability, considering the extent of human involvement, explanation assessment, and uncertainty quantification in this topic. Research articles associated with the subject, since 2015 to 2021, were selected from five databases following the PRISMA methodology, several of them related to sensors. The data were extracted from selected articles and examined obtaining diverse findings that were synthesized as follows. First, while the discipline is still young, the analysis indicates a growing acceptance of XAI in PHM. Second, XAI offers dual advantages, where it is assimilated as a tool to execute PHM tasks and explain diagnostic and anomaly detection activities, implying a real need for XAI in PHM. Third, the review shows that PHM-XAI papers provide interesting results, suggesting that the PHM performance is unaffected by the XAI. Fourth, human role, evaluation metrics, and uncertainty management are areas requiring further attention by the PHM community. Adequate assessment metrics to cater to PHM needs are requested. Finally, most case studies featured in the considered articles are based on real industrial data, and some of them are related to sensors, showing that the available PHM-XAI blends solve real-world challenges, increasing the confidence in the artificial intelligence models’ adoption in the industry.

This essay argues that the popular misrepresentation of the nature of AI has important consequences concerning how we view the need for regulations. Considering AI as something that exists in itself, rather than as a set of cognitive technologies whose characteristics—physical, cognitive, and systemic—are quite different from ours (and that, at times, differ widely among the technologies) leads to inefficient approaches to regulation. This paper aims at helping the practitioners of responsible AI to address the way in which the technical aspects of the tools they are developing and promoting directly have important social and political consequences.

Recently, the emerging technologies have been constantly shaping the education domain, especially the use of artificial intelligence (AI) for language learning, which has attracted significant attention. Many of the AI tools are being used for learning foreign languages, in both formal and informal ways. There are many studies that have explored the potential of the recent technology “ChatGPT” for education and learning languages, but none of the existing studies have conducted any exploratory study for assessing the usability of ChatGPT. This paper conducts an assessment for usability of ChatGPT for formal English language learning. The study uses a standard questionnaire-based approach to ask participants about their feedback for usefulness and effectiveness of ChatGPT. The participants were asked for their feedback after performing series of tasks related to formal English language learning with ChatGPT. A variety of student participants were selected for this study with diverse English language proficiency levels, education levels, and nationalities. The quantitative analysis of the participant responses shed light on their experience with regards to the usability of ChatGPT for performing different English language learning tasks such as conversation, writing, grammar, and vocabulary. The findings from this study are quite promising and indicate that ChatGPT is an effective tool to be used for formal English language learning. Overall, this study contributes to the fast-growing research domain on using emerging technologies for formal English language learning by conducting in-depth assessment of usability for ChatGPT in formal English language learning.

Large-scale fading models play an important role in estimating radio coverage, optimizing base station deployments and characterizing the radio environment to quantify the performance of wireless networks. In recent times, multi-frequency path loss models are attracting much interest due to their expected support for both sub-6 GHz and higher frequency bands in future wireless networks. Traditionally, linear multi-frequency path loss models like the ABG model have been considered, however such models lack accuracy. The path loss model based on a deep learning approach is an alternative method to traditional linear path loss models to overcome the time-consuming path loss parameters predictions based on the large dataset at new frequencies and new scenarios. In this paper, we proposed a feed-forward deep neural network (DNN) model to predict path loss of 13 different frequencies from 0.8 GHz to 70 GHz simultaneously in an urban and suburban environment in a non-line-of-sight (NLOS) scenario. We investigated a broad range of possible values for hyperparameters to search for the best set of ones to obtain the optimal architecture of the proposed DNN model. The results show that the proposed DNN-based path loss model improved mean square error (MSE) by about 6 dB and achieved higher prediction accuracy R2 compared to the multi-frequency ABG path loss model. The paper applies the XGBoost algorithm to evaluate the importance of the features for the proposed model and the related impact on the path loss prediction. In addition, the effect of hyperparameters, including activation function, number of hidden neurons in each layer, optimization algorithm, regularization factor, batch size, learning rate, and momentum, on the performance of the proposed model in terms of prediction error and prediction accuracy are also investigated.

Many activities and sectors have come to a halt due to the COVID-19 crisis. People and workers’ habits and behaviors have changed dramatically, as the use of technologies and connections, virtual reality, and remote support have been enhanced. Businesses and cities have been forced to quickly adapt to the new challenges. Digital technologies have allowed people to have better access to public services due to improved use of resources. Smart cities have significant potential for linking people to work and services as never done before. Additionally, the technological convergence produces data that can enhance interactions and decisions toward the “new normal”. In this paper, the aim is to assess how Portugal is prepared to respond to the accelerated process that this context demands from cities. Portuguese SMEs have developed a good capacity for entrepreneurship and innovation; however, they are still behind in converting the knowledge acquired in sales and exports and there is still limited collaboration at the public-private level. The acceleration of smart cities through the Internet of Things (IoT) may encourage changes in these issues. A more assertive alignment between the emergent technologies and the digitization goals of companies is required. This paper opens a discussion around major needs and trends of IoT (and related technologies) since the pandemic has leveraged them. The relationship between innovation and city smartness is approached to assess main contributing and limiting variables (through the European Innovation Scoreboard), to clarify future directions toward smarter services. The tourism sector, as the largest export economic activity in the country, is addressed in this matter. An analytical framework (using, for example, Power BI and Azure IoT Hub) around this approach can choose and support the most suitable areas of development in the country.