Exploring the evolution of emerging technology using text mining method based on machine learning: evidence from intelligent ship technology

Machine-Learning Implementation in Clinical Anesthesia: Opportunities and Challenges.

In this paper, the current situation of research on Internet of things technology to carry on the analysis, the RFID technology, sensor network and detection technology, intelligent technology, nanotechnology, address of the IPv6 technology are summarized, analysis of existing problems in the development of the Internet of things, the development and application of the Internet of things technology is prospected.

Digital transformation of public services in the context of modern information systems and technologies is a relevant area of research, which is due to the growing demands of society for the quality and speed of public services. In the digital economy, artificial intelligence, machine learning and big data processing technologies play an important role, which can significantly increase the efficiency of public administration. The purpose of the article is to study the possibilities and effectiveness of using intelligent technologies in reengineering business processes that accompany the digital transformation of the public administration sector. The scientific novelty of the study lies in the development of conceptual models that describe the methodology for integrating modern artificial intelligence methods into the digital environment of public services, as well as in the formation of practical recommendations for their implementation. In particular, the use of machine learning algorithms is proposed to predict the load on services, detect anomalies and optimize decision-making based on big data. The main conclusions of the study are confirmation that the integration of intelligent technologies provides a reduction in time costs, increased transparency, a decrease in the number of errors in the processes of providing public services, and also strengthens citizens' trust in state digital platforms. Special attention is paid to the issues of overcoming technical and organizational barriers that may arise during the implementation of the proposed solutions. The results obtained have practical significance for the further improvement of e-government and can be used by state authorities to optimize the processes of digital transformation of public services. Key words: Intelligent technologies, reengineering, digital transformation, artificial intelligence, machine learning, public services, big data.

In the big data era, data-driven machine learning technology acts as a vital role in the development of energy materials. Innovative research on high-quality energy material databases, appropriate material feature descriptors, efficient prediction and generation models are urgent and will bring significant breakthroughs to the discovery of new energy materials. To realize the sustainable development of society, advanced materials for energy storage and conversion are urgently needed. For a long time, the development of new materials relies heavily on tedious trial and error experiments, which have long cycles and high costs, far from modern requirements for advanced materials. With the rapid development of supercomputers and the wide application of density functional theory, high-precision first principle theoretical calculation has been widely used in the process of material design. With the help of quantum mechanics, researchers can select the best-performed materials from thousands of candidates quickly, which greatly shortens the process of new material development. However, the cost of this ab initio calculation method is still high, and sometimes would be stuck in large-scale complex systems. The above disadvantages limit its application in further material discovery. In the big data era, with the explosive growth of new material samples, the effective management and utilization of existing data is the key to accelerating material design. Therefore, the greatest challenge is how to evaluate and analyze quickly and efficiently existing data sets to find out hidden rules. Artificial intelligence (AI) technology, which could extract information from massive data, has been applied in all aspects of our daily life and has gradually penetrated the field of natural science to help experimental scheme design or functional material screen. Machine learning is the core of AI as well as the key to realizing computer intelligence. Based on machine learning, computers can automatically learn the relationship between the characteristics and properties of data samples usually beyond human cognition and discover the hidden laws behind high-dimensional data. Then it could be used to predict the properties of unknown samples and generate potentially new ones. In recent years, with the rapid growth of material databases, machine learning has also been applied in materials science. The introduction of machine learning has greatly reduced the cost of high-throughput computational material screening and accelerated the research and development process of energy materials. Meanwhile, the feature analysis of the built models can further deepen our understanding of material structure–property relationships and help to explore new theoretical mechanisms. At present, machine learning-assisted material development methods have been used in the field of new energy materials. For example, several breakthroughs have been made in photovoltaic materials, thermoelectric materials, lithium-ion batteries, catalytic hydrogen production, and so on.1-3 In photovoltaic material discovery, machine learning models have already been used to predict the optical band gaps, photoelectric conversion efficiencies, and other related properties, with appropriate data-mining and feature-extracting algorithms, which avoid the tedious density functional theory calculation. At the same time, several effective generation algorithms can automatically generate potential high-performance materials in the prescreening process. For example, the machine learning model can realize the full-space search of perovskite photovoltaic materials by screening key parameters such as band gap and formation energy, and afterward, unexplored high-performance photovoltaic materials could be created. In addition, after the model training, the weight analysis can help us to understand the sample characteristics in depth, which is of great significance in inspiring and designing new materials. In addition to photovoltaic materials, thermoelectric material is another kind of clean energy material that can convert thermal energy into electrical energy directly. The thermoelectric properties are related to the thermoelectric conversion Seebeck coefficient, thermal conductivity, electrical conductivity, and many other properties of the material. Through natural language recognition and first-principles calculations, some experimental and computational databases for thermoelectric materials have been developed. These databases collected hundreds of thermoelectric materials with their thermoelectric merit (ZT), Seebeck coefficient, thermal conductivity, and other related information at different temperatures. With these samples, researchers trained predictive models to explore the most important factors of thermoelectric conversion properties, such as ZT or Seebeck coefficients. Besides, machine learning models are also widely used to predict the lattice thermal conductivity, one of the most important factors determining the energy conversion between thermal and electrical energy. In addition to periodic solid materials, machine learning algorithms have significant advantages in large-scale and irregular systems. In the field of new energy materials, apart from energy conversion, energy storage is also very crucial. Lithium-ion battery is the most important energy storage device, while it is still unsatisfactory in energy density, power density, cycle life, cost and safety. Compared with traditional ways, AI methods could significantly accelerate the development of new battery systems. By learning the data in literature or existing databases, efficient machine learning models can be used to develop novel electrode materials, greatly improving the screening efficiency and finding reliable new materials. Currently, machine learning has shown excellent performances in the prediction of electrolyte and electrode materials properties, battery state, and lifetimes. In addition, catalysts play a vital role in the new energy industry, such as the photo-dissociation of water to hydrogen, carbon dioxide reduction, and fuel cells. Therefore, efficient catalyst materials are highly desired to achieve higher reaction efficiency. In design of new catalytic materials, machine learning models can screen catalysts via rapidly predicting adsorption energy of crucial intermediate on catalysts. At the same time, through the weight analysis of model parameters, we can fully understand the relationship between catalyst structure and performance. In short, machine learning acts as a vital role in the development of energy materials. The explosive growth of high-performance algorithms and material databases provides fertile importantground for the booming of machine learning in energy materials development. However, as a new attempt, machine learning still faces not only opportunities but challenges. First, as a data-driven method, sufficient data is a necessary condition to ensure the accuracy of the training model. With the development of material genetic engineering, more and more commercial or open-source material databases have been developed. There still are many published data not being collected and collated. Compared with databases in stock exchange, public transportation, or biomedicine, most material databases were limited with their versatility, normalization, and scale. At the same time, few databases collect negative data and materials with poor performance, which also play important roles in model training. To solve the above-mentioned problems, researchers, in one hand, should establish a generally accepted data storage standard and develop an open collaboration framework for machine-readable format data. It will achieve data standardization and promote data sharing. In the other hand, the development of professional natural language recognition technology is also helpful to enlarge the scale of material databases. For example, text recognition and mining technology has been applied to chemistry and materials science. Secondly, the performance of the training model to predict material properties largely depends on the appropriate description of materials. In most situations, the processes of feature selection and material description rely on the intuition of researchers. Generally, the descriptions are constructed by encoding their structural or electronic attributes, such as mass, atomic number, atomic type, electronic bandgap, dielectric constant, work function, electron density, electron affinity, and so on. Therefore, researchers' understanding and cognition of the problem play a decisive role in feature engineering, which will further affect the trained model. However, even excellent researchers cannot exhaust and figure out all the best features and the most efficient encoding. Therefore, automated feature engineering is proposed. Compared with the manual way, automated feature engineering is more efficient and repeatable. For example, deep learning provides an opportunity for automatic feature extraction and continuous training, which can reduce the incompleteness of manual operation, and is an important research trend in the future. Third, it is also to select the appropriate machine learning algorithm or algorithm collection before training the model. The selection of the algorithm depends on not only the training datasets, including their size, distribution, and internal correlation but also the problems to be solved. There is no universal algorithm suitable for all problems. And sometimes, we have to integrate multiple algorithms to obtain effective models. In addition, with the expansion of databases, time consumption also should be paid attention in the process of algorithm selection and model optimization. Besides, machine learning models and material generation models are also important directions of machine learning in the field of energy materials. The machine learning model has long been considered a "black box" connecting input and output. It is difficult for us to extract knowledge from the model and summarize it into general scientific laws. Therefore, the interpretability of machine learning models is also a key challenge. Developing interpretable algorithms, converting models into formulas, and summarizing scientific laws are important directions in the future. In conclusion, as a relatively new direction in computational materials science, data-driven machine learning material screening methods will provide great opportunities for the design of new energy materials. Meanwhile, chemical synthesis robots equipped with artificial intelligence technology have also been paid attention to in recent years.4-6 Combining machine learning material screening with intelligent manufacturing will further accelerate the development of new energy materials (Figures 1 and 2). This study was supported by the National Natural Science Foundation of China (22003046 and 22071172) and the research program "A Multi-Scale and High-Efficiency Computing Platform for Advanced Functional Materials," funded by Haihe Laboratory in Tianjin (Grants No. 22HHXCJC00007). The authors declare no conflicts of interest.

Due to the particularity of the artificial intelligence major and the machine learning courses learned, the traditional course teaching model is not suitable for artificial intelligence major machine learning courses. Based on this background, this article proposes a new system based on machine learning curriculum teaching reform. It mainly includes the reform of curriculum teaching mode, curriculum practice reform, and teaching process reform. In order to verify the effect of the proposed new model on the teaching quality of machine learning courses, this article also proposes an evaluation method based on intelligent technology. Firstly, the feasibility of evaluation based on intelligent technology is described. Secondly, it lists the application details of the existing teaching evaluation based on intelligent technology. Finally, a novel teaching quality evaluation system based on intelligent technology is proposed. The system collects student facial expression data and uses classification algorithms to make classification decisions on the data. The result of the decision can give feedback on the quality of classroom teaching. The comparison of experiments based on different intelligent technologies shows that the teaching quality evaluation system proposed in this article is feasible and effective.

In times when machine learning (ML) and other artificial intelligence (AI) technologies are expanding the role and definition of network learning in schools, this short paper reports from a practice-centred research project that explores how K-12 teachers affect and are affected by educational technologies with AI. Accelerated by the COVID-19 pandemic, data-driven and decision-making systems with ML are already entering various educational policy and practice realms, often underpinned by promises of automation and personalization. A growing number of research, drawing from the theoretical orientations and empirical approaches from Science & Technology Studies is increasingly unpacking such promises as well as addressing controversies directly related to the constitutions of ML AI in education. Still, little research explores the adoption of data-driven AI technologies in classrooms from a socio-material, networked learning stance. This short paper introduces such work (in progress) drawing on ethnographic fieldwork conducted in Sweden. Guided by the ontological and methodological approaches of Actor-Network-Theory (ANT), the study focuses on the interactions in K-12 classrooms between commercial ML technologies and teachers. Methodologically this means engaging with both human and non-human actors through ethnographic approaches striving for very specific descriptions of interactions within the actor-network and its enacted realities. Preliminary findings from the first of two envisaged case studies in which a ML-based teaching aid in mathematics was tried out in 22 classrooms indicate how compensatory and contradictory actions and accounts emerge within the network of heterogeneous actors. Human actors seem to compensate for the algorithmic actions of the specific educational technology with ML. This is however not a fait accompli but a continuous and unsettled process in the making between humans and the (non-human) technology. Preliminary results also suggest how controversies of ML algorithms in teaching aids, such as their lack of transparency and algorithmic “governance” play out in authentic learning contexts. In conclusion, the paper argues that theoretical and methodological principles of ANT grant for non-deterministic narrative of the heterogeneous nature of educational practice and have the potential to open the black-box of machine learning in the emerging networked learning settings of K-12 classrooms.

Sensors support machine learning

The main objective of the annual International Conference on Intelligent Technologies (InTech) is to bring together researchers and practitioners who implement intelligent and fuzzy technologies in real-world environment. The Fifth International Conference on Intelligent Technologies InTech'04 was held in Houston, Texas, on December 2-4, 2004. Topics of InTech'04 included mathematical foundations of intelligent technologies, traditional Artificial Intelligent techniques, uncertainty processing and methods of soft computing, learning/adaptive systems/data mining, and applications of intelligent technologies. This special issue contains versions of 15 selected papers originally presented at InTech'04. These papers cover most of the topics of the conference. Several papers describe new applications of the existing intelligent techniques. R. Aló{o} et al. show how traditional <I>statistical</I> hypotheses testing techniques – originally designed for processing measurement results – need to be modified when applied to simulated data – e.g., when we compare the quality of two algorithms. Y. Frayman et al. use <I>mathematical morphology</I> and <I>genetic algorithms</I> in the design of a machine vision system for detecting surface defects in aluminum die casting. Y. Murai et al. propose a new faster <I>entropy</I>-based placement algorithm for VLSI circuit design and similar applications. A. P. Salvatore et al. show how <I>expert system</I>-type techniques can help in scheduling botox treatment for voice disorders. H. Tsuji et al. propose a new method, based on <I>partial differential equations</I>, for automatically identifying and extracting objects from a video. N. Ward uses <I>Ordered Weighted Average</I> (OWA) techniques to design a model that predicts admission of computer science students into different graduate schools. An important aspect of intelligence is ability to <I>learn</I>. In A. Mahaweerawat et al., neural-based machine learning is <I>used</I> to identify and predict software faults. J. Han et al. show that we can drastically <I>improve</I> the quality of machine learning if, in addition to discovering traditional (positive) rules, we also search for negative rules. A serious problem with many neural-based machine learning algorithms is that often, the results of their learning are un-intelligible rules and numbers. M. I. Khan et al. show, on the example of robotic arm applications, that if we allow neurons with different input-output dependencies – including linear neurons – then we can <I>extract</I> meaningful <I>knowledge</I> from the resulting network. Several papers analyze the Equivalent Transformation (ET) model, that allows the user to <I>automatically generate code from specifications</I>. A general description of this model is given by K. Akama et al. P. Chippimolchai et al. describe how, within this model, we can transform a user's query into an equivalent more efficient one. H. Koike et al. apply this approach to <I>natural language processing</I>. Y. Shigeta et al. show how the existing <I>constraint</I> techniques can be translated into equivalent transformation rules and thus, combined with other specifications. I. Takarajima et al. extend the ET approach to situations like <I>parallel computations</I>, where the order in which different computations are performed on different processors depends on other processes and is, thus, non-deterministic. Finally, a paper by J. Chandra – based on his invited talk at InTech'04 – describes a <I>general framework</I> for robust and resilient critical infrastructure systems, with potential applications to transportation systems, power grids, communication networks, water resources, health delivery systems, and financial networks. We want to thank all the authors for their outstanding work, the participants of InTech'04 for their helpful suggestions, the anonymous reviewers for their thorough analysis and constructive help, and – last but not the least – to Professor Kaoru Hirota for his kind suggestion to host this issue and to the entire staff of the journal for their tireless work.

The aim of the paper is twofold. First to examine the role of the board of directors in facilitating the adoption of AI and ML in Saudi Arabian banking sector. Second, to explore the effectiveness of artificial intelligence and machine learning in protection of Saudi Arabian banking sector from cyberattacks. A qualitative research approach was applied using in-depth interviews with 17 board of directors from prominent Saudi Arabian banks. The present study highlights both the opportunities and challenges of integrating artificial intelligence and machine learning advanced technologies in this highly regulated industry. Findings reveal that advanced artificial intelligence and machine learning technologies offer substantial benefits, particularly in areas like threat detection, fraud prevention, and process automation, enabling banks to meet regulatory standards and mitigate cyber threats efficiently. However, the research also identifies significant barriers, including limited technological infrastructure, a lack of cohesive artificial intelligence strategies, and ethical concerns around data privacy and algorithmic bias. Interviewees emphasized the board of directors’ critical role in providing strategic direction, securing resources, and fostering partnerships with artificial intelligence technology providers. The study further highlights the importance of aligning artificial intelligence and machine learning initiatives with national development goals, such as Saudi Vision 2030, to ensure sustained growth and competitiveness. The findings from the present study offer valuable implications for policymakers in banking in navigating the complexities of artificial intelligence and machine learning adoption in financial services, particularly in emerging markets.

Multiple research disciplines, from cognitive sciences to biology, finance, physics, and the social sciences, as well as many companies, believe that data-driven and intelligent solutions are necessary. Unfortunately, current artificial intelligence (AI) and machine learning (ML) technologies are not sufficiently democratized - building complex AI and ML systems requires deep expertise in computer science and extensive programming skills to work with various machine reasoning and learning techniques at a rather low level of abstraction. It also requires extensive trial and error exploration for model selection, data cleaning, feature selection, and parameter tuning. Moreover, there is a lack of theoretical understanding that could be used to abstract away these subtleties. Conventional programming languages and software engineering paradigms have also not been designed to address challenges faced by AI and ML practitioners. In 2016, companies invested $26–39 billion in AI and McKinsey predicts that investments will be growing over the next few years. Any AI/ML-based systems will need to be built, tested, and maintained, yet there is a lack of established engineering practices in industry for such systems because they are fundamentally different from traditional software systems. This Dagstuhl Seminar brought together two rather disjoint communities together, software engineering and programming languages (PL/SE) and artificial intelligence and machine learning (AI-ML) to discuss open problems on how to improve the productivity of data scientists, software engineers, and AI-ML practitioners in industry.

The digital revolution has profoundly reshaped the landscape of global finance, giving rise to an era where speed, security, and intelligence define the success of financial services. Innovations in Digital Finance and Intelligent Technologies: A Deep Dive into AI, Machine Learning, Cloud Computing, and Big Data in Transforming Global Payments and Financial Services aims to illuminate this transformation by exploring disruptive technologies at the core of today’s financial evolution. In recent years, Artificial Intelligence (AI), Machine Learning (ML), Cloud Computing, and Big Data have moved from theoretical concepts to practical tools that power every facet of modern finance—from algorithmic trading and fraud detection to credit scoring and personalized banking. These innovations are not just enhancing operational efficiency; they are enabling real-time decision-making, improving financial inclusion, and reshaping the way individuals, businesses, and institutions engage with money. This book is crafted for a wide spectrum of readers including financial professionals, technologists, researchers, policy-makers, and students. It unpacks the mechanisms behind intelligent financial systems, provides real-world case studies, and discusses emerging trends such as decentralized finance (DeFi), embedded banking, digital identity, and AI-driven risk assessment. Special attention is given to the regulatory, ethical, and cybersecurity challenges that come with this rapid digitalization. What makes this work timely is its holistic view of innovation—not just as a product of technology, but as a driver of systemic change in global economies. From digital wallets and instant cross-border payments to cloud-native banking platforms, the book outlines how intelligent technologies are setting new standards for agility, transparency, and customer experience. As financial ecosystems grow increasingly complex and interconnected, this book serves as a compass guiding stakeholders toward building secure, inclusive, and intelligent financial futures.

Maximising the Opportunities of Artificial Intelligence for People Living With Cancer

Artificial intelligence (AI) technology has been rapidly reshaping all aspects of our lives since the 1990s. The recent release of ChatGPT in November 2022 represents one of the biggest advancements in AI since AlphaGo won the first-ever game against a human professional Go player in 2015. Machine learning (ML)—one of the AI tools—is able to solve complex relationships in a system, handle big data, improve its own efficiency and predictability with more data, learn new knowledge (i.e., unknowns) through deep learning (DL), and evolve open-access algorithms and data from diverse disciplines (LeCun et al., 2015). These advantageous capabilities, coupled with rapid advancements in computing power, have been recognized in all fields of science and engineering, as is evidenced by the cascading escalations in their applications. One major reason for the widespread and speedy adoption of ML technology is because the human brain has a limited capacity for comprehending large, complex systems. Scholars and practitioners in natural science have adopted various MLs to address basic and applied challenges, such as modeling complex causes, processes, and consequences in the Earth system at multiple temporal and spatial scales (Reichstein et al., 2019). The Earth system is traditionally examined through the construction of simulation models, also known as Earth system models (ESMs; sometimes called terrestrial biosphere models, Sun et al., 2023), built to estimate past, current, and future conditions. ESMs evolved from a suite of simple algorithms (Chen, 2021) to approximate the complex interactions among components of the Earth system (Fisher & Koven, 2020; Gettelman et al., 2022). ESMs are capable of integrating critical processes from atmospheric science, biogeochemistry, biological systems, human influences, and ecosystem processes to meet the diverse needs of multiple disciplines. Yet, modern ESMs pose unprecedented challenges, such as the number and types of ESMs (30+ in CMIP6 of IPCC; Smith et al., 2020), the high number of potential parameters (hundreds to thousands), complex architecture and structure, accurate values of the parameters (i.e., parameterizations), large discrepancies among the models and high uncertainty for their predictions, and applications at high spatial and temporal resolutions (Schaefer et al., 2012). An even greater challenge arises from the computing time, which hinders their practical use, especially at landscape-regional scales. Sun and colleagues recently looked into whether ML tools could be good alternatives of conventional ESMs for predicting the functions of terrestrial ecosystems (Sun et al., 2023). In their pioneering work, they applied the same input variables (27) to three versions of ORCHIDEE (i.e., a major ESM) and Baggin decision trees (i.e., a ML tool) for predicting terrestrial carbon, nitrogen, and phosphorous at global scale. They demonstrated that ML reduced the computing demand by 78–80% while maintaining similar or even more accurate predictions than ORCHIDEEs. Such reductions are substantial, though computing power soon may no longer be a bottleneck hindering ESM runs due to rapid advancements in computing technology. An equally important finding in the Sun et al. study relates to the contributions of input variables for predicting carbon, nitrogen, and phosphorus production. It was especially interesting to see that ML only required a small number of input variables (20–25) compared to a similar number of input variables to reach better accuracy in predictions using ORCHIDEE for different components of carbon, nitrogen, and phosphorus (figures 2–4, respectively, in Sun et al., 2023). Over the past century, quantitative models in climatology, forestry, agronomy, ecology, and other fields have evolved from a few algorithms with a few input variables for predicting system functions to hundreds of equations and thousands of variables so that all interested processes are modeled (Chen, 2021). Consequently, the number of input variables for ESMs continues to increase despite difficulties in generating reasonable values at pixel level (a.k.a. tiles). For example, we often are faced with a lack of accurate and dynamic global land cover maps, as well as associated land surface properties, for parameterizing ESMs. Even more challenging is deriving accurate values for all the intermediate variables and their relationships for a specified time and location. Understanding and revising the structure, logic connections, and parameterizations are the major focuses of modelers. While inclusion of more processes (i.e., more algorithms) has the benefit of integrating more knowledge into the models and meet the needs of diverse disciplines, there are also values in reducing the number of parameters and algorithms so that an ESM can be applied for various purposes without the need to understand the details of all mechanisms, as well as without access of supercomputers. We can anticipate an increase in the development of ESMs that can be parameterized and run by non-modelers and applied to broader applications, including ecosystem, landscape, and regional applications where spatial resolutions of 10–25 km and temporal resolutions of less than a month (e.g., day, hours, etc.) are often needed for location-specific applications (e.g., Zou et al., 2022). Were ML tools applied with a small number of input variables, without the need for in-depth knowledge of detailed processes and algorithms, and with accurate predictions as demonstrated in Sun et al. (2023) and others (e.g., Pal & Sharma, 2021), our knowledge, resource management, adaptation strategies, and policy making would be significantly and promptly improved. I am particularly excited to see that predicted C, N, and P from MLs do not always match well with those of ORCHIDEE (figure 5 in Sun et al., 2023), and the ranks of important forcing factors are not the same among plant functional types (figure 6). Among the 15 PFTs, not only are the lists of significant drivers different, but their ranks in importance vary greatly as well. While these results are expected and have been reported in other recent publications (e.g., Irvin et al., 2021), a major take-away is that our efforts to estimate the values of input variables should be weighted by PFT, prediction purposes (e.g., carbon vs. nitrogen), location, and so forth. These lessons have direct implications for ML applications when used as alternatives. In sum, Sun and colleagues provide a unique and promising exploration of the application of ML on the complex contingent regulations of biogeochemical processes that are the focuses of ESMs. Based on a recent review of the literature, Pal and Sharma (2021) concluded that that ML-based techniques can enhance performance, reduce uncertainties, improve parameter optimization, and improve predictions. The needs for incorporating MLs into conventional process-based modeling frameworks, such as improving parameterization or replacing a less-constrained or semi-empirical sub-model, have also been highlighted (Reichstein et al., 2019). However, there are thousands of ML algorithms, with new ones emerging daily. Traditional ML learns and predicts based on passive observations (e.g., random forests, boosted decision trees, etc.), whereas DL focuses on an agent interacting with an environment to learn and take actions that maximize its chance of success in achieving its goals (Alpaydin, 2020). For example, both recurrent neural networks (RNNs) and graphic neural networks (GNNs) have recently gained the ability to grasp temporal and spatial relationships and variable-length data (Hinton et al., 2012; Reed et al., 2021)—the exact cases in ESMs where pixels are spatially and temporally correlated. Here, RNN and GNN enable us to connect nodes as a directed graph along a temporal/spatial sequence, thereby addressing the temporal and spatial dependencies of input, intermediate, and output variables in ESMs. Because conventional ML assumes that data are independent and identically distributed, it cannot take advantage of the information available in temporal correlations, nor can it address the consequences of rare, abrupt changes in climate forcings (e.g., extremes) or human disturbances on variables in ESMs. Future efforts will likely be expanded from Sun et al. (2023) to explore different DL tools that may be more computationally effective, easy-to-use, and more accurate projections than ESMs. No data were as used for this commentary.

How can agricultural water production be promoted？ a review on machine learning for irrigation

Evaluation of multispectral imaging for freeze damage assessment in strawberries using AI-based computer vision technology