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Artificial intelligence is more present than ever, both in our society in general and in science. At the center of this development has been the concept of deep learning, the use of artificial neural networks that are many layers deep and can often reproduce human‐like behavior much better than other machine‐learning techniques. The articles in this collection are some recent examples of its application for X‐ray photon science and crystallography that have been published in Journal of Applied Crystallography. [ABSTRACT FROM AUTHOR]

An editorial is presented the journal's success with an impact factor of 3.0 and offers readers an extensive collection of publications in critical care nursing research; it discusses the capabilities and limitations of large language models (LLMs) in research, presents various research articles with diverse topics and methodologies, emphasizing the use of LLMs in healthcare research, and highlights the importance of domain-specific expertise when utilizing LLMs.

This paper presents a comprehensive review of the most recent papers and research trends in the fields of wind energy and artificial intelligence. Our study aims to guide future research by identifying the potential application and research areas of artificial intelligence and machine learning techniques in the wind energy sector and the knowledge gaps in this field. Artificial intelligence techniques offer significant benefits and advantages in many sub‐areas, such as increasing the efficiency of wind energy facilities, estimating energy production, optimizing operation and maintenance, providing security and control, data analysis, and management. Our research focuses on studies indexed in the Web of Science library on wind energy between 2000 and 2023 using sub‐branches of artificial intelligence techniques such as artificial neural networks, other machine learning methods, data mining, fuzzy logic, meta‐heuristics, and statistical methods. In this way, current methods and techniques in the literature are examined to produce more efficient, sustainable, and reliable wind energy, and the findings are discussed for future studies. This comprehensive evaluation is designed to be helpful to academics and specialists interested in acquiring a current and broad perspective on the types of uses of artificial intelligence in wind energy and seeking what research subjects are needed in this field. [ABSTRACT FROM AUTHOR]

Smooth interaction with a disaster‐affected community can create and strengthen its social capital, leading to greater effectiveness in the provision of successful post‐disaster recovery aid. To understand the relationship between the types of interaction, the strength of social capital generated, and the provision of successful post‐disaster recovery aid, intricate ethnographic qualitative research is required, but it is likely to remain illustrative because it is based, at least to some degree, on the researcher's intuition. This paper thus offers an innovative research method employing a quantitative artificial intelligence (AI)‐based language model, which allows researchers to re‐examine data, thereby validating the findings of the qualitative research, and to glean additional insights that might otherwise have been missed. This paper argues that well‐connected personnel and religiously‐based communal activities help to enhance social capital by bonding within a community and linking to outside agencies and that mixed methods, based on the AI‐based language model, effectively strengthen text‐based qualitative research. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and machine learning (ML) pose a challenge for achieving science that is both reproducible and replicable. The challenge is compounded in supervised models that depend on manually labeled training data, as they introduce additional decision‐making and processes that require thorough documentation and reporting. We address these limitations by providing an approach to hand labeling training data for supervised ML that integrates quantitative content analysis (QCA)—a method from social science research. The QCA approach provides a rigorous and well‐documented hand labeling procedure to improve the replicability and reproducibility of supervised ML applications in Earth systems science (ESS), as well as the ability to evaluate them. Specifically, the approach requires (a) the articulation and documentation of the exact decision‐making process used for assigning hand labels in a "codebook" and (b) an empirical evaluation of the reliability" of the hand labelers. In this paper, we outline the contributions of QCA to the field, along with an overview of the general approach. We then provide a case study to further demonstrate how this framework has and can be applied when developing supervised ML models for applications in ESS. With this approach, we provide an actionable path forward for addressing ethical considerations and goals outlined by recent AGU work on ML ethics in ESS. Plain Language Summary: Artificial intelligence and machine learning can make it hard to do science in a way that can be repeated. This can mean redoing a study in the exact same way to see if you can get the same or similar results (reproducibility) or trying to use the same study design on a new problem to see if the results are the same or similar (replicability). These types of scientific repetitions is important for developing robust knowledge, but is hard to do with certain types of machine learning that rely on data that were categorized by researchers. The researchers have to make decisions and categorize their data, which the machine learning algorithm then uses as a guide to make its own decisions. Generally, there is not enough information shared by the researchers about how these decisions were made to repeat the science or evaluate how good it is. In this paper, we provide a way to address these shortcomings. The approach and example we offer illustrates how to (a) create a rulebook that can be shared for how to make decisions and (b) quantitatively measure how consistent the researchers are at using that rulebook to make their decisions. Key Points: We provide a rigorous hand labeling procedure to improve the replicability and reproducibility of supervised machine learning (ML)Our case study and step‐by‐step guide clearly outline how the procedure can be appliedThe procedure is an actionable path forward for addressing ethical considerations and goals for ML development in Earth systems science [ABSTRACT FROM AUTHOR]

Power system protection and asset management present persistent technical challenges, particularly in the context of the smart grid and renewable energy sectors. This paper aims to address these challenges by providing a comprehensive assessment of machine learning applications for effective asset management in power systems. The study focuses on the increasing demand for energy production while maintaining environmental sustainability and efficiency. By harnessing the power of modern technologies such as artificial intelligence (AI), machine learning (ML), and deep learning (DL), this research explores how ML techniques can be leveraged as powerful tools for the power industry. By showcasing practical applications and success stories, this paper demonstrates the growing acceptance of machine learning as a significant technology for current and future business needs in the power sector. Additionally, the study examines the barriers and difficulties of large‐scale ML deployment in practical settings while exploring potential opportunities for these tactics. Through this overview, insights into the transformative potential of ML in shaping the future of power system asset management are provided. [ABSTRACT FROM AUTHOR]

There is a growing trend of using artificial intelligence, particularly deep learning algorithms, in medical diagnostics, revolutionizing healthcare by improving efficiency, accuracy, and patient outcomes. However, the use of artificial intelligence in medical diagnostics comes with the critical need to explain the reasoning behind artificial intelligence‐based predictions and ensure transparency in decision‐making. Explainable artificial intelligence has emerged as a crucial research area to address the need for transparency and interpretability in medical diagnostics. Explainable artificial intelligence techniques aim to provide insights into the decision‐making process of artificial intelligence systems, enabling clinicians to understand the factors the algorithms consider in reaching their predictions. This paper presents a detailed review of saliency‐based (visual) methods, such as class activation methods, which have gained popularity in medical imaging as they provide visual explanations by highlighting the regions of an image most influential in the artificial intelligence's decision. We also present the literature on non‐visual methods, but the focus will be on visual methods. We also use the existing literature to experiment with infrared breast images for detecting breast cancer. Towards the end of this paper, we also propose an "attention guided Grad‐CAM" that enhances the visualizations for explainable artificial intelligence. The existing literature shows that explainable artificial intelligence techniques are not explored in the context of infrared medical images and opens up a wide range of opportunities for further research to make clinical thermography into assistive technology for the medical community. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) technology has brought new reconstruction opportunities for the intelligence of the advertisement industry through the help of AI technologies such as machine learning and deep learning. First, the relationship between AI and the attractiveness of green advertisements is investigated, and the influence of different AI technologies in green advertisements on consumers' perception of the attractiveness of green advertisements is summarized. Second, based on the green advertisement dissemination rate data set, the data visualization exploration is carried out, and the data deletion and coding processing are carried out aiming at different characteristic variables. Finally, according to the problems existing in the current green advertisement communication and the high‐dimensional and sparse characteristics of the communication rate data set. In this paper, based on Deep FM (Factorization Machine), Gradient Boost Decision Tree (GBDT) is added to assist the experiment, and the prediction performance of green advertising communication is tested. The results are as follows. (1) Different AI expressions in green advertisements will affect consumers' perception of the attractiveness of green advertisements. (2) The prediction ability of Deep FM model after feature engineering is better than that of data cleaning only. The prediction effect of the model is obviously improved. The purpose of this paper is to integrate green advertising media communication into the ecological concept of harmonious coexistence between man and nature, strengthen the political belief of ecological civilization construction, and conform to the communication trend of today's severe ecological situation. [ABSTRACT FROM AUTHOR]

Background: Driven by the ongoing need to provide high‐quality learning and teaching, universities recently have shown an increased interest in using learning analytics (LA) for improving learning design (LD). However, the evidence of such improvements is scarce, and the maturity of such research is unclear. Objectives: This study is aimed to evaluate the maturity of research discussing LA‐driven LD improvements in higher education. Methods: The systematic review analyses 49 empirical papers, assesses their quality and suggests further research directions. The review elaborates on methodological (research questions, strategy and methods, LA‐LD integration theoretical backgrounds) and substantial (LA‐driven LD improvements, types of data used, LA software development) features of the papers. Results and Conclusions: The findings demonstrated the lack of theoretical alignment between LA and LD, with research tending towards user experience studies. The most frequently used research strategy was a case study; experiments were very rare. Researchers predominantly used parsing for collecting data and AI methods for analysing it; mostly used data types related to registering learners' engagement with learning activities as well as resources and tools provided in digital learning environments. Takeaways: The research area discussing LA‐driven LD improvements still has a way to go before attaining the level of full maturity. Only a third of the papers reported actual LA‐driven LD improvements; moreover, only three papers measured their effectiveness. The presented LA software was mostly at the beta or implementation stages and did not assess the impact of using this software. Lay Description: What is already known about this topic: Universities show an increased interest in using LA for improving LD.The evidence of such improvements being efficient is scarce.Learning analytics is weakly grounded in learning and teaching. What this paper adds: LA‐LD research presented in the reviewed papers cannot yet be considered fully mature.Almost half of the research was carried out using a case study approach, intrinsically challenging in terms of replication and validity. As few as three out of the reviewed studies applied an experimental approach, capable of stating the cause–effect relationship between LA and LD.All the studied papers mentioned improvements in LD, but only around a third of them reported actual improvements, another one‐third suggested potential improvements, and the rest did not come down to describing any.The LA software presented in the papers was mostly at the testing or implementation stages, and the impact of implemented software on LD has yet to be evaluated. Implications for practice and/or policy: Future LA‐LD research to become more mature may well elaborate on theoretical footing, contribute to the cause–effect relationship between LA and LD utilising experimental research design, ensure sufficient sample size.Practitioners' efforts should be directed to operationalising LD for efficient measurements, addressing pedagogical challenges when applying LA, and assessing LA‐driven improvements introduced into LD. [ABSTRACT FROM AUTHOR]

Background Objective Methods Results Conclusion Cosmetic dermatology is a growing field as more patients are seeking treatments for esthetic concerns. Traditionally, practitioners and patients utilize their own perceptions, current beauty standards, and manual observation to determine their satisfaction with cosmetic interventions. Artificial intelligence (AI) can be introduced into cosmetic dermatology to provide objective data‐driven recommendations to both dermatologists and patients.The purpose of this paper is to compose a unified review that illustrates the various facets of artificial intelligence and formulate a hypothesis regarding the new implications of artificial intelligence in cosmetic dermatology specifically.A comprehensive search on PubMed was conducted to identify the available information related to AI in cosmetic dermatology. The search was conducted using a combination of keywords including “cosmetic dermatology” and “artificial intelligence.”The current literature indicates that AI models offer personalized, efficient, and result‐driven outputs that can enhance cosmetic outcomes, patient satisfaction, and overall experience.Artificial intelligence integration in cosmetic dermatology shows a promising future, offering the ability to analyze vast data sets and deliver a tailored patient experience. By incorporating AI into cosmetic dermatology, there is an opportunity to balance evidence‐based decision‐making with the artistic human touch of cosmetic dermatologists. [ABSTRACT FROM AUTHOR]

Background: Education is often the primary arena for exploring and integrating new technologies. AI and human‐machine communication (HMC) are prevalent in the classroom, yet we are still learning how student perceptions of these tools will impact education. Objectives: We sought to understand student perceptions of credibility related to written feedback attributed to a human or an AI grader (Study One). We also investigated how corrective messages containing verbal immediacy and social support influenced student perceptions of an AI grader's credibility based on feedback in an evaluated essay (Study Two). Methods: We used an online experimental design to assess the perceived credibility of a grader. In Study One, we randomly assigned students (N = 155) to a condition that contained a paragraph they were told was evaluated by a human or an AI grader. In Study Two (N = 222), we investigated ways of increasing perceptions of an AI grader's credibility by writing messages with higher/lower levels of immediacy and social support. Results: In Study One, the students rated both the human and AI grader as credible (yet rated the AI grader lower on goodwill). The data suggest that students in Study Two attributed more goodwill (i.e., caring) to the AI grader when the feedback included more verbal immediacy. Conclusions: Our results highlight the importance of student perceptions and communication styles when integrating technology into education. The two studies imply that students viewed the human and AI graders as competent, caring, and trustworthy, specifically when feedback included more immediacy cues. Lay Description: What is currently known about this topic: An instructor's use of verbal and non‐verbal behaviours is a crucial component of establishing credibility within a student‐instructor relationship.Non‐human actors (artificial intelligence and social robots) are perceived as credible when they use appropriate verbal and non‐verbal messages.Education plays a central role in exploring and integrating new technologies, but initial impressions and perceived utility impact acceptance and adoption. What does this paper add: Students perceived both human and machine graders as credible based on measures of competence, goodwill, and trustworthiness.This is the first study that sought to alter perceptions of a machine grader's credibility by manipulating verbal and non‐verbal immediacy cues.Results suggest that a machine grader's communication style, especially verbal immediacy, would influence student engagement. Implications for practice/or policy: This study offers initial insights into using machine grading as a supplementary engagement tool.Communication style is a key factor to consider when designing courses that include automated scoring systems or AI graders. [ABSTRACT FROM AUTHOR]

The cost for covering active power losses makes a significant item in transmission system operators (TSO) annual budgets, and still it received limited attention in the existing literature. The focus of accurate power loss forecasting and procurement is of high increase during the past 2 years due to spikes in electricity prices, making the cost of covering the active power losses a dominant factor of TSO operational costs. This paper presents practical aspects of the highly accurate models for transmission loss forecast in the day ahead time frame for the Croatian transmission system. The contributions are two‐fold: 1) Practical insights into usable TSO data are provided, filling a critical research gap and a foundational literature review is established on transmission loss forecasting. 2) A novel method utilizing only electricity transit data as input which outperforms existing practices is presented. For this, several algorithms such as gradient boosted decision tree model (XGB), support vector regressors, multiple linear regression and fully connected feedforward artificial neural networks are developed, and implemented and validated on data obtained from the Croatian TSO. The results show that the XGB model outperforms current TSO model by 32% for 4 months of comparison and TSCNET's commercial solution by 25% during a year‐long testing period. The developed XGB model is also implemented as a software tool and put into everyday operation with the Croatian TSO. [ABSTRACT FROM AUTHOR]

Engagement has been defined as an attitude toward work, as a positive, satisfying, work‐related state of mind characterized by high levels of vigour, dedication, and absorption. Both its definition and its assessment have been controversial; however, new methods for its assessment, including artificial intelligence (AI), have been introduced in recent years. Therefore, this research aims to determine the state of the art of AI in the study of engagement. To this end, we conducted a systematic review in accordance with PRISMA to analyse the publications to date on the use of AI for the analysis of engagement. The search, carried out in six databases, was filtered, and 15 papers were finally analysed. The results show that AI has been used mainly to assess and predict engagement levels, as well as to understand the relationships between engagement and other variables. The most commonly used AI techniques are machine learning (ML) and natural language processing (NLP), and all publications use structured and unstructured data, mainly from self‐report instruments, social networks, and datasets. The accuracy of the models varies from 22% to 87%, and its main benefit has been to help both managers and HR staff understand employee engagement, although it has also contributed to research. Most of the articles have been published since 2015, and the geography has been global, with publications predominantly in India and the US. In conclusion, this study highlights the state of the art in AI for the study of engagement and concludes that the number of publications is increasing, indicating that this is possibly a new field or area of research in which important advances can be made in the study of engagement through new and novel techniques. [ABSTRACT FROM AUTHOR]

This paper describes a novel method for detecting child malnutrition based on artificial intelligence and facial photography. Estimates of severe and moderate acute malnutrition in children are critical for rapid emergency responses. However, the two traditional measurement methods, mid‐upper arm circumference (MUAC) and weight‐for‐height (WFH), are impractical in conflict and catastrophic disaster situations. They require well‐trained enumerators, cumbersome equipment, and close supervision. The Method for Extremely Rapid Observation of Nutritional Status (MERON) addresses the problem, using simple facial photographs. Facial features are extracted to predict Body Mass Index (BMI) in adults and Weight for Height Z Score (WFHZ) in children under five. MERON correctly predicts adult BMI classification with 78% accuracy. A variant of the model, trained on a sample of 3167 children in Kenya, successfully classified 60% of cases. On most measures, MERON was easier and more culturally acceptable to use than the traditional measurement methods. If MERON were to be trained and validated on a larger sample, with more extreme cases, it would provide a practical solution to a recurrent humanitarian problem. [ABSTRACT FROM AUTHOR]

One important goal of cognitive science is to understand the mind in terms of its representational and computational capacities, where computational modeling plays an essential role in providing theoretical explanations and predictions of human behavior and mental phenomena. In my research, I have been using computational modeling, together with behavioral experiments and cognitive neuroscience methods, to investigate the information processing mechanisms underlying learning and visual cognition in terms of perceptual representation and attention strategy. In perceptual representation, I have used neural network models to understand how the split architecture in the human visual system influences visual cognition, and to examine perceptual representation development as the results of expertise. In attention strategy, I have developed the Eye Movement analysis with Hidden Markov Models method for quantifying eye movement pattern and consistency using both spatial and temporal information, which has led to novel findings across disciplines not discoverable using traditional methods. By integrating it with deep neural networks (DNN), I have developed DNN+HMM to account for eye movement strategy learning in human visual cognition. The understanding of the human mind through computational modeling also facilitates research on artificial intelligence's (AI) comparability with human cognition, which can in turn help explainable AI systems infer humans' belief on AI's operations and provide human‐centered explanations to enhance human−AI interaction and mutual understanding. Together, these demonstrate the essential role of computational modeling methods in providing theoretical accounts of the human mind as well as its interaction with its environment and AI systems. In this paper, I summarize my research in visual cognition, language processing, and explainable AI to demonstrate the essential role of computational modeling methods in providing theoretical accounts of the human mind as well as its interaction with its environment and AI systems. [ABSTRACT FROM AUTHOR]

This paper addresses a question about the moral status of Artificial Intelligence (AI): will AIs ever become moral patients? I argue that, while it is in principle possible for an intelligent machine to be a moral patient, there is no good reason to believe this will in fact happen. I start from the plausible assumption that traditional artifacts do not meet a minimal necessary condition of moral patiency: having a good of one's own. I then argue that intelligent machines are no different from traditional artifacts in this respect. To make this argument, I examine the feature of AIs that enables them to improve their intelligence, i.e., machine learning. I argue that there is no reason to believe that future advances in machine learning will take AIs closer to having a good of their own. I thus argue that concerns about the moral status of future AIs are unwarranted. Nothing about the nature of intelligent machines makes them a better candidate for acquiring moral patiency than the traditional artifacts whose moral status does not concern us. [ABSTRACT FROM AUTHOR]

Some scholars claim that epistemology of science and machine learning are actually overlapping disciplines studying induction, respectively affected by Hume's problem of induction and its formal machine‐learning counterpart, the "no‐free‐lunch" (NFL) theorems, to which even advanced AI systems such as LLMs are not immune. Extending Kevin Korb's view, this paper envisions a hierarchy of disciplines where the lowermost is a basic science, and, recursively, the metascience at each level inductively learns which methods work best at the immediately lower level. Due to Hume's dictum and NFL theorems, no exact metanorms for the good performance of each object science can be obtained after just a finite number of levels up the hierarchy, and the progressive abstractness of each metadiscipline and consequent ill‐definability of its methods and objects makes science—as defined by a minimal standard of scientificity—cease to exist above a certain metalevel, allowing for a still rational style of inquiry into science that can be called "philosophical." Philosophical levels, transitively reflecting on science, peculiarly manifest a non–empirically learned urge to self‐reflection constituting the properly normative aspect of philosophy of science. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) revolutionizes vehicle communication in dynamic networks. Message dissemination in IoV involves sharing critical information for the safety and convenience of the IoV network. It is very crucial to secure message dissemination due to potential cyber‐attacks, traffic disruptions, and privacy breaches. Data integrity, authentication, and privacy are vital to maintaining trust and safety in the IoV network. This network consists of resource‐constrained IoV devices with limited resources due to the availability of embedded components in vehicular systems. Therefore, optimizing algorithms and protocols is crucial for efficient vehicle‐to‐everything (V2X) communication, enhancing safety and transportation efficiency. Solutions often include lightweight protocols and secure message exchange. This paper proposes a machine learning (ML) based secure and lightweight message dissemination framework for resource‐constrained IoV. First, we present an ML‐based threat classification model capable of effectively categorizing adversarial and nonadversarial data streams and delivering an optimized model with superior accuracy. Furthermore, we also propose a secure message dissemination scheme using lightweight cryptographic primitives, which significantly reduces computational, communication, and energy overhead. To validate the robustness of our proposed ML‐based secure and lightweight message dissemination framework, we evaluate it using various security parameters and performance measures such as computation cost, communication cost, energy cost, accuracy, precision, recall, and F1‐score. Our contributions promise to significantly enhance the security and efficiency of message dissemination in IoV environments and advance lightweight, secure, and reliable transportation systems for the future. [ABSTRACT FROM AUTHOR]

Parkinson's Disease (PD) is a brain‐related disease that eventually causes disability and disrupts a person's normal life. Most physicians and researchers try to diagnose it quickly and treat it on time. In recent years, computer science and the field of artificial intelligence (machine learning) have helped researchers find a way to detect the disease early. This article proposes a method that diagnoses Parkinson's disease by analyzing the hand drawing shaped by the individuals using Bi‐Directional Long Short‐Term Memory (Bi‐LSTM) neural network. In addition, this paper proposes a Fuzzy Inferential Classifier for the Dense layer, which classifies the output of LSTM Blocks to the associated classes by modifying the Soft‐max function. Our decision to propose this classifier was because, in some cases, hand‐drawing data related to people with Parkinson's disease have no significant difference with the healthy subjects for the distinguishing and are often very similar to each other. A standard dataset has been used in this paper, which includes spirals drawn test (including static spiral, dynamic spiral, and stability specific point) by a group of healthy people and people with Parkinson's disease. The proposed method has reached 97%, 98.5%, and 100% accuracy rates for the three mentioned spiral tests with a smoother training loss and accuracy plots. In addition, the results outperform state‐of‐the‐art research conducted on this dataset and show at least more than 2.5% improvement in the accuracy rate in comparison. [ABSTRACT FROM AUTHOR]

Objectives Methods Results Conclusion To investigate health consumers' ethical concerns towards the use of artificial intelligence (AI) in EDs.Qualitative semi‐structured interviews with health consumers, recruited via health consumer networks and community groups, interviews conducted between January and August 2022.We interviewed 28 health consumers about their perceptions towards the ethical use of AI in EDs. The results discussed in this paper highlight the challenges and barriers for the effective and ethical implementation of AI from the perspective of Australian health consumers. Most health consumers are more likely to support AI health tools in EDs if they continue to be involved in the decision‐making process. There is considerably more approval of AI tools that support clinical decision‐making, as opposed to replacing it. There is mixed sentiment about the acceptability of AI tools influencing clinical decision‐making and judgement. Health consumers are mostly supportive of the use of their data to train and develop AI tools but are concerned with who has access. Addressing bias and discrimination in AI is an important consideration for some health consumers. Robust regulation and governance are critical for health consumers to trust and accept the use of AI.Health consumers view AI as an emerging technology that they want to see comprehensively regulated to ensure it functions safely and securely with EDs. Without considerations made for the ethical design, implementation and use of AI technologies, health consumer trust and acceptance in the use of these tools will be limited. [ABSTRACT FROM AUTHOR]

Background: Malnutrition is associated with increased morbidity, mortality, and healthcare costs. Early detection is important for timely intervention. This paper assesses the ability of a machine learning screening tool (MUST‐Plus) implemented in registered dietitian (RD) workflow to identify malnourished patients early in the hospital stay and to improve the diagnosis and documentation rate of malnutrition. Methods: This retrospective cohort study was conducted in a large, urban health system in New York City comprising six hospitals serving a diverse patient population. The study included all patients aged ≥ 18 years, who were not admitted for COVID‐19 and had a length of stay of ≤ 30 days. Results: Of the 7736 hospitalisations that met the inclusion criteria, 1947 (25.2%) were identified as being malnourished by MUST‐Plus‐assisted RD evaluations. The lag between admission and diagnosis improved with MUST‐Plus implementation. The usability of the tool output by RDs exceeded 90%, showing good acceptance by users. When compared pre‐/post‐implementation, the rate of both diagnoses and documentation of malnutrition showed improvement. Conclusion: MUST‐Plus, a machine learning‐based screening tool, shows great promise as a malnutrition screening tool for hospitalised patients when used in conjunction with adequate RD staffing and training about the tool. It performed well across multiple measures and settings. Other health systems can use their electronic health record data to develop, test and implement similar machine learning‐based processes to improve malnutrition screening and facilitate timely intervention. Key points/Highlights: Malnutrition is prevalent among hospitalised patients and frequently goes unrecognised, with the potential for severe sequelae. Accurate diagnosis, documentation and treatment of malnutrition have the potential of having a positive impact on morbidity rate, mortality rate, length of inpatient stay, readmission rate and hospital revenue. The tool's successful application highlights its potential to optimise malnutrition screening in healthcare systems, offering potential benefits for patient outcomes and hospital finances. [ABSTRACT FROM AUTHOR]

WeatherBench 2 is an update to the global, medium‐range (1–14 days) weather forecasting benchmark proposed by (Rasp et al., 2020, https://doi.org/10.1029/2020ms002203), designed with the aim to accelerate progress in data‐driven weather modeling. WeatherBench 2 consists of an open‐source evaluation framework, publicly available training, ground truth and baseline data as well as a continuously updated website with the latest metrics and state‐of‐the‐art models: https://sites.research.google/weatherbench. This paper describes the design principles of the evaluation framework and presents results for current state‐of‐the‐art physical and data‐driven weather models. The metrics are based on established practices for evaluating weather forecasts at leading operational weather centers. We define a set of headline scores to provide an overview of model performance. In addition, we also discuss caveats in the current evaluation setup and challenges for the future of data‐driven weather forecasting. Plain Language Summary: Traditionally, weather forecasts are made by models that attempt to replicate the physical processes of the atmosphere. This has been very successful over the last few decades as better computers, better observations and model upgrades have lead to steadily improving weather forecasts. However, with rapid advances in artificial intelligence (AI), the question can be asked whether one can simply learn a weather model from past observations or reanalyzes. In the last couple of years, we have seen tremendous progress with state‐of‐the‐art AI models rivaling the best "traditional" weather models in skill. WeatherBench 2 is a benchmark data set designed to evaluate and compare the quality of AI and traditional models. By setting a standard for evaluation, alongside providing open‐source data and code, this project aims to accelerate this research direction and lead to better weather prediction. Key Points: WeatherBench 2 is a framework for evaluating and comparing data‐driven and traditional numerical weather forecasting modelsIt provides an evaluation framework, publicly available data sets and a website to assess the state‐of‐the‐art weather modelsThe evaluation protocol has been designed following best practices established in the operational weather forecasting community [ABSTRACT FROM AUTHOR]

Background: The use of artificial intelligence (AI) is increasing in many areas of healthcare, including mental healthcare. The automated nature of such technologies has the potential to be developed to work with large numbers of people. This paper examines the way that student therapists experience using an interactive text‐based machine client as a training tool. Methodology: Chatbot technology has been used to develop an emotionally responsive interactive client (ERIC). This introduces individuals to concepts of person‐centred therapy (empathy, congruence and unconditional positive regard) by using a series of pre‐programmed scenarios. Twenty‐eight student therapists evaluated ERIC's potential as a learning tool. Individuals were recruited from one university from a postgraduate and an undergraduate counselling programme. Findings: Feedback was generally positive, with all reporting that they enjoyed engaging with ERIC as a learning method. ERIC helped individuals consider their understanding of counselling skills in a non‐judgemental environment. Participants felt the scenarios were realistic and engaging, with many reporting that they felt they were engaging with a real client/person due to ERIC's ability to express emotions. Discussion: ERIC is at the proof‐of‐concept phase. From the feedback presented here, it is evident that it can be a useful learning tool. Further development of ERIC with feedback from a larger sample is, however, required. ERIC is currently a text‐based client, and further development would like to see the intervention be voice‐activated to enhance the experience. ERIC can be further enhanced and adapted to be a useful learning platform for student therapists, as well as for students in other (healthcare‐related) disciplines, whereby a client or patient is required. [ABSTRACT FROM AUTHOR]

Material science has historically evolved in tandem with advancements in technologies for characterization, synthesis, and computation. Another type of technology to add to this mix is machine learning (ML) and artificial intelligence (AI). Now increasingly sophisticated AI‐models are seen that can solve progressively harder problems across a variety of fields. From a material science perspective, it is indisputable that machine learning and artificial intelligence offer a potent toolkit with the potential to substantially accelerate research efforts in areas such as the development and discovery of new functional materials. Less clear is how to best harness this development, what new skill sets will be required, and how it may affect established research practices. In this paper, those question are explored with respect to increasingly more sophisticated ML/AI‐approaches. To structure the discussion, a conceptual framework of an AI‐ladder is introduced. This AI‐ladder ranges from basic data‐fitting techniques to more advanced functionalities such as semi‐autonomous experimentation, experimental design, knowledge generation, hypothesis formulation, and the orchestration of specialized AI modules as stepping‐stones toward general artificial intelligence. This ladder metaphor provides a hierarchical framework for contemplating the opportunities, challenges, and evolving skill sets required to stay competitive in the age of artificial intelligence. [ABSTRACT FROM AUTHOR]

Nowadays, the development of Artificial Intelligence of Things (AIoT) is advancing rapidly, and intelligent devices are increasingly exposed to more security risks on the network. Deep learning‐based intrusion detection is an effective security defence approach. Federated learning (FL) is capable of enabling deep learning models to be trained on local clients without uploading their data to a central server. This paper proposes a novel federated learning aggregation algorithm called fed‐dynamic gravitational search algorithm (Fed‐DGSA), which incorporates the GSA algorithm to optimize the weight updating process of FL local models. During the updating process, the decay rate of the gravity coefficient is optimized and random perturbations and dynamic weights are introduced to ensure a more stable and efficient FL aggregation process. The experimental results show that the detection accuracy of Fed‐DGSA has reached about 97.8%, and it is demonstrated that the model trained using Fed‐DGSA achieves higher accuracy compared to Fed‐Avg. [ABSTRACT FROM AUTHOR]

This study aimed to comprehensively evaluate the current utilization and future potential of ChatGPT, an AI-based chat model, in the field of radiology. The primary focus is on its role in enhancing decision-making processes, optimizing workflow efficiency, and fostering interdisciplinary collaboration and teaching within healthcare. A systematic search was conducted in PubMed, EMBASE and Web of Science databases. Key aspects, such as its impact on complex decision-making, workflow enhancement and collaboration, were assessed. Limitations and challenges associated with ChatGPT implementation were also examined. Overall, six studies met the inclusion criteria and were included in our analysis. All studies were prospective in nature. A total of 551 chatGPT (version 3.0 to 4.0) assessment events were included in our analysis. Considering the generation of academic papers, ChatGPT was found to output data inaccuracies 80% of the time. When ChatGPT was asked questions regarding common interventional radiology procedures, it contained entirely incorrect information 45% of the time. ChatGPT was seen to better answer US board-style questions when lower order thinking was required (P = 0.002). Improvements were seen between chatGPT 3.5 and 4.0 in regard to imaging questions with accuracy rates of 61 versus 85%(P = 0.009). ChatGPT was observed to have an average translational ability score of 4.27/5 on the Likert scale regarding CT and MRI findings. ChatGPT demonstrates substantial potential to augment decision-making and optimizing workflow. While ChatGPT’s promise is evident, thorough evaluation and validation are imperative before widespread adoption in the field of radiology. [ABSTRACT FROM AUTHOR]

In the domain of modern manufacturing, computer numerical control (CNC) milling machines have emerged as instrumental assets. However, the data they generate is of vast amount, but usually contains redundancies and displays consistent patterns, making it inefficient for deep learning training. This paper proposes a novel sampling algorithm tailored for CNC milling machine data, emphasizing both diversity and efficiency. The proposed method leverages the entropy concept from the information‐theoretic perspective to evaluate and enhance data diversity, aiming to achieve efficient learning with high accuracy. This in turn enables to not only facilitates a deeper understanding of CNC data characteristics but also contributes significantly to the optimization of deep learning training processes in the context of CNC milling data. [ABSTRACT FROM AUTHOR]

For example, a manuscript that examines the application of machine learning methods in reviewing irregularity reports from test administration is in scope. Machine learning, or artificial intelligence as a broader term, has been very popular in recent years. [Extracted from the article]

In the past years, multimedia content has improved in realism and plausibility owing to the development of deep learning techniques, particularly the generative adversarial networks and variational auto‐encoders. Though digital content, especially digital movies shot from a certain viewpoint gives a true representation of reality, yet the ubiquitous usage of content manipulation techniques casts doubt on its veracity. Deepfaking an AI based tampering technique, is able to map facial and acoustic features of a source person onto the target with an intention to make target say or enact the things that has not happened in real. Numerous approaches have been proposed in the literature for detection of image and video deepfakes. With technological advancement, researchers have also started to examine audio deepfakes and ways to detect them. As there is currently no comprehensive overview of audio deepfake generation and detection techniques, this paper aims to provide a survey of the relevant literature in this area. This survey paper intends to help research fraternity about the available audio generation and detection approaches for design of reliable detection models in future to classify fake and real audios. [ABSTRACT FROM AUTHOR]

Frozen sections are a useful pathologic tool, but variable image quality may impede the use of artificial intelligence and machine learning in their interpretation. We aimed to identify the current research on machine learning models trained or tested on frozen section images. We searched PubMed and Web of Science for articles presenting new machine learning models published in any year. Eighteen papers met all inclusion criteria. All papers presented at least one novel model trained or tested on frozen section images. Overall, convolutional neural networks tended to have the best performance. When physicians were able to view the output of the model, they tended to perform better than either the model or physicians alone at the tested task. Models trained on frozen sections performed well when tested on other slide preparations, but models trained on only formalin‐fixed tissue performed significantly worse across other modalities. This suggests not only that machine learning can be applied to frozen section image processing, but also use of frozen section images may increase model generalizability. Additionally, expert physicians working in concert with artificial intelligence may be the future of frozen section histopathology. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) research began in theoretical neurophysiology, and the resulting classical paper on the McCulloch‐Pitts mathematical neuron was written in a psychiatry department almost 80 years ago. However, the application of AI in digital neuropathology is still in its infancy. Rapid progress is now being made, which prompted this article. Human brain diseases represent distinct system states that fall outside the normal spectrum. Many differ not only in functional but also in structural terms, and the morphology of abnormal nervous tissue forms the traditional basis of neuropathological disease classifications. However, only a few countries have the medical specialty of neuropathology, and, given the sheer number of newly developed histological tools that can be applied to the study of brain diseases, a tremendous shortage of qualified hands and eyes at the microscope is obvious. Similarly, in neuroanatomy, human observers no longer have the capacity to process the vast amounts of connectomics data. Therefore, it is reasonable to assume that advances in AI technology and, especially, whole‐slide image (WSI) analysis will greatly aid neuropathological practice. In this paper, we discuss machine learning (ML) techniques that are important for understanding WSI analysis, such as traditional ML and deep learning, introduce a recently developed neuropathological AI termed PathoFusion, and present thoughts on some of the challenges that must be overcome before the full potential of AI in digital neuropathology can be realized. [ABSTRACT FROM AUTHOR]

With the ever‐growing adoption of artificial intelligence (AI)‐based systems, the carbon footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to hold themselves accountable for the carbon emissions of the AI models they design and use. This led in recent years to the appearance of researches tackling AI environmental sustainability, a field referred to as Green AI. Despite the rapid growth of interest in the topic, a comprehensive overview of Green AI research is to date still missing. To address this gap, in this article, we present a systematic review of the Green AI literature. From the analysis of 98 primary studies, different patterns emerge. The topic experienced a considerable growth from 2020 onward. Most studies consider monitoring AI model footprint, tuning hyperparameters to improve model sustainability, or benchmarking models. A mix of position papers, observational studies, and solution papers are present. Most papers focus on the training phase, are algorithm‐agnostic or study neural networks, and use image data. Laboratory experiments are the most common research strategy. Reported Green AI energy savings go up to 115%, with savings over 50% being rather common. Industrial parties are involved in Green AI studies, albeit most target academic readers. Green AI tool provisioning is scarce. As a conclusion, the Green AI research field results to have reached a considerable level of maturity. Therefore, from this review emerges that the time is suitable to adopt other Green AI research strategies, and port the numerous promising academic results to industrial practice. This article is categorized under:Technologies > Machine Learning [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal IET Generation, Transmission & Distribution. It discusses the use of artificial intelligence (AI) in reliable forecasting for energy sectors. The editorial highlights the challenges of integrating renewable energy sources and fluctuating electricity demand, and emphasizes the importance of accurate forecasting for system operators. The document also provides summaries of several papers included in a special issue on AI-empowered forecasting in energy sectors, covering topics such as load forecasting, wind power prediction, and control parameter optimization. The editorial concludes by recommending further research and practical implementations of AI approaches in the energy sectors. [Extracted from the article]

Data extraction is a time‐consuming and resource‐intensive task in the systematic review process. Natural language processing (NLP) artificial intelligence (AI) techniques have the potential to automate data extraction saving time and resources, accelerating the review process, and enhancing the quality and reliability of extracted data. In this paper, we propose a method for using Bing AI and Microsoft Edge as a second reviewer to verify and enhance data items first extracted by a single human reviewer. We describe a worked example of the steps involved in instructing the Bing AI Chat tool to extract study characteristics as data items from a PDF document into a table so that they can be compared with data extracted manually. We show that this technique may provide an additional verification process for data extraction where there are limited resources available or for novice reviewers. However, it should not be seen as a replacement to already established and validated double independent data extraction methods without further evaluation and verification. Use of AI techniques for data extraction in systematic reviews should be transparently and accurately described in reports. Future research should focus on the accuracy, efficiency, completeness, and user experience of using Bing AI for data extraction compared with traditional methods using two or more reviewers independently. [ABSTRACT FROM AUTHOR]

Multiple tools exist for separately simulating and estimating the parameters of system dynamics models. Artificial intelligence (AI) has been increasingly used to estimate the parameters of system dynamics models. The development of modeling tools and advanced environments has resulted in great benefits to the community at large. The incorporation of AI tools into system dynamics presents opportunities for expanding on current decision‐making methods. As systems become complex, the need to incorporate evidence‐based data‐driven methods increases. By integrating system dynamics tools and facilitating AI and system dynamics simulation in an integrated environment, model parameters can be estimated with the latest data, and the integrity of the model can be retained effectively. This provides an advantage to the efficiency and capabilities of the system dynamics model and its analysis. This paper presents a general methodology to incorporate regression AI into system dynamics models for simulation and analysis. To demonstrate the validity of the methodology, a case study involving a susceptible‐infected‐recovered model and empirical data from the COVID‐19 pandemic is performed using support vector machines (SVMs), artificial neural networks (ANNs), and random forests. [ABSTRACT FROM AUTHOR]

Software‐defined networks (SDNs) have gained popularity in recent years as a solution for the fundamental issues that affect traditional dispersed networks. The primary advantage of SDNs is the decoupling of the control plane from the data plane, which increases the flexibility of the network. The SDN represents a network architecture of the next generation, however, its configuration options are centralized, leaving it open for cyber‐attacks. This paper concentrates on the early identification of attacks in an SDN environment. When malicious traffic is affecting in an SDN topology, an artificial intelligence (AI) module in the topology is used to detect the attack and stop the attack source using machine learning (ML) techniques. The architecture presented in this research allows for the comparison of several ML classification techniques that are used to identify different sorts of network attacks. For attack detection, eight ML techniques are used, namely logistic regression (LR), linear discriminant analysis (LDA), Naïve Bayes (NB), k‐nearest neighbor (KNN), classification and regression tree (CART), AdaBoost (AB), random forest (RF), and support‐vector machine (SVM) classifiers. These techniques are tested on the InSDN dataset, which is a novel attack‐specific SDN dataset. The results show that the highest accuracy of 98.6% is achieved with the LDA classifier. Further improvement in the accuracy of classification models is observed when random over‐sampling, synthetic minority oversampling technique (SMOTE), random under‐sampling, and under‐sampling with Tomek links and near‐miss concept are applied to address the class imbalance problem. After applying these methods, the LDA classifier showed an accuracy of 98.79%. [ABSTRACT FROM AUTHOR]

Fatigue limit states often govern the design of shear connectors in steel‐concrete composite bridges. AASHTO LRFD bridge design specifications provides a linear equation in a semi‐logarithmic S‐N curve for predicting the fatigue life of shear connectors. However, this equation can be too conservative in some cases, as supported by the available experimental data. In this paper, artificial intelligence (AI) was incorporated into the prediction of the fatigue life of shear connectors. Six different machine learning (ML) algorithms were considered for this purpose. The predictions of ML algorithms were compared both with the available experimental data and the equation provided by AASHTO. The results showed that the fatigue life predicted by ML methods is more accurate than that predicted by the current equation of AASHTO. The results of this study showed that AI can be a proper alternative to the existing methods for predicting the fatigue life of shear connectors. Highlights: AI was used for the first time to evaluate fatigue life of shear connectors in bridges.AI predicted fatigue life of shear connectors more accurately than the current methods.GPR and DT algorithms were the best algorithms for assessing fatigue life of connectors.The current equations in AASHTO for fatigue may need to be redeveloped based on AI. [ABSTRACT FROM AUTHOR]

Stock prices forecasting has always been a challenging task. Although many research projects adopt machine learning and deep learning algorithms to address the problem, few of them pay attention to the varying degrees of dependencies between stock prices. In this paper we introduce a hybrid model that improves stock price prediction by emphasizing the dependencies between adjacent stock prices. The proposed model, ResNLS, is mainly composed of two neural architectures, ResNet and LSTM. ResNet serves as a feature extractor to identify dependencies between stock prices across time windows, while LSTM analyses the initial time‐series data with the combination of dependencies which considered as residuals. In predicting the SSE Composite Index, our experiment reveals that when the closing price data for the previous five consecutive trading days is used as the input, the performance of the model (ResNLS‐5) is optimal compared to those with other inputs. Furthermore, ResNLS‐5 outperforms vanilla CNN, RNN, LSTM, and BiLSTM models in terms of prediction accuracy. It also demonstrates at least a 20% improvement over the current state‐of‐the‐art baselines. To verify whether ResNLS‐5 can help clients effectively avoid risks and earn profits in the stock market, we construct a quantitative trading framework for back testing. The experimental results show that the trading strategy based on predictions from ResNLS‐5 can successfully mitigate losses during declining stock prices and generate profits in the periods of rising stock prices. [ABSTRACT FROM AUTHOR]

Summary: Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever‐growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi‐society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. [ABSTRACT FROM AUTHOR]

Nanofluids are thermoelectric substance with a greater thermal transmission effectiveness than traditional thermal fluids. Nanofluid's specific heat capacity (SHC) is a crucial thermophysical parameter since it controls the fluid's heat transfer coefficient. Surprisingly few research investigations have been done on the prognostic modelling of the specific heat capacity of fusion nanofluids, despite the fact that numerous experiments have been conducted on the heat transfer capacitance of blended nanofluids. This study focuses on the use of algorithms based on machine learning procedures (MLP) to estimate the SHC of blended nanofluids. Numerous numbers of hybrid nanofluids are investigated in this investigation. Total of 984 samples of hybrid nanofluids for specific heat capacity has been collected from nine experimental research papers. Various MLP techniques were utilized in this analysis, including extreme boost gradient boost regression (XGB), support vector regression augmented with a genetic algorithm (support vector regression [SVR]‐genetic algorithm [GA]), grid search optimized based gradient boost regression algorithm (GBR) (GBR‐GSO), and voting ensemble procedure (VE). The obtained correlation coefficients of the SVR‐GA, XGB, VE and GBR‐GSO models for the testing dataset are 98.43%, 96.29%, 94.4% and 96.55% respectively. The SVR‐GA model showed a better predictive accuracy relative to other ML models. This SVR‐GA anticipated model could be used for quick and reliable prediction of the SHC of blended nanofluids which reduces the burden associated with experimental measurement possible future work includes applying a machine‐learning strategy to the problem of determining the diffusivity of hybrid nanofluids. [ABSTRACT FROM AUTHOR]

Deep learning techniques can recognize complex patterns in noisy, multidimensional data. In recent years, researchers have started to explore the potential of deep learning in the field of structural biology, including protein crystallography. This field has some significant challenges, in particular producing high‐quality and well ordered protein crystals. Additionally, collecting diffraction data with high completeness and quality, and determining and refining protein structures can be problematic. Protein crystallographic data are often high‐dimensional, noisy and incomplete. Deep learning algorithms can extract relevant features from these data and learn to recognize patterns, which can improve the success rate of crystallization and the quality of crystal structures. This paper reviews progress in this field. [ABSTRACT FROM AUTHOR]

Artificial intelligence, as a nonhuman entity, is increasingly used to inform, direct, or supplant nursing care and clinical decision‐making. The boundaries between human‐ and nonhuman‐driven nursing care are blurred with the advent of sensors, wearables, camera devices, and humanoid robots at such an accelerated pace that the critical evaluation of its influence on patient safety has not been fully assessed. Since the pivotal release of To Err is Human, patient safety is being challenged by the dynamic healthcare environment like never before, with nursing at a critical juncture to steer the course of artificial intelligence integration in clinical decision‐making. This paper presents an overview of artificial intelligence and its application in healthcare and highlights the implications which affect nursing as a profession, including perspectives on nursing education and training recommendations. The legal and policy challenges which emerge when artificial intelligence influences the risk of clinical errors and safety issues are discussed. [ABSTRACT FROM AUTHOR]

As an emerging desalination technology, forward osmosis (FO) can potentially become a reliable method to help remedy the current water crisis. Introducing uncomplicated and precise models could help FO systems' optimization. This paper presents the prediction and evaluation of FO systems' membrane flux using various artificial intelligence‐based models. Detailed data gathering and cleaning were emphasized because appropriate modeling requires precise inputs. Accumulating data from the original sources, followed by duplicate removal, outlier detection, and feature selection, paved the way to begin modeling. Six models were executed for the prediction task, among which two are tree‐based models, two are deep learning models, and two are miscellaneous models. The calculated coefficient of determination (R2) of our best model (XGBoost) was 0.992. In conclusion, tree‐based models (XGBoost and CatBoost) show more accurate performance than neural networks. Furthermore, in the sensitivity analysis, feed solution (FS) and draw solution (DS) concentrations showed a strong correlation with membrane flux. Practitioner Points: The FO membrane flux was predicted using a variety of machine‐learning models.Thorough data preprocessing was executed.The XGBoost model showed the best performance, with an R2 of 0.992.Tree‐based models outperformed neural networks and other models. [ABSTRACT FROM AUTHOR]

Machine learning for design and analysis of steel frame halls The use of digital tools in design and construction processes increases efficiency, especially for recurring tasks and processes, such as the dimensioning of components or structures, which follow similar patterns. This paper develops an artificial intelligence (AI)‐based assistant for use in the pre‐design and design of steel frame halls as a prototypical co‐pilot. For this purpose, the necessary database of steel frame halls is first compiled from project archives of TRAGRAUM Partnerschaft Beratender Ingenieure mbB. This will be examined in the context of data analyses according to the decisive description parameters and similarities, so that finally suitable AI surrogate models for the prediction of the hall parameters are trained by means of regression analyses. Furthermore, an interface to the finite element program SOFiSTiK is implemented, which passes the predictions of the AI regarding the parameters of the steel frame halls to a parameterized input file and triggers the calculation and design. In the course of the calibration of the AI substitute models, in addition to the approximation qualities, possible safety margins to be provided are also discussed in the context of this article. This article shows that the prototypical co‐pilot for the preliminary design of steel frame halls could be successfully implemented. Furthermore the AI newly generated hall frames can be classified as stable with respect to the validation considerations. An outlook on future further developments of this approach as well as the transfer to other applications complete this paper. [ABSTRACT FROM AUTHOR]

GLAM organizations have been digitizing their collections and making them available for the public for several decades. Recent methods for publishing digital collections such as "GLAM Labs" and "Collections as Data" provide guidelines for the application of computational methods to reuse the contents of cultural heritage institutions in innovative and creative ways. Jupyter Notebooks have become a powerful tool to foster use of these collections by digital humanities researchers. Based on previous approaches for quality assessment, which have been adapted for cultural heritage collections, this paper proposes a methodology for assessing the quality of projects based on Jupyter Notebooks published by relevant GLAM institutions. A list of projects based on Jupyter Notebooks using cultural heritage data has been evaluated. Common features and best practices have been identified. A detailed analysis, that can be useful for organizations interested in creating their own Jupyter Notebooks projects, has been provided. Open issues requiring further work and additional avenues for exploration are outlined. [ABSTRACT FROM AUTHOR]

Alzheimer's disease, often known as dementia, is a severe neurodegenerative disorder that causes irreversible memory loss by destroying brain cells. People die because there is no specific treatment for this disease. Alzheimer's is most common among seniors 65 years and older. However, the progress of this disease can be reduced if it can be diagnosed earlier. Recently, artificial intelligence has instilled hope in the diagnosis of Alzheimer's disease by performing sophisticated analyses on extensive patient datasets, enabling the identification of subtle patterns that may elude human experts. Researchers have investigated various deep learning and machine learning models to diagnose this disease at an early stage using image datasets. In this paper, a new Deep learning (DL) methodology is proposed, where MRI images are fed into the model after applying various pre‐processing techniques. The proposed Alzheimer's disease detection approach adopts transfer learning for multi‐class classification using brain MRIs. The MRI Images are classified into four categories: mild dementia (MD), moderate dementia (MOD), very mild dementia (VMD), and non‐dementia (ND). The model is implemented and extensive performance analysis is performed. The finding shows that the model obtains 97.31% accuracy. The model outperforms the state‐of‐the‐art models in terms of accuracy, precision, recall, and F‐score. [ABSTRACT FROM AUTHOR]

This study aims to develop a new artificial intelligence model that can produce explainable rules to predict the mix design and early‐age concrete compressive strength classes of recycled aggregate concrete (RAC). Unlike other black‐box machine learning methods and rule‐based algorithms, the study relies on a metaheuristic mechanism for explainability. This metaheuristic mechanism is not used for a traditional parameter optimization, but to automatically extract interpretable and interpretable rules from the experimental data. In the study, 30 series of RACs are produced, and the samples' 1‐ and 3‐day early‐age concrete compressive strength values are determined. Concretes produced using these strength values are classified. The labels defined for each concrete class are Class A (C 8/10), Class B (C 12/15), Class C (C 16/20), and Class D (C 20/25). The proposed intelligent classification model that consists of rule set automatically produces interpretable and comprehensible rules from data to determine the early‐age concrete compressive strength class and RAC mix amounts. In addition, the proposed method eliminates the black‐box disadvantages of classical machine learning methods with its explainability and interpretability feature. The sunflower optimization algorithm is adapted as the metaheuristic mechanism and a special fitness function and representative solution form are developed for automatic extraction of high‐quality comprehensible rules by simultaneously optimizing many different metrics. This paper is the first interpretable and comprehensible artificial intelligence model attempt used for early‐age compressive strength classification and mixture design of recycled aggregate concrete by balancing and optimizing both the accuracy and explainability. Proposed explainable intelligent classification model is tested against both well‐known state‐of‐the‐art machine learning algorithms and standard rule‐based methods on the produced real data. Promising results in terms of accuracy, precision, recall are obtained along with the explainability feature. [ABSTRACT FROM AUTHOR]

Artificial intelligence is increasingly becoming important to businesses because many companies have realized the benefits of applying machine learning (ML) and deep learning (DL) in their operations. ML and DL have become attractive technologies for organizations looking to automate repetitive tasks to reduce manual work and free up resources for innovation. Unlike rule‐based automation, typically used for standardized and predictable processes, machine learning, especially deep learning, can handle more complex tasks and learn over time, leading to greater accuracy and efficiency improvements. One of such promising applications is to use AI to reduce manual engineering work. This paper discusses a particular case within McDermott where the research team developed a DL model to do a quality check of complex blueprints. We describe the development and the final product of this case—AI‐based software for the engineering, procurement, and construction (EPC) industry that helps to find the design mistakes buried inside very complex engineering drawings called piping and instrumentation diagrams (P&IDs). We also present a cost‐benefit analysis and potential scale‐up of the developed software. Our goal is to share the successful experience of AI‐based product development that can substantially reduce the engineering hours and, therefore, reduce the project's overall costs. The developed solution can also be potentially applied to other EPC companies doing a similar design for complex installations with high safety standards like oil and gas or petrochemical plants because the design errors it captures are common within this industry. It also could motivate practitioners and researchers to create similar products for the various fields within engineering industry. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a decentralized machine learning (ML) technique that enables multiple clients to collaboratively train a common ML model without them having to share their raw data with each other. A typical FL process involves (1) FL client(s) selection, (2) global model distribution, (3) local training, and (4) aggregation. As such FL clients are heterogeneous edge devices (i.e., mobile phones) that differ in terms of computational resources, training data quality, and distribution. Therefore, FL client(s) selection has a significant influence on the execution of the remaining steps of an FL process. There have been a variety of FL client(s) selection models proposed in the literature, however, their critical review and/or comparative analysis is much less discussed. This paper brings the scattered FL client(s) selection models onto a single platform by first categorizing them into five categories, followed by providing a detailed analysis of the benefits/shortcomings and the applicability of these models for different FL scenarios. Such understanding can help researchers in academia and industry to develop improved FL client(s) selection models to address the requirement challenges and shortcomings of the current models. Finally, future research directions in the area of FL client(s) selection are also discussed. This article is categorized under:Technologies > Machine LearningTechnologies > Artificial Intelligence [ABSTRACT FROM AUTHOR]