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Games have been designed to be challenging and fun for humans to play, so we can test the AI system against the best human players in the world to quantify how good our AI systems are getting to be. We went from there to AlphaGo, which was our AI system to master the ancient and complex game of Go. DeepMind: From Games to Scientific Discovery: This paper is an edited version of Demis Hassabis' 2021 IRI Medal talk. He discussed his personal AI journey - from games to scientific discovery, some of his breakthrough results in complex games of strategy, and some of the exciting ways that lessons from the world of games are helping to accelerate scientific discovery. [Extracted from the article]

In this paper, the author introduced a sentiment analysis-based customer loyalty prediction model in mobile applications using word embedding models, deep learning algorithms, and deep contextualized word representations. This method showed better performances than usual state-of-the-art methods in separate data sets along with the combined data set on both gender and region classification. Intelligent Systems can be thought of as a concept with a very broad scope. [Extracted from the article]

Delayed graft function (DGF) is an early complication after kidney transplantation. The literature on DGF has experienced substantial growth. However, there is a lack of bibliometric analysis of DGF. This study aimed to analyze the scientific outputs of DGF and explore its hotspots from 2013 to 2023 by using CiteSpace and VOSviewer. The 2058 pieces of literature collected in the Web of Science Core Collection (WOSCC) from 1 January 2013 to 31 December 2023 were visually analyzed in terms of the annual number of publications, authors, countries, journals, literature co-citations, and keyword clustering by using CiteSpace and VOSviewer. We found that the number of papers published in the past ten years showed a trend of first increasing and then decreasing; 2021 was the year with the most posts. The largest number of papers was published by the University of California System, and the largest number of papers was published by the United States. The top five keyword frequency rankings are: 'delayed graft function', 'kidney transplantation', 'renal transplantation', 'survival', and 'recipients'. These emerging trends include 'brain death donors', 'blood absence re-injection injuries', 'tacrolimus', 'older donors and recipients', and 'artificial intelligence and DGF'. In summary, this study reveals the authors and institutions that could be cooperated with and discusses the research hotspots in the past ten years. It provides a reference and direction for future research and application of DGF. [ABSTRACT FROM AUTHOR]

Human error estimating food intake is a major source of bias in nutrition research. Artificial intelligence (AI) methods may reduce bias, but the overall accuracy of AI estimates is unknown. This study was a systematic review of peer-reviewed journal articles comparing fully automated AI-based (e.g. deep learning) methods of dietary assessment from digital images to human assessors and ground truth (e.g. doubly labelled water). Literature was searched through May 2023 in four electronic databases plus reference mining. Eligible articles reported AI estimated volume, energy, or nutrients. Independent investigators screened articles and extracted data. Potential sources of bias were documented in absence of an applicable risk of bias assessment tool. Database and hand searches identified 14,059 unique publications; fifty-two papers (studies) published from 2010 to 2023 were retained. For food detection and classification, 79% of papers used a convolutional neural network. Common ground truth sources were calculation using nutrient tables (51%) and weighed food (27%). Included papers varied widely in food image databases and results reported, so meta-analytic synthesis could not be conducted. Relative errors were extracted or calculated from 69% of papers. Average overall relative errors (AI vs. ground truth) ranged from 0.10% to 38.3% for calories and 0.09% to 33% for volume, suggesting similar performance. Ranges of relative error were lower when images had single/simple foods. Relative errors for volume and calorie estimations suggest that AI methods align with – and have the potential to exceed – accuracy of human estimations. However, variability in food image databases and results reported prevented meta-analytic synthesis. The field can advance by testing AI architectures on a limited number of large-scale food image and nutrition databases that the field determines to be adequate for training and testing and by reporting accuracy of at least absolute and relative error for volume or calorie estimations. These results suggest that AI methods are in line with – and have the potential to exceed – accuracy of human estimations of nutrient content based on digital food images. Variability in food image databases used and results reported prevented meta-analytic synthesis. The field can advance by testing AI architectures on a limited number of large-scale food image and nutrition databases that the field determines to be accurate and by reporting accuracy of at least absolute and relative error for volume or calorie estimations. Overall, the tools currently available need more development before deployment as stand-alone dietary assessment methods in nutrition research or clinical practice. [ABSTRACT FROM AUTHOR]

Based on the panel data of 285 cities in China from 2000 to 2019, this paper searches the number of patent applications related to urban artificial intelligence from five dimensions: algorithm, data, computing power, application scenario and related technology. Combining the two perspectives of industrial upgrading and rationalization, we analyze the internal influence theory of the research topic from the theoretical and empirical perspectives. The results show that artificial intelligence is not only conducive to industrial upgrading, but also significantly inhibit the deviation of industrial structure from equilibrium, which is conducive to industrial rationalization. In addition, the conclusion of this paper is still valid after a series of robustness tests, such as eliminating the samples of central cities, winsorize treatment and instrumental variables method. Through the heterogeneity test, it is found that the promoting effect of artificial intelligence on industrial upgrading is more obvious in big cities and cities with high level of industrial upgrading. The internal mechanism test results show that artificial intelligence promotes industrial upgrading by promoting technological innovation. In cities with a high degree of marketization and Internet development, the role of artificial intelligence in promoting industrial upgrading can be strengthened. The research conclusions of this paper will be conducive to accelerating the development of artificial intelligence to promote industrial upgrading, and provide a useful reference for realizing high-quality development. [ABSTRACT FROM AUTHOR]

Internet-based psychological treatments for depression have been around for more than 20 years. There has been a continuous line of research with new research questions being asked and studies conducted. In this paper, the author reviews studies with a focus on papers published from 2020 and onwards based on a Medline and Scopus search. Internet-based cognitive behavior therapy (ICBT) programs have been developed and tested for adolescents, older adults, immigrant groups and to handle a societal crisis (e.g. COVID-19). ICBT works in regular clinical settings and long-term effects can be obtained. Studies on different treatment orientations and approaches such as acceptance commitment therapy, unified protocol, and tailored treatments have been conducted. Effects on quality-of-life measures, knowledge acquisition and ecological momentary assessment as a research tool have been reported. Factorial design trials and individual patient data meta-analysis are increasingly used in association with internet intervention research. Finally, prediction studies and recent advances in artificial intelligence are mentioned. Internet-delivered treatments are effective, in particular if therapist guidance is provided. More target groups have been covered but there are many remaining challenges including how new tools like artificial intelligence will be used when treating depression. [ABSTRACT FROM AUTHOR]

Cyberattacks are becoming more sophisticated and ubiquitous. Cybercriminals are inevitably adopting Artificial Intelligence (AI) techniques to evade the cyberspace and cause greater damages without being noticed. Researchers in cybersecurity domain have not researched the concept behind AI-powered cyberattacks enough to understand the level of sophistication this type of attack possesses. This paper aims to investigate the emerging threat of AI-powered cyberattacks and provide insights into malicious used of AI in cyberattacks. The study was performed through a three-step process by selecting only articles based on quality, exclusion, and inclusion criteria that focus on AI-driven cyberattacks. Searches in ACM, arXiv Blackhat, Scopus, Springer, MDPI, IEEE Xplore and other sources were executed to retrieve relevant articles. Out of the 936 papers that met our search criteria, a total of 46 articles were finally selected for this study. The result shows that 56% of the AI-Driven cyberattack technique identified was demonstrated in the access and penetration phase, 12% was demonstrated in exploitation, and command and control phase, respectively; 11% was demonstrated in the reconnaissance phase; 9% was demonstrated in the delivery phase of the cybersecurity kill chain. The findings in this study shows that existing cyber defence infrastructures will become inadequate to address the increasing speed, and complex decision logic of AI-driven attacks. Hence, organizations need to invest in AI cyber-security infrastructures to combat these emerging threats. [ABSTRACT FROM AUTHOR]

We introduce the Theory and Methods Special Issue on Precision Medicine and Individualized Policy Discovery. The issue consists of four discussion papers, grouped into two pairs, and sixteen regular research papers that cover many important lines of research on data-driven decision making. We hope that the many provocative and original ideas presented herein will inspire further work and development in precision medicine and personalization. [ABSTRACT FROM AUTHOR]

Delayed graft function (DGF) is an early complication after kidney transplantation. The literature on DGF has experienced substantial growth. However, there is a lack of bibliometric analysis of DGF. This study aimed to analyze the scientific outputs of DGF and explore its hotspots from 2013 to 2023 by using CiteSpace and VOSviewer. The 2058 pieces of literature collected in the Web of Science Core Collection (WOSCC) from 1 January 2013 to 31 December 2023 were visually analyzed in terms of the annual number of publications, authors, countries, journals, literature co-citations, and keyword clustering by using CiteSpace and VOSviewer. We found that the number of papers published in the past ten years showed a trend of first increasing and then decreasing; 2021 was the year with the most posts. The largest number of papers was published by the University of California System, and the largest number of papers was published by the United States. The top five keyword frequency rankings are: ‘delayed graft function’, ‘kidney transplantation’, ‘renal transplantation’, ‘survival’, and ‘recipients’. These emerging trends include ‘brain death donors’, ‘blood absence re-injection injuries’, ‘tacrolimus’, ‘older donors and recipients’, and ‘artificial intelligence and DGF’. In summary, this study reveals the authors and institutions that could be cooperated with and discusses the research hotspots in the past ten years. It provides a reference and direction for future research and application of DGF. [ABSTRACT FROM AUTHOR]

Aim: Bibliometric surveys are time-consuming endeavors, which cannot be scaled up to meet the challenges of ever-expanding fields, such as bone regeneration. Artificial intelligence, however, can provide smart tools to screen massive amounts of literature, and we relied on this technology to automatically identify research topics. Materials & methods: We used the BERTopic algorithm to detect the topics in a corpus of MEDLINE manuscripts, mapping their similarities and highlighting research hotspots. Results: Using BERTopic, we identified 372 topics and were able to assess the growing importance of innovative and recent fields of investigation such as 3D printing and extracellular vescicles. Conclusion: BERTopic appears as a suitable tool to set up automatic screening routines to track the progress in bone regeneration. [ABSTRACT FROM AUTHOR]

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

This paper reviews the application of artificial intelligence (AI)-based models in modeling vehicular road traffic noise. A computerized search method was used to conduct the literature search. Fifty published articles from 2007 to 2023 were reviewed regarding observation time, input data, countries where studies were performed, and modeling techniques. Sixty-three percent of the studies used an observation period of 60 min, and 29% used 15 min. All the reviewed papers considered traffic flow as the major input parameter, followed by average speed, with 95% of the researchers using it as an input parameter. It was found that using AI-based models for traffic noise prediction was popular in countries with no established empirical models. The primary input parameters for the AI-based models are traffic volume and speed. Traffic volume is used either as total traffic volume or classified into subcategories, and each category is used as an independent input parameter. Although AI-based models have demonstrated reliable performance regarding prediction error and goodness of fit, the accuracy of the AI-based models' performance should be compared with the results of the empirical models in countries with established models, such as the UK (CoRTN) and the USA (FHWA). [ABSTRACT FROM AUTHOR]

Urban traffic attributed to commercial and industrial transportation is observed to largely affect living standards in cities due to external factors like pollution and congestion. To counter this, smart cities deploy technologies such as digital twins (DT)s to achieve sustainability. Research suggests that DTs can be beneficial in optimizing the physical systems they are linked with. The concept has been extensively studied in many technology-driven industries like manufacturing. However, little work has been done with regards to their application in urban logistics. In this paper, we seek to provide a framework by which DTs could be easily adapted to urban logistics applications. To do this, we survey previous research on DT applications in urban logistics as we found that a holistic overview is lacking. Using this knowledge in combination with the identification of key factors in urban logistics, we produce a conceptual model for the general design of an urban logistics DT through a knowledge graph. We provide an illustration on how the conceptual model can be used in solving a relevant problem and showcase the integration of relevant DDO methods. We finish off with a discussion on research opportunities and challenges based on previous research and our practical experience. [ABSTRACT FROM AUTHOR]

Antimicrobial resistance results from the widespread use of antimicrobial agents and is a significant obstacle to the effectiveness of these agents. Numerous methods are used to overcome this problem with moderate success. Besides efforts of antimicrobial stewards, several artificial intelligence (AI)-based technologies are being explored for preventing resistance development. These first-generation systems mainly focus on improving patients' adherence. Chronobiology is inherent in all biological systems. Host response to infections and pathogens activity are assumed to be affected by the circadian clock. This paper describes the problem of antimicrobial resistance and reviews some of the current AI technologies. We present the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance. An algorithm-controlled regimen that improves the long-term effectiveness of antimicrobial agents is being developed based on the implementation of variability in dosing and drug administration times. The method provides a means for ensuring a sustainable response and improved outcomes. Ongoing clinical trials determine the effectiveness of this second-generation system in chronic infections. Data from these studies are expected to shed light on a new aspect of resistance mechanisms and suggest methods for overcoming them. IMPORTANCE SECTION The paper presents the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance. Antimicrobial resistance results from the widespread use of antimicrobial agents and is a significant obstacle to the effectiveness of these agents. We present the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance. [ABSTRACT FROM AUTHOR]

Over the years, several achievements have been made in power generation and optimising hybrid renewable energy systems (HRES) to achieve nature conservation, achieve energy security, and reduce carbon emissions. However, there are many complexities in Renewable energy (RE) conversion, sizing, design, and implementation, that require optimisation techniques to achieve optimal results in terms of reliability, cost, and environmental protection over time. This paper presents an overview of research trends in Optimization methods in HRES which are classified into modern and conventional methods. These two classifications are further divided into control methods, Artificial intelligence, Iterative and mathematical operations. However, all mentioned techniques have inherent advantages and disadvantages which will be discussed in this survey. In addition, the review paper explored different types of research in computing intelligence (CI), an aspect of Artificial Intelligence (AI) that involves the development of nature-inspired algorithms for optimisation. Finally, general optimisation criteria, system sizing methods used in RES, Mathematical modelling of RES, and gaps for future work to achieve sustainability were also presented. [ABSTRACT FROM AUTHOR]

Among the most significant solutions suggested for estimating energy consumption and cooling load, one can refer to enhancing energy efficiency in non-residential and residential buildings. A structure's characteristics must be considered when estimating how much heating and cooling is required. To design and develop energy-efficient buildings, it can be helpful to research the characteristics of connected structures, such as the kinds of cooling and heating systems needed to ensure sui interior air quality. As an important part of energy consumption and demand of buildings, the assessment of cooling load conditions from the envelope of large buildings has not been comprehensively understood yet. In the present paper, a new conceptual system has been developed to anticipate cooling load in the sector of residential buildings. Also, the paper briefly describes the major models of the developed system to maintain continuity and concentrate on the prediction model of the cooling load. To predict cooling load, authors have modelled two methods of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in conjunction with teaching-learning-based optimization (TLBO). This article aims to illustrate how artificial intelligence (AI) approaches play an essential role in addressing the mentioned necessity and help estimate the optimal design parameters for various stations. The value of the multiple determination coefficient is also determined. The values of the training R² (coefficient of multiple determination) are 0.96446 and 0.97585 for TLBO-MLP and TLBO-ANFIS in the training stage and 0.95855 and 0.9721 in the testing stage, respectively, with an unknown dataset which is acceptable. The training RMSE values for TLBO-MLP and TLBO-ANFIS are 0.0685 and 0.11176 for training and 0.07074 and 0.12035 for testing, respectively, for the unknown dataset, which is acceptable. The lowest RMSE value and the higher R² value indicate the favourable accuracy of the TLBO-MLP technique. According to the high value of R² (97%) and the low value of RMSE, TLBO-MLP can predict residential buildings' cooling load. [ABSTRACT FROM AUTHOR]

In numerical fire simulations, the calculation of thermal feedback from the flame to the solid and liquid fuel surface plays a critical role as it connects the fundamental gas-phase flame burning and condensed-phase fuel gasification. However, it is a computationally intensive task in CFD fire modelling methods because of the requirement of a high-resolution grid for calculating the interface heat transfer. This paper proposed a real-time prediction of the flame-to-fuel heat transfer by using simulated flame images and a computer-vision deep learning method. Different methanol pool fires were selected to produce the image database for training the model. As the pool diameters increase from 20 to 40 cm, the dominant flame-to-fuel heat transfer shifts from convection to radiation. Results show that the proposed AI algorithm trained by flame images can predict both the convective and radiative heat flux distributions on the condensed fuel surface with a relative error below 20%, based on the input of real-time flame morphology that can be captured by a larger grid size. Regardless of growing or decaying fires or puffing flames induced by buoyancy, this method can further predict the non-uniform distribution of heat transfer coefficient on the interface rather than using empirical correlations. This work demonstrates the use of AI and computer vision in accelerating numerical fire simulation, which helps simulate complex fire behaviours with simpler models and smaller computational costs. [ABSTRACT FROM AUTHOR]

This paper presents a pioneering artificial intelligence (AI) solution - the Arctic Tern-Optimized Weighted Feature Least Squares Support Vector Regression (ATO-WFLSSVR) system to aid civil engineers in accurately predicting scour depth at bridges. This prediction system amalgamates the strengths of hybrid models by uniting a metaheuristic optimization algorithm with weighted features and least squares support vector regression (WFLSSVR). The metaheuristic algorithm concurrently optimizes all hyperparameters of constituent WFLSSVR models, resulting in a highly effective system. Validation involves a comprehensive assessment using two case studies, which include datasets of scour depths across various complexities and pier foundation types. Comparative analyses against single AI models, conventional ensemble models, hybrid techniques, and empirical methods demonstrate that ATO-WFLSSVR's reliability outperforms others in performance evaluation metrics. Specifically, for the field dataset, ATO-WFLSSVR achieves MAPE and R values of 20.92% and 0.9435, respectively, and for scour depth data at complex pier foundations, it records MAPE and R values of 6.49% and 0.9384, respectively. The automated predictive analytics underscore the robustness, efficiency, and stability of ATO-WFLSSVR compared to existing methods. This study's notable contributions include the development of an innovative optimization algorithm named Arctic Terns Optimizer (ATO), proficiency in solving high-dimensional optimization problems, and the creation of a user-friendly graphical interface system, a promising tool for civil engineers to estimate scour depth at bridges. Further testing and evaluation of ATO-WFLSSVR across diverse datasets encompassing more complex scenarios are recommended. The data and source code for this study are currently accessible at https://www.researchgate.net/profile/Jui-Sheng-Chou/publications. [ABSTRACT FROM AUTHOR]

Agent-based evolutionary, computational systems have been proven to be an efficient concept for solving complex computational problems. This paper is an extension of [Biełaszek, S., Piętak, K., & Kisiel-Dorohinicki, M. (2021). New extensions of reproduction operators in solving LABS problem using EMAS meta-heuristic. Springer, cop. 2021. – Lecture Notes in Artificial Intelligence, Computational collective intelligence 12876 304-316. 13th International Conference, ICCCI 2021: Rhodes, Greece, September 29ŰOctober 1, 2021.] where we proposed new variants of reproduction operators together with new heuristics for the generation of initial population, dedicated to LABS – a hard discrete optimization problem. In this research, we verify if the proposed recombination operators improve EMAS efficiency also with different local optimization techniques such as Tabu Search and Self-avoiding walk, and therefore can be seen as better recombination operators dedicated to LABS problem in general. This paper recalls the definition of new recombination variants dedicated to LABS and verify if they can be successfully used in many different evolutionary configurations. [ABSTRACT FROM AUTHOR]

The identification of early-stage colorectal cancers (CRC) and the resection of pre-cancerous neoplastic lesions through colonoscopy allows to decrease both CRC incidence and mortality. However, colonoscopy miss rates up to 26% for adenomas and 9% for advanced adenomas have been reported. In recent years, artificial intelligence (AI) systems have been emerging as easy-to-use tools, potentially lowering the risk of missing lesions. This review paper focuses on GI Genius device (Medtronic Co. Minneapolis, MN, U.S.A.) a computer-assisted tool designed to assist endoscopists during standard white-light colonoscopies in detecting mucosal lesions. Randomized controlled trials (RCTs) suggest that GI Genius is a safe and effective tool for improving adenoma detection, especially in CRC screening and surveillance colonoscopies. However, its impact seems to be less significant among experienced endoscopists and in real-world clinical scenarios compared to the controlled conditions of RCTs. Furthermore, it appears that GI Genius mainly enhances the detection of non-advanced, small polyps, but does not significantly impact the identification of advanced and difficult-to-detect adenoma. When using GI Genius, no complications were documented. Only a small number of studies reported an increased in withdrawal time or the removal of non-neoplastic lesions. [ABSTRACT FROM AUTHOR]

With the development of IoT technology, more and more IoT devices are connected to the network. Due to the hardware constraints of IoT devices themselves, it is difficult for developers to embed security software into them. Therefore, it is better to protect IoT devices at the traffic level. The effect of malicious traffic detection based on neural networks is promising. Still, the slow computation brings some difficulties to deploying AI-based detection systems on edge servers. Time Convolutional Network (TCN) is a high-speed neural network suitable for massively parallel computation. In this paper, we propose Multi-class S-TCN, an improved network supporting multiple classifications based on TCN for the practical needs of IoT scenarios. Besides, we implement a complete IoT traffic security detection procedure based on deep packet inspection and protocol analysis. The proposed Multi-class S-TCN significantly improves the detection speed without degrading the detection effect. Experiments show that this work has better detection performance and faster detection speed compared to existing approaches, proving the effectiveness of the proposed detection flow and Multi-class S-TCN in IoT scenarios. [ABSTRACT FROM AUTHOR]

Identity recognition technology is a type of technology that realizes identity verification based on certain biological characteristics. After entering the Internet era, this technology has become a popular research direction in the computer field. In this paper, the image of the tooth print is used as the biological feature to carry out the research on the identification algorithm. This paper adopts the target detection algorithm based on neural network to detect a single tooth imprint area of the target, build a target detection network. The experimental results show that the method has a good segmentation effect on the target area, and the accuracy rate is 91.66%. According to the contour features of the collected tooth print images, a set of tooth pore area ratio feature extraction methods are designed. To objectively evaluate the recognition and classification method, the support vector machine is used as the final classifier. The recognition accuracy rate is 94.09%, and the verification accuracy rate is 94.09%. The test accuracy rate is 91.46%, and the classification effect is excellent. This paper has made a lot of breakthroughs and obvious progress based on the previous research on the tooth impression model. [ABSTRACT FROM AUTHOR]

In order to explore the intelligent path of agricultural sustainable development, this paper combines big data technology to process agricultural sustainable development data with the support of machine learning technology. Moreover, in order to solve the evaluation problem of regional agricultural sustainable development as well as understand the evolution path and evolution law of regional agricultural sustainable growth, this paper starts from the framework model and combines big data in addition to artificial intelligence expertise to construct a sustainable agricultural analysis model. In addition, this paper combines the needs of sustainable agricultural development to set the system function and analyse the realisation process of the system function. At last, this paper proposes a digital simulation experiment to authenticate the accomplishment of the model built in this paper. Through experimental research, it can be known that the intelligent agricultural sustainable development system constructed in this paper has certain effects, and this system can be used in subsequent research to analyse agricultural sustainable development. Based on algorithm verification as well as evaluation of the model on agricultural sustainability analysis, the statistical results are varying from 80 to 90. [ABSTRACT FROM AUTHOR]

Stroke presents a formidable global health threat, carrying significant risks and challenges. Timely intervention and improved outcomes hinge on the integration of Explainable Artificial Intelligence (XAI) into medical decision-making. XAI, an evolving field, enhances the transparency of conventional Artificial Intelligence (AI) models. This systematic review addresses key research questions: How is XAI applied in the context of stroke diagnosis? To what extent can XAI elucidate the outputs of machine learning models? Which systematic evaluation methodologies are employed, and what categories of explainable approaches (Model Explanation, Outcome Explanation, Model Inspection) are prevalent We conducted this review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our search encompassed five databases: Google Scholar, PubMed, IEEE Xplore, ScienceDirect, and Scopus, spanning studies published between January 1988 and June 2023. Various combinations of search terms, including "stroke," "explainable," "interpretable," "machine learning," "artificial intelligence," and "XAI," were employed. This study identified 17 primary studies employing explainable machine learning techniques for stroke diagnosis. Among these studies, 94.1% incorporated XAI for model visualization, and 47.06% employed model inspection. It is noteworthy that none of the studies employed evaluation metrics such as D, R, F, or S to assess the performance of their XAI systems. Furthermore, none evaluated human confidence in utilizing XAI for stroke diagnosis. Explainable Artificial Intelligence serves as a vital tool in enhancing trust among both patients and healthcare providers in the diagnostic process. The effective implementation of systematic evaluation metrics is crucial for harnessing the potential of XAI in improving stroke diagnosis. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive survey of the state-of-the-art use of Artificial Intelligence (AI) and Machine Learning (ML) in the field of cybersecurity. The paper illuminates key applications of AI and ML in cybersecurity, while also addressing existing challenges and posing unresolved questions for future research. The paper also emphasizes the ethical and legal implications associated with their implementation. The researchers conducted a thorough survey by reviewing numerous papers and articles from respected sources such as IEEE, ACM, and Springer. Their focus centered on the latest AI and ML breakthroughs in cybersecurity, while also exploring current challenges and open research questions. The results indicate that integrating AI and ML into cybersecurity systems shows great potential for future research and development. Intrusion detection and response, malware detection, and network security are among the most promising applications identified. According to the survey, 45% of organizations have already implemented AI and ML in their cybersecurity systems, while an additional 35% plan to do so. However, 20% of organizations believe that it is not yet the right time for adopting these technologies. Overall, this paper serves as a reliable reference for researchers and practitioners in the field of cybersecurity, offering a comprehensive overview of the use of AI and ML. It not only highlights the potential applications but also addresses the challenges and research gaps. Additionally, the paper raises awareness about the ethical and legal considerations associated with leveraging AI and ML in the cybersecurity domain. [ABSTRACT FROM AUTHOR]

Block Adjustment (BA) is a critical procedure in the geometric processing of satellite images, responsible for compensating and correcting the geometric positioning errors of the images. The accuracy of the photogrammetric products, including Digital Orthophoto Map (DOM), Digital Elevation Model (DEM), Digital Line Graphic (DLG), and Digital Raster Graphic (DRG), directly depends on the accuracy of BA results. In recent years, the rapid development of related technologies such as Artificial Intelligence (AI), Computer Vision (CV), Unmanned Aerial Vehicles (UAVs) and big data has greatly facilitated and transformed the classical BA in photogrammetry. This paper first reviews the current status of BA and then looks into the future. First, this paper provides a brief review of the key technologies involved in BA, including image matching, the establishment of adjustment model, the determination of the parameters and the detection of gross error. Then, taking the intercross and fusion of current technologies such as AI, cloud computing and big data with photogrammetry into account, this paper explores the future trends of photogrammetry. Finally, four typical cases of large-scale adjustment are introduced, including large-scale BA without Ground Control Points (GCPs) for optical stereo satellite images, large-scale BA with laser altimetry data for optical stereo satellite images, large-scale BA for UAV oblique photogrammetry, and large-scale BA for indoor photogrammetry in caves with a large number of close-range images. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to study the impact of database accuracy on system learning. The paper assumes a basic model of an information system with a database, a rulebase, and an embedded machine learning approach that is used to add rules to the rulebase. The system learns from its database, changes to that database, and the examination of other databases. The results in this paper can be of use in the analysis of the design and behavior of such learning systems. It is found that the information system accuracy impacts the magnitude of a measure of goodness of individual roles. Thus, if only rules of a certain magnitude are kept, then some rules will be discarded because of database inaccuracy, unless that inaccuracy is accounted for. In addition, by accounting for database inaccuracy, the direction of the impact on measure of goodness can be determined. In some cases, the impact on the direction is monotonic. This finding allows us to understand the impact of database inaccuracy, without explicitly taking account of that inaccuracy. Further, information system accuracy can impact the resulting order of importance of rules, within a set of rules. Since only those higher-ranked rules are kept, database accuracy and measure of goodness can impact what rules are retained in the rulebase of the system. As a result, it is important to account for the information system accuracy in learning information systems. [ABSTRACT FROM AUTHOR]

China is a Pacific coastal country, adjacent to the Circum-Pacific seismic belt. The tsunami from the western North Pacific is very dangerous to China because of the short distance. This paper analyzes historical earthquakes and tsunamis in the western North Pacific, and gives the focal parameters characteristics of tsunamigenic earthquakes. Among the analysis results, the reverse fault is the major tsunamigenic fault type, accounting for 73.1%, and the strike-slip faults can also trigger tsunami. Data mining method is used to find useful information behind historical tsunami events. The magnitude of tsunamigenic earthquake is generally greater than 5.3, and the focal depth is usually less than 69 km. Based on the analysis results, this paper assesses the hazard of potential tsunamis using the numerical model. The calculation results indicate that the tsunamis from Ryukyu Trench and Manila Trench are more dangerous, with the tsunami amplitude near China coast reaching 3–5 m. The statistical analysis results and tsunami hazard assessment of this paper can help to determine whether earthquake can trigger tsunami and understand the tsunami hazard, providing scientific reference for tsunami warning and mitigation. [ABSTRACT FROM AUTHOR]

The Vision Guided Robotic systems (VGR) is an essential aspect of modern intelligent robotics. The VGR is rapidly transforming manufacturing processes by enabling robots to be highly adaptable and intelligent reducing the cost and com- plexity. For any sensor-based intelligent robots, vision-based planning is considered as one of the most prominent steps followed by controlled actions using visual feedback. To develop robust vision-based autonomous systems in robotic applica- tions, path-planning and localization can be implemented along with Visual Servoing (VS) for robust feedback control. In the available literature, most of the reviews are focused on a particular module of autonomous systems like path planning, motion planning strategies, or Visual Servoing techniques. In this paper overall review of different modules in vision-guided robotic systems is presented. So, this review provides researchers with broader in-depth knowledge about different modules that exist in the vision-guided autonomous system. The review also includes different vision sensors that are commonly used in industries covering their characteristics and applications. In this work, overall, 227 research papers in path planning and vision-based control algorithms are reviewed with recent intelligent techniques based on optimization and learning-based approaches. The graphical analysis illustrating the advancements of research in the field of vision-based robotics using Artificial Intelligence (AI) is also discussed. Lastly, this paper con- cludes by discussing some of the research gaps, challenges, and future directions existing in vision-based planning and control. [ABSTRACT FROM AUTHOR]

Understanding the mechanisms behind memory, learning, and behavior is crucial to human development and significant research has been done in this area. Classical and operant conditioning and other theories of learning have elucidated different mechanisms of learning and how it modulates behavior. Even with advances in this area, questions remain on how to unlearn faulty ideas or extinguish maladaptive behaviors. In this paper, a novel theory to improve our understanding of this area is proposed. The theory proposes that as a consequence of the brain's energy efficiency evolutionary adaptations, all learning following memory consolidation, reconsolidation, and repeated reinforcements or strengthening over time, results in a phenomenon called mental representation block. The implications of this block on learning and behavior are significant and broad and include cognitive biases, belief in a creator or God, close-mindedness, dogmatism, physician misdiagnosis, racism, homophobia, and transphobia, susceptibility to deception and indoctrination, hate and love, artificial intelligence and creativity. [ABSTRACT FROM AUTHOR]

Most manufactured electrical cables suffer from reductions in their physical, mechanical and electrical properties. These setbacks are mainly attributed to the improper control of wire tension during the cable manufacturing process. Hence, this paper systematically reviewed different control algorithms involved in controlling tension in moving webs, which include conventional control, advanced control, observer-based control, artificial intelligence-based control and hybrid control techniques. Thus, the review provided information about existing tension control techniques in moving webs, including their strengths and weaknesses. It was observed in this review that although a significant research effort has been made on web tension control systems, a thorough literature review is still lacking. It was concluded that controller optimisation using hybrid control algorithms is gaining popularity in web tension control due to their improved control response. Hence, its application in wire tension control can better help cable manufacturers improve the quality of manufactured cables. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is expected to impact all facets of inflammatory bowel disease (IBD) management, including disease assessment, treatment decisions, discovery and development of new biomarkers and therapeutics, as well as clinician–patient communication. This perspective paper provides an overview of the application of AI in the clinical management of IBD through a review of the currently available AI models that could be potential tools for prognosis, shared decision-making, and precision medicine. This overview covers models that measure treatment response based on statistical or machine-learning methods, or a combination of the two. We briefly discuss a computational model that allows integration of immune/biological system knowledge with mathematical modeling and also involves a 'digital twin', which allows measurement of temporal trends in mucosal inflammatory activity for predicting treatment response. A viewpoint on AI-enabled wearables and nearables and their use to improve IBD management is also included. Although challenges regarding data quality, privacy, and security; ethical concerns; technical limitations; and regulatory barriers remain to be fully addressed, a growing body of evidence suggests a tremendous potential for integration of AI into daily clinical practice to enable precision medicine and shared decision-making. Advances in artificial intelligence (AI) show promise for improving treatment response prediction, decision-making, and precision medicine in inflammatory bowel disease (IBD). In particular, AI could improve precision medicine for IBD by enabling identification of disease subtypes, prediction of disease progression and treatment response, selection of personalized treatments, and remote monitoring. Predictive models can benefit clinicians and patients alike by optimizing shared decision-making processes; patients can also use AI to cope with daily and long-term challenges of the disease. Beyond patients and practitioners, predictive models may positively impact healthcare structures and payers by enabling effective healthcare-resource utilization. To increase the accuracy and efficiency of AI models, biomarkers, patient-reported outcomes, and disease scores should be combined within predictive models, and the outputs should be compared with clinical trial data and real-world data for validation. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is the third most common malignancy and second leading cause of cancer-related mortality in the world. Adenoma detection rate (ADR), a quality indicator for colonoscopy, has gained prominence as it is inversely related to CRC incidence and mortality. As such, recent efforts have focused on developing novel colonoscopy devices and technologies to improve ADR. The main objective of this paper is to provide an overview of advancements in the fields of colonoscopy mechanical attachments, artificial intelligence-assisted colonoscopy, and colonoscopy optical enhancements with respect to ADR. We accomplished this by performing a comprehensive search of multiple electronic databases from inception to September 2023. This review is intended to be an introduction to colonoscopy devices and technologies. Numerous mechanical attachments and optical enhancements have been developed that have the potential to improve ADR and AI has gone from being an inaccessible concept to a feasible means for improving ADR. While these advances are exciting and portend a change in what will be considered standard colonoscopy, they continue to require refinement. Future studies should focus on combining modalities to further improve ADR and exploring the use of these technologies in other facets of colonoscopy. [ABSTRACT FROM AUTHOR]

This paper comprehensively elaborates the differences in normal body and mind levels of high-altitude migrant college students between low-altitude and high-altitude regions, as well as the changes in normal body and mind levels of high-altitude migrant college students during their migration to high-altitude regions and the adaptation mechanism, so as to reveal the influence of altitude on the normal body and mind of high-altitude migrant college students and provide a comprehensive theoretical basis for the evaluation and standardization of normal body and mind levels of high-altitude migrant college students from different altitude regions. In view of the current situation of martial arts morning practice of college students and its problems, and the objective analysis of the reasons for the poor effect of martial arts morning practice, the article adopts the artificial neural network method and establishes a comprehensive evaluation model of normal body and mind of high-altitude migrant college students who perform martial arts morning practice based on the artificial neural network method, circumvents the empirical and arbitrary nature of the traditional threshold setting, takes the improvement of students' physical fitness as the guiding ideology, establishes The concept of a new model of cultivating the awareness of college students' martial arts morning practice, which includes relevant organizations and establishing a guarantee system, improving supervision, strengthening guidance, increasing the investment in hardware for students' physical exercise, and playing the role of associations. In the experimental validation, we find the data connection between each test sample and determine the threshold value of each index, and finally establish a scientific comprehensive evaluation model of normal body and mind of high-altitude migrant college students in martial arts morning practice, which makes up for the shortcomings of the original research method and evaluation model. [ABSTRACT FROM AUTHOR]

Industry 4.0 refers to a new generation of connected and intelligent factories that is driven by the emergence of new technologies such as artificial intelligence, Cloud computing, Big Data and industrial control systems (ICS) in order to automate all phases of industrial operations. The presence of connected systems in industrial environments poses a considerable security challenge, moreover with the huge amount of data generated daily, there are complex attacks that occur in seconds and target production lines and their integrity. But, until now, factories do not have all the necessary tools to protect themselves, they mainly use traditional protection. In order to improve industrial control systems in terms of efficiency and response time, the present paper propose a new distributed intrusion detection approach using artificial intelligence methods, Big Data techniques and deployed in a cloud environment. A variety of Machine Learning and Deep Learning algorithms, basically convolutional neural networks (CNN), have been tested to compare performance and choose the most suitable model for the classification. We test the performance of our model by using the industrial dataset SWat. [ABSTRACT FROM AUTHOR]

Artificial intelligence systems require large amounts of data to allow them to learn and achieve high performance. That data is increasingly collected in extractive and exploitative ways, which transfer value and power from individuals to AI system owners. Our research focuses on data that is collected from users of digital platforms, through direct and indirect interaction with those platforms, in ways that are not communicated to users, without consent or compensation. This paper presents our findings from a series of interviews and workshops in the Aotearoa New Zealand context to identify common themes and concerns from a variety of perspectives. Reframing this type of interaction as work or labour brings into view an otherwise unrecognised harm of using this data for training AI systems, and illustrates a new class of exploitative data practices that have become normalised in the digital age. We found that participants particularly emphasised moral or ethical justifications for intervention over financial or economic reasons to act. [ABSTRACT FROM AUTHOR]

The sudden outbreak of the novel Coronavirus (nCoV-19, COVID-19) and its rampant spread led to a significant number of people being infected worldwide and disrupted several businesses. With most of the countries imposing serious lockdowns due to the increasing number of fatalities, the social lives of millions of people were affected. Although the lockdown led to an increase in network activities, online shopping, and social network usage, it also raised questions On the mental wellness of society. Interestingly, excessive usage of social networks also witnessed humor traveling across the Internet in the form of Internet Memes during the lockdown period. Humor is known to affect our well-being, decision-making, and psychological systems. In this paper, we have analyzed the Internet Meme activity in Social Networks during the COVID-19 Lockdown period. As humor is known to relieve individuals from psychological stress, it is necessary to understand how human beings adopted Internet Memes for coping up with the lockdown stress and stress-relieving mechanism during the lockdown period. In this paper, we have considered thirty popular memes and the increase in the number of their captions within the period (September 2017 to August 2020). An increase in Internet Meme activity since the lockdown period (March 2020) depicts an increase in online social behavior. We analyze the internet meme activity in social networks during the COVID-19 lockdown period using random forest, multi-layer perceptron, and instance-based learning algorithms followed by data visualization using line graph and Heat Map (8 & 15 clustered). We also compared the performance of the models using evaluation parameters like mean absolute error, root-mean-squared error & Kappa statistics and observed that random forest and instance-based learning algorithms perform better than multi-layer perceptrons. The result indicates that random forest and instance-based learning classifiers are having near perfect classification tendencies whereas multi-layer perceptrons showed around 97% classification accuracy. [ABSTRACT FROM AUTHOR]

Aiming at the structural learning problem of the additive noise model in causal discovery and the challenge of massive data processing in the era of artificial intelligence, this paper combines partial rank correlation coefficients and proposes two new Bayesian network causal structure learning algorithms: PRCB algorithm based on threshold selection and PRCS algorithm based on hypothesis testing. We mainly made three contributions. First, we proved that the partial rank correlation coefficient can be used as the standard of independence test, and explored the distribution of corresponding statistics. Second, the partial rank correlation coefficient is associated with the correlation, and a causal discovery algorithm PRCB based on partial rank correlation and an improved PRCS algorithm based on hypothesis testing are proposed. Finally, comparing with the existing technology on seven classic Bayesian networks, it proves the superiority of the algorithm in low-dimensional networks; the processing of millions of data on three high- dimensional Bayesian networks verifies the high-efficiency performance of the algorithm in high-dimensional large sample data; the application performance of the algorithm is tested by performing fault prediction on the real power plant equipment measurement point data set. Theoretical analysis and experimental results have proved the superiority of the algorithm. [ABSTRACT FROM AUTHOR]

Different types of research have been done on video data using Artificial Intelligence (AI) deep learning techniques. Most of them are behavior analysis, scene understanding, scene labeling, human activity recognition (HAR), object localization, and event recognition. Among all these, HAR is one of the challenging tasks and thrust areas of video data processing research. HAR is applicable in different areas, such as video surveillance systems, human-computer interaction, human behavior characterization, and robotics. This paper aims to present a comparative review of vision-based human activity recognition with the main focus on deep learning techniques on various benchmark video datasets comprehensively. We propose a new taxonomy for categorizing the literature as CNN and RNN-based approaches. We further divide these approaches into four sub-categories and present various methodologies with their experimental datasets and efficiency. A short comparison is also made with the handcrafted feature-based approach and its fusion with deep learning to show the evolution of HAR methods. Finally, we discuss future research directions and some open challenges on human activity recognition. The objective of this survey is to give the current progress of vision-based deep learning HAR methods with the up-to-date study of literature. [ABSTRACT FROM AUTHOR]

The fourth industrial revolution shapes today's private and industrial environments, implying increased digitalization, connectivity, and artificial intelligence. Wearable devices support digital communication by displaying information and monitoring health-related aspects by measuring vital signs. Even though various wearables for measuring vital signs are already used in private life, they have not yet found their way into the production environment. This could be due to poor data quality or a lack of acceptance among employees regarding the usability of wearables during work activities. This paper aims to evaluate the accuracy and usability of selected wearable devices in manual assembly. Therefore, two user studies were conducted in a rebuilt production environment. The first study focuses on the data accuracy of the heart rate measurement of different wearables during manual assembly. In the second study, the usability of the selected wearables is evaluated with the thinking-aloud method during a manual assembly task and questionnaires. [ABSTRACT FROM AUTHOR]

The current sea monitoring equipment's are being used for a variety of purposes around the world. Currently used vehicles have some drawbacks. The first is the high fuel cost. The Vehicle engines cost more fuel as they have to release more power and environment and pollution. As well as not being able to stay under the sea for long days, there will often be a need for vehicles to come to the surface to refuel. The second is the vibrations and noise of these vehicles. The vibrations caused by these can be detrimental to the biodiversity of the ocean. Also, the noise makes it easier for enemies to identify our vehicles. Similarly when these vehicles go under water, water waves form on the surface. With this in mind, radar can detect what a vehicle under the sea looks like. In this paper, an artificial intelligence based microbiological model was proposed to monitor the sea level. With this biological model can greatly reduce fuels. It can get more capacity than normal vehicles. As fuel consumption decreases, so it does environmental pollution and since it operates quietly and without high vibrations, there is no threat to the biodiversity of the ocean. [ABSTRACT FROM AUTHOR]

The United Nations established the 17 Sustainable Development Goals (SDGs) in 2015 to address issues like gender equality, clean water, health, education, and hunger by 2030. Of the 17 SDGs, health and education have an outsized impact on countries’ socioeconomic development, so providing insights into progress on these two goals is crucial. Machine learning can help solve many real-world problems, including working towards the SDGs. This paper proposes using a metaheuristic ensemble of Cat Swarm Optimization algorithms with Feed Forward Extreme Learning Machines, called Sustainable Health And Educational Goal Development (SHEGD) Prediction, to effectively contribute to countries’ economic growth by achieving health and education SDGs through machine learning. The model is assessed using UN SDG datasets and performance metrics like accuracy, precision, recall, specificity, and F1-score. Comparisons to other machine learning models demonstrate this model’s superiority in designing a recommendation system for progressing towards the health and education SDGs. The proposed model outperforms the other approaches, proving its value for an SDG recommendation system design. [ABSTRACT FROM AUTHOR]

This paper presents the results of research into applying an object-oriented approach to systems analysis and design. Characteristics of object-oriented model building are briefly reviewed. A methodology is described which aids development of system modeling and simulation as the analysis and design process proceeds. The paper also describes the advantages of modeling analysis tools as objects which are integrated into the environment being analyzed. These principles are demonstrated by an object-oriented systems analysis and design recently conducted by the authors. [ABSTRACT FROM AUTHOR]

In the third industrial revolution (IR), mechatronics engineering has emerged, which is the merger of mechanical and electronics engineering along with information technology to an extent. In the current industrial revolution (fourth), the amalgamation of mechanical with Artificial intelligence (AI) has been used to simulate human behaviour in machines. In the earlier decades, the application of AI in mechanical engineering has expedited and the increased volume of related research papers is a proof of this fact. The broad domain of mechanical engineering has a lot of potential for machine learning applications in order to detect errors, minimise rejection, improve product quality, optimise a system, and so on, as it has not been intensely explored although. This article’s goal is to give a structured evaluation of works that employ vision systems and to attract researchers to analyse its potential integration with machine learning (ML) techniques from the industry 4.0 viewpoint. Current trends and research gaps in the area of manufacturing in collaboration with ML are also discussed at appropriate places. A general procedure to carry out research in the field of mechanical engineering is also presented. The results of various studies highlighted in this paper confirm that the implementation of AI in ME has provided an optimum solution to many mechanical problems that improved product quality and minimised the associated cost. Finally, the authors have led a direction to empower researchers in order to explore various areas in mechanical engineering from Industry 4.0 and AI perspective and enable possible topics for future research. The article concludes by providing challenges that may be faced in the coming future about the use of machine learning in mechanical engineering problems. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of the parameter sensitivity of a process-based model at the global scale using large-sample data. The analysis was carried out using the HYdrological Prediction of the Environment (HYPE) model, for which soil and snow parameters were evaluated using 187 river flow gauges spread worldwide. As a result, 6 out of 12 soil parameters and 7 out of 10 snow parameters were found to be sensitive. Taking advantage of the global dataset, an additional analysis was used to investigate links between catchment characteristics and parameter sensitivity. Different patterns of sensitivity were observed for different Köppen climate classes, which indicates that parameter regionalization would benefit from calibration based on climate zones. This numerical sensitivity method was compared with the judgement of a set of expert HYPE modellers to understand how numerical results compare with modellers' experience. [ABSTRACT FROM AUTHOR]

In this study, a bibliometric analysis technique is used for performance analysis and science mapping of artificial intelligence (AI) applications in streamflow research. This paper examines the current trends in the literature using the Scopus database over the last 37 years. RStudio Bibliometrix software was used to analyse the titles, keywords, abstracts, and full texts of 3000 publications to identify trends in AI models, publication types, journals, citations, authors, countries, and regions. The highest frequency AI-related keyword is "artificial neural networks," which was used in a total of 25587 times. The most common publication type, at 82.1%, is journal articles, and the highest rate of country production is 25% for China. In recent years, streamflow research studies have significantly increased their use of AI applications. [ABSTRACT FROM AUTHOR]

The rapid processing, analysis, and mining of remote-sensing big data based on intelligent interpretation technology using remote-sensing cloud computing platforms (RS-CCPs) have recently become a new trend. The existing RS-CCPs mainly focus on developing and optimizing high-performance data storage and intelligent computing for common visual representation, which ignores remote sensing data characteristics such as large image size, large-scale change, multiple data channels, and geographic knowledge embedding, thus impairing computational efficiency and accuracy. We construct a LuoJiaAI platform composed of a standard large-scale sample database (LuoJiaSET) and a dedicated deep learning framework (LuoJiaNET) to achieve state-of-the-art performance on five typical remote sensing interpretation tasks, including scene classification, object detection, land-use classification, change detection, and multi-view 3D reconstruction. The details of the LuoJiaAI application experiment can be found at the white paper for LuoJiaAI industrial application. In addition, LuoJiaAI is an open-source RS-CCP that supports the latest Open Geospatial Consortium (OGC) standards for better developing and sharing Earth Artificial Intelligence (AI) algorithms and products on benchmark datasets. LuoJiaAI narrows the gap between the sample database and deep learning frameworks through a user-friendly data-framework collaboration mechanism, showing great potential in high-precision remote sensing mapping applications. [ABSTRACT FROM AUTHOR]

Age-related macular degeneration (AMD) is a leading cause of irreversible visual impairment worldwide. The endpoint of AMD, both in its dry or wet form, is macular atrophy (MA) which is characterized by the permanent loss of the RPE and overlying photoreceptors either in dry AMD or in wet AMD. A recognized unmet need in AMD is the early detection of MA development. Artificial Intelligence (AI) has demonstrated great impact in detection of retinal diseases, especially with its robust ability to analyze big data afforded by ophthalmic imaging modalities, such as color fundus photography (CFP), fundus autofluorescence (FAF), near-infrared reflectance (NIR), and optical coherence tomography (OCT). Among these, OCT has been shown to have great promise in identifying early MA using the new criteria in 2018. There are few studies in which AI-OCT methods have been used to identify MA; however, results are very promising when compared to other imaging modalities. In this paper, we review the development and advances of ophthalmic imaging modalities and their combination with AI technology to detect MA in AMD. In addition, we emphasize the application of AI-OCT as an objective, cost-effective tool for the early detection and monitoring of the progression of MA in AMD. [ABSTRACT FROM AUTHOR]