Artificial intelligence(AI)is reshaping financial systems and services,as intelligent AI agents increasingly form the foundation of autonomous,goal-driven systems capable of reasoning,learning,and action.This review s...Artificial intelligence(AI)is reshaping financial systems and services,as intelligent AI agents increasingly form the foundation of autonomous,goal-driven systems capable of reasoning,learning,and action.This review synthesizes recent research and developments in the application of AI agents across core financial domains.Specifically,it covers the deployment of agent-based AI in algorithmic trading,fraud detection,credit risk assessment,roboadvisory,and regulatory compliance(RegTech).The review focuses on advanced agent-based methodologies,including reinforcement learning,multi-agent systems,and autonomous decision-making frameworks,particularly those leveraging large language models(LLMs),contrasting these with traditional AI or purely statistical models.Our primary goals are to consolidate current knowledge,identify significant trends and architectural approaches,review the practical efficiency and impact of current applications,and delineate key challenges and promising future research directions.The increasing sophistication of AI agents offers unprecedented opportunities for innovation in finance,yet presents complex technical,ethical,and regulatory challenges that demand careful consideration and proactive strategies.This review aims to provide a comprehensive understanding of this rapidly evolving landscape,highlighting the role of agent-based AI in the ongoing transformation of the financial industry,and is intended to serve financial institutions,regulators,investors,analysts,researchers,and other key stakeholders in the financial ecosystem.展开更多
The deep integration of artificial intelligence technology and agricultural industry has pushed smart agriculture into a new stage of"AI+scenario",and put forward a transformation requirement for the talent ...The deep integration of artificial intelligence technology and agricultural industry has pushed smart agriculture into a new stage of"AI+scenario",and put forward a transformation requirement for the talent cultivation of smart agriculture major in universities from"technology application"to"intelligent innovation".In response to the problems of insufficient AI integration,lack of contextualization,and insufficient collaboration between industry and education in the traditional"technology+"practical course system,this paper takes the smart agriculture major at Yulin Normal University as an example to construct a"AI+agriculture"practical course reconstruction framework and propose a four-dimensional transformation path of"goal-content-mode-evaluation".Through the practical exploration of modular curriculum design,scenario based practical design,integration of industry and education,and intelligent evaluation reform,a practical teaching system with local application-oriented university characteristics has been formed,providing a reference example for the cultivation of smart agriculture professionals under the background of new agricultural science.展开更多
The integration of machine learning(ML)into geohazard assessment has successfully instigated a paradigm shift,leading to the production of models that possess a level of predictive accuracy previously considered unatt...The integration of machine learning(ML)into geohazard assessment has successfully instigated a paradigm shift,leading to the production of models that possess a level of predictive accuracy previously considered unattainable.However,the black-box nature of these systems presents a significant barrier,hindering their operational adoption,regulatory approval,and full scientific validation.This paper provides a systematic review and synthesis of the emerging field of explainable artificial intelligence(XAI)as applied to geohazard science(GeoXAI),a domain that aims to resolve the long-standing trade-off between model performance and interpretability.A rigorous synthesis of 87 foundational studies is used to map the intellectual and methodological contours of this rapidly expanding field.The analysis reveals that current research efforts are concentrated predominantly on landslide and flood assessment.Methodologically,tree-based ensembles and deep learning models dominate the literature,with SHapley Additive exPlanations(SHAP)frequently adopted as the principal post-hoc explanation technique.More importantly,the review further documents how the role of XAI has shifted:rather than being used solely as a tool for interpreting models after training,it is increasingly integrated into the modeling cycle itself.Recent applications include its use in feature selection,adaptive sampling strategies,and model evaluation.The evidence also shows that GeoXAI extends beyond producing feature rankings.It reveals nonlinear thresholds and interaction effects that generate deeper mechanistic insights into hazard processes and mechanisms.Nevertheless,several key challenges remain unresolved within the field.These persistent issues are especially pronounced when considering the crucial necessity for interpretation stability,the demanding scholarly task of reliably distinguishing correlation from causation,and the development of appropriate methods for the treatment of complex spatio-temporal dynamics.展开更多
The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats.The early detection of threa...The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats.The early detection of threats is both necessary and complex,yet these interconnected healthcare settings generate enormous amounts of heterogeneous data.Traditional Intrusion Detection Systems(IDS),which are generally centralized and machine learning-based,often fail to address the rapidly changing nature of cyberattacks and are challenged by ethical concerns related to patient data privacy.Moreover,traditional AI-driven IDS usually face challenges in handling large-scale,heterogeneous healthcare data while ensuring data privacy and operational efficiency.To address these issues,emerging technologies such as Big Data Analytics(BDA)and Federated Learning(FL)provide a hybrid framework for scalable,adaptive intrusion detection in IoT-driven healthcare systems.Big data techniques enable processing large-scale,highdimensional healthcare data,and FL can be used to train a model in a decentralized manner without transferring raw data,thereby maintaining privacy between institutions.This research proposes a privacy-preserving Federated Learning–based model that efficiently detects cyber threats in connected healthcare systems while ensuring distributed big data processing,privacy,and compliance with ethical regulations.To strengthen the reliability of the reported findings,the resultswere validated using cross-dataset testing and 95%confidence intervals derived frombootstrap analysis,confirming consistent performance across heterogeneous healthcare data distributions.This solution takes a significant step toward securing next-generation healthcare infrastructure by combining scalability,privacy,adaptability,and earlydetection capabilities.The proposed global model achieves a test accuracy of 99.93%±0.03(95%CI)and amiss-rate of only 0.07%±0.02,representing state-of-the-art performance in privacy-preserving intrusion detection.The proposed FL-driven IDS framework offers an efficient,privacy-preserving,and scalable solution for securing next-generation healthcare infrastructures by combining adaptability,early detection,and ethical data management.展开更多
In recent years, the digital economy has become a key driver of deepening China-Thailand cooperation. As trade and industrial digitalization advance, artificial intelligence (AI), an essential force in the new wave of...In recent years, the digital economy has become a key driver of deepening China-Thailand cooperation. As trade and industrial digitalization advance, artificial intelligence (AI), an essential force in the new wave of technological revolution, is increasingly embedded in the bilateral agenda, steering collaboration beyond efficiencydriven “digital coordination” toward more structured and systemic“intelligent co-development.”Building on five decades of mutual trust since the establishment of diplomatic ties, this cooperation not only addresses the practical needs of industrial upgrading and governance transformation but offers a valuable reference for ASEAN countries exploring replicable pathways in the era of digital transformation.展开更多
The year 2025 marked the inaugural volume of Artificial Intelligence Science and Engineering(AISE).Withthe strong support of the global academic community in artificial intelligence and control science,and through the...The year 2025 marked the inaugural volume of Artificial Intelligence Science and Engineering(AISE).Withthe strong support of the global academic community in artificial intelligence and control science,and through thededicated efforts of our editorial board members,authors,and reviewers,all four issues were successfullypublished.These issues brought together significant original contributions and authoritative surveys from leadingscholars,laying a solid academic foundation for the long-term development of the journal.展开更多
Building reliable intent-based,task-oriented dialog systems typically requires substantial manual effort:designers must derive intents,entities,responses,and control logic from raw conversational data,then iterate unt...Building reliable intent-based,task-oriented dialog systems typically requires substantial manual effort:designers must derive intents,entities,responses,and control logic from raw conversational data,then iterate until the assistant behaves consistently.This paper investigates how far large language models(LLMs)can automate this development.In this paper,we use two reference corpora,Let’s Go(English,public transport)and MEDIA(French,hotel booking),to prompt four LLM families(GPT-4o,Claude,Gemini,Mistral Small)and generate the core specifications required by the rasa platform.These include intent sets with example utterances,entity definitions with slot mappings,response templates,and basic dialog flows.To structure this process,we introduce a model-and platform-agnostic pipelinewith two phases.The first normalizes and validates LLM-generated artifacts,enforcing crossfile consistency andmaking slot usage explicit.The second uses a lightweight dialog harness that runs scripted tests and incrementally patches failure points until conversations complete reliably.Across eight projects,all models required some targeted repairs before training.After applying our pipeline,all reached≥70%task completion(many above 84%),while NLU performance ranged from mid-0.6 to 1.0 macro-F1 depending on domain breadth.These results show that,with modest guidance,current LLMs can produce workable end-to-end dialog prototypes directly fromraw transcripts.Our main contributions are:(i)a reusable bootstrap method aligned with industry domain-specific languages(DSLs),(ii)a small set of high-impact corrective patterns,and(iii)a simple but effective harness for closed-loop refinement across conversational platforms.展开更多
External forces of marine weather, such as waves, currents and wind flows, affect the course and speed of a ship under way. As a result, marine accidents, such as collisions or grounding, may occur, particularly in in...External forces of marine weather, such as waves, currents and wind flows, affect the course and speed of a ship under way. As a result, marine accidents, such as collisions or grounding, may occur, particularly in inshore areas. On coasts where earthquakes and tsunamis occur frequently, such as the Japanese coast, a tsunami that advances into a bay from the open sea is influenced by the submarine topography. It grows into a huge wave that could cause tremendous damage to ships under way and at anchorage. A massive earthquake occurred in the Tohoku and Kanto regions of Japan on March 11,2011. In response, a tsunami alert, a tsunami warning and an advisory were issued for the entire Pacific coast region. This research investigates the evacuation behaviour of ships by AIS （automatic identification system） data in Tokyo Bay after the tsunami warning was issued. The unusual behaviour observed was attributed to the emergency evacuation of ships.展开更多
基金supported by the Ministry of Education and Science of the Republic of North Macedonia through the project“Utilizing AI and National Large Language Models to Advance Macedonian Language Capabilties”。
文摘Artificial intelligence(AI)is reshaping financial systems and services,as intelligent AI agents increasingly form the foundation of autonomous,goal-driven systems capable of reasoning,learning,and action.This review synthesizes recent research and developments in the application of AI agents across core financial domains.Specifically,it covers the deployment of agent-based AI in algorithmic trading,fraud detection,credit risk assessment,roboadvisory,and regulatory compliance(RegTech).The review focuses on advanced agent-based methodologies,including reinforcement learning,multi-agent systems,and autonomous decision-making frameworks,particularly those leveraging large language models(LLMs),contrasting these with traditional AI or purely statistical models.Our primary goals are to consolidate current knowledge,identify significant trends and architectural approaches,review the practical efficiency and impact of current applications,and delineate key challenges and promising future research directions.The increasing sophistication of AI agents offers unprecedented opportunities for innovation in finance,yet presents complex technical,ethical,and regulatory challenges that demand careful consideration and proactive strategies.This review aims to provide a comprehensive understanding of this rapidly evolving landscape,highlighting the role of agent-based AI in the ongoing transformation of the financial industry,and is intended to serve financial institutions,regulators,investors,analysts,researchers,and other key stakeholders in the financial ecosystem.
基金Supported by the Autonomous Region-level Research and Practice Projects for New Engineering,New Medicine,New Agriculture,and New Humanities of Guangxi Department of Education(XNK202409)the Undergraduate Teaching Reform Project of Guangxi Higher Education(2024JGB332+1 种基金2024JGA304)the Guangxi Degree and Graduate Education Reform Project(JGY2025382).
文摘The deep integration of artificial intelligence technology and agricultural industry has pushed smart agriculture into a new stage of"AI+scenario",and put forward a transformation requirement for the talent cultivation of smart agriculture major in universities from"technology application"to"intelligent innovation".In response to the problems of insufficient AI integration,lack of contextualization,and insufficient collaboration between industry and education in the traditional"technology+"practical course system,this paper takes the smart agriculture major at Yulin Normal University as an example to construct a"AI+agriculture"practical course reconstruction framework and propose a four-dimensional transformation path of"goal-content-mode-evaluation".Through the practical exploration of modular curriculum design,scenario based practical design,integration of industry and education,and intelligent evaluation reform,a practical teaching system with local application-oriented university characteristics has been formed,providing a reference example for the cultivation of smart agriculture professionals under the background of new agricultural science.
文摘The integration of machine learning(ML)into geohazard assessment has successfully instigated a paradigm shift,leading to the production of models that possess a level of predictive accuracy previously considered unattainable.However,the black-box nature of these systems presents a significant barrier,hindering their operational adoption,regulatory approval,and full scientific validation.This paper provides a systematic review and synthesis of the emerging field of explainable artificial intelligence(XAI)as applied to geohazard science(GeoXAI),a domain that aims to resolve the long-standing trade-off between model performance and interpretability.A rigorous synthesis of 87 foundational studies is used to map the intellectual and methodological contours of this rapidly expanding field.The analysis reveals that current research efforts are concentrated predominantly on landslide and flood assessment.Methodologically,tree-based ensembles and deep learning models dominate the literature,with SHapley Additive exPlanations(SHAP)frequently adopted as the principal post-hoc explanation technique.More importantly,the review further documents how the role of XAI has shifted:rather than being used solely as a tool for interpreting models after training,it is increasingly integrated into the modeling cycle itself.Recent applications include its use in feature selection,adaptive sampling strategies,and model evaluation.The evidence also shows that GeoXAI extends beyond producing feature rankings.It reveals nonlinear thresholds and interaction effects that generate deeper mechanistic insights into hazard processes and mechanisms.Nevertheless,several key challenges remain unresolved within the field.These persistent issues are especially pronounced when considering the crucial necessity for interpretation stability,the demanding scholarly task of reliably distinguishing correlation from causation,and the development of appropriate methods for the treatment of complex spatio-temporal dynamics.
文摘The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats.The early detection of threats is both necessary and complex,yet these interconnected healthcare settings generate enormous amounts of heterogeneous data.Traditional Intrusion Detection Systems(IDS),which are generally centralized and machine learning-based,often fail to address the rapidly changing nature of cyberattacks and are challenged by ethical concerns related to patient data privacy.Moreover,traditional AI-driven IDS usually face challenges in handling large-scale,heterogeneous healthcare data while ensuring data privacy and operational efficiency.To address these issues,emerging technologies such as Big Data Analytics(BDA)and Federated Learning(FL)provide a hybrid framework for scalable,adaptive intrusion detection in IoT-driven healthcare systems.Big data techniques enable processing large-scale,highdimensional healthcare data,and FL can be used to train a model in a decentralized manner without transferring raw data,thereby maintaining privacy between institutions.This research proposes a privacy-preserving Federated Learning–based model that efficiently detects cyber threats in connected healthcare systems while ensuring distributed big data processing,privacy,and compliance with ethical regulations.To strengthen the reliability of the reported findings,the resultswere validated using cross-dataset testing and 95%confidence intervals derived frombootstrap analysis,confirming consistent performance across heterogeneous healthcare data distributions.This solution takes a significant step toward securing next-generation healthcare infrastructure by combining scalability,privacy,adaptability,and earlydetection capabilities.The proposed global model achieves a test accuracy of 99.93%±0.03(95%CI)and amiss-rate of only 0.07%±0.02,representing state-of-the-art performance in privacy-preserving intrusion detection.The proposed FL-driven IDS framework offers an efficient,privacy-preserving,and scalable solution for securing next-generation healthcare infrastructures by combining adaptability,early detection,and ethical data management.
文摘In recent years, the digital economy has become a key driver of deepening China-Thailand cooperation. As trade and industrial digitalization advance, artificial intelligence (AI), an essential force in the new wave of technological revolution, is increasingly embedded in the bilateral agenda, steering collaboration beyond efficiencydriven “digital coordination” toward more structured and systemic“intelligent co-development.”Building on five decades of mutual trust since the establishment of diplomatic ties, this cooperation not only addresses the practical needs of industrial upgrading and governance transformation but offers a valuable reference for ASEAN countries exploring replicable pathways in the era of digital transformation.
文摘The year 2025 marked the inaugural volume of Artificial Intelligence Science and Engineering(AISE).Withthe strong support of the global academic community in artificial intelligence and control science,and through thededicated efforts of our editorial board members,authors,and reviewers,all four issues were successfullypublished.These issues brought together significant original contributions and authoritative surveys from leadingscholars,laying a solid academic foundation for the long-term development of the journal.
基金This publication is part of the TrustBoost project,that has received funding from MICIU/AEI/10.13039/501100011033,from FEDER,UEIt is a coordinated project by a multidisciplinary team from the Universidad Politécnica de Madrid(UPM)and University of Granada(UGR),with two subprojects that address TrustBoost’s objectives:“Enhancing Trustworthiness in Conversational AI through Multimodal Affective Awareness”(Trust Boost-UPM,ref.PID2023-150584OB-C21)“Breaking the Duality of Conversational AI:Going beyond Guided Conversations While Ensuring Compliance with Domain Rules and Constraints”(Trust Boost-UGR,ref.PID2023-150584OB-C22).
文摘Building reliable intent-based,task-oriented dialog systems typically requires substantial manual effort:designers must derive intents,entities,responses,and control logic from raw conversational data,then iterate until the assistant behaves consistently.This paper investigates how far large language models(LLMs)can automate this development.In this paper,we use two reference corpora,Let’s Go(English,public transport)and MEDIA(French,hotel booking),to prompt four LLM families(GPT-4o,Claude,Gemini,Mistral Small)and generate the core specifications required by the rasa platform.These include intent sets with example utterances,entity definitions with slot mappings,response templates,and basic dialog flows.To structure this process,we introduce a model-and platform-agnostic pipelinewith two phases.The first normalizes and validates LLM-generated artifacts,enforcing crossfile consistency andmaking slot usage explicit.The second uses a lightweight dialog harness that runs scripted tests and incrementally patches failure points until conversations complete reliably.Across eight projects,all models required some targeted repairs before training.After applying our pipeline,all reached≥70%task completion(many above 84%),while NLU performance ranged from mid-0.6 to 1.0 macro-F1 depending on domain breadth.These results show that,with modest guidance,current LLMs can produce workable end-to-end dialog prototypes directly fromraw transcripts.Our main contributions are:(i)a reusable bootstrap method aligned with industry domain-specific languages(DSLs),(ii)a small set of high-impact corrective patterns,and(iii)a simple but effective harness for closed-loop refinement across conversational platforms.
文摘External forces of marine weather, such as waves, currents and wind flows, affect the course and speed of a ship under way. As a result, marine accidents, such as collisions or grounding, may occur, particularly in inshore areas. On coasts where earthquakes and tsunamis occur frequently, such as the Japanese coast, a tsunami that advances into a bay from the open sea is influenced by the submarine topography. It grows into a huge wave that could cause tremendous damage to ships under way and at anchorage. A massive earthquake occurred in the Tohoku and Kanto regions of Japan on March 11,2011. In response, a tsunami alert, a tsunami warning and an advisory were issued for the entire Pacific coast region. This research investigates the evacuation behaviour of ships by AIS （automatic identification system） data in Tokyo Bay after the tsunami warning was issued. The unusual behaviour observed was attributed to the emergency evacuation of ships.
