Artificial Intelligence Architecture Research Articles

In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back.

Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equationsand the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materialsscience.

Integrating generative AI into financial market prediction for improved decision making

This study provides an in-depth analysis of the model architecture and key technologies of generative artificial intelligence, combined with specific application cases, and uses conditional generative adversarial networks ( cGAN ) and time series analysis methods to simulate and predict dynamic changes in financial markets. The research results show that the cGAN model can effectively capture the complexity of financial market data, and the deviation between the prediction results and the actual market performance is minimal, showing a high degree of accuracy. Through investment return analysis, the application value of model predictions in actual investment strategies is confirmed, providing investors with new ways to improve the decision-making process. In addition, the evaluation of model stability and reliability also shows that although there are still challenges in responding to market emergencies, overall, GAI technology has shown great potential and application value in the field of financial market prediction. The conclusion points out that integrating generative artificial intelligence into financial market forecasts can not only improve the accuracy of forecasts, but also provide powerful data support for financial decisions, helping investors make more informed decisions in a complex and ever-changing market environment. choose.

An in-depth evaluation of deep learning-enabled adaptive approaches for detecting obstacles using sensor-fused data in autonomous vehicles

This paper delivers an exhaustive analysis of the fusion of multi-sensor technologies, including traditional sensors such as cameras, Light Detection and Ranging(LiDAR), Radio Detection and Ranging(RADAR), and ultrasonic sensors, with Artificial Intelligence(AI) powered methodologies in obstacle detection for Autonomous Vehicles(AVs). With the growing momentum in AVs adoption, a heightened need exists for versatile and resilient obstacle detection systems. Our research delves into study of literatures, where proposed approaches assimilate data from this diverse sensor suite, integrated through Deep Learning(DL) techniques, to refine AV performance. Recent advancements and prevailing challenges within the domain are thoroughly examined, with particular focus on the integration of sensor fusion techniques, the facilitation of real-time processing via edge and fog computing, and the implementation of advanced artificial intelligence architectures, including Convolutional Neural Networks(CNNs), Recurrent Neural Networks(RNNs), and Generative Adversarial Networks(GANs), to enhance data interpretation efficacy. In conclusion, the paper underscores the critical contribution of multi-sensor arrays and deep learning in enhancing the safety and reliability of autonomous vehicles, offering significant perspectives for future research and technological progress.

Abstract PO4-01-10: Multi-modal artificial intelligence models from baseline histopathology predict prognosis in HR+ HER2- early breast cancer

Abstract Background: Prognostic assessment in HR+ HER2- early breast cancer (EBC) remains challenging given relatively low rates of disease progression. Nuanced risk stratification is needed for decisions regarding systemic therapy. Modern artificial intelligence (AI)-based techniques have already provided substantial medical progress, particularly in prostate cancer. Here, we leverage multi-modal artificial intelligence (MMAI) trained using digital histopathology and clinical data to evaluate whether the MMAI technology can be expanded to other disease indications by applying the technology to the WSG PlanB and ADAPT trials in HR+ HER2- EBC. Methods: Pre-treatment breast biopsy and surgical hematoxylin and eosin (H&E) slides were digitized from the WSG PlanB and ADAPT trials for HR+ HER2- EBC patients receiving endocrine therapy +/- chemotherapy. Median follow-up in both trials exceeded 5 years. A multi-modal artificial intelligence architecture was developed and validated on predicting risk of distant recurrence (DR). Time-to-event endpoints were summarized using cumulative incidence curves. Univariable and multivariable Fine-Gray models were used to assess performance; hazard ratios were reported per standard deviation increase of the model score. Results: A total of 5539 patients from the two trials with H&E images and available follow-up data were used for development and validation of a multi-modal artificial intelligence architecture. The MMAI score derived using the training set was significantly associated with risk of DR in a validation cohort. To put the results into the clinical context in HR+ HER2- EBC, comprehensive validation analyses are currently ongoing and will be presented at the meeting. Conclusions: We have successfully developed a multiple-instances, learning-based deep neural network for outcome prediction using H&E-stained images. This study provides important evidence that MMAI technology can further personalize breast cancer management by adding standardizable information. Citation Format: Daniel Kates-Harbeck, Hans-Heinrich Kreipe, Oleg Gluz, Matthias Christgen, Sherko Kuemmel, Monika Graeser, Ulrike Nitz, Sven Mahner, Doris Mayr, Rachel Wuerstlein, Akinori Mitani, Jingbin Zhang, Hans Pinckaers, Yi Ren, Peter Wood, Jacqueline Griffin, Felix Feng, Andre Esteva, Jess Keim-Malpass, Ronald Kates, Nadia Harbeck. Multi-modal artificial intelligence models from baseline histopathology predict prognosis in HR+ HER2- early breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-01-10.

State of the Art on Diffusion Models for Visual Computing

AbstractThe field of visual computing is rapidly advancing due to the emergence of generative artificial intelligence (AI), which unlocks unprecedented capabilities for the generation, editing, and reconstruction of images, videos, and 3D scenes. In these domains, diffusion models are the generative AI architecture of choice. Within the last year alone, the literature on diffusion‐based tools and applications has seen exponential growth and relevant papers are published across the computer graphics, computer vision, and AI communities with new works appearing daily on arXiv. This rapid growth of the field makes it difficult to keep up with all recent developments. The goal of this state‐of‐the‐art report (STAR) is to introduce the basic mathematical concepts of diffusion models, implementation details and design choices of the popular Stable Diffusion model, as well as overview important aspects of these generative AI tools, including personalization, conditioning, inversion, among others. Moreover, we give a comprehensive overview of the rapidly growing literature on diffusion‐based generation and editing, categorized by the type of generated medium, including 2D images, videos, 3D objects, locomotion, and 4D scenes. Finally, we discuss available datasets, metrics, open challenges, and social implications. This STAR provides an intuitive starting point to explore this exciting topic for researchers, artists, and practitioners alike.

A systematic review of artificial intelligence-powered (AI-powered) chatbot intervention for managing chronic illness

Background Utilizing artificial intelligence (AI) in chatbots, especially for chronic diseases, has become increasingly prevalent. These AI-powered chatbots serve as crucial tools for enhancing patient communication, addressing the rising prevalence of chronic conditions, and meeting the growing demand for supportive healthcare applications. However, there is a notable gap in comprehensive reviews evaluating the impact of AI-powered chatbot interventions in healthcare within academic literature. This study aimed to assess user satisfaction, intervention efficacy, and the specific characteristics and AI architectures of chatbot systems designed for chronic diseases. Method A thorough exploration of the existing literature was undertaken by employing diverse databases such as PubMed MEDLINE, CINAHL, EMBASE, PsycINFO, ACM Digital Library and Scopus. The studies incorporated in this analysis encompassed primary research that employed chatbots or other forms of AI architecture in the context of preventing, treating or rehabilitating chronic diseases. The assessment of bias risk was conducted using Risk of 2.0 Tools. Results Seven hundred and eighty-four results were obtained, and subsequently, eight studies were found to align with the inclusion criteria. The intervention methods encompassed health education (n = 3), behaviour change theory (n = 1), stress and coping (n = 1), cognitive behavioural therapy (n = 2) and self-care behaviour (n = 1). The research provided valuable insights into the effectiveness and user-friendliness of AI-powered chatbots in handling various chronic conditions. Overall, users showed favourable acceptance of these chatbots for self-managing chronic illnesses. Conclusions The reviewed studies suggest promising acceptance of AI-powered chatbots for self-managing chronic conditions. However, limited evidence on their efficacy due to insufficient technical documentation calls for future studies to provide detailed descriptions and prioritize patient safety. These chatbots employ natural language processing and multimodal interaction. Subsequent research should focus on evidence-based evaluations, facilitating comparisons across diverse chronic health conditions.

Groups of Persons in the Proposed AI Act Amendments

Abstract This article explores the proposed amendments to the AI Act, which introduce the concept of “groups of persons”. The inclusion of this notion has the potential to broaden the traditional individual-centric approach in data protection. The analysis explores the context and the challenges posed by the rapid evolution of technology, with an emphasis on the role of artificial intelligence (AI) systems. It discusses both the potential benefits and challenges of recognising groups of people, including issues such as discrimination prevention, public trust and redress mechanisms. The analysis also identifies key challenges, including the lack of a clear definition for “group”, the difficulty in explaining AI architecture concerning groups and the need for well-defined redress mechanisms. The article also puts forward recommendations aimed at addressing these challenges in order to enhance the effectiveness and clarity of the proposed amendments.

Diagnostic Performance of Artificial Intelligence for Detection of Scaphoid and Distal Radius Fractures: A Systematic Review

To review the existing literature to (1) determine the diagnostic efficacy of artificial intelligence (AI) models for detecting scaphoid and distal radius fractures and (2) compare the efficacy to human clinical experts. PubMed, OVID/Medline, and Cochrane libraries were queried for studies investigating the development, validation, and analysis of AI for the detection of scaphoid or distal radius fractures. Data regarding study design, AI model development and architecture, prediction accuracy/area under the receiver operator characteristic curve (AUROC), and imaging modalities were recorded. A total of 21 studies were identified, of which 12 (57.1%) used AI to detect fractures of the distal radius, and nine (42.9%) used AI to detect fractures of the scaphoid. AI models demonstrated good diagnostic performance on average, with AUROC values ranging from 0.77 to 0.96 for scaphoid fractures and from 0.90 to 0.99 for distal radius fractures. Accuracy of AI models ranged between 72.0% to 90.3% and 89.0% to 98.0% for scaphoid and distal radius fractures, respectively. When compared to clinical experts, 13 of 14 (92.9%) studies reported that AI models demonstrated comparable or better performance. The type of fracture influenced model performance, with worse overall performance on occult scaphoid fractures; however, models trained specifically on occult fractures demonstrated substantially improved performance when compared to humans. AI models demonstrated excellent performance for detecting scaphoid and distal radius fractures, with the majority demonstrating comparable or better performance compared with human experts. Worse performance was demonstrated on occult fractures. However, when trained specifically on difficult fracture patterns, AI models demonstrated improved performance. AI models can help detect commonly missed occult fractures while enhancing workflow efficiency for distal radius and scaphoid fracture diagnoses. As performance varies based on fracture type, future studies focused on wrist fracture detection should clearly define whether the goal is to (1) identify difficult-to-detect fractures or (2) improve workflow efficiency by assisting in routine tasks.

Soil Loss Estimation by Water Erosion in Agricultural Areas Introducing Artificial Intelligence Geospatial Layers into the RUSLE Model

The existing digital soil maps are mainly characterized by coarse spatial resolution and are not up to date; thus, they are unable to support the physical process-based models for improved predictions. The overarching objective of this work is oriented toward a data-driven approach and datacube-based tools (Soil Data Cube), leveraging Sentinel-2 imagery data, open access databases, ground truth soil data and Artificial Intelligence (AI) architectures to provide enhanced geospatial layers into the Revised Universal Soil Loss Equation (RUSLE) model, improving both the reliability and the spatial resolution of the final map. The proposed methodology was implemented in the agricultural area of the Imathia Regional Unit (northern Greece), which consists of both mountainous areas and lowlands. Enhanced soil maps of Soil Organic Carbon (SOC) and soil texture were generated at 10 m resolution through a time-series analysis of satellite data and an XGBoost (eXtrene Gradinent Boosting) model. The model was trained by 84 ground truth soil samples (collected from agricultural fields) taking into account also additional environmental covariates (including the digital elevation model and climatic data) and following a Digital Soil Mapping (DSM) approach. The enhanced layers were introduced into the RUSLE’s soil erodibility factor (K-factor), producing a soil erosion layer with high spatial resolution. Notable prediction accuracy was achieved by the AI model with R2 0.61 for SOC and 0.73, 0.67 and 0.63 for clay, sand, and silt, respectively. The average annual soil loss of the unit was found to be 1.76 ton/ha/yr with 6% of the total agricultural area suffering from severe erosion (>11 ton/ha/yr), which was mainly found in the mountainous border regions, showing the strong influence of the mountains in the agricultural fields. The overall methodology could strongly support regional decision making and planning and environmental policies such as the European Common Agricultural Policy (CAP) and the Sustainable Development Goals (SDGs).

End to End AI Architecture for Next Generation Network

Targeting 6G network architecture with native intelligence, a holistic view on the end to end (E2E) Artificial Intelligence (AI) flow across Operation Administration and Maintenance (OAM), Core Network (CN), Edge, Radio Access Network (RAN), and User Equipment (UE) domains is presented in this article. The AI Task Management (AITM) is introduced as the enabler in such network, and a User Equipment Data Analytics (UEDA) is proposed to take advantage of the increasing computing capability of UEs while taking their power consumption limitation into consideration. Conventional control plane and user plane connections assume fixed one-to-one end points and a single chosen transmission mode. Depending on the goal of the AI task and what network entities need to participate, the endpoints of the AI task connection is uncertain, with a group of participant UEs on one side and base station, core network or application server on the other. The transmission mode of such connection may need to be dynamically reconfigured as well. This article proposes an approach of AI task-oriented connections, where logical connections among UE, RAN, CN, and applications are established via AITM for AI model distribution, data collection, as well as parameter synchronization across different domains. A new wireless bearer type, named AI Radio Bearer (AIRB), for AI task-oriented connections is proposed with due design considerations and its associated performance.

Neural Networks and Cyber Resilience: Deep Insights into AI Architectures for Robust Security Framework

With the goal of applying artificial intelligence architectures to strengthen security frameworks, this paper explores the integration of Neural Networks into cybersecurity. The review of the literature highlights the advancements made in the use of neural networks for a range of cyber defense uses, with a focus on spam filtering and intrusion detection. Notable obstacles highlight the changing cybersecurity scene, such as the interpretability of AI and the need for explainable models.The study paper that goes along with it offers helpful advice on how to put AI architectures for strong security frameworks into practice. It emphasizes the methodical fusion of various AI techniques to strengthen defenses against the ever-changing array of cyberthreats. Tabulated performance ratings of several Neural Network types provide a comprehensive grasp of their strengths and capabilities across a range of metrics. These evaluations show that hybrid RCNN-ML performs very well. The potential and difficulties presented by the changing cybersecurity landscape are explored in discussions of AI technologies and their effects, with a focus on how traditional defenses must evolve in order to effectively use AI-driven solutions. At the end this work offers a nuanced viewpoint on the use of neural networks to improve cybersecurity resilience. Understanding the nuances of various Neural Network types is crucial for creating flexible and reliable security frameworks in the face of constantly evolving cyber threats as AI continues to advance.

Transparency in AI Decision Making: A Survey of Explainable AI Methods and Applications

Artificial Intelligence (AI) systems have become pervasive in numerous facets of modern life, wielding considerable influence in critical decision-making realms such as healthcare, finance, criminal justice, and beyond. Yet, the inherent opacity of many AI models presents significant hurdles concerning trust, accountability, and fairness. To address these challenges, Explainable AI (XAI) has emerged as a pivotal area of research, striving to augment the transparency and interpretability of AI systems. This survey paper serves as a comprehensive exploration of the state-of-the-art in XAI methods and their practical applications. We delve into a spectrum of techniques, spanning from model-agnostic approaches to interpretable machine learning models, meticulously scrutinizing their respective strengths, limitations, and real-world implications. The landscape of XAI is rich and varied, with diverse methodologies tailored to address different facets of interpretability. Model-agnostic approaches offer versatility by providing insights into model behavior across various AI architectures. In contrast, interpretable machine learning models prioritize transparency by design, offering inherent understandability at the expense of some predictive performance. Layer-wise Relevance Propagation (LRP) and attention mechanisms delve into the inner workings of neural networks, shedding light on feature importance and decision processes. Additionally, counterfactual explanations open avenues for exploring what-if scenarios, elucidating the causal relationships between input features and model outcomes. In tandem with methodological exploration, this survey scrutinizes the deployment and impact of XAI across multifarious domains. Successful case studies showcase the practical utility of transparent AI in healthcare diagnostics, financial risk assessment, criminal justice systems, and more. By elucidating these use cases, we illuminate the transformative potential of XAI in enhancing decision-making processes while fostering accountability and fairness. Nevertheless, the journey towards fully transparent AI systems is fraught with challenges and opportunities. As we traverse the current landscape of XAI, we identify pressing areas for further research and development. These include refining interpretability metrics, addressing the scalability of XAI techniques to complex models, and navigating the ethical dimensions of transparency in AI decision-making.Through this survey, we endeavor to cultivate a deeper understanding of transparency in AI decision-making, empowering stakeholders to navigate the intricate interplay between accuracy, interpretability, and ethical considerations. By fostering interdisciplinary dialogue and inspiring collaborative innovation, we aspire to catalyze future advancements in Explainable AI, ultimately paving the way towards more accountable and trustworthy AI systems.

Perspectives on AI Architectures and Codesign for Earth System Predictability

Abstract Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, laboratory, modeling, and analysis activities, called model experimentation (ModEx). BER’s ModEx is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the AI Architectures and Codesign session and associated outcomes. The AI Architectures and Codesign session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including 1) DOE high-performance computing (HPC) systems, 2) cloud HPC systems, and 3) edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this codesign area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as 1) reimagining codesign, 2) data acquisition to distribution, 3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with Earth system modeling and simulation, and 4) AI-enabled sensor integration into Earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper. Significance Statement This study aims to provide perspectives on AI architectures and codesign approaches for Earth system predictability. Such visionary perspectives are essential because AI-enabled model-data integration has shown promise in improving predictions associated with climate change, perturbations, and extreme events. Our forward-looking ideas guide what is next in codesign to enhance Earth system models, observations, and theory using state-of-the-art and futuristic computational infrastructure.

Retracted: Exploring Artificial Intelligence Architecture in Data Cleaning Based on Bayesian Networks

Retracted: Exploring Artificial Intelligence Architecture in Data Cleaning Based on Bayesian Networks

Preface

The purpose of the three-day 2023 3rd International Conference on Electronic Communication, Computer Science and Technology (ECCST 2023), held via hybrid form on September 15th to 17th, 2023 in Shanghai, China, was to create a timely forum for multi-disciplinary discussions related to the recent developments on electronic communication, computer science and technology.As usual, the program of the meeting mainly consisted of keynote speeches, oral presentations and poster presentations, with the former including speeches delivered by six sophisticated professors from different countries and regions. The keynote speeches covered six main topics, including the one Network AI: Enabling 6G Pervasive Intelligence given by Prof. Yang Yang (The Hong Kong University of Science and Technology (Guangzhou), China). In this presentation, he first coined the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. Then, he proposed a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions.In the framework of experimental and theoretical approaches, the Conference gathered about 80 delegates from all over the world and addressed a number of highly relevant aspects of electronic communication, computer science and technology. Covering Nonlinear Circuits and Systems, Low Power Design and VLSI Physical Design, Electromagnetic Compatibility, Computer and Data Communication, Computer Programming, etc., the selected contributions of this special issue provide guidance for future interdisciplinary developments. In this way, the multi-scale aspects of electronic communication, computer science and technology should be considered.We received various manuscripts of invited papers and contributions presented at the Conference. All of these have been peer reviewed and are collected in this volume. We would like to thank all the anonymous colleagues who have acted as referees to assess the suitability of the various articles for publication in Journal of Physics: Conference Series. We are confident that the high quality of both invited and contributed papers contained in this Proceedings will be appreciated by electronic and computer communities.We would like to express our thanks to all the authors for their time and genuine efforts, and to the reviewers for their fruitful comments during the preparation of this volume. We also acknowledge the support provided in various ways by Shanghai Pudong New Area Association For Computer and Donghua University.The Committee of ECCST 2023List of Committee Member is available in this Pdf.

Soil Data Cube and Artificial Intelligence Techniques for Generating National-Scale Topsoil Thematic Maps: A Case Study in Lithuanian Croplands

There is a growing realization among policymakers that in order to pave the way for the development of evidence-based conservation recommendations for policy, it is essential to improve the capacity for soil-health monitoring by adopting multidimensional and integrated approaches. However, the existing ready-to-use maps are characterized mainly by a coarse spatial resolution (>200 m) and information that is not up to date, making their use insufficient for the EU’s policy requirements, such as the common agricultural policy. This work, by utilizing the Soil Data Cube, which is a self-hosted custom tool, provides yearly estimations of soil thematic maps (e.g., exposed soil, soil organic carbon, clay content) covering all the agricultural area in Lithuania. The pipeline exploits various Earth observation data such as a time series of Sentinel-2 satellite imagery (2018–2022), the LUCAS (Land Use/Cover Area Frame Statistical Survey) topsoil database, the European Integrated Administration and Control System (IACS) and artificial intelligence (AI) architectures to improve the prediction accuracy as well as the spatial resolution (10 m), enabling discrimination at the parcel level. Five different prediction models were tested with the convolutional neural network (CNN) model to achieve the best accuracy for both targeted indicators (SOC and clay) related to the R2 metric (0.51 for SOC and 0.57 for clay). The model predictions supported by the prediction uncertainties based on the PIR formula (average PIR 0.48 for SOC and 0.61 for clay) provide valuable information on the model’s interpretation and stability. The model application and the final predictions of the soil indicators were carried out based on national bare-soil-reflectance composite layers, generated by employing a pixel-based composite approach to the overlaid annual bare-soil maps and by using a combination of a series of vegetation indices such as NDVI, NBR2, and SCL. The findings of this work provide new insights for the generation of soil thematic maps on a large scale, leading to more efficient and sustainable soil management, supporting policymakers and the agri-food private sector.

Photonic optical accelerators: The future engine for the era of modern AI?

Photonic accelerators for Artificial Intelligence (AI) are rapidly advancing, promising to provide revolutionary computational speed for modern AI architectures. By leveraging photons with a bandwidth higher than 100 THz, photonic accelerators tackle the computational demands of AI tasks that GHz electronics alone cannot meet. Photonics accelerators integrate circuitry for matrix–vector operators and ultra-fast feature extractors, enabling energy-efficient and parallel computations that prove crucial for the training and inference of AI models in various applications, including classification, segmentation, and feature extraction. This Perspective discusses modern challenges and opportunities that optical computations open in AI for research and industry.

XAI SHAP 기반 토지 피복 구성에 따른 지표면 온도의 영향 분석

Recently, with the development of artificial intelligence architecture and the rapid improvement of hardware performance, various artificial intelligence models are attracting attention and their usefulness are increasing. However, unlike the rate of development of performance, the development of model's explanatory ability is slow to progress. The increasingly complex decision branching points of AI models are increasing exponentially, which hinders researchers' ease of model interpretation. Explainable Artificial Intelligence, XAI has emerged to solve the above complexity, and helps to improve interpretability and reliability by decomposing the black box of the model to a level that researchers can understand. This study estimates the surface temperature of a specific spatial unit using land cover data of a cell unit in Korea, Seoul. The limitations of the existing statistical model for high-dimensional data were examined, and the estimation results were compared using a machine learning model. Models used include Lasso regression(Least absolute linkage and selection operator), random forest regression, and XGBoost (eXtreme Gradient Boosting) regression. Finally, based on the fitted result of the XGBoost regression model, XAI SHAP was carried out.

Transforming Organizational Development with AI: Navigating Change and Innovation for Success

Effective change management emerges as a deciding element for an organization's survival and success in the changing terrain of today's fiercely competitive business climate. The variety of change management theories and approaches that are currently available, however, paints a complicated picture that is plagued by inconsistencies, a lack of strong empirical support, and unproven assumptions about contemporary organizational dynamics. This essay seeks to set the basis for a fresh paradigm for effective change administration by critically analyzing popular change management ideas. The gap between theory and practice is addressed in the paper, which concludes with suggestions for more research. In parallel, artificial intelligence (AI) has made incredible progress, giving rise to computers that mimic human autonomy and cognition. Industry-wide excitement has been sparked by the enthusiasm among academics, executives, and the general public, which has resulted in significant investments in utilizing AI's potential through creative business models. However, the lack of thorough academic guidance forces managers to struggle with AI integration issues, increasing the risk of project failure. An in-depth analysis of AI's complexities and its function as a spark for revolutionary business model innovation is provided in this article. A thorough literature assessment, which involves sifting through a sizable library of published works, combines up-to-date information on how AI is affecting the development of new business models. The findings come together to form a roadmap for seamless AI integration that includes four steps: understanding the fundamentals of AI and the skills needed for digital transformation, understanding current business models and their innovation potential, nurturing key proficiencies for AI assimilation, and gaining organizational acceptance while developing internal competencies. This article combines the fields of organizational change management and AI-driven business model innovation with ease, providing a thorough explanation to assist businesses in undergoing a successful transformation and innovation. These disciplines' confluence offers a practical vantage point for successfully adapting to, thriving in, and profiting within a dynamic business environment. Artificial intelligence (AI), a massively disruptive force that is altering international businesses, is at the vanguard of this revolution. The ability of AI to make decisions automatically, based on data analysis and observation, opens up hitherto untapped possibilities for value creation and competitive dominance, with broad consequences spanning several industries. With its quick scaling, ongoing improvement, and self-learning capabilities, this evolutionary invention functions as an agile capital-labor hybrid. Significantly, AI's architecture serves as the cornerstone for data-driven decision support by deftly sifting through large and complicated datasets to extract insights. Thus, the symbiotic marriage of organizational change management and AI-driven business model innovation gives a thorough narrative, directing businesses towards not just surviving, but thriving in an ever-evolving business environment. It is underlined how business models (BMs) interact with technology to affect how well business’s function, underlining the need of taking BMs into account while using AI. Business model innovation (BMI) that AI unlocks may improve goods, streamline processes, and save costs. However, there is a void between technological improvements and their operationalization via BMs. Successful AI integration depends on a well-structured BM, which promotes agility and makes the most of technological resources. BMI is accelerated by AI, which reshapes sectors via innovation. Although interest in AI is high, strategic, cultural, and technological constraints sometimes prevent large investments from producing positive economic results. To fully utilize AI's capabilities, structured BMs are required. Despite an increase in research, there is still little cohesive information about the business uses of AI. In an effort to close this gap, we examine implementation-related AI problems. Analyzing AI-driven BM transformation and risk management is aided by a study on BMI and digital transformation at the same time. The purpose of this study is to further our understanding of AI-driven business model innovation and to provide a useful framework to help practitioners navigate the potential and difficulties of AI implementation. The suggested roadmap aims to identify current knowledge gaps and future research initiatives.

Artificial Intelligence (AI) Architecture for Integrated Smart Digital Banking System

Using artificial intelligence, physicists have condensed a daunting quantum problem that until recently required 100,000 equations into a task of as few as four equations - all without sacrificing accuracy. Artificial intelligence is becoming the most current achievement of the computing world. The human brain is to some extent represented and spliced with the ability to solve problems. The purpose of this study is to describe in detail how the power of artificial intelligence with its complex system can help the needs of digital technology in the banking sector. The research method used is the elaboration of great thoughts and facts about artificial intelligence. Scientific data is interpreted with analytical power that is as precise as possible, so as to produce a description that meets the logic of structured thinking. The data is taken from relevant and up-to-date literature, the work of scientists who have been disseminated in various weighty scientific publications at the world level. The results of the study found how artificial intelligence has folded the complexity of the problem into a simple solution. Artificial intelligence in the banking world becomes an instrument that integrates consumers, smartphones, databases, security systems, financial services authorities, agencies, law enforcement officers, and various derivative digital systems.