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Artificial intelligence for geoscience:Progress,challenges,and perspectives

The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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Location embedding based pairwise distance learning for fine-grained diagnosis of urinary stones

arXiv

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arXiv 2024年

作者： Jin, Qiangguo Huang, Jiapeng Sun, Changming Cui, Hui Xuan, Ping Su, Ran Wei, Leyi Wu, Yu-Jie Wu, Chia-An Duh, Henry B.L. Lu, Yueh-Hsun School of Software Northwestern Polytechnical University Shaanxi China Yangtze River Delta Research Institute of NPU Suzhou China CSIRO Data61 Sydney Australia Department of Computer Science and Information Technology La Trobe University Melbourne Australia School of Engineering Shantou University Guangdong China College of Intelligence and Computing Tianjin University Tianjin China School of Information Xiamen University Xiamen China AIDD Faculty of Applied Science Macao Polytechnic University China Department of Radiology Shuang-Ho Hospital Taipei Medical University Taipei Taiwan Master of Public Health Program National Yang Ming Chiao Tung University Taipei Taiwan School of Design The Hong Kong Polytechnic University Hong Kong Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

The precise diagnosis of urinary stones is crucial for devising effective treatment strategies. The diagnostic process, however, is often complicated by the low contrast between stones and surrounding tissues, as well as the variability in stone locations across different patients. To address this issue, we propose a novel location embedding based pairwise distance learning network (LEPD-Net) that leverages low-dose abdominal X-ray imaging combined with location information for the fine-grained diagnosis of urinary stones. LEPD-Net enhances the representation of stone-related features through context-aware region enhancement, incorporates critical location knowledge via stone location embedding, and achieves recognition of fine-grained objects with our innovative fine-grained pairwise distance learning. Additionally, we have established an in-house dataset on urinary tract stones to demonstrate the effectiveness of our proposed approach. Comprehensive experiments conducted on this dataset reveal that our framework significantly surpasses existing state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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A Comparative Study of Gaussian Process Machine Learning and Time Series Analysis Techniques for Predicting Unemployment Rate

A Comparative Study of Gaussian Process Machine Learning and...

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International Conference on Computer and Automation Engineering, ICCAE

作者： Muhammad Naeim Mohd Aris Shalini Nagaratnam Nurul Nnadiah Zakaria Muhammad Fadhirul Anuar Mohd Azami Muhammad Afiq Ikram Samsudin Ernee Sazlinayati Othman Department of American Degree Transfer Program School of American Education Sunway University Bandar Sunway Malaysia Department of Econometrics and Business Statistics School of Business Monash University Malaysia Bandar Sunway Malaysia Department of Fundamental and Applied Sciences Faculty of Science and Information Technology Universiti Teknologi PETRONAS Seri Iskandar Malaysia Department of American Degree Transfer Programme School of American Education Sunway University Bandar Sunway Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Seremban 3 Malaysia Centre of Foundation Studies Universiti Teknologi MARA Cawangan Selangor Kampus Dengkil Dengkil Malaysia

ISBN: (数字)9798350370058

ISBN: (纸本)9798350370164

This study explores and compares the capability of Gaussian process machine learning (GPML) with time series analysis techniques, which are autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA) and cubic spline interpolation, in modeling unemployment rate in Malaysia over the period of 1991 to 2022. Six predictive models are developed based on the observations. The predictive performance of each model is quantified using mean absolute error (MAE) and mean squared error (MSE). GPML demonstrated the best predictive model, exhibiting minimal MAE and MSE compared to the time series analysis techniques. The statistical analysis also concluded no significant mean difference between the GPML model and the actual observations, implying a robust predictive model in predicting the unemployment rate in Malaysia.

关键词： Analytical models Interpolation Statistical analysis Time series analysis Gaussian processes Machine learning Predictive models

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Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Classification

Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Cl...

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International Conference on Informatics, Multimedia, Cyber and information System (ICIMCIS)

作者： Devi Fitrianah Kristien Margi Suryaningrum Noviyanti Tri Maretta Sagala Vina Ayumi Siew Mooi Lim Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Mathematics Statistics Department School of Computer Science Bina Nusantara University Jakarta Indonesia Informatics Engineering Program Faculty of Computer Science Universitas Mercu Buana Jakarta Indonesia Computer Science Department Faculty of Computing and Information Technology Tunku Abdul Rahman University College Kuala Lumpur Malaysia

ISBN: (纸本)9781665473286

Study on the identification and classification of fish is challenging and valuable because of its role in advancing the marine and agricultural fields. This research has benefits interms of monitoring fish populations and ecosystems in a particular area. Furthermore, this research helps monitor fish that are considered threatened or endangered so that it makes iteasier to map prohibited areas for fishing. This research aims to know performance of MobileNetV2 and VGG16 with parameter tuning process by identifying the value of batch size, epoch, learning rate, and optimizer for fish image dataset. The proposed research phase consists of five main stages, including experimental setup, dataset construction, dataset preprocessing, dataset training and modelling and evaluation. As the result, VGG16 obtained the highest accuracy value. For VGG16 without fine-tuning, the testing accuracy is 98.07%. For VGG16 with fine-tuning, the testing accuracy is 96.56%.

关键词： Training Biological system modeling Sociology Ecosystems Fish Statistics Monitoring

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Assessment of CYP2D6 gene expression in liver tissue: Variability in CYP2D6 mRNA levels within genotype-predicted metabolizer phenotype groups

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Chemico-Biological Interactions 2025年 416卷

作者： Meaddough, Erika L. Sarasua, Sara M. Kunkel, Deborah Boccuto, Luigi Ganakammal, Satishkumar R. Moersen, Matt Farrell, Christopher L. School of Nursing Healthcare Genetics & Genomics Program College of Behavioral Social and Health Sciences Clemson University Clemson SC United States School of Mathematical and Statistical Sciences College of Science Clemson University Clemson SC United States Research Computing & Data Clemson Computing and Information Technology Clemson University Clemson SC United States

Pharmacogenetic (PGx) testing can be used to help guide drug therapy and decrease or avoid the risk of adverse drug reactions. CYP2D6 is an important pharmacogene in pharmacogenomics testing panels. However, phenoconversion, whereby an individual's ability to metabolize a drug does not match the genotype-predicted metabolizer status, is a confounding factor to the accurate application of PGx testing results to patient care. To address this issue, CYP2D6 expression between and within genotype-predicted CYP2D6 metabolizer phenotype groups was compared using WGS and RNA-Seq data from 134 normal liver tissue donors obtained from the GTEx program. Wide variability in CYP2D6 mRNA levels was observed within metabolizer phenotype groups. The median expression level for ultrarapid metabolizers (UMs) was 738.9 TPM (transcripts per million;196.8–778.9 TPM), 212.5 TPM (32.1–666.5 TPM) for normal metabolizers (NMs), 219.6 TPM for intermediate metabolizers (IMs) (22–389.8 TPM), and 121.2 TPM for poor metabolizers (PMs) (9.3–298.2 TPM). The PM and UM phenotypes were significant predictors of CYP2D6 expression (p = 0.0004 and p = 0.019, respectively). Interestingly, expression of the gene encoding human serum albumin (ALB) was also a significant predictor of CYP2D6 expression (p = 0.0003). Data from 50 patients with hepatocellular carcinoma obtained from the TCGA program showed no significant difference in expression between tumor tissue (median = 119.7 TPM, range 0.16–817.7 TPM) and normal matched tissue (median = 143.3 TPM, range 26.2–810.7 TPM). Transcriptional regulation of CYP2D6 expression may contribute to differences in drug response and risk for CYP2D6 phenoconversion. Efforts to understand the role of gene expression to predict CYP2D6 phenoconversion may inform the use of PGx testing in the clinical setting. © 2025 The Authors

关键词： CYP2D6 Drug-metabolizing enzymes Gene expression Hepatocellular carcinoma Metabolizer phenotype Pharmacogenetics Phenoconversion

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Blockchain technology? Visual Art Marketing Through Non-Fungible Token (NFT) from the Perspectives of Islamic Shariah in Malaysia: A Need Assessment Analyses

Blockchain Technology? Visual Art Marketing Through Non-Fung...

引用

computing and information technology (ICCIT-1441), 2020 International Conference on

作者： Ishak Ramli Mohamad Noorman Masrek Mohd Daud Abdul Rahim Thuraiya Mohd Mohd Ashrof Zaki Yaacob Mohd Radzuan Mohd Rafee Issarezal Ismail Mohamad Rahimi Osman Mohd Khalik Mustafa Muhammad Redza Rosman Program of Fine Art College of Creative Arts Universiti Teknologi MARA Perak Branch Perak Malaysia School of Information Management College of Computing Informatics and Mathematic Universiti Teknologi MARA Shah Alam Selangor Malaysia Program of Fine Art College of Creative Arts Universiti Teknologi MARA Perak Branch Seri Iskandar Campus Perak Malaysia Program of Real Estate Management College of Buit Environment Universiti Teknologi MARA Perak Branch Seri Iskandar Campus Perak Malaysia Division of Zakat Charity and Waqf Academy of Contemporary Islamic Studies Universiti Teknologi MARA Shah Alam Selangor Malaysia Program of Photography College of Creative Arts Universiti Teknologi MARA Perak Branch Seri Iskandar Campus Perak Malaysia Academy of Contemporary Islamic Studies Universiti Teknologi MARA Shah Alam Selangor Malaysia Program of Fashion Design College of Creative Arts Universiti Teknologi MARA Perak Branch Seri Iskandar Campus Perak Malaysia Program of Building Technology College of Built Environment Universiti Teknologi MARA Perak Branch Seri Iskandar Campus Perak Malaysia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

The development of blockchain technology across the globe has seen to be vast, including those related to visual arts. In Islam, anything new: especially those that never existed before, needs to be, obligatory discussed from the Islamic Shariah perspective. Therefore, the vast development of blockchain technology relating to non-fungible token (NFT) towards marketing visual art needs assessment from the Islamic Shariah perspective as there has not been any empirical studies carried out towards it. This paper intends to discuss a need assessment analyses regarding marketing visual art through the NFT platform blockchain technology from the Islamic Shariah perspective in Malaysia. Using the systematic literature review technique, researchers have discussed briefly regarding the development of visual arts, NFT and its trend, and reviewing Islamic perspectives on NFT. As a result, it is necessary for the Islamic Council of Malaysia to explain in detail the marketing of visual arts through NFT. A proper paperwork is crucial to give an overview on the practices regarding NFT that are 1) crypto currency; 2) artwork visual appearance; 3) copyright and consent issue; 4) pricing and value; and 5) royalty and blockchain record - to market of visual arts for Islamic Shariah assessment; in ensuring the muamalat activities amongst Muslims are centred on the faith of Islam.

关键词： Visualization Art Pricing Market research Cryptocurrency Nonfungible tokens information technology Systematic literature review

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Android Based Heart-rate Monitoring System using ECG Sensor

Android Based Heart-rate Monitoring System using ECG Sensor

引用

International Conference on Sustainable Engineering and Creative computing (ICSECC)

作者： Rosalina Grace Selina Rila Mandala Faculty of Computing Information Technology Study Program President University Bekasi Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9781728175898

This research aims to help the user monitor their heart’s condition and notify other people in case of an emergency due to an abnormal heartbeat (normal heartbeat around 60-100 beats per minute). From the heart rate graph, the user could do a consultation with an expert in the nearest hospital using a map feature that will show the nearest hospital with a 1 KM radius from the user’s current location. The heart is undeniably an essential part of the human body system. The heart's missions are to pump blood to the lungs to expel the waste carbon dioxide that resulted from the respiration and pump blood that will deliver oxygen throughout the human body's circulation system. While the blood that carries oxygen being pumped throughout the human body, the heart will provide the oxygen to all cells. Body cells need to use oxygen to get the energy from food through a process called cellular respiration. Monitoring our heartbeat and pulse rate can increase people’s awareness about their heart’s health, motivating, and encouraging people to maintain a healthy eating plan and exercise. This research will be implemented ECG Sensor. The sensor will then send the data to the android application via HC-06 Bluetooth module. Monitoring the heart rate and analyzing the user’s heartbeat will be done in the android with a microcontroller device's help.

关键词： Bluetooth Heart beat Hospitals Microcontrollers Lung Electrocardiography Biomedical monitoring

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Interactive 3D Segmentation for Primary Gross Tumor Volume in Oropharyngeal Cancer

arXiv

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arXiv 2024年

作者： Saukkoriipi, Mikko Sahlsten, Jaakko Jaskari, Joel Orsmaa, Lotta Kangas, Jari Rasouli, Nastaran Raisamo, Roope Hirvonen, Jussi Mehtonen, Helena Järnstedt, Jorma Mäkitie, Antti Naser, Mohamed Fuller, Clifton Kann, Benjamin Kaski, Kimmo Department of Computer Science Aalto University School of Science Espoo Finland Faculty of Information Technology and Communication Sciences Computing Sciences University of Tampere Tampere Finland Department of Radiology Tampere University Faculty of Medicine and Health Technology Tampere University Hospital Tampere Finland Department of Otorhinolaryngology-Head and Neck Surgery Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki University Hospital Helsinki Finland Department of Radiation Oncology The University of Texas MD Anderson Cancer Center HoustonTX United States Artificial Intelligence in Medicine Program Mass General Brigham Harvard Medical School BostonMA United States Department of Radiation Oncology Dana-Farber Cancer Institute Brigham and Women's Hospital Harvard Medical School BostonMA United States The Alan Turing Institute British Library 96 Euston Rd LondonNW1 2DB United Kingdom

The main treatment modality for oropharyngeal cancer (OPC) is radiotherapy, where accurate segmentation of the primary gross tumor volume (GTVp) is essential. However, accurate GTVp segmentation is challenging due to significant interobserver variability and the time-consuming nature of manual annotation, while fully automated methods can occasionally fail. An interactive deep learning (DL) model offers the advantage of automatic high-performance segmentation with the flexibility for user correction when necessary. In this study, we examine interactive DL for GTVp segmentation in OPC. We implement state-of-the-art algorithms and propose a novel two-stage Interactive Click Refinement (2S-ICR) framework. Using the 2021 HEad and neCK TumOR (HECKTOR) dataset for development and an external dataset from The University of Texas MD Anderson Cancer Center for evaluation, the 2S-ICR framework achieves a Dice similarity coefficient of 0.713 ± 0.152 without user interaction and 0.824 ± 0.099 after five interactions, outperforming existing methods in both cases. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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How a Global Elective Engineering class impacts Engineering Students’ Global Efficacy

How a Global Elective Engineering class impacts Engineering ...

引用

World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC)

作者： Jamie R. Gurganus Michael M. Malschützky João Carlos Do Carmo Santos Marc Zupan Abel Dias dos Santos Engineering & Computing Education Program & Graduate School University of Maryland Baltimore County (UMBC) Baltimore USA Department of Management Sciences Hochschule Bonn-Rhein-Sieg Centre for Teaching Development and Innovation University of Applied Sciences (H-BRS) Sankt Augustin Germany Department of Mechanical Engineering College of Engineering and Information Technology University of Maryland Baltimore County (UMBC) Baltimore USA Departamento de Engenharia Mecânica Faculdade de Engenharia da Universidade do Porto (FEUP) Porto Portugal

Today’s pluralistic and globalized society requires a global perspective in how we think, view ourselves, and relate to others to collaborate successfully with people with different cultural backgrounds. To support the development of a global perspective, universities cultivate international co-operations to enable international mobility for students, faculty, and staff. However, in-person mobility can be inhibited due to various reasons. To support a wider pool of students gaining international experience, modern digital mediated course formats can be used to offer virtual instead of in-person mobility. This contribution evaluates a digital mediated course for mechanical engineering students (Global Engineering, GE) held in co-operation between an US and a Portuguese university, where internationally mixed student groups working on design projects in an Hyflex setting. Asides a literature driven discussion about the need for Global Engineers and a detailed description of the GE course, a longitudinal analysis was performed to assess the impact of the GE course on the students’ development of a global perspective. The longitudinal analysis showed strong statistical support that the digitally mediated GE course improved the participating students’ global perspective, assessed with the Global Perspective Inventory (GPI), tremendously. The observed improvements were in three of the six GPI dimensions comparable to an in-person semester abroad. Limitations of the study and future investigations are discussed.

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Icd: A New Interpretable Cognitive Diagnosis Model for Intelligent Tutor Systems

SSRN

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SSRN 2022年

作者： Qi, Tianlong Ren, Meirui Guo, Longjiang Li, Xiaokun Li, Jin Zhang, Lichen Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an710062 China Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi’an710119 China Engineering Laboratory of Teaching Information Technology of Shaanxi Province Xi’an710119 China School of Computer Science Shaanxi Normal University Xi’an710119 China Xi’an Key Laboratory of Culture Tourism Resources Development and Utilization Xi’an710062 China Postdoctoral Program of Heilongjiang Hengxun Technology Co. Ltd Harbin150090 China

Cognitive diagnosis plays a crucial role in Intelligent Tutor Systems, which aims to diagnose learners' cognitive states according to learners' observable records, such as exercise records. However, this paper observes that the existing cognitive diagnosis models still have rooms for performance improvements: (1) they ignore the interaction between knowledge concepts;(2) they ignore the quantitative relation between exercises and concepts. In order to solve the aforementioned problems, this paper proposes a new interpretable cognitive diagnosis model named ICD based on a novel neural network designed by us, which can conduct answer records with different lengths. The neural network can fully utilize the interaction among concepts, the quantitative relation between exercises and concepts, and considers bias replacement by slip and guess, which makes the ICD more interpretable. The paper has conducted extensive experiments on four real publicity datasets, and the results show that both the performances and the interpretability of ICD are better than the latest state-of-the-art CDMs such as RCD, NCDM, and CDGK, and the classical CDMs such as DINA and MIRT. © 2022, The Authors. All rights reserved.

关键词： Neural networks

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