World Sci. Annu. Rev. Artif. Intell.最新文献

Artificial Intelligence in Evidence-based Medicine: Challenges and Opportunities 循证医学中的人工智能:挑战与机遇

World Sci. Annu. Rev. Artif. Intell. Pub Date : 2023-06-16 DOI: 10.1142/s2811032323300025

Xue Li, Catherine Zou, R. Boots, Sen Wang, Weitong Chen, G. Zuccon

引用次数: 0

A Survey on Code Representation 代码表示研究综述

World Sci. Annu. Rev. Artif. Intell. Pub Date : 2023-05-22 DOI: 10.1142/s2811032323500017

Peter D. Nagy, Marzieh Ahmadi Najafabadi, Heidar Davoudi

引用次数: 0

Recommendation System: A Survey and New Perspectives 推荐系统:调查与新视角

World Sci. Annu. Rev. Artif. Intell. Pub Date : 2023-03-08 DOI: 10.1142/s2811032323300013

Wei Wei, Sen Zhao, Ding Zou

引用次数: 3

Black-Box Attack using Adversarial Examples: A New Method of Improving Transferability 基于对抗性实例的黑盒攻击:一种提高可转移性的新方法

World Sci. Annu. Rev. Artif. Intell. Pub Date : 2022-12-16 DOI: 10.1142/s2811032322500059

Tao Wu, Tie Luo, D. Wunsch

引用次数: 0

Multimodal Learning for Multi-Omics: A Survey 面向多组学的多模态学习研究综述

World Sci. Annu. Rev. Artif. Intell. Pub Date : 2022-11-29 DOI: 10.1142/S2811032322500047

Sina Tabakhi, M. N. I. Suvon, Pegah Ahadian, Haiping Lu

{"title":"Multimodal Learning for Multi-Omics: A Survey","authors":"Sina Tabakhi, M. N. I. Suvon, Pegah Ahadian, Haiping Lu","doi":"10.1142/S2811032322500047","DOIUrl":"https://doi.org/10.1142/S2811032322500047","url":null,"abstract":"With advanced imaging, sequencing, and profiling technologies, multiple omics data become increasingly available and hold promises for many healthcare applications such as cancer diagnosis and treatment. Multimodal learning for integrative multi-omics analysis can help researchers and practitioners gain deep insights into human diseases and improve clinical decisions. However, several challenges are hindering the development in this area, including the availability of easily accessible open-source tools. This survey aims to provide an up-to-date overview of the data challenges, fusion approaches, datasets, and software tools from several new perspectives. We identify and investigate various omics data challenges that can help us understand the field better. We categorize fusion approaches comprehensively to cover existing methods in this area. We collect existing open-source tools to facilitate their broader utilization and development. We explore a broad range of omics data modalities and a list of accessible datasets. Finally, we summarize future directions that can potentially address existing gaps and answer the pressing need to advance multimodal learning for multi-omics data analysis.","PeriodicalId":404894,"journal":{"name":"World Sci. Annu. Rev. Artif. Intell.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122831954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Modeling and Analysis of Discrete Facial Expressions with Dense Optical Flow-derived Features 具有密集光流衍生特征的离散面部表情建模与分析

World Sci. Annu. Rev. Artif. Intell. Pub Date : 2022-11-11 DOI: 10.1142/s2811032322500035

Shivangi Anthwal, D. Ganotra

引用次数: 0

Early Detection of Gestures using Context-and-Gap-aware Predictive Pose Framework 使用上下文和间隙感知预测姿势框架的手势早期检测

World Sci. Annu. Rev. Artif. Intell. Pub Date : 2022-10-28 DOI: 10.1142/s2811032322500023

Nishant Bhattacharya, Suresh Sundaram

引用次数: 0

Deep Learning-Based Downscaling of Temperatures for Monitoring Local Climate Change Using Global Climate Simulation Data 基于深度学习的温度降尺度全球气候模拟数据监测局部气候变化

World Sci. Annu. Rev. Artif. Intell. Pub Date : 2022-09-30 DOI: 10.1142/s2811032322500011

Firas Gerges, M. Boufadel, E. Bou‐Zeid, H. Nassif, J. T. Wang

{"title":"Deep Learning-Based Downscaling of Temperatures for Monitoring Local Climate Change Using Global Climate Simulation Data","authors":"Firas Gerges, M. Boufadel, E. Bou‐Zeid, H. Nassif, J. T. Wang","doi":"10.1142/s2811032322500011","DOIUrl":"https://doi.org/10.1142/s2811032322500011","url":null,"abstract":"The impact of climate change on the environment has become increasingly visible today, and foreseeing future climate events, which involves long-term prediction of climate variables (e.g., temperature, wind speed, precipitation, etc.) at a local small scale in a local region, is crucial for disaster risk management. General Circulation Models (GCMs) allow for the simulation of multiple climate variables, decades into the future (often till the year 2100). GCM simulations, however, are at a global large scale (from 100 km to 600 km) and are too coarse to monitor climate change at the local small scale. Statistical downscaling approaches are often applied to the GCM simulations to allow the evaluation of the GCM outputs at the local scale. Machine learning-based techniques are popular approaches for statistical downscaling. In this paper, we provide an overview of GCM downscaling with machine learning and present a case study that leverages deep learning to downscale weekly averages of the daily minimum and maximum temperatures in the Hackensack–Passaic watershed in New Jersey.","PeriodicalId":404894,"journal":{"name":"World Sci. Annu. Rev. Artif. Intell.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134086771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1