2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G)最新文献

Theory of Machine Learning Based in Quantum Mechanics 基于量子力学的机器学习理论

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9311015

H. Nieto-Chaupis

引用次数: 0

eVision: Influenza Forecasting Using CDC, WHO, and Google Trends Data 展望:利用疾病预防控制中心、世界卫生组织和谷歌趋势数据进行流感预测

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9311072

Navid Shaghaghi, Andrés Calle, Yuhang Qian

{"title":"eVision: Influenza Forecasting Using CDC, WHO, and Google Trends Data","authors":"Navid Shaghaghi, Andrés Calle, Yuhang Qian","doi":"10.1109/AI4G50087.2020.9311072","DOIUrl":"https://doi.org/10.1109/AI4G50087.2020.9311072","url":null,"abstract":"Influenza, more commonly known as the flu, is a contagious respiratory illness caused by viruses which in the 2018–19 flu season, infected 37.4 to 42.9 million people in the United States alone. Of those, 431 to 647 thousand were hospitalized and 36,400 to 61,200 (most of them elderly and children) succumbed to the disease. At the time of this writing, the best known defense against influenza is vaccination. However, due to the annual mutation of the very many strands of the flu virus, new vaccines must be administered every flu season. Hence, the prediction of the rate of growth in reported infection cases of each strand of the flu is paramount to ensuring the correct supply of vaccines per strand. Machine learning - specifically Neural Networks - are a great tool for making future predictions using existing data. Long Short-Term Memory (LSTM) neural networks are utilized by Santa Clara University's EPIC (Ethical, Pragmatic, and Intelligent Computing) and BioInnovation & Design laboratories for continued research and development of a tool named eVision (Epidemic Vision) to predict the trend of influenza cases throughout the flu season. eVision has been trained on data gathered across 4 flu seasons from the 2014–15 season to the 2017–18 season of the Center for Disease Control and Prevention (CDC) records as well as the World Health Organization (WHO) and Google Trends search result data gathered across the same period of time. eVision has been able to make 7 weeks in advance predictions about the flu trend in the 2018–19 United States flu season with 90.15% accuracy. This paper is to report the achievements of eVision thus far and to delineate next phases for the project which aims to provide a tool for the pharmaceutical and healthcare industries to more accurately predict the trend of flu (and other) epidemics in order to meet the demands for vaccines and test kits ahead of time.","PeriodicalId":286271,"journal":{"name":"2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125589023","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: 4

AI for monitoring the Sustainable Development Goals and supporting and promoting action and policy development 人工智能用于监测可持续发展目标，支持和促进行动和政策制定

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9311014

Lynn Miller, M. Bolton, Julie Boulton, Michael Mintrom, Ann E Nicholson, C. Rüdiger, R. Skinner, R. Raven, Geoffrey I. Webb

{"title":"AI for monitoring the Sustainable Development Goals and supporting and promoting action and policy development","authors":"Lynn Miller, M. Bolton, Julie Boulton, Michael Mintrom, Ann E Nicholson, C. Rüdiger, R. Skinner, R. Raven, Geoffrey I. Webb","doi":"10.1109/AI4G50087.2020.9311014","DOIUrl":"https://doi.org/10.1109/AI4G50087.2020.9311014","url":null,"abstract":"The United Nations sustainable development goals (SDGs) were ratified with much enthusiasm by all UN member states in 2015. However, subsequent progress to meet these goals has been hampered by a lack of data available to measure the SDG indicators (SDIs), and a lack of evidence-based insights to inform effective policy responses. We outline an interdisciplinary program of research into the use of artificial intelligence techniques to support measurement of the SDIs, using both machine learning methods to model SDI measurements and explainable AI techniques to present the outputs in a human-friendly manner. As well as addressing the technical concerns, we will investigate the governance issues of what forms of evidence, methods of collecting that evidence and means of its communication will most usefully inform effective policy development. By addressing these fundamental challenges, we aim to provide policy makers with the evidence needed to take effective action towards realising the Sustainable Development Goals.","PeriodicalId":286271,"journal":{"name":"2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121991195","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: 5

AI and Robotics-Based Cognitive Training for Elderly: A Systematic Review 基于人工智能和机器人的老年人认知训练:系统综述

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9311076

Munkhjargal Gochoo, Alistair A. Vogan, Sumayya Khalid, F. Alnajjar

引用次数: 2

Effective, Explainable and Ethical: AI for Law Enforcement and Community Safety 有效，可解释和道德:执法和社区安全的人工智能

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9311021

Campbell Wilson, Janis Dalins, Gregory Rolan

引用次数: 1

DASSL: Dynamic, AI-assisted, Scalable System for Labelling Used Bottle Images DASSL:动态的，人工智能辅助的，可扩展的标签用瓶图像系统

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9311013

Parnmet Daengphruan, Orathai Sangpetch, Akkarit Sangpetch

{"title":"DASSL: Dynamic, AI-assisted, Scalable System for Labelling Used Bottle Images","authors":"Parnmet Daengphruan, Orathai Sangpetch, Akkarit Sangpetch","doi":"10.1109/AI4G50087.2020.9311013","DOIUrl":"https://doi.org/10.1109/AI4G50087.2020.9311013","url":null,"abstract":"To ensure sustainable consumption and production, one way is to reduce waste generation by increasing the reuse rate. We have been working with the bottle classification facility to enhance the efficiency and productivity. Many used bottles come in with unimaginable ways of dirty, defective conditions. To manage the sheer volume of used bottles, we create an AI-enabled, bottle classification system. However, it requires many labelled images for training to improve accuracy. Unfortunately, the traditional approach, having human label individual images, is very time consuming. Even worse, it is not effective for our dataset because conditions of used bottles are not well defined and studied. From our experiments, the human experts cannot agree on the same labelling for similar bottle conditions, especially when impurities or defects are not separable objects. For 42%-99% of images in certain subcategories, human experts assign different labels to bottles with similar conditions. With huge inconsistency in data labelling, it deteriorates the accuracy of our classification models. To alleviate this problem, we propose a Dynamic, AI-assisted, Scalable System for Labelling used bottle images, called DASSL. DASSL employs multiple algorithms to extract and/or quantize different features of used bottle images, and cluster the images into groups with the supervision of human. With DASSL, we can achieve labelling consistency and improve scalability by reducing the data labelling time by at least 10x. To enhance agility, we can dynamically adjust DASSL to adapt to changes of cleaning machines' capabilities or bottle demand.","PeriodicalId":286271,"journal":{"name":"2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G)","volume":"166 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115565153","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: 0

Proposal for a Chatbot Prototype in the Plant Health Department of Brazilian Ministry of Agriculture 巴西农业部植物卫生部门的聊天机器人原型提案

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9311048

W. Feitosa, Flora Oyama do Patrocínio, Sara Rosa Santos, Susana Costa e Silva

{"title":"Proposal for a Chatbot Prototype in the Plant Health Department of Brazilian Ministry of Agriculture","authors":"W. Feitosa, Flora Oyama do Patrocínio, Sara Rosa Santos, Susana Costa e Silva","doi":"10.1109/AI4G50087.2020.9311048","DOIUrl":"https://doi.org/10.1109/AI4G50087.2020.9311048","url":null,"abstract":"Around the world, governments are implementing services combined with artificial intelligence to improve service to the population. The main objective of this study is to develop a chatbot prototype able to answer the most frequently asked questions about the Plant Health department of the Brazilian Ministry of Agriculture, Livestock, and Supply (MAPA), reducing the workload dedicated to responding emails and phone calls, in addition to providing face-to-face care. Initially, investigations were made on the concepts of artificial intelligence, the different types of chatbots and the tools needed to build the prototype. Subsequently, an analysis of the chosen tool and programming language was elaborated. After that, the development of the application and the configuration of the platforms began. Considering how a chatbot can spread information, including good practices about plant health and agriculture, this project can contribute to food security, increasing productivity and fighting against waste, helping to achieve UN SDG number 2 - zero hunger.","PeriodicalId":286271,"journal":{"name":"2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114741741","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: 2

A conceptual framework for AI system development and sustainable social equality 人工智能系统发展和可持续社会平等的概念框架

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9310984

L. Chen

{"title":"A conceptual framework for AI system development and sustainable social equality","authors":"L. Chen","doi":"10.1109/AI4G50087.2020.9310984","DOIUrl":"https://doi.org/10.1109/AI4G50087.2020.9310984","url":null,"abstract":"Artificial intelligence (AI) technology has been used for some years and is growing rapidly. We are living in a world where AI has been involved in many different ways; from helping us to perform online search, shopping on the internet, customer service over internet, medical research, advices over banking activities, advices for legal matters, or to determine different stages of our life, even since when a baby is born. AI has also been significantly having a strong impact on the way we conduct business; for example, customer analysis, product research, trend analysis, making price policy, and recruiting process. However, awareness levels among end-users is still low. Authorities and industries are still looking for possibilities to regulate, optimise and harmonise negative issues that have been raised. In addition to just following general policies, developers and companies also need to take ethical issues into consideration in order to build trustworthy AI powered systems. This paper is aiming to design a conceptual framework by seeking possibilities in/among known debates, issues, theories, policies, dilemmas, and with personal view, instead of finding general solutions. The opinion of this paper does not necessarily reflect the views of the organisation.","PeriodicalId":286271,"journal":{"name":"2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G)","volume":"26 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116587374","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

Using Machine Learning for Anticipating a Diabetes Crisis Through a Sensors-based Internet of Bio-nano Things Network 通过基于传感器的生物纳米物联网网络，使用机器学习预测糖尿病危机

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9311028

H. Nieto-Chaupis

引用次数: 0

Artificial Intelligence in Manufacturing: Bibliometric and Content Analysis 制造业中的人工智能:文献计量学和内容分析

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9311087

G. Zeba, Marina Dabić, M. Čičak, T. Daim

引用次数: 1