2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G)最新文献_第3页

A Review of Artificial Intelligence Applications to Achieve Water-related Sustainable Development Goals 人工智能在实现与水有关的可持续发展目标中的应用综述

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9311018

H. Mehmood, Danielle Liao, Kimberly Mahadeo

{"title":"A Review of Artificial Intelligence Applications to Achieve Water-related Sustainable Development Goals","authors":"H. Mehmood, Danielle Liao, Kimberly Mahadeo","doi":"10.1109/AI4G50087.2020.9311018","DOIUrl":"https://doi.org/10.1109/AI4G50087.2020.9311018","url":null,"abstract":"This paper reviews the Artificial Intelligence (AI) applications that help achieve water-related Sustainable Development Goals (SDGs). Current applications of AI in the water sector include i) predictive maintenance of water infrastructure, ii) forecasting water demand and consumption, iii) monitoring water reservoirs and dams, iv) tracking water quality, and v) monitoring and predicting water-related disasters. These applications contribute to achieving water-related SDG targets, specifically 3, 6, 11, and 15. The literature review shows that: i) the rate of adoption of AI-based solutions in predictive maintenance of water infrastructure has accelerated, as AI becomes increasingly accessible, and data analytics and smart sensors become more efficient and affordable; ii) deep learning technology has enabled a new generation of water management systems, which can generate short-term (daily) and long-term (annual) forecasts. iii) as Asia and South America experience an increase in water reservoir and dam construction, AI-based techniques are being successfully implemented in reservoir development and operation; iv) water quality monitoring has been the most significantly impacted by AI relative to other applications, as AI is used to examine small samples and large water bodies, and for real time water quality monitoring; v) AI can be used to forecast water-related disasters with higher accuracy, frequency and lead time, allowing for focused management of post-disaster activity. The paper ends by highlighting the challenges of adopting AI to achieve water-related SDGs.","PeriodicalId":286271,"journal":{"name":"2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G)","volume":"12 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":"123625538","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: 9

Maximising Value of Frugal Soil Moisture Sensors for Precision Agriculture Applications 节约土壤水分传感器在精准农业应用中的最大价值

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9311008

Prachin Jain, Swagatam Bose Choudhury, Prakruti V. Bhatt, Sanat Sarangi, S. Pappula

{"title":"Maximising Value of Frugal Soil Moisture Sensors for Precision Agriculture Applications","authors":"Prachin Jain, Swagatam Bose Choudhury, Prakruti V. Bhatt, Sanat Sarangi, S. Pappula","doi":"10.1109/AI4G50087.2020.9311008","DOIUrl":"https://doi.org/10.1109/AI4G50087.2020.9311008","url":null,"abstract":"Rugged soil moisture sensors with stable measurement profiles are usually expensive for a common farmer. The moisture readings for frugal, inexpensive, and often resistive, sensors are usually jittery where the sensor health tends to degrade over a period of time. Failing to catch the reduced reliability due to degraded sensor health would lead to imprecise irrigation decisions. We propose a soil moisture calibration and health management system that adds a layer of reliability to a distributed IoT-edge solution involving a frugal soil moisture sensor to help make its adoption pervasive for precision farming applications. Our approach offers a multi-step process based on artificial intelligence that maximizes the value of a low-cost soil moisture sensor. The sensor is first calibrated to give volumetric water content (a derived irrigation-related parameter) equivalent to a rugged sensor with a 5% root mean square error (RMSE). A classification model is then developed to predict the health of the sensor based on the sensor values and image analytics with an overall accuracy of 93%. We believe the outcomes would significantly help increase the adoption of precision agriculture, especially in emerging geographies, by making technology-driven intelligent solutions more affordable.","PeriodicalId":286271,"journal":{"name":"2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G)","volume":"60 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":"114464551","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

Towards an Audio-based CNN for Classroom Observation on a Smartwatch 基于音频的CNN在智能手表上的课堂观察

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9311083

I. Zualkernan, Muhammed S. Khan

引用次数: 2

Innovative Multi-Step Anomaly Detection Algorithm with Real-World Implementation: Case Study in Supply Chain Management 创新的多步骤异常检测算法与现实世界的实现:供应链管理案例研究

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9311045

E. Žunić, Zlatan Tucakovic, Sead Delalic, H. Hasic, K. Hodzic

{"title":"Innovative Multi-Step Anomaly Detection Algorithm with Real-World Implementation: Case Study in Supply Chain Management","authors":"E. Žunić, Zlatan Tucakovic, Sead Delalic, H. Hasic, K. Hodzic","doi":"10.1109/AI4G50087.2020.9311045","DOIUrl":"https://doi.org/10.1109/AI4G50087.2020.9311045","url":null,"abstract":"In all information systems it is very important to operate with correct information. Incorrect information can lead to many problems that can cause direct financial and reputation loss of the company. Data used by the system can be gathered by sensors, scripts or by hand. In all those cases, mistakes are possible. It is important to detect mistakes on time and stop them from propagating further into the system. In this paper, a novel multi-step anomaly detection algorithm based on the greatest common divisor and median value is described. The algorithm for anomaly detection in historical sales data is used as a part of the smart warehouse management system which is implemented in some of the largest distribution companies in Bosnia and Herzegovina. The algorithm showed significant results in anomaly detection on company orders and improved a number of processes in the operation of the smart warehouse management system. The algorithm described can also be used in other areas where the transaction data is collected, such as sales and banking,","PeriodicalId":286271,"journal":{"name":"2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G)","volume":"68 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":"126321679","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

Transforming Digital Employee Experience with Artificial Intelligence 用人工智能改变数字化员工体验

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9311088

Serap Zel, E. Kongar

引用次数: 14

Misinformation in Crises: A Conceptual Framework for Examining Human-Machine Interactions 危机中的错误信息:检验人机交互的概念框架

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9311010

T. Tran, P. Rad, Rohit Valecha, H. Rao

引用次数: 0

Artificial Intelligence Driven Crop Protection Optimization for Sustainable Agriculture 人工智能驱动的可持续农业作物保护优化

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9311082

Priyamvada Shankar, Nicolas Werner, S. Selinger, Ole Janssen

{"title":"Artificial Intelligence Driven Crop Protection Optimization for Sustainable Agriculture","authors":"Priyamvada Shankar, Nicolas Werner, S. Selinger, Ole Janssen","doi":"10.1109/AI4G50087.2020.9311082","DOIUrl":"https://doi.org/10.1109/AI4G50087.2020.9311082","url":null,"abstract":"This paper introduces digital farming solutions offered by xarvio™ and how these solutions contribute towards achieving the United Nations Sustainable Development Goals. By leveraging recent advancements in Artificial Intelligence, farmers can apply crop protection more efficiently by targeted usage. Respective modules presented in this paper, namely Spray Timer, Zone Spray, Buffer Zones and Product Recommendation ensure crop protection products are applied at the right time and only where they are needed while also ensuring the right product at the optimal rate. This not only reduces the impact on the environment, but moreover increases the productivity and profitability of the farmer. The impact of our digital solutions is exemplified by real world case studies in two major food production regions: Europe and Brazil. In Europe the use of Artificial Intelligence driven spray timing, variable rate application maps and product recommendation have led to a 30% decrease in fungicide usage on field trial cereal crops and a 72% decrease in tank leftovers reducing environmental pollution. In Brazil the Zone Spray weed maps solution created using Computer Vision techniques resulted in a 61% average savings, cutting back on almost two thirds of herbicide and water consumption. As a result the solutions presented in this paper cater to the UN Sustainable Development Goals of zero hunger and responsible consumption and production.","PeriodicalId":286271,"journal":{"name":"2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G)","volume":"62 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":"117049293","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

A Novel Application for the Efficient and Accessible Diagnosis of ADHD Using Machine Learning (Extended Abstract) 机器学习在ADHD诊断中的新应用(扩展摘要)

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9311012

S. Khanna, William Das

{"title":"A Novel Application for the Efficient and Accessible Diagnosis of ADHD Using Machine Learning (Extended Abstract)","authors":"S. Khanna, William Das","doi":"10.1109/AI4G50087.2020.9311012","DOIUrl":"https://doi.org/10.1109/AI4G50087.2020.9311012","url":null,"abstract":"Attention-deficit/hyperactivity disorder is the most pervasive neurodevelopmental disorder among children and adolescents. Current clinical diagnosis, however, is inaccurate, inefficient, and inaccessible in developing nations, hindering the administration of proper treatment regimens. Clinical assessments are based on qualitative observations of perceived behavior. They are time-consuming and costly, preventing minorities and socioeconomically disadvantaged groups from gaining the support they need to succeed academically, socially, and occupationally. A more accurate and accessible method of detection is necessary to ensure that all children are able to be diagnosed and given proper treatment regimens. This research proposes a novel machine learning-based method to analyze pupil-dynamics data as an objective biomarker to characterize ADHD. After visualizing and engineering pupillometric features, a voting ensemble classification algorithm and meta learner were developed and yielded the most optimal leave-one-out-cross-validation metrics on a declassified dataset. The ensemble model, in particular, classified ADHD with. 821 sensitivity, 0.727 specificity, and 0.856 AUROC. This model was implemented in a web application that administers a memory task and captures pupil biometrics in realtime. This application is the first to use pupil-size dynamics as a biomarker, and offers a time-efficient, accurate, and accessible approach to diagnose ADHD in developing nations.","PeriodicalId":286271,"journal":{"name":"2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G)","volume":"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":"125936782","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

The Kullback-Leibler divergence as the logarithm of the quantum bit error rate and the lost of information at the BB84 protocol 在BB84协议中，Kullback-Leibler散度作为量子误码率和信息丢失的对数

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/AI4G50087.2020.9311065

H. Nieto-Chaupis

引用次数: 0

Index 指数

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) Pub Date : 2020-09-21 DOI: 10.1109/ai4g50087.2020.9311090

引用次数: 0