Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference最新文献_第3页

Affect recognition using simplistic 2D skeletal features from the upper body movement 影响识别使用简单的2D骨骼特征从上半身的运动

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582115

Saba Baloch, Syed A. R. Syed Abu Bakar, M. Mokji, Saima Waseem, Adel Hafeezallah

引用次数: 0

Implementation of an Undergraduate Admission Chatbot Using Microsoft Azure's Question Answering and Bot Framework 基于Microsoft Azure问答和Bot框架的本科招生聊天机器人的实现

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582135

Alvin Gorom Usigan, Ma. Isabela Salomeo, Glan Jazler Lieau Jover Zafe, C. Centeno, A. A. R. Sison, Andrew G. Bitancor

引用次数: 1

Implementation of a Liquefied Petroleum Gas Leakage Detection System 液化石油气泄漏检测系统的实现

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582138

Kyle Andrey Tajale Dahab, Andrea Nicole Esconde Medina, Mark Anthony Umali Siena, Joseph Darwin Celso Co, C. Centeno, Domingo Valenzuela Tanael

{"title":"Implementation of a Liquefied Petroleum Gas Leakage Detection System","authors":"Kyle Andrey Tajale Dahab, Andrea Nicole Esconde Medina, Mark Anthony Umali Siena, Joseph Darwin Celso Co, C. Centeno, Domingo Valenzuela Tanael","doi":"10.1145/3582099.3582138","DOIUrl":"https://doi.org/10.1145/3582099.3582138","url":null,"abstract":"Gas leakage is the unintentional discharge of gaseous substances into any location where they should not be present. The potential for numerous accidents that result in property damage and physical injuries is due to the gas' properties, such as toxicity, flammability, explosivity, etc. Liquefied petroleum gas can also be quite hard to detect as it can be obscured by stronger odors or smells that may come from food. It is also invisible to the naked eye. In this study, the researchers developed a system that detects liquefied petroleum gas leakage and immediately notifies nearby users that are affected through the use of an ESP32 microcontroller, MQ-6 gas sensor, and a passive buzzer module. The results show that the researchers have successfully achieved their objective of detecting the inadvertent outflow of liquefied petroleum gas and notifying affected users via buzzer and email application. The evaluation of the study using ISO 25010 shows that the system has excellent Functional Suitability and Usability with a mean of 4.84 and 4.72, respectively.","PeriodicalId":222372,"journal":{"name":"Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132000932","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

Exploring Semi-Supervised Learning for Camera Trap Images from the Wild 探索半监督学习相机陷阱图像从野外

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582122

A. Sajun, I. Zualkernan

{"title":"Exploring Semi-Supervised Learning for Camera Trap Images from the Wild","authors":"A. Sajun, I. Zualkernan","doi":"10.1145/3582099.3582122","DOIUrl":"https://doi.org/10.1145/3582099.3582122","url":null,"abstract":"Camera traps are an important tool for ecologists in their fight against ever increasing animal extinction. However, the use of these camera traps involves the tedious process of manually labeling the animals in captured images. An added hinderance is that of empty images triggered by wind movement and other stimuli called ghost images. Deep learning techniques have previously been applied to automate this task but have been prevented from being entirely effective due to two problems. Firstly, a lack of labeled data due to the expertise of ecologists being required to perform the labeling and secondly the training data being imbalanced in nature due to the high presence of ghost images and images of common animals. Many semi-supervised learning (SSL) algorithms perform well using very small amount of labelled data however need to be evaluated when trained with imbalance data. This paper explores the performance of FixMatch and a derivative called the Auxiliary Balanced Classifier (ABC) under a variety of data imbalance and proportions of labelled data. The algorithms were evaluated using a in the wild imbalanced dataset from camera traps in addition to benchmark datasets such as CIFAR-10, CIFAR-100 and SVHN. While FixMatch showed a consistent drop in performance as the data imbalance was increased, the algorithm generally outperformed ABC. However, the ABC derivative performed better than FixMatch in cases of very high imbalance.","PeriodicalId":222372,"journal":{"name":"Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126825524","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

Food Production Forecasting with Time Series and Ensemble Modeling Methods 基于时间序列和集成建模方法的粮食产量预测

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582109

Kittisak Kerdprasop, P. Chuaybamroong, Nittaya Kerdprasop

{"title":"Food Production Forecasting with Time Series and Ensemble Modeling Methods","authors":"Kittisak Kerdprasop, P. Chuaybamroong, Nittaya Kerdprasop","doi":"10.1145/3582099.3582109","DOIUrl":"https://doi.org/10.1145/3582099.3582109","url":null,"abstract":"Safe and sufficient food production is important to the achievement of the sustainable development goal targeting \"zero hunger by the year 2030\" agreed by all member states of the United Nations upon the summit meeting in 2015. This research supports such goal by performing insightful analysis over a long duration of global food production spanning from the year 1971 to 2020. Analysis methodology adopts the application of time series forecasting using the ARIMA algorithm with varied parameter values as well as the machine learning modeling methods through six learning algorithms, which are linear regression (MLR), support vector regression (SVR), artificial neural network (ANN), random forest (RF), gradient boosting (GB), and AdaBoost (AB). The algorithms MLR, SVR, ANN are in the category of single modeling method that a single model is enough for predicting future value, whereas RF, GB, AB are ensemble in which a group of models are used cooperatively to predict the output. To observe characteristics of modeling results, the global models trained from food production index of 164 countries are compared against the minor scale of Thailand. For time series forecasting results, we found that ARIMA (p,d,q) model yields the best prediction at a global scale when setting the parameters (p,d,q) to be (1,1,1), but the parameter values (1,1,2) works better for the minor scale of a single country. In the case of machine learning modeling methods, the ensemble of gradient boosting produces the most accurate forecasting result in both global and regional scales.","PeriodicalId":222372,"journal":{"name":"Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116315166","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

Real-time Pose Estimation in Mobile with Dense Upsampling Convolution 基于密集上采样卷积的移动设备实时姿态估计

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582120

Yingxian Chen, Baoheng Zhang, W. Fok

引用次数: 0

Application of Artificial Intelligence in Early–Stage Diagnosis of Sepsis 人工智能在脓毒症早期诊断中的应用

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582129

Oznur Esra Par, E. Sezer, H. Sever

{"title":"Application of Artificial Intelligence in Early–Stage Diagnosis of Sepsis","authors":"Oznur Esra Par, E. Sezer, H. Sever","doi":"10.1145/3582099.3582129","DOIUrl":"https://doi.org/10.1145/3582099.3582129","url":null,"abstract":"Patient care is a critical task, which requires a lot of effort. Medical practitioners face many challenges, especially during diagnosing different diseases. Sepsis is one of the riskiest diseases, which proves to be lethal for Intensive Care Unit (ICU) patients. World Health Organization (WHO) has declared it a major cause of death worldwide. Early-stage diagnosis of sepsis can help in terminating it in the start. But unfortunately, medical practitioners encounter hitches in the early-stage diagnosis of sepsis. The study used SOFA (Sequential Organ Failure Assessment) for measuring the severity of sepsis in patients. The study employs artificial intelligence techniques such as Multilayer Perceptron (MLP) and Random Forest (RF) to diagnose early-stage of sepsis. The study compared the performance of MLP (connected and non-connected) and Random Forest (connected and non-connected) algorithms. The results indicate that for both of the algorithms, the connected method yielded better results than the non-connected method. Further, it was found that RF both connected and non-connected algorithms yielded better results than MLP algorithms and the Random Forest connected algorithm yielded highly accurate results for diagnosing early-stage sepsis in the 3rd hour.","PeriodicalId":222372,"journal":{"name":"Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128463672","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

Discussion on Designing the Service Path of Humanoid Robots 仿人机器人服务路径设计的探讨

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582134

Tingsheng Weng, Chien-Kuo Li, Isaac Chao

引用次数: 0

Support Vector Machine based Handwritten Letters and Digits Recognition using Deep Learning 基于深度学习的手写字母和数字识别的支持向量机

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582116

A. Balobaid, Saahirabanu Ahamed, Shermin Shamsudheen

引用次数: 0

The Search for Areas with High Solar Energy Based on Clustering Analysis 基于聚类分析的太阳能高能量区搜索

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582112

Isara Soisom, Kittisak Kerdprasop, Nittaya Kerdprasop

引用次数: 0