2022 IEEE World AI IoT Congress (AIIoT)最新文献_第8页

Using Machine Learning and Regression Analysis to Classify and Predict Danger Levels in Burning Sites 使用机器学习和回归分析来分类和预测燃烧地点的危险程度

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817232

Adenrele A. Ishola, Damian Valles

{"title":"Using Machine Learning and Regression Analysis to Classify and Predict Danger Levels in Burning Sites","authors":"Adenrele A. Ishola, Damian Valles","doi":"10.1109/aiiot54504.2022.9817232","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817232","url":null,"abstract":"Firefighters go into burning structures to rescue trapped victims and put out the fire as soon as possible. Factors such as extreme temperatures, smoke, toxic gases, explosions, and falling objects inhibit their efficiency and risk their safety. These factors could change within a twinkle of an eye. Firefighters must be provided with accurate information and data about the burning site. They can make informed decisions about their duties and know when it is safe to enter and evacuate to reduce casualties. This research work presents Machine Learning (ML) and regression models for predicting the danger levels in burning sites and utilizes autonomous embedded system vehicles (AESV) to validate the models' performance to increase firefighters' safety. We investigated the classification performance of three ML methods: Support Vector Machines (SVM), Logistic Regression (LR), and k- Nearest Neighbors (k-NN) on the Cross Laminated Timber (CLT) data collected by the National Institute of Standards and Technology (NIST) and the National Research Council Canada while testing the impacts of laminated timber in a controlled fire temperature. We have reported promising results for danger levels classification with the three models, but the k-NN performed slightly better than the other two classifiers.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116195115","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

Smart Three In One System For Indoor Safety Assurance 智能三合一系统，保证室内安全

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817286

A. Sandanayake, Yehan Kodithuwakku, Chanuka Bandara, V. Logeeshan

引用次数: 0

Pulse and Signal Data Classification Using Conventional and Few-Shot Machine Learning 脉冲和信号数据分类使用常规和少次机器学习

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817223

Kayla Lee, K. George

{"title":"Pulse and Signal Data Classification Using Conventional and Few-Shot Machine Learning","authors":"Kayla Lee, K. George","doi":"10.1109/aiiot54504.2022.9817223","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817223","url":null,"abstract":"Signal detection is a key component in a radar system; however, signals are often muddled with noise and interference, which can make singling out the pure signals difficult. Also, signals are often interleaved with other signals, which makes it difficult to tell from first glance where a signal starts and ends. This paper will focus on classifying pulses and signals that were generated in MATLAB using few-shot machine learning and conventional machine learning techniques. The signals will be filtered using the Hilbert transform, and the envelope will be taken in order for the data to be used to train machine learning models. The few-shot learning method used in this study involves meta-learning and utilizes an algorithm that was adapted to handle data rather than images. Specifically, the models will be trained using pure time domain data, and the validation accuracies of each model will be compared to see which technique fares best when using minimal data. The trained models will then be used to try to classify a test set and observe if they correctly classify whether a given sample of data represents a pulse or a signal. In the second portion of this experiment, the data will also be labeled based on the number of pulses or signals present in the given sample, using the same methodology but with eight classes instead of two. These results will be compared to see not only how the models fare against one another, but also how having a larger number of classes with a certain attribute to identify affects the accuracy as well.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134334499","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

Applying Aspect-Oriented Design Methodology to Manage Time-Validity of Information in Internet-of-Things Systems 应用面向方面的设计方法管理物联网系统信息的时效性

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817345

Vyas O'Neill, B. Soh

引用次数: 1

Automated Medicinal-Pill Dispenser with Cellular and Wi-Fi IoT Integration 具有蜂窝和Wi-Fi物联网集成的自动药物分配器

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817226

Chanuka Bandara, A. Sandanayake, Yehan Kodithuwakku, V. Logeeshan

引用次数: 0

An Approach for Automatic Discovery of Rules Based on ECG Data Using Learning Classifier Systems 基于学习分类器系统的心电数据规则自动发现方法

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817370

Muthana Zouri, A. Ferworn

{"title":"An Approach for Automatic Discovery of Rules Based on ECG Data Using Learning Classifier Systems","authors":"Muthana Zouri, A. Ferworn","doi":"10.1109/aiiot54504.2022.9817370","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817370","url":null,"abstract":"Personalized medicine aims to understand the underlying relationships between the multitudes of factors affecting a patient's health and provide physicians with an evidence-based approach to customize the treatment based on patient-specific characteristics. Machine-learning techniques can examine available data and discover relationships and patterns that may not be explicitly expressed within the data. In this case, physicians can use this knowledge for hypothesis testing and conduct investigations into the possible conditions that affect the patients' health. The benefits of personalized medicine include improved patient satisfaction, reduced length of hospitalization, enhanced treatment outcomes, and increased overall efficiency of the health care system. In this paper, we present an approach based on Learning Classifier Systems (LCS) to automatically discover rules that can support medical decision-making in evaluating the patient's heart condition. LCS are considered adaptive rule-based systems that can evolve a set of classifiers called rules based on a learning component that assigns credit to existing rules and an evolutionary component that helps discover new ones. The proposed approach is based on the implementation of an accuracy-based LCS that has been modified to support rules learning for personalized medical decision-making. The experimental results in the case study section provide a proof of concept for rules learning based on ECG data to discover rules that can support physicians in the medical decision-making process.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125767027","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

Using Amazon Managed Blockchain for ePHI An Analysis of Hyperledger Fabric and Ethereum 使用亚马逊管理的区块链进行ePHI超级账本结构和以太坊的分析

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817198

Audrey Long, Daniel Choi, Joel Coffman

引用次数: 1

A Review on Energy Efficient Strategies for Corona Based Architecture in Wireless Sensor Networks 基于电晕架构的无线传感器网络节能策略综述

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817369

Aaniya Agrawala, Vidhi Katyal, Neha Pandey, Astha Jain, Vivekanand Jha

引用次数: 0

Genetic Algorithm for Logistics-Route Optimization in Urban Area 城市物流路径优化的遗传算法

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817173

Nur Uddin, Hendi Hermawan, Nur Layi Rachmawati, H. Tannady

{"title":"Genetic Algorithm for Logistics-Route Optimization in Urban Area","authors":"Nur Uddin, Hendi Hermawan, Nur Layi Rachmawati, H. Tannady","doi":"10.1109/aiiot54504.2022.9817173","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817173","url":null,"abstract":"This paper presents an optimal solution of logistics-route planning especially in urban areas. This study is motivated by a significant increment of parcel deliveries due to the fast growing e-commerce sector. The parcel deliveries are a logistics activity, where transportation is one of the main concerns. The transportation gives a significant contribution to the logistics cost such that minimising it is desired. The transportation cost can be reduced through several methods and one of them is minimizing the travel distance. There are many route options for delivering the parcels but not all of them are the minimum-distance route. Finding the minimum-distance route is an optimization problem which is not an easy problem. In this study, the genetic algorithm (GA) is applied to obtain the optimal route which is the shortest distance route. A case study of planning a route for delivering 20 parcels in Tangerang Selatan City is presented. The route planning and GA are implemented in a computer program written in Python. Executing the program resulted in an optimal route represented in a map visualization and the total distance. The optimal route was obtained through 800 iterations which were completed in less than one minute of computation.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126886753","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

To Offload or Not? An Analysis of Big Data Offloading Strategies from Edge to Cloud 要不要卸载?从边缘到云的大数据卸载策略分析

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817276

Raghubir Singh, J. Kovács, T. Kiss

{"title":"To Offload or Not? An Analysis of Big Data Offloading Strategies from Edge to Cloud","authors":"Raghubir Singh, J. Kovács, T. Kiss","doi":"10.1109/aiiot54504.2022.9817276","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817276","url":null,"abstract":"Large reductions in completion times can result from transfer of Big Data tasks from edge nodes to cloud resources, which can reduce the completion times by up to 97 % and meet client deadlines for computational tasks with responsive and agile solutions. Using scientific programs of varying computational complexity to model resource-intensive tasks, we demonstrate that the task complexity of the computational jobs, the Wide Area Network (WAN) speed and the potential overload of edge servers (as reflected by CPU workloads) are crucial for achieving total reductions in task completion time edge-cloud orchestrators are situated in edge nodes. With continuous access to the parameters of Wireless Local Area Network (WLAN) speed (for data exchanges between client and edge resources), WAN speed (for data exchanges between edge and cloud resources) edge server CPU workload and the complexities in Big Data analytics requirements, accurate edge-to-cloud offloading decisions can be made to minimise total task completion time by the use of cloud computing resources. This work supports the major research efforts have been recently made to develop novel resource orchestration solutions to flexibly link edge nodes with centralised cloud resources so as to maximise the efficiency with which such a continuum of resources can be accessed by users.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122270548","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: 6