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Computational Intelligence in the Internet of Things最新文献

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Machine Learning Algorithms 机器学习算法
Computational Intelligence in the Internet of Things Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-7955-7.CH009
Namrata Dhanda, S. Datta, Mudrika Dhanda
{"title":"Machine Learning Algorithms","authors":"Namrata Dhanda, S. Datta, Mudrika Dhanda","doi":"10.4018/978-1-5225-7955-7.CH009","DOIUrl":"https://doi.org/10.4018/978-1-5225-7955-7.CH009","url":null,"abstract":"Human intelligence is deeply involved in creating efficient and faster systems that can work independently. Creation of such smart systems requires efficient training algorithms. Thus, the aim of this chapter is to introduce the readers with the concept of machine learning and the commonly employed learning algorithm for developing efficient and intelligent systems. The chapter gives a clear distinction between supervised and unsupervised learning methods. Each algorithm is explained with the help of suitable example to give an insight to the learning process.","PeriodicalId":283602,"journal":{"name":"Computational Intelligence in the Internet of Things","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125674488","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
Comprehensive Overview of Neural Networks and Its Applications in Autonomous Vehicles 神经网络及其在自动驾驶汽车中的应用综述
Computational Intelligence in the Internet of Things Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-7955-7.CH007
J. Rodge, S. Jaiswal
{"title":"Comprehensive Overview of Neural Networks and Its Applications in Autonomous Vehicles","authors":"J. Rodge, S. Jaiswal","doi":"10.4018/978-1-5225-7955-7.CH007","DOIUrl":"https://doi.org/10.4018/978-1-5225-7955-7.CH007","url":null,"abstract":"Deep learning and Artificial intelligence (AI) have been trending these days due to the capability and state-of-the-art results that they provide. They have replaced some highly skilled professionals with neural network-powered AI, also known as deep learning algorithms. Deep learning majorly works on neural networks. This chapter discusses about the working of a neuron, which is a unit component of neural network. There are numerous techniques that can be incorporated while designing a neural network, such as activation functions, training, etc. to improve its features, which will be explained in detail. It has some challenges such as overfitting, which are difficult to neglect but can be overcome using proper techniques and steps that have been discussed. The chapter will help the academician, researchers, and practitioners to further investigate the associated area of deep learning and its applications in the autonomous vehicle industry.","PeriodicalId":283602,"journal":{"name":"Computational Intelligence in the Internet of Things","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133224122","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
Motion Planning Method for In-Pipe Walking Robots Using Height Maps and CNN-Based Pipe Branches Detector 基于高度映射和cnn管道分支检测器的管道行走机器人运动规划方法
Computational Intelligence in the Internet of Things Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-7955-7.CH001
S. Savin
{"title":"Motion Planning Method for In-Pipe Walking Robots Using Height Maps and CNN-Based Pipe Branches Detector","authors":"S. Savin","doi":"10.4018/978-1-5225-7955-7.CH001","DOIUrl":"https://doi.org/10.4018/978-1-5225-7955-7.CH001","url":null,"abstract":"In this chapter, the problem of motion planning for an in-pipe walking robot is studied. One of the key parts of motion planning for a walking robot is a step sequence generation. In the case of in-pipe walking robots it requires choosing a series of feasible contact locations for each of the robot's legs, avoiding regions on the inner surface of the pipe where the robot cannot step to, such as pipe branches. The chapter provides an approach to localization of pipe branches, based on deep convolutional neural networks. This allows including the information about the branches into the so-called height map of the pipeline and plan the step sequences accordingly. The chapter shows that it is possible to achieve prediction accuracy better than 0.5 mm for a network trained on a simulation-based dataset.","PeriodicalId":283602,"journal":{"name":"Computational Intelligence in the Internet of Things","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121156933","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
Analysis of Industrial and Household IoT Data Using Computationally Intelligent Algorithm 使用计算智能算法分析工业和家庭物联网数据
Computational Intelligence in the Internet of Things Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-7955-7.CH002
Soumen Mukherjee, A. Bhattacharjee, D. Bhattacharya, Moumita Ghosal
{"title":"Analysis of Industrial and Household IoT Data Using Computationally Intelligent Algorithm","authors":"Soumen Mukherjee, A. Bhattacharjee, D. Bhattacharya, Moumita Ghosal","doi":"10.4018/978-1-5225-7955-7.CH002","DOIUrl":"https://doi.org/10.4018/978-1-5225-7955-7.CH002","url":null,"abstract":"In this chapter, data mining approaches are applied on standard IoT dataset to identify relationship among attributes of the dataset. IoT is not an exception; data mining can be used in this domain also. Various rule-based classifiers and unsupervised classifiers are implemented here. Using these approaches relation between various IoT features are determined based on different properties of classification like support, confidence, etc. For classification, a real-time IoT dataset is used, which consists of household figures collected from various sources over a long duration. A brief comparison is also shown for different classification approaches on the IoT dataset. Kappa coefficient is also calculated for these classification techniques to measure the robustness of these approaches. In this chapter, standard and popular power utilization in household dataset is used to show the association between the different intra-data dependency. Classification accuracy of more than 86% is found with the Almanac of Minutely Power Dataset (AMPds) in the present work.","PeriodicalId":283602,"journal":{"name":"Computational Intelligence in the Internet of Things","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130929695","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
Rainfall Prediction Model Using Exponential Smoothing Seasonal Planting Index (ESSPI) For Determination of Crop Planting Pattern 利用指数平滑季节种植指数(ESSPI)确定作物种植模式的降雨预测模型
Computational Intelligence in the Internet of Things Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-7955-7.CH010
K. Hartomo, S. Y. Prasetyo, M. T. Anwar, H. Purnomo
{"title":"Rainfall Prediction Model Using Exponential Smoothing Seasonal Planting Index (ESSPI) For Determination of Crop Planting Pattern","authors":"K. Hartomo, S. Y. Prasetyo, M. T. Anwar, H. Purnomo","doi":"10.4018/978-1-5225-7955-7.CH010","DOIUrl":"https://doi.org/10.4018/978-1-5225-7955-7.CH010","url":null,"abstract":"The traditional crop farmers rely heavily on rain pattern to decide the time for planting crops. The emerging climate change has caused a shift in the rain pattern and consequently affected the crop yield. Therefore, providing a good rainfall prediction models would enable us to recommend best planting pattern (when to plant) in order to give maximum yield. The recent and widely used rainfall prediction model for determining the cropping patterns using exponential smoothing method recommended by the Food and Agriculture Organization (FAO) suffered from short-term forecasting inconsistencies and inaccuracies for long-term forecasting. In this study, the authors developed a new rainfall prediction model which applied exponential smoothing onto seasonal planting index as the basis for determining planting pattern. The results show that the model gives better accuracy than the original exponential smoothing model.","PeriodicalId":283602,"journal":{"name":"Computational Intelligence in the Internet of Things","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131086485","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: 7
Neural Network for Big Data Sets 大数据集的神经网络
Computational Intelligence in the Internet of Things Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-7955-7.CH012
V. Phu, Vo Thi Ngoc Tran
{"title":"Neural Network for Big Data Sets","authors":"V. Phu, Vo Thi Ngoc Tran","doi":"10.4018/978-1-5225-7955-7.CH012","DOIUrl":"https://doi.org/10.4018/978-1-5225-7955-7.CH012","url":null,"abstract":"Machine learning (ML), neural network (NN), evolutionary algorithm (EA), fuzzy systems (FSs), as well as computer science have been very famous and very significant for many years. They have been applied to many different areas. They have contributed much to developments of many large-scale corporations, massive organizations, etc. Lots of information and massive data sets (MDSs) have been generated from these big corporations, organizations, etc. These big data sets (BDSs) have been the challenges of many commercial applications, researches, etc. Therefore, there have been many algorithms of the ML, the NN, the EA, the FSs, as well as computer science which have been developed to handle these massive data sets successfully. To support for this process, the authors have displayed all the possible algorithms of the NN for the large-scale data sets (LSDSs) successfully in this chapter. Finally, they have presented a novel model of the NN for the BDS in a sequential environment (SE) and a distributed network environment (DNE).","PeriodicalId":283602,"journal":{"name":"Computational Intelligence in the Internet of Things","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126791069","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
Machine Learning Applications for Anomaly Detection 异常检测中的机器学习应用
Computational Intelligence in the Internet of Things Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-7955-7.CH003
T. Wahyono, Y. Heryadi
{"title":"Machine Learning Applications for Anomaly Detection","authors":"T. Wahyono, Y. Heryadi","doi":"10.4018/978-1-5225-7955-7.CH003","DOIUrl":"https://doi.org/10.4018/978-1-5225-7955-7.CH003","url":null,"abstract":"The aim of this chapter is to describe and analyze the application of machine learning for anomaly detection. The study regarding the anomaly detection is a very important thing. The various phenomena often occur related to the anomaly study, such as the occurrence of an extreme climate change, the intrusion detection for the network security, the fraud detection for e-banking, the diagnosis for engines fault, the spacecraft anomaly detection, the vessel track, and the airline safety. This chapter is an attempt to provide a structured and a broad overview of extensive research on anomaly detection techniques spanning multiple research areas and application domains. Quantitative analysis meta-approach is used to see the development of the research concerned with those matters. The learning is done on the method side, the techniques utilized, the application development, the technology utilized, and the research trend, which is developed.","PeriodicalId":283602,"journal":{"name":"Computational Intelligence in the Internet of Things","volume":"451 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133284953","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: 3
Enhancing E-Government With Internet of Things 以物联网加强电子政务
Computational Intelligence in the Internet of Things Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-7955-7.CH005
Panagiota Papadopoulou, Kostas Kolomvatsos, S. Hadjiefthymiades
{"title":"Enhancing E-Government With Internet of Things","authors":"Panagiota Papadopoulou, Kostas Kolomvatsos, S. Hadjiefthymiades","doi":"10.4018/978-1-5225-7955-7.CH005","DOIUrl":"https://doi.org/10.4018/978-1-5225-7955-7.CH005","url":null,"abstract":"Internet of things (IoT) brings unprecedented changes to all contexts of our lives, as they can be informed by smart devices and real-time data. Among the various IoT application settings, e-government seems to be one that can be greatly benefited by the use of IoT, transforming and augmenting public services. This chapter aims to contribute to a better understanding of how IoT can be leveraged to enhance e-government. IoT adoption in e-government encompasses several challenges of technical as well as organizational, political, and legal nature, which should be addressed for developing efficient applications. With the application of IoT in e-government being at an early stage, it is imperative to investigate these challenges and the ways they could be tackled. The chapter provides an overview of IoT in e-government across several application domains and explores the aspects that should be considered and managed before it can reach its full potential.","PeriodicalId":283602,"journal":{"name":"Computational Intelligence in the Internet of Things","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126432484","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
Deep Learning 深度学习
Computational Intelligence in the Internet of Things Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-7955-7.CH006
R. Kashyap
{"title":"Deep Learning","authors":"R. Kashyap","doi":"10.4018/978-1-5225-7955-7.CH006","DOIUrl":"https://doi.org/10.4018/978-1-5225-7955-7.CH006","url":null,"abstract":"The vast majority of the examination on profound neural systems so far has been centered on acquiring higher exactness levels by building progressively vast and profound structures. Preparing and assessing these models is just practical when a lot of assets; for example, handling power and memory are easy run of the mill applications that could profit by these models. The system starts handling the compelled gadget and depends on the remote part when the neighborhood part does not give a sufficiently precise outcome. The falling system takes into account a new ceasing component amid the review period of the system. This chapter empowers an entire assortment of independent frameworks where sensors, actuators, and registering hubs can cooperate and demonstrate that the falling design takes into account a free change in assessment speed on obliged gadgets while the misfortune in precision is kept to a base.","PeriodicalId":283602,"journal":{"name":"Computational Intelligence in the Internet of Things","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130568836","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: 3
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