A Feature-Based Learning System for Internet of Things Applications

Wang, Dapeng; Shi, Hang; Wang, Honggang; Wang, Ruyan; Fang, Hua

doi:10.1109/jiot.2018.2884485

Cited by 134 publications

(60 citation statements)

References 28 publications

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“…Such big data is created from various sources, such as audio, video, photographs, social networking, cell phones, etc. In August 2012, a study was sent to the U.S. "The Congress notes big data as" huge quantities of high-speed, complicated and variable data requiring advanced techniques and technologies to collect, store, transmit, handle and analyse information" [1]. So, the emerging challenges that the different organizations face is the fast-growing data sets of large volume which needs modern technology to organize, store and analyze the proper useful data.…”

Section: Imentioning

confidence: 99%

Lung Cancer Prediction Using Machine Learning Algorithms on Big Data: Survey

Kasthuri¹,

Jency²

2020

IJCSMC

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Lung cancer is a malignant lung tumour that is characterised by the regulated growth of cells in the lung tissue. The most common cancer diagnosed worldwide is lung cancer. More deaths than any other kind of cancer occur due to lung cancer. Early diagnosis and care are very useful and efficient for the survival of cancer patients. Different image processing and soft computing methods may be used for identifying cancer cells from medical images. Classification depends on features extracted from the images. In order to produce better classification results, the focus is on the feature extraction level. In order to distinguish a pattern that can provide some useful insights into what combination of features is most likely to result in an abnormality, this knowledge is then given to machine learning algorithms. The prediction of lung cancer is analysed using various machine learning classification algorithms such as Naive Bayes, Support Vector Machine, Artificial Neural Network and Logistic Regression. The key aim of this paper is to diagnose lung cancer early by examining the performance of exist classification algorithms.

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Section: Imentioning

confidence: 99%

Lung Cancer Prediction Using Machine Learning Algorithms on Big Data: Survey

Kasthuri¹,

Jency²

2020

IJCSMC

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“…During the past three decades, significant developments have taken place in the filed of information and communication technology (ICT), e.g., 3G/4G/5G, Cloud Computing, Big Data, Internet of Things [1]- [3], which has revolutionized the way we live and work. However, manufacturing, as the foundation of the real economy, does not actually use these advanced ICTs very well.…”

Section: Introductionmentioning

confidence: 99%

Modeling Identifiable Data in Industrial Internet

et al. 2020

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As the cornerstone of Industrial Internet, heterogeneous data interoperability is aiming to develop intelligence through the data-driven industry. Hence, standardized data model and information service system become more and more important for different countries, different industries, and different enterprises. In this paper, we provide a comprehensive study of the latest industrial data models, and propose the exploration of Identifiable Digital Object (IDO) data model. We hope that this paper will inspire more researches into practice.

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“…Besides, associated devices furnished with sensors or potentially actuators see their environment, comprehend what is happening, and perform likewise. These interconnected gadget systems can prompt an expansive number of clever and self-ruling applications and services that can bring critical individual, professional, and monetary advantages bringing about the rise of more information driven organizations [1] [2]. IoT devices need to make their information available to invested individuals, which can be web services, advanced mobile phone, cloud asset, and so forth.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Machine Learning for IoT Networks

Thamaraiselvi¹,

Mary²

2020

IJCSMC

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In practice applications like Security Systems, intelligent infrastructure, traffic management and weather systems (among others), internet of things (IoT) is becoming more and more popular. Recently, the Internet of Things (IoT) has become more common because the number of intelligent devices used in everyday human life with minimal network life. The transfer of routing information plays a major role in providing communication between nodes. The IoT produces big data in addition to its increased volume, with a variety of multiple modalities and differing data quality, distinguished by its time and position dependence. Intelligent data processing and analysis are the main elements for the creation of intelligent IoT applications. This paper focuses on IoT network security aspects by exploring the serviceability of machine learning algorithms to identify anomalies in data from these networks. Taxonomy of machine learning algorithms is introduced to illustrate how various methods are applied to the data in order to obtain higher level knowledge. There will also be discussions on the ability and problems of Machine Learning for IoT data processing.

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A Feature-Based Learning System for Internet of Things Applications

Cited by 134 publications

References 28 publications

Lung Cancer Prediction Using Machine Learning Algorithms on Big Data: Survey

Lung Cancer Prediction Using Machine Learning Algorithms on Big Data: Survey

Modeling Identifiable Data in Industrial Internet

A Survey of Machine Learning for IoT Networks

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