Predictive Analytics of Sensor Data Based on Supervised Machine Learning Algorithms

Gupta, Shreya; Mittal, Mohit; Padha, Anupama

doi:10.1109/icngcis.2017.12

Cited by 15 publications

(8 citation statements)

References 10 publications

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“…People are exploring more and more over their smartphones, such as paying bills [8], ordering the food [9], play games for entertainment [10], watching videos [11], etc. People, from the young generation to the older generation, are using smartphones, which are easy to understand and learn to use [12,13]. The big impact seen in the past few years is that on online game industry.…”

Section: Introductionmentioning

confidence: 99%

A Technology Acceptance Model-Based Analytics for Online Mobile Games Using Machine Learning Techniques

et al. 2021

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In recent years, the enhancement in technology has been envisioning for people to complete tasks in an easier way. Every manufacturing industry requires heavy machinery to accomplish tasks in a symmetric and systematic way, which is much easier with the help of advancement in the technology. The technological advancement directly affects human life as a result. It is found that humans are now fully dependent on it. The online game industry is one example of technology breakthrough. It is now a prominent industry to develop online games at world level. In this paper, our main objective is to analyze major factors which encourage mobile games industry to expand. Analyzing the system and symmetric relations inside can be done into two phases. The first phase is through a TAM Model, which is a very efficient way to solve statistical problems, and the second phase is with machine learning (ML) techniques, such as SVM, logistic regression, etc. Both strategies are popular and efficient in analyzing a system while maintaining the symmetry in a better way. Therefore, according to results from both the TAM model and ML approach, it is clear that perceived usefulness, attitude, and symmetric flow are important factors for game industry. The analytics provide a clear insight that perceived usefulness is an important parameter over behavior intention for the online mobile game industry.

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Section: Introductionmentioning

confidence: 99%

A Technology Acceptance Model-Based Analytics for Online Mobile Games Using Machine Learning Techniques

et al. 2021

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“…The system predicts output data (y) depending upon the type of input and system function. The objective of supervised learning is to approximate the mapping function so that when a new unknown input data is applied then, the output (y) can be predicted [39,40].…”

Section: Literature Survey Of Supervised Machine Learning For Wsn-iotmentioning

confidence: 99%

Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Sharma¹,

2021

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Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author’s knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications.

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“…Artificial intelligence concepts are the base of machine learning techniques that are utilized in computational tasks where conventional techniques cannot be applied. In [10], the author has used many machine learning algorithms on different ranges of datasets and tested the classification accuracy of Naive Bayes (NB), linear regression, and Stochastic Gradient Decent (SGD). Classifiers were used for the classification purpose, and Gaussian naive Bayes outperformed the other classifiers.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Algorithms and Fault Detection for Improved Belief Function Based Decision Fusion in Wireless Sensor Networks

Javaid

Wadud

et al. 2019

Sensors

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Decision fusion is used to fuse classification results and improve the classification accuracy in order to reduce the consumption of energy and bandwidth demand for data transmission. The decentralized classification fusion problem was the reason to use the belief function-based decision fusion approach in Wireless Sensor Networks (WSNs). With the consideration of improving the belief function fusion approach, we have proposed four classification techniques, namely Enhanced K-Nearest Neighbor (EKNN), Enhanced Extreme Learning Machine (EELM), Enhanced Support Vector Machine (ESVM), and Enhanced Recurrent Extreme Learning Machine (ERELM). In addition, WSNs are prone to errors and faults because of their different software, hardware failures, and their deployment in diverse fields. Because of these challenges, efficient fault detection methods must be used to detect faults in a WSN in a timely manner. We have induced four types of faults: offset fault, gain fault, stuck-at fault, and out of bounds fault, and used enhanced classification methods to solve the sensor failure issues. Experimental results show that ERELM gave the first best result for the improvement of the belief function fusion approach. The other three proposed techniques ESVM, EELM, and EKNN provided the second, third, and fourth best results, respectively. The proposed enhanced classifiers are used for fault detection and are evaluated using three performance metrics, i.e., Detection Accuracy (DA), True Positive Rate (TPR), and Error Rate (ER). Simulations show that the proposed methods outperform the existing techniques and give better results for the belief function and fault detection in WSNs.

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Predictive Analytics of Sensor Data Based on Supervised Machine Learning Algorithms

Cited by 15 publications

References 10 publications

A Technology Acceptance Model-Based Analytics for Online Mobile Games Using Machine Learning Techniques

A Technology Acceptance Model-Based Analytics for Online Mobile Games Using Machine Learning Techniques

Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Machine Learning Algorithms and Fault Detection for Improved Belief Function Based Decision Fusion in Wireless Sensor Networks

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