Machine Learning for IoT Systems
Added on 2021-06-17
12 Pages9299 Words186 Views
Machine Learning for Intelligent Decision Making in IoTName of the StudentName of the UniversityAuthor’s Note:Total Words: 6015
Machine Learning for Intelligent Decision Making inIoT
ABSTRACT – Machine learning refers to the significant field of computer science, which utilizes the statisticaltechniques for providing various computer systems, the specific ability in learning with datum, without even gettingexplicitly programmed. It is one of the most significant technologies that are utilized in today’s world. It was firstevolved from the various theories of artificial intelligence, computational learning theory and pattern recognition. Iteventually explores the proper construction of algorithms for the purpose of learning to make several vital predictionsabout data. Decisions could be easily undertaken with the help of this machine learning. Internet of Things or IoT playsthe most important role in intelligent decision making with machine learning. The main objective of this research reportis to understand the entire concept of machine learning for intelligent decision making in Internet of Things. Variousissues are present within the technology and could be mitigated subsequently. Moreover, the research report also focuseson the several advantages and disadvantages of the technology. 1.INTRODUCTION (15 MARKS)The Internet of Things or IoT is defined as the network ofvehicles, home appliances, physical devices or any otherproduct, which is subsequently embedded with the software,sensors, electronics, actuators and connectivity, which help inenabling each and every object in the proper connection andexchanging of data (Gubbi et al., 2013). Each and every itemcan be easily as well as uniquely identified by the specificembedded system for computing; however comprises of thecore capability to interoperate within the infrastructure ofInternet, which is previously existing. In simple words,internet of things is the significant interconnection through theInternet of various computing devices that are embeddedwithin regular items and thus enabling these objects forsuccessful sending as well as receiving of data or information(Kelly, Suryadevara & Mukhopadhyay, 2013). The mostimportant advantages of this internet of things is that itincreases the machine to machine or M2M connectivity.Since, it is of ingenious innovation, the physical deviceseventually stay in touch with each and other, hence leadinghigher quality and great efficiency. 1.1. MACHINE LEARNING1.1.1. BackgroundMachine learning is the specific type of AI or artificialintelligence, which eventually allows the softwareapplications in becoming explicitly accurate for the predictionof outcomes without being perfectly programmed (Lee & Lee,2015). The main objective of this machine learning is toconstruct the algorithms, which could eventually receive theinput data as well as utilize the statistical analysis forpredicting the output value within a proper acceptable range.Each and every machine learning algorithm is categorized forgetting supervised or unsupervised (He, Yan & Da Xu, 2014).All of these supervised algorithms need human beings inproviding the input as well as the desired output with thefurnishing feedback regarding prediction accuracy during thetraining period. When this training is completed, the specificalgorithm is applied to the new data. The unsupervisedalgorithms on the other hand, do not require significanttraining for bringing out the desired resulting data. Rather,they can utilize a specific iterative approach, known as deeplearning for proper data review and thus arriving atconclusions (Tukker et al., 2013). These unsupervisedalgorithms for learning are utilized for tasks that are morecomplicated and complex processing than the learningsystems that are supervised. 1.1.2. Machine Learning for Intelligent Decision MakingMachine learning could be easily utilized by all the usersfor the purpose of intelligent decision making (Sanchez et al.,2014). It is the specific cognitive procedure that eventuallyresults in the perfect selection of any belief or action courseamongst the various alternative possibilities. Each and everyprocess of decision making subsequently provides the finalchoice that might or might not have a prompt action. Theprocedure for identification and selection of variousalternatives on the basis of values, beliefs and preferences ofthe particular decision maker is called decision making.Machine learning is one of the most significant technologiesthat help to take intelligent decision making (Xu et al., 2014).The major advantage of involving machine learning indecision making is that it helps in improving the casualinterference of big data. All these technologies or methodscould be used with any type of complexity and thus areextremely popular for the end users. 1.1.3. Standout of Machine Learning in Internet of ThingsThe growth of Internet of Things or IoT has eventuallyacquired the entire technological world (Bellavista et al.,2013). The major involvement of Internet of Things withmachine learning is for the decision making algorithms orrather for taking various decisions. There are three mostcommon scenarios, where machine learning is working withthe Internet of Things for enabling the business operations.The first scenario is anomaly monitoring, where anomalies areeasily detected in shorter time. The second scenario ispredictive maintenance, where the organizational costs aredirectly impacted. Thus, the machine learning solution is wellaccepted by all (Amendola et al., 2014). The final scenario isin vehicle telemetry, where the safety as well as reliability ofvehicles is eventually improved. Hence, machine learning ismuch popular for the users to take proper decisions. 2.BACKGROUND/LITERATURE REVIEW (40 MARKS)2.1. Intelligent Decision Making2.1.1. Data Analytics
End of preview
Want to access all the pages? Upload your documents or become a member.
Related Documents
Research Report on MITS Advanced Research Techniques 2022lg...
|5
|1432
|20
Use of Machine Learning to Improve the Internet of Things Based Applications | PPTlg...
|1
|1527
|9
IoT Mining Industry: Benefits and Challengeslg...
|20
|5832
|307
Big Data Challenges in IoT and Cloudlg...
|6
|6502
|62
Securing the IoTlg...
|12
|3225
|243
Machine Learning in Internet of Things (IOT)lg...
|8
|2103
|124