scholarly journals Intelligent Network Office System Based on Cloud Computing and Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yinfang Guo ◽  
Yanjun Guo
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Large Scale ◽  
Intelligent System ◽  
Functional Module ◽  
Module Structure ◽  
Intelligent Network ◽  
Office System

The traditional data automatic office system has limited mining and computing capabilities. Due to the iterative complexity of data mining algorithms, it is difficult to discover the relationships and rules existing in the Internet of Things data as well as impossible to advance the efficiency of the office system based on the existing Internet of Things data. This paper combines cloud computing and machine learning to construct an intelligent network office system, realizes large-scale IoT data processing through the combination of IoT data mining technology and cloud computing framework, and constructs the functional module structure of the intelligent network office system through demand analysis. On this basis, this paper conducts system performance verification and conducts experimental design based on network intelligent system demand. The experimental results show that the system constructed in this paper has certain practical effects, which can provide theoretical reference for subsequent related research.

scholarly journals Study report on Indian agriculture with IoT

2020 ◽  
Vol 10 (3) ◽  
pp. 2322
Author(s):  
G. Balakrishna ◽  
Nageswara Rao Moparthi
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Emerging Technologies ◽  
Agricultural Policies ◽  
Quality Work ◽  
Agriculture Sector ◽  
Indian Agriculture ◽  
Work Done

Most of the population of our country are depends on agriculture for their survival. Agriculture plays an important role in our country economy. But since past few years production from agriculture sector is decreasing drastically. Agriculture sector saw a drastic downfall in its productivity from past few years, there are many reasons for this downfall. In this paper we will discuss about past, present and future of agriculture in our country, agricultural policies which are provided by government to improve the growth of agriculture and reasons why we are not able see the growth in agriculture. And also we will see how can we adopt automation into agriculture using various emerging technologies like IoT (Internet of Things), data mining, cloud computing and machine learning and some authors done some quality work previously on this topic we will discuss that also. Here we will see previous work done by various authors which can be useful to increase the productivity of agriculture sector

A Review of Machine Learning and Data Mining Approaches for Business Applications in Social Networks

2013 ◽  
Vol 9 (1) ◽  
pp. 36-53
Author(s):  
Evis Trandafili ◽  
Marenglen Biba
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Networks ◽  
Large Scale ◽  
Viral Marketing ◽  
Social Network Mining ◽  
Business Applications ◽  
Mining Community ◽  
Recent Developments ◽  
Analysis Of Social Networks

Social networks have an outstanding marketing value and developing data mining methods for viral marketing is a hot topic in the research community. However, most social networks remain impossible to be fully analyzed and understood due to prohibiting sizes and the incapability of traditional machine learning and data mining approaches to deal with the new dimension in the learning process related to the large-scale environment where the data are produced. On one hand, the birth and evolution of such networks has posed outstanding challenges for the learning and mining community, and on the other has opened the possibility for very powerful business applications. However, little understanding exists regarding these business applications and the potential of social network mining to boost marketing. This paper presents a review of the most important state-of-the-art approaches in the machine learning and data mining community regarding analysis of social networks and their business applications. The authors review the problems related to social networks and describe the recent developments in the area discussing important achievements in the analysis of social networks and outlining future work. The focus of the review in not only on the technical aspects of the learning and mining approaches applied to social networks but also on the business potentials of such methods.

scholarly journals IMPROVING INTERNET OF THINGS PARKING SYSTEMS

2021 ◽  
Vol 19 (3) ◽  
pp. 163
Author(s):  
Dušan Bogićević
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Information Needs ◽  
Vehicle Classification ◽  
The Internet ◽  
Parking Space ◽  
Automated Systems ◽  
The Internet Of Things

Edge data processing represents the new evolution of the Internet and Cloud computing. Its application to the Internet of Things (IoT) is a step towards faster processing of information from sensors for better performance. In automated systems, we have a large number of sensors, whose information needs to be processed in the shortest possible time and acted upon. The paper describes the possibility of applying Artificial Intelligence on Edge devices using the example of finding a parking space for a vehicle, and directing it based on the segment the vehicle belongs to. Algorithm of Machine Learning is used for vehicle classification, which is based on vehicle dimensions.

Internet of Things-Based Architecture of Web and Smart Home Interface

2020 ◽  
pp. 1-46
Author(s):  
Jasjit Singh ◽  
Ankur Kohli ◽  
Bhupendra Singh ◽  
Simranjeet Kaur
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Web Service ◽  
Smart Home ◽  
Intelligent System ◽  
Home Appliances ◽  
Whole Process ◽  
Living Things ◽  
User Intervention

Internet has revolutionized the technological era, which has a significant impact on us by making communication much better not only with the living beings but also with non-living things through the medium of internet of things (IoT). Thus, this topic highlights how internet of things can minimize user intervention in controlling home appliances and monitoring its setting. Integrating IoT with cloud computing and web service helps us in providing feasibility in accessing home appliances (i.e., monitoring appliances and measuring home condition). The whole process of integration aims to create an intelligent system. Thus, smart home is one of the application of IoT aimed at improving comfort, safety, and wellbeing within our homes.

A Review: Map Reduce Framework for Cloud Computing

2018 ◽  
Vol 7 (4.6) ◽  
pp. 13
Author(s):  
Mekala Sandhya ◽  
Ashish Ladda ◽  
Dr. Uma N Dulhare ◽  
. . ◽  
. .
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Distributed Computing ◽  
Data Storage ◽  
High Performance ◽  
Large Scale ◽  
Distributed Storage ◽  
Large Data ◽  
Mass Data ◽  
Internet Information

In this generation of Internet, information and data are growing continuously. Even though various Internet services and applications. The amount of information is increasing rapidly. Hundred billions even trillions of web indexes exist. Such large data brings people a mass of information and more difficulty discovering useful knowledge in these huge amounts of data at the same time. Cloud computing can provide infrastructure for large data. Cloud computing has two significant characteristics of distributed computing i.e. scalability, high availability. The scalability can seamlessly extend to large-scale clusters. Availability says that cloud computing can bear node errors. Node failures will not affect the program to run correctly. Cloud computing with data mining does significant data processing through high-performance machine. Mass data storage and distributed computing provide a new method for mass data mining and become an effective solution to the distributed storage and efficient computing in data mining. 

Research on the Distributed Data Mining Cloud Framework Oriented Internet of Things

2014 ◽  
Vol 686 ◽  
pp. 306-310
Author(s):  
Wei Guan ◽  
Hui Juan Lu ◽  
Jing Jing Chen ◽  
Jie Wu
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Rapid Development ◽  
Distributed Data Mining ◽  
Distributed Data ◽  
Mining System ◽  
Cloud Framework ◽  
Distributed Networking ◽  
Mining Model

The rapid development of Internet of Things imposes new requirements on the data mining system, due to the weak capability of traditional distributed networking data mining. To meet the needs of the Internet of Things, this paper proposes a novel distributed data-mining model to realize the seamless access between cloud computing and distributed data mining. The model is based on the cloud computing architecture, which belongs to the type of incredible nodes.

scholarly journals Growing self-organizing tree-based kernel smoother for machine learning and data mining

2021 ◽  
Author(s):  
Bohan Zheng
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Internet Of Things ◽  
Multivariate Regression ◽  
Regression Tree ◽  
Local Regression ◽  
Self Organizing Maps ◽  
Electrical Vehicle ◽  
Self Organizing

With Internet of Things (IoT) being prevalently adopted in recent years, traditional machine learning and data mining methods can hardly be competent to deal with the complex big data problems if applied alone. However, hybridizing those who have complementary advantages could achieve optimized practical solutions. This work discusses how to solve multivariate regression problems and extract intrinsic knowledge by hybridizing Self-Organizing Maps (SOM) and Regression Trees. A dual-layer SOM map is developed in which the first layer accomplishes unsupervised learning and then regression tree layer performs supervised learning in the second layer to get predictions and extract knowledge. In this framework, SOM neurons serve as kernels with similar training samples mapped so that regression tree could achieve regression locally. In this way, the difficulties of applying and visualizing local regression on high dimensional data are overcome. Further, we provide an automated growing mechanism based on a few stop criteria without adding new parameters. A case study of solving Electrical Vehicle (EV) range anxiety problem is presented and it demonstrates that our proposed hybrid model is quantitatively precise and interpretive. key words: Multivariate Regression, Big Data, Machine Learning, Data Mining, Self-Organizing Maps (SOM), Regression Tree, Electrical Vehicle (EV), Range Estimation, Internet of Things (IoT)

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