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

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 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.

Emerging Technologies and Universities

2021 ◽  
pp. 97-114
Author(s):  
Fausto E. Jacome
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
New Technologies ◽  
Emerging Technologies ◽  
The Internet ◽  
New Paradigm ◽  
New Era ◽  
Technological Revolution ◽  
Goods And Services ◽  
The Internet Of Things

Emerging technologies such as machine learning, the cloud, the internet of things (IoT), social web, mobility, robotics, and blockchain, among others, are powering a technological revolution in such a way that are transforming all human activities. These new technologies have generated creative ways of offering goods and services. Today's consumers demand in addition to quality, innovation, a real-time and ubiquitous service. In this context, what is the challenge that academy faces? What is the effect of these new technologies on the universities mission? What are people's expectations about academy in this new era? This chapter tries to get answers to these questions and explain how these emerging technologies are converting universities to lead society transformation to the digital age. Under this new paradigm, there are only two roads: innovate or perish. As might be expected universities are embracing these technologies for innovating themselves.

Information Privacy and Emerging Technologies in the UAE

2021 ◽  
pp. 1134-1152
Author(s):  
Dimitrios Xanthidis ◽  
Christos Manolas ◽  
Ourania Koutzampasopoulou Xanthidou ◽  
Han-I Wang
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Emerging Technologies ◽  
Personal Information ◽  
Local Environment ◽  
Information Privacy ◽  
Paper Briefly ◽  
Related Research ◽  
Research Themes

The rapid developments of emerging technologies, including Big Data, Cloud Computing, and Internet of Things, are causing many societies to struggle whilst trying to keep up with, and adopt them. As a consequence, serious concerns and issues are being raised. The threat to personal information privacy is one of these issues. This review paper briefly introduces the aforementioned technologies and explores concepts related to concerns on information privacy and disclosure in the U.A.E. in the context of these technologies. In addition, related research themes that could be interesting to explore are identified, with a focus on the local environment.

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)

scholarly journals A Supervised Learning Algorithm to Forecast Weather Conditions for Playing Cricket

2019 ◽  
Vol 9 (1) ◽  
pp. 1560-1565
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Supervised Learning ◽  
Learning Algorithm ◽  
Weather Conditions ◽  
Redundant Data ◽  
Classification Technique

Now days, Machine learning is considered as the key technique in the field of technologies, such as, Internet of things (IOT), Cloud computing, Big data and Artificial Intelligence etc. As technology enhances, lots of incorrect and redundant data are collected from these fields. To make use of these data for a meaningful purpose, we have to apply mining or classification technique in the real world. In this paper, we have proposed two nobel approaches towards data classification by using supervised learning algorithm

A new approach for predicting crop yield prediction using data mining techniques

2019 ◽  
Author(s):  
Surya A. Venkaiah ◽  
Surya A. Venkaiah ◽  
Kondajji Swati Sunitha
Keyword(s):  
Data Mining ◽  
Climatic Conditions ◽  
Random Forest Algorithm ◽  
New Approach ◽  
Agriculture Sector ◽  
Prediction Algorithms ◽  
Indian Agriculture ◽  
Using Data ◽  
Prediction Techniques ◽  
Day By Day

Agriculture is the most important sector of Indian Economy. Indian agriculture sector accounts for 18 per cent of India's gross domestic product (GDP) and provides employment to 50% of the countries workforce. We all know agriculture is the most important factor which influence the economy of India and it also offers employment to 50% population of India. People of India are practicing agriculture for many years and the result were never satisfying due to many factors that affect the crop. Day by day environment is changing and is not stable at various places. It is very important for the farmer to cultivate their farm in good climatic conditions, under such conditions they need technology that predict the environment. In such cases data mining is the apt technology for prediction. Data mining contains various prediction algorithms like id3, cart, c4.5, random forest algorithm. In this Project we are using Id3 and cart algorithms as a prediction techniques and it is possible obtain the information from the prediction algorithms which helps farmers to cultivate the appropriate crop. Available online at https://int-scientific-journals.com

Internet of Things in Healthcare

2020 ◽  
pp. 23-39
Author(s):  
Rajasekaran Thangaraj ◽  
Sivaramakrishnan Rajendar ◽  
Vidhya Kandasamy
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Deep Learning ◽  
Internet Of Things ◽  
Healthcare Systems ◽  
Wearable Devices ◽  
Healthcare Research ◽  
Learning Technologies ◽  
Learning Approaches ◽  
And Performance

Healthcare motoring has become a popular research in recent years. The evolution of electronic devices brings out numerous wearable devices that can be used for a variety of healthcare motoring systems. These devices measure the patient's health parameters and send them for further processing, where the acquired data is analyzed. The analysis provides the patients or their relatives with the medical support required or predictions based on the acquired data. Cloud computing, deep learning, and machine learning technologies play a prominent role in processing and analyzing the data respectively. This chapter aims to provide a detailed study of IoT-based healthcare systems, a variety of sensors used to measure parameters of health, and various deep learning and machine learning approaches introduced for the diagnosis of different diseases. The chapter also highlights the challenges, open issues, and performance considerations for future IoT-based healthcare research.

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