scholarly journals Prediction Model of Football World Cup Championship Based on Machine Learning and Mobile Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yanyang Bai ◽  
Xuesheng Zhang
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Clustering Algorithm ◽  
Technological Development ◽  
Rapid Development ◽  
Evidence Theory ◽  
Predictive Analysis ◽  
Learning Technology ◽  
World Cup ◽  
Python Programming

With the technological development and change of the times in the current era, with the rapid development of science and technology and information technology, there is a gradual replacement in the traditional way of cognition. Effective data analysis is of great help to all societies, thereby drive the development of better interests. How to expand the development of the overall information resources in the process of utilization, establish a mathematical analysis–oriented evidence theory system model, improve the effective utilization of the machine, and achieve the goal of comprehensively predicting the target behavior? The main goal of this article is to use machine learning technology; this article defines the main prediction model by python programming language, analyzes and forecasts the data of previous World Cup, and establishes the analysis and prediction model of football field by K-mean and DPC clustering algorithm. Python programming is used to implement the algorithm. The data of the previous World Cup football matches are selected, and the built model is used for the predictive analysis on the Python platform; the calculation method based on the DPC-K-means algorithm is used to determine the accuracy and probability of the variables through the calculation results, which develops results in specific competitions. Research shows how the machine wins and learns the efficiency of the production process, and the machine learning process, the reliability, and accuracy of the prediction results are improved by more than 55%, which proves that mobile algorithm technology has a high level of predictive analysis on the World Cup football stadium.

scholarly journals Crop Yield and Rainfall Prediction in Tumakuru District using Machine Learning

2019 ◽  
Author(s):  
Girish L
Keyword(s):  
Machine Learning ◽  
Call Center ◽  
Market Price ◽  
Predictive Analysis ◽  
Learning Technology ◽  
Rainfall Prediction ◽  
Analysis Technique ◽  
The Future ◽  
The Government ◽  
Smart Agriculture

Smart Agriculture is a development that emphasizes the use of information technology in the farming. Mostof the population in India depending on agriculture. This situation is one of the reason, that hindering the developmentof country. Nowadays, even though farmers get more yield for their crop but the market price for that crop will be less,in that case farmers get loss for their product and vice versa. Particularly, when growing new crops, farmers face therisks of both market price and production problems. To overcome these problems, a machine learning technology isused. Predictive analysis is a branch of data mining which predicts the future probabilities and trends. The predictionwill help the farmers to choose whether the particular crop is suitable for specific rainfall and crop price values. Thisapproach is to increase the net yield rate of the crop, based on rainfall. Prediction can be carried out by using variousmachine learning algorithms like linear regression, SVM, K NN method and decision tree algorithm out of which SVMis giving the highest efficiency. The predictive analysis technique can be implemented in several government sectors likeAPMC, kissan call center etc., by which the government and farmers can get the information of the future rainfall, cropyield and the market price.

Prediction of Football Match Results Based on Edge Computing and Machine Learning Technology

2022 ◽  
Vol 13 (2) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Rapid Development ◽  
Prediction Method ◽  
Edge Computing ◽  
Machine Learning Algorithms ◽  
Learning Technology ◽  
Cloud Server ◽  
Football Match ◽  
The Common ◽  
Characteristic Features

With the rapid development of artificial intelligence, various machine learning algorithms have been widely used in the task of football match result prediction and have achieved certain results. However, traditional machine learning methods usually upload the results of previous competitions to the cloud server in a centralized manner, which brings problems such as network congestion, server computing pressure and computing delay. This paper proposes a football match result prediction method based on edge computing and machine learning technology. Specifically, we first extract some game data from the results of the previous games to construct the common features and characteristic features, respectively. Then, the feature extraction and classification task are deployed to multiple edge nodes.Finally, the results in all the edge nodes are uploaded to the cloud server and fused to make a decision. Experimental results have demonstrated the effectiveness of the proposed method.

scholarly journals AI and Digital Platforms: The Market [Part 1]

2021 ◽  
Author(s):  
Örjan Larsson
Keyword(s):  
Machine Learning ◽  
Dynamic Range ◽  
Technological Development ◽  
Learning Technology ◽  
Basic Approach ◽  
Digital Platforms ◽  
Practical Applications ◽  
Strategic Innovation ◽  
Ongoing Work ◽  
Innovation Projects

This essay aims to describe the dynamics at play in the field of industrial AI, where the significant efficiency potential is driving demand. There are rapid technological development and increasing use of AI technology within the industry. Meanwhile, practical applications rather than technical development itself are creating value. The primary purpose of the article is to spread knowledge to industry. It is also intended to form the basis of the Swedish innovation program PiiAs ongoing work around open calls and targeted strategic innovation projects. The basic approach taken is to investigate both industry demand for AI and how the supply of technology is developing. AI takes in a broad and dynamic range of concepts, but it should also be considered in an even broader context of industrial digitalisation. The article has been divided into two sections: The Market, in which we assess the development and the consequences on the factory floor; and The Technology, which provides a more in-depth understanding of the structures of industrial IT and machine-learning technology. The article concludes with four practical examples from the industry.

scholarly journals Classification of Tennis Video Types Based on Machine Learning Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xun Gong ◽  
Fucheng Wang
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Rapid Development ◽  
Video Data ◽  
Learning Technology ◽  
Video Content ◽  
Video Classification ◽  
The World ◽  
Spatial Detection

With the rapid development of online video data, how to find the required information has become an urgent problem to be solved. This article focuses on sports videos and studies video classification and content-based retrieval techniques. Its purpose is to establish a mark and index of video content and to promote user acquisition through computer processing, analysis, and understanding of video content. Video tennis classification has high research and application value. This article focuses on video tennis based on the selection of the basic frame of each shot and proposes an algorithm for classification of shots based on average grouping. Based on this, we use a color-coded spatial detection method to detect the type of tennis match. Then, it integrates the results of audiovisual analysis to identify and classify exciting events in tennis matches. According to statistics, although the number of people participating in tennis cannot enter the top ten, the number of spectators ranks fourth. Four tennis tournaments, masters, and crown tournaments are held every year around the world. Watching large-scale international tennis matches has become a pillar of leisure and vacation for many people. Tennis matches last from two hours to four hours or more, and there are countless large and small tennis matches around the world every year, so the number of tennis records created is staggering. And artificial intelligence technology is rarely used in tennis in the sports world (5%), but football has reached 50%. Therefore, when dealing with such a large amount of data, we urgently need to find a fast and effective video retrieval classification method to find the required information. The experiment of tennis video classification research based on machine learning technology proves that the accuracy of tennis video classification reaches 98%, so this system has high feasibility.

scholarly journals A Framework for Implementing Machine Learning algorithms using Data sets

2019 ◽  
Vol 8 (11) ◽  
pp. 155-160
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Mathematical Analysis ◽  
Rapid Development ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Commercial Application ◽  
Data Sets ◽  
Learning Technology ◽  
Human Society

The rapid development of cloud computing, big data, machine learning and datamining made information technology and human society to enter new era of technology. Statistical and mathematical analysis on data given a new way of research on prediction and estimation using samples and data sets. Data mining is a mechanism that explores and analyzes many dis-organized or dis-ordered data to obtain potentially useful information and model it based on different algorithms. Machine learning is an iterative process rather than a linear process that requires each step to be revisited as more is learned about the problem. We discussed different machine learning algorithms that can manipulate data and analyses datasets based on best cases for accurate results. Design and Implementation of a framework that is associated with different machine learning algorithms. This paper expounds the definition, model, development stage, classification and commercial application of machine learning, and emphasizes the role of machine learning in data mining by deploying the framework. Therefore, this paper summarizes and analyzes machine learning technology, and discusses the use of machine learning algorithms in data mining. Finally, the mathematical analysis along with results and graphical analysis is given

scholarly journals Indonesia Network Infrastructures and Workforce Adequacy to Implement Machine Learning for Large-Scale Manufacturing

2021 ◽  
Vol 8 (1) ◽  
pp. 1-16
Author(s):  
Steven Anderson ◽  
Ansarullah Lawi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Large Scale ◽  
Manufacturing Industry ◽  
Industrial Revolution ◽  
Technological Development ◽  
Learning Technology ◽  
Monitoring And Control ◽  
Manufacturing Activity ◽  
Industrial Monitoring

Technological development prior to industrial revolution 4.0 incentivized manufacturing industries to invest into digital industry with the aim of increasing the capability and efficiency in manufacturing activity. Major manufacturing industry has begun implementing cyber-physical system in industrial monitoring and control. The system itself will generate large volumes of data. The ability to process those big data requires algorithm called machine learning because of its ability to read patterns of big data for producing useful information. This study conducted on premises of Indonesia’s current network infrastructure and workforce capability on supporting the implementation of machine learning especially in large-scale manufacture. That will be compared with countries that have a positive stance in implementing machine learning in manufacturing. The conclusions that can be drawn from this research are Indonesia current infrastructure and workforce is still unable to fully support the implementation of machine learning technology in manufacturing industry and improvements are needed.

scholarly journals Load Balancing Algorithm of Controller Based on SDN Architecture Under Machine Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 578-588
Author(s):  
Siyuan Liang ◽  
Wenli Jiang ◽  
Fangli Zhao ◽  
Feng Zhao
Keyword(s):  
Machine Learning ◽  
Load Balancing ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Network Control ◽  
Learning Technology ◽  
Related Services ◽  
Network Transmission ◽  
Optimal Action ◽  
Load Balancing Algorithm

Abstract With the rapid development of cloud computing and other related services, higher requirements are put forward for network transmission and delay. Due to the inherent distributed characteristics of traditional networks, machine learning technology is difficult to be applied and deployed in network control. The emergence of SDN technology provides new opportunities and challenges for the application of machine learning technology in network management. A load balancing algorithm of Internet of things controller based on data center SDN architecture is proposed. The Bayesian network is used to predict the degree of load congestion, combining reinforcement learning algorithm to make optimal action decision, self-adjusting parameter weight to adjust the controller load congestion, to achieve load balance, improve network security and stability.

scholarly journals Smart Medical Prediction for Guidance: A Mechanism Study of Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xiangming Wang ◽  
Baobao Dong
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Prediction Model ◽  
Learning Technologies ◽  
Practical Significance ◽  
Learning Technology ◽  
Enterprise Strategy ◽  
Mechanism Study ◽  
Smart Healthcare ◽  
Medical Analysis

Data analysis and prediction have gradually attracted more and more attention in the smart healthcare industry. The smart medical prediction system is of great importance to the enterprise strategy and business development, and it is also of great value to provide medical advices for patients and assist patient guidance. The research theme is the use of machine learning technologies with the application in the areas of smart medical analysis. In this paper, the actual data of the smart medical industry were statistically analysed and visualized according to the features, and the most influential feature combinations were selected for the establishment of the prediction model. Based on machine learning technology, namely, random forest, the guidance prediction model is established, and the combination of features is repeatedly adjusted to improve its accuracy. The practical significance of this paper is to provide a high-precision solution for smart medical data analysis and to realize the proposed data analysis and prediction on the cloud platform based on the Spark environment.

scholarly journals Machine learning-based prediction of survival prognosis in cervical cancer

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Dongyan Ding ◽  
Tingyuan Lang ◽  
Dongling Zou ◽  
Jiawei Tan ◽  
Jia Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cervical Cancer ◽  
Survival Rate ◽  
Prediction Model ◽  
Clustering Algorithm ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
High Survival ◽  
Survival Prognosis ◽  
Cancer Management

Abstract Background Accurately forecasting the prognosis could improve cervical cancer management, however, the currently used clinical features are difficult to provide enough information. The aim of this study is to improve forecasting capability by developing a miRNAs-based machine learning survival prediction model. Results The expression characteristics of miRNAs were chosen as features for model development. The cervical cancer miRNA expression data was obtained from The Cancer Genome Atlas database. Preprocessing, including unquantified data removal, missing value imputation, samples normalization, log transformation, and feature scaling, was performed. In total, 42 survival-related miRNAs were identified by Cox Proportional-Hazards analysis. The patients were optimally clustered into four groups with three different 5-years survival outcome (≥ 90%, ≈ 65%, ≤ 40%) by K-means clustering algorithm base on top 10 survival-related miRNAs. According to the K-means clustering result, a prediction model with high performance was established. The pathways analysis indicated that the miRNAs used play roles involved in the regulation of cancer stem cells. Conclusion A miRNAs-based machine learning cervical cancer survival prediction model was developed that robustly stratifies cervical cancer patients into high survival rate (5-years survival rate ≥ 90%), moderate survival rate (5-years survival rate ≈ 65%), and low survival rate (5-years survival rate ≤ 40%).

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