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.

The Rain Prediction in Australia Based Big Data Analysis and Machine Learning Technology

2021 ◽  
Author(s):  
Bohdan Polishchuk ◽  
Andrii Berko ◽  
Lyubomyr Chyrun ◽  
Myroslava Bublyk ◽  
Vadim Schuchmann
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Learning Technology

Model of predicting customer behavior based on big data analysis technologies

2021 ◽  
Vol 1 (2 (6)) ◽  
Author(s):  
Anastasiia Ivanitska ◽  
Dmytro Ivanov ◽  
Ludmila Zubik
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Customer Behavior ◽  
Learning Technologies ◽  
User Preferences ◽  
Data Sets ◽  
Learning Technology ◽  
Behavior Prediction ◽  
Network Information ◽  
Selection Of

The analysis of the available methods and models of formation of recommendations for the potential buyer in network information systems for the purpose of development of effective modules of selection of advertising is executed. The effectiveness of the use of machine learning technologies for the analysis of user preferences based on the processing of data on purchases made by users with a similar profile is substantiated. A model of recommendation formation based on machine learning technology is proposed, its work on test data sets is tested and the adequacy of the RMSE model is assessed. Keywords: behavior prediction; advertising based on similarity; collaborative filtering; matrix factorization; big data; machine learning

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 Editorial for the Special Issue “Frontiers in Spectral Imaging and 3D Technologies for Geospatial Solutions”

2019 ◽  
Vol 11 (14) ◽  
pp. 1714
Author(s):  
Eija Honkavaara ◽  
Konstantinos Karantzalos ◽  
Xinlian Liang ◽  
Erica Nocerino ◽  
Ilkka Pölönen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Data Analysis ◽  
Spectral Imaging ◽  
Learning Technologies ◽  
Special Issue ◽  
Multimodal Data

This Special Issue hosts papers on the integrated use of spectral imaging and 3D technologies in remote sensing, including novel sensors, evolving machine learning technologies for data analysis, and the utilization of these technologies in a variety of geospatial applications. The presented results showed improved results when multimodal data was used in object analysis.

scholarly journals Review of Machine Learning Technologies and Neural Networks in Drug Synergy Combination pharmacological research

2020 ◽  
Vol 6 (3) ◽  
pp. 27-32
Author(s):  
Artur S. Ter-Levonian ◽  
Konstantin A. Koshechkin
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Technologies ◽  
Learning Technology ◽  
Preclinical Research ◽  
Processing Technologies ◽  
Drug Synergy ◽  
Positive Effects ◽  
The Past ◽  
The Right

Introduction: Nowadays an increase in the amount of information creates the need to replace and update data processing technologies. One of the tasks of clinical pharmacology is to create the right combination of drugs for the treatment of a particular disease. It takes months and even years to create a treatment regimen. Using machine learning (in silico) allows predicting how to get the right combination of drugs and skip the experimental steps in a study that take a lot of time and financial expenses. Gradual preparation is needed for the Deep Learning of Drug Synergy, starting from creating a base of drugs, their characteristics and ways of interacting. Aim: Our review aims to draw attention to the prospect of the introduction of Deep Learning technology to predict possible combinations of drugs for the treatment of various diseases. Materials and methods: Literary review of articles based on the PUBMED project and related bibliographic resources over the past 5 years (2015–2019). Results and discussion: In the analyzed articles, Machine or Deep Learning completed the assigned tasks. It was able to determine the most appropriate combinations for the treatment of certain diseases, select the necessary regimen and doses. In addition, using this technology, new combinations have been identified that may be further involved in preclinical studies. Conclusions: From the analysis of the articles, we obtained evidence of the positive effects of Deep Learning to select “key” combinations for further stages of preclinical research.

scholarly journals Constructing an Efficient Machine Learning Model for Tornado Prediction

2020 ◽  
Vol 19 (05) ◽  
pp. 1177-1187
Author(s):  
Fuad Aleskerov ◽  
Sergey Demin ◽  
Michael B. Richman ◽  
Sergey Shvydun ◽  
Theodore B. Trafalis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Prediction Model ◽  
Superposition Principle ◽  
Learning Models ◽  
Analysis Techniques ◽  
Machine Learning Model ◽  
Choice Procedures ◽  
Efficient Machine ◽  
Machine Learning Models

Tornado prediction variables are analyzed using machine learning and decision analysis techniques. A model based on several choice procedures and the superposition principle is applied for different methods of data analysis. The constructed model has been tested on a database of tornadic events. It is shown that the tornado prediction model developed herein is more efficient than a previous set of machine learning models, opening the way to more accurate decisions.

scholarly journals Social Big-Data Analysis of Particulate Matter, Health, and Society

2019 ◽  
Vol 16 (19) ◽  
pp. 3607 ◽  
Author(s):  
Juyoung Song ◽  
Tae Min Song
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Particulate Matter ◽  
Data Analysis ◽  
Prediction Model ◽  
Opinion Mining ◽  
Big Data Analysis ◽  
Online News ◽  
Text Documents ◽  
Social Big Data

The study collected particulate matter (PM)-related documents in Korea and classified main keywords related to particulate matter, health, and social problems using text and opinion mining. The study attempted to present a prediction model for important causes related to particulate matter by using social big-data analysis. Topics related to particulate matter were collected from online (online news sites, blogs, cafés, social network services, and bulletin boards) from 1 January 2015, to 31 May 2016, and 226,977 text documents were included in the analysis. The present study applied machine-learning analysis technique to forecast the risk of particulate matter. Emotions related to particulate matter were found to be 65.4% negative, 7.7% neutral, and 27.0% positive. Intelligent services that can detect early and prevent unknown crisis situations of particulate matter may be possible if risk factors of particulate matter are predicted through the linkage of the machine-learning prediction model.

scholarly journals Application of machine learning technologies for Drone Network in logistics and port activities of Russia

2021 ◽  
pp. 149-157
Author(s):  
О.М. Михов ◽  
Н.В. Шаталова ◽  
О.В. Бородина ◽  
Ю.И. Васильев
Keyword(s):  
Machine Learning ◽  
Learning Technologies ◽  
Scientific Article ◽  
Learning Technology ◽  
Foreign Companies ◽  
International Trends ◽  
Logistics Enterprises ◽  
The Cost ◽  
Network Technologies ◽  
Transport Logistics

Cтатья посвящена проведению исследовательского анализа особенностей практического использования беспилотных дронов и квадрокоптеров. Актуальность исследования обусловлена тем, что технологии Drone Network позволяют решить основную проблему логистики – провести оптимизацию финансовых расходов, путем сокращения затрат на реализацию цепочек поставок. Это помощь интеграции логистики, дронов и технологии машинного обучения. В рамках статьи рассмотрены теоретические аспекты понятия технологии «Drone Network». Проанализирован зарубежный опыт и международные тенденции в использовании беспилотных дронов при формировании цепочки поставок логистики морских предприятий. Рассмотрены ключевые преимущества, которые предоставляют данные технологии в совершенствовании транспортной логистики компаний. Проанализированы перспективы развития технологий Drone Network на территории Российской Федерации. Рассмотрены основные проблемы, препятствующие их практическому применению российскими компаниями. Проанализированы недостатки, с которыми сталкиваются организации в рамках использования технологий беспилотных дронов в логистике. Описаны перспективы развития технологий Drone Network в международном и российском рынке. Проанализированы перспективы применения беспилотных дронов, управляемых технологиями машинного обучения, в рамках развития портовой деятельности, внутрипортовой логистики и для поиска бедствующих кораблей. The scientific article is devoted to the research analysis of the features of the practical use of unmanned drones and frame copters in the framework of the transport logistics of goods and orders by foreign companies. The relevance of the study is due to the fact that Drone Network technologies allow solving the main problem of logistics - to optimize financial costs by reducing the cost of implementing supply chains. Perhaps this is helping the integration of logistics, drones and machine learning technology. The article discusses the theoretical aspects of the concept of the "Drone Network" technology. Analyzed foreign experience and international trends in the use of unmanned drones in the formation of the supply chain of logistics enterprises. The key advantages that these technologies provide in improving the transport logistics of companies are considered. The prospects for the development of Drone Network technologies on the territory of the Russian Federation are analyzed. The main problems that hinder their practical application by Russian companies are considered. The paper analyzes the shortcomings faced by organizations in the use of unmanned drone technologies in logistics. The prospects for the development of Drone Network technologies in the international and Russian markets are described. The prospects for the use of unmanned drones controlled by machine learning technologies in the development of port activities, intra-port logistics and for the search for distressed ships are analyzed.

scholarly journals Investigation of Classifiers Algorithms of ML for Heart Disease Prediction

2021 ◽  
Vol 9 (VI) ◽  
pp. 3025-3033
Author(s):  
Ved Prakash Singh
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Data Analysis ◽  
Movement Disorders ◽  
Learning Technologies ◽  
The Body ◽  
Data Sets ◽  
K Nearest Neighbors ◽  
Key Factor ◽  
Ultimate Objective

A ML computer plays an important role in predicting the presence or absence of movement disorders and heart disease. The resting part of the body as compared to the Heart s, is the largest and most concentrated organ in the human body. Data analysis helps in predicting heart disease in the medical field is an important task. Machine learning is recycled in the medical industry throughout the world. The presence or absence of movement disorders and cardiac diseases is a key factor in machine learning. Data analysis helps predict more information and prevents various diseases in medical centers. The main impartial of the research paper is toward predict a patient cardiac disease using an algorithm for machine learning as a random forest is most predictable. A large number of patient data are kept every month. The data stored can be used to predict future diseases. Certain data mining and machine learning technologies are used to forecast heart disease, including artificial neural networks (ANN), decision trees, fuzzy logic, K-Nearest neighbors (KNN), naive bays and vector supporting equipment (SVM). The ultimate objective of this paper is to inspect the best logistic regression which signifies the machine's python learning. The UCI machine learning depot used the data sets of heart disease.

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