Study of Methods of Complex Data Analysis that Based on Machine Learning Technologies

2019 ◽  
Author(s):  
Anastasiia Ivanytska ◽  
Liudmyla Zubyk ◽  
Dmitry Ivanov ◽  
Kateryna Domracheva
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Learning Technologies ◽  
Complex Data

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 Trend analysis of university placement by using machine learning algorithms

2018 ◽  
Vol 7 (2.4) ◽  
pp. 178
Author(s):  
Chandrasekhar Kumbhar ◽  
Dr S. S. Sridhar
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Data Analysis ◽  
Trend Analysis ◽  
Model Building ◽  
Machine Learning Algorithms ◽  
Complex Data ◽  
Student Placement ◽  
The University ◽  
Different Sources

Machine learning is a method of data analysis that automates analytical model building. These models help you to make a trend analysis of university placements data, to predict a placement rate for the students of an upcoming year which will help the university to analyze the performance during placements. Many students look at universities as a means of investment which can help them make a great future by getting placed in good companies and which will relieve their stress and unease from their lives before graduating from the university. The trend will also help in giving the companies reasons as to why they should visit university again and again. Some attributes play the very important role while analyzing the student for e.g. Student’s name, Department, Company, Location and Annual package. So, classification can help you to classify those data and clustering helps to make the clusters department wise. In this paper we have used neural networks to predict the upcoming student placement and got 77% of accuracy while testing were iteration are 1000. Through extensive trend analysis of varies complex data collected from different sources, we can demonstrate that our analysis can provide a good pragmatic solution for future placement of students. 

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.

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.

Machine learning and data mining techniques for medical complex data analysis

2018 ◽  
Vol 276 ◽  
pp. 1 ◽  
Author(s):  
Hamid Alinejad-Rokny ◽  
Esmaeil Sadroddiny ◽  
Vinod Scaria
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Data Analysis ◽  
Complex Data ◽  
Data Mining Techniques

Model of artificial neural network for complex data analysis in slag glass-ceramic

2004 ◽  
Vol 95 (2) ◽  
pp. 97-101 ◽  
Author(s):  
Hongyuan Sun ◽  
Qiye Wen ◽  
Peixin Zhang ◽  
Jianhong Liu ◽  
Qianling Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Analysis ◽  
Glass Ceramic ◽  
Complex Data ◽  
Slag Glass ◽  
Artificial Neural

Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer.

2020 ◽  
Vol 21 ◽  
Author(s):  
Sukanya Panja ◽  
Sarra Rahem ◽  
Cassandra J. Chu ◽  
Antonina Mitrofanova
Keyword(s):  
Machine Learning ◽  
Missing Values ◽  
Therapeutic Response ◽  
Patient Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Complex Data ◽  
Human Machine Interaction ◽  
Data Repositories ◽  
Response Modeling

Background: In recent years, the availability of high throughput technologies, establishment of large molecular patient data repositories, and advancement in computing power and storage have allowed elucidation of complex mechanisms implicated in therapeutic response in cancer patients. The breadth and depth of such data, alongside experimental noise and missing values, requires a sophisticated human-machine interaction that would allow effective learning from complex data and accurate forecasting of future outcomes, ideally embedded in the core of machine learning design. Objective: In this review, we will discuss machine learning techniques utilized for modeling of treatment response in cancer, including Random Forests, support vector machines, neural networks, and linear and logistic regression. We will overview their mathematical foundations and discuss their limitations and alternative approaches all in light of their application to therapeutic response modeling in cancer. Conclusion: We hypothesize that the increase in the number of patient profiles and potential temporal monitoring of patient data will define even more complex techniques, such as deep learning and causal analysis, as central players in therapeutic response modeling.

Machine Learning Based Predictive Action on Categorical Non-Sequential Data

2020 ◽  
Vol 13 (5) ◽  
pp. 1020-1030
Author(s):  
Pradeep S. ◽  
Jagadish S. Kallimani
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Categorical Data ◽  
Numerical Data ◽  
Processing Technique ◽  
Machine Learning Algorithms ◽  
Sequential Data ◽  
Industry Standard ◽  
Robust Model ◽  
Future Work

Background: With the advent of data analysis and machine learning, there is a growing impetus of analyzing and generating models on historic data. The data comes in numerous forms and shapes with an abundance of challenges. The most sorted form of data for analysis is the numerical data. With the plethora of algorithms and tools it is quite manageable to deal with such data. Another form of data is of categorical nature, which is subdivided into, ordinal (order wise) and nominal (number wise). This data can be broadly classified as Sequential and Non-Sequential. Sequential data analysis is easier to preprocess using algorithms. Objective: The challenge of applying machine learning algorithms on categorical data of nonsequential nature is dealt in this paper. Methods: Upon implementing several data analysis algorithms on such data, we end up getting a biased result, which makes it impossible to generate a reliable predictive model. In this paper, we will address this problem by walking through a handful of techniques which during our research helped us in dealing with a large categorical data of non-sequential nature. In subsequent sections, we will discuss the possible implementable solutions and shortfalls of these techniques. Results: The methods are applied to sample datasets available in public domain and the results with respect to accuracy of classification are satisfactory. Conclusion: The best pre-processing technique we observed in our research is one hot encoding, which facilitates breaking down the categorical features into binary and feeding it into an Algorithm to predict the outcome. The example that we took is not abstract but it is a real – time production services dataset, which had many complex variations of categorical features. Our Future work includes creating a robust model on such data and deploying it into industry standard applications.

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