scholarly journals Popularity Based Recommendation System

2020 ◽  
Vol 9 (3) ◽  
pp. 1561-1566
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
General Public ◽  
Recommendation System ◽  
Data Extraction ◽  
Test Dataset

A Recommendation engine recommends the most relevant items to the user by using different algorithms to filter the data. A Recommendation system is more useful in the context of data extraction relating to applications of big data and machine learning. As the name indicates Popularity based recommendation system works with the current vogue. It basically uses the items which are in swing at present. This is the most basic recommendation system which provides generalized recommendation to every user depending on the popularity. Whatever is more popular among the general public that is more likely to be recommended to new customers. The generalized recommendation not personalized is based on the count. In this paper I am going to use a class that we created which includes the methods to create recommendations and to recommend the item to the user. Next I will load the data of Comma Separated Value (CSV). After that sort the sound name based on the how many users have listened to the sound name. After the collection of data code splits the dataset into training and the test dataset using 80–20 ratio. This creates an instance of popularity based recommenders class. At last I will use the popularity model to make the predictions.

scholarly journals Machine Learning Based On Big Data Extraction of Massive Educational Knowledge

2017 ◽  
Vol 12 (11) ◽  
pp. 151 ◽  
Author(s):  
Abdelladim Hadioui ◽  
Nour-eddine El Faddouli ◽  
Yassine Benjelloun Touimi ◽  
Samir Bennani
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Learning Analytics ◽  
Data Extraction ◽  
Learning System ◽  
Massive Data ◽  
Data Usage ◽  
E Learning ◽  
Uniform Model ◽  
Educational Field

A learning environment generates massive knowledge by means of the services provided in MOOCs. Such knowledge is produced via learning actor interactions. This result is a motivation for researchers to put forward solutions for big data usage, depending on learning analytics techniques as well as the big data techniques relating to the educational field. In this context, the present article unfolds a uniform model to facilitate the exploitation of the experiences produced by the interactions of the pedagogical actors. The aim of proposing the said model is to make a unified analysis of the massive data generated by learning actors. This model suggests making an initial pre-processing of the massive data produced in an e-learning system, and it’s subsequently intends to produce machine learning, defined by rules of measures of actors knowledge relevance. All the processing stages of this model will be introduced in an algorithm that results in the production of learning actor knowledge tree.

scholarly journals Machine learning-driven automatic storage space recommendation for object-based cloud storage system

2021 ◽  
Author(s):  
Anindita Sarkar Mondal ◽  
Anirban Mukhopadhyay ◽  
Samiran Chattopadhyay
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Big Data ◽  
Cloud Storage ◽  
Recommendation System ◽  
Storage System ◽  
Sparse Learning ◽  
Storage Space ◽  
Object Based ◽  
Selection Algorithms

AbstractAn object-based cloud storage system is a storage platform where big data is managed through the internet and data is considered as an object. A smart storage system should be able to handle the big data variety property by recommending the storage space for each data type automatically. Machine learning can help make a storage system automatic. This article proposes a classification engine framework for this purpose by utilizing a machine learning strategy. A feature selection approach wrapped with a classifier is proposed to automatically predict the proper storage space for the incoming big data. It helps build an automatic storage space recommendation system for an object-based cloud storage platform. To find out a suitable combination of feature selection algorithms and classifiers for the proposed classification engine, a comparative study of different supervised feature selection algorithms (i.e., Fisher score, F-score, Lll21) from three categories (similarity, statistical, sparse learning) associated with various classifiers (i.e., SVM, K-NN, Neural Network) is performed. We illustrate our study using RSoS system as it provides a cloud storage platform for the healthcare data as experimental big data by considering its variety property. The experiments confirm that Lll21 feature selection combined with K-NN classifier provides better performance than the others.

scholarly journals Healthcare Prediction and Analysis System with Constant Data Polling

2020 ◽  
Vol 5 (11) ◽  
pp. 1-8
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Extraction ◽  
Community Services ◽  
Complex Data ◽  
Accurate Analysis ◽  
Early Disease Detection ◽  
Huge Data ◽  
Scope Of Application ◽  
Analysis System

The applications of Big data and machine learning in the fields of healthcare, bioinformatics and information sciences are the most important things that a researcher takes into consideration when doing predictive analysis. The Data production at this stage has never been higher and it is increasing at an alarming rate. Hence, it is difficult to store, process and visualise this huge data using customary technologies. However, abstract design for a specific massive information application has been restricted. With advancement of big data in the field of biomedical and healthcare domain, accurate analysis of medical data can be proved beneficial for early disease detection, patient care and community services. Machine learning is being used in a wide scope of application domains to discover patterns in huge datasets. Moreover, the results from machine learning drive critical decisions in applications relating healthcare and biomedicine. The transformation of data to actionable insights from complex data remains a key challenge. In this paper we have introduced a new method of polling of data before analysis is conducted on it. This method will be valuable for dealing with the issue of incomplete data and will progressively prompt suitable and more precise data extraction.

The Cross-Sectional Pricing of Corporate Bonds Using Big Data and Machine Learning

2020 ◽  
Author(s):  
Turan G. Bali ◽  
Amit Goyal ◽  
Dashan Huang ◽  
Fuwei Jiang ◽  
Quan Wen
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Corporate Bonds ◽  
Cross Sectional ◽  
The Cross
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