scholarly journals Optimized Recommendation System for E-Commerce on Product Features and User Behavior

2019 ◽  
Vol 8 (2) ◽  
pp. 748-759
Keyword(s):  
Big Data ◽  
Recommendation System ◽  
User Behavior ◽  
Scheduling Algorithm ◽  
Previous History ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Market Place ◽  
Hadoop Distributed File System ◽  
Current Scenario

Big data is a late of huge information stored in it and all we need is to dig into get the important information out of it and create a useful system which can be very helpful in improving the current scenario. There are various applications where big data is being used and even there are few fields that are learning techniques to go with big data and evaluate their work and get an improve decision. This paper particularly concentrates on the e commerce system which is highly trending on the market field. [20] E commerce also known as electronic commerce is a market place which gives you a platform to enjoy various services from both buyers as well as sellers. It is a place with various varieties are provided that can help the consumer to choose from and the buyer can get a platform where he can show case his product and get millions of the customer at the same time and he does not have to look for site all the time, it’s the system that take care of it. Now big data is playing a vital role in e commerce as it reads about user behavior and provides him a suitable product that he may need according to his behavior and query. There are various machine learning algorithms that are working on this and improving the services. [11] Basically in this paper we will read the user information and combine it with the product attributes and get a suitable suggestion for the user that will be most likely to be purchased by him. In the existing system we just look at one part of the case and give suggestion but in this paper we looked at both the sides, that is we looked after the product entities (the attributes and features that it poses) and the user behavior (the information given by the user and its previous history) that will better prediction and improve the system. Moreover for the optimized working of the system we included an enhanced version of HPCA scheduling algorithm for the Hadoop distributed file system also known as HDFS, which is very suitable for the heterogeneous system, the existing algorithm looks after the overall capacity of the node and then the tasks were assigned but here we will consider the health and the left over capacity of the nodes and arrange the queue for the same which will be refreshed all the time after the task is completed by any node.[18] The aim of the paper is to provide fast and most suitable suggestions to the users which can play a vital role in improving the sales of the company and getting the target done soon and faster

Big Data Security Challenges and Solution of Distributed Computing in Hadoop Environment: A Security Framework

2020 ◽  
Vol 13 (4) ◽  
pp. 790-797
Author(s):  
Gurjit Singh Bhathal ◽  
Amardeep Singh Dhiman
Keyword(s):  
Big Data ◽  
Data Security ◽  
Data Sets ◽  
Security Framework ◽  
Hadoop Distributed File System ◽  
Current Scenario ◽  
Hadoop Cluster ◽  
Ciphertext Policy ◽  
In Transit ◽  
Hadoop Framework

Background: In current scenario of internet, large amounts of data are generated and processed. Hadoop framework is widely used to store and process big data in a highly distributed manner. It is argued that Hadoop Framework is not mature enough to deal with the current cyberattacks on the data. Objective: The main objective of the proposed work is to provide a complete security approach comprising of authorisation and authentication for the user and the Hadoop cluster nodes and to secure the data at rest as well as in transit. Methods: The proposed algorithm uses Kerberos network authentication protocol for authorisation and authentication and to validate the users and the cluster nodes. The Ciphertext-Policy Attribute- Based Encryption (CP-ABE) is used for data at rest and data in transit. User encrypts the file with their own set of attributes and stores on Hadoop Distributed File System. Only intended users can decrypt that file with matching parameters. Results: The proposed algorithm was implemented with data sets of different sizes. The data was processed with and without encryption. The results show little difference in processing time. The performance was affected in range of 0.8% to 3.1%, which includes impact of other factors also, like system configuration, the number of parallel jobs running and virtual environment. Conclusion: The solutions available for handling the big data security problems faced in Hadoop framework are inefficient or incomplete. A complete security framework is proposed for Hadoop Environment. The solution is experimentally proven to have little effect on the performance of the system for datasets of different sizes.

Drug Prediction in Healthcare Using Big Data and Machine Learning

2019 ◽  
pp. 79-92 ◽  
Author(s):  
Mamoon Rashid ◽  
Vishal Goyal ◽  
Shabir Ahmad Parah ◽  
Harjeet Singh
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Side Effects ◽  
Previous History ◽  
Distributed File System ◽  
Optimal Drug ◽  
Hadoop Distributed File System ◽  
History Of ◽  
Medicinal Drugs

The healthcare system is literally losing patients due to improper diagnosis, accidents, and infections in hospitals alone. To address these challenges, the authors are proposing the drug prediction model that will act as informative guide for patients and help them for taking right medicines for the cure of particular disease. In this chapter, the authors are proposing use of Hadoop distributed file system for the storage of medical datasets related to medicinal drugs. MLLib Library of Apache Spark is to be used for initial data analysis for drug suggestions related to symptoms gathered from particular user. The model will analyze the previous history of patients for any side effects of the drug to be recommended. This proposal will consider weather and maps API from Google as well so that the patients can easily locate the nearby stores where the medicines will be available. It is believed that this proposal of research will surely eradicate the issues by prescribing the optimal drug and its availability by giving the location of the retailer of that drug near the customer.

A Big Data Recommendation Engine Framework Based on Local Pattern Analytics Strategy for Mining Multi-Sourced Big Data

2019 ◽  
Vol 18 (01) ◽  
pp. 1950009
Author(s):  
T. Venkatesan ◽  
K. Saravanan ◽  
T. Ramkumar
Keyword(s):  
Big Data ◽  
Recommendation System ◽  
Vital Role ◽  
User Preferences ◽  
Data Sources ◽  
Web Based ◽  
Local Pattern ◽  
Business Operations ◽  
Business Applications ◽  
Big Data Technologies

Organisations that perform business operations in a multi-sourced big data environment are in imperative need to discover meaningful patterns of interest from their diversified data sources. With the advent of big data technologies such as Hadoop and Spark, commodity hardwares play vital role in the task of data analytics and process the multi-sourced and multi-formatted big data in a reasonable cost and time. Though various data analytic techniques exist in the context of big data, recommendation system is more popular in web-based business applications to suggest suitable products, services, and items to potential customers. In this paper, we put forth a big data recommendation engine framework based on local pattern analytics strategy to explore user preferences and taste for both branch level and central level decisions. The framework encourages the practice of moving computing environment towards the data source location and avoids forceful integration of data. Further it assists decision makers to reap hidden preferences and taste of users from branch data sources for an effective customer campaign. The novelty of the framework has been evaluated in the benchmark dataset, MovieLens100k and results clearly confirm the advantages of the proposal.

scholarly journals Comparative Study of Machine Learning Algorithms for Recommendation System

2020 ◽  
Vol 22 (12) ◽  
Author(s):  
Sonam Singh ◽  
◽  
Kriti Srivastva ◽  
Keyword(s):  
Machine Learning ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Research Work ◽  
Previous History ◽  
Machine Learning Algorithms ◽  
The Given ◽  
User History

The role of recommender system is very vital in recent times for a lot of individuals. It helps in taking decisions without exploring physically. Broadly there are two types of recommender system: Content based and Collaborative Filtering. The first one focus on user’s history and takes decisions. But there could be times when decisions based on only user history is not sufficient. For this, there is a need to analyze many parameters influencing the decision such as previous history, Age, gender, location etc. In the second approach it finds similar group of users based on several parameters and then takes decisions. Over the last few decades machine learning algorithms have proved their worth in this area because of their ability to learn from the given data and identify various hidden patterns. With this learning, these algorithms are able to generalize very well for unknown data. In this research work, a survey on three different machine learning based collaborative filtering methods are presented using Movie Lens dataset. The comparison of all three methods based on RMSE and MAE error is also discussed.

Energy Efficiency Analysis in ERA using NFL-BA Algorithm

2020 ◽  
pp. 340-348
Author(s):  
Palky Mehta ◽  
H. L. Sharma
Keyword(s):  
Cluster Head ◽  
Vital Role ◽  
Wireless Sensor ◽  
Energy Usage ◽  
Cluster Heads ◽  
Comparison Results ◽  
Current Scenario ◽  
Energy Efficiency Analysis ◽  
Selection Of

In the current scenario of Wireless Sensor Network (WSN), power consumption is the major issue associated with nodes in WSN. LEACH technique plays a vital role of clustering in WSN and reduces the energy usage effectively. But LEACH has its own limitation in order to search cluster head nodes which are randomly distributed over the network. In this paper, ERA-NFL- BA algorithm is being proposed for selects the cluster heads in WSN. This algorithm help in selection of cluster heads can freely transform from global search to local search. At the end, a comparison has been done with earlier researcher using protocol ERA-NFL, which clearly shown that proposed Algorithm is best suited and from comparison results that ERA-NFL-BA has given better performance.

scholarly journals Accelerating organic solar cell material's discovery: high-throughput screening and big data

2021 ◽  
Author(s):  
Xabier Rodríguez-Martínez ◽  
Enrique Pascual-San-José ◽  
Mariano Campoy-Quiles
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
High Throughput ◽  
Organic Solar Cells ◽  
High Throughput Screening ◽  
Organic Solar Cell ◽  
State Of The Art ◽  
Review Article ◽  
Machine Learning Algorithms ◽  
Device Optimization

This review article presents the state-of-the-art in high-throughput computational and experimental screening routines with application in organic solar cells, including materials discovery, device optimization and machine-learning algorithms.

scholarly journals PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features

2021 ◽  
Vol 22 (5) ◽  
pp. 2704
Author(s):  
Andi Nur Nilamyani ◽  
Firda Nurul Auliah ◽  
Mohammad Ali Moni ◽  
Watshara Shoombuatong ◽  
Md Mehedi Hasan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Web Application ◽  
Computational Prediction ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Recursive Feature Elimination ◽  
Post Translational Modification ◽  
Multiple Sequence ◽  
Sequence Features

Nitrotyrosine, which is generated by numerous reactive nitrogen species, is a type of protein post-translational modification. Identification of site-specific nitration modification on tyrosine is a prerequisite to understanding the molecular function of nitrated proteins. Thanks to the progress of machine learning, computational prediction can play a vital role before the biological experimentation. Herein, we developed a computational predictor PredNTS by integrating multiple sequence features including K-mer, composition of k-spaced amino acid pairs (CKSAAP), AAindex, and binary encoding schemes. The important features were selected by the recursive feature elimination approach using a random forest classifier. Finally, we linearly combined the successive random forest (RF) probability scores generated by the different, single encoding-employing RF models. The resultant PredNTS predictor achieved an area under a curve (AUC) of 0.910 using five-fold cross validation. It outperformed the existing predictors on a comprehensive and independent dataset. Furthermore, we investigated several machine learning algorithms to demonstrate the superiority of the employed RF algorithm. The PredNTS is a useful computational resource for the prediction of nitrotyrosine sites. The web-application with the curated datasets of the PredNTS is publicly available.

Sign in / Sign up

Export Citation Format

Share Document