scholarly journals Implementation of Fund Recommendation System Using Machine Learning

2021 ◽  
Vol 8 (3) ◽  
pp. 183-190
Author(s):  
Chae-eun Park ◽  
Dong-seok Lee ◽  
Sung-hyun Nam ◽  
Soon-kak Kwon
Keyword(s):  
Machine Learning ◽  
Recommendation System

Development of Recommendation System in e-Commerce using Emotional Analysis and Machine Learning Methods

2021 ◽  
Author(s):  
Taras Mykhalchuk ◽  
Tetiana Zatonatska ◽  
Oleksandr Dluhopolskyi ◽  
Alina Zhukovska ◽  
Tetiana Dluhopolska ◽  
...  
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Comparison Study on Improved Movie Recommender Systems

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 1470-1478
Author(s):  
R. Lavanya ◽  
Ebani Gogia ◽  
Nihal Rai
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Recommendation System ◽  
Singular Value ◽  
Experimental Result ◽  
Comparison Study ◽  
Deep Learning Neural Network ◽  
Value Decomposition ◽  
Crucial Part

Recommendation system is a crucial part of offering items especially in services that offer streaming. For streaming movie services on OTT, RS are a helping hand for users in finding new movies for leisure. In this paper, we propose a machine learning an approach based on auto encoders to produce a CF system which outputs movie rating for a user based on a huge DB of ratings from other users. Utilising Movie Lens dataset, we explore the use of deep learning neural network based Stacked Auto encoders to predict user s ratings on new movies, thereby enabling movie recommendations. We consequently implement Singular Value Decomposition (SVD) to recommend movies to users. The experimental result showcase that our R S out performs a user-based neighbourhood baseline in terms of MSE on predicted ratings and in a survey in which user judge between recommendation s from both systems.

scholarly journals Machine Learning Based Crop Recommendation System

2021 ◽  
pp. 891-897
Author(s):  
Dhruv Piyush Parikh ◽  
Jugal Jain ◽  
Tanishq Gupta ◽  
Rishit Hemant Dabhade
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Scientific Knowledge ◽  
Recommendation System ◽  
Human Being

The three most basic amenities required for the survival of a human being are food, shelter and clothing. In today’s tech-savvy generation, the latter two have witnessed a huge scientific boost. Unfortunately, even today, agriculture is considered as more of a man-power oriented field. Most of the farmers are untutored and have little to no scientific knowledge of farming. So, they have to rely on the hit and trial method to learn from experience which leads to wastage of time and resources. Our system focuses on building a predictive model to recommend the most suitable crops to grow in a particular farm based on various parameters. This can be helpful for the farmers to be more productive and competent without wasting any resources by farming the most competent crops.

scholarly journals Movie Recommenadation System

2021 ◽  
Vol 9 (4) ◽  
pp. 13-16
Author(s):  
Gandhali Malve ◽  
Lajree Lohar ◽  
Tanay Malviya ◽  
Shirish Sabnis
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Recommendation Systems ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Amount Of Information ◽  
System Recommendation ◽  
User Ratings

Today the amount of information in the internet growth very rapidly and people need some instruments to find and access appropriate information. One of such tools is called recommendation system. Recommendation systems help to navigate quickly and receive necessary information. Many of us find it difficult to decide which movie to watch and so we decided to make a recommender system for us to better judge which movie we are more likely to love. In this project we are going to use Machine Learning Algorithms to recommend movies to users based on genres and user ratings. Recommendation system attempt to predict the preference or rating that a user would give to an item.

scholarly journals ViraMiner: Deep Learning on Raw DNA Sequences for Identifying Viral Genomes in Human Samples

2019 ◽  
Author(s):  
Ardi Tampuu ◽  
Zurab Bzhalava ◽  
Joakim Dillner ◽  
Raul Vicente
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Dna Sequences ◽  
Recommendation System ◽  
Clinical Importance ◽  
Training Dataset ◽  
Viral Genomes ◽  
Human Samples ◽  
Types Of Information ◽  
Improved Accuracy

ABSTRACTDespite its clinical importance, detection of highly divergent or yet unknown viruses is a major challenge. When human samples are sequenced, conventional alignments classify many assembled contigs as “unknown” since many of the sequences are not similar to known genomes. In this work, we developed ViraMiner, a deep learning-based method to identify viruses in various human biospecimens. ViraMiner contains two branches of Convolutional Neural Networks designed to detect both patterns and pattern-frequencies on raw metagenomics contigs. The training dataset included sequences obtained from 19 metagenomic experiments which were analyzed and labeled by BLAST. The model achieves significantly improved accuracy compared to other machine learning methods for viral genome classification. Using 300 bp contigs ViraMiner achieves 0.923 area under the ROC curve. To our knowledge, this is the first machine learning methodology that can detect the presence of viral sequences among raw metagenomic contigs from diverse human samples. We suggest that the proposed model captures different types of information of genome composition, and can be used as a recommendation system to further investigate sequences labeled as “unknown” by conventional alignment methods. Exploring these highly-divergent viruses, in turn, can enhance our knowledge of infectious causes of diseases.

scholarly journals The algorithm and the flow: Netflix, machine learning and recommendation algorithms

Intexto ◽  
2019 ◽  
pp. 166-184
Author(s):  
João Damasceno Martins Ladeira
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Recommendation System ◽  
Cultural Impact ◽  
Recommendation Algorithms ◽  
Definition Of

This article discusses the Netflix recommendation system, expecting to understand these techniques as a part of the contemporary strategies for the reorganization of television and audiovisual. It renders problematic a technology indispensable to these suggestions: the tools for artificial intelligence, expecting to infer questions of cultural impact inscribed in this technique. These recommendations will be analyzed in their relationship with the formerly decisive form for the constitution of broadcast: the television flow. The text investigates the meaning such influential tools at the definition of a television based on the manipulation of collections, and not in the predetermined programming, decided previously to the transmission of content. The conclusion explores the consequences of these archives, which concedes to the user a sensation of choice in tension with the mechanical character of those images.

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