scholarly journals Music Recommendation System using Machine Learning Algorithms

2021 ◽  
Vol 9 (VII) ◽  
pp. 2682-2688
Author(s):  
Kartik Kaushik
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Music Recommendation ◽  
Listening Habits ◽  
Music Recommendation System

Music рlаys аn imроrtаnt rоle in humаn lifestyles. Humans рrefers tо hear tо musiс/songs mоre оften thаn аbig apple оther pursuit. With internet teсhnоlоgies, large quantity оf musiс соntent hold musiс оf several genres hаs beсоme’s eаsily аccessible tо milliоns оf user аrоund whole wоrld. Musiс group sinсe deсаde аnd соmрgrowing оf many genres оf musiс is accessible. The mаjоr diffiсulties thаt customer fасe is tо choose аррrорriаte song/musiс frоm suсh big collection of music. The objective оf our рrоjeсt wаs tо reсоmmend sоngs tо customers built exclusively оn their listening habits, with nо knowledge аbоut the musiс. Musiс аррliсаtiоns аre аttemрting tо imрrоve their reсоmmendаtiоn structures in оrder tо оffer their customers the quality роssible listening exрerienсe аnd keeр them оn their рlаtfоrm. For better reсоmmendаtiоns, view аnаlysis will be рerfоrm оn the lyriсs оf sоng and the use of rаndоm-fоrest аlgоrithm will be use fоr сlаssified the song lines intо vаriоus саtegоry (hаррy, sаd).

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.

Statistical Analysis of Human Emotions to Suggest Suitable Music as per Individual's Mood

2021 ◽  
Vol 11 (3) ◽  
pp. 34-67
Author(s):  
Rohit Rastogi ◽  
Prabhat Yadav ◽  
Jayash Raj Singh Yadav
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Statistical Analysis ◽  
Leisure Time ◽  
Recommendation System ◽  
Music Industry ◽  
Music Recommendation ◽  
Emotion Detection ◽  
Current Mood ◽  
Music Recommendation System

There is music recommendation software and music providers that are well explored and commonly used, but those are generally based on simple similarity calculations and manually tagged parameters. This project proposes a music recommendation system based on emotion detection of users, automatic computing, and classification. Music is recommended based on the emotion expressed and temper of the user. Like artists and genre, emotion of the user can also be a crucial recommendation point for music listeners. The different mооds in whiсh the system will сlаssify the imаges аre hаррy, neutrаl, аnd sаd. The system will рre-sоrt the songs according to their genre in the above-mentioned categories. This research project gives us advancement in the music industry with the help of machine learning and artificial intelligence and will reduce the hassle of selecting songs in our leisure time and will automatically play songs by detecting the emotion of the user. This data can be used to play the songs that match the current mood detected from the provided input by the user.

scholarly journals Crop Recommendation System by Artificial Neural Network

2021 ◽  
Author(s):  
Bangaru Kamatchi S ◽  
R. Parvathi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Recommendation System ◽  
Climatic Factors ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Crop Type ◽  
Artificial Neural

Abstract The agriculture yield mostly depends on climate factors. Any information associated with climatic factors will help farmers in foreordained farming. Choosing a right crop at right time is most important to get proper yield. To help the farmers in decision making process a classification model is built by considering the agro climatic parameters of a crop like temperature, relative humidity, type of soil, soil pH and crop duration and a recommendation system is built based on three factors namely crop, type of crop and the districts. Predicting the districts is the novel approach in which crop pattern of 33 districts of Tamilnadu is marked and based on that classification model is built. Thorough analysis of machine learning algorithms incorporating pre-processing, data augmentation and comparison of optimizers and activation function of ANN. Log loss metric is used to validate the models. The results shows that artificial neural network is the best predictive model for classification of crops crop type and district based on agrometeorological climatic condition. The accuracy of artificial neural network model is compared with five different machine learning algorithms to analyse the performance.

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.

scholarly journals A Machine Learning Approach to Career Path Choice for Information Technology Graduates

2020 ◽  
Vol 10 (6) ◽  
pp. 6589-6596
Author(s):  
H. Al-Dossari ◽  
F. A. Nughaymish ◽  
Z. Al-Qahtani ◽  
M. Alkahlifah ◽  
A. Alqahtani
Keyword(s):  
Machine Learning ◽  
Information Technology ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Career Path ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
It Professionals ◽  
Machine Learning Approach

Enterprises rely more and more on well-qualified and highly specialized IT professionals. Although the increasing availability of IT jobs is a good indicator for IT graduates, they nonetheless may find themselves confused about the most appropriate career for their future. In this paper, a recommendation system called CareerRec is proposed, which uses machine learning algorithms to help IT graduates select a career path based on their skills. CareerRec was trained and tested using a dataset of 2255 employees in the IT sector in Saudi Arabia. We conducted a performance comparison between five machine learning algorithms to assess their accuracy for predicting the best-suited career path among 3 classes. Our experiments demonstrate that the XGBoost algorithm outperforms other models and gives the highest accuracy (70.47%).

scholarly journals AI based Music Recommendation system using Deep Learning Algorithms

2021 ◽  
Vol 785 (1) ◽  
pp. 012013
Author(s):  
R Anand ◽  
R.S Sabeenian ◽  
Deepika Gurang ◽  
R Kirthika ◽  
Shaik Rubeena
Keyword(s):  
Deep Learning ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Music Recommendation ◽  
Music Recommendation System

scholarly journals Development of machine learning algorithms for a recommendation system for selecting musical compositions

Connectivity ◽  
2020 ◽  
Vol 148 (6) ◽  
Author(s):  
D. D. Demydov ◽  
◽  
I. S. Shcherbyna ◽  
N. A. Trintina ◽  
A. M. Shtimmerman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Recommendation Systems ◽  
Main Tool ◽  
Machine Learning Algorithms ◽  
Separate Analysis ◽  
Data Set ◽  
Economic Point ◽  
Musical Compositions

The article analyzes the method of singular value decomposition (SVD) as an effective way to build a recommender system. With the development of information technologies and their introduction into public life, there is a need to search for accentuated information in conditions of uncertainty. To solve such problems, recently created intelligent recommendation systems [1]. The popularity of recommendation systems is growing in every segment of goods and services, in particular music. From a socio-economic point of view, such systems are the main tool for the dissemination of new compositions in the field of music promotes the promotion of these compositions in accordance with the preferences of the target audience and encourages users to purchase new music tracks. In addition, such systems significantly reduce the time and facilitate the search for appropriate musical compositions under conditions of uncertainty. The main problem in developing machine learning algorithms is the lack of an individual approach to each user. All recommendations are based on the statistical behavior of the majority, resulting in a percentage of people who do not receive recommendations that match their personal preferences. In the case of a separate analysis of each of the users and the implementation of recommendations in accordance with their personal use of Internet resources, the number of quality and more accurate proposals in the list of recommendations would increase significantly. Machine learning methods are effectively used to build recommendation systems, namely: the k-nearest neighbors method, the Bayesian algorithm and the singular matrix decomposition method. Among these methods, the SVD method is the most widely used in practice. This method is used to reduce the number of non-significant data set factors. Factors in recommendation systems are properties that describe the user or subject. In music recommendation systems, this can be a genre. SVD reduces the dimension of the matrix by removing its hidden factors.

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