scholarly journals Resolving Cold Start Problem Using User Demographics and Machine Learning Techniques for Movie Recommender Systems

2018 ◽  
Author(s):  
Sahil Motadoo
Keyword(s):  
Machine Learning ◽  
Recommender Systems ◽  
Cold Start ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Cold Start Problem

Stress Detection Using Machine Learning Classifiers in Internet of Things Environment

2019 ◽  
Vol 16 (10) ◽  
pp. 4214-4219
Author(s):  
Richa Sharma ◽  
Shalli Rani ◽  
Deepali Gupta
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Recommender Systems ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Smart Healthcare ◽  
Learning Techniques ◽  
Internet Of Things Environment ◽  
Novel Applications

Over the years, Recommender systems have emerged as a means to provide relevant content to the users, be it in the field of entertainment, social-network, health, education, travel, food or tourism. Further,with the expeditious development of Big Data and Internet of Things (IoT), technology has successfully associated with our everyday life activities with smart healthcare being one. The global acceptance towards smart watches, wearable devices or wearable biosensors have paved the way for the evolution of novel applications for personalized eHealth and mHealth technologies. The data gathered by wearables can further be interpreted using Machine learning algorithms and shared with healthcare experts to provide suitable recommendations. In this work, we study the role of recommender systems in IoT and Cloud and vice-versa. Further, we have analyzed the performance of different machine learning techniques on SWELL dataset. Based on the results, it is observed that 2 Class Neural network performs the best with 98% accuracy.

scholarly journals Student Career Prediction Using Advanced Machine Learning Techniques

2018 ◽  
Vol 7 (2.20) ◽  
pp. 26 ◽  
Author(s):  
K Sripath Roy ◽  
K Roopkanth ◽  
V Uday Teja ◽  
V Bhavana ◽  
J Priyanka
Keyword(s):  
Machine Learning ◽  
Computer Science ◽  
Recommender Systems ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Science Domain ◽  
Job Role

As students are going through their academics and pursuing their interested courses, it is very important for them to assess their capabilities and identify their interests so that they will get to know in which career area their interests and capabilities are going to put them in. This will help them in improving their performance and motivating their interests so that they will be directed towards their targeted career and get settled in that. Also recruiters while recruiting the candidates after assessing them in all different aspects, these kind of career recommender systems help them in deciding in which job role the candidate should be kept in based on his/her performance and other evaluations. This paper mainly concentrates on the career area prediction of computer science domain candidates.  

Recommender Systems for E-Learning

2021 ◽  
pp. 221-247
Author(s):  
Mohamed Abdullah Amanullah ◽  
Abdessalem Khedher
Keyword(s):  
Machine Learning ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommendation Systems ◽  
Machine Learning Techniques ◽  
User Needs ◽  
Learning Techniques ◽  
E Learning ◽  
Minimal Deviation ◽  
Learning Platforms

The recommender systems are really important in this phase because the users want to be concentrated and to be focused on the domain in which they are interested. There should be minimal deviation in the topics suggested by the recommendation engines. Some of the famous e-learning platforms suggest recommendations based on tags such as highest rated, bestsellers, and so on in various domains. This ultimately makes the users deviate from the domain in which they have to master, and it results in not satisfying the user needs. So, to address this problem, effective recommendation engines will help provide recommendations according to the users by implementing the machine learning techniques such as collaborative filtering and content-based techniques. In this chapter, the authors discuss the recommendation systems, types of recommendation systems, and challenges.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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