Empirical analysis of Machine Learning Techniques for context aware Recommender Systems in the environment of IoT

2016 ◽  
Author(s):  
Nitin Sachdeva ◽  
Renu Dhir ◽  
Akshi Kumar
Keyword(s):  
Machine Learning ◽  
Recommender Systems ◽  
Empirical Analysis ◽  
Machine Learning Techniques ◽  
Context Aware ◽  
Learning Techniques

scholarly journals ContextPCA: Predicting Context-Aware Smartphone Apps Usage Based On Machine Learning Techniques

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 499 ◽  
Author(s):  
Iqbal H. Sarker ◽  
Yoosef B. Abushark ◽  
Asif Irshad Khan
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Real Life ◽  
Machine Learning Techniques ◽  
Model Complexity ◽  
Context Aware ◽  
Smartphone Apps ◽  
Data Set ◽  
Machine Learning Classification ◽  
Learning Techniques

This paper mainly formulates the problem of predicting context-aware smartphone apps usage based on machine learning techniques. In the real world, people use various kinds of smartphone apps differently in different contexts that include both the user-centric context and device-centric context. In the area of artificial intelligence and machine learning, decision tree model is one of the most popular approaches for predicting context-aware smartphone usage. However, real-life smartphone apps usage data may contain higher dimensions of contexts, which may cause several issues such as increases model complexity, may arise over-fitting problem, and consequently decreases the prediction accuracy of the context-aware model. In order to address these issues, in this paper, we present an effective principal component analysis (PCA) based context-aware smartphone apps prediction model, “ContextPCA” using decision tree machine learning classification technique. PCA is an unsupervised machine learning technique that can be used to separate symmetric and asymmetric components, and has been adopted in our “ContextPCA” model, in order to reduce the context dimensions of the original data set. The experimental results on smartphone apps usage datasets show that “ContextPCA” model effectively predicts context-aware smartphone apps in terms of precision, recall, f-score and ROC values in various test cases.

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.

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