Role of Ontology and Machine Learning in Recommender Systems

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.

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Exchange Spin Coupling from Gaussian Process Regression

10.26434/chemrxiv.12589541.v3 ◽

2020 ◽

Author(s):

Marc Philipp Bahlke ◽

Natnael Mogos ◽

Jonny Proppe ◽

Carmen Herrmann

Keyword(s):

Machine Learning ◽

Gaussian Process ◽

Gaussian Process Regression ◽

Molecular Magnets ◽

Molecular Structures ◽

Spin Coupling ◽

Structure Property ◽

Data Set ◽

Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

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