ISLAB/CAISR - User contributions [en]

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2018-07-04T14:54:21Z

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2018-07-04T14:51:57Z

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2018-06-26T09:39:51Z

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Modelling Health Recommender System using Hybrid Techniques

2018-01-12T15:29:21Z

Rebeen:

{{StudentProjectTemplate
|Summary=The goal of this project is to develop a health recommender system using existing machine learning techniques.
|Keywords=Recommendation system, Machine learning, Expert system,
|Prerequisites=Completed courses in basic machine learning are required.
|Supervisor=Hassan Mashad Nemati, Rebeen Hamad,
|Level=Master
|Status=Ongoing
}}
This project has the purpose of exploring the use of existing AI methods and machine learning algorithms for health data assessment in order to develop build a recommender system. The primary goal is to plan, develop and test a knowledge-base of health recommendations to be used for automation, increased health and performance.
Our methodology for building the Diagnostics and recommender (D-R) system is sub-divided into three steps: building a model for analyzing the structured
data, building a model for extrapolating the unstructured data and then finally a model that correlates them to produce an appropriate recommendation.

Modelling Health Recommender System using Hybrid Techniques

2018-01-12T15:24:50Z

Rebeen:

{{StudentProjectTemplate
|Summary= The goal of this project to develop a health recommender system using existing machine learning techniques.
|Keywords=Recommendation system, Machine learning, Expert system,
|Prerequisites=Completed courses in basic machine learning are required.
|Supervisor=Hassan Mashad Nemati, Rebeen Hamad,
|Level=Master
|Status=Ongoing
}}
This project has the purpose of exploring the use of existing AI methods and machine learning algorithms for health data assessment in order to develop build a recommender system. The primary goal is to plan, develop and test a knowledge-base of health recommendations to be used for automation, increased health and performance.
Our methodology for building the Diagnostics and recommender (D-R) system is sub-divided into three steps: building a model for analyzing the structured
data, building a model for extrapolating the unstructured data and then finally a model that correlates them to produce an appropriate recommendation.

Modelling Health Recommender System using Hybrid Techniques

2018-01-12T15:18:11Z

Rebeen: Created page with "{{StudentProjectTemplate |Summary=This project has the purpose of exploring the use of existing AI methods and machine learning algorithms for health data assessment |Keywords..."

{{StudentProjectTemplate
|Summary=This project has the purpose of exploring the use of existing AI methods and machine learning algorithms for health data assessment
|Keywords=Recommendation system, Machine learning, Expert system,
|Prerequisites=Completed courses in basic machine learning is required.
|Supervisor=Hassan Mashad Nemati, Rebeen Hamad,
|Level=Master
|Status=Ongoing
}}
This project has the purpose of exploring the use of existing AI methods and machine learning algorithms for health data assessment in order to develop build a recommender system. The primary goal is to plan, develop and test a knowledge-base of health recommendations to be used for automation, increased health and performance.
Our methodology for building the Diagnostics and recommender (D-R) system is sub-divided into three steps: building a model for analyzing the structured
data, building a model for extrapolating the unstructured data and then finally a model that correlates them to produce an appropriate recommendation.