A hybridized red deer and rough set clinical information retrieval system for hepatitis B diagnosis.

Healthcare is a big concern in the current booming population. Many approaches for improving health are imposed, such as early disease identification, treatment, and prevention. Therefore, knowledge acquisition is highly essential at different stages of decision-making.

A hybrid rough set shuffled frog leaping knowledge inference system for diagnosis of lung cancer disease.

Abundant medical data are generated in the digital world every second. However, gathering helpful information from these data is difficult. Gathering useful information from the dataset is very advantageous and demanding.

Extracting information and inferences from a large text corpus.

The usage of various software applications has grown tremendously due to the onset of Industry 4.0, giving rise to the accumulation of all forms of data. The scientific, biological, and social media text collections demand efficient machine learning methods for data interpretability, which organizations need in decision-making of all sorts.

A Hybrid Scheme for Heart Disease Diagnosis Using Rough Set and Cuckoo Search Technique.

Large volumes of raw data are created from the digital world every day. Acquiring useful information from these data is challenging, and it turned into a prime zone of momentum explore. More research is done in this direction.

Neural network and rough set hybrid scheme for prediction of missing associations.

Currently, internet is the best tool for distributed computing, which involves spreading of data geographically. But, retrieving information from huge data is critical and has no relevance unless it provides certain information. Prediction of missing associations can be viewed as fundamental problems in machine learning where the main objective is to determine decisions for the missing associations.