Data mining has from long been human beings friend and savior in numerous ways and one of the methods is through decision making. With increasing health issues polygenic disorder incorporates a modern-day scourge with millions round the world affected. Data mining is growing in connection to finding such globe unwellness issues through its tools. The following study proposes to use the UCI repository polygenic disorder dataset and generate call tree models for classification mistreatment LAD tree, NB tree and a Genetic J48 tree. The decision tree based classifier models study includes various parameters like computational overheads consumed, features, efficiency and accuracy and provides the results. This genetic J48 model accurately classifies the dataset compared with the opposite 2 models in terms of accuracy and speed.
In this paper, a two-tier cellular wireless network is characterized by overlapping the service area for managing the new calls users having different mobility speed. The overlapping property of the two-tier system provides the advantages that share the traffic load to improve the efficiency of new calls subscriber with guard channels in cell to handle the handoff calls. Using Guard channels, our strategy have the prioritization to the handoff calls. Micro cells are used to provide the services to slow-speed, high-intensity traffic area users and macro cells are overlaid over more than one micro cell cater mainly to lower density, high-speed users. The two-tier of micro cells and macro cells provide the secondary resource to provide the service for new calls as well as handoff calls with guard channels as overflow the slow speed users in macro cell by sharing the frequency in vertical direction as well as sharing the frequency in horizontal direction in the upper layer. The call lose probability of aggregate calls are developed through numerical analysis. The results justify the advantages of proposed strategy.
Data mining plays a vital role in prediction of diseases in health care industry. Diabetes is one of the major health issues in the world. According to World Health Organization 2014 report, around 422 million people worldwide are suffering from diabetes. Diabetes is considered as one of deadliest and chronic disease which causes an increase in blood sugar. Many complications occur if diabetes remains untreated and unidentified. Data mining is a process of obtaining the information from a dataset and transforms it into unambiguous structure. Medical Data mining techniques are used to find hidden patterns in the data sets of medical domain for medical diagnosis and treatment. There are various data mining techniques for prediction of diseases like heart diseases, cancer, and kidney etc. Prediction of diabetes is a fastest growing technology. This paper helps in predicting polygenic disorder by applying data processing techniques. Using various data mining techniques we can predict Diabetes from the data set of a patient. This paper concentrates on the overall survey related to data mining techniques for predicting diabetes.
In the current pandemic of COVID-19, artificial intelligence has become crucial and plays a vital role in dealing with and caring for patients because of the infectious nature of the disease. Many research institutions are stressing on new challenges and future issues of AI, and business houses are funding research institutes and scholars as artificial intelligence is transforming every aspect of human life. Artificial intelligence is grooming itself as a multidisciplinary field of study as it borrows concept of machine learning, data analytics, deep learning, neural network, statics, and soft computing. This chapter presents various applications of AI in a variety of fields and shows how it has become essential and significant in the current pandemic of COVID-19 and helps in societal development and global wellbeing. The chapter end with future issues and challenges of artificial intelligence and research prospects.
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