An Intelligent IoT-Cloud-Based Air Pollution Forecasting Model Using Univariate Time-Series Analysis.

Air pollution is a significant environmental issue affecting public health and ecosystems worldwide, resulting from various sources such as industrial activities, vehicle emissions, and fossil fuel burning. Air pollution contributes to climate change and can cause several health problems, such as respiratory illnesses, cardiovascular disease, and cancer. A potential solution to this problem has been proposed by using different artificial intelligence (AI) and time-series models.

Big Data for Treatment Planning: Pathways and Possibilities for Smart Healthcare Systems.

Background: Treatment planning is one of the crucial stages of healthcare assessment and delivery. Moreover, it also has a significant impact on patient outcomes and system efficiency. With the evolution of transformative healthcare technologies, most areas of healthcare have started collecting data at different levels, as a result of which there is a splurge in the size and complexity of health data being generated every minute.

Recurrent neural network based hybrid model for reconstructing gene regulatory network.

One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer.

A survey of application: genomics and genetic programming, a new frontier.

The aim of this paper is to provide an introduction to the rapidly developing field of genetic programming (GP). Particular emphasis is placed on the application of GP to genomics. First, the basic methodology of GP is introduced.