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Decision trees have been widely used for classification in Data mining. Number of decision tree algorithms has been developed in the past. In order to reduce the computational time the On Improving the efficiency of SLIQ (OIESLIQ) algorithm has been developed with an aim to reduce diversity of the decision tree at each split. In order to improve the accuracy, the paper proposes a novel approach (Pioneer classifier algorithm) to embark upon the other two algorithms.

The introduction of self-driving automobiles in today's society necessitates the development of high-quality algorithms to guide them. Convolutional neural networks are extensively utilised because of their ability to classify images based on observed attributes. The proposed solution entails steering a car autonomously using just the windshield view as input. This is accomplished by using Convolutional neural networks in an end-to-end deep learning strategy, as proposed by NVIDIA for self-driving automobiles. To improve the accuracy of existing models, the neural network will be supported with inputs from image processing modules that identify lane markers and cars.Reduced costs, increased safety, and increased mobility are all advantages of providing such an algorithm for retrofitting existing cars.

The number of studies in big data aspects of biomedical domain are tremendously increasing because of the growing technical knowledge, need for reduced computation costs and the availability of internet facilities almost over everywhere. A considerable amount of data in the biomedical domain are stored across platforms that are semantically, structurally and semantically different. With this heterogeneity, it becomes extremely difficult to access and derive meaningful insights from data. Data Integration plays a significant role in merging the data and making access to these data faster and easier. Ontology, a form of knowledge representation, is widely used in data integration to denote the semantic relationship between the data stored in heterogeneous data sources and aid in easier retrieval. This paper surveys the ontology engineering methods used in various biomedical domains like cardiology, nephrology, diabetes, Covid-19, traditional medicine and the recent advancements in ontology development.