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In today's world redundancy is the most vital problem faced in almost all domains. Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. The problem becomes more intense when it comes to "Research

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Speech is a verbal communication used by humans through language. Likewise speech recognition is a process of converting speech to text. This paper provides a study of use of artificial neural networks(ANN) in speech recognition. Hidden Markov models (HMM) is a traditional statistical techniques for performing speech recognition. In speech detection software, Mel frequency cepstral coefficients (MFCCs) are frequently used. With different approaches evolving, we deal with the features used to recognize the speech pattern and implementation of speech recognition in the efficient types of artificial neural network (ANN).

The Plant Leaf infection attestation tallies gathers on segregating different sorts of highlights from leaf pictures of weakened plants. The Leaf disorders are critical segments, as it can cause colossal abatement in both quality and number of harvests in agribusiness creation. Thusly, area and portrayal of ailments is a huge task. The highlights extricated from the leaf pictures depends on the grouping cycle. The leaf picture-based infection acknowledgment approach comprises of three techniques: locale division of unhealthy leaf pictures utilizing K-implies bunching, extraction of surface and shape highlights from grouped injury pictures, and arranging the ailing leaf pictures utilizing Adaptive Neuro Fuzzy characterization. The framework produced for leaf sicknesses acknowledgment is used to remove the features from tainted leaves and will be used to gather leaf ailments using ANFIS.