In the present scenario, it is quite aware that almost every field is moving into machine based automation right from fundamentals to master level systems. Among them, Machine Learning (ML) is one of the important tool which is most similar to Artificial Intelligence (AI) by allowing some well known data or past experience in order to improve automatically or estimate the behavior or status of the given data through various algorithms. Modeling a system or data through Machine Learning is important and advantageous as it helps in the development of later and newer versions. Today most of the information technology giants such as Facebook, Uber, Google maps made Machine learning as a critical part of their ongoing operations for the better view of users. In this paper, various available algorithms in ML is given briefly and out of all the existing different algorithms, Linear Regression algorithm is used to predict a new set of values by taking older data as reference. However, a detailed predicted model is discussed clearly by building a code with the help of Machine Learning and Deep Learning tool in MATLAB/ SIMULINK. Keywords: Machine Learning (ML), Linear Regression algorithm, Curve fitting, Root Mean Squared Error
Generally, image restoration is an important task in any image processing system. This study discusses a novel colour image denoising approach by using new decision based morpho filter for salt and pepper noise corrupted digital color images.The key drawback of the various denoising technique is incapability of edge preservation while image restoration. The proposed denoising algorithm overcomes the aforementioned problem and efficiently removes the corrupted pixels by morphological operation. The proposed system is evaluated by bench mark images such as Lena, Barbara and Peppers. A satisfactory denoising performance is achieved and the result of the system is compared with existing restoration methods such as standard median filter (SMF), adaptive median filter (AMF) and decision based algorithm (DBA).
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