PhD Journal Paper Writing In Machine Learning Cooja

The highly growing massive data and emerging AI (Artificial Intelligence) demand the need of Machine Learning (ML). It is the detailed subfield of AI (artificial intelligence) that find out the ways to analyze the abundant data chunks and execute the system to make the data work automatically without taking the assistance of the programming. The algorithms used in machine learning seeks the ways to disclose the fine-grained patterns from the unequalled data under the multitude of prospects and to create a reliable prediction model as never made before. Moreover, the machine learning algorithm is differentiated in four types and the classifications are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

In any circumstance, when the new data is used as input in the machine learning algorithm, it itself learns and finds out the nearing by utilizing the preceding experience over time. MI is consecutively disseminating its features in a wide range of applications such as computer vision, online recommendation system, natural language processing, predictive analytics, cyber security, speech processing, neuroscience, fraud detection, IoT (Internet of things), and more.

Concisely, the research ideas on machine level language has been used on pattern recognition, network intrusion detection, image recognition, recommendation and filtering, self-driving cars, malware detection, cryptosystems, spam detection, unmanned autonomous systems, PUF (unclonable function), cybersecurity, homeland security, sales, marketing, electric grid, etc.

The research idea classification includes; Techniques are proposed for the analysis of prediction in data mining by using the algorithms of machine learning. With glcm algorithm in ML, reorganization system and the iris detection has made. The face spoof detection needed in artificial neural networks is done with the concepts of machine learning. Besides, the plant disease identification is made by K-Nearest-Neighbours and gray-level co-occurrence matrix classification in neural networks used in machine learning.

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