Machine learning is an approach of artificial intelligence (AI) where the machine can automatically learn and improve its performance on experience. It is not explicitly programmed; the data is fed into the generic algorithm and it builds logic based on the data provided. Traditional algorithms have to define new rules or massive rules when the pattern varies or the number of patterns increases, which reduces the accuracy or efficiency of the algorithms. But the machine learning algorithms learn new input patterns capable of handling complex situations while maintaining accuracy and efficiency. Due to its effectual benefits, machine learning algorithms are used in various domains like healthcare, industries, travel, game development, social media services, robotics, and surveillance and information security. In this chapter, the application of machine learning technique in healthcare is discussed in detail.

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Problem statement: In mobile ad hoc networks, frequent mobility during the transmission of data causes route failure which results in route rediscovery. In this, we propose multipath routing protocol for effective local route recovery in Mobile Ad hoc Networks (MANET). In this protocol, each source and destination pair establishes multiple paths in the single route discovery and they are cached in their route caches. Approach: The cached routes are sorted on the basis of their bandwidth availability. In case of route failure in the primary route, a recovery node which is an overhearing neighbor, detects it and establishes a local recovery path with maximum bandwidth from its route cache. Results: By simulation results, we show that the proposed approach improves network performance. Conclusion: The proposed route recovery management technique prevents the frequent collision and degradation in the network performance

A low-complexity standard transmit diversity scheme for coded orthogonal frequency division multiplexing (OFDM) frameworks is cyclic delay diversity (CDD). As the cyclic delay esteem expands the diversity effect also increases. However, the channel estimation turns out to be more troublesome because of increased frequency selectivity as the increased cyclic delay value. In this paper we discussed about dissimilar channel estimation schemes and to conclude we proposed a novel pilot-aided channel estimation scheme for CDD to estimate the channel in an enhanced way. Our scheme estimates each channel separately and achieves significant performance improvement.

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