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Wireless communications deployed in the current epoch claims ceaseless connection among its users thereby leading to the investigation of Cognitive Radio Networks (CRN) which enables to make use of unallocated spectrum optimally and provides uninterrupted connection. Establishing interminable connectivity during the handoff process in spectrum mobility of CRN is a challenging task. This paper elucidates the optimization of handoff process carried out in CRN by incorporating an intelligent method. This includes fuzzy logic wherein the handoff parameters are processed thereby indicating the need of handoff. The proffered method also comprises of a part of genetic algorithm which yields fitness value for reducing the handoff occurrences and enhancing the overall performance of the system is promoted using cuckoo search which decides the mobile node from which the handoff process has to initiate based on the priority generated. This technique ensures that decision is taken ahead of link failure rather than range failure which are the key point in comparison to the existing system. Results obtained through the simulation are satisfactory in terms of delay, throughput, number of failed handoff and handoffs performed in comparison to the existing fuzzy based handoff process in CRN.

The objectives of this study are to determine (1) when the stock market first perceives the impending bankruptcy of a potentially bankrupt firm and (2) what firm‐specific factors explain the interval between the perception time and the eventual date of bankruptcy (i.e., market lead time). A computational methodology based on the Hillmer‐Yu technique is used to determine the month in which a structural change in the mean and variance of monthly stock return occurs for a potentially bankrupt firm. This parametric change month or the “market perception time” is computed for a sample of 47 industrial firms. The range of market lead times cautions against the common assumption of a uniform event period in event studies. The lead time interval (for both the mean and variance of monthly market return) of poteintially bankrupt firms is found to be positively related to the firm's earnings per share at the time of stock market perception of eventual bankruptcy. Neither the firm's asset size nor systematic risk appear to be significant indicators of lead time interval. Also, change in investment at market perception time is positively related to percentage change in the market lead times. This suggests that innovations in the investment variable are a source of new information to the security market in assesing the probability of future bankruptcy of a firm.

Many researches in the past ignore the need to encrypt the data for security perspective. However in recent years, researchers have given top priority for data security for smooth transmission of data over network by incorporating many encryption strategies along with actual data. In this paper, we consciously discuss the need to secure the data for patient monitoring using various algorithms and plug out the one which is best suit for inbound data security for future healthcare application. As the fields of IoT and Cloud are distinct by their intrinsic technologies, there is a need for integration of Cloud with IoT is obligatory to facilitate and resolve issues involved in data storage as well as data security. In the field of modern healthcare environment, automation has emerged to be more necessary to route and stock the facts about employers (doctors), employees (staffs) and customer (patients). Hence doctors in need of such a stored voluminous information's about a particular person, whom which the condition has to be diagnosed. The clinical and other facts about a person is indeed to be private (trust worthy) and should not be revealed by any other private identity. While establishing bi-directional connections to the internet, communication is a threat and has to be secured without involving any security threads. The offered work makes use of blow fish data encryption and IPv6 based addressing scheme for high data security and increased probability of number of nodes to reduce network congestion.

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