The objective of the research explored the development of Fuzzy Systems and Applications Evolutionary Genetic algorithms in the Aviation Industry, and their demonstrated capability of solving different sets of problem emanating from a wide range of application domains affecting the aviation industry. The research problem statement develop investigated the limitations of approximate reasoning use in cascaded genetic algorithms for automatically generate high performance fuzzy systems used in automated aviation flight control in hazardous flight conditions with  using minimal fuzzy sets and rules. The research methodology consisted of qualitative and quantitative research designs through analysis of systematic peer reviewed scholarly journals in the field of Fuzzy Systems and Applications Evolutionary Genetic algorithms in the Aviation Industry, as well as an analysis a sample of relevant case studies. The researcher found out that Fuzzy Systems and Applications Evolutionary Genetic algorithms in the Aviation Industry can be design in a varied number of ways depending on the required shape of the membership functions and appropriate degrees of affinity, and a major limitation of fuzzy inference systems included the limitations concerning the balance between accuracy and interpretability.  Therefore, Evolutionary neuro fuzzy inference systems and applications hybridize the approximate reasoning with learning capabilities of evolutionary algorithms and neural networks, which promotes aviation safety personnel capabilities, and aircraft performance. Future research should be devoted in real time planning, and therefore developments should consider more research into delays on airports, weather conditions, and other aviation events that compromise safety and performance such as aviation accidents and attacks.

Keywords- Approximate Reasoning, Aviation Industry, Evolutionary Genetic Algorithms, Fuzzy Rule Sets, Fuzzy Systems, Hybridization, Membership Functions, Neuro Fuzzy Inference Systems, Safety Procedures

Wireless sensor network (WSN) is one of the emerging technologies to attract the researchers with its research challenges and can be applied on various domains. Now a day WSN applications are used on environmental detection, monitoring system, medical system, military and industrial is monitoring for transforming human life to different aspects. Depending on the applications used on the WSN, Security is one of the greatest challenges in WSNs and it is the essential part before designing the WSNs. In this paper a survey is taken related to the security on the wireless sensor network (WSN).

Keywords—WSN, security, challenges, survey

Voice morphing is a technique for modifying asource speaker’s speech to sound as if it was spoken by some designated target speaker. One of the most recent models for voice conversion is the classical LPC analysis-synthesis model combined with GMM, which aims to separate information from excitation and vocal tract and to learn the transformation rules with statistical methods. However, it does not work well as it is supposed to be due to the inaccuracy of the extracted feature information as well as the overly-smoothed spectral converted by traditional GMM. In this paper, we propose a novel method to solve the problem which is based on the technique of the separation of glottal waveforms. Also, a new strategy to convert excitation information based on residual prediction technique is proposed. The final result shows that not only are the transformed vocal tract parameters matching the target one better, but also is the high quality of the synthesized speech preserved.

Keywords—Morphing, GMM,Extraction, LPC, analysis, synthesis , Glottal Waveform Separation.

In this paper a two server password authentication protocol described. In the sense that two symmetric servers are used for the authentication purpose with making preliminary caution that if one server shut down due to some reason then another server should continue to provide the services to the client. Many protocols are established for the security in server. Client communicates to server through two different secure channels. The authentication information of client on server1 is kept with backup on server2 and vice versa. For avoiding the data redundancy the periodic backup facility is adopted in this system. In this way two server authentications with backup can be created.

Keywords— PAKE, Diffie-Hellman, Elgamal Encryption

The computing scenario is shifting from cloud computing towards edge networking which is known as Fog computing. It is now becoming an  important facilitator in upcoming internet of things applications.  With the rapid increase in the number of internet connected devices, the increased demand of real-time, low-latency services is proving to be a challenge for the traditional cloud computing framework. Fog computing is observed to be an aerial compared to the traditional cloud. It is hierarchical distributed architecture. This computing  ensures the low and predictable latency in the latency-sensitive of Internet of Things (IoT) applications such as the healthcare services which is primary objective of this computing. This paper focuses on architecture, policy manager involved in this novel computing technology and with its usage and  applicabilty.

Keywords: Fog device ,software defined network, internet of things, actuator, foglet , orchestration .

In enormous information world, Hadoop Distributed File System (HDFS) is extremely well known. It gives a system to putting away information in a conveyed situation furthermore has set of instruments to recover and prepare. These information set utilizing guide decrease idea. In this paper, an intensive examination has been conveyed to talk about that how enormous information investigation can be performed on information put away on Hadoop dispersed document framework utilizing Pig and Hive. Apache Pig and Hive are two undertakings which are layered on top of Hadoop, and give more elevated amount dialect to utilize Hadoop's MapReduce library. In this paper, as a matter of first importance, the fundamental ideas of MapReduce, Pig and Hive are presented and their performance comparison.

Keywords: Hadoop Distributed File System, Pig, Hive, mapreduce, Framework

This paper proposes a feature-selection method that minimizes the data needed to represent the appearance of objects by learning the most appropriate feature set for the task person reidentification. The computational cost for feature extraction and the cost for storing the feature descriptor are considered jointly with feature performance to select cost-effective good features. This selection allows improving intercamera reidentification while reducing the bandwidth that is necessary to share data across the cameras. It also rank the selected features in the order of effectiveness for the task to enable a further reduction of the feature set by dropping the least effective features when application constraints require this adaptation and show that the proposed approach considerably reduces cost by selecting inexpensive features and improves re-identification performance.
Index Terms- Cost of features, data reduction, feature selection, person re-identification, camera networks.