Genetic Folding algorithm uses linear chromosomes composed of organized genes in floating-numbers manner, in which each genes chain fold back on themselves to form the final GF chromosome. In this paper, a novel genotype representation and a novel genetic operator were proposed. The paper was applied using MATLAB code to illustrate the beneficiary, flexibility and powerful of the Genetic Folding algorithm solving Santa Fe Trail problem. The problem of programming an artificial ant to follow the Santa Fe Trail is used as an example of program search space. 
To evaluate the efficiency and feasibility of the proposed methods, a comparison was held between the various types and sizes through the Santa Fe Trail problem. Several test functions along with various levels of difficulty were also conducted. Results of this proposal clearly show significant results of the proposed genotype and the genetic operator also. 

Now-a-days, one of the most exciting technology is cloud computing. Accessing dynamically virtualized resources through internet is called as cloud computing. Security and confidentiality are the major concerns in public cloud. Though EduCloud (Educational Cloud) uses public cloud, moving data from one location to another location may lead to risk. Information related to staff, student and management or admin that can be shared in EduCloud, are to be secured in public educational cloud environment. In this scenario, data security is the most critical issue in cloud. But present authentication system available does not provide enough security in public EduCloud. Hence, we propose new authentication framework to enhance security in public educational cloud. The features of various authentication techniques are discussed in this paper and a novel framework is proposed for pubic EduCloud, which provides not only security but also increases the response time. The developed software tool is best suited and provably a secured solution to the public educational cloud environment.

The key aim of this assessment is to characterize the rate of corrosion of buried Nickel plated and non-plated AISI 1015 steel pipes using a Modified Artificial Neural Network on Matlab and taking the oil and gas area of Nigeria as a case study. Ten (10) metal specimens were used. Five (5) were nickel electroplated specimens buried differently in 5 plastic containers containing 5 different soil samples with the other 5 non-plated specimens also buried into the same 5 soil samples but different plastic containers. In carrying out the experiment, the data that was collected for 25 consecutive days were grouped into sets of input and output data. This was required so as to appropriately feed the modelling tool (Artificial Neural Network). The input data were; temperature of the soil sample, temperature of the immediate surroundings, and pH of the soil sample while the output data was weight loss. Conclusively, Modified Artificial Neural Network relationships between the varied selected input parameters that affects corrosion rate (soil sample temperature, immediate environment temperature and pH value) and the output parameter (Corrosion Penetration Rate) were derived. Also, soil sample temperature and the immediate surrounding temperature combined conditions had the strongest effect on corrosion penetration rate while the immediate surrounding temperature and the pH value combined conditions had the weakest effect on corrosion penetration rate.

Big Data is the collection of large data sets which contains large amount of data. There are different areas which are generating huge data, this data may be present in the form of semi-structured or unstructured and to get useful information from such raw data there is need of data analysis. Due to Big Data’s excessive volume, variety, and velocity it is very difficult to store and process huge data. The process of extracting the information from such raw data is called Big Data Analytics. Big data Analytics processes data gives result in the form of structured data. Again this data is huge size and very difficult to understand since it is present in the form of CSV or excel or simple text files. So for effective decision making and to understand the information quickly the data need to be visualized as human mind understands images and graphs better and faster than text data. In this paper a model called Data Visualization (Viz) is designed which integrates big data analytics and the data visualization. This model first takes the data from various sources and then processes it and converts it into structured form, if want this data can be stored to RDBMS. Finally the text result can be visualized with the help of Visualization module of the DataViz. Here text result is represented in the form of charts and graphs.

Android, an operating system developed by Google for smartphones, tablets and such type of touchscreen phones. Android app is a software running on Android platform. Typically an app is designed for specific purpose on Android platform such as smartphones and tablets. In this work a designing and implementing process of an Android based spy robot is described. An Android based application, RC Bluetooth controller is used to control in this spy robot. This robot can be controlled with the help an app of Android phones. Arduino based microcontroller is used for instruction processing and giving proper instructions. Bluetooth technology is used to interface between Arduino and Android. A hand for collecting and holding objects and a camera for live video steaming is used to observe any object & movement of robot. This robot can move to any place and perform smartly within Bluetooth network range.

Pipeline vandalization is one of Nigeria's economy killer, since Nigeria economy to a great extent relies on oil in that capacity. Therefore, a third party damage to petroleum pipelines can be cataclysmic if undetected and prevented. This act results in budgetary misfortunes, ecological contamination and incessant death and loss of properties worth millions as an aftereffect of vandalization. Consequently, it is very paramount to protect these pipelines from vandals through intelligent monitoring systems. Several efforts have been made towards providing a reliable monitoring system for oil pipeline, however, no practically implementable solution have been achieved. Therefore, a pipeline monitoring system using multi-sensors is presented herewith. The sensor array consists of a Passive Infrared (PIR), vibration and sound sensor. An uninvolved infrared (PIR) sensor was utilized to detect intruders before they get in contact with the pipeline and for affirmation of intruders, sound and vibration sensor were set up. As the PIR recognizes an on-coming human the sound and vibration sensors affirms if the human is an intruder(s). An intrusion message containing the location of the vandals is sent to the appropriate authority by the microcontroller via a connected GSM module. Results obtained proved the system as a viable solution for detecting pipeline vandals.