Asian Journal of Research in Computer Science

Image compression research has increased dramatically as a result of the growing demands for image transmission in computer and mobile environments. It is needed especially for reduced storage and efficient image transmission and used to reduce the bits necessary to represent a picture digitally while preserving its original quality. Fractal encoding is an advanced technique of image compression. It is based on the image's forms as well as the generation of repetitive blocks via mathematical conversions. Because of resources needed to compress large data volumes, enormous programming time is needed, therefore Fractal Image Compression's main disadvantage is a very high encoding time where decoding times are extremely fast. An artificial intelligence technique similar to a neural network is used to reduce the search space and encoding time for images by employing a neural network algorithm known as the “back propagation” neural network algorithm. Initially, the image is divided into fixed-size and domains. For each range block its most matched domain is selected, its range index is produced and best matched domains index is the expert system's input, which reduces matching domain blocks in sets of results. This leads in the training of the neural network. This trained network is now used to compress other images which give encoding a lot less time. During the decoding phase, any random original image, converging after some changes to the Fractal image, reciprocates the transformation parameters. The quality of this FIC is indeed demonstrated by the simulation findings. This paper explores a unique neural network FIC that is capable of increasing neural network speed and image quality simultaneously.

The aim of this article describes the program of computerized how to calculate the feeder fault current in a distribution substation. This article adopts Thevenin theory as the basis of calculation, and narrates them in two ways: the artificial and the computerized algorithm. It leaves aside the artificial and delves the computerized algorithm.  The latter is divided for two computerized algorithm - separate and all of equipment. In the computerized algorithm, all data inputting, procedure steps, and report form were carefully been designed by MATLAB application software. As for data Inputting refers to the specification parameters of equipment component. The characteristics of this article are described with both text and Fig. to achieve operation simple and understanding easy. References include a representative textbook and several journal articles. Verify with real cases and reveal the pros and cons of artificial and program algorithms. The purpose of this article is to discard waste - an artificial calculation that is time - consuming, cumbersome and prone to clerical errors. The computer programs algorithm can compensates for defects and improves accuracy and timeliness. This method has been proven to be an economical design aid tool that is of great help to maintenance or designers in the field of electrical engineering.

With the internet fast-penetrating the Nigerian populace, e-commerce businesses have become commonplace, this has given rise to an increase in the number of Nigerians shopping online. However, there is a growing concern that most Nigerian e-shoppers prefer foreign to local online shops, resulting in an online fund-leak from the local economy. This work presents a comparative analysis of the usability of e-commerce websites in Nigeria, highlights the key findings viz: security and lack of trust. The findings were then related to why Nigerians prefer shopping from foreign rather than local e-commerce websites.  We argued that for e-commerce to thrive; usability should be given prime consideration, security should be guaranteed and trust-building ethos is practiced. We conclude that despite the ‘pay on delivery’ mode applied by e-commerce websites to woo customers and gain trust, the insecurity posed by the prevalence of online fraud in Nigeria has created apprehension and distrust among Nigerians towards local e-commerce websites and is contributing to why Nigerians prefer to buy from foreign rather than local e-commerce websites.

The rapid advancement in the Internet of things applications generates a considerable amount of data and requires additional computing power. These are serious challenges that directly impact the performance, latency, and network breakdown of cloud computing. Fog Computing can be depended on as an excellent solution to overcome some related problems in cloud computing. Fog computing supports cloud computing to become nearer to the Internet of Things. The fog's main task is to access the data generated by the IoT devices near the edge. The data storage and data processing are performed locally at the fog nodes instead of achieving that at cloud servers. Fog computing presents high-quality services and fast response time. Therefore, Fog computing can be the best solution for the Internet of things to present a practical and secure service for various clients. Fog computing enables sufficient management for the services and resources by keeping the devices closer to the network edge. In this paper, we review various computing paradigms, features of fog computing, an in-depth reference architecture of fog with its various levels, a detailed analysis of fog with different applications, various fog system algorithms, and also systematically examines the challenges in Fog Computing which act as a middle layer between IoT sensors or devices and data centers of the cloud.

Android is now the world's (or one of the world’s) most popular operating system. More and more malware assaults are taking place in Android applications. Many security detection techniques based on Android Apps are now available. The open environmental feature of the Android environment has given Android an extensive appeal in recent years. The growing number of mobile devices are incorporated in many aspects of our everyday lives. This  paper gives a detailed comparison that summarizes and analyses various detection techniques. This work examines the current status of Android malware detection methods, with an emphasis on Machine Learning-based classifiers for detecting malicious software on Android devices. Android has a huge number of apps that may be downloaded and used for free. Consequently, Android phones are more susceptible to malware. As a result, additional research has been done in order to develop effective malware detection methods. To begin, several of the currently available Android malware detection approaches are carefully examined and classified based on their detection methodologies. This study examines a wide range of machine-learning-based methods to detecting Android malware covering both types dynamic and static.