·         Table of Contents (Volume #4, Issue #2)

·         Cover Page (Volume #4, Issue #2)

·         Editorial (Volume #4, Issue #2)

Paper #1

Automating the Assessment of Networking and Security in Higher Education

Neville Palmer

Abstract: In the subject of computer networking students must understand the concepts and be confident in configuring and securing a range of network devices and systems. Challenges are faced in assessing practical networking and security exercises in a laboratory environment utilising multivariate devices and operating systems that involve real or simulated systems operating in real or virtualized environments. The working environment for a practical exercise must be pre-configured and once an exercise has been completed the results extracted to enable formative or summative assessment of the outcomes. Using manual methods this process can be time consuming and it would therefore be beneficial to implement some form of automation. To facilitate this a new application has been developed that automates the configuration management and assessment processes within a computer networking laboratory. The new application has been successfully used to assess the extent to which students have been able to configure secure networks using case-study based exercises. The development challenges of the application and rationale for the implementation of the prototype application are discussed. A test methodology is presented in which the application is used to assess the outcomes of a computer networking exercise, comparing the results obtained from the application with those of a proprietary simulator. This demonstrates that the prototype application is able to successfully and accurately automate the assessment of the practice of networking and security in the context of defined parameters. Further work is suggested to improve the assessment mechanism employed by the application, seek enhancements and to conduct additional tests.

Keywords: Computer Networking; Security; Laboratory; Automation; Assessment.

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Paper #2

Comparative Analysis of Ranking Algorithms Used On Web

Sandeep Suri, Arushi Gupta and Kapil Sharma

Abstract: With the evolution in technology huge amount of data is being generated, and extracts the necessary data from large volumes of data. This process is significantly complex. Generally the web contains bulk of raw data and the process of converting this data to information mining process can be performed. At whatever point the user places some inquiry on particular web search tool, outcomes are produced with respect to the requests which are dependent on the magnitude of the document created via web information retrieval tools. The results are obtained using calculations and implementation of well written algorithms. Well known web search tools like Google and other varied engines contain their specific manner to compute the page rank, various outcomes are obtained on various web crawlers for a same inquiry because the method for deciding the importance of the sites contrasts among number of algorithm. In this research, an attempt to analyze well-known page ranking calculation on the basis of their quality and shortcomings. This paper places the light on a portion of the extremely mainstream ranking algorithm and attempts to discover a better arrangement that can optimize the time spent on looking through the list of sites.

Keywords: Time Rank; Hybrid Rank; Web Mining; TF-IDF; EigenRumor; TagRank; Weighted Page Rank.

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Paper #3

Mobile-based Skin Lesions Classification Using Convolution Neural Network

Nazia Hameed, Antesar Shabut, Fozia Hameed, Silvia Cirstea, Sorrel Harriet and Alamgir Hossain

Abstract: This research work is aimed at investing skin lesions classification problem using Convolution Neural Network (CNN) using cloud-server architecture. Using the cloud services and CNN, a real-time mobile-enabled skin lesions classification expert system “i-Rash” is proposed and developed. i-Rash aimed at early diagnosis of acne, eczema and psoriasis at remote locations. The classification model used in the “i-Rash” is developed using the CNN model “SqueezeNet”. The transfer learning approach is used for training the classification model and model is trained and tested on 1856 images. The benefit of using SqueezeNet results in a limited size of the trained model i.e. only 3 MB. For classifying new image, cloud-based architecture is used, and the trained model is deployed on a server. A new image is classified in fractions of seconds with overall accuracy, sensitivity and specificity of 97.21%, 94.42% and 98.14% respectively. i-Rash can serve in initial classification of skin lesions, hence, can play a very important role early classification of skin lesions for people living in remote areas.

Keywords: Skin lesions classification; mobile-enabled skin lesion classification; convolution neural network acne classification; eczema classification; psoriasis classification; deep learning; SqueezeNet.

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Paper #4

A Low-Cost Wireless Interface Linking a Microcontroller to a Microcomputer Server

Ivano Folgosa and Peter Excell

Abstract: The development of a low-cost solution for the connection of wireless sensor nodes to the Internet is presented. The solution has the additional benefit of extremely low power consumption and hence very long battery life and low maintenance cost. The core of the solution is the Atmel ATtiny series of wireless transceivers and the designs of minimised printed circuit boards for three versions of the node are presented. To provide the fixed node at minimum cost, the Raspberry Pi microcomputer was used, attached to the Nordic Semiconductor nRF24L01+ transceiver module. Software to implement the system in each of the nodes was developed from zero-cost sources, the software being tailored for the particular application and devices selected. A World Wide Web interface for the Raspberry Pi was created, having an intuitive display and offering functionalities relevant for general automation tasks, data display and manual control, in conjunction with wirelessly connected clients. The Web interface can serve as the basis for a home automation system, a topic which is becoming more and more important and popular nowadays, but industrial applications are equally feasible.

Keywords: ISM wireless band; Low-power Transceiver; Raspberry Pi microcomputer; ShockBurst wireless functionality; Web interface; Wireless sensor network.

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Paper #5

Real Time Identification of Railway Track Surface Faults using Canny Edge Detector and 2D Discrete Wavelet Transform

Ali Akbar Shah, Bhawani S. Chowdhry, Tayab D. Memon, Imtiaz H. Kalwar and J. Andrew Ware

Abstract: Usually, railway accidents are caused by train derailment, the mechanical failure of tracks, such as broken rails often caused by lack of railway condition monitoring. Such monitoring could identify track surface faults, such as squats, that act as a catalyst for the track to crack and ultimately break. The research presented in this paper enables real-time identification of railway track faults using image processing techniques such as Canny edge detection and 2D discrete wavelet transformation. The Canny edge detection outperforms traditional track damage detection techniques including Axle Based Acceleration using Inertial Measurement Units and is as reliable as Fiber Bragg Grating. The Canny edge detection employed can identify squats in real-time owing to its specific threshold amplitude using a camera module mounted on a specially designed handheld Track Recording Vehicle (TRV). The 2D discrete wavelet transformation validates the insinuation of the Canny edge detector regarding track damage and furthermore determines damage severity, by applying high sub band frequency filter. The entire algorithm works on a Raspberry Pi 3 B+ utilizing an OpenCV API. When tested using an actual rail track, the algorithm proved reliable at determining track surface damage in real-time. Although wavelet transformation performs better than Canny edge detection in terms of determining the severity of track surface damage, it has processing overheads that become a bottleneck in real-time. To overcome this deficiency a very effective two-stage process has been developed.

Keywords: Time Rank; Hybrid Rank; Web Mining; TF-IDF; EigenRumor; TagRank; Weighted Page Rank.

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