SoC Student Conference 2019, University of Portsmouth UK

The School of Computing’s Student Conference 2019 is being held on Wednesday 3rd April, in the Future Technology Centre at the University of Portsmouth.

1.00pm–3.00pm Presentations – PO 0.28 (North Lecture Theatre)
3.00pm–5.00pm Exhibition – FTC Floor 1 and Floor 2

This conference format includes presentations, software demonstrations and electronic posters. It is your opportunity to view our final year students’ projects, PhD research and good work from other years. We also have representatives from our careers, placement and enterprise departments attending. A good chance to network and to see what the University is doing in the technology space.

The presentations start at 1pm and the exhibition of software demos and posters starts at 3.00pm. Student prizes will be awarded before the event closes at 5.00pm.

If you would like more information, please contact David Williams / Penny Hart.

Free tickets available here

If you are a student and would like to showcase your work in the SoC Student Conference 2019, please complete this form.

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A Data Mining Framework to Detect Issues of Assessment in Higher Education

Zainab Mutlaq Ibrahim, PhD

A Data Mining Framework to Detect Issues of Assessment in Higher Education

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Activity tracker with mood tracking

Hashir Ahmad, BSc Web Technologies

A software tool which can be installed on your device to record time, current mood (optional) and any notes of the activity that you want to track.

For example student may want to know how much time and how they feel over a period of time while studying. So they can create a Studying activity to which they can later start when they start studying and stop when they finish. User can select a mood of how they feel. Over a period of time the app can tell good days and bad days etc. The app has wide array of Charts to show activities in relation to the mood.

Additionally the user can also set if an activity is a habit in which case the app will try to encourage the user to do it by a specified time every day or decrease it if its a negative habit until the average time of an activity is x which is again specified by the user.

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Concrete Surface Crack Detection using Hierarchal Convolutional Neural Network

Davis Agyemang, BSc Software Engineering

Cracks on concrete walls can imply that a building possesses issues with its structural integrity. Surveyors who inspect these defects are expected to provide their customers with excellent evaluation regarding its severity. The process is currently conducted through visual inspection, resulting in occasions of subjective judgements being made on the classification and severity of the concrete surface. This poses danger for customers and the environment as it not being analysed objectively. Many researchers have applied numerous classification techniques to tackle this issue but from the author’s knowledge, their methods do not allow the classification result to be sent back to the user for them look back at the concrete surface, or provide the severity level of the evaluated concrete crack and there is no feedback mechanism in terms of adaptability of when their method classifies a concrete surface incorrectly. In this paper, the author proposes in building a hybrid web mobile application with the capabilities of giving in-depth information about a concrete surface’s severity, users being able to send the classification results to themselves via email and the ability to improve the accuracy of the application via user feedback. The application will be build using Python, Flask, Keras with Tensorflow backend, HTML, CSS and JavaScript, in which a trained Hierarchal-Convolutional Neural Network(H-CNN) will be used to evaluate the concrete surface via images taken by a mobile device or uploaded via a desktop.

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Corona: Threat Intelligence Aggregation Platform

Charlie Bricknell, BSc Forensic Computing

Corona is a threat intelligence platform that aggregates intelligence from many sources including its own honeypot network.

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Using machine learning to predict season transition within agriculture

Omar Ali, BSc Computer Science

Using KNN and linear regression for time series forecasting of the weather to further predict when our seasons transition.

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Can Deep Learning aid bird watching and monitoring?

Lucinda Ashdown, BSc Computing

The proposed solution is to utilise Amazon Web Services (AWS) with an AWS DeepLens machine learning camera to detect and record the different bird species. This camera allows deep learning models from AWS to be deployed and run locally on the device for visual analysis. The data can then be used by conservation charities to help analyse birds at most risk of extinction. This Proof of Concept (POC) will aim to support these charities by providing additional species population data to increase the reliability of their conservation categorisation, as well as promote awareness of their decline and be an additional tool to support their work.

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Information vs Poverty

Rubie Targema-Takema, BSc Software Engineering

Statistics show that only 53.5% of Africa’s population have access to the internet, of which only 18.6% are female. The remaining 46.5% of the population are left without access to the needed  information and knowledge that could improve their lives.
This disadvantage leaves individuals in Less developed parts of Africa without access to information that could potentially save lives, and thus widens the already existing and growing digital divide.
The aim of my research is to investigate whether the use of low-cost technology, in this case a Raspberry pi (which is a small programmable device) provides people in these less-developed communities access to information and improves their life opportunities.

The target countries are Nigeria and South Africa and the target audience in these areas are women and school children.

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Evolution of Android Malware

Michelle Fradgley, BSc Forensic Computing

This project will help establish and analyse previous significant cases of android malware, defining the drivers for change as well as looking into trends over time.


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Smart Greenhouse

Ben Rose, BSc Computer Science

An automated personal greenhouse with climate control, web interface and intelligent agricultural-focused management software using readily accessible and affordable hardware.

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Using Novel Data Sets to predict elections

Thomas Chate, BSc Computing

A dashboard showing data correlation between Twitter, Weather, Crime and election data sets.

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