Computer Science and Information Technology

Permanent URI for this community

https://ir.bellsuniversity.edu.ng/handle/123456789/251

Browse

Browsing Computer Science and Information Technology by Subject "Data"

Now showing 1 - 3 of 3
  • No Thumbnail Available
    ItemMetadata only
    A Mobile Application for Potholes Detection and Analysis System
    (Bells University of Technology, 2020-03-08) KUDETI, Waziri Omolade; Prof. M. O. Abass
    The presence of potholes on roads has over the years been a great source of concern to everyone.it has been identified as one of the major causes of road accidents as well as the wear and tear of vehicle tyres. The mobile sensing method for pothole detection is considered as the most suitable method to detect pothole for mobile devices. This is because the other methods require huge computation power for image recognition. Three different algorithms were implemented: the Z-Diff algorithm, STDEV(z)algorithm which are existing algorithms and a new improved algorithm. The data used for the experiments were obtained by driving through a selected test track which is 18.9km (11.7miles) long .it includes major single lane streets as well as minor multi-lane from Sango Ota, Ogun state to Agege, Lagos state, and is characterized by a range of degrees in road surface smoothness. This research shows that the proposed improved algorithm outperforms both the Z-DIFF algorithm and STDEV(Z) metrics. The performance metrics gives different results because each metric covers a part and considers some parameters while leaving some others. Therefore, a combination of metrics is recommended to be used to measure the performance of each algorithm against others.
  • No Thumbnail Available
    ItemMetadata only
    Comparative Analysis of Certain Data Analytic Languages Using Code Based Metrics
    (Bells University of Technology, 2019-10-17) ADIGUN, Maria Funmilayo; Prof. M. O. Abass
    Software complexity metrics is developed and used by the various software organizations for evaluating and assuring software code quality, operation, and maintenance. Software metrics measure various types of software complexity like size metrics, control flow metrics and data flow metrics. These software complexities must be continuously calculated, followed, and controlled because high complexity may result in more errors and difficulties in maintenance, understandability, modification and testing effort. The revival of data science due to the presence of large amount of data has resulted in the need for a good programming language on which many data science applications can be developed. Hence the need to apply programming complexity metrics to existing data analytic languages so as to guide programmers in choosing tools for building data science applications. For application, binary search and quicksort algorithms are considered. The programs are written in three different data analytic languages: Python, R and Scala. Software complexity for each is found using LOC, McCabe and Halstead models. The results are compared and Scala is realized to be the most complex for all the metrics while Python and R programs are averagely at par.
  • No Thumbnail Available
    ItemMetadata only
    Privacy Enforcement on Subscriber's Data in Cloud Computing
    (Bells University of Technology, Ota., 2018-04-09) ASANGA, Ukeme Joseph; Prof. O. M. Abass
    Data stored in the cloud are susceptible to an array of threats from hackers and also to undetectable and unauthorized access by the cloud service provider. This is because the subscriber has no access to the internal operations of the cloud and cannot control access to his data. Therefore confidentially, availability and integrity of subscriber's data must be maintained to gain their trust in cloud-based systems. This work made use of privacy with non-trusted provider algorithm to ensure privacy of subscriber's data. Here, the subscriber encrypts their data before sending it to the se4rvfice provider that performs a second encryption before storage in the cloud. Simulation was done on multimedia dataset download from Stanford University dataset. The system results were then compared with an existing systems using a trusted cloud provided and it ensured a higher level of privacy on subscriber's data. The results were evaluated using encrypted time, decryption time and brute force hack. Results showed that with the proposed system, the subscriber had a high level of control over his data sending it to the cloud service provider. It also promises an increase adopted of cloud computing by businesses and organization with highly sensitive information as it contains measures to ensure that the subscriber's data is not readily available to unauthorized user.