Faculty of Computing

Permanent URI for this communityhttps://repository.nileuniversity.edu.ng/handle/123456789/13

Browse

Has files: YesSubject: search.filters.subject.CNN

Search Results

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    Improving DDoS Detection in Software-Defined Networks Through a Hybrid Machine Learning Approach
    (IRE Journals, 2025-09-03) FRANCIS ONOJAH; PROF. PREMA KIRUBAKARAN; DR. RIDWAN KOLAPO; Atoyebi, Temitope Olufunmi; DR. R. RENUGA DEV
    (DDoS) Attacks remain a significant concern for network security, utilizing flood-like traffic at the volume, protocol, and application levels to exploit vulnerabilities in today's infrastructure. To lessen these risks, Software-Defined Networking (SDN) offers programmability and centralized control. However, current machine learning (ML)-based detection techniques have a high false positive rate, are not very flexible against zero-day attacks, and are ineffective when handling high-dimensional flow data. To enhance the detection of DDoS attacks in software-defined networks, this paper proposes a hybrid machine-learning approach. Tapping into SDNs broad view of all network flows, the system studies traffic in real time by merging supervised deep learning- in this case, Long Short-Term Memory- with unsupervised anomaly detection called Isolation Forest. The LSTM sorts incoming packets and learns new normal behavior, while the Isolation Forest flags any stray patterns that don’t fit.
  • No Thumbnail Available
    Item
    Improving the Accuracy of Animal Species Classification in Camera Trap Images Using Transfer Learning
    (International Conference on Artificial Intelligence, Computer, Data Sciences and Application, 2024-02-02) Moussa Mahamat Boukar
    Understanding biodiversity, monitoring endangered species, and estimating the possible effect of climate change on particular regions all rely on animal species identification. Closed-circuit television (CCTV) cameras, which can collect huge volumes of video data, are an excellent environmental monitoring tool. However, manually evaluating these massive datasets is time-consuming, difficult, and expensive, emphasizing the need for automated ecological analysis.Deep learning models have transformed computer vision, handling problems such as object and species detection. Their cutting-edge performance qualifies them for this application. The purpose of this work was to create and test machine learning models for distinguishing diverse animal species using camera trap images. On VGG19, GoogLeNet (InceptionV3), ResNet50, and DenseNet121, we used transfer learning. The best multi- classification accuracy was attained by GoogLeNet (87%), followed by ResNet50 (83%), DenseNet (81%), and VGG19 (53%). This evidence suggests that transfer learning outperforms training models from scratch for this task.
  • No Thumbnail Available
    Item
    Development of Hausa Acoustic Model for Speech Recognition
    ((IJACSA) International Journal of Advanced Computer Science and Applications, 2022-01-02) Umar Adam Ibrahim; Moussa Mahamat Boukar; Muhammad Aliyu Suleiman
    Acoustic modeling is essential for enhancing the accuracy of voice recognition software. To build an automatic speech system and application for any language, building an acoustic model is essential. In this regard, this research is concerned with the development of the Hausa acoustic model for automatic speech recognition. The goal of this work is to design and develop an acoustic model for the Hausa language. This is done by creating a word-level phonemes dataset from the Hausa speech corpus database. Then implement a deep learning algorithm for acoustic modeling. The model was built using Convolutional Neural Network that achieved 83% accuracy. The developed model can be used as a foundation for the development and testing of the Hausa speech recognition system.