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  1. 1

    Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems by Mohammad Khamees Khaleel, Alsajri

    Published 2022
    “…Most of the currently existing intrusion detection systems (IDS) use machine learning algorithms to detect network intrusion. …”
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    Thesis
  2. 2

    Development Of Human Skin Detection Algorithm Using Multilayer Perceptron Neural Network And Clustering Method by Al-Mohair, Hani Kaid Saif

    Published 2017
    “…The performance of the developed system has been compared with the existing intelligent skin detection systems. …”
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    Thesis
  3. 3

    DEVELOPMENT OF DRIVER DROWSINESS DETECTION ALGORITHM by YVONNE, PHUA YEE WUN

    Published 2022
    “…To contribute to the existing works of driver drowsiness detection systems, this project aims to develop a more useful drowsiness detection algorithm with low complexity and high performance using Python 3.10.1 software. …”
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    Final Year Project Report / IMRAD
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    Adaptive glioblastoma detection using evolutionary-based algorithm / Nurul Amira Mohd Ali by Mohd Ali, Nurul Amira

    Published 2020
    “…The objectives of the project are to design and develop a prototype of adaptive Glioblastoma detection using Evolutionary-based algorithm to assist in detecting brain tumor and also to test the prototype’s functionality and detection accuracy. …”
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    Thesis
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    Extended development of a Computer Aided Detection (CAD) system for brain bleed in CT / Muhammad Illyas Abdul Muhji by Muhammad Illyas, Abdul Muhji

    Published 2018
    “…Final output of the algorithm summarise the bleeding detection for all patient in an excel file. …”
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    Thesis
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    Development of cost efficient vision system for defects detection / Muhammad Zarif Kamarudin by Kamarudin, Muhammad Zarif

    Published 2010
    “…In order to develop a vision system, full understanding regarding how the system works is the most important aspect. …”
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    Thesis
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    Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions by Gorment N.Z., Selamat A., Cheng L.K., Krejcar O.

    Published 2024
    “…This paper seeks to conduct a thorough systematic literature review (SLR) and offer a taxonomy of machine learning methods for malware detection that considers these problems by analyzing 77 chosen research works related to malware detection using machine learning algorithm. …”
    Article
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    WEBCAM MOTION DETECTION USINGVISUAL BASIC by Abdullah, Ilyana

    Published 2005
    “…The main focus of the system is to ensure that the motion can be detected only using the skin tones. Combination of several related algorithms helps in developing the skin tones algorithm. …”
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    Final Year Project
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    Polymorphic malware detection based on dynamic analysis and supervised machine learning / Nur Syuhada Selamat by Selamat, Nur Syuhada

    Published 2021
    “…The benefit of this work indicated that the implementation of a feature selection technique plays an important role in machine learning algorithms to increase the performance of detection.…”
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    Thesis
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    Intelligent DNA signature detection for internet worms by Ghazali, Osman

    Published 2011
    “…Active worms spread in an automated fashion flooding the Internet in a very short time.Slammer worm infected more than 90% of vulnerable machines within 10 minutes on January 25th, 2003.Hence it is necessary to monitor and detect the worms as soon as they are introduced to minimize the damage caused by them.This project concentrates on developing an anti-scanning worm detection system that can automatically detect and control the spread of internet scanning worms without any manual intervention.The Intelligent Failure Connection Algorithm (IFCA) developed in this project can detect both stealth and normal worms within a short time.Experiments conducted as part of the evaluation shows that IFCA detects a worm within two scanning cycles of the worm.This is faster than any of the currently available algorithms or mechanisms reported in the literature.The IFCA uses Artificial Immune System (AIS) for the purpose of monitoring and detecting the worms.The Traffic Signature Algorithm (TSA) developed in the project captures the traffic signature of the worm from the infector when it sends the traffic to the victim.The Intelligent DNA Signature Detection Algorithm (IDNASDA) algorithm works by breaking an infection session into different infection phases, each phase containing a number of different traffic such as Internet Control Message Protocol (ICMP), Transmission Control Protocol (TCP), or User Datagram Protocol (UDP).Finally it converts the traffic signature to DNA signature.The tests carried out show that the IDNASD could detect DNA signature for MSBlaster worm.…”
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