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    Shot boundary detection based on orthogonal polynomial by Abdulhussain, Sadiq H., Ramli, Abd Rahman, Mahmmod, Basheera M., Saripan, Mohd Iqbal, Al-Haddad, Syed Abdul Rahman, Jassim, Wissam A.

    Published 2019
    “…SBD is the process of automatically partitioning video into its basic units, known as shots, through detecting transitions between shots. …”
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    Article
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    Metaheuristic-Based Neural Network Training And Feature Selector For Intrusion Detection by Ghanem, Waheed Ali Hussein Mohammed

    Published 2019
    “…Considering the wide success of swarm intelligence methods in optimization problems, the main objective of this thesis is to contribute to the improvement of intrusion detection technology through the application of swarm-based optimization techniques to the basic problems of selecting optimal packet features, and optimal training of neural networks on classifying those features into normal and attack instances. …”
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    Thesis
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    Development of control algorithm for a new 12s-6p single phase field excited flux switching motor by Amin, Faisal

    Published 2020
    “…For position detection, algorithms merely need a basic infrared transceiver sensor. …”
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    Thesis
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    Autonomous Fire Fighting Mobile Platform by Teh, Nam Khoon, Saman, Abu Bakar Sayuti, Sebastian , Patrick

    Published 2012
    “…This paper describes the development of an Autonomous Fire Fighting Mobile Platform (AFFMP) that is equipped with the basic fighting equipment that can patrol through the hazardous site via a guiding track with the aim of early detection for fire. …”
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    Conference or Workshop Item
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    The development of virtual product life cycle design tool using artificial intelligence technique by Harun, Habibollah, Ismail @ Ishak, Hasrul Haidar, Sukimin, Zuraini

    Published 2008
    “…The generated features from code classification algorithm give the information of machining parameter through the mapping algorithm. …”
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    Monograph
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    HRI for interactive humanoid head Amir-II for visual tracking and servoing of human face. by Iqbal, Aseef, A Shafie, Amir, Khan, Md. Raisuddin, Alias, Mohd Farid, Radhi, Jamil

    Published 2011
    “…The algorithm basically compares the locations of the face in the image plane that is detected from the static face image captured from real-time video stream. …”
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    Article
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    Visual tracking and servoing of human face for robotic head Amir-II by Shafie, Amir Akramin, Iqbal, Aseef, Khan, Md. Raisuddin

    Published 2010
    “…The algorithm basically compares the locations of the face in the image plane that is detected from the static face image captured from real-time video stream. …”
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    Proceeding Paper
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    Human Spontaneous Emotion Detection System by Radin Monawir, Radin Puteri Hazimah

    Published 2018
    “…Having smart computerized system which can understand and instantly gives appropriate response to human is the utmost motive in human and computer interaction (HCI) field.It is argued either HCI is considered advance if human could not have natural and comfortable interaction like human to human interaction.Besides,despite of several studies regarding emotion detection system, current system mostly tested in laboratory environment and using mimic emotion.Realizing the current system research lack of real life or genuine emotion input,this research work comes up with the idea of developing a system that able to recognize human emotion through facial expression.Therefore,the aims of this study are threefold which are to enhance the algorithm to detect spontaneous emotion,to develop spontaneous facial expression database and to verify the algorithm performance.This project used Matlab programming language,specifically Viola Jones method for features tracking and extraction,then pattern matching for emotion classification purpose.Mouth feature is used as main features to identify the emotion of the expression.For verification purpose,the mimic and spontaneous database which are obtained from internet,open source database or novel (own) developed databases are used.Basically,the performance of the system is indicated by emotion detection rate and average execution time.At the end of this study,it is found that this system is suitable for recognizing spontaneous facial expression (63.28%) compared to posed facial expression (51.46%).The verification even better for positive emotion with 71.02% detection rate compared to 48.09% for negative emotion detection rate.Finally,overall detection rate of 61.20% is considered good since this system can execute result within 3s and use spontaneous input data which known as highly susceptible to noise.…”
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    Thesis
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    Website assurance monitoring application with MD5 Hashing and SHA-26 Algorithm / Abdullah Sani Abd Rahman and Samsiah Ahmad by Abd Rahman, Abdullah Sani, Ahmad, Samsiah

    Published 2022
    “…This paper presents a new framework for websites integrity assurance through a website monitoring application. The monitoring application able to detect any modification on a website to be reverted to the actual version. …”
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    Article
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    Deep learning-based breast cancer detection and classification using histopathology images / Ghulam Murtaza by Ghulam , Murtaza

    Published 2021
    “…Thus, this research is aimed to develop two models. First, the BrC detection model is developed to diagnose BrT basic types like benign and malignant. …”
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    Thesis
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    Cluster-based spectrum sensing scheme in heterogeneous network by Hasan, Mohammad Kamrul, Ismail, Ahmad Fadzil, Hassan Abdalla Hashim, Aisha, Mohd. Ramli, Huda Adibah, Hashim, Wahidah, Islam, Shayla, Badron, Khairayu

    Published 2014
    “…A cluster formation algorithm is also proposed in where, cluster head (CH) and signaling node (SN) will detect implies the multi-channel SS technique. …”
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    Proceeding Paper
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    Empirical study on intelligent android malware detection based on supervised machine learning by Abdullah, Talal A.A., Ali, Waleed, Abdulghafor, Rawad Abdulkhaleq Abdulmolla

    Published 2020
    “…In response, specific tools and anti-virus programs used conventional signature-based methods in order to detect such Android malware applications. However, the most recent Android malware apps, such as zero-day, cannot be detected through conventional methods that are still based on fixed signatures or identifiers. …”
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    Article