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    Development of IoT-based automated dynamic emergency response system against fire incidents in academic building by Al-Hady, Syed Mohammed Zakaria, Islam, Md Rafiqul, Rashid, Muhammad Mahbubur

    Published 2023
    “…The proposed system leverages IoT technology, wireless and bluetooth sensor networks to gather real-time data from various sensors and devices installed in the site and uses machine learning algorithms to predict and prevent potential fire incidents. …”
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    Article
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    Density based subspace clustering: a case study on perception of the required skill by Rahmat Widia, Sembiring

    Published 2014
    “…This research aims to develop an improved model for subspace clustering based on density connection. …”
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    Thesis
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    Density subspace clustering: a case study on perception of the required skill by Sembiring, Rahmat Widia

    Published 2014
    “…This research aims to develop an improved model for subspace clustering based on density connection. …”
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    Thesis
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    Development of a smart edge device for fire detection by Ng, Wei Yuan

    Published 2023
    “…This project proposes a smart edge fire detection system that overcomes the limitations of conventional fire warning systems by utilizing deep learning models and edge computing. …”
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    Final Year Project / Dissertation / Thesis
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    Multi sensor network system for early detection and prediction of forest fires in southeast asia by Kadir, Evizal Abdul, Alomainy, Akram H., Hanita, Daud, Maharani, Warih, Noryanti, Muhammad, Syafitri, Nesi

    Published 2023
    “…The collected data is then processed and analyzed using machine learning algorithms to identify fire patterns and predict the likelihood of fire outbreaks. …”
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    Conference or Workshop Item
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    The development of a tracking algorithm for ambulance detection using squaring of RGB and HSV color processing techniques by Mohammad Syawaludin Syafiq, Hassan

    Published 2016
    “…In this study, a tracking algorithm is developed by means of image processing technique in detecting ambulance. …”
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    Thesis
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    ReSTiNet: An efficient deep learning approach to improve human detection accuracy by Shahriar Shakir, Sumi, Dayang Rohaya, Awang Rambli, Mirjalili, Seyedali, Miah, M. Saef Ullah, Muhammad Mudassir, Ejaz

    Published 2023
    “…ReSTiNet is a novel small convolutional neural network that overcomes the problems of network size, detection speed, and accuracy. The developed ReSTiNet contains fire modules by evaluating their number and position in the network to minimize the model parameters and network size. …”
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    Article
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    ReSTiNet: An Efficient Deep Learning Approach to Improve Human Detection Accuracy by Sumit, S.S., Rambli, D.R.A., Mirjalili, S., Miah, M.S.U., Ejaz, M.M.

    Published 2023
    “…ReSTiNet is a novel small convolutional neural network that overcomes the problems of network size, detection speed, and accuracy. The developed ReSTiNet contains fire modules by evaluating their number and position in the network to minimize the model parameters and network size. …”
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    Article
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    ReSTiNet : An efficient deep learning approach to improve human detection accuracy by Sumit, Shahriar Shakir, Dayang Rohaya, Awang Rambli, Seyedali, Mirjalili, Miah, Md Saef Ullah, Muhammad Mudassir, Ejaz

    Published 2023
    “…ReSTiNet is a novel small convolutional neural network that overcomes the problems of network size, detection speed, and accuracy. The developed ReSTiNet contains fire modules by evaluating their number and position in the network to minimize the model parameters and network size. …”
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    Article
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    Hybrid Intelligent Warning System for Boiler tube Leak Trips by Singh, D., Ismail, F.B., Shakir Nasif, M.

    Published 2017
    “…The Extreme Learning Machine (ELM) methodology was also adopted in IWS-1 and compared with traditional training algorithms. …”
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    Article
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    Prediction of oil and gas pipeline failures through machine learning approaches: A systematic review by Al-Sabaeei, A.M., Alhussian, H., Abdulkadir, S.J., Jagadeesh, A.

    Published 2023
    “…The rapid progress of machine learning (ML) technologies provides an advantageous opportunity to develop predictive models that can effectively tackle these challenges. …”
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    SVM, ANN, and PSF modelling approaches for prediction of iron dust minimum ignition temperature (MIT) based on the synergistic effect of dispersion pressure and concentration by Arshad, U., Taqvi, S.A.A., Buang, A., Awad, A.

    Published 2021
    “…Data-driven models for predicting fire and explosion-related properties have been improved greatly in recent years using machine-learning algorithms. …”
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    Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions by Saraswati, Galuh Wilujeng

    Published 2017
    “…The project has also implemented the optimization process onto the proposed ANFIS model through the hybrid of Genetic Algorithm on the fuzzy membership function of the ANFIS model. …”
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    Thesis