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    Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition by Al-Qammaz, Abdullah Yousef, Ahmad, Farzana Kabir, Yusof, Yuhanis

    Published 2018
    “…Due to some limitations of current heuristics and evolutionary algorithms, this paper proposed a new swarm based algorithm for feature selection method called Social Spider Optimization (SSO-FS). …”
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    Article
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    The development of parameter estimation method for Chinese hamster ovary model using black widow optimization algorithm by Nurul Aimi Munirah, ., Muhammad Akmal, Remli, Noorlin, Mohd Ali, Hui, Wen Nies, Mohd Saberi, Mohamad, Khairul Nizar Syazwan, Wan Salihin Wong

    Published 2020
    “…The proposed algorithm has been compared with the other three famous algorithms, which are Particle Swarm Optimization (PSO), Differential Evolutionary (DE), and Bees Optimization Algorithm (BOA). …”
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    Air Pollution Index Estimation Model Based On Artificial Neural Network by Mohammed Nasser, Al-Subaie

    Published 2021
    “…However, choosing a correct model structure, and optimum training algorithm with minimum complexity is crucial. Therefore, a dimensional reduction method was implemented based on the multiway principal component analysis (MPCA) method. …”
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    Forecasting the air pollution index using artifical neural network at Muar, Johor, Malaysia / Ahmad Farid Rasdi by Rasdi, Ahmad Farid

    Published 2021
    “…This study mainly focuses on forecasting the Air Pollution Index. In this study, secondary data was used which is obtained from the Department of Environment (DOE) regarding the Air Pollution Index in Malaysia. …”
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    Thesis
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    Air pollution forecasting in Kuala Terengganu using Artificial Neural Network (ANN) / Nur Raudzah Abdullah by Abdullah, Nur Raudzah

    Published 2020
    “…In this study, ANN algorithm is used to forecast air pollution index (API) for the next day in Kuala Terengganu. …”
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    Thesis
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    An IoT based system for magnify air pollution monitoring and prognosis using hybrid artificial intelligence technique by Almalawi, A., Alsolami, F., Khan, A.I., Alkhathlan, A., Fahad, A., Irshad, K., Qaiyum, S., Alfakeeh, A.S.

    Published 2022
    “…The hypothesized artificial intelligence models are evaluated to the Root Mean Squares Error, Mean Squared Error and Mean absolute error, depending upon the performance measurements and a lower error value model is chosen. Based on the algorithm of the Artificial Intelligent System, the level of 5 air pollutants like CO2, SO2, NO2, PM 2.5 and PM10 can be predicted immediately by integrating the observations with errors. …”
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    An IoT based system for magnify air pollution monitoring and prognosis using hybrid artificial intelligence technique by Almalawi, A., Alsolami, F., Khan, A.I., Alkhathlan, A., Fahad, A., Irshad, K., Qaiyum, S., Alfakeeh, A.S.

    Published 2022
    “…The hypothesized artificial intelligence models are evaluated to the Root Mean Squares Error, Mean Squared Error and Mean absolute error, depending upon the performance measurements and a lower error value model is chosen. Based on the algorithm of the Artificial Intelligent System, the level of 5 air pollutants like CO2, SO2, NO2, PM 2.5 and PM10 can be predicted immediately by integrating the observations with errors. …”
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    Article
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    ScannerVision: Scanner-based image acquisition of medically important arthropods for the development of computer vision and deep learning models by Ong, Song Quan, Nathan Pinoy, Lim, Min Hui, Kim Bjerge, Francisco Javier Peris-Felipo, Rob Lind, Jordan P. Cuff, Samantha M. Cook, Toke Thomas Høye

    Published 2025
    “…In this paper, we present a high-throughput scanner-based method for capturing images of arthropods that can be used to generate large datasets suitable for training machine learning algorithms for identification. …”
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    Article
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    Investigating the reliability of machine learning algorithms as an advanced tool for ozone concentration prediction by Ayman Mohammed Shaher Yafouz, Mr.

    Published 2023
    “…The hybrid technique has been developed by using deep learning algorithms with the structure of multiple layers (with several neurons) of CNN and LSTM. …”
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