Search Results - (( mobile relation bees algorithm ) OR ( panel classification _ algorithm ))*

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    Identifying Damage Types in Solar Panels Through Surface Image Analysis with Naive Bayes by Wiliani, Ninuk, T.K.A, Rahman, Ramli, Suzaimah

    Published 2024
    “…This work illustrates the potential of statistical feature extraction approaches for defect classification, while emphasizing the necessity for future improvements to boost the efficacy of feature extraction and classification techniques in practical applications.…”
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    Journal
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    ABC-PSO for vertical handover in heterogeneous wireless networks by Goudarzi, Shidrokh, Hassan, Wan Haslina, Anisi, Mohammad Hossein, Soleymani, Seyed Ahmad, Sookhak, Mehdi, Khan, Muhammad Khurram, Hassan Abdalla Hashim, Aisha, Zareei, Mahdi

    Published 2017
    “…Nevertheless, attributes of mobile devices need algorithms that are quick and effective in order to select best available network near real-time. …”
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    Article
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    A study on obstacle detection for IoT based automated guided vehicle (AGV) by Loh, Jit Hao, M. Nafis, O. Z.

    Published 2022
    “…The study produced several significant results related to obstacle detection of AGV with IoT based technology. …”
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    Article
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    Defect Detection And Classification Of Silicon Solar Wafer Featuring Nir Imaging And Improved Niblack Segmentation by Mahdavipour, Zeinab

    Published 2016
    “…Meanwhile, a set of descriptors corresponding to Elliptic Fourier Features shape description is extracted for each defect and is evaluated for each cluster to use for clustering and classification part. The classification combines the analysis of defect intensity features, the application of unsupervised k-mean clustering and multi-class SVM algorithms. …”
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    Thesis
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    Development of electronic nose for classification of aromatic herbs using Artificial Intelligent techniques by Che Soh, Azura, Mohamad Radzi, Nur Fadzilah, Mohamad Yusof, Umi Kalsom, Ishak, Asnor Juraiza, Hassan, Mohd Khair

    Published 2018
    “…Two classification methods, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used in order to investigate the performance of classification accuracy for this E-nose system. …”
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    Article
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