Search Results - (( intelligence based window algorithm ) OR ( intelligence valid mapping algorithm ))

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  1. 1

    Implementation of an intelligent SINS navigator based on ANFIS by Ahjebory, Karim M., Ismaeel, Salam A., Alqaissi, Ahmed M.

    Published 2009
    “…As in previous work, which is based on Artificial Neural Network, the window based weight updating strategy was used, and the intelligent navigator evaluated using several SINS hypothetical field tests data. …”
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    Conference or Workshop Item
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    Tacit knowledge for business intelligence framework using cognitive-based approach by Surbakti, Herison

    Published 2022
    “…The framework is validated through Power BI and reviewed by seven experts. …”
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    Thesis
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    Hybrid Spatial-Artificial Intelligence Approach for Renewable Energy Sources Sites Identification and Integration in Sarawak State by Far Chen, Jong

    Published 2022
    “…The minimum total distances in all four cases are acquired and validated as both the TSP-GA algorithm and the Traveling Salesman Problem-Mixed Integer Linear Programming (TSP-MILP) algorithm produced the same routing pattern. …”
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    Thesis
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    Artificial intelligent power prediction for efficient resource management of WCDMA mobile network by Tee Y.K., Tinng S.K., Koh J., David Y.

    Published 2023
    “…This artificial intelligent call admission control (CAC) was validated using a dynamic WCDMA mobile network simulator. …”
    Conference Paper
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    Snake detection system using convolutional neural network / Muhammad Danial Ahmad Tarmizi by Ahmad Tarmizi, Muhammad Danial

    Published 2020
    “…The algorithm is built using Tensorflow software. The development of the project is based on Waterfall methodology. …”
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    Thesis
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    LSR-YOLO: a lightweight and fast model for retail products detection by Zhao, Yawen, Solihin, Mahmud Iwan, Yang, Defu, Cai, Bingyu, Chow, Li Sze, Handayani, Dini Oktarina Dwi, Prabuwono, Anton Satria

    Published 2025
    “…Experiments on the Locount dataset demonstrate that LSR-YOLO achieves an inference speed of 357.1 FPS with mAP50 of 72.2% and mAP50-95 of 47.8%. Compared with the baseline YOLOv8n, LSR-YOLO increases inference speed by 246.7 FPS, making it substantially faster and more suitable for real-time retail applications. …”
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    Article
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    Design and development of latex mark visual detection system by Chong, Kai Zhe, Zakaria, Azrul Abidin, Mohamed, Hassan, Baharuddin, Mohd Zafri

    Published 2025
    “…With the 14 stages of algorithm development, including tagging and training, the YOLO v5 model achieved an average loss of 5.2% and mAP performance of 99.3% accuracy, achieving the AQL 2.5 standard with less than 15 pieces of false detection.…”
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    Article
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    Development of an accurate AI-based dermatology assistant for skin disease recognition using YOLOv8 models by Huzaini, Muhammad Irfan Darwish, Mansor, Hasmah, Gunawan, Teddy Surya, Ahmad, Izanoordina

    Published 2024
    “…After 500 training epochs, the YOLOv8 Small Model was the most accurate, achieving a precision of 84%, a recall of 77.1%, and a mean average precision (mAP) of 84%. The potential of the proposed AI-based assistant to significantly improve healthcare accessibility and diagnostic accuracy in underserved areas is demonstrated through rigorous testing, which validates its effectiveness. …”
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    Proceeding Paper
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    Experiment and analysis of computer vision-based wrist radial and ulnar deviation exercises by M. Zabri, Abu Bakar, Rosdiyana, Samad, Pebrianti, Dwi, Mahfuzah, Mustafa, Nor Rul Hasma, Abdullah

    Published 2017
    “…The researchers have kept on improving the existing methods by creating novel method or developing new algorithms in image processing and artificial intelligence. …”
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    Conference or Workshop Item
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    Enhancing Driving Assistance System with YOLO V8-Based Normal Visual Camera Sensor by Beg, Mohammad Sojon, Muhammad Yusri, Ismail, Miah, Md Saef Ullah, Mohamad Heerwan, Peeie

    Published 2023
    “…After conducting a performance validation, the system achieved a mean average precision (mAP) of 88.2% on train dataset and was able to detect different types of vehicles such as cars, motorcycles, and traffic lights. …”
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    Article
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    Disease detection of solanaceous crops using deep learning for robot vision by Ahmad Radzi, Syafeeza, A.Halim, Nurul Hidayah, Abd Razak, Norazlina, Mohd Saad, Wira Hidayat, Wong, Yan Chiew, Amsan, Azureen Naja

    Published 2022
    “…The performances of YOLOv5 were more robust in terms of 94.2% mAP, and the speed was slightly faster than Scaled-YOLOv4. …”
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    Article
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