Search Results - (( intelligence based _aps algorithm ) OR ( intelligence based m algorithm ))

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    Vehicle detection for vision-based intelligent transportation systems using convolutional neural network algorithm by Khalifa, Othman Omran, Wajdi, Muhammad H., Saeed, Rashid A., Hassan Abdalla Hashim, Aisha, Ahmed, Muhammed Z., Ali, Elmustafa Sayed

    Published 2022
    “…Results from the simulated and evaluated algorithm showed that the proposed model was able to achieve a mAP of 97.8 in the daytime dataset and 95.1 in the nighttime dataset.…”
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    Article
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    A Novel Path Prediction Strategy for Tracking Intelligent Travelers by Motlagh, Omid Reza Esmaeili

    Published 2009
    “…It is proposed and shown that route-like intelligent motion is based on a combination of decisional and kinematical factors. …”
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    Thesis
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    Artificial intelligent integrated into sun-tracking system to enhance the accuracy, reliability and long-term performance in solar energy harnessing by Tan, Jun You

    Published 2022
    “…Therefore, a fully artificial intelligent (AI)-integrated sun-tracking algorithm is proposed and can be used in any type of sun-tracking systems such as concentrated photovoltaic (CPV), flat photovoltaic (PV) or heliostat systems. …”
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    Final Year Project / Dissertation / Thesis
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    Development and Integration of Metocean Data Interoperability for Intelligent Operations and Automation Using Machine Learning: A Review by Danyaro, K.U., Hussain, H.H., Abdullahi, M., Liew, M.S., Shawn, L.E., Abubakar, M.Y.

    Published 2022
    “…In this paper, we demonstrate the capabilities of ML for the development of Metocean data integration interoperability based on intelligent operations and automation. A comprehensive review of several research studies, which explore the needs of ML in oil and gas industries by investigating the inâ��depth integration of Metocean data interoperability for intelligent operations and automation using an MLâ��based ap-proach, is presented. …”
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    Development and Integration of Metocean Data Interoperability for Intelligent Operations and Automation Using Machine Learning: A Review by Danyaro, K.U., Hussain, H.H., Abdullahi, M., Liew, M.S., Shawn, L.E., Abubakar, M.Y.

    Published 2022
    “…In this paper, we demonstrate the capabilities of ML for the development of Metocean data integration interoperability based on intelligent operations and automation. A comprehensive review of several research studies, which explore the needs of ML in oil and gas industries by investigating the inâ��depth integration of Metocean data interoperability for intelligent operations and automation using an MLâ��based ap-proach, is presented. …”
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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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    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
    “…To address the high computational cost and slow detection speed of existing methods, this study proposes LSR-YOLO, a lightweight object detection framework based on YOLOv8n, designed for deployment in robots and intelligent devices. …”
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    Automated underwater vision system for detection and classification of marine life using CNN YOLO-based model / Mohamed Syazwan Asyraf Rosli by Rosli, Mohamed Syazwan Asyraf

    Published 2022
    “…Recently, the integration of computer vision and machine learning has given solutions to improve the underwater detection system by using intelligent classifier algorithm in real-time computer vision to detect underwater animals with challenging environments. …”
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    Thesis
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    Analysis of the effectiveness of natural treatments for preserving apricots and the YOLOv7 application for early damage detection by Al-Sammarraie, Mustafa A.J., Gokalp, Zeki, Abd Aziz, Samsuzana

    Published 2025
    “…For the performance indicators of the YOLOv7 algorithm, precision of 84.5%, recall of 87%, F1 of 0.77, and mAP@0.5 of 77.2 were obtained, respectively. …”
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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
    “…Other research works are focusing on improving the mean average precision and the best result reported so far is 93% of mean Average Precision (mAP) by YOLOv5. This paper focuses on object detection of the Convolutional Neural Network (CNN) architecture-based to detect the disease of solanaceous crops for robot vision. …”
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    Real-time vehicle counting using custom YOLOv8n and DeepSORT for resource-limited edge devices by Saadeldin, Abuelgasim, Rashid, Muhammad Mahbubur, Shafie, Amir Akramin, Hasan, Tahsin Fuad

    Published 2024
    “…Recently, there has been a significant increase in the use of deep learning and low-computing edge devices for analysis of video-based systems, particularly in the field of intelligent transportation systems (ITS). …”
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    Retinal Microvascular Feature Extraction Using Faster Region-based Convolutional Neural Network by Mohammed Enamul, Hoque

    Published 2021
    “…This proposed method obtained 91.82% Mean Average Precision (mAP), 92.81% sensitivity, and 63.34% Positive Predictive Value (PPV). …”
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    Thesis
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    Smart cashierless checkout system for retail using machine vision by Lee, Ren Yi

    Published 2022
    “…After training the model with dataset generated in previous stage, the proposed model with MobileNet V3 surpassed baseline model in terms of inference time, with only 0.142s while maintaining high Mean Average Precision (mAP) of 98.2% and Checkout Accuracy (cAcc) of 89.17% on Jetson Nano.…”
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    Final Year Project / Dissertation / Thesis
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