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    Recurrent Adaptive Classifier Ensemble for Handling Recurring Concept Drifts by Tinofirei Museba, Fulufhelo Nelwamondo, Khmaies Ouahada, Ayokunle Akinola

    Published 2021-01-01
    “…A common remedy for most machine learning algorithms is to retain and reuse previously learned models, but the process is time-consuming and computationally prohibitive in nonstationary environments to appropriately select any optimal ensemble classifier capable of accurately adapting to recurring concepts. …”
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  4. 8904

    A New Self-Assessment “TQM Hybrid MCDM Fuzzy Model” For Enhancing the KPIs in Mega Universities by Abdul Hamid M. Ragab, Amin Y. Noaman, Ahmed A. A. Gad-Elrab, Ezz H. Abdul Fatah

    Published 2022-01-01
    “…Each of these submodels is computed and evaluated analytically. A comparison between them is achieved. …”
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  5. 8905
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    Application of Optimized Convolution Neural Network Model in Mural Segmentation by Zhiqiang Chen, Leelavathi Rajamanickam, Xiaodong Tian, Jianfang Cao

    Published 2022-01-01
    “…The model combines the Deeplabv3+ structure and MobileNetV2 network and adopts the unique spatial pyramid structure of DeeplabV3+ to process convolutional features for multi-scale fusion, which reduces the loss of detail in the mural segmentation images. …”
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    Inflow Forecast of Iranamadu Reservoir, Sri Lanka, under Projected Climate Scenarios Using Artificial Neural Networks by Chamaka Karunanayake, Miyuru B. Gunathilake, Upaka Rathnayake

    Published 2020-01-01
    “…However, traditional computational models like hydrologic models need many data about the catchment itself. …”
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  9. 8909

    A Novel Hybrid Deep Learning Model Based on Simulated Annealing and Cuckoo Search Algorithms for Automatic Radiomics-Based COVID-19 Diagnosis by Basma Jumaa Saleh, Zaid Omar, Muhammad Amir As’ari, Vikrant Bhateja, Lila Iznita Izhar

    Published 2025-01-01
    “…Automated segmentation of COVID-19 lung computed tomography (CT) scans is a crucial diagnostic tool that aids physicians in providing accurate and timely diagnoses, as it contains significant radiomics information. …”
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    Near-field extremely large-scale MIMO data rate prediction based on deep learning by Guozhi Rong, Rugui Yao, Yifeng He

    Published 2025-07-01
    “…Abstract The channel matrix dimension of the near-field ultra-large-scale MIMO (Multiple-Input Multiple-Output) system is extremely high due to the significant increase in the number of antennas, thus aggravating the computational and storage burdens, and posing challenges to real-time processing and system resource management. …”
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