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Showing 21 - 40 results of 110 for search '(( implement learning algorithm ) OR ((( element data algorithm ) OR ( element td3 algorithm ))))', query time: 0.19s Refine Results
  1. 21
    Multi-Agent Learning of Strategies in Abstract Argumentation Mechanisms

    Multi-Agent Learning of Strategies in Abstract Argumentation Mechanisms by Nemer, Rama

    Published 2009
    “…As for the effect of the learning algorithm on the choice of strategy, the results confirm that WPL is biased toward mixed strategies while GIGA is faster in convergence to pure strategy Nash equilibria. …”
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  2. 22
    A Data-Driven Decision-Making Framework for Fleet Management in the Government Sector of Dubai

    A Data-Driven Decision-Making Framework for Fleet Management in the Government Sector of Dubai by ALGHANEM, HANI SUBHI MOHD

    Published 2024
    “…The proposed framework comprises key elements: Important Decisions derived from interviews with transportation leaders, Knowledge Management enhanced by AI algorithms, Data Mining/Analysis utilizing historical data, the Fleet Management System employing Oracle ERP, and a Data-Driven Decision Support Framework that leans towards the extended framework approach. …”
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  3. 23
    Adaptive Secure Pipeline for Attacks Detection in Networks with set of Distribution Hosts

    Adaptive Secure Pipeline for Attacks Detection in Networks with set of Distribution Hosts by ALSHAMSI, SUROUR

    Published 2022
    “…The research work carried out, in relation to attack detection ensemble learning, mainly aims to increase the performance of machine learning algorithms by combining their results. …”
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  4. 24
    Brain Source Localization in the Presence of Leadfield Perturbations

    Brain Source Localization in the Presence of Leadfield Perturbations by Momin, Rabiya Nakhat

    Published 2015
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  5. 25
    Benchmarking Concept Drift Detectors for Online Machine Learning

    Benchmarking Concept Drift Detectors for Online Machine Learning by Mahgoub, Mahmoud

    Published 2022
    “…Upon drift detection, the classifica tion algorithm may reset its model or concurrently grow a new learning model. …”
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  6. 26
    A machine learning approach for localization in cellular environments

    A machine learning approach for localization in cellular environments by Abdallah, Ali A.

    Published 2018
    “…A machine learning approach is developed for localization based on received signal strength (RSS) from cellular towers. …”
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  7. 27
    LDSVM: Leukemia Cancer Classification Using Machine Learning

    LDSVM: Leukemia Cancer Classification Using Machine Learning by Abdul Karim (417009)

    Published 2022
    “…This study proposes a novel method using machine learning algorithms based on microarrays of leukemia GSE9476 cells. …”
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  8. 28
    A Fast and Robust Gas Recognition Algorithm Based on Hybrid Convolutional and Recurrent Neural Network

    A Fast and Robust Gas Recognition Algorithm Based on Hybrid Convolutional and Recurrent Neural Network by Xiaofang Pan (1895950)

    Published 2019
    “…Featuring the capability of learning the correlations of time-series data, the proposed deep learning method is well-suited for extracting the valuable transient feature contained in the very beginning of the response curve. …”
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  9. 29
    A multi-class discriminative motif finding algorithm for autosomal genomic data. (c2015)

    A multi-class discriminative motif finding algorithm for autosomal genomic data. (c2015) by Wehbe, Gioia Wahib

    Published 2016
    “…Then, it searches for population discriminative motifs or differentiable sequence of SNPs, by implementing Probabilistic Suffix Trees data structures. …”
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  10. 30
    FPGA-Based Network Traffic Classification Using Machine Learning

    FPGA-Based Network Traffic Classification Using Machine Learning by Elnawawy, Mohammed

    Published 2019
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  11. 31
    FPGA-Based Network Traffic Classification Using Machine Learning

    FPGA-Based Network Traffic Classification Using Machine Learning by Elnawawy, Mohammed

    Published 2020
    “…Classification approaches based on machine learning techniques have shown promising results with high levels of accuracy. …”
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  12. 32
    Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial Systems

    Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial Systems by Tarek Berghout (16905132)

    Published 2021
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  13. 33
    PSYCHOLOGICAL EMOTION RECOGNITION OF STUDENTS USING MACHINE LEARNING BASED CHATBOT

    PSYCHOLOGICAL EMOTION RECOGNITION OF STUDENTS USING MACHINE LEARNING BASED CHATBOT by Khalil Assayed, Suha

    Published 2023
    “…In future, other neural network algorithms such as the RNN, LSTM will be implemented, and Arabic tweets will be included in the future.…”
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  14. 34
    PSYCHOLOGICAL EMOTION RECOGNITION OF STUDENTS USING MACHINE LEARNING BASED CHATBOT

    PSYCHOLOGICAL EMOTION RECOGNITION OF STUDENTS USING MACHINE LEARNING BASED CHATBOT by Khalil Assayed, Suha

    Published 2023
    “…In future, other neural network algorithms such as the RNN, LSTM will be implemented, and Arabic tweets will be included in the future.…”
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  15. 35
    Analysis of Using Machine Learning to Enhance the Efficiency of Facilities Management in the UAE

    Analysis of Using Machine Learning to Enhance the Efficiency of Facilities Management in the UAE by ULLAH, SAAD

    Published 2022
    “…This study addresses these issues by Implementing Machine Learning (ML) algorithms using data from Building Management Systems (BMS) and FM maintenance reports, focussing on predictive maintenance for Fresh Air Handling Units. …”
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  16. 36
    Recent advances on artificial intelligence and learning techniques in cognitive radio networks

    Recent advances on artificial intelligence and learning techniques in cognitive radio networks by Abbas, Nadine

    Published 2015
    “…This paper also discusses the cognitive radio implementation and the learning challenges foreseen in cognitive radio applications.…”
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  17. 37
    Reinforcement R-learning model for time scheduling of on-demand fog placement

    Reinforcement R-learning model for time scheduling of on-demand fog placement by Farhat, Peter

    Published 2020
    “…Our model aims to decrease the cloud’s load by utilizing the maximum available fogs resources over different locations. An implementation of our proposed R-learning model is provided in the paper, followed by a series of experiments on a real dataset to prove its efficiency in utilizing fog resources and minimizing the cloud’s load. …”
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  18. 38
    After generative AI : preparing faculty to transform education, learning, and pedagogy

    After generative AI : preparing faculty to transform education, learning, and pedagogy by Hardey, Mariann

    Published 2025
    “…This is a foundational understanding for readers, ensuring they are equipped to make informed decisions about integrating GAI into their teaching and learning processes. Going beyond basic explanations, Hardey and Aad provide practical insights and implementation strategies that recognize the concerns and ethical challenges related to GAI, such as bias in algorithms, privacy issues, and the need for inclusivity. …”
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  19. 39
    Online Recruitment Fraud (ORF) Detection Using Deep Learning Approaches

    Online Recruitment Fraud (ORF) Detection Using Deep Learning Approaches by Natasha Akram (20749538)

    Published 2024
    “…In recent studies, traditional machine learning and deep learning algorithms have been implemented to detect fake job postings; this research aims to use two transformer-based deep learning models, i.e., Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT-Pretraining Approach (RoBERTa) to detect fake job postings precisely. …”
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  20. 40
    Detecting Arabic Cyberbullying Tweets in Arabic Social Using Deep Learning

    Detecting Arabic Cyberbullying Tweets in Arabic Social Using Deep Learning by ALFALASI, FARIS Jr

    Published 2023
    “…A sizable dataset of electronic text data was gathered from multiple social media platforms like Twitter, Instagram, YouTube, and many more sites in order to examine cyberbullying in social media using machine learning and deep learning techniques. The data needs to be initially prepared so that deep learning algorithms may be trained on it before cyberbullying analysis can be done. …”
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