Showing 81 - 100 results of 439 for search 'Binary research algorithm', query time: 0.12s Refine Results
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    Use of hybrid quantum-classical algorithms for enhancing biomarker classification. by Aninda Astuti, Pin-Keng Shih, Shan-Chih Lee, Venugopala Reddy Mekala, Ezra B Wijaya, Ka-Lok Ng

    Published 2025-01-01
    “…Our study addresses the need for improved execution time and accuracy in QSVC and QNN algorithms compared to SVC and NN for binary classification. …”
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    Efficient Big Integer Multiplication and Squaring Algorithms for Cryptographic Applications by Shahram Jahani, Azman Samsudin, Kumbakonam Govindarajan Subramanian

    Published 2014-01-01
    “…Improving the efficiency of public-key cryptosystem through speeding up calculation and using fewer resources are among the main goals of cryptography research. In this paper, we introduce new symbols extracted from binary representation of integers called Big-ones. …”
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    Predicting Voluntary Auditor Change Using the Water Cycle Algorithm by Erfan Safdel, Hoda Eskandar, Hassan Yazdifar

    Published 2024-06-01
    “…In this regard, the purpose of this research is to predict voluntary auditor change (non-continuation of the relationship with the current client) using a meta-heuristic algorithm (Water Cycle Algorithm, WCA) and to compare the results with the logistic regression method.MethodsThe statistical sample is 185 companies listed on the Tehran Stock Exchange, selected by the systematic elimination method from 2017 to 2023, and their information was collected. …”
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    Analyzing cryptographic algorithm efficiency with in graph-based encryption models by Yashmin Banu, Biplab Kumar Rath, Debasis Gountia

    Published 2025-07-01
    “…Consequently, this research conducts a comparative analysis of RSA and ElGamal cryptographic algorithms by applying them to mixed data, including binary, text, and image files. …”
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    Investigating the contributory factors influencing speeding behavior among long-haul truck drivers traveling across India: Insights from binary logit and machine learning technique... by Balamurugan Shandhana Rashmi, Sankaran Marisamynathan

    Published 2024-12-01
    “…While conventional statistical methods like binary logit technique lacked prediction capabilities, machine learning (ML) algorithms including decision tree (DT), random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost) were employed to model speeding behavior among LHTDs. …”
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    Digital Art Feature Association Mining Based on the Machine Learning Algorithm by Zhiying Wu, Yuan Chen

    Published 2021-01-01
    “…The research scope is set in the field of digital art, and all kinds of accidental factors of digital art creation based on the machine learning algorithm are mined and analyzed for feature correlation. …”
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    Sustainable Selection of Machine Learning Algorithm for Gender-Bias Attenuated Prediction by Raik Orbay, Evelina Wikner

    Published 2025-01-01
    “…As the vast dataset for engineering rules, standards and experiments are based on men, a bias towards women is the subject of research. Accordingly, any bias should be contained before the algorithms are deployed to the service of the sustainable society. …”
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    Optimizing Data Filtering in Multi-Armed Bandit Algorithms for Reinforcement Learning by Zhang Shengshi

    Published 2025-01-01
    “…The algorithms were evaluated in static and dynamic environments using the MovieLens dataset and transferred to binary rewards to measure performance. …”
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    Brain Computer Interface using Genetic Algorithm with modified Genome and Phenotype Structures by Rohtash Dhiman, A Anshul

    Published 2023-08-01
    “…The human machine interface research in the light of modern fast computers and advanced sensors is taking new heights. …”
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    Performance Comparison of Random Forest and Decision Tree Algorithms for Anomaly Detection in Networks by Rafiq Fajar Ramadhan, Wahid Miftahul Ashari

    Published 2024-11-01
    “…From the study result, it can be conclude that the Decision Tree algorithm performs better in detecting anomalies in binary data with an accuracy of 99,71%. …”
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