Raman spectral pattern recognition of breast cancer: A machine learning strategy based on feature fusion and adaptive hyperparameter optimization. [PDF]
Heliyon, 2023 Raman spectroscopy, as a kind of molecular vibration spectroscopy, provides abundant information for measuring components and molecular structure in the early detection and diagnosis of breast cancer.
Li Q, Zhang Z, Ma Z.
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Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis
Informatics, 2021 Machine learning models are used today to solve problems within a broad span of disciplines. If the proper hyperparameter tuning of a machine learning classifier is performed, significantly higher accuracy can be obtained.
Enas Elgeldawi +3 more
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Understanding Bitcoin Price Prediction Trends under Various Hyperparameter Configurations
Computers, 2022 Since bitcoin has gained recognition as a valuable asset, researchers have begun to use machine learning to predict bitcoin price. However, because of the impractical cost of hyperparameter optimization, it is greatly challenging to make accurate ...
Jun-Ho Kim, Hanul Sung
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Optimizing Machine Learning Algorithms for Landslide Susceptibility Mapping along the Karakoram Highway, Gilgit Baltistan, Pakistan: A Comparative Study of Baseline, Bayesian, and Metaheuristic Hyperparameter Optimization Techniques. [PDF]
Sensors (Basel), 2023 Algorithms for machine learning have found extensive use in numerous fields and applications. One important aspect of effectively utilizing these algorithms is tuning the hyperparameters to match the specific task at hand. The selection and configuration
Abbas F +6 more
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Federated learning with hyper-parameter optimization
Journal of King Saud University: Computer and Information Sciences, 2023 Federated Learning is a new approach for distributed training of a deep learning model on data scattered across a large number of clients while ensuring data privacy.
Majid Kundroo, Taehong Kim
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Hyperparameter Tuning on Classification Algorithm with Grid Search
Sistemasi: Jurnal Sistem Informasi, 2022 Currently, machine learning algorithms continue to be developed to perform optimization with various methods to produce the best-performing model. In Supervised learning or classification, most of the algorithms have hyperparameters.
Wahyu Nugraha, Agung Sasongko
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Improving stroke diagnosis accuracy using hyperparameter optimized deep learning
IJAIN (International Journal of Advances in Intelligent Informatics), 2019 Stroke may cause death for anyone, including youngsters. One of the early stroke detection techniques is a Computerized Tomography (CT) scan. This research aimed to optimize hyperparameter in Deep Learning, Random Search and Bayesian Optimization for ...
Tessy Badriyah +3 more
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Exploratory Landscape Validation for Bayesian Optimization Algorithms
MathematicsBayesian optimization algorithms are widely used for solving problems with a high computational complexity in terms of objective function evaluation. The efficiency of Bayesian optimization is strongly dependent on the quality of the surrogate models of ...
Taleh Agasiev, Anatoly Karpenko
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EMPLOYING GENETIC ALGORITHM INSPIRED HYPERPARAMETER OPTIMIZATION IN MOBILE NET V2 ARCHITECTURE [PDF]
Proceedings on Engineering SciencesThis paper presents a novel approach for hyperparameter optimization for the MobileNetV2 architecture using a genetic algorithm. The proposed approach aims to automate the hyperparameter tuning leading to performance enhancement.
Baljinder Kaur +3 more
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Machine Learning: Science and Technology, 2023 Unsupervised and semi-supervised ML methods such as variational autoencoders (VAE) have become widely adopted across multiple areas of physics, chemistry, and materials sciences due to their capability in disentangling representations and ability to find
Arpan Biswas +3 more
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