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bayesian optimization » based optimization (Expand Search)
data bayesian » a bayesian (Expand Search), art bayesian (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
binary wave » binary image (Expand Search)
bayesian optimization » based optimization (Expand Search)
data bayesian » a bayesian (Expand Search), art bayesian (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
binary wave » binary image (Expand Search)
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Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things
Published 2025“…This research presents Data-Driven Intrusion Detection System in Internet of Things utilizing Optimized Bayesian Regularization-Back Propagation Neural Network (DIDS-BRBPNN-BBWOA-IoT) to overcome these issues. …”
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Bayesian sequential design for sensitivity experiments with hybrid responses
Published 2023“…To deal with the problem of complex computation involved in searching for optimal designs, fast algorithms are presented using two strategies to approximate the optimal criterion, denoted as SI-optimal design and Bayesian D-optimal design, respectively. …”
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Data_Sheet_1_Posiform planting: generating QUBO instances for benchmarking.pdf
Published 2023“…<p>We are interested in benchmarking both quantum annealing and classical algorithms for minimizing quadratic unconstrained binary optimization (QUBO) problems. …”
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Data_Sheet_1_Pneumonia detection by binary classification: classical, quantum, and hybrid approaches for support vector machine (SVM).pdf
Published 2024“…A support vector machine (SVM) is attractive because binary classification can be represented as an optimization problem, in particular as a Quadratic Unconstrained Binary Optimization (QUBO) model, which, in turn, maps naturally to an Ising model, thereby making annealing—classical, quantum, and hybrid—an attractive approach to explore. …”
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MCLP_quantum_annealer_V0.5
Published 2025“…Theoretical and applied experiments are conducted using four solvers: QBSolv, D-Wave Hybrid binary quadratic model 2, D-Wave Advantage system 4.1, and Gurobi. …”
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Image1_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF
Published 2021“…Its current hardware implementation relies on D-Wave’s Quantum Processing Units, which are limited in terms of number of qubits and architecture while being restricted to solving quadratic unconstrained binary optimization (QUBO) problems. …”
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Image3_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF
Published 2021“…Its current hardware implementation relies on D-Wave’s Quantum Processing Units, which are limited in terms of number of qubits and architecture while being restricted to solving quadratic unconstrained binary optimization (QUBO) problems. …”
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Image2_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF
Published 2021“…Its current hardware implementation relies on D-Wave’s Quantum Processing Units, which are limited in terms of number of qubits and architecture while being restricted to solving quadratic unconstrained binary optimization (QUBO) problems. …”
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DataSheet1_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.pdf
Published 2021“…Its current hardware implementation relies on D-Wave’s Quantum Processing Units, which are limited in terms of number of qubits and architecture while being restricted to solving quadratic unconstrained binary optimization (QUBO) problems. …”