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"Machine Learning"
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25291
Panoramic radiographic features for machine learning based detection of mandibular third molar root and inferior alveolar canal contact
Enthalten in Scientific reports Bd. 15, 4.2.2025, Nr. 1, date:12.2025: 1-14
25292
Panoramic tongue imaging and deep convolutional machine learning model for diabetes diagnosis in humans
Enthalten in Scientific reports Bd. 12, 7.1.2022, Nr. 1, date:12.2022: 1-18
25293
Paper-based fluorescence sensor array with functionalized carbon quantum dots for bacterial discrimination using a machine learning algorithm
Enthalten in Analytical and bioanalytical chemistry Bd. 416, 17.4.2024, Nr. 13, date:5.2024: 3139-3148
25294
Paralinguistic and spectral feature extraction for speech emotion classification using machine learning techniques
Enthalten in European Association for Speech, Signal and Image Processing: EURASIP Journal on audio, speech, and music processing Bd. 2023, 15.5.2023, Nr. 1, date:12.2023: 1-14
25295
Paralinguistic singing attribute recognition using supervised machine learning for describing the classical tenor solo singing voice in vocal pedagogy
Enthalten in European Association for Speech, Signal and Image Processing: EURASIP Journal on audio, speech, and music processing Bd. 2022, 15.4.2022, Nr. 1, date:12.2022: 1-16
25296
Parallel hybrid quantum-classical machine learning for kernelized time-series classification
Enthalten in Quantum machine intelligence Bd. 6, 10.3.2024, Nr. 1, date:6.2024: 1-17
25297
Parameter estimation of an SRGM using teaching learning based optimization
Enthalten in International journal of information technology Bd. 15, 4.8.2023, Nr. 6, date:8.2023: 2941-2950
25298
Parameter identifiability of a deep feedforward ReLU neural network
Enthalten in Machine learning Bd. 112, 3.8.2023, Nr. 11, date:11.2023: 4431-4493
25299
Parameter tuning in metaheuristics: a bibliometric and gap analysis
Enthalten in International journal of information technology Bd. 16, 19.1.2024, Nr. 3, date:3.2024: 1645-1651
25300
Parameterization of empirical forcefields for glassy silica using machine learning
Enthalten in Materials Research Society: MRS communications Bd. 9, 20.6.2019, Nr. 2, date:6.2019: 593-599
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