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Surgical interventions for spontaneous supratentorial intracerebral haemorrhage: a systematic review and network meta-analysisResearch in context
Published 2025-01-01“…The frequentist pairwise and network meta-analysis (NMA) were performed. The GRADE approach was used to evaluate the certainty of evidence. …”
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Risk of aortic aneurysm or dissection following use of fluoroquinolones: a retrospective multinational network cohort studyResearch in context
Published 2025-03-01“…Methods: A retrospective cohort study was conducted using a large, distributed network analysis across 14 databases from 5 countries (United States, South Korea, Japan, Taiwan, and Australia). …”
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Reach and engagement on the history of nursing on social media in light of Pierre Lévy
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Effects of non-pharmacological interventions on depressive and anxiety symptoms in pregnant women: a systematic review and network meta-analysisResearch in context
Published 2025-01-01“…We performed both pairwise meta-analyses and random-effects network meta-analyses (NMAs), calculating standardised mean differences (SMDs) with 95% credible intervals (CrI). …”
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Estimating the Prevalence of Schizophrenia in the General Population of Japan Using an Artificial Neural Network–Based Schizophrenia Classifier: Web-Based Cross-Sectional Survey
Published 2025-01-01“…To address these issues, we previously developed an artificial neural network (ANN)–based schizophrenia classification model (SZ classifier) using data from a large-scale Japanese web-based survey to enhance the comprehensiveness of schizophrenia case identification in the general population. …”
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Association between demographic, clinical characteristics and severe complications by SARS-CoV-2 infection in a community-based healthcare network in Chile.
Published 2024-01-01“…We included people 18+ years old enrolled in the Primary Care Network between 1st January 2020 and 31st December 2021. …”
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Assessment of using transfer learning with different classifiers in hypodontia diagnosis
Published 2025-01-01“…Pretrained convolutional neural network models (AlexNet, DarkNet-19, DarkNet-53, DenseNet-201, EfficientNet, GoogLeNet, InceptionV3, IncResV2, MobileNetV2, NasNet-Mobile, Places365, ResNet-18, ResNet-50, ResNet-101, ShuffleNet, SqueezeNet, VGG-16, VGG-19, and Xception) were used for training with the fine-tuning method and different machine learning classifiers (decision trees, discriminant analysis, logistic regression, naive Bayes, support vector machines, nearest neighbor, ensemble method, and artificial neural network). …”
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Perbandingan Arsitektur Convolutional Neural Network Pada Klasifikasi Pneumonia, COVID-19, Lung Opacity, dan Normal Menggunakan Citra Sinar-X Thoraks
Published 2022-12-01“…Inception-ResNet, DenseNet201, InceptionV3, ResNet50v1, ResNet101, ResNet152, ResNet50v2, ResNet101v2 and ResNet152v2. …”
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Engineering Circuit Analysis /
Published 2012Table of Contents: “…Mesh Analysis: A Comparison -- 4.6.Computer-Aided Circuit Analysis -- Summary And Review -- Reading Further -- Exercises -- ch. 5 Handy Circuit Analysis Techniques -- 5.1.Linearity and Superposition -- 5.2.Source Transformations -- 5.3.Thevenin and Norton Equivalent Circuits -- 5.4.Maximum Power Transfer -- 5.5.Delta-Wye Conversion -- 5.6.Selecting an Approach: A Summary of Various Techniques -- Summary And Review -- Reading Further -- Exercises -- ch. 6 The Operational Amplifier -- 6.1.Background -- 6.2.The Ideal Op Amp: A Cordial Introduction -- 6.3.Cascaded Stages -- 6.4.Circuits for Voltage and Current Sources -- 6.5.Practical Considerations -- 6.6.Comparators and the Instrumentation Amplifier -- Summary And Review -- Reading Further -- Exercises -- ch. 7 Capacitors And Inductors -- 7.1.The Capacitor -- 7.2.The Inductor -- 7.3.Inductance and Capacitance Combinations -- 7.4.Consequences of Linearity -- 7.5.Simple Op Amp Circuits with Capacitors -- 7.6.Duality -- 7.7.Modeling Capacitors and Inductors with PSpice -- Summary And Review -- Reading Further -- Exercises -- ch. 8 Basic Rl And Rc Circuits -- 8.1.The Source-Free RL Circuit -- 8.2.Properties of the Exponential Response -- 8.3.The Source-Free RC Circuit -- 8.4.A More General Perspective -- 8.5.The Unit-Step Function -- 8.6.Driven RL Circuits -- 8.7.Natural and Forced Response -- 8.8.Driven AC Circuits -- 8.9.Predicting the Response of Sequentially Switched Circuits -- Summary And Review -- Reading Further -- Exercises -- ch. 9 The Rcl Circuit -- 9.1.The Source-Free Parallel Circuit -- 9.2.The Overdamped Parallel RLC Circuit -- 9.3.Critical Damping -- 9.4.The Underdamped Parallel RLC Circuit -- 9.5.The Source-Free Series RLC Circuit -- 9.6.The Complete Response of the RLC Circuit -- 9.7.The Lossless LC Circuit -- Summary And Review -- Reading Further -- Exercises -- ch. 10 Sinusoidal Steady-State Analysis -- 10.1.Characteristics of Sinusoids -- 10.2.Forced Response to Sinusoidal Functions -- 10.3.The Complex Forcing Function -- 10.4.The Phasor -- 10.5.Impedance and Admittance -- 10.6.Nodal and Mesh Analysis -- 10.7.Superposition, Source Transformations and Thevenin's Theorem -- 10.8.Phasor Diagrams -- Summary And Review -- Reading Further -- Exercises -- ch. 11 Ac Circuit Power Analysis -- 11.1.Instantaneous Power -- 11.2.Average Power -- 11.3.Effective Values of Current and Voltage -- 11.4.Apparent Power and Power Factor -- 11.5.Complex Power -- Summary And Review -- Reading Further -- Exercises -- ch. 12 Polyphase Circuits -- 12.1.Polyphase Systems -- 12.2.Single-Phase Three-Wire Systems -- 12.3.Three-Phase Y-Y Connection -- 12.4.The Delta (A) Connection -- 12.5.Power Measurement in Three-Phase Systems -- Summary And Review -- Reading Further -- Exercises -- ch. 13 Magnetically Coupled Circuits -- 13.1.Mutual Inductance -- 13.2.Energy Considerations -- 13.3.The Linear Transformer -- 13.4.The Ideal Transformer -- Summary And Review -- Reading Further -- Exercises -- ch. 14 Complex Frequency And The Laplace Transform -- 14.1.Complex Frequency -- 14.2.The Damped Sinusoidal Forcing Function -- 14.3.Definition of the Laplace Transform -- 14.4.Laplace Transforms of Simple Time Functions -- 14.5.Inverse Transform Techniques -- 14.6.Basic Theorems for the Laplace Transform -- 14.7.The Initial-Value and Final-Value Theorems -- Summary And Review -- Reading Further -- Exercises -- ch. 15 Circuit Analysis In The s-Domain -- 15.1.Z(s) and Y(s) -- 15.2.Nodal and Mesh Analysis in the s-Domain -- 15.3.Additional Circuit Analysis Techniques -- 15.4.Poles, Zeros, and Transfer Functions -- 15.5.Convolution -- 15.6.The Complex-Frequency Plane -- 15.7.Natural Response and the s Plane -- 15.8.A Technique for Synthesizing the Voltage Ratio H(s) = V out/V in -- Summary And Review -- Reading Further -- Exercises -- ch. 16 Frequency Response -- 16.1.Parallel Resonance -- 16.2.Bandwidth and High-Q Circuits -- 16.3.Series Resonance -- 16.4.Other Resonant Forms -- 16.5.Scaling -- 16.6.Bode Diagrams -- 16.7.Basic Filter Design -- 16.8.Advanced Filter Design -- Summary And Review -- Reading Further -- Exercises -- ch. 17 Two-Port Networks -- 17.1.One-Port Networks -- 17.2.Admittance Parameters -- 17.3.Some Equivalent Networks -- 17.4.Impedance Parameters -- 17.5.Hybrid Parameters -- 17.6.Transmission Parameters -- Summary And Review -- Reading Further -- Exercises -- ch. 18 Fourier Circuit Analysis -- 18.1.Trigonometric Form of the Fourier Series -- 18.2.The Use of Symmetry -- 18.3.Complete Response to Periodic Forcing Functions -- 18.4.Complex Form of the Fourier Series -- 18.5.Definition of the Fourier Transform -- 18.6.Some Properties of the Fourier Transform -- 18.7.Fourier Transform Pairs for Some Simple Time Functions -- 18.8.The Fourier Transform of a General Periodic Time Function -- 18.9.The System Function and Response in the Frequency Domain -- 18.10.The Physical Significance of the System Function -- Summary And Review -- Reading Further -- Exercises.…”
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Fuzzy Enhanced Kidney Tumor Detection: Integrating Machine Learning Operations for a Fusion of Twin Transferable Network and Weighted Ensemble Machine Learning Classifier
Published 2025-01-01“…Two pre-trained deep convolutional neural networks (PT-DCNNs), DenseNet121 and ResNet101, are used to extract features from the enhanced CT images. …”
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Exploiting the determinant factors on the available flexibility area of ADNs at TSO‐DSO interface
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Adversarial examples detection method based on boundary values invariants
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Design and implementation of software router supporting segment routing
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Leveraging Deep Learning for Real-Time Coffee Leaf Disease Identification
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The history of Aristotelian society of the early period: the genesis and preconditions for development
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Service-oriented dynamic resource configuration optimization scheme in RFID system
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