Showing 21 - 40 results of 181 for search 'The 101 Network', query time: 0.09s Refine Results
  1. 21

    Surgical interventions for spontaneous supratentorial intracerebral haemorrhage: a systematic review and network meta-analysisResearch in context by Jiayidaer Huan, Minghong Yao, Yu Ma, Fan Mei, Yanmei Liu, Lu Ma, Xiaochao Luo, Jiali Liu, Jianguo Xu, Chao You, Hunong Xiang, Kang Zou, Xiao Liang, Xin Hu, Ling Li, Xin Sun

    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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    Effects of non-pharmacological interventions on depressive and anxiety symptoms in pregnant women: a systematic review and network meta-analysisResearch in context by Guowei Zeng, Jianfeng Niu, Ke Zhu, Fei Li, Liwen Li, Kaiming Gao, Yanlong Zhuang, Boyang Zhang, Xiaoqiang Han, Gang Ye, Zhikun Gao, Haobai Li

    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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  8. 28

    Estimating the Prevalence of Schizophrenia in the General Population of Japan Using an Artificial Neural Network–Based Schizophrenia Classifier: Web-Based Cross-Sectional Survey by Pichsinee Choomung, Yupeng He, Masaaki Matsunaga, Kenji Sakuma, Taro Kishi, Yuanying Li, Shinichi Tanihara, Nakao Iwata, Atsuhiko Ota

    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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    Assessment of using transfer learning with different classifiers in hypodontia diagnosis by Tansel Uyar, Didem Sakaryalı Uyar

    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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  11. 31

    Perbandingan Arsitektur Convolutional Neural Network Pada Klasifikasi Pneumonia, COVID-19, Lung Opacity, dan Normal Menggunakan Citra Sinar-X Thoraks by Agung Wahyu Setiawan

    Published 2022-12-01
    “…Inception-ResNet, DenseNet201, InceptionV3, ResNet50v1, ResNet101, ResNet152, ResNet50v2, ResNet101v2 and ResNet152v2. …”
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  12. 32

    Engineering Circuit Analysis / by Hayt, William H. (William Hart), Jr., 1920-1999

    Published 2012
    Table 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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  13. 33

    Fuzzy Enhanced Kidney Tumor Detection: Integrating Machine Learning Operations for a Fusion of Twin Transferable Network and Weighted Ensemble Machine Learning Classifier by Ananya Ghosh, Jyotismita Chaki

    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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