PREDIKSI PERTUMBUHAN PRODUK DOMESTIK REGIONAL BRUTO (PDRB) MENGGUNAKAN ADAFTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS) (Studi Kasus : PDRB Provinsi Bali) by , Made Agung Raharja, , Dra. Sri Hartati, M.Sc, Ph.D.

“…Adaptive Neuro-fuzzy inference system (ANFIS) is the merger mechanism described fuzzy inference system in neural network architectures. In this study conducted by the ANFIS prediction of GDRP growth that has the main goal is to make the design and construction of a model that can predict the growth of the Gross Domestic Regional Product (GDRP) in the Province of Bali on a regular basis. …”

Pengesanan Kerosakan Bahan Penebat Transformer Dengan Menggunakan Rangkaian Neural Buatan by Che Osman, Suzita

“…A multilayer perceptron (MLP) is the choice among several neural network architectures that is used in this study. A three layer neural network has been used throughout this study. …”
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Prognostic techniques for aeroengine health assessment and Remaining Useful Life estimation by Caricato A., Ficarella A., Chiodo L. Spada

“…In turn, different regression models and neural network architectures have been compared, namely tree regression with different levels of tree depth, Gaussian Process Regression (GPR) with different kernel functions and Multilayer Perceptron (MLP) with one to three hidden layers and varying number of nodes. …”
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Detection of trachoma using machine learning approaches. by Damien Socia, Christopher J Brady, Sheila K West, R Chase Cockrell

“…We developed classifiers by implementing two state-of-the-art Convolutional Neural Network architectures, ResNet101 and VGG16, and applying a suite of data augmentation/oversampling techniques to the positive images. …”
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Identifying cross-lingual plagiarism using rich semantic features and deep neural networks: A study on Arabic-English plagiarism cases by Salha Alzahrani, Hanan Aljuaid

“…For this purpose, we proposed different neural network architectures to solve the PD task as either binary classification (plagiarism/independently written), or even deeper classification (literally translated/paraphrased/summarized/independently written). …”
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Paired Trial Classification: A Novel Deep Learning Technique for MVPA by Jacob M. Williams, Ashok Samal, Prahalada K. Rao, Matthew R. Johnson

“…Many recent developments in machine learning have come from the field of “deep learning,” or the use of advanced neural network architectures and techniques. While these methods have produced state-of-the-art results and dominated research focus in many fields, such as image classification and natural language processing, they have not gained as much ground over standard multivariate pattern analysis (MVPA) techniques in the classification of electroencephalography (EEG) or other human neuroscience datasets. …”
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RadNet 1.0: exploring deep learning architectures for longwave radiative transfer by Y. Liu, R. Caballero, J. M. Monteiro

“…We compare multiple neural-network architectures, including a convolutional neural network, and our results suggest that the performance loss from the use of conventional convolutional networks is not offset by gains in accuracy. …”
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A neurophysiologically interpretable deep neural network predicts complex movement components from brain activity by Neelesh Kumar, Konstantinos P. Michmizos

“…Deep neural networks are most suitable for decoding real-time data but their use in EEG is hindered by the gross classes of motor tasks in the currently available datasets, which are solvable even with network architectures that do not require specialized design considerations. …”
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Machine Learning Sequential Methodology for Robot Inverse Kinematic Modelling by Franco Luis Tagliani, Nicola Pellegrini, Francesco Aggogeri

“…The ANN is implemented with the following settings: scaled conjugate gradient as training function and the mean squared error as the loss function. Different network architectures are examined to validate the IK procedure, ranging from global to sequential and considering the computation direction (from end-effector or from basement). …”
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Application of Machine Learning in Battery: State of Charge Estimation Using Feed Forward Neural Network for Sodium-Ion Battery by Devendrasinh Darbar, Indranil Bhattacharya

“…This model comprises the Dropout technique, which can have many neural network architectures by dropping the neuron/mode at each epoch/training cycle using the same weights and biases. …”
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Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review by Saman Ghaffarian, João Valente, Mariska van der Voort, Bedir Tekinerdogan

“…This study provides an overview of the developed attention mechanisms and how to integrate them with different deep learning neural network architectures. In addition, it aims to investigate the effect of the attention mechanism on deep learning-based RS image processing. …”
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An Optical 1×4 Power Splitter Based on Silicon–Nitride MMI Using Strip Waveguide Structures by Aviv Frishman, Dror Malka

“…By leveraging the advantages of Si₃N₄ and the MMI coupler, this design opens possibilities for advanced optical network architectures and enables efficient transmission of optical signals in the O-band range.…”
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Improved UNet with Attention for Medical Image Segmentation by Ahmed AL Qurri, Mohamed Almekkawy

“…In this paper, we propose a new model that combines the strengths of both CNNs and Transformer, with network architectural improvements designed to enrich the feature representation captured by the skip connections and the decoder. …”
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Comparison of Fully Convolutional Networks (FCN) and U-Net for Road Segmentation from High Resolution Imageries by Ozan Ozturk, Batuhan Sarıturk, Dursun Zafer Şeker

“…To train the system using these datasets, two different artificial neural network architectures U-Net and Fully Convolutional Networks (FCN), which are used for object segmentation on high-resolution images, were selected. …”

An imbalance-aware deep neural network for early prediction of preeclampsia. by Rachel Bennett, Zuber D Mulla, Pavan Parikh, Alisse Hauspurg, Talayeh Razzaghi

“…We also investigated the effectiveness of multiple network architectures using three hyperparameter optimization algorithms: Bayesian optimization, Hyperband, and random search. …”
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Cancer-Net SCa: tailored deep neural network designs for detection of skin cancer from dermoscopy images by James Ren Hou Lee, Maya Pavlova, Mahmoud Famouri, Alexander Wong

“…To the best of the authors’ knowledge, Cancer-Net SCa comprises the first machine-driven design of deep neural network architectures tailored specifically for skin cancer detection, one of which leverages attention condensers for an efficient self-attention design. …”
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AutoFL: Towards AutoML in a Federated Learning Context by Davy Preuveneers

“…This decision is based on the server’s ability to accomplish the task by either reusing well-established neural network architectures suitable for the specific task (e.g., ResNet-50 for image classification) or evaluating the adequacy of a model using the limited data it has access to. …”
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Investigation of Hyperparameter Setting of a Long Short-Term Memory Model Applied for Imputation of Missing Discharge Data of the Daihachiga River by Weilisi, Toshiharu Kojima

“…Being one of the network architectures used in deep learning, Long Short-Term Memory (LSTM) has been applied largely in related research, such as flood forecasting and discharge prediction, and the performance of an LSTM model has been compared with other deep learning models. …”
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A Novel CNN pooling layer for breast cancer segmentation and classification from thermograms. by Esraa A Mohamed, Tarek Gaber, Omar Karam, Essam A Rashed

“…The VBP-based results were as follows: global accuracy = 98.3%, mean accuracy = 97.9%, mean IoU = 95.87%, and mean BF score = 88.68% while the AVG-MAX VPB-based results were as follows: global accuracy = 99.2%, mean accuracy = 98.97%, mean IoU = 98.03%, and mean BF score = 94.29%. Other network architectures also demonstrate superior improvement considering the use of VPB and AVG-MAX VPB.…”
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Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data. by Guillermo López-García, José M Jerez, Leonardo Franco, Francisco J Veredas

“…Using the public Pan-Cancer dataset, in this study we pre-train convolutional neural network architectures for survival prediction on a subset composed of thousands of gene-expression samples from thirty-one tumor types. …”
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