GuardianMPC: Backdoor-Resilient Neural Network Computation by Mohammad Hashemi, Domenic Forte, Fatemeh Ganji

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Retracted: Analysis of Comprehensive Artificial Neural Network Computer Media Aided Construction of Economic Forecasting Model by Journal of Electrical and Computer Engineering

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PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts by Jordi Armengol-Estapé, Felipe Soares, Montserrat Marimon, Martin Krallinger

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The Use of Machine Learning to Support the Diagnosis of Oral Alterations by Rosana Leal do Prado, Juliane Avansini Marsicano, Amanda Keren Frois, Jacques Duílio Brancher

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Complex application of mathematical methods in diagnostics of diffuse liver diseases by I. Yu. Pirogova, S. A. Pyshkin, A. A. Bolotov

“…To construct the «images» of stages of chronic HCV-and HBV-infections discriminant analysis, neural networks, computer programming were applied. The result of non-invasive diagnostics of patients with HСV-infection in 71% of cases coincided with LB data by LF stage, in 62% – by HAI. …”
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Low-power artificial neuron networks with enhanced synaptic functionality using dual transistor and dual memristor. by Keerthi Nalliboyina, Sakthivel Ramachandran

“…Artificial neurons with bio-inspired firing patterns have the potential to significantly improve the performance of neural network computing. The most significant component of an artificial neuron circuit is a large amount of energy consumption. …”
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LazyAct: Lazy actor with dynamic state skip based on constrained MDP. by Hongjie Zhang, Zhenyu Chen, Hourui Deng, Chaosheng Feng

“…Specifically, each decision made by an agent requires a complete neural network computation, leading to a linear increase in computational cost with the number of interactions and agents. …”
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ADA-NAF: Semi-Supervised Anomaly Detection Based on the Neural Attention Forest by Andrey Ageev, Andrei Konstantinov, Lev Utkin

“…In this study, we present a novel model called ADA-NAF (Anomaly Detection Autoencoder with the Neural Attention Forest) for semi-supervised anomaly detection that uniquely integrates the Neural Attention Forest (NAF) architecture which has been developed to combine a random forest classifier with a neural network computing attention weights to aggregate decision tree predictions. …”
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