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DNA sequences performs as natural language processing by exploiting deep learning algorithm for the identification of N4-methylcytosine.
Scientific reports
Jan 8, 2021
N4-methylcytosine is a biochemical alteration of DNA that affects the genetic operations without modifying the DNA nucleotides such as gene expression, genomic imprinting, chromosome stability, and the development of the cell. In the proposed work, a...
Predicting the reproductive toxicity of chemicals using ensemble learning methods and molecular fingerprints.
Toxicology letters
Jan 6, 2021
Reproductive toxicity endpoints are a significant safety concern in the assessment of the adverse effects of chemicals in drug discovery. Computational models that can accurately predict a chemical's toxic potential are increasingly pursued to replac...
GA-based implicit stochastic optimization and RNN-based simulation for deriving multi-objective reservoir hedging rules.
Environmental science and pollution research international
Jan 4, 2021
Management of reservoir systems is a complicated process involving many uncertainties regarding future events and the diversity of purposes these reservoirs serve; therefore, an effective management of these systems could help improve resource utiliz...
Extended Robust Exponential Stability of Fuzzy Switched Memristive Inertial Neural Networks With Time-Varying Delays on Mode-Dependent Destabilizing Impulsive Control Protocol.
IEEE transactions on neural networks and learning systems
Jan 4, 2021
This article investigates the problem of robust exponential stability of fuzzy switched memristive inertial neural networks (FSMINNs) with time-varying delays on mode-dependent destabilizing impulsive control protocol. The memristive model presented ...
Multistability of Fractional-Order Neural Networks With Unbounded Time-Varying Delays.
IEEE transactions on neural networks and learning systems
Jan 4, 2021
This article addresses the multistability and attraction of fractional-order neural networks (FONNs) with unbounded time-varying delays. Several sufficient conditions are given to ensure the coexistence of equilibrium points (EPs) of FONNs with conca...
Global Exponential Synchronization of Coupled Delayed Memristive Neural Networks With Reaction-Diffusion Terms via Distributed Pinning Controls.
IEEE transactions on neural networks and learning systems
Jan 4, 2021
This article presents new theoretical results on global exponential synchronization of nonlinear coupled delayed memristive neural networks with reaction-diffusion terms and Dirichlet boundary conditions. First, a state-dependent memristive neural ne...
A Neural Network-Based Joint Prognostic Model for Data Fusion and Remaining Useful Life Prediction.
IEEE transactions on neural networks and learning systems
Jan 4, 2021
With the rapid development of sensor and information technology, now multisensor data relating to the system degradation process are readily available for condition monitoring and remaining useful life (RUL) prediction. The traditional data fusion an...
A Two-Timescale Duplex Neurodynamic Approach to Mixed-Integer Optimization.
IEEE transactions on neural networks and learning systems
Jan 4, 2021
This article presents a two-timescale duplex neurodynamic approach to mixed-integer optimization, based on a biconvex optimization problem reformulation with additional bilinear equality or inequality constraints. The proposed approach employs two re...
How to teach neural networks to mesh: Application on 2-D simplicial contours.
Neural networks : the official journal of the International Neural Network Society
Jan 2, 2021
A machine learning meshing scheme for the generation of 2-D simplicial meshes is proposed based on the predictions of neural networks. The data extracted from meshed contours are utilized to train neural networks which are used to approximate the num...
Exploring the potential of transfer learning for metamodels of heterogeneous material deformation.
Journal of the mechanical behavior of biomedical materials
Dec 31, 2020
From the nano-scale to the macro-scale, biological tissue is spatially heterogeneous. Even when tissue behavior is well understood, the exact subject specific spatial distribution of material properties is often unknown. And, when developing computat...
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