Bidirectional recurrent neural network approach for predicting cervical cancer recurrence and survival by S. Geeitha, K. P. Rama Prabha, Jaehyuk Cho, Sathishkumar Veerappampalayam Easwaramoorthy

Subjects: Get full text

Global Exponential Stability of Discrete-Time Neural Networks with Time-Varying Delays by S. Udpin, P. Niamsup

“…This paper presents some global stability criteria of discrete-time neural networks with time-varying delays. Based on a discrete-type inequality, a new global stability condition for nonlinear difference equation is derived. …”
Get full text

BEARING REMAINING LIFE PREDICTION BASED ON DEEP SEPARABLE CONVOLUTIONAL NEURAL NETWORK by XU HaiMing, XIA QiaoYang, LI Yong, ZHANG LanZhu

Subjects: Get full text

Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network by Xiaoling Wei, Jimin Li, Chenghao Zhang, Ming Liu, Peng Xiong, Xin Yuan, Yifei Li, Feng Lin, Xiuling Liu

“…Then, a convolution neural network is used to detect atrial fibrillation by analyzing the eigenvalues of the Recurrence Complex Network. …”
Get full text

Emei Martial Arts Promotion Model and Properties Based on Neural Network Technology by Cheng Xing, N.E. Zainal Abidin, Yudong Tang

“…In order to enhance the effectiveness of the standard recommendation algorithm, a deep neural network-based recommendation algorithm is paired with a neural network-based recommendation algorithm that is proposed in this article. …”
Get full text

Combination of Artificial Neural Network and Particle Swarm Intelligence Algorithm for Diagnosing Diabetes by Cillian Thompson, Oscar Higgins

Subjects: Get full text

Compressive Strength Prediction of Stabilized Dredged Sediments Using Artificial Neural Network by Van Quan Tran

“…In this investigation, the artificial neural network (ANN) model is introduced to forecast the compressive strength. …”
Get full text

Convolutional neural network model over encrypted data based on functional encryption by Chen WANG, Jiarun LI, Jian XU

Subjects: “…convolutional neural network…”
Get full text

Type-K Exponential Ordering with Application to Delayed Hopfield-Type Neural Networks by Bin-Guo Wang

“…As an application, the model of delayed Hopfield-type neural networks with a type-K monotone interconnection matrix is considered, and the attractor result is obtained.…”
Get full text

Predicting Water Saturation in a Greek Oilfield with the Power of Artificial Neural Networks by Mohammed Gad, Ahmed Abdulhamid Mahmoud, George Panagopoulos, Paschalia Kiomourtzi, Panagiotis Kirmizakis, Salaheldin Elkatatny, Umair bin Waheed, Pantelis Soupios

Get full text

Security evaluation for parameters of SIMON-like cipher based on neural network distinguisher by Zezhou HOU, Jiongjiong REN, Shaozhen CHEN

Subjects: Get full text

Target Recognition Technology of Multimedia Platform Based on a Convolutional Neural Network by Jie Liu, Jiamin Zhang

“…Aiming at the above problems, we propose a multitarget retrieval method based on a convolutional neural network, which uses multitarget detection algorithm to locate multitarget regions and extract regional features and uses cosine distance as a similarity measure for multitarget recognition. …”
Get full text

A Differential Evolution-Oriented Pruning Neural Network Model for Bankruptcy Prediction by Yajiao Tang, Junkai Ji, Yulin Zhu, Shangce Gao, Zheng Tang, Yuki Todo

“…Among them, Artificial Neural Networks (ANNs) have been widely and effectively applied in bankruptcy prediction. …”
Get full text

Implementation of Genetic Algorithm Integrated with the Deep Neural Network for Estimating at Completion Simulation by Karrar Raoof Kareem Kamoona, Cenk Budayan

“…In this research, a relatively new intelligent model called deep neural network (DNN) is proposed to calculate the EAC. …”
Get full text

Prediction-Based Maintenance of Existing Bridges Using Neural Network and Sensitivity Analysis by Pengyong Miao

“…This study proposed a methodology to resolve these issues by integrating an artificial neural network (ANN) and sensitivity analysis method. …”
Get full text

A Bayesian Neural Network-Based Method to Calibrate Microscopic Traffic Simulators by Qinqin Chen, Anning Ni, Chunqin Zhang, Jinghui Wang, Guangnian Xiao, Cenxin Yu

“…The paper proposes a Bayesian neural network (BNN)-based method to calibrate parameters of microscopic traffic simulators, which reduces repeated running of simulations in the calibration and thus significantly improves the calibration efficiency. …”
Get full text

Multi-branch convolutional neural network with cross-attention mechanism for emotion recognition by Fei Yan, Zekai Guo, Abdullah M. Iliyasu, Kaoru Hirota

Subjects: Get full text

Personalized tourism recommendation model based on temporal multilayer sequential neural network by XueFei Xiao, ChunHua Li, XingJie Wang, AnPing Zeng

Subjects: Get full text

Reservoir Flood Forecasting Based on Long-Short-Term Memory Neural Network by LUO Zhaolin, ZHANG Bo, MENG Qingkui, CHEN Wufen

“…Accurate flood forecasting is one of the main means to well perform flood control and drainage,and the long-short-term memory neural network (LSTM) has a strong ability to fit time series relationships,which thus is very suitable for simulating and forecasting the complex time series process of basin runoff generation and confluence.To explore the applicability of LSTM in the field of reservoir flood forecasting,this paper established an LSTM model according to different forecast periods in the Baipenzhu Basin and compared it with Xinanjiang model.The LSTM model uses the rainfall and water level data in the basin as input and adopts the water levels of the reservoir at different forecast periods as output.The calibration period is five years,and the verification period is one year.The results show that LSTM has high forecast accuracy when the forecast period is 1~6 h,and the forecast accuracy is the highest when the forecast period is 1h,reaching 0.991.As the forecast period increases,the accuracy of the LSTM model gradually decreases,but its forecast accuracy is higher than that of Xinanjiang model.In addition,reflecting the complexity of the neural network,the prediction period and the number of neurons in the hidden layer will affect not only the forecast accuracy but also the training speed of the model.It is proven that the LSTM model has high forecast accuracy and is of guiding significance to reservoir flood forecasting.…”
Get full text

Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction Motors by David Camarena-Martinez, Martin Valtierra-Rodriguez, Arturo Garcia-Perez, Roque Alfredo Osornio-Rios, Rene de Jesus Romero-Troncoso

“…In this work, a novel digital structure to implement the empirical mode decomposition (EMD) for processing nonstationary and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE)-based frequency estimator and a feed forward neural network (FFNN)-based classifier to provide an intelligent methodology for the automatic diagnosis during the startup transient of motor faults such as: one and two broken rotor bars, bearing defects, and unbalance. …”
Get full text