A Bi‐level stacked LSTM‐DNN‐based decoder network for AGC dispatch under regulation market framework in presence of VPP and EV aggregators by Kingshuk Roy, Sanjoy Debbarma, Siddhartha Deb Roy, Liza Debbarma

“…In this context, a bi‐level AGC dispatch approach based on a stacked long short‐term memory (LSTM)‐deep neural network (DNN)‐based decoder framework is proposed for a power system comprising diverse CIGs forming a virtual power plant and electric vehicle aggregators. …”
Get full text

Deep Learning Approach for Automatic Classification of Ocular and Cardiac Artifacts in MEG Data by Ahmad Hasasneh, Nikolas Kampel, Praveen Sripad, N. Jon Shah, Jürgen Dammers

“…We propose an artifact classification scheme based on a combined deep and convolutional neural network (DCNN) model, to automatically identify cardiac and ocular artifacts from neuromagnetic data, without the need for additional electrocardiogram (ECG) and electrooculogram (EOG) recordings. …”
Get full text

MFEMDroid: A Novel Malware Detection Framework Using Combined Multitype Features and Ensemble Modeling by Wei Gu, Hongyan Xing, Tianhao Hou

“…Furthermore, we design an ensemble network based on SENet, ResNet, and the evolutionary convolutional neural network Squeeze Excitation Residual Network (SEResNet) to explore the hidden associations between different types of features from multiple perspectives. …”
Get full text

Long short‐term memory‐based forecasting of uncertain parameters in an islanded hybrid microgrid and its energy management using improved grey wolf optimization algorithm by Raji Krishna, Hemamalini S

“…The LSTM outperforms the artificial neural network (ANN) model in terms of mean square error (MSE) and prediction accuracy (R2) for both training and testing datasets. …”
Get full text

Optimization of an Intelligent Sorting and Recycling System for Solid Waste Based on Image Recognition Technology by Haitao Chen

“…The convolutional layer, pooling layer, and fully connected layer in a convolutional neural network are responsible for feature extraction, reducing the number of parameters, integrating features into high-level features, and finally classifying them by SoftMax classifier in turn. …”
Get full text

Adaptive Image Denoising Method Based on Diffusion Equation and Deep Learning by Shaobin Ma, Lan Li, Chengwen Zhang

“…Then, the threshold function is adaptively designed and improved so that it can automatically control the threshold of the function according to the maximum gray value of the image and the number of iterations, so as to further preserve the important details of the image such as edge and texture. A neural network is used to realize image denoising because of its good learning ability of image statistical characteristics, mainly by the diffusion equation and deep learning (CNN) algorithm as the foundation, focus on the effects of activation function of network optimization, using multiple feature extraction technology in-depth networks to study the characteristics of the input image richer, and how to better use the adaptive algorithm on the depth of diffusion equation and optimization backpropagation learning. …”
Get full text

Abnormal neural circuits and altered brain network topological properties in patients with persistent postural-perceptual dizziness by Kangzhi Li, Xia Ling, Jing Zhao, Zhiqun Wang, Xu Yang

“…Network-based statistic results reveal an abnormal neural network in PPPD patients with key nodes in the occipital visual cortex, precuneus, sensorimotor cortex, multisensory vestibular cortex and cerebellum. …”
Get full text

Research on the evaluation method of cooperative jamming effectiveness based on IPSO-ELM by A. Tianjian Yang, B. Xing Wang, C. Siyi Cheng, D. You Chen, E. Xi Zhang

“…On this basis, the cooperative jamming decision schemes and their corresponding cooperative jamming effectiveness values are solved at different locations in the target space, and the results are detected as outliers using box plots, thus constructing sample data for cooperative jamming effectiveness evaluation. Subsequently, a neural network based on the extreme learning machine methodology is developed, with its initial weights and biases fine-tuned through an improved particle swarm optimization, which is termed IPSO-ELM. …”
Get full text

Rapid Fluid Velocity Field Prediction in Microfluidic Mixers via Nine Grid Network Model by Qian Li, Yuwei Chen, Taotao Sun, Junchao Wang

“…Using this theory, we developed and trained an artificial neural network (ANN) to predict the fluid dynamics within microfluidic mixers. …”
Get full text

Multi-Objective Optimal Design of Dropping Shock of Series Cushioning Packaging System by Yang Xue, Wei-Sheng Song, Hong-Tao Miao, Jing Wang, Yu Li

“…This paper adopts a BP neural network to develop a more precise constitutive relationship. …”
Get full text

Deep Learning Automated System for Thermal Defectometry of Multilayer Materials by A. S. Momot, R. M. Galagan, V. Yu. Gluhovskii

“…The proposed system consists of a heating source, an infrared camera for recording sequences of thermograms and a digital information processing unit. Three neural network modules are used for automated data processing, each of which performs one of the tasks: defects detection and classification, determination of the defect depth and thickness. …”
Get full text

Systematic Research on the Application of Steel Slag Resources under the Background of Big Data by Le Kang, Hui Ling Du, Hao Zhang, Wan Li Ma

“…Secondly, the steel slag prediction model based on the convolution neural network (CNN) is established. The material data of steelmaking, the operation data of steelmaking process, and the data of steel slag composition are put into the model from the Hadoop platform, and the prediction of the slag composition is further realized. …”
Get full text

Robust adaptive optimization for sustainable water demand prediction in water distribution systems by Ke Wang, Jiayang Meng, Zhangquan Wang, Kehua Zhao, Banteng Liu

“…The predictive power of the proposed model is harnessed through the construction of deep neural networks that utilize the decomposed data to forecast minutely water demand. …”
Get full text

A Robust License Plate Detection and Character Recognition Algorithm Based on a Combined Feature Extraction Model and BPNN by Fei Xie, Ming Zhang, Jing Zhao, Jiquan Yang, Yijian Liu, Xinyue Yuan

“…This paper has proposed a novel license plate detection and character recognition algorithm based on a combined feature extraction model and BPNN (Backpropagation Neural Network) which is adaptable in weak illumination and complicated backgrounds. …”
Get full text

Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism by Dong Yun Lee, Jang Yeop Kim, Soo Young Cho

“…To address this challenge, this study proposes a convolutional neural network (CNN)-based super-resolution architecture, utilizing a melanoma dataset to enhance image resolution through deep learning techniques. …”
Get full text

Analysis and Dynamic Prediction of Bus Dwell Time Under Rainfall Conditions by Baoyun SUN, Yaping YANG, Lei DONG, Honglin LU, Zimin WANG

“…Support vector machine, k-nearest neighbour and backpropagation (BP) prediction models were established, and the BP neural network model, having the best prediction effect, was optimised using a genetic algorithm (GA). …”
Get full text

Dynamic Response of a Casting Crane Rigid-Flexible Coupling System to High Temperature by Yunsheng Xin, Qing Dong, Qisong Qi, Qinglu Shi

“…The constitutive equation for the elastic modulus of Q355 alloy steel at different temperatures was predicted using test data and a neural network algorithm. Based on crane structural characteristics and the principle of system dynamics, a coupling vibration model was established that included the crane flexible girder, cabin, trolley, crane, and temperature. …”
Get full text

Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning by Yi Chen, Jun Bin, Congming Zou, Mengjiao Ding

“…Therefore, an objective and reliable discriminant technique for tobacco leaf maturity level based on near-infrared (NIR) spectroscopy combined with a deep learning approach of convolutional neural networks (CNNs) is proposed in this study. To assess the performance of the proposed maturity discriminant model, four conventional multiclass classification approaches—K-nearest neighbor (KNN), backpropagation neural network (BPNN), support vector machine (SVM), and extreme learning machine (ELM)—were employed for a comparative analysis of three categories (upper, middle, and lower position) of tobacco leaves. …”
Get full text

Comparison of Different Machine Learning Methodologies for Predicting the Non‐Specific Treatment Response in Placebo Controlled Major Depressive Disorder Clinical Trials by Roberto Gomeni, Françoise Bressolle‐Gomeni

“…At this purpose, six machine learning methodologies (gradient boosting machine, lasso regression, logistic regression, support vector machines, k‐nearest neighbors, and random forests) were compared to the multilayer perceptrons artificial neural network (ANN) methodology for predicting the probability of individual non‐specific treatment response. …”
Get full text

Deep learning and explainable AI for classification of potato leaf diseases by Sarah M. Alhammad, Doaa Sami Khafaga, Walaa M. El-hady, Farid M. Samy, Khalid M. Hosny

“…Transfer learning enables the model to benefit from pre-trained neural network architectures and weights, enhancing its ability to learn meaningful representations from limited labeled data. …”
Get full text