Brain tumor segmentation by deep learning transfer methods using MRI images by E.Y. Shchetinin

“…In this paper, a deep neural network model based on U-Net neural network architecture is proposed for brain glioma segmentation. …”
Get full text

Multilevel Attention and Multiscale Feature Fusion Network for Author Classification of Chinese Ink-Wash Paintings by Wei Jiang, Xianglian Meng, Ji Xi

“…The model extracts three types of feature maps from the low-level, middle-level and high-level layers of the pretrained deep neural network firstly. Then, the low-level and middle-level feature maps are processed by the spatial attention module, nevertheless the high-level feature maps are processed by the scale invariance module to increase the scale-invariance properties. …”
Get full text

Recent advances in journal bearings: wear fault diagnostics, condition monitoring and fault diagnosis methodologies by Nazik Jebur, Wafa Soud

“…Key findings indicate that ensemble models, such as the CNN and deep neural network (CNNEPDNN) model, significantly improve convergence speed, test accuracy, and F-Score in bearing fault diagnosis by 15-20% compared to individual models. …”
Get full text

NuFold: end-to-end approach for RNA tertiary structure prediction with flexible nucleobase center representation by Yuki Kagaya, Zicong Zhang, Nabil Ibtehaz, Xiao Wang, Tsukasa Nakamura, Pranav Deep Punuru, Daisuke Kihara

“…To address this challenge, we developed NuFold, a novel computational approach that leverages state-of-the-art deep learning architecture to accurately predict RNA tertiary structures. NuFold is a deep neural network trained end-to-end for the output structure from the input sequence. …”
Get full text

An End-to-End Rumor Detection Model Based on Feature Aggregation by Aoshuang Ye, Lina Wang, Run Wang, Wenqi Wang, Jianpeng Ke, Danlei Wang

“…In this paper, a deep neural network- (DNN-) based feature aggregation modeling method is proposed, which makes full use of the knowledge of propagation pattern feature and text content feature of social network event without feature engineering and domain knowledge. …”
Get full text

An Integrated Bearing Fault Diagnosis Method Based on Multibranch SKNet and Enhanced Inception-ResNet-v2 by Baoquan Hu, Jun Liu, Yue Xu, Tianlong Huo

“…To solve the above problems, this paper proposes an integrated deep neural network that combines an improved selective kernel network (SKNet) with an enhanced Inception-ResNet-v2, named SIR-CNN. …”
Get full text

A New Approach to Estimate Concentration Levels with Filtered Neural Nets for Online Learning by Woodo Lee, Junhyoung Oh, Jaekwoun Shim

“…This research aims to estimate more subtle levels as specified states by using a minimum amount of body movement data. The deep neural network is used to continuously recognize the human concentration model, and the concentration levels can be predicted and estimated by the Kalman filter. …”
Get full text

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

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

Balanced coarse-to-fine federated learning for noisy heterogeneous clients by Longfei Han, Ying Zhai, Yanan Jia, Qiang Cai, Haisheng Li, Xiankai Huang

“…However, heterogeneous clients have different deep neural network structures, and these models have different sensitivity to various noise types, the fixed noise-detection based methods may not be effective for each client. …”
Get full text

A Target Domain-Specific Classifier Weight Partial Transfer Adversarial Network for Bearing Fault Diagnosis by Yin Bai, Xiangdong Hu, Kai Zheng, Yunnong Chen, Yi Tang

“…Finally, five mainstream deep neural network methods are taken for comparison using the data from Western Reserve University and the motor-magnetic brake test designed by us. …”
Get full text

Predicting the heat capacity of strontium-praseodymium oxysilicate SrPr4(SiO4)3O using machine learning, deep learning, and hybrid models by Amir Hossein Sheikhshoaei, Ali Khoshsima, Davood Zabihzadeh

“…In this study, the capability of five advanced machine learning models, including Random Forest (RF), Gradient Boosting (GBoost), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Decision Tree (DT) models, and three deep learning models, TabNet, Deep Belief Network (DBN), and Deep Neural Network (DNN) was investigated. Our analysis indicates that the Random Forest and Deep Belief Network models outperform all other competing models. …”
Get full text

Application of IoT-Based Drones in Precision Agriculture for Pest Control by Mohamad Reda. A. Refaai, Vinjamuri SNCH Dattu, N. Gireesh, Ekta Dixit, CH. Sandeep, David Christopher

“…These deep neural networks are adapted to the immediate situation using transfer learning and deep extraction of features approaches. …”
Get full text

Research on Mental Health Monitoring Scheme of Migrant Children Based on Convolutional Neural Network Based on Deep Learning by Guangyan Yang

“…This paper studies children’s smiling face recognition based on deep neural network. In order to obtain a better identification effect of mental health problems of children, this paper attempts to use multisource data, including consumption data, access control data, network logs, and grade data, and proposes a multisource data-based mental health problem identification algorithm. …”
Get full text

Raw Camera Data Object Detectors: An Optimisation for Automotive Video Processing and Transmission by Pak Hung Chan, Chuheng Wei, Anthony Huggett, Valentina Donzella

“…Whilst Deep Neural Networks (DNNs) have been developing swiftly, most of the research has been focused on videos based on RGB frames. …”
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

“…In this paper, R wave peak interval independent atrial fibrillation detection algorithm is proposed based on the analysis of the synchronization feature of the electrocardiogram signal by a deep neural network. Firstly, the synchronization feature of each heartbeat of the electrocardiogram signal is constructed by a Recurrence Complex Network. …”
Get full text

NeuralACT: Accounting Analytics Using Neural Network for Real-Time Decision Making From Big Data by Leonidas Theodorakopoulos, Alexandra Theodoropoulou, Georgios Kampiotis, Ioanna Kalliampakou

“…This paper presents a deep neural network (DNN)-based accounting analytics, NeuralACT, to support the decision-making process using real-time predictions made from big data. …”
Get full text

Speech Enhancement Using Joint DNN-NMF Model Learned with Multi-Objective Frequency Differential Spectrum Loss Function by Matin Pashaian, Sanaz Seyedin

“…We propose a multi-objective joint model of non-negative matrix factorization (NMF) and deep neural network (DNN) with a new loss function for speech enhancement. …”
Get full text

Instance-level semantic segmentation of nuclei based on multimodal structure encoding by Bo Guan, Guangdi Chu, Ziying Wang, Jianmin Li, Bo Yi

“…However, existing deep neural network-based methods often struggle to capture complex morphological features and global spatial distributions of cell nuclei due to their reliance on local receptive fields. …”
Get full text

Variational Methods in Optical Quantum Machine Learning by Marco Simonetti, Damiano Perri, Osvaldo Gervasi

“…The goal was to create a quantum deep neural network that could recognise and categorise points accurately with the fewest trainable parameters possible.…”
Get full text