Monitoring the Maize Canopy Chlorophyll Content Using Discrete Wavelet Transform Combined with RGB Feature Fusion by Wenfeng Li, Kun Pan, Yue Huang, Guodong Fu, Wenrong Liu, Jizhong He, Weihua Xiao, Yi Fu, Jin Guo

“…Images of maize canopies during the jointing, tasseling, and grouting stages were captured using unmanned aerial vehicle (UAV) remote sensing to extract color, texture, and wavelet features and to construct a color and texture feature dataset and a fusion of wavelet, color, and texture feature datasets. Backpropagation neural network (BP), Stacked Ensemble Learning (SEL), and Gradient Boosting Decision Tree (GBDT) models were employed to develop CHL monitoring models for the maize canopy. …”
Get full text

A multi-task model for failure identification and GPS assessment in metro trains by Pratik Vinayak Jadhav, Sairam V. A, Siddharth Sonkavade, Shivali Amit Wagle, Preksha Pareek, Ketan Kotecha, Tanupriya Choudhury

“…A multi-task artificial neural network was developed for the simultaneous identification of failures and GPS quality assessment. …”
Get full text

End-to-end neural automatic speech recognition system for low resource languages by Sami Dhahbi, Nasir Saleem, Sami Bourouis, Mouhebeddine Berrima, Elena Verdú

“…An on-the-fly data augmentation method is applied to these mel-spectrograms, treating them as images from which features are extracted to train a convolutional neural network (CNN) and a bidirectional long short-term memory (BLSTM)-based ASR. …”
Get full text

Bringing Intelligence to the Edge for Structural Health Monitoring: The Case Study of the Z24 Bridge by Ali Dabbous, Riccardo Berta, Matteo Fresta, Hadi Ballout, Luca Lazzaroni, Francesco Bellotti

“…To this end, we study the application of two convolutional neural network architectures that have emerged in the literature for efficient feature extraction from time series, namely WaveNet and MINImally RandOm Convolutional KErnel Transform (MiniRocket). …”
Get full text

Recent Developments in Heavy Metals Detection: Modified Electrodes, Pretreatment Methods, Prediction Models and Algorithms by Yujie Shi, Shijie Zhang, Hang Zhou, Yue Dong, Gang Liu, Wenshuai Ye, Renjie He, Guo Zhao

“…To address these issues, two potential solutions have been proposed: the development of advanced algorithms (such as machine learning (ML), back-propagation neural network (BPNN), support vector machines (SVM), random forests (RF), etc.) for signal processing and the use of pretreatment methods (such as Fenton oxidation (FO), ozone oxidation, and photochemical oxidation) to suppress such interferences. …”
Get full text

G-UNETR++: A Gradient-Enhanced Network for Accurate and Robust Liver Segmentation from Computed Tomography Images by Seungyoo Lee, Kyujin Han, Hangyeul Shin, Harin Park, Seunghyon Kim, Jeonghun Kim, Xiaopeng Yang, Jae Do Yang, Hee Chul Yu, Heecheon You

“…Accurate liver segmentation from computed tomography (CT) scans is essential for liver cancer diagnosis and liver surgery planning. Convolutional neural network (CNN)-based models have limited segmentation performance due to their localized receptive fields. …”
Get full text

Diagnosis of COVID-19 Using a Deep Learning Model in Various Radiology Domains by Yousef Alhwaiti, Muhammad Hameed Siddiqi, Madallah Alruwaili, Ibrahim Alrashdi, Saad Alanazi, Muhammad Hasan Jamal

“…In this work, we designed a model using convolutional neural network in order to detect COVID-19 from X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI) images. …”
Get full text

Deep learning empowered sensor fusion boosts infant movement classification by Tomas Kulvicius, Dajie Zhang, Luise Poustka, Sven Bölte, Lennart Jahn, Sarah Flügge, Marc Kraft, Markus Zweckstetter, Karin Nielsen-Saines, Florentin Wörgötter, Peter B. Marschik

“…Various combinations and two sensor fusion approaches (late and early fusion) for infant movement classification were tested to evaluate whether a multi-sensor system outperforms single modality assessments. Convolutional neural network (CNN) architectures were used to classify movement patterns. …”
Get full text

Empathetic Deep Learning: Transferring Adult Speech Emotion Models to Children With Gender-Specific Adaptations Using Neural Embeddings by Elina Lesyk, Tomás Arias-Vergara, Elmar Nöth, Andreas Maier, Juan Rafael Orozco-Arroyave, Paula Andrea Perez-Toro

“…To address the dataset limitations, we employ transfer learning by training a neural network to classify adult emotional speech using a Wav2Vec model for feature extraction, followed by a classification head for the downstream task. …”
Get full text

Study on the Forecasting of Internal Solitary Wave Propagation in the Andaman Sea Using Joint Ascending-Descending Orbit Sentinel-1A Data and Machine Learning by Zexiang Cao, Junmin Meng, Jing Wang, Lina Sun, Hao Zhang

“…A CNN-LSTM hybrid neural network model, based on multiple features and the self-attention mechanism, has been constructed by incorporating the characteristics of ISWs (ISW-Attention-CNN-LSTM Net, IACL Net), aiming to predict the propagation of ISWs in the Andaman Sea. …”
Get full text

Prior Knowledge-Based Two-Layer Energy Management Strategy for Fuel Cell Ship Hybrid Power System by Lin Liu, Xiangguo Yang, Xin Li, Xingwei Zhou, Yufan Wang, Telu Tang, Qijia Song, Yifan Liu

“…Distribution results are then used to train an SSA-BP neural network, creating an offline strategy library. …”
Get full text

Attention-Based CNN Fusion Model for Emotion Recognition During Walking Using Discrete Wavelet Transform on EEG and Inertial Signals by Yan Zhao, Ming Guo, Xiangyong Chen, Jianqiang Sun, Jianlong Qiu

“…These serve as input to the attention-based convolutional neural network (CNN) fusion model. The designed network structure is simple and lightweight while integrating the channel attention mechanism to extract and enhance features. …”
Get full text

DITC control strategy for semi-direct drive system with switched reluctance motor in coal mine belt conveyor by LIU Peng, ZHANG Lei, BAO Jiusheng, CHEN Huaxin, WEI Chunji, MA Chuanming, WANG Lei, WANG Xiaolong

“…To address these limitations, the drive system was retrofitted with a 2×400 kW switched reluctance motor semi-direct drive(SRSD) system utilizing a switched reluctance motor(SRM) for the belt conveyor. A BP neural network was used to predict the flux linkage and torque of the SRM, and a highly accurate SRM nonlinear model was developed based on the predictions. …”
Get full text

Safety assessment of rosuvastatin-fenofibrate combination in the treatment of hyperlipidemia based on FDA’s adverse event reporting system database by Qun Li, Wenya Shan, Saiwei Wu

“…The proportional report ratio (PRR), reporting odds ratio (ROR), and Bayesian Confidence Propagation Neural Network (BCPNN) analysis were used to extract data from FAERS for suspected signals referring to the combination of rosuvastatin and fenofibrate.ResultsA total of 68 safety signals were detected from the top 250 AEs in 3,587 reports, of which 28 signals were not included in the drug labels. …”
Get full text

Machine learning-based modelling and analysis of carbonation depth of recycled aggregate concrete by Xuyong Chen, Xuan Liu, Shukai Cheng, Xiaoya Bian, Xixuan Bai, Xin Zheng, Xiong Xu, Zhifeng Xu

“…On this basis, six machine learning models were employed to predict RAC carbonation depth: Artificial Neural Network, Decision Tree, Support Vector Regression, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting. …”
Get full text

XCF-LSTMSATNet: A Classification Approach for EEG Signals Evoked by Dynamic Random Dot Stereograms by Tingting Zhang, Xu Yan, Xin Chen, Yi Mao

“…To effectively classify the EEG signals induced by DRDS, we introduced a novel hybrid neural network model, XCF-LSTMSATNet, which integrates an XGBoost Channel Feature Optimization Module with the EEGNet and an LSTM Self-Attention Modules. …”
Get full text

Classification of tomato leaf disease using Transductive Long Short-Term Memory with an attention mechanism by Aarthi Chelladurai, D.P. Manoj Kumar, S. S. Askar, Mohamed Abouhawwash, Mohamed Abouhawwash

“…The experimental outcome shows that the proposed classifier achieved an accuracy of 99.98% which is comparably better than existing convolutional neural network models with transfer learning and IBSA-NET.…”
Get full text

Influences of Solar Wind Parameters on Energetic Electron Fluxes at Geosynchronous Orbit Revealed by the Deep SHAP Method by Jianhang Wang, Zheng Xiang, Binbin Ni, Deyu Guo, Yangxizi Liu, Junhu Dong, Jingle Hu, Haozhi Guo

“…In this study, we use the Deep SHAP method to quantify contributions of different solar wind parameters with an artificial neural network (ANN) model. Backpropagating the prediction results of this ANN model from 2011 to 2020, SHAP values for four solar wind parameters (interplanetary magnetic field (IMF) BZ, solar wind speed, solar wind dynamic pressure, and proton density) are calculated and comprehensively analyzed. …”
Get full text

ADMNet: adaptive deformable convolution large model combining multi-level progressive fusion for Building Change Detection by Liye Mei, Haonan Yu, Zhaoyi Ye, Chuan Xu, Cheng Lei, Wei Yang

“…First, we propose a Siamese neural network based on adaptive deformable convolution (ADC) modules. …”
Get full text

Unveiling shadows: A data-driven insight on depression among Bangladeshi university students by Sanjib Kumar Sen, Md. Shifatul Ahsan Apurba, Anika Priodorshinee Mrittika, Md. Tawhid Anwar, A.B.M. Alim Al Islam, Jannatun Noor

“…Seven machine learning models, including Support Virtual Machine (SVM), K-Nearest Neighbor (K-NN), Gaussian Naive Bayes (GNB), Decision Tree (DT), Random Forest Classifier (RFC), Artificial Neural Network (ANN), and Gradient Boosting (GB), were trained and tested using the collected data (n = 750) to identify the most effective method for predicting depression. …”
Get full text