Segmented Curve-Fitting Method for Continuum Removal in CRISM MTRDR data by P. Kumari, S. Soor, A. Shetty, S. G. Koolagudi

“…The identification score is improved by around 8% for the similarity matching method Weighted Sum of Spectrum Correlation and by around 1.5% for a Convolutional Neural Network. Furthermore, an SCF-based mineral identification framework demonstrates its effectiveness in identifying the dominant minerals on CRISM MTRDR hyperspectral data collected from different locations on the Martian surface.…”
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Employing sentinel-2 time-series and noisy data quality control enhance crop classification in arid environments: A comparison of machine learning and deep learning methods by Zahra Mohammadi Mobarakeh, Saeid Pourmanafi, Mohsen Ahmadi

“…In this study, we employed a novel hybrid approach, integrating time-series analysis, noisy data quality control, and different machine learning and deep learning models to classify croplands of complex multi-crop systems in central Iran. …”
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Predictive performance and uncertainty analysis of ensemble models in gully erosion susceptibility assessment by Congtan Liu, Haoming Fan, Yixuan Wang

“…The optimal feature datasets S7 included factors such as the Convergence Index (CI), Topographic Wetness Index (TWI), Terrain Ruggedness Index (TRI), distance from river, annual rainfall, distance from road, drainage density, elevation, Normalized Difference Vegetation Index NDVI, slope, and Slope Length (LS). …”
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Quantification of CO₂ hotspot emissions from OCO-3 SAM CO₂ satellite images using deep learning methods by J. Dumont Le Brazidec, J. Dumont Le Brazidec, P. Vanderbecken, A. Farchi, G. Broquet, G. Kuhlmann, M. Bocquet

“…The results are very promising, showing a relative difference in the predictions to reported emissions only slightly higher than the relative error diagnosed from the experiments with synthetic images. …”
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A modified deep neural network enables identification of foliage under complex background by Xiaolong Zhu, Junhao Zuo, Honge Ren

“…Experimental results show that the modified approach can identify out different leaves with similar characteristics in one scene, and demonstrate the superiority of our proposed approach over some state-of-the-art deep neural networks, when it comes to recognise foliage in complicated environments.…”
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Automated detection of submarine pipelines in the Yellow River Estuary: a deep learning approach for side-scan sonar data in dynamic deltaic systems by Min Wei, Min Wei, Yongqing Yu, Xing Du, Yupeng Song, Lifeng Dong, Qikun Zhou, Linfeng Wang, Longying Zhang, Yamei Wang

“…However, there is a notable gap in research regarding the comparative performance of different models and the impact of data expansion. …”
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Image data-driven intelligent recognition of permafrost strength and feature visualization based analysis by Zhaoming YAO, Xun WANG, Hang WEI, Xiaolong WANG

“…By comparing the training processes and test results of different models, it was found that the ResNet-34 model performed the best, achieving an accuracy of 92.8% with no signs of overfitting. …”
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Inferring Mechanical Properties of Wire Rods via Transfer Learning Using Pre-Trained Neural Networks by Adriany A. F. Eduardo, Gustavo A. S. Martinez, Ted W. Grant, Lucas B. S. Da Silva, Wei-Liang Qian

“…Firstly, different possible architectures are compared, particularly between multi-output and multi-label convolutional neural networks (CNNs). …”
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Daily soil temperature prediction using hybrid deep learning and SHAP for sustainable soil management by Meysam Alizamir, Kaywan Othman Ahmed, Salim Heddam, Sungwon Kim, Jeong Eun Lee

“…Based on the results of this study, various deep learning models demonstrated optimal performance for soil temperature prediction at different depths across the two stations. At Chamchamal station, the hybrid deep learning model that combines bidirectional gated recurrent unit (BiGRU) and convolutional neural network (CNN), denoted as BiGRU-CNN achieved the best result for the 05 cm depth (RMSE = 1.298°C), while the hybrid model based on gated recurrent unit (GRU) and convolutional neural network (CNN), referred to as GRU–CNN yielded the best performance at 10 cm (RMSE = 1.333°C). …”
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Detection of <i>Aspergillus flavus</i> in Figs by Means of Hyperspectral Images and Deep Learning Algorithms by Cristian Cruz-Carrasco, Josefa Díaz-Álvarez, Francisco Chávez de la O, Abel Sánchez-Venegas, Juan Villegas Cortez

“…The images were taken after inoculation of the microtoxin using 3 different concentrations, related to three different classes and healthy figs (healthy controls). …”
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An Integrated Lightweight Neural Network Design and FPGA-Accelerated Edge Computing for Chili Pepper Variety and Origin Identification via an E-Nose by Ziyu Guo, Yong Yin, Haolin Gu, Guihua Peng, Xueya Wang, Ju Chen, Jia Yan

“…The system uses the AIRSENSE PEN3 e-nose from Germany to collect gas data from thirteen different varieties of chili peppers and two specific varieties of chili peppers originating from seven different regions. …”
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Linear-nonlinear cascades capture synaptic dynamics. by Julian Rossbroich, Daniel Trotter, John Beninger, Katalin Tóth, Richard Naud

“…Short-term synaptic dynamics differ markedly across connections and strongly regulate how action potentials communicate information. …”
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MOD3NN: A Framework for Automatic Signal Modulation Detection Using 3D CNN by Vishal Perekadan, Chaity Banerjee, Tathagata Mukherjee, Eduardo Pasiliao, Hovannes Kulhandjian, Michel Kulhandjian

“…Raw I/Q signal data exhibits a special “helical” structure that can be exploited with three-dimensional convolutions (3D convolutions) to learn spatio-temporal features from the signal for the problem of modulation recognition. …”
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Selective Feature Sets Based Fake News Detection for COVID-19 to Manage Infodemic by Manideep Narra, Muhammad Umer, Saima Sadiq, Ala' Abdulmajid Eshmawi, Hanen Karamti, Abdullah Mohamed, Imran Ashraf

“…Additionally, the influence of different preprocessing steps is also analyzed regarding fake news detection. …”
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Early Detection of Inter-Turn Short Circuits in Induction Motors Using the Derivative of Stator Current and a Lightweight 1D-ResNet by Carlos Javier Morales-Perez, David Camarena-Martinez, Juan Pablo Amezquita-Sanchez, Jose de Jesus Rangel-Magdaleno, Edwards Ernesto Sánchez Ramírez, Martin Valtierra-Rodriguez

“…This work presents a lightweight and practical methodology for detecting inter-turn short-circuit faults in squirrel-cage induction motors under different mechanical load conditions. The proposed approach utilizes a one-dimensional convolutional neural network (1D-CNN) enhanced with residual blocks and trained on differentiated stator current signals obtained under different load mechanical conditions. …”
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Complex-Valued CNN Nonlinear Equalization Enabled 36-Tbit&#x002F;s (45&#x00D7;800-Gbit&#x002F;s) WDM Transmission Over 3150 Km Using Silicon-Based IC-TROSA by Yuhan Gong, Xiaoshuo Jia, Ying Zhu, Kailai Liu, Ming Luo, Jin Tao, Zhixue He, Chao Li, Zichen Liu, Yan Li, Jian Wu, Chao Yang

“…The paper also demonstrates the application of CVCNN in WDM systems, enhancing system performance across different WDM encoding schemes. Finally, the experiment verified that CVCNN requires fewer computational resources than real-valued convolutional neural networks (RVCNN).…”
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Quality assurance of hyperspectral imaging systems for neural network supported plant phenotyping by Justus Detring, Abel Barreto, Anne-Katrin Mahlein, Stefan Paulus

“…To test the spatial accuracy at different working distances, the sine-wave-based spatial frequency response (s-SFR) was analysed. …”
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A Hybrid AI Approach for Fault Detection in Induction Motors Under Dynamic Speed and Load Operations by Muhammad Irfan Ishaq, Muhammad Adnan, Muhammad Ali Akbar, Amine Bermak, Nimra Saeed, Maaz Ansar

“…From existing literature, conventional fault diagnosis approaches in an IM struggle to reliably identify fault patterns at different speeds, particularly under variable speed and changing load conditions. …”
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Multi-modal denoised data-driven milling chatter detection using an optimized hybrid neural network architecture by Haining Gao, Haoyu Wang, Hongdan Shen, Shule Xing, Yong Yang, Yinlin Wang, Wenfu Liu, Lei Yu, Mazhar Ali, Imran Ali Khan

“…Multi-modal data features of different machining states are then obtained using time–frequency domain methods and Markov transition field methods. …”
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GearFaultNet: Novel Network for Automatic and Early Detection of Gearbox Faults by Proma Dutta, Kanchon Kanti Podder, Md. Shaheenur Islam Sumon, Muhammad E. H. Chowdhury, Amith Khandakar, Nasser Al-Emadi, Moajjem Hossain Chowdhury, M. Murugappan, Mohamed Arselene Ayari, Sakib Mahmud, S. M. Muyeen

“…This shallow network can also be applied to estimate other mechanical faults in different machinery.…”
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