Towards rigorous dataset quality standards for deep learning tasks in precision agriculture: A case study exploration by A. Carraro, G. Saurio, F. Marinello

“…Deep Learning (DL) through Convolutional Neural Networks (CNNs) has emerged as a critical player in classifying plant diseases from images. …”
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Recognizing Special Art Pieces Through EEG: A Journey in Neuroaesthetics Classification by Maurizio Palmieri, Marco Avvenuti, Francesco Marcelloni, Alessio Vecchio

“…The first two methods exploit classical machine learning approaches based on various sets of features extracted using different techniques for EEG analysis. In particular, the first method analyzes features extracted from time and frequency domains using an ensemble classifier, while the second one analyzes the Phase Locking Values of different channels using a classifier based on K-Nearest Neighbors. …”
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Deep Learning-Based Carotid Plaque Ultrasound Image Detection and Classification Study by Hongzhen Zhang, Feng Zhao

“…Background: This study aimed to develop and evaluate the detection and classification performance of different deep learning models on carotid plaque ultrasound images to achieve efficient and precise ultrasound screening for carotid atherosclerotic plaques. …”
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OriLoc: Unlimited-FoV and Orientation-Free Cross-View Geolocalization by Boni Hu, Haowei Li, Shuhui Bu, Lin Chen, Pengcheng Han

“…</label> <caption><p>Cross-view geolocalization performance comparison in different FoV and orientation-free scenarios. (a) Recall@1, (b) Recall@5, (c) Recall@10, and (d) Recall@1&#x0025; accuracy on the CVUSA benchmark. …”
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Robust low frequency seismic bandwidth extension with a U-net and synthetic training data by P. Zwartjes, J. Yoo

“…This work focuses on enhancing low frequency seismic data using a convolutional neural network trained on synthetic data. …”
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STDNet: Improved lip reading via short-term temporal dependency modeling by Xiaoer Wu, Zhenhua Tan, Ziwei Cheng, Yuran Ru

“…In particular, we designed a local–temporal block, which aggregates interframe differences, strengthening the relationship between various local lip regions through multiscale convolution. …”
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Machine Learning Monitoring Model for Fertilization and Irrigation to Support Sustainable Cassava Production: Systematic Literature Review by Ahmad Chusyairi, Yeni Herdiyeni, Heru Sukoco, Edi Santosa

“…Important new information on the application of UAV technology, multispectral imaging, thermal imaging, among the vegetation indices are the Soil-Adjusted Vegetation Index (SAVI), Leaf Color Index (LCI), Leaf Area Index (LAI), Normalized Difference Water Index (NDWI), Normalized Difference Red Edge Index (NDRE), and Green Normalized Difference Vegetation Index (GNDVI).…”
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Electrical Impedance Tomography for Hip Stem Implant Monitoring by Krukewitt Lisa, Spors Sascha

“…As a first step towards a new method for noninvasive hip-stem implant monitoring, this work aims to detect bone defects from time- and frequency-difference electrical impedance tomography. As a proof-of concept, an in-silico model of a thigh with bone and implant is used, and convolutional neural networks are applied to predict size and position of areas where conductivity of the bone is increased. …”
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DAMI-YOLOv8l: A multi-scale detection framework for light-trapping insect pest monitoring by Xiao Chen, Xinting Yang, Huan Hu, Tianjun Li, Zijie Zhou, Wenyong Li

“…The DMC module improves multi-scale feature extraction to enable the effective capture and merging of features across different detection scales while reducing network parameters. …”
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Predicting Dysglycemia in Patients with Diabetes Using Electrocardiogram by Ho-Jung Song, Ju-Hyuck Han, Sung-Pil Cho, Sung-Il Im, Yong-Suk Kim, Jong-Uk Park

“…A residual block-based one-dimensional convolution neural network model was used to predict dysglycemia. …”
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Efficient one-stage detection of shrimp larvae in complex aquaculture scenarios by Guoxu Zhang, Tianyi Liao, Yingyi Chen, Ping Zhong, Zhencai Shen, Daoliang Li

“…This paper proposes an efficient one-stage shrimp larvae detection method, FAMDet, specifically designed for complex scenarios in intensive aquaculture. Firstly, different from the ordinary detection methods, it exploits an efficient FasterNet backbone, constructed with partial convolution, to extract effective multi-scale shrimp larvae features. …”
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Facial emotion based smartphone addiction detection and prevention using deep learning and video based learning by C. Joseph, P. Uma Maheswari

“…Additionally, face emotion detection algorithms tuned with MnasNet-Teaching Learning Based Optimization (TLBO) and Convolution Neural Networks (CNN)-Cuckoo Search Optimization (CSO) are employed for accurate emotion recognition. …”
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DFPF-Net: Dynamically Focused Progressive Fusion Network for Remote Sensing Change Detection by Chengming Wang, Peng Duan, Jinjiang Li

“…In recent years, various CD methods represented by convolutional neural network (CNN) and transformer have achieved significant success in effectively detecting difference areas in bitemporal remote sensing images. …”
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Surface Defect Detection Algorithm for Wind Turbine Blades Based on HSCA-YOLOv7 by Bing LI, Yunshan BAI, Kuan ZHAO, Congbin GUO, Yongjie ZHAI

“…Then, deep separable convolutions with different expansion rates are introduced into the improved spatial pyramid pooling (ISPP) module to reduce the loss of details caused by pooling operations. …”
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Implementation of MF block in CNN for advanced REB fault diagnosis by M. Pandiyan, Narendiranath Babu T.

“…Using the Balance Cross-Entropy (BCE) loss function for training has helped the model optimize prediction accuracy by minimizing the difference between the actual class and the predicted class probabilities. …”
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The Essential Role of Monte Carlo Simulations for Lung Dosimetry in Liver Radioembolization—Part B: ¹⁶⁶Ho Microspheres by Edoardo d’Andrea, Andrea Politano, Bartolomeo Cassano, Nico Lanconelli, Marta Cremonesi, Vincenzo Patera, Massimiliano Pacilio

“…Significant underestimations in the mean absorbed dose (AD) were observed, with relative differences with respect to the reference (MC) of −64% for MIRD, −93% for kST, −56% for kST<i>_L</i>, −76% for kLT, −68% for kLT₂₂₁, and −60% for kLT<i>_L</i>. …”
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A Spatial–Frequency Combined Transformer for Cloud Removal of Optical Remote Sensing Images by Fulian Zhao, Chenlong Ding, Xin Li, Runliang Xia, Caifeng Wu, Xin Lyu

“…FreSA leverages fast Fourier transform to convert features into the frequency domain, using frequency differences between object and cloud regions to achieve precise cloud detection and fine-grained removal. …”
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A Novel Dual-Branch Global and Local Feature Extraction Network for SAR and Optical Image Registration by Xuanran Zhao, Yan Wu, Xin Hu, Zhikang Li, Ming Li

“…However, the inherent differences between the two modalities pose a challenge to the existing deep-learning algorithms that only depend on local features. …”
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A Deep Learning-Based Echo Extrapolation Method by Fusing Radar Mosaic and RMAPS-NOW Data by Shanhao Wang, Zhiqun Hu, Fuzeng Wang, Ruiting Liu, Lirong Wang, Jiexin Chen

“…However, most of these extrapolation network architectures are built upon convolutional neural networks, using radar echo images as input. …”
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Multi-Modal Deep Embedded Clustering (MM-DEC): A Novel Framework for Mineral Detection Using Hyperspectral Imagery by Priyanka Nair, Devesh Kumar Srivastava, Roheet Bhatnagar

“…Preprocessing pipeline includes denoising using Machine Learning(ML) and statistical techniques, followed by major land cover classification based on spectral indices including Normalized Difference Vegetation Index (NDVI); Normalized Difference Water Index (NDWI) and Normalized Difference Soil Index (NDSI). …”
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