Machine learning-based estimation of crude oil-nitrogen interfacial tension by Safia Obaidur Rab, Subhash Chandra, Abhinav Kumar, Pinank Patel, Mohammed Al-Farouni, Soumya V. Menon, Bandar R. Alsehli, Mamata Chahar, Manmeet Singh, Mahmood Kiani

“…In this work, we aim to utilize eight machine learning methods of Decision Tree (DT), AdaBoost (AB), Random Forest (RF), K-nearest Neighbors (KNN), Ensemble Learning (EL), Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) to construct data-driven intelligent models to predict crude oil – nitrogen IFT based upon experimental data of real crude oils samples encountered in underground oil reservoirs. …”
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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

“…Convolutional neural network (CNN)-based models have limited segmentation performance due to their localized receptive fields. …”
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Satellite-Based Forest Stand Detection Using Artificial Intelligence by Patrik Kovacovic, Rastislav Pirnik, Julia Kafkova, Mario Michalik, Alzbeta Kanalikova, Pavol Kuchar

“…Several models, including YOLOv8, YOLOv5 and Mask R-CNN, were tested and compared. An optimal model was selected based on parameters such as detection accuracy, total training time, and the precision of labeling detected image elements. …”
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An Empirical Analysis of Transformer-Based and Convolutional Neural Network Approaches for Early Detection and Diagnosis of Cancer Using Multimodal Imaging and Genomic Data by S. K. B. Sangeetha, Sandeep Kumar Mathivanan, V. Muthukumaran, Jaehyuk Cho, and Sathishkumar Veerappampalayam Easwaramoorthy

“…The proposed study assesses the effectiveness of Transformer-based models and Convolutional Neural Networks (CNN) in cancer diagnosis with respect to multimodal imaging and genomic data. …”
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QoE-Driven Big Data Management in Pervasive Edge Computing Environment by Qianyu Meng, Kun Wang, Xiaoming He, Minyi Guo

“…Then, with respect to accuracy, we propose a Tensor-Fast Convolutional Neural Network (TF-CNN) algorithm based on deep learning, which is suitable for high-dimensional big data analysis in the pervasive edge computing environment. …”
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Advancing breast cancer diagnosis: token vision transformers for faster and accurate classification of histopathology images by Mouhamed Laid Abimouloud, Khaled Bensid, Mohamed Elleuch, Mohamed Ben Ammar, Monji Kherallah

“…These results highlight the potential of our hybrid deep ViT-CNN architecture for advancing tumor classification in histopathological images. …”
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Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms by D. Ciani, C. Fanelli, B. Buongiorno Nardelli

“…To address these issues, we developed and tested different deep learning methodologies, specifically convolutional neural network (CNN) models that were originally proposed for single-image super resolution. …”
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Introduction to deep learning methods for multi‐species predictions by Yuqing Hu, Sara Si‐Moussi, Wilfried Thuiller

“…Specifically, we introduced four distinct deep learning models that use site × species community data but differ in their internal structure or on the input environmental data structure: (1) a multi‐layer perceptron (MLP) model for tabular data (e.g. in‐situ/raster climate or soil data), (2) a convolutional neural network (CNN) and (3) a vision transformer (ViT) models tailored for image data (e.g. aerial ortho‐photographs, satellite imagery), and a multimodal model that integrates both tabular and image data. …”
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Application of Image Denoising Method Based on Two-Way Coupling Diffusion Equation in Public Security Forensics by Yiqun Wang, Changpeng He, Zhenjiang Li

“…When the noise intensity increases, visually, it can be clearly seen that the two-way coupled diffusion equation and DnCNN have better denoising effects. When the noise level is high, the two-way coupled diffusion equation network is used to use the clear image and the denoised image for indistinguishable calculation. …”
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Deep-Learning-Driven Insights into Nitrogen Leaching for Sustainable Land Use and Agricultural Practices by Caixia Hu, Jie Li, Yaxu Pang, Lan Luo, Fang Liu, Wenhao Wu, Yan Xu, Houyu Li, Bingcang Tan, Guilong Zhang

“…A machine learning (ML) model for predicting nitrate leaching was then developed, with the random forest (RF) model outperforming the support vector machine (SVM), extreme gradient boosting (XGBoost), and convolutional neural network (CNN) models, achieving an R² of 0.75. …”
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Tomato Stem and Leaf Segmentation and Phenotype Parameter Extraction Based on Improved Red Billed Blue Magpie Optimization Algorithm by Lina Zhang, Ziyi Huang, Zhiyin Yang, Bo Yang, Shengpeng Yu, Shuai Zhao, Xingrui Zhang, Xinying Li, Han Yang, Yixing Lin, Helong Yu

“…The framework uses a four-layer Convolutional Neural Network (CNN) for stem and leaf segmentation by incorporating an improved swarm intelligence algorithm with an accuracy of 0.965. …”
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Validation of deep-learning accelerated quantitative susceptibility mapping for deep brain nuclei by Ying Zhou, Ying Zhou, Lingyun Liu, Shan Xu, Yongquan Ye, Ruiting Zhang, Minming Zhang, Jianzhong Sun, Peiyu Huang

“…The DL-QSM employed Poisson disk style under-sampling scheme and a previously developed cascaded CNN based reconstruction model, with acquisition time of 4:35, 3:15, and 2:11 for AF of 3, 4, and 5, respectively. …”
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SiC MOSFET with Integrated SBD Device Performance Prediction Method Based on Neural Network by Xiping Niu, Ling Sang, Xiaoling Duan, Shijie Gu, Peng Zhao, Tao Zhu, Kaixuan Xu, Yawei He, Zheyang Li, Jincheng Zhang, Rui Jin

“…Meanwhile, in the comparison of convolutional neural networks and machine learning, the CNN accuracy is much higher than the machine learning methods. …”
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A Study on Multi-Scale Behavior Recognition of Dairy Cows in Complex Background Based on Improved YOLOv5 by Zheying Zong, Zeyu Ban, Chunguang Wang, Shuai Wang, Wenbo Yuan, Chunhui Zhang, Lide Su, Ze Yuan

“…Moreover, it outperformed comparison models, including YOLOv4, YOLOv3, and Faster R-CNN, in complex background scenarios, multi-scale behavior detection, and behavior type discrimination. …”
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Enhanced automated text categorization via Aquila optimizer with deep learning for Arabic news articles by Muhammad Swaileh A. Alzaidi, Alya Alshammari, Abdulkhaleq QA Hassan, Shouki A. Ebad, Hanan Al Sultan, Mohammed A. Alliheedi, Ali Abdulaziz Aljubailan, Khadija Abdullah Alzahrani

“…In the area of text classification for Arabic news articles, deep learning (DL) methods, namely recurrent neural network (RNN) and convolutional neural network (CNN), were effectively used. This model is trained on labelled datasets around many news topics to automatically categorize articles into predetermined classes. …”
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Development and validation of a deep learning-enhanced prediction model for the likelihood of pulmonary embolism by Yu Tian, Yu Tian, Jingjie Liu, Shan Wu, Yucong Zheng, Rongye Han, Qianhui Bao, Lei Li, Lei Li, Tao Yang

“…Our prediction model uses a convolutional neural network (CNN), enhanced with three custom-designed modules for better performance. …”
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Automatic Detection of the Aortic Annular Plane and Coronary Ostia from Multidetector Computed Tomography by Patricio Astudillo, Peter Mortier, Johan Bosmans, Ole De Backer, Peter de Jaegere, Francesco Iannaccone, Matthieu De Beule, Joni Dambre

“…The detection strategy used these three CNN models to analyse a single MDCT image and yield three segmentation volumes as output. …”
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Needle tracking and segmentation in breast ultrasound imaging based on spatio-temporal memory network by Qiyun Zhang, Jiawei Chen, Jinhong Wang, Haolin Wang, Yi He, Yi He, Bin Li, Zhemin Zhuang, Huancheng Zeng

“…The proposed network integrates a hybrid encoder that employs CNN-Transformer architectures, along with an optical flow estimation method. …”
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Optimizing AI models to predict esophageal squamous cell carcinoma risk by incorporating small datasets of soft palate images by Kotaro Waki, Katsuya Nagaoka, Keishi Okubo, Masato Kiyama, Ryosuke Gushima, Kento Ohno, Munenori Honda, Akira Yamasaki, Kenshi Matsuno, Yoki Furuta, Hideaki Miyamoto, Hideaki Naoe, Motoki Amagasaki, Yasuhito Tanaka

“…The Bilinear convolutional neural network (CNN) model (especially when pre-trained on fractal images) demonstrated diagnostic precision that was comparable to or better than other models for distinguishing between high-risk and non-high-risk groups. …”
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Comparison of Deep-Learning-Based Segmentation Models: Using Top View Person Images by Imran Ahmed, Misbah Ahmad, Fakhri Alam Khan, Muhammad Asif

“…The encoder consists of trained Convolutional Neural Network (CNN) to encode feature maps of the input image. …”
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