Medical image segmentation based on frequency domain decomposition SVD linear attention by Liu Qiong, Li Chaofan, Teng Jinnan, Chen Liping, Song Jianxiang

“…Abstract Convolutional Neural Networks (CNNs) have achieved remarkable segmentation accuracy in medical image segmentation tasks. …”
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Spatial transcriptome reveals histology-correlated immune signature learnt by deep learning attention mechanism on H&E-stained images for ovarian cancer prognosis by Chun Wai Ng, Kwong-Kwok Wong, Barrett C. Lawson, Sammy Ferri-Borgogno, Samuel C. Mok

“…We sought to deconvolute the attention feature learnt by a deep learning convolutional neural networks trained with whole-slide images (WSIs) of hematoxylin-and-eosin (H&E)–stained tumor samples using spatial transcriptomic data. …”
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Fault diagnosis of a CNC hobbing cutter through machine learning using three axis vibration data by Nagesh Tambake, Bhagyesh Deshmukh, Sujit Pardeshi, Sachin Salunkhe, Robert Cep, Emad Abouel Nasr

“…Various machine learning algorithms, including Decision Trees, Efficient Linear models, Neural Networks, and Ensemble methods, were evaluated for their classification accuracy. …”
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Multi-label classification with deep learning techniques applied to the B-Scan images of GPR by El Karakhi, Soukayna, Reineix, Alain, Guiffaut, Christophe

“…With the emergence of deep neural networks and with a learning phase on a large number of Bscan, it becomes possible to extract almost instantaneously the characteristics of GPR radar data. …”
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An Efficient CNN Model for COVID-19 Disease Detection Based on X-Ray Image Classification by Aijaz Ahmad Reshi, Furqan Rustam, Arif Mehmood, Abdulaziz Alhossan, Ziyad Alrabiah, Ajaz Ahmad, Hessa Alsuwailem, Gyu Sang Choi

“…Artificial intelligence (AI) techniques in general and convolutional neural networks (CNNs) in particular have attained successful results in medical image analysis and classification. …”
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Digital technologies for water use and management in agriculture: Recent applications and future outlook by Carlos Parra-López, Saker Ben Abdallah, Guillermo Garcia-Garcia, Abdo Hassoun, Hana Trollman, Sandeep Jagtap, Sumit Gupta, Abderrahmane Aït-Kaddour, Sureerat Makmuang, Carmen Carmona-Torres

“…UAV-mounted multispectral cameras) can accurately monitor soil moisture to optimise irrigation scheduling, while AI-driven models (e.g. random forest or neural networks) can predict groundwater recharge or forecast rainfall events. …”
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Innovative segmentation technique for aerial power lines via amplitude stretching transform by Pengfei Xu, Nor Anis Asma Sulaiman, Yafei Ding, Jiangwei Zhao

“…The proposed algorithm is compared with the main power line segmentation algorithms, such as Region Convolutional Neural Networks(R-CNN) and Phase Stretch Transform(PST). …”
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Quantifying 3D coral reef structural complexity from 2D drone imagery using artificial intelligence by Aviv Suan, Simone Franceschini, Joushua Madin, Elizabeth Madin

“…In this study, we present an approach that integrates drones, various color space information, and deep learning neural networks to predict three-dimensional complexity metrics—height range, rugosity and fractal dimension—across large (thousands of m2) spatial scales. …”
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Integrated information theory reveals the potential role of the posterior parietal cortex in sustaining conditioning responses in classical conditioning tasks by Tien Cuong Phi, Shin Ishii, Shin Ishii, Shin Ishii, Masashi Kondo, Masanori Matsuzaki, Masanori Matsuzaki, Masanori Matsuzaki, Masanori Matsuzaki, Ken Nakae

“…Using the core complex framework derived from Integrated Information Theory (IIT), we explored the dynamics of neural networks during sessions of high CR performance. …”
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Adulteration detection in cactus seed oil: Integrating analytical chemistry and machine learning approaches by Said El Harkaoui, Cristina Ortiz Cruz, Aaron Roggenland, Micha Schneider, Sascha Rohn, Stephan Drusch, Bertrand Matthäus

“…The MC-simulated data were then used to simulate larger datasets, a critical step for training and testing two classification models: Random Forest (RF) and Neural Networks (NN), as robust training cannot be achieved with small sample sizes. …”
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Impact of stain variation and color normalization for prognostic predictions in pathology by Siyu Lin, Haowen Zhou, Mark Watson, Ramaswamy Govindan, Richard J. Cote, Changhuei Yang

“…Abstract In recent years, deep neural networks (DNNs) have demonstrated remarkable performance in pathology applications, potentially even outperforming expert pathologists due to their ability to learn subtle features from large datasets. …”
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Evaluation of Satellite Rainfall Products over the Mahaweli River Basin in Sri Lanka by Helani Perera, Shalinda Fernando, Miyuru B. Gunathilake, T. A. J. G. Sirisena, Upaka Rathnayake

“…Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) outperformed among all SRPs, while Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products showed dire performances. …”
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A Block-Based and Highly Parallel CNN Accelerator for Seed Sorting by Xiaoting Sang, Zhenghui Hu, Huanyu Li, Chunlei Li, Zhoufeng Liu

“…The seed sorting methods based on convolutional neural networks (CNNs) have achieved excellent recognition accuracy on large-scale pretrained network models. …”
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“…Landslide susceptibility assessment and factor effect analysis: bad propagation artificial neural networks and comparison with frequency ratio and bivariate logistic regression modeling. …”
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Forecasting the Applied Deep Learning Tools in Enhancing Food Quality for Heart Related Diseases Effectively: A Study Using Structural Equation Model Analysis by Sunil L. Bangare, Deepali Virmani, Girija Rani Karetla, Pankaj Chaudhary, Harveen Kaur, Syed Nisar Hussain Bukhari, Shahajan Miah

“…Furthermore, deep neural networks can be applied for analyzing and detecting heart failures effectively and supporting medical practitioners in making better and more critical clinical decisions making. …”
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Web-Based Healthcare Delivery Integrated System to Forecast COVID-19 Hospitalizations in a Marginalized Patient Population: A Case Study in Broome County, New York by Anemone Kasasbeh, Elie Issa, Naseem Khan, Mehmet Yildirim, Amy Booth, Hiroki Sayama

“…We tested the following methods to forecast the number of COVID-19 hospitalizations 7 days in advance: simple exponential smoothing, autoregressive integrated moving average (ARIMA), and multilayer perceptron neural networks. The candidate model was selected based on the akaike information criterion, Bayesian information criterion, and the root-mean-square error (RMSE). …”
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Intrusion detection in metaverse environment internet of things systems by metaheuristics tuned two level framework by Milos Antonijevic, Miodrag Zivkovic, Milica Djuric Jovicic, Bosko Nikolic, Jasmina Perisic, Marina Milovanovic, Luka Jovanovic, Mahmoud Abdel-Salam, Nebojsa Bacanin

“…This study revolves around hybrid framework that combines convolutional neural networks (CNN) and machine learning (ML) classifying models, like categorical boosting (CatBoost) and light gradient-boosting machine (LightGBM), further optimized through metaheuristics optimizers for leveraged performance. …”
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Exploration of transfer learning techniques for the prediction of PM10 by Michael Poelzl, Roman Kern, Simonas Kecorius, Mario Lovrić

“…Common ML models such as Random Forests, Multilayer Perceptrons, Long-Short-Term Memory, and Convolutional Neural Networks are explored to predict particulate matter in both cities. …”
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Uncertainty-aware deep learning in healthcare: A scoping review. by Tyler J Loftus, Benjamin Shickel, Matthew M Ruppert, Jeremy A Balch, Tezcan Ozrazgat-Baslanti, Patrick J Tighe, Philip A Efron, William R Hogan, Parisa Rashidi, Gilbert R Upchurch, Azra Bihorac

“…All imaging model architectures used convolutional neural networks or a variation thereof. The predominant method for quantifying uncertainty was Monte Carlo dropout, producing predictions from multiple networks for which different neurons have dropped out and measuring variance across the distribution of resulting predictions. …”
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Design of an Integrated Model for Video Summarization Using Multimodal Fusion and YOLO for Crime Scene Analysis by Sai Babu Veesam, Aravapalli Rama Satish

“…Spatio-temporal action localization is achieved with the help of 3D Convolutional Neural Networks, along with Temporal Attention Networks that capture all weapon-related actions with the best set of critical frames. …”
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