Improving GOCI ocean color data under high solar-zenith angle over open oceans using neural networks by Xiaoming Liu, Menghua Wang

“…With hourly measurements available during daytime between local times of 09:00–16:00, ocean color data derived from the Geostationary Ocean Color Imager (GOCI) onboard the Korean Communication, Ocean, and Meteorological (COMS) satellite have been useful for research and surveillance of diurnal processes in the western Pacific Ocean region. …”
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Performance analysis of smart digital signage system based on software-defined IoT and invisible image sensor communication by Mohammad Arif Hossain, Amirul Islam, Nam Tuan Le, Yong Tae Lee, Hyun Woo Lee, Yeong Min Jang

“…The future of the interactive world depends on the future Internet of Things (IoT). Software-defined networking (SDN) technology, a new paradigm in the networking area, can be useful in creating an IoT because it can handle interactivity by controlling physical devices, transmission of data among them, and data acquisition. …”
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Correlating pore space morphology with numerically computed soil gas diffusion for structured loam and sand, including stochastic 3D microstructure modeling by Benedikt Prifling, Matthias Weber, Maximilian Rötzer, Nadja Ray, Alexander Prechtel, Maxime Phalempin, Steffen Schlüter, Doris Vetterlein, Volker Schmidt

“…In particular, nutrient transport depends on diffusivity and permeability within the soil’s pore network. A deeper understanding of the relationship between microscopic soil structure and such effective macroscopic properties can be obtained by tomographic imaging combined with a quantitative analysis of soil morphology and numerical simulations of effective macroscopic properties. …”
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A comparative analysis of urban development and the tram line network in Lviv in 1932−2016 by Cegiełko Szczepan, Karsznia Izabela

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Selective protection of cereals using artificial neural networks by Wiesław Golka, Magdalena Szechyńska-Hebda, Jolanta Kowalska, Adrian Golka, Robert Grolik, Magdalena Jakubowska, Mateusz Góra, Aleksander Bomberski

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BiLSTM-based Approach to the Natural Language Text Dependencies Analysis by A. Chernyshov

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Development of a model for detection and analysis of inclusions in tomographic images of iron castings using decision trees by Dorota Wilk-Kołodziejczyk, Aleksandra Nowotny, Izabela Krzak, Adam Tchórz, Krzysztof Jaśkowiec, Marcin Małysza, Adam Bitka, Mirosław Głowacki, Marzanna Książek, Łukasz Marcjan

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ES-UNet: efficient 3D medical image segmentation with enhanced skip connections in 3D UNet by Minyoung Park, Seungtaek Oh, Junyoung Park, Taikyeong Jeong, Sungwook Yu

“…Abstract Background Deep learning has significantly advanced medical image analysis, particularly in semantic segmentation, which is essential for clinical decisions. …”
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Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings by David Verstraete, Andrés Ferrada, Enrique López Droguett, Viviana Meruane, Mohammad Modarres

“…To address this problem a deep learning enabled featureless methodology is proposed to automatically learn the features of the data. Time-frequency representations of the raw data are used to generate image representations of the raw signal, which are then fed into a deep convolutional neural network (CNN) architecture for classification and fault diagnosis. …”
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Detection and classification of hypertensive retinopathy based on retinal image analysis using a deep learning approach by Bambang Krismono Triwijoyo, Ahmat Adil, Muhammad Zulfikri

“…Methods: This research utilizes secondary data, specifically a retinal image dataset from the open-source Messidor database. …”
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Improving long‐tail classification via decoupling and regularisation by Shuzheng Gao, Chaozheng Wang, Cuiyun Gao, Wenjian Luo, Peiyi Han, Qing Liao, Guandong Xu

“…Abstract Real‐world data always exhibit an imbalanced and long‐tailed distribution, which leads to poor performance for neural network‐based classification. …”
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Automatic Blob Detection Method for Cancerous Lesions in Unsupervised Breast Histology Images by Vincent Majanga, Ernest Mnkandla, Zenghui Wang, Donatien Koulla Moulla

“…The early detection of cancerous lesions is a challenging task given the cancer biology and the variability in tissue characteristics, thus rendering medical image analysis tedious and time-inefficient. In the past, conventional computer-aided diagnosis (CAD) and detection methods have heavily relied on the visual inspection of medical images, which is ineffective, particularly for large and visible cancerous lesions in such images. …”
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Multimodal learning for enhanced SPECT/CT imaging in sports injury diagnosis by Zhengzheng Jiang, YaWen Shen

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Uncertainty-Aware Adaptive Multiscale U-Net for Low-Contrast Cardiac Image Segmentation by A. S. M. Sharifuzzaman Sagar, Muhammad Zubair Islam, Jawad Tanveer, Hyung Seok Kim

“…Medical image analysis is critical for diagnosing and planning treatments, particularly in addressing heart disease, a leading cause of mortality worldwide. …”
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Advances in weed identification using hyperspectral imaging: A comprehensive review of platform sensors and deep learning techniques by Bright Mensah, Nitin Rai, Kelvin Betitame, Xin Sun

“…Techniques like image calibration, standard normal variate, multiplicative scatter correction, Savitsky-Golay smoothing, derivatives, and features selection are among the most used techniques, (d) traditional machine learning models namely support vector machines (SVM), partial least square discriminant analysis (PLS-DA), maximum likelihood classifiers (MLC), and random forest (RF) are the widely employed classifiers for weed identification, (e) the application of deep learning technique, namely convolutional neural networks (CNNs) are limited, but its application demonstrated superior performance accuracies compared to traditional machine learning models. …”
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Harnessing multi-source data for AI-driven oncology insights: Productivity, trend, and sentiment analysis by Wissal EL HABTI, Abdellah AZMANI

“…Among 8339 authors, Kather JN was the third most prolific author and held a central position in the co-authorship network. The most prominent article emphasized the Explainability of AI methods (XAI) with a profound discussion of their potential implications and privacy in data fusion contexts. …”
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Multi-scale eddy identification and analysis based on deep learning method and ocean color data by Meng Hou, Lixing Fang, Kai Wu, Jie Yang, Ge Chen

“…The algorithm integrates high resolution ocean color data, digital image processing, artificial intelligence, and multi-scale object detection technologies. …”
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SRFNet: Multimodal Based Selective Receptive Field Neural Network for Time Series Forecast of Flood Range by Zhiqing Li, Zeqiang Chen, Lai Chen, Xu Tang, Nengcheng Chen

“…Nonetheless, many existing methods are developed for natural images and do not take into account the unique characteristics of remote-sensing images and other modal data. …”
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Dynamic mode decomposition for analysis and prediction of metabolic oscillations from time-lapse imaging of cellular autofluorescence by Daniel Wüstner, Henrik Helge Gundestrup, Katja Thaysen

“…DMD with TDE can also discern other types of oscillations, as demonstrated for simulated calcium traces, and its forecasting ability is on par with that of Long Short-Term Memory (LSTM) neural networks. Our results demonstrate the potential of DMD for analysis of oscillatory dynamics at the single-cell level.…”
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Application of deep learning models in gastric cancer pathology image analysis: a systematic scoping review by Sijun Xia, Yuanze Xia, Ting Liu, Yiming Luo, Patrick Cheong-Iao Pang

“…The emergence of deep learning (DL) models provides new ways to automate and improve the analysis of GC pathology images. This systematic review aims to evaluate the current application, challenges, and future directions of DL in GC pathology image analysis. …”
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