Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography Images by M. Kumar, S. K. Mishra, S. S. Sahu

“…Gaussian noise is one of the dominant noises, which degrades the quality of acquired Computed Tomography (CT) image data. It creates difficulties in pathological identification or diagnosis of any disease. …”
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Investigation of TsGAN-based multimodal image fusion to augment image pre-processing abilities by Priyanka Bhatambarekar, Gayatri Phade

“…An extensive range of image fusion techniques is available, across from traditional approaches such as intensity-hue-saturation (IHS), principal component analysis (PCA), discrete cosine transform (DCT), and discrete wavelet transform (DWT) to more advanced methods such as deep learning (DL) and generative adversarial networks (GANs). …”
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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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Adaptive Elastic GAN for High-Fidelity Blood Cell Image Hallucination and Classification by Issac Neha Margret, K. Rajakumar

“…The model utilizes image hallucination, which generates artificial but realistic images to supplement datasets, addressing data imbalance and improving model generalization. …”
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Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review Paper by Kiranmayee Janardhan, Vinay Martin D’Sa Prabhu, T. Christy Bobby

“…Accurate segmentation ensures proper identification of the glioma’s size and position, transforming images into applicable data for analysis. Classification of brain gliomas is also essential because different types require different treatment approaches. …”
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CGD-CD: A Contrastive Learning-Guided Graph Diffusion Model for Change Detection in Remote Sensing Images by Yang Shang, Zicheng Lei, Keming Chen, Qianqian Li, Xinyu Zhao

“…With the rapid development of remote sensing technology, the question of how to leverage large amounts of unlabeled remote sensing data to detect changes in multi-temporal images has become a significant challenge. …”
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Determination of Pear Cultivars (Pyrus communis L.) Based on Colour Change Levels by Using Data Mining by Dilara Gerdan, Abdullah Beyaz, Mustafa Vatandaş

“…For this aim the colour change of the damaged pears were determined, in another term, colour change value from red to green and yellow to blue at the damaged pears were determined with lightness values by using image analysis technique and analysed with data mining methods. …”
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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

“…Here, we show that dynamic mode decomposition (DMD), a numerical algorithm for linear approximation of non-linear dynamics, can be combined with time-delay embedding (TDE) to dissect damped and sustained glycolytic oscillations in simulations and experiments in a fully data-driven manner. Together with an assessment of spurious eigenvalues via residual DMD, this provides a unique spectrum for each scenario, allowing for high-fidelity time-series and image reconstruction. …”
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Decoding Mental States in Social Cognition: Insights from Explainable Artificial Intelligence on HCP fMRI Data by José Diogo Marques dos Santos, Luís Paulo Reis, José Paulo Marques dos Santos

“…Artificial neural networks (ANNs) have been used for classification tasks involving functional magnetic resonance imaging (fMRI), though typically focusing only on fractions of the brain in the analysis. …”
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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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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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SlicerBatchBrainMRTumorSegmentation: Automating brain tumor segmentation in 3D slicer for improved efficiency and research support by Saima Safdar, Nathaniel Barry, Michael Bynevelt, Suki Gill, Pejman Rowshan Farzad, Martin A Ebert

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A CNN-Based Method for Quantitative Assessment of Steel Microstructures in Welded Zones by Cássio Danelon de Almeida, Thales Tozatto Filgueiras, Moisés Luiz Lagares, Bruno da Silva Macêdo, Camila Martins Saporetti, Matteo Bodini, Leonardo Goliatt

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Neural network approaches, including use of topological data analysis, enhance classification of human induced pluripotent stem cell colonies by treatment condition. by Alexander Ruys de Perez, Paul E Anderson, Elena S Dimitrova, Melissa L Kemp

“…We use topological data analysis to derive input information about the cells' positions to a four-layer feedforward neural network. …”
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Research on intelligent classification of coastal land cover by integrating remote sensing images and deep learning by Xinhao Lin, Junmiao Hei, Yixiao Wang, Ang Zhang

“…Traditional methods, like pixel-based and object-oriented classification, often struggle with high complexity and inaccurate results due to limitations in handling spatial relationships and spectral data.MethodsThis research addresses these shortcomings by integrating deep learning models, particularly convolutional neural networks (CNNs) and spatially dependent learning techniques, to develop a robust classification model for coastal land cover using remote sensing data. …”
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A Mathematical Survey of Image Deep Edge Detection Algorithms: From Convolution to Attention by Gang Hu

“…This survey presents a mathematically grounded analysis of edge detection’s evolution, spanning traditional gradient-based methods, convolutional neural networks (CNNs), attention-driven architectures, transformer-backbone models, and generative paradigms. …”
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Improving trend prediction of agricultural futures price using image encoding and attention mechanisms by Dabin Zhang, Huiqiang Xie, Huanling Hu, Zehui Yu

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Data-dependent visualization of biological networks in the web-browser with NDExEdit. by Florian Auer, Simone Mayer, Frank Kramer

“…Since the network data is only stored locally within the web browser, users can edit their private networks without concerns of unintentional publication. …”
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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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Multi-User Activity Recognition Using Plot Images Based on Ambiental Sensors by Anca Roxana Alexan, Alexandru Iulian Alexan, Stefan Oniga

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