Image Quality Assessment and Reliability Analysis of Artificial Intelligence-Based Tumor Classification of Stimulated Raman Histology of Tumor Biobank Samples by Anna-Katharina Meißner, Tobias Blau, David Reinecke, Gina Fürtjes, Lili Leyer, Nina Müller, Niklas von Spreckelsen, Thomas Stehle, Abdulkader Al Shugri, Reinhard Büttner, Roland Goldbrunner, Marco Timmer, Volker Neuschmelting

“…Analysis of SRH images using Convolutional Neural Networks (CNN) has shown promising results for predicting the main histopathological classes of neurooncological tumors. …”
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Aided Greenway Design Approach Based on Internet Big Data and AIGC Fine-Tuning Model by Yifan WU, Lu MENG, Liang LI

“…The framework can be divided into four major processes: Network big data collection, intelligent evaluation of network big data, AIGC image fine-tuning model construction, and AI-aided design generation. 1) Network big data collection: Obtain datasets related to the required landscape architecture segmentation scenarios through online social platforms for evaluation and fine-tuning model training. 2) Intelligent evaluation of network big data: Analyze and categorize image data, and filter out the scenario images with excellent user evaluation based on text sentiment evaluation and subsidiary information analysis. 3) AIGC image fine-tuning model construction: Utilize the high-quality image dataset obtained in the previous stage to conduct fine-tuning model training based on a mature pre-trained general model, and inject relevant knowledge and experience from the sub-scenarios of landscape architecture in a cost-effective manner, thereby enhancing the model’s generative capabilities. 4) AI-aided design generation: Employ the fine-tuning model obtained through training to assist in generating scenario images according to the needs of design practice, and based on the intensity of control over the generated content, divide the aided scenario generation into “weakly controlled” and “strongly controlled” aided design scenarios. …”
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A deep transfer learning model for green environment security analysis in smart city by Madhusmita Sahu, Rasmita Dash, Sambit Kumar Mishra, Mamoona Humayun, Majed Alfayad, Mohammed Assiri

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Salient object detection dataset with adversarial attacks for genetic programming and neural networksMendeley Data by Matthieu Olague, Gustavo Olague, Roberto Pineda, Gerardo Ibarra-Vazquez

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Federated learning with tensor networks: a quantum AI framework for healthcare by Amandeep Singh Bhatia, David E Bernal Neira

“…Currently, there are no known classical tensor networks (TNs) implemented in federated settings. Furthermore, we investigated the effectiveness and feasibility of the proposed framework by conducting a differential privacy analysis to ensure the security of sensitive data across healthcare institutions. …”
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A hybrid model for multimedia data compression using generative adversarial networks and chaotic encryption by P.T. Sivagurunathan, A. Sridevi

“…The encrypted images are then subjected for compression using generative adversarial network model. …”
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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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Présentation by Christèle Couleau, Oriane Deseilligny, Pascale Hellégouarc’h

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Exploiting K-Space in Magnetic Resonance Imaging Diagnosis: Dual-Path Attention Fusion for K-Space Global and Image Local Features by Congchao Bian, Can Hu, Ning Cao

“…In this paper, we present a novel MRI K-space-based global feature extraction and dual-path attention fusion network. Our proposed method extracts global features from MRI K-space data and fuses them with local features from the image domain using a dual-path attention mechanism, thereby achieving accurate MRI segmentation for diagnosis. …”
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Small Scale Multi-Object Segmentation in Mid-Infrared Image Using the Image Timing Features–Gaussian Mixture Model and Convolutional-UNet by Meng Lv, Haoting Liu, Mengmeng Wang, Dongyang Wang, Haiguang Li, Xiaofei Lu, Zhenhui Guo, Qing Li

“…In this model, the maximum pooling layer is replaced with a convolutional layer, addressing the challenge of limited training data and improving the network’s ability to preserve spatial features. …”
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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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Visual nutrition analysis: leveraging segmentation and regression for food nutrient estimation by Yaping Zhao, Yaping Zhao, Ping Zhu, Ping Zhu, Yizhang Jiang, Kaijian Xia, Kaijian Xia

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An Analysis of the Social Media Presence of the Brands by OTILIA-ELENA PLATON

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Automatic Fault Classification in Photovoltaic Modules Using Denoising Diffusion Probabilistic Model, Generative Adversarial Networks, and Convolutional Neural Networks by Carlos Roberto da Silveira Junior, Carlos Eduardo Rocha Sousa, Ricardo Henrique Fonseca Alves

“…In this study, three data augmentation techniques—geometric transformations (GTs), generative adversarial networks (GANs), and the denoising diffusion probabilistic model (DDPM)—were combined with a CNN to classify faults in PV modules through thermographic images and identify the type of fault in 11 different classes (i.e., soiling, shadowing, and diode). …”
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Dual smart sensor data-based deep learning network for premature infant hypoglycemia detection by Muhammad Shafiq, J. Kavitha, Dhruva R. Rinku, N. K. Senthil Kumar, Kamal Poon, Amar Y. Jaffar, V. Saravanan

“…This device tracks changes in the infant’s motion, skin colour, and breathing patterns; this is done through a PPG sensor strapped either on the belly or chest of an infant, logging information on heart functioning. The digital data gathered by this PPG sensor and image data captured from the smart camera are then processed by a Raspberry Pi Zero 2 W. …”
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Attention-based multi-residual network for lung segmentation in diseased lungs with custom data augmentation by Md. Shariful Alam, Dadong Wang, Yulia Arzhaeva, Jesse Alexander Ende, Joanna Kao, Liz Silverstone, Deborah Yates, Olivier Salvado, Arcot Sowmya

“…In this study, we propose AMRU++, an attention-based multi-residual UNet++ network designed for robust and accurate lung segmentation in CXR images with both normal and severe abnormalities. …”
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A Cloud Computing Framework for Space Farming Data Analysis by Adrian Genevie Janairo, Ronnie Concepcion, Marielet Guillermo, Arvin Fernando

“…The corresponding sensor readings and image data were securely streamed through Amazon Web Service Internet of Things (AWS IoT) to an ESP-NOW receiver and Roboflow. …”
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Susceptibility evaluation of valley debris flow based on dual-channel network with fusion attention mechanism by Yumeng LUO, Baoyun WANG, Ruohao YUAN, Xu WANG, Cunxi LIU, Kuayue CHEN

“…The main contributions of this paper are as follows: Firstly, based on historical debris flow records and using Digital Elevation Maps (DEMs) and remote sensing images as data sources, a dual-channel network structure is designed as the basic technical framework. …”
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Deep learning driven methodology for the prediction of mushroom moisture content using a novel LED-based portable hyperspectral imaging system by Kai Yang, Ming Zhao, Dimitrios Argyropoulos

“…This study proposes a deep-learning driven methodology for the analysis of mushroom moisture content (MC) datasets acquired using a novel portable hyperspectral imaging (HSI) system. …”
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Association of IL-17A levels with immuneinflammatory profile and structural MRI data in patients with schizophrenia by I. K. Malashenkova, V. L. Ushakov, S. A. Krynskiy, D. P. Ogurtsov, N. A. Khailov, A. Yu. Ratushnyy, E. I. Chekulaeva, N. V. Zakharova, G. P. Kostyuk, N. A. Didkovsky

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