Hybrid CNN and RNN Model for Histopathological Sub-Image Classification in Breast Cancer Analysis Using Self-Learning by Alaa Hussein Abdulaal, Morteza Valizadeh, Riyam Ali Yassin, Mehdi Chehel Amirani, A. F. M. Shahen Shah, Baraa M. Albaker, Ammar Saad Mustaf Mustaf

“…By leveraging localized features from a pre-trained CNN and insights from the RNN, this innovative approach aims to enhance accuracy. …”
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Identification of cell chemistries in lithium-ion batteries: Improving the assessment for recycling and second-life by Christopher Wett, Jörg Lampe, Dominik Görick, Thomas Seeger, Bugra Turan

“…The dV/dQ characteristics are chosen as features and four machine learning algorithms are trained on different lengths of OCV curves. …”
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Electroencephalographic signatures of migraine in small prospective and large retrospective cohorts by Bihua Bie, Samer Ghosn, Shehryar R. Sheikh, Matheus Lima Diniz Araujo, Reena Mehra, MaryAnn Mays, Carl Y. Saab

“…In the prospective analysis, no significant difference between migraine and control groups was detected in the mean power spectral density (PSD) of an all-electrodes montage in the frequency range of 1–32 Hz, whereas a significant PSD increase in single occipital electrodes was found at 12 Hz in migraine patients. We then trained machine learning models on the binary classification of migraine versus control using EEG power features, resulting in high accuracies (82–83%) with occipital electrodes’ power at 12 Hz ranking highest in the contribution to the model’s performance. …”
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Optimal Partitioning of Unbalanced Datasets for BGP Anomaly Detection by Rahul Deo Verma, Pankaj Kumar Keserwani, Vinesh Kumar Jain, Mahesh Chandra Govil, M. W. P. Maduranga, Valmik Tilwari

“…The existing solutions are based on the classical machine learning (ML) models, which need to be advanced. …”
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Towards full integration of explainable artificial intelligence in colon capsule endoscopy’s pathway by Esmaeil S. Nadimi, Jan-Matthias Braun, Benedicte Schelde-Olesen, Smith Khare, Vinay C. Gogineni, Victoria Blanes-Vidal, Gunnar Baatrup

“…The characterisation DNN trained on an unaugmented database of 317 images featuring neoplastic polyps and 162 images of non-neoplastic polyps reached a sensitivity of $$84.3\%$$ and a specificity of $$81.5\%$$ in classifying polyps. …”
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High Resolution Sea Ice Concentration Using a Sentinel-1 U-Net Ice-Water Classifier by Jozef Rusin, Anthony P. Doulgeris, K. Andrea Scott, Thomas Lavergne, Catherine Taelman

“…To address SAR noise challenges, we incorporate multilooking, adaptive noise correction, and overlapping patches at inference to improve SIC accuracy while preserving fine-scale features. We trained the U-Net across multilooking levels to balance resolution, noise reduction, and computational efficiency, allowing the model to handle noise artefacts effectively. …”
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Automated Detection of Galactic Rings from Sloan Digital Sky Survey Images by Linn Abraham, Sheelu Abraham, Ajit K. Kembhavi, N. S. Philip, A. K. Aniyan, Sudhanshu Barway, Harish Kumar

“…Morphological features in galaxies—like spiral arms, bars, rings, and tidal tails, etc.…”
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The CVFEM approach to study the Cattaneo-Christov heat flux model using thin film Williamson hybrid nanofluid flowing over a disk by M.M. Alqarni, Abdul Samad Khan, M.A. Aljohani, Taza Gul, Emad E. Mahmoud

“…The input data, hidden layer, and output data are the main features of the AI-based artificial neural network (ANN) which are used to predict the results of the fluid flow problem. …”
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Estimations of Dynamic Water Depth and Volume of Global Lakes Using Machine Learning by Yunzhe Lv, Li Jia, Massimo Menenti, Chaolei Zheng, Jing Lu, Min Jiang, Qiting Chen, Yiqing Zhang

“…By incorporating various lake features and employing multiple innovative water depth extraction methods, we generated an extensive water depth dataset to train the model. …”
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Multi-Pathway 3D CNN With Conditional Random Field for Automated Segmentation of Multiple Sclerosis Lesions in MRI by Reeda Saeed, Shahab U. Ansari, Muhammad Hanif, Kamran Javed, Usman Haider, Iffat Maab, Saeed Mian Qaisar, Pawel Plawiak

“…Multiple Sclerosis (MS) is a chronic and autoimmune disease that causes lesions in the central nervous system. It is diagnosed based on accurate identification and segmentation of lesions in magnetic resonance imaging (MRI). …”
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kMoL: an open-source machine and federated learning library for drug discovery by Romeo Cozac, Haris Hasic, Jun Jin Choong, Vincent Richard, Loic Beheshti, Cyrille Froehlich, Takuto Koyama, Shigeyuki Matsumoto, Ryosuke Kojima, Hiroaki Iwata, Aki Hasegawa, Takao Otsuka, Yasushi Okuno

“…It demonstrates extensive customization possibilities, advanced security features, straightforward implementation of user-specific models, and high adaptability to custom datasets without additional programming requirements. kMoL is evaluated through locally trained benchmark settings and distributed federated learning experiments using various datasets to assess the features and flexibility of the library, as well as the ability to facilitate fast and practical experimentation. …”
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Automated Skin Cancer Report Generation via a Knowledge-Distilled Vision-Language Model by Lawhori Chakrabarti, Boyu Zhang, Hengyi Tian, Aleksandar Vakanski, Min Xian

“…This study presents a multimodal vision-language model (VLM) trained using a two-stage knowledge distillation (KD) framework to generate structured medical reports from dermoscopic images, with descriptive features based on the 7-point melanoma checklist. …”
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Automatic Paddy Planthopper Detection and Counting Using Faster R-CNN by Siti Khairunniza-Bejo, Mohd Firdaus Ibrahim, Marsyita Hanafi, Mahirah Jahari, Fathinul Syahir Ahmad Saad, Mohammad Aufa Mhd Bookeri

“…The datasets were subjected to data augmentation and utilised to train four convolutional object detection models based on transfer learning. …”
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Development of a Multi-Channel Ultra-Wideband Electromagnetic Transient Measurement System by Shaoyin He, Xiangyu Chen, Bohao Zhang, Liang Song

“…In complex electromagnetic environments, such as substations, converter stations in power systems, and the compartments of aircraft, trains, and automobiles, electromagnetic immunity testing is crucial. …”
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Latent spectral-spatial diffusion model for single hyperspectral super-resolution by Yingsong Cheng, Yong Ma, Fan Fan, Jiayi Ma, Yuan Yao, Xiaoguang Mei

“…To address this complexity, we leverage a large-scale pre-trained autoencoder to map the data into a low-dimensional space conducive to diffusion. …”
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Adaptive Echo State Network for crop yield prediction incorporating Fall Armyworm dynamicsMendeley Data by Mulima Chibuye, Jackson Phiri, Phillip Nkunika

“…FAW severity is quantified on a 0–100 scale by blending trap counts (40 %) and larval density (60 %) per month. First, the ESN is trained on all available data to predict crop yields based on environmental features. …”
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Diet quality of Chilean schoolchildren: How is it linked to adherence to dietary guidelines? by Anna Christina Pinheiro Fernandes, Jacqueline Araneda Flores, Daiana Quintiliano Scarpelli Dourado, Tito Pizarro Quevedo, Maria Rita Marques de Oliveira

“…Dietary intake was assessed using a validated semi-quantitative frequency survey featuring images of food groups aligned with DGBF, as well as HPF. …”
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Enhancing Post-Disaster Damage Detection and Recovery Monitoring by Addressing Class Imbalance in Satellite Imagery Using Enhanced Super-Resolution GANs (ESRGAN) by U. Lagap, S. Ghaffarian

“…To generate super-resolution (SR) images, LR images were first paired with HR images to train ESRGAN. Numerical evaluations using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) between SR and HR images confirm that ESRGAN effectively reconstructs high-resolution features, with Not Damaged (PSNR: 29.2, SSIM: 0.78) and New Buildings (PSNR: 30.3, SSIM: 0.81) exhibiting the highest reconstruction quality. …”
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Practical Aspects of Using Artificial Intelligence Programs in the Analytical Work of the Department by Chupryna Leonid

“…In particular, one of this work features is the limited access of AI to regional sites. …”
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Deep learning for gender estimation using hand radiographs: a comparative evaluation of CNN models by Hilal Er Ulubaba, İpek Atik, Rukiye Çiftçi, Özgür Eken, Monira I. Aldhahi

“…This study aimed to develop a deep learning-based model for gender classification using hand radiographs, offering a rapid and objective alternative to conventional methods. …”
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