A Hybrid Artificial Intelligence Approach for Down Syndrome Risk Prediction in First Trimester Screening by Emre Yalçın, Serpil Aslan, Mesut Toğaçar, Süleyman Cansun Demir

“…The final features are classified using machine learning algorithms, including Bagged Trees and Naive Bayes. …”
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Continuous Train Positioning Using Visible Light Communication Assisted with Zero Velocity Update by Yanpeng ZHANG, Rongrong ZHANG, Nan MENG, Bingqing ZHANG, Xia XIAO

“…The algorithm is simple, easy to implement, and has many applications in vehicle inertial navigation, robot positioning, human motion recognition, and other fields. …”
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Volcanic emission estimates from the inversion of ACTRIS lidar observations and their use for quantitative dispersion modeling by A. Kampouri, A. Kampouri, V. Amiridis, T. Georgiou, T. Georgiou, S. Solomos, A. Gialitaki, A. Gialitaki, M. Tsichla, M. Tsichla, M. Rennie, S. Scollo, P. Zanis

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Inverse Rendering of Near-Field mmWave MIMO Radar for Material Reconstruction by Nikolai Hofmann, Vanessa Wirth, Johanna Braunig, Ingrid Ullmann, Martin Vossiek, Tim Weyrich, Marc Stamminger

“…To avoid bias from conventional radar image reconstruction algorithms in the optimization process, we directly derive gradients from raw radar outputs, resulting in more accurate material characterization. …”
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YOLO-SBA: A Multi-Scale and Complex Background Aware Framework for Remote Sensing Target Detection by Yifei Yuan, Yingmei Wei, Xiaoyan Zhou, Yanming Guo, Jiangming Chen, Tingshuai Jiang

“…On the DIOR dataset, YOLO-SBA improves mAP by 16.6% and single-category detection AP by 0.8–23.8% compared to the existing state-of-the-art algorithm.…”
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STRATEGICITY AS A BASIC PRINCIPLE OF PROFESSIONAL AND PEDAGOGICAL ACTIVITY by Olha O. Lilik

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Multimodal adaptive temporal phase unwrapping using deep learning and physical priors by Yiheng Liu, Xinsheng Li, Ziheng Jin, Wenwu Chen, Edmund Y. Lam, Shijie Feng, Qian Chen, Chao Zuo

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Tomato classification with YOLOv8: Enhancing automated sorting and quality assessment by Viviana Moya, Michael Guerra, Karina Pazmiño, Faruk Abedrabbo, Fernando A. Chicaiza, David Pozo-Espín

“…The classification model was trained on a dataset of 2,145 images of tomatoes taken from different sources and lighting conditions to enhance performance during training. …”
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Artificial intelligence-based Raynaud’s quantification index (ARTIX): an objective mobile-based tool for patient-centered assessment of Raynaud’s phenomenon by Marco Di Battista, Seda Colak, Anna Howard, Francesca Donadoni, Chris Owen-Smith, Andrea Rindone, Stefano Di Donato, Collette Hartley, Lesley-Anne Bissell, Francesco Del Galdo

“…Abstract Background We aimed to develop an artificial intelligence algorithm able to assess Raynaud’s phenomenon (RP) from mobile phone photography, ensuring as a patient-centered, image-based method for RP quantification. …”
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Uncertainty analysis of clinically relevant distances derived using photogrammetric intersection for the assessment of motor-speech-control in children by L. Boyle, P. Helmholz, R. Ward, R. Palmer, D. Lichti

“…In this paper, we use a combination of the markerless BlazeFace algorithm and photogrammetry to examine how different weightings of the image observations, introduced for the photogrammetric intersection, impact the assessment of whether the calculated inter-landmark distances significantly change during the production of spoken words. …”
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Robust Multi-Input Multi-Output Analysis for Crop Row Segmentation and Furrow Line Detection in Diverse Agricultural Fields by Muhammad Ibrahim Zain Ul Abideen, Dewa Made Sri Arsa, Talha Ilyas, Hyunggi Jo, Sang Cheol Kim, Hyongsuk Kim

“…This paper introduces a comprehensive deep-learning model designed to detect furrow centerlines across diverse types of furrows, thereby improving accuracy and robustness. Our algorithm utilizes RGB and depth images, processed through a dual encoder that combines their features. …”
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Tomato leaf disease detection method based on improved YOLOv8n by Ming Chen, Chunping Wang, Chengwei Liu, Ying Yu, Yuan Yuan, Jiaxuan Ma, Kaisheng Zhang

“…To address this issue, this paper proposes an optimized YOLOv8n algorithm, incorporating a C2f-DynamicConv optimization module. …”
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Research on Recognition of Green Sichuan Pepper Clusters and Cutting-Point Localization in Complex Environments by Qi Niu, Wenjun Ma, Rongxiang Diao, Wei Yu, Chunlei Wang, Hui Li, Lihong Wang, Chengsong Li, Pei Wang

“…A dataset containing 14,504 training images under diverse lighting and occlusion scenarios was constructed. …”
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A Meta-Learning-Based Ensemble Model for Explainable Alzheimer’s Disease Diagnosis by Fatima Hasan Al-bakri, Wan Mohd Yaakob Wan Bejuri, Mohamed Nasser Al-Andoli, Raja Rina Raja Ikram, Hui Min Khor, Zulkifli Tahir, The Alzheimer’s Disease Neuroimaging Initiative

“…The core contribution lies in the exclusive use of mid-slice MRI images, which highlight the lateral ventricles, thus improving the transparency and clinical relevance of the decision-making process. …”
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Remote sensing-based assessment of four decades of land use/land cover change in the Sigi River Watershed, East Usambara Mountains, Tanzania by Simon Chidodo, Simon Chidodo, Lulu Zhang, Azin Zarei, Karl-Heinz Feger

“…This study analyzed LULC dynamics from 1983 to 2022 in the Sigi River watershed (887 km2) in the East Usambara Mountains, a biodiversity hotspot and critical water source in northeastern Tanzania.Methods:Multi-temporal Landsat satellite images were classified using the Random Forest algorithm to assess LULC transitions across elevation and slope gradients.Results:The results revealed marked variations in LULC change across topographic zones. …”
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Automatic crop type mapping based on crop-wise indicative features by Junru Yu, Longcai Zhao, Yanfu Liu, Qingrui Chang, Na Wang

“…However, large-scale crop type mapping is often hindered by the absence of ground truth data and spatiotemporal heterogeneity of available cloud-free optical images. In this study, the conception of daily crop-wise indicative features (DCIF) was proposed for automatic crop mapping to address this issue. …”
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Construction and Application of Feature Recommendation Model for Remote Sensing Interpretation of Rock Strata Based on Knowledge Graph by Liufeng Tao, Qirui Wu, Miao Tian, Zhong Xie, Jianguo Chen, Yueyu Wu, Qinjun Qiu

“…These include limited use of cross-modal data (e.g., geochemical tables, outcrop images) and a reliance on static knowledge representations. …”
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Prospects for Using 3D Modeling and Biomarkers in Differential Diagnosis of Endometrioid Ovarian Cysts by Е. М. Blazhnova, R. B. Balter, Т. V. Ivanova, Т. А. Pugacheva, А. R. Ibragimova, L. S. Tselkovich, А. Т. Oganisyan

“…In 59 infertile women with endometrioid ovarian cysts, in the dynamics of the menstrual cycle, ultrasound examinations were performed using Acuson S2000 (Siemens, Germany) and Voluson E8 (General Electric, USA) with the possibility of Doppler measurements and three-dimensional reconstruction of tissue images. Also, in laboratories “Invitro”, “Hemotest”, “Nauka” (Samara, Russia) the biomarkers were determined: interleukins IL-1β, IL-6, cancer antigen 125 (CA-125), human epididymis protein 4 (HE4) and the index by Risk of Ovarian Malignancy Algorithm (ROMA). …”
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Enhancing coastal bathymetric mapping with physics-informed recurrent neural networks synergizing Gaofen satellite imagery and ICESat-2 lidar data: A case in the South China Sea by Congshuang Xie, Siqi Zhang, Zhenhua Zhang, Peng Chen, Delu Pan

“…The key steps in our process included developing a new algorithm to extract accurate seafloor data from ICESat-2 laser measurements, and a neural network that incorporated both spectral information from satellite images and the physical principles of light interaction in water bodies. …”
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Does Artificial Intelligence Bring New Insights in Diagnosing Phlebological Diseases?—A Systematic Review by Sergiu-Ciprian Matei, Sorin Olariu, Ana-Maria Ungureanu, Daniel Malita, Flavia Medana Petrașcu

“…The future of AI in venous diagnostics is promising, and several areas of development were noted, including AI algorithms embedding directly into ultrasound devices to provide instantaneous diagnostic insights during patient evaluations; combining AI-processed Doppler data with other imaging modalities, such as computed tomography or MRI, for comprehensive assessments; AI usage in order to predict disease progression and tailor treatment strategies based on individual patient profiles; and constructing large-scale, multicenter datasets to improve the robustness and generalizability of AI algorithms.…”
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