Establishing a radiomics model using contrast-enhanced ultrasound for preoperative prediction of neoplastic gallbladder polyps exceeding 10 mm by Dong Jiang, Yi Qian, Yijun Gu, Ru Wang, Hua Yu, Zhenmeng Wang, Hui Dong, Dongyu Chen, Yan Chen, Haozheng Jiang, Yiran Li

“…This model, derived from machine learning frameworks including Support Vector Machine (SVM), Logistic Regression (LR), Multilayer Perceptron (MLP), k-Nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGBoost) with fivefold cross-validation, showed AUCs of 0.95 (95% CI: 0.90–0.99) and 0.87 (95% CI: 0.72–1.0) in internal validation. …”
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Development of a digital treatment analyzer for the management of prostate cancer patients, with the help of real world data and use of predictive modelling by Lev Korolkov, Heather A Robinson, Konstantinos Mouratis

“…We aim to present the concept of a digital treatment analyzer (TA) for the management of prostate cancer (PC) patients, leveraging real-world data (RWD) and predictive modeling to enhance personalized disease management strategies and adherence to PC guidelines, ultimately aiming to optimize therapeutic efficacy and improve outcomes. …”
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A Track-Type Orchard Mower Automatic Line Switching Decision Model Based on Improved DeepLabV3+ by Lixing Liu, Pengfei Wang, Jianping Li, Hongjie Liu, Xin Yang

“…To achieve unmanned line switching operations for a track-type mower in orchards, an automatic line switching decision model based on machine vision has been designed. …”
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3D modeling of transport properties on the surface of a textronic structure produced using a physical vapor deposition process by Mączka Mariusz, Korzeniewska Ewa, Lebioda Marcin, Pawłowski Stanisław

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NeSyDPP-4: discovering DPP-4 inhibitors for diabetes treatment with a neuro-symbolic AI approach by Delower Hossain, Delower Hossain, Ehsan Saghapour, Ehsan Saghapour, Jake Y. Chen, Jake Y. Chen, Jake Y. Chen

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A Custom-Developed Device for Testing Tensile Strength and Elasticity of Vascular and Intestinal Tissue Samples for Anastomosis Regeneration Research by Zoltan Attila Godo, Laszlo Adam Fazekas, Gergo Fritsch, Balazs Szabo, Norbert Nemeth

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EHA-YOLOv5: An Efficient and Highly Accurate Improved YOLOv5 Model for Workshop Bearing Rail Defect Detection Application by Jiyong Hu, Hongfei Yang, Jiatang He, Dongxu Bai, Hongda Chen

“…To validate the superiority of our model, a comparison with popular current models was conducted, achieving optimal values in recall rate, accuracy, and mAP, which were 91.4%, 92.6%, and 88.9%, respectively. …”
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Advanced neural network architectures for tomato leaf disease diagnosis in precision agriculture by Hritwik Ghosh, Irfan Sadiq Rahat, Md. Mintajur Rahman Emon, Md. Jisan Mashrafi, Mohammed Abdul Al Arafat Tanzin, Sachi Nandan Mohanty, Shashi Kant

“…The system incorporates a dataset of 6,000 images, which underwent extensive preprocessing, including resizing to 256 × 256 pixels, grayscale conversion, normalization, masking, and augmentation, to optimize input quality for the model. Our approach stands out through the integration of state-of-the-art neural network architectures—VGG19, Vision Transformer (ViT), EfficientNetV2, ConvNeXt—and a novel hybrid model specifically designed to leverage the strengths of diverse architectures. …”
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RETRACTED ARTICLE: Screening and identification of susceptibility genes for cervical cancer via bioinformatics analysis and the construction of an mitophagy-related genes diagnosti... by Zhang Zhang, Fangfang Chen, Xiaoxiao Deng

“…Results MRGs were extracted from single-cell RNA sequencing data, and a network graph was constructed on the basis of intercellular interaction data. Furthermore, using machine learning algorithms, we constructed a clinical prognostic model and validated and optimized it via extensive clinical data. …”
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Construction of a predictive model for rebleeding risk in upper gastrointestinal bleeding patients based on clinical indicators such as Helicobacter pylori infection by Wei Zang, Ze Lin, Yanduo Zhao, Tianshi Jia, Xinglong Zhang

“…The rebleeding detection rates of Rockall score, AIMS65 score and Glasgow Blatchford score were 16.14%, 0 and 77.17%, respectively. Of the four machine learning models, Random Forest (RF) had the highest accuracy on the test set at 0.68. …”
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A Levelized Multiple Workflow Heterogeneous Earliest Finish Time Allocation Model for Infrastructure as a Service (IaaS) Cloud Environment by Farheen Bano, Faisal Ahmad, Mohammad Shahid, Mahfooz Alam, Faraz Hasan, Mohammad Sajid

“…This paper proposes a Levelized Multiple Workflow Heterogeneous Earliest Finish Time (LMHEFT) model to optimize makespan in the cloud computing environment. …”
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Development of a Weighted Average Ensemble Model for Predicting Officially Assessed Land Prices Using Grid Map Data and SHAP by Surin Im, Kangmin Kim, Geunhee Lee, Hoi-Jeong Lim

“…The optimal ensemble model, weighted as GBM:XGBoost:LightGBM =0:9:1, achieved a high predictive performance with R2 of 0.8964. …”
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Multivariate predictive modeling of compressive strength in ground granulated blast furnace slag/fly ash-based alkali-activated concrete by Dina A. Emarah

“…Additionally, the study introduced advanced performance metrics, including R2, RMSE, Scatter Index (SI), Objective Function (OBJ), and Scatter Index (SI), to provide a more robust validation of the models. By incorporating diverse parameters, employing advanced machine learning techniques, and performing a comprehensive sensitivity analysis, this research establishes a new benchmark for predictive modeling of AAC's CS. …”
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A Novel Local Binary Patterns-Based Approach and Proposed CNN Model to Diagnose Breast Cancer by Analyzing Histopathology Images by Mehmet Gul

“…The histopathology images improved with the QS-LBP method were then analyzed with the most commonly used Random Forest and Optimized Forest algorithms among machine learning algorithms. …”
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Numerical examination of the chemically reactive MHD flow of hybrid nanofluids over a two-dimensional stretching surface with the Cattaneo–Christov model and slip conditions by Algehyne Ebrahem A., Alamrani Fahad Maqbul, Seada M. M., Saeed Anwar, Bognár Gabriella

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MODELING OF A DUPLEX MILLER AND CONDITIONS OF FORMATION OF TREATMENT OF WHEEL PAIRS OF MAIN ELECTRIC CARDS, DUAL CARS, ELECTRO SECTIONS by Victor Shapovalov, Alexandr Permyakov, Alexander Klochko

“…The article solves the following  tasks: substantiation of the importance of upgrading the most promising designs of "duplex" mills, taking into account the reduction of vibrations due to chip crushing, to increase tool wear resistance, machining accuracy, reduce tool readjustment time and, accordingly, reduce equipment downtime, increase processing productivity, determine the mathematical apparatus for calculating model parameters; development of a method for modeling and optimization of shaping processes in the processing of locomotive wheel sets. …”
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YOLO-WTB: Improved YOLOv12n Model for Detecting Small Damage of Wind Turbine Blades From Aerial Imagery by Phat T. Nguyen, Duy C. Huynh, Loc D. Ho, Matthew W. Dunnigan

“…In addition, in the backbone part, we also propose to remove a Convolution module and an Area Attention Concatenate-Convolution-Fusion module and add an improved SoftPool Feature Spatial Pyramid Pooling - Fast module to increase the feature extraction ability while maintaining the complexity of the model. The proposed model not only optimizes wind turbine maintenance efficiency but also contributes to advancements in the field of computer vision.…”
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Conflict-based model predictive control for multi-agent path finding experimentally validated on a magnetic planar drive system by Kai Janning, Abdalsalam Housin, Christopher Schulte, Frederik Erkens, Luca Frenken, Laura Herbst, Bastian Nießing, Robert H. Schmitt, Robert H. Schmitt

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The PM_2.5-Bound Polycyclic Aromatic Hydrocarbon Behavior in Indoor and Outdoor Environments, Part III: Role of Environmental Settings in Elevating Indoor Concentrations... by Gordana Jovanović, Mirjana Perišić, Timea Bezdan, Svetlana Stanišić, Kristina Radusin, Aleksandar Popović, Andreja Stojić

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European sovereign debt control through reinforcement learning by Tato Khundadze, Willi Semmler, Willi Semmler, Willi Semmler

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