Infrared dim tiny-sized target detection based on feature fusion by Peng Zhang, Yaman Jing, Guodong Liu, Ziyang Chen, Xiaoyan Wu, Osami Sasaki, Jixiong Pu

“…The comparison of the proposed network with other algorithms are performed on open-source dataset and experimentally generated infrared images. …”
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Extrapolation of cavitation and hydrodynamic pressure in lubricated contacts: a physics-informed neural network approach by Faras Brumand-Poor, Freddy Kokou Azanledji, Nils Plückhahn, Florian Barlog, Lukas Boden, Katharina Schmitz

“…To overcome these experimental and computational limitations, machine learning algorithms offer a promising solution. Physics-informed machine learning (PIML) improves on traditional data-driven models by incorporating physical principles. …”
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A Multi-Branch Convolution and Dynamic Weighting Method for Bearing Fault Diagnosis Based on Acoustic–Vibration Information Fusion by Xianming Sun, Yuhang Yang, Changzheng Chen, Miao Tian, Shengnan Du, Zhengqi Wang

“…Experimental results demonstrate that our method significantly outperforms other algorithms in terms of convergence speed and final performance. …”
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Performance Analysis of Iteratively Decoded Convergent Source Mapping with Sphere Packing-Assisted Differential Space-Time Spreading Technique for Efficient Video Transmission by Ishtiaque Ahmed, Nasru Minallah, Jaroslav Frnda, Jan Nedoma

“…The SP-assisted DSTS systems are mainly useful in decoding algorithms that operate without requiring Channel State Information (CSI). …”
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Optimized Path Planning and Scheduling in Robotic Mobile Fulfillment Systems Using Ant Colony Optimization and Streamlit Visualization by Isam Sadeq Rasham

“…However, in a manner to implement such systems, one needs to find enhanced algorithms pertaining to resource mapping and the planning of movement of robots in sensitive environments. …”
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Progressive Bitwidth Assignment Approaches for Efficient Capsule Networks Quantization by Mohsen Raji, Amir Ghazizadeh Ahsaei, Kimia Soroush, Behnam Ghavami

“…Capsule Networks (CapsNets) are a class of neural network architectures that can be used to more accurately model hierarchical relationships due to their hierarchical structure and dynamic routing algorithms. However, their high accuracy comes at the cost of significant memory and computational resources, making them less feasible for deployment on resource-constrained devices. …”
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Integrative single-cell and multi-omics analyses reveal ferroptosis-associated gene expression and immune microenvironment heterogeneity in gastric cancer by Shupeng Zhang, Zhaojin Li, Gang Hu, Hekai Chen

“…In the gene selection and model validation section, critical genes were identified using machine learning algorithms, constructing a model with high predictive accuracy. …”
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Milk adulteration identification using hyperspectral imaging and machine learning by Muhammad Aqeel, Ahmed Sohaib, Muhammad Iqbal, Syed Sajid Ullah

“…Machine learning techniques (logistic regression, decision tree, support vector machine, and linear discriminant analysis [LDA]), are employed, with LDA excelling in adulteration identification by learning the spectral signatures. These algorithms are trained and validated using a developed milk adulteration dataset. …”
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Decision tree-based learning and laboratory data mining: an efficient approach to amebiasis testing by Enas Al-khlifeh, Ahmad S. Tarawneh, Khalid Almohammadi, Malek Alrashidi, Ramadan Hassanat, Ahmad B. Hassanat

“…Patient demographics, clinical signs, microscopic diagnoses, and leukocyte counts were used to train eight decision tree algorithms and compare their accuracy of predictions. …”
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Emerging Strategies in Cartilage Repair and Joint Preservation by Mircea Adrian Focsa, Sorin Florescu, Armand Gogulescu

“…Future research should focus on refining integrated therapeutic protocols, conducting long-term clinical evaluations, and embracing personalised treatment models driven by artificial intelligence and predictive algorithms.…”
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A Fusion Method Incorporating Dual-Attention Mechanism and Transfer Learning Into UNet++ for Remote Sensing Image Coastline Extraction by Yanru Song, Bai Xue, Yueyue Meng, Xiang Qin, Yixiao Li, Qi Liu

“…Experimental results using the GID dataset for coastline segmentation demonstrate that compared to the latest algorithms such as PSPNet, CS-Deeplab v3+, and UNet++, the improved UNet++ network achieves lower boundary blurriness and more accurate segmentation results for coastlines, with fewer missed and false detections. …”
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SSMM: Semi-supervised manifold method with spatial-spectral self-training and regularized metric constraints for hyperspectral image dimensionality reduction by Bei Zhu, Yao Jin, Xuehua Guan, Yanni Dong

“…In the dimensionality reduction experiments, the overall accuracy of SSMM in any dimension is higher than that of other algorithms. In the classification experiments, the KAPPA coefficient of SSMM on the three data sets is improved by 1.41%, 0.61%, and 0.27% respectively. …”
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Online learning: Attitudes of Russian students by D. V. Rudenkin

“…The research materials can be used for the optimisation of Internet technologies implementation in the learning process and for the clarification of optimal algorithms for using such technologies when working with students.…”
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Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach. by Hamidreza Moradi, H Timothy Bunnell, Bradley S Price, Maryam Khodaverdi, Michael T Vest, James Z Porterfield, Alfred J Anzalone, Susan L Santangelo, Wesley Kimble, Jeremy Harper, William B Hillegass, Sally L Hodder, National COVID Cohort Collaborative (N3C) Consortium

“…Then, the most accurate model is utilized by eXplainable Artificial Intelligence (XAI) algorithms to provide insights about the learned treatment combination impacts on the model's final outcome prediction.…”
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PCCN: Polarimetric Contexture Convolutional Network for PolSAR Image Super-Resolution by Lin-Yu Dai, Ming-Dian Li, Si-Wei Chen

“…Compared with state-of-the-art algorithms, experimental studies demonstrate and validate the effectiveness and superiority of the proposed method in both visualization examination and quantitative metrics. …”
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Anomaly Detection Using Explainable Random Forest for the Prediction of Undesirable Events in Oil Wells by Nida Aslam, Irfan Ullah Khan, Aisha Alansari, Marah Alrammah, Atheer Alghwairy, Rahaf Alqahtani, Razan Alqahtani, Maryam Almushikes, Mohammed AL Hashim

“…Accordingly, this study aimed to utilize ML techniques to reduce the time spent manually to establish rules that detect abnormalities in oil wells, leading to rapid and more precise detection. Four ML algorithms were utilized, including random forest (RF), logistic regression (LR), k-nearest neighbor (K-NN), and decision tree (DT). …”
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Fully Automated Bone Age Assessment on Large-Scale Hand X-Ray Dataset by Xiaoying Pan, Yizhe Zhao, Hao Chen, De Wei, Chen Zhao, Zhi Wei

“…In the end, a variety of ensemble regression algorithms are applied to perform BAA. Besides, we choose a specific CNN to extract features and explain why we select that CNN. …”
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Mf-net: multi-feature fusion network based on two-stream extraction and multi-scale enhancement for face forgery detection by Hanxian Duan, Qian Jiang, Xin Jin, Michal Wozniak, Yi Zhao, Liwen Wu, Shaowen Yao, Wei Zhou

“…Current face forgery detection algorithms achieve high detection accuracy within-dataset. …”
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DualTransAttNet: A Hybrid Model with a Dual Attention Mechanism for Corn Seed Classification by Fei Pan, Dawei He, Pengjun Xiang, Mengdie Hu, Daizhuang Yang, Fang Huang, Changmeng Peng

“…Existing classification algorithms often suffer from limitations such as reliance on single information sources, constrained feature extraction capabilities, time consumption, low accuracy, and the potential to cause irreversible damage to seeds. …”
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Stimulation of MMP-1 and CCL2 by NAMPT in PDL Cells by Marjan Nokhbehsaim, Sigrun Eick, Andressa Vilas Boas Nogueira, Per Hoffmann, Stefan Herms, Holger Fröhlich, Søren Jepsen, Andreas Jäger, Joni Augusto Cirelli, James Deschner

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