Cross-language dissemination of Chinese classical literature using multimodal deep learning and artificial intelligence by Yulan Bai, Songhua Lei

“…The proposed model innovatively integrates visual features generated by conditional diffusion models and leverages knowledge distillation techniques to achieve efficient transfer learning, fully exploiting the latent information in multilingual corpora. …”
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Combination of gray level features with deep transfer learning for copra classification using machine learning and neural networks by A. Stephen Sagayaraj, T. Kalavathi Devi

“…A novel approach is introduced by combining GLCM (Gray-Level Co-Occurrence Matrix) features with features extracted from four transfer learning models. …”
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Multi-Level Feature Fusion Attention Generative Adversarial Network for Retinal Optical Coherence Tomography Image Denoising by Yiming Qian, Yichao Meng

“…Evaluations on three public OCT datasets compared traditional methods and deep learning models using PSNR, SSIM, CNR, and ENL metrics. <b>Results</b>: MFFA-GAN achieved good performance (PSNR:30.107 dB, SSIM:0.727, CNR:3.927, ENL:529.161) on smaller datasets, outperforming benchmarks and further enhanced interpretability through pixel error maps. …”
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Deep learning based fine-grained recognition technology for basketball movements by Lin Zhang

“…The model based on 3D attention feature fusion module exhibits stronger robustness when the feature dimension changes. …”
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Assessing nanocrystalline pulsed transformer core performance using Maxwell ANSYS by D. Wright, K. Kelp, T. Klein, J. Stephens, J. Dickens, J. Mankowski, Z. C. Shaw, A. Neuber

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Experimental and numerical investigations of hydrodynamic performance for horizontal-axis hydrokinetic turbines by Fatih Mehmet Kale, Naz Yilmaz, Kemal Furkan Sokmen, Weichao Shi

“…The CFD investigations of HKTs revealed that the Detached Eddy Simulation (DES) model has less relative errors compared to the other turbulence models at the same Tip Speed Ratio (TSR) values, while the Sliding Mesh (SM) method describing rotation gives more consistent and closer results to the experiments, with the investigation of y+ point of view.…”
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Utilizing Enhanced Particle Swarm Optimization for Feature Selection in Gender-Emotion Detection From English Speech Signals by Ammar Amjad, Li-Chia Tai, Hsien-Tsung Chang

“…We extract pitch and acoustic-related statistical features from speech samples and develop separate models for gender prediction and emotion detection. …”
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A computational approach and software package RNAexploreR for grouping RNA molecules of human genes by exon features by M. M. Yatskou, V. V. Skakun, V. V. Grinev

“…A certain part of the research is aimed at developing reliable prediction models for global exon combinatorics during the formation of mature RNA. …”
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Artificial intelligence driven platform for rapid catalytic performance assessment of nanozymes by Wenjie Xuan, Xiaofo Li, Honglei Gao, Luyao Zhang, Jili Hu, Liping Sun, Hongxing Kan

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Integration of UAV-sensed features using machine learning methods to assess species richness in wet grassland ecosystems by Clara Oliva Gonçalves Bazzo, Bahareh Kamali, Murilo dos Santos Vianna, Dominik Behrend, Hubert Hueging, Inga Schleip, Paul Mosebach, Almut Haub, Axel Behrendt, Thomas Gaiser

“…RF models generally outperformed PLS models across different feature sets, with the CH + VI + GLCM combination yielding the best results. …”
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Tau PET correlates with different Alzheimer’s disease‐related features compared to CSF and plasma p‐tau biomarkers by Rik Ossenkoppele, Juhan Reimand, Ruben Smith, Antoine Leuzy, Olof Strandberg, Sebastian Palmqvist, Erik Stomrud, Henrik Zetterberg, the Alzheimer's Disease Neuroimaging Initiative, Philip Scheltens, Jeffrey L Dage, Femke Bouwman, Kaj Blennow, Niklas Mattsson‐Carlgren, Shorena Janelidze, Oskar Hansson

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Comparison of clinicopathological features and prognostic analysis between early-onset and late-onset advanced rectal mucinous adenocarcinoma by Du Rui, Chang Yue, Li Yonghai, Cheng Yuanguang, Zhang Juan, Zhang Chengyue, Xu Liejuan, Liu Yuancheng

“…The clinicopathological features of the two groups were compared, and survival analysis was performed. …”
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Predicting Sugar Yield From Sugarcane Using Machine Learning for Jaggery Production by Kathirvel Narayanasamy, Ilayaraja Venkatachalam

“…The feature importance results highlighted a clear hierarchy in predictor significance, with water content and Brix values collectively driving approximately 90% of the model&#x2019;s predictive strength. …”
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HierbaNetV1: a novel feature extraction framework for deep learning-based weed identification by Justina Michael, Thenmozhi Manivasagam

“…The proposed method integrates low-level and high-level features thus allowing the model to learn intensive and diversified features. …”
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The features of the kinematics for the joint functioning of the main mechanisms and working equipment of an open-pit excavator during excavation of rocks by Komissarov A.P., Lukashuk O.A., Nabiullin R.Sh., Letnev K.Yu.

“…Methods of mechanism analysis, mathematical modeling and computational experiment were used. On the basis of a simulation model of the excavation process, a computational experiment was performed to calculate the operating parameters of the main mechanisms for the EKG-20A excavator manufactured by PJSC Uralmashplant when working out an excavator face. …”
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A multimodal framework for pepper diseases and pests detection by Jun Liu, Xuewei Wang

“…This model decomposes complex multimodal features of language and images into explicit attribute features and employs fine-grained multimodal attribute contrast learning strategies. …”
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Exploring Voice Acoustic Features Associated with Cognitive Status in Korean Speakers: A Preliminary Machine Learning Study by Jiho Lee, Nayeon Kim, Ji-Wan Ha, Kyunghun Kang, Eunhee Park, Janghyeok Yoon, Ki-Su Park

“…Voice acoustic features were extracted and analyzed. <b>Results</b>: The Deep Neural Network model performed best in two classification tasks, with Precision-Recall Area Under the Curve scores of 0.737 for severe vs. no impairment and 0.726 for mild vs. no impairment, while Random Forest achieved 0.715 for severe + mild vs. no impairment. …”
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A Deep Learning Framework for Chronic Kidney Disease stage classification by Gayathri Hegde M, P Deepa Shenoy, Venugopal KR, Arvind Canchi

“…The models were assessed using performance metrics such as accuracy, precision, recall, F1-Score, Loss, Validation Loss and computation time. …”
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Feature extraction and spatial imaging of synchrotron radiation X-ray diffraction patterns using unsupervised machine learning by Kentaro Kutsukake, Takefumi Kamioka, Kota Matsui, Ichiro Takeuchi, Takashi Segi, Takuo Sasaki, Seiji Fujikawa, Masamitu Takahasi

“…We analyzed a number of complicated X-ray diffraction patterns using feature patterns obtained through unsupervised machine learning. …”
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Semantic Segmentation of Aerial Laser Point Clouds Based on Deep-Residual Enhanced Coding of Multi-Feature Information by Xin Luo, Peng Lin, Xiaoxi Li, Zuqi Wei, Hai Li

“…Then, a novel structure for feature aggregation, named SEP–PointNet, is introduced to encode the auxiliary and geometric features of points separately. …”
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