DualRep: Knowledge Graph Completion by Utilizing Dual Representation of Relational Paths and Tail Node Density Insights by Haji Gul, Feras Al-Obeidat, Adnan Amin, Muhammad Wasim, Fernando Moreira

“…We validate the effectiveness of our technique by conducting comprehensive tests on many benchmark datasets, revealing substantial enhancements compared to conventional approaches. The Dual-Rep model, which incorporates relational paths and node density features, has continuously shown improved performance across several metrics, such as Mean Reciprocal Rank (MRR), Hit at 1 (Hit@1), and Hit at 3 (Hit@3). …”
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Multimodal Adaptive Identity-Recognition Algorithm Fused with Gait Perception by Changjie Wang, Zhihua Li, Benjamin Sarpong

“…To overcome this deficiency, this paper proposes several gait feature identification algorithms. First, in combination with the collected gait information of individuals from triaxial accelerometers on smartphones, the collected information is preprocessed, and multimodal fusion is used with the existing standard datasets to yield a multimodal synthetic dataset; then, with the multimodal characteristics of the collected biological gait information, a Convolutional Neural Network based Gait Recognition (CNN-GR) model and the related scheme for the multimodal features are developed; at last, regarding the proposed CNN-GR model and scheme, a unimodal gait feature identity single-gait feature identification algorithm and a multimodal gait feature fusion identity multimodal gait information algorithm are proposed. …”
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HoRNS-CNN model: an energy-efficient fully homomorphic residue number system convolutional neural network model for privacy-preserving classification of dyslexia neural-biomarkers by Opeyemi Lateef Usman, Ravie Chandren Muniyandi, Khairuddin Omar, Mazlyfarina Mohamad, Ayoade Akeem Owoade, Morufat Adebola Kareem

“…The findings show that the HoRNS-CNN model effectively manages cipher-image expansion with an asymptotic complexity of $$O\left({n}^{3}\right)$$ O n 3 , offering better performance and faster feature extraction compared to its peers. …”
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Online Asynchronous Learning over Streaming Nominal Data by Hongrui Li, Shengda Zhuo, Lin Li, Jiale Chen, Tianbo Wang, Jun Tang, Shaorui Liu, Shuqiang Huang

“…Specifically, OALN is grounded in three core principles: (1) It utilizes a Gaussian mixture copula in the latent space to preserve class structure and numerical relationships, thereby addressing the encoding and relational learning challenges posed by mixed feature types. (2) It performs adaptive imputation through conditional covariance matrices to seamlessly handle random missing values and feature drift, while incrementally updating copula parameters to accommodate dynamic changes in the feature space. (3) It incorporates a model pool and hint mechanism to efficiently process asynchronous label feedback. …”
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Robust EEG Characteristics for Predicting Neurological Recovery from Coma After Cardiac Arrest by Meitong Zhu, Meng Xu, Meng Gao, Rui Yu, Guangyu Bin

“…Approach: Standard machine learning models were applied sequentially as feature selectors and filters. …”
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Structural Performance of a Hollow-Core Square Concrete Column Longitudinally Reinforced with GFRP Bars under Concentric Load by AbdulMuttalib I. Said, Hussein A. Hilfi, Abbas A. Allawi, George Wardeh

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Thermal performance analysis of a low-cost and eco-friendly rotary desiccant wheel dehumidification system with recycled materials by Pinar Mert Cuce, Erdem Cuce, Ali Ahmed Alqahtani, Saad Alshahrani, Manzoore Elahi M. Soudagar, Jingyu Cao, Abdallah Bouabidi, Mohammed El Hadi Attia, Yusuf Nadir Yilmaz

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Machine Learning for Long COVID Inference Based on the Acute Phase: A Case Study in Healthcare Professionals by Caio B. S. Maior, Sandrely P. Silva, Isis D. Lins, Ana Lisa Gomes, Marcio C. Moura

“…In addition to five ML (i.e., models such asRandom Forest, K-Nearest Neighbors, Logistic Regression, Support Vector Machine, and Multilayer Perceptron), we applied dimensionality reduction techniques such as Principal Components Analysis, Linear Discriminant Analysis, and Feature Selection. …”
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Non-Destructive Detection of Pesticide-Treated Baby Leaf Lettuce During Production and Post-Harvest Storage Using Visible and Near-Infrared Spectroscopy by Dimitrios S. Kasampalis, Pavlos I. Tsouvaltzis, Anastasios S. Siomos

“…In this study, the performance of visible and near-infrared (vis/NIR) spectroscopy was tested in discriminating pesticide-free against pesticide-treated lettuce plants. …”
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Exploratory integration of near-infrared spectroscopy with clinical data: a machine learning approach for HCV detection in serum samples by Eloy Pérez-Gómez, José Gómez, José Gómez, Jennifer Gonzalo, Sergio Salgüero, Daniel Riado, María Luisa Casas, María Luisa Gutiérrez, Elena Jaime, Enrique Pérez-Martínez, Rafael García-Carretero, Javier Ramos, Conrado Fernández-Rodríguez, Conrado Fernández-Rodríguez, Myriam Catalá, Myriam Catalá, Luca Martino, Óscar Barquero-Pérez

“…Our dataset comprised 137 serum samples from 38 patients, each represented by a NIRS spectrum and clinical data from blood tests.ResultsAfter preprocessing with Standard Normal Variate (SNV) correction and downsampling, the best-performing RF model, which combined NIRS features and clinical data, achieved an accuracy of 72.2% and an AUC-ROC of 0.850, outperforming models using only clinical or spectral data. …”
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Sequence characterization and evolutionary analysis of S-RNase gene among five genera Pomoideae by LIANG Wenjie, XIE Zhiliang, Wuyun Tana

“…The Find Best DNA/Protein Models program of MEGA11 software was used to find out the optimal model suitable for the sequence of 5 genera of Pyridae S-RNase gene, and the corresponding model and algorithm were used to calculate the differentiation between the sequences. …”
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Diagnostic performance of dual-layer spectral CT Radiomics and deep learning for differentiating osteoblastic bone metastases from bone islands by Yuchao Xiong, Wei Guo, Xuwen Zeng, Fan Xu, Li Wu, Jiahui Ou

“…Background: This study aimed to compare the diagnostic performance of radiomic features derived from dual-layer spectral detector computed tomography (DLSCT) and a deep learning (DL) model applied to conventional CT images in the differentiation of osteoblastic bone metastases (OBM) from bone islands (BI). …”
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A colonic polyps detection algorithm based on an improved YOLOv5s by Jianjun Li, Jinhui Zhao, Yifan Wang, Jinhui Zhu, Yanhong Wei, Junjiang Zhu, Xiaolu Li, Shubin Yan, Qichun Zhang

“…The YOLOv5s + SEBiFPN model demonstrate a substantial improvement over the YOLOv5s algorithm, and establishing a benchmark technology for advancing computer-assisted diagnostic systems is feasible.…”
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Efficient-ViT B0Net: A high-performance light weight transformer for rice leaf disease recognition and classification by Santosh Kumar Upadhyay, Rajesh Prasad

“…The proposed model utilizes the strengths of CNN and Vision Transformer, where CNN successfully extracts local fine-grained texture features quickly. …”
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An Interpretable Method for Asphalt Pavement Skid Resistance Performance Evaluation Under Sand-Accumulated Conditions Based on Multi-Scale Fractals by Yuhan Weng, Zhaoyun Sun, Huiying Liu, Yingbin Gu

“…The three-dimensional (3D) texture of asphalt pavements is decomposed at multiple scales, and fractal and multifractal features are extracted to build a dataset. The performance of mainstream machine learning models is compared, and the eXtreme Gradient Boosting (XGBoost) model is optimized using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. …”
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Towards Precision Medicine in Sinonasal Tumors: Low-Dimensional Radiomic Signature Extraction from MRI by Riccardo Biondi, Giacomo Gravante, Daniel Remondini, Sara Peluso, Serena Cominetti, Francesco D’Amore, Maurizio Bignami, Alberto Daniele Arosio, Nico Curti

“…<b>Results:</b> The results showed that ML classification using both data types achieved a median Matthews Correlation Coefficient (MCC) of 0.60 ± 0.07. The best-performing DNetPRO models reached an MCC of 0.73 (T1-w + T2-w) and 0.61 (T1-w only). …”
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An enhanced text classification model by the inverted attention orthogonal projection module by Hong Zhao, Chenpeng Zhang, Aolong Wang

“…The orthogonal projection method has made significant progress in text classification, especially in generating discriminative features. This method obtains more pure and suitable for classification features by projecting text features onto the orthogonal direction of common features (which are not helpful for classification and actually confuse performance). …”
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Construction of a risk prediction model for occupational noise-induced hearing loss using routine blood and biochemical indicators in Shenzhen, China: a predictive modelling study by Wenting Feng, Wen Zhang, Yan Guo, Naixing Zhang, Liang Zhou, Dafeng Lin, Linlin Chen, Caiping Li, Liuwei Shi, Xiangli Yang, Peimao Li, Dianpeng Wang

“…On the test data set, the model achieved an AUC of 0.990. After implementing feature selection, the model was refined to include only 16 features, while maintaining strong performance on a newly acquired independent data set, with an AUC of 0.872, a balanced accuracy of 0.798, a sensitivity of 0.755 and a specificity of 0.840. …”
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Application of artificial intelligence sensor and visual image technology in the analysis of hydrophilic space landscape characteristics by Dayan Li

“…Combined with the key nodes of 3D object AI feature recognition, multi-sensor collaborative Dempster Shafer evidence theory and 3D convolutional neural network waterfront space landscape feature recognition sub-model are constructed, and the waterfront space landscape recognition analysis model is tested and analyzed. …”
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Prediction of pathological complete response to neoadjuvant chemoimmunotherapy in non–small cell lung cancer using 18F-FDG PET radiomics features of primary tumour and lymph nodes... by Xingbiao Liu, Zhilin Ji, Libo Zhang, Linlin Li, Wengui Xu, Qian Su

“…The performance of these models was evaluated based on the area under the curve (AUC). …”
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