Data Reconstruction Methods in Multi-Feature Fusion CNN Model for Enhanced Human Activity Recognition by Jae Eun Ko, SeungHui Kim, Jae Ho Sul, Sung Min Kim

“…Background: Human activity recognition (HAR) plays a pivotal role in digital healthcare, enabling applications such as exercise monitoring and elderly care. …”
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Parameters Estimation and Stability Analysis of Nonlinear Fractional-Order Economic System Based on Empirical Data by Lei He, Xiong Wang

“…This paper is devoted to propose a novel method for studying the macroeconomic system with fractional derivative, which can depict the memory property of actual data of economic variables. First of all, we construct a constrained optimal problem to evaluate the coefficients of nonlinear fractional financial system based on empirical data and design the corresponding genetic algorithm. …”
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Knee Osteoarthritis Diagnosis With Unimodal and Multi-Modal Neural Networks: Data From the Osteoarthritis Initiative by Xin Yu Teh, Pauline Shan Qing Yeoh, Tao Wang, Xiang Wu, Khairunnisa Hasikin, Khin Wee Lai

“…Multi-modal learning, which integrates information from various modalities, is increasingly recognized for its potential to enhance diagnostic performance in medical applications. However, such models incur a higher computational load due to the additional data required. …”
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Balancing AI-assisted learning and traditional assessment: the FACT assessment in environmental data science education by Ahmed S. Elshall, Ahmed S. Elshall, Ahmed S. Elshall, Ashraf Badir

“…To address these challenges, the Fundamental, Applied, Conceptual, critical Thinking (FACT) assessment was implemented in an Environmental Data Science course for upper-level undergraduate and graduate students from civil and environmental engineering, and Earth sciences. …”
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Development of a Distributed Physics‐Informed Deep Learning Hydrological Model for Data‐Scarce Regions by Liangjin Zhong, Huimin Lei, Jingjing Yang

“…Furthermore, transfer learning DL models pre‐trained on large data sets still necessitate local data for retraining, thereby constraining their applicability. …”
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Assessing the Feasibility of Persistent Scatterer Data for Operational Dam Monitoring in Germany: A Case Study by Jonas Ziemer, Jannik Jänichen, Carolin Wicker, Daniel Klöpper, Katja Last, Andre Kalia, Thomas Lege, Christiane Schmullius, Clémence Dubois

“…With the launch of nationwide and continent-wide ground motion services (GMSs), freely available deformation data can now be analyzed on a large scale. However, their applicability for monitoring critical infrastructure, such as dams, has not yet been thoroughly assessed, and several challenges have hindered the integration of MT-InSAR into existing monitoring frameworks. …”
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MuCST: restoring and integrating heterogeneous morphology images and spatial transcriptomics data with contrastive learning by Yu Wang, Zaiyi Liu, Xiaoke Ma

“…MuCST accurately identifies spatial domains and is applicable to diverse datasets platforms. Overall, MuCST provides an alternative for integrative analysis of multi-modal SRT data ( https://github.com/xkmaxidian/MuCST ).…”
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Boosting EEG and ECG Classification with Synthetic Biophysical Data Generated via Generative Adversarial Networks by Archana Venugopal, Diego Resende Faria

“…Techniques such as discrete wavelet transform, downsampling, and upsampling were employed to enhance data quality. This method shows significant potential in addressing biophysical data scarcity and advancing applications in assistive technologies, human-robot interaction, and mental health monitoring, among other medical applications.…”
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“Ensembled transfer learning approach for error reduction in landslide susceptibility mapping of the data scare region” by Ankit Singh, Nitesh Dhiman, K. C. Niraj, Dericks Praise Shukla

“…Abstract Landslide susceptibility map (LSM) plays an important role in providing the knowledge of slopes prone to future landslides. However, the applicability of LSM is often hindered due to high cost of data collection especially in mountainous region such as Himalayas. …”
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A Preliminary Assessment of the VIIRS Cloud Top and Base Height Environmental Data Record Reprocessing by Qian Liu, Xianjun Hao, Cheng-Zhi Zou, Likun Wang, John J. Qu, Banghua Yan

“…This preliminary assessment enhances data applicability of remote sensing products for atmospheric and climate research, allowing for more accurate cloud measurements and advancing environmental monitoring efforts.…”
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Stretched Gelatin Phantom for Detection of Residual Dipolar Couplings in MR Spectra and Data Analysis of Carnosine by Karel Bernášek, Marián Grocký, Martin Burian, Jan Lang

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Data-driven hybrid SARIMAX-MLP framework for energy consumption prediction in residential micro-grid by Ibrahim Ali Kachalla, Christian Ghiaus, Adeniran Ademuwagun, Olufemi Babajide Odeyinde, Majid Baseer

“…A case study of two residential blocks, with one year six months (18 months) of energy consumption data, was utilised to evaluate the model's prediction accuracy using performance metrics (RMSE, MAE, R2) and computational cost. …”
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Two Anonymous Cooperative Cache-Based Data Access Schemes in Mobile Ad Hoc Networks by Chang-Ji Wang, Xi-Lei Xu, Dong-Yuan Shi

“…Mobile ad hoc network has been extensively studied in recent years due to its potential applications in civilian and military environments. Cooperative caching, which allows the sharing and coordination of cached data among multiple nodes, could be employed to improve data accessibility and reduce data access cost in mobile ad hoc networks. …”
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Analysis of kidney patients and pump failure data using a new unit interval distribution by Tariq S. Alshammari, Khudhayr A. Rashedi

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From test data to FE code: a straightforward strategy for modelling the structural bonding interface by M. A. Lepore, M. Perrella

“…The algorithm is applicable both to dominant mode I or dominant mode II debonding simulations. …”
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From test data to FE code: a straightforward strategy for modelling the structural bonding interface by M. A. Lepore, M. Perrella

“…The algorithm is applicable both to dominant mode I or dominant mode II debonding simulations. …”
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An evaluation of urbanisation processes in suburban zones using land-cover data and fuzzy set theory by Cieślak Iwona, Górecka Kamila

“…The study explored the applicability of GIS as a data source and a tool for evaluating urbanisation processes in studies that rely on modern methods such as fuzzy set theory. …”
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Discovering action insights from large-scale assessment log data using machine learning by Minyoung Yun, Minjeong Jeon, Heyoung Yang

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Nearest neighbor search algorithm for high dimensional data based on weighted self-taught hashing by Cong PENG, Jiangbo QIAN, Huahui CHEN, Yihong DONG

“…Because of efficiency in query and storage,learning hash is applied in solving the nearest neighbor search problem.The learning hash usually converts high-dimensional data into binary codes.In this way,the similarities between binary codes from two objects are conserved as they were in the original high-dimensional space.In practical applications,a lot of data which have the same distance from the query point but with different code will be returned.How to reorder these candidates is a problem.An algorithm named weighted self-taught hashing was proposed.Experimental results show that the proposed algorithm can reorder the different binary codes with the same Hamming distances efficiently.Compared to the naive algorithm,the F1-score of the proposed algorithm is improved by about 2 times and it is better than the homologous algorithms,furthermore,the time cost is reduced by an order of magnitude.…”
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Nearest neighbor search algorithm for high dimensional data based on weighted self-taught hashing by Cong PENG, Jiangbo QIAN, Huahui CHEN, Yihong DONG

“…Because of efficiency in query and storage,learning hash is applied in solving the nearest neighbor search problem.The learning hash usually converts high-dimensional data into binary codes.In this way,the similarities between binary codes from two objects are conserved as they were in the original high-dimensional space.In practical applications,a lot of data which have the same distance from the query point but with different code will be returned.How to reorder these candidates is a problem.An algorithm named weighted self-taught hashing was proposed.Experimental results show that the proposed algorithm can reorder the different binary codes with the same Hamming distances efficiently.Compared to the naive algorithm,the F1-score of the proposed algorithm is improved by about 2 times and it is better than the homologous algorithms,furthermore,the time cost is reduced by an order of magnitude.…”
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