Optimizing quantum convolutional neural network architectures for arbitrary data dimension by Changwon Lee, Israel F. Araujo, Dongha Kim, Junghan Lee, Siheon Park, Ju-Young Ryu, Ju-Young Ryu, Daniel K. Park, Daniel K. Park

“…The number of input qubits determines the dimensions (i.e., the number of features) of the input data that can be processed, restricting the applicability of QCNN algorithms to real-world data. …”
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An open data collection of 3D tool and equipment models for neonatology by Serena Bardelli, Gianpaolo Coro, Rosa T. Scaramuzzo, Massimiliano Ciantelli, Armando Cuttano

“…Eventually, we published the 3D models and the virtual training room as an open-access data collection on Sketchfab (a free-to-use online digital platform for 3D model publication), from which all models can be freely downloaded and inspected through Web browsers, mobile applications, and Virtual and Augmented Reality devices. …”
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Reconstruction of gene regulatory networks from single cell transcriptomic data by M. A. Rybakov, N. A. Omelyanchuk, E. V. Zemlyanskaya

“…The review also considers the applicability of GRNs reconstructed from single-cell omics data to recover and characterize various biological processes. …”
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When Poor-Quality Data Meet Anonymization Models: Threats and Countermeasures by Abdul Majeed, Seong Oun Hwang

“…However, some automated tools can curate data with many vulnerabilities (e.g., missing values, wrong values, outliers, skewed distributions, missing labels, etc.) which can hamper its usage in underlying applications. …”
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Modern Deep Learning Techniques for Big Medical Data Processing in Cloud by Mohammed Y. Shakor, Mustafa Ibrahim Khaleel

“…The paper then explores a wide spectrum of ML techniques, including unsupervised, supervised, and reinforcement learning methods, and their applications in big data contexts. This study explores recent advancement in DL architectures, while examining their effectiveness. …”
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Time Series Data Generation Method with High Reliability Based on ACGAN by Fang Liu, Yuxin Li, Yuanfang Zheng

“…In the process of big data processing, especially in fields like industrial fault diagnosis, there is often the issue of small sample sizes. …”
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phylospatial: An R package for spatial phylogenetic analysis with quantitative community data by Matthew M. Kling

“…By facilitating spatial phylogenetic analysis of quantitative data types that more accurately represent the reality of species distributions, these methods and tools help broaden the range of questions and applications that can be addressed, and can help to increase the detail and statistical rigour of these studies.…”
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Learning a prior on regulatory potential from eQTL data. by Su-In Lee, Aimée M Dudley, David Drubin, Pamela A Silver, Nevan J Krogan, Dana Pe'er, Daphne Koller

“…Genome-wide RNA expression data provide a detailed view of an organism's biological state; hence, a dataset measuring expression variation between genetically diverse individuals (eQTL data) may provide important insights into the genetics of complex traits. …”
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Local public investment drivers in Morocco: A panel data analysis by Hicham El Bakkali, Rizlane Guati

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Optimization of data allocation in hierarchical memory for blocked shortest paths algorithms by A. A. Prihozhy

“…This paper is devoted to the reduction of data transfer between the main memory and direct mapped cache for blocked shortest paths algorithms (BSPA), which represent data by a D[M×M] matrix of blocks. …”
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Dynamic Ensemble Selection for EEG Signal Classification in Distributed Data Environments by Małgorzata Przybyła-Kasperek, Jakub Sacewicz

“…This study presents a novel approach to EEG signal classification in distributed environments using dynamic ensemble selection. In scenarios where data dispersion arises due to privacy constraints or decentralized data collection, traditional global modelling is impractical. …”
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Semi-supervised prediction of protein fitness for data-driven protein engineering by Alicia Olivares-Gil, José A. Barbero-Aparicio, Juan J. Rodríguez, José F. Díez-Pastor, César García-Osorio, Mehdi D. Davari

“…However, the combinatorial complexity of the protein sequence space and the limited availability of assay-labelled data hinder the efficient optimization of protein properties. …”
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Generative AI-Enhanced Cybersecurity Framework for Enterprise Data Privacy Management by Geeta Sandeep Nadella, Santosh Reddy Addula, Akhila Reddy Yadulla, Guna Sekhar Sajja, Mohan Meesala, Mohan Harish Maturi, Karthik Meduri, Hari Gonaygunta

“…Unlike analog systems that achieve > 0.99 accuracy at the cost of higher resource consumption and limited scalability, this framework emphasizes practical applications in diverse sectors. Additionally, it employs differential privacy, encryption, and data masking to ensure data security while addressing modern cybersecurity challenges. …”
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Smart adaptive ensemble model for multiclass imbalanced nonstationary data streams by Abdul Sattar Palli, Jafreezal Jaafar, Mohamad Hanif Md Saad, Ainul Akmar Mokhtar, Heitor Murilo Gomes, Afzal Ahmed Soomro, Abdul Rehman Gilal

“…Most of the existing work is limited to addressing these issues for binary class data streams. Very little focus is given to the multi-class data streams. …”
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Estimating canopy fuels across Europe with satellite data and allometric equations by E. Kutchartt, E. Kutchartt, J. R. González-Olabarria, J. R. González-Olabarria, N. Aquilué, N. Aquilué, A. Trasobares, F. Pirotti, F. Pirotti

“…Validation was carried out by comparing the results with higher-accuracy sampling methods, combining LiDAR data and field measurements in different European latitudes, typically applied on a smaller scale and with greater detail. …”
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Research Progress in Formation Pressure Prediction Methods Based on Logging Data by SUN Jianmeng, ZHANG Ying, WANG Yuexiang, WU Yanhui, CHEN Hongtao

“…Then, the principles of commonly used formation pressure prediction methods based on logging data are elaborated and their applicability is analyzed, including commonly used methods that rely on normal compaction trend lines, methods based on effective stress theorem, methods based on mudstone prediction, while drilling formation pressure prediction methods, and other formation pressure prediction methods. …”
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Blockchain oracles for decentralized agricultural insurance using trusted IoT data by Manoj T, Krishnamoorthi Makkithaya, Narendra V. G., Vijaya Murari T

“…However, blockchain operates as a deterministic and synchronized state system, which means it cannot directly access real-world data for decentralized applications. A mechanism called oracle is required for the trusted access of agricultural risk factor data to smart contracts from external sources such as Internet of Things (IoT) devices, web services and databases. …”
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Integrating Drone-Based LiDAR and Multispectral Data for Tree Monitoring by Beatrice Savinelli, Giulia Tagliabue, Luigi Vignali, Roberto Garzonio, Rodolfo Gentili, Cinzia Panigada, Micol Rossini

“…Traditional field-based methods are often labour-intensive, costly, and logistically challenging, limiting their use for large-scale applications. Drones offer advantages such as low operating costs, versatility, and rapid data collection. …”
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DRomics, a workflow to exploit dose-response omics data in ecotoxicology by Delignette-Muller, Marie Laure, Siberchicot, Aurélie, Larras, Floriane, Billoir, Elise

“…DRomics has been augmented especially to be able to handle varied omics data considering the nature of the measured signal (e.g. counts of reads in RNAseq) and the way data were collected (e.g. batch effect, situation with no experimental replicates). …”
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Leveraging the Potential of Novel Data in Power Line Communication of Electricity Grids by Christoph Balada, Max Bondorf, Sheraz Ahmed, Markus Zdrallek, Andreas Dengel

“…In addition, we present different use cases in asset management, grid state visualization, forecasting, predictive maintenance, and novelty detection to highlight the benefits of these types of data. For these applications, we particularly highlight the use of novel machine learning architectures to extract rich information from real-world data that cannot be captured using traditional approaches. …”
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