Arabic paraphrased parallel synthetic datasetGitHub by Noora Al-shameri, Hend Al-Khalifa

“…The Arabic paraphrased parallel dataset plays a crucial role in advancing NLP and other language-related applications by leveraging data from diverse sources and expanding it through data augmentation techniques. …”
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Correction of the Relative Coordinates by Range using the Maximum Likelihood Method by V. K. Orlov, V. S. Ramadanov

“…The applicability of the analyses is given, the coordinate filter, implemented the developed assessment, is constructed.…”
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Validation of sentinel 2 based machine learning models for Czech National Forest Inventory by Richard Kovárník, Jitka Janová

“…By systematically comparing model predictions with official NFI data, we address a key gap in remote sensing applications: the need for real-world validation beyond training datasets. …”
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Security Challenges and Performance Trade-Offs in On-Chain and Off-Chain Blockchain Storage: A Comprehensive Review by Haluk Eren, Özgür Karaduman, Muharrem Tuncay Gençoğlu

“…This study examines the fundamental components of blockchain-based data storage systems, their sector-specific applications, and the technical challenges they present. …”
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Study on lifecycle management of high-speed rail catenary system under the MDD-APC theory by Rui Li, Ping Li, Chenkang Wu, Xue Zhang

“…Purpose – The rapid development of China’s railway construction has led to an increase in data generated by the high-speed rail (HSR) catenary system. …”
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Self-Supervised Drift-Resilient Classification for Time Series Industrial Anomaly Detection by Myung-Kyo Seo, Byeong Hoon Yoon, Junseung Ryu, Hyung Ju Hwang

“…Validation in NASA bearing datasets, as well as real-world vibration data from industrial fans, motors, and gearboxes confirms the model scalability and practical applicability. …”
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Noisier2Inverse: Self-Supervised Learning for Image Reconstruction With Correlated Noise by Nadja Gruber, Johannes Schwab, Markus Haltmeier, Ander Biguri, Clemens Dlaska, Gyeongha Hwang

“…Our approach learns a reconstruction function without requiring ground truth data and is applicable in settings where measurement noise is statistically correlated. …”
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Quantum-Inspired Statistical Frameworks: Enhancing Traditional Methods with Quantum Principles by Theodoros Kyriazos, Mary Poga

“…These principles are illustrated through domain-specific applications in finance, cryptography (distinct from direct quantum cryptography applications), healthcare, and climate modeling, demonstrating how amplitude-based confidence measures, density matrices, and measurement analogies can enrich standard statistical models by capturing more nuanced correlation structures and enhancing predictive performance. …”
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Machine Learning for Human Activity Recognition: State-of-the-Art Techniques and Emerging Trends by Md Amran Hossen, Pg Emeroylariffion Abas

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Local Transmissibility-Based Identification of Structural Damage Utilizing Positive Learning Strategies by Oguz Gunes, Burcu Gunes

“…This study introduces an innovative, unsupervised learning framework leveraging transmissibility functions (TFs) as DSFs due to their local sensitivity to changes in dynamic behavior and their ability to operate without requiring input excitation measurements—an advantage in civil engineering applications where such data are often difficult to obtain. …”
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Steganography in IoT: A Comprehensive Survey on Approaches, Challenges, and Future Directions by Maha Driss, Lamia Berriche, Safa Ben Atitallah, Siwar Rekik

“…While effective, traditional encryption techniques are often computationally expensive and easily identifiable, making them unsuitable for many IoT applications. Steganography is an intriguing approach that allows hiding sensitive information within seemingly ordinary data, preventing unauthorized parties from detecting and accessing it. …”
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Enhanced Framework for Lossless Image Compression Using Image Segmentation and a Novel Dynamic Bit-Level Encoding Algorithm by Erdal Erdal, Alperen Önal

“…The novel approaches were validated on four different datasets, demonstrating strong performance and broad applicability. A dedicated data structure was developed to facilitate lossless storage and precise reconstruction of compressed data, ensuring data integrity throughout the process. …”
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An Overview of Deep Neural Networks for Few-Shot Learning by Juan Zhao, Lili Kong, Jiancheng Lv

“…However, these methods often require extensive labeled data for optimal performance, posing challenges and high costs in practical applications. …”
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An innovative hybrid grid generation for urban ventilation simulation by John Xin Wang, Richard Kwok Kit Yuen, Lan Wang

“…The multiple buildings example shows that this proposed grid generation can expand to general urban-scale built environment applications. Finally, a case study with the hybrid grid arrangement is carried out to validate the simulation results with experimental measurement data. …”
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A Novel Approach to Enhancing the Accuracy of Prediction in Ship Fuel Consumption by Tianrui Zhou, Jinggai Wang, Qinyou Hu, Zhihui Hu

“…However, enhancing the accuracy of these predictions poses significant challenges due to data limitations and the methods employed. Due to these factors, such as data variability and equipment characteristics, ship fuel consumption exhibits certain fluctuations under specific conditions. …”
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Defect Detection and Correction in OpenMP: A Static Analysis and Machine Learning-Based Solution by Norah A. Al-Johany, Fathy E. Eassa, Sanaa A. Sharaf, Eynas H. Balkhair, Sara M. Assiri

“…Concurrency defects such as race conditions, deadlocks, and improper synchronization remain a critical challenge in developing reliable OpenMP-based parallel applications. Traditional static analysis tools often focus only on defect detection, offering limited or no automated correction capabilities. …”
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Statistical package for computing precision covariance matrices via modified Cholesky decomposition by Elías D. Niño-Ruiz, Dylan Samuel Cantillo Arrieta, Giuliano Raffaele Frieri Quiroz, Nicolas Quintero Quintero

“…Leveraging ensemble modeling, the method ensures robustness and accuracy, with direct applications in data assimilation tasks. The package also features seamless integration with prominent climate datasets, such as ERA5 and MPAS, enabling streamlined workflows for climate modeling and prediction.…”
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Personalized Clustering for Emotion Recognition Improvement by Laura Gutiérrez-Martín, Celia López-Ongil, Jose M. Lanza-Gutiérrez, Jose A. Miranda Calero

“…An initial approach for clustering labeled data compiled (physiological data, together with emotional labels) is presented in this work, as well as the method to ensure the enrollment of new users with unlabeled data once the AI models are generated. …”
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Identification of comorbid genes between type 2 diabetes and migraine through peripheral blood single-cell and Mendelian randomization analysis by Bobo Yuan, Jianrui Li, Qing Shu, Xiaoye Wang, Guogang Luo, Ranran Ma

“…Results Analysis of single-cell data identified 2,128 migraine-associated and 3,833 T2DM-associated genes, with 714 genes shared between the two diseases. …”
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Evaluating the impact of digital technologies on the excursion services’ management by Olesia Suntsova

“…To achieve this objective, the research employs a combination of analytical, synthetic, and comparative methods, allowing for a comprehensive evaluation of current technological trends and their implications. The findings highlight a substantial increase in engagement and customer satisfaction resulting from the adoption of AR and VR technologies, interactive multimedia guides, and mobile applications that provide real-time navigation, personalized recommendations, and enhanced accessibility. …”
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