What are the future directions for microplastics characterization? A regex-llama data mining approach for identifying emerging trends by FERNANDO GOMES, SHEKHAR BHANSALI, FABÍOLA MARANHÃO, VIVIANE VALLADÃO, KARINE VELASCO

“…This combination enhances the accuracy and speed of identifying complex terminology related to microplastics research. Data analysis and visualization were conducted using VOSDataAnalyzer and VOSviewer, allowing us to map connections and trends within a network of related terms. …”
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Enabling Advanced Land Cover Analytics: An Integrated Data Extraction Pipeline for Predictive Modeling With the Dynamic World Dataset by Victor Radermecker, Andrea Zanon, Nancy Thomas, Annita Vapsi, Saba Rahimi, Rama Ramakrishnan, Daniel Borrajo

“…Understanding land cover holds considerable potential for a myriad of practical applications, particularly as data accessibility transitions from being exclusive to governmental and commercial entities to now including the broader research community. …”
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Double cropping detection in the moderate continental climate region of Serbia using machine learning and Sentinel-2 data by Miljana Marković, Predrag Lugonja, Sanja Brdar, Ioannis Athanasiadis, Mirjana Radulović, Branislav Pejak, Minučer Mesaroš, Vladimir Crnojević

“…This approach simplifies data requirements while maintaining high accuracy, making it applicable to regions with similar geographical characteristics. …”
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A Novel Air-to-Ground Communication Scheme for Advanced Big Data Collection in Smart Farming Using UAVs by Georgios A. Kakamoukas, Thomas D. Lagkas, Vasileios Argyriou, Sotirios K. Goudos, Panagiotis Radoglou-Grammatikis, Stamatia Bibi, Panagiotis G. Sarigiannidis

“…This conventional approach is often inefficient because of its inability to enable real-time data transmission and its vulnerability to operational failures. …”
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Multi-objective optimization of EMS facilities using multi-source data: A case study in Dangtu, China by Jinze Li, Xiao Wang, Qiyan Zhang, Peng Tang

“…We demonstrate the applicability of our framework through a quantitative and qualitative case study in Dangtu, China. …”
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Mixed Layer Depth Estimation From Multisource Remote Sensing Data Using Clustering-Machine Learning Method by Zengxin Guan, Kaijun Ren, Senliang Bao, Hengqian Yan, Huizan Wang, Yanlai Zhao, Jianbin Liu

“…To assess practical applicability, comparative experiments using remote sensing data were performed, highlighting the model's feasibility and utility. …”
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Big Data-Driven 3D Visualization Analysis System for Promoting Regional-Scale Digital Geological Exploration by Yiping Tian, Jiongqi Wu, Genshen Chen, Gang Liu, Xialin Zhang

“…This study develops a big data-driven 3D visual analysis system for regional-scale applications. …”
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Performance Comparison Between Deep Learning Models for Fault Classification in Transmission Lines Using Time Series Data by Nadeem Ahmed Tunio, Mohsin Ali Tunio, Muhammad Amir Raza, Muhammad Faheem, Ashfaque Ahmed Hashmani, Rumaisa Nadeem

“…Leveraging a comprehensive data set encompassing diverse fault scenarios like single‐phase to ground fault (AG), double line to ground fault (ABG), three‐phase fault (ABC) from both simulated and real transmission line data, the study provides a rigorous evaluation of these models’ performance under realistic conditions. …”
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Benchmarking Machine Learning Algorithms for Bearing Fault Classification Using Vibration Data: A Deployment-Oriented Study by Prasanta Kumar Samal, R. Srinidhi, Pramod Kumar Malik, H. J. Manjunatha, Imran M. Jamadar

“…This study presents a comprehensive benchmarking of 33 machine learning (ML) algorithms for bearing fault classification using vibration data, with a focus on real-world deployment in condition monitoring systems. …”
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CNN-Based Multi-Object Detection and Segmentation in 3D LiDAR Data for Dynamic Industrial Environments by Danilo Giacomin Schneider , Marcelo Ricardo Stemmer

“…Furthermore, we integrate our CNN-based detection and segmentation model into a Robot Operating System 2 (ROS2) framework, facilitating communication between mobile robots and a centralized node for data aggregation and map creation. Our experimental results demonstrate promising performance, showcasing the potential applicability of our approach in future assembly systems. …”
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The Effect of Various Text Representation Methods for Sentiment Analysis on Movie Review Data with Different Machine Learning Methods by Veysel Göç, Muhammet Sinan Başarslan

“…On the open source IMDB movie reviews dataset, we first undertake data cleaning and text representation with data preprocessing steps. …”
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A Common Data Environment Framework Applied to Structural Life Cycle Assessment: Coordinating Multiple Sources of Information by Lini Xiang, Gang Li, Haijiang Li

“…In Building Information Modeling (BIM)-driven collaboration, the workflow for information management utilizes a Common Data Environment (CDE). The core idea of a CDE is to serve as a single source of truth, enabling efficient coordination among diverse stakeholders. …”
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Ship behavior prediction and anomaly detection using LSTM-DCross model based on AIS and remote sensing data by Wanrong Wu, Dongmei Yan, Jun Yan, Xiaowei Wang

“…Notably, the historical data-based prediction method with LSTM-DCross not only demonstrates high accuracy but also offers greater applicability, successfully predicting future ship behaviours using only historical data, even during complete AIS failure. …”
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SPT-AD: Self-Supervised Pyramidal Transformer Network-Based Anomaly Detection of Time Series Vibration Data by Seokhyun Gong, Taeyong Kim, Jongpil Jeong

“…Our proposed model tackles the data imbalance issue by generating four types of synthetic anomalies from normal vibration data and incorporates a pyramid-structured attention module to reduce computational costs and enhance the handling of long-term dependencies. …”
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HCCD: A handwritten camera-captured dataset for document enhancement under varied degradation conditionsMendeley Data by K.S. Koushik, Bipin Nair B J, N. Shobha Rani

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Comprehensive VR dataset for machine learning: Head- and eye-centred video and positional dataThe Adaptive Mind Datahub by Alexander Kreß, Markus Lappe, Frank Bremmer

“…Using a VR motion platform, participants navigated these environments freely while their eye movements and positional data were captured and stored in CSV format. The dataset spans six distinct environments, including one specifically for calibrating the motion platform, and provides a cumulative playtime of over 10 h for both head- and eye-centred perspectives.The data collection was conducted in naturalistic VR settings, where participants collected virtual coins scattered across diverse landscapes such as grassy fields, dense forests, and an abandoned urban area, each characterized by unique ecological features. …”
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Reducing annotation effort in agricultural data: simple and fast unsupervised coreset selection with DINOv2 and K-means by Laura Gómez-Zamanillo, Laura Gómez-Zamanillo, Nagore Portilla, Artzai Picón, Artzai Picón, Itziar Egusquiza, Itziar Egusquiza, Ramón Navarra-Mestre, Andoni Elola, Arantza Bereciartua-Perez

“…The need for large amounts of annotated data is a major obstacle to adopting deep learning in agricultural applications, where annotation is typically time-consuming and requires expert knowledge. …”
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Association of IUCN-threatened Indian mangroves: A novel data-driven rule filtering approach for restoration strategy by Moumita Ghosh, Sourav Mondal, Rohmatul Fajriyah, Kartick Chandra Mondal, Anirban Roy

“…To assess the framework’s broader applicability, we extend our analysis to species presence-absence data from sacred groves in the laterite regions of eastern India. …”
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Efficient Tactile Perception in Robotics: Reducing Data Redundancy through Compression and Normalization in Spiking Graph Convolutional Networks by Elahe Rezaee Ahvanooii, Sheis Abolmaali

“…This research presents a Spiking Graph Convolutional Network (SGCN) designed for processing tactile data in object recognition tasks. The model addresses the redundancy in spiking-format input data by employing two key techniques: (1) data compression to reduce the input size and (2) batch normalization to standardize the data. …”
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Genomic surveillance of SARS-CoV-2 in Taiwan: A perspective on evolutionary data interpretation and sequencing issues by Yu-Nong Gong, Nai-Yu Kuo, Ting-Syuan Yeh, Shin-Ru Shih, Guang-Wu Chen

“…This integration is crucial to enhance the precision and applicability of genomic data. Overall, we have provided an overview of the significance of genomic surveillance in tracking SARS-CoV-2 variants globally and the pivotal role of data science methodologies in interpreting these data for effective public health interventions.…”
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