LLM in the Loop: A Framework for Contextualizing Counterfactual Segment Perturbations in Point Clouds by Veljka Kocic, Niko Lukac, Dzemail Rozajac, Stefan Schweng, Christoph Gollob, Arne Nothdurft, Karl Stampfer, Javier del Ser, Andreas Holzinger

“…Point Cloud Data analysis has seen a major leap forward with the introduction of PointNet algorithms, revolutionizing how we process 3D environments. …”
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Selective Reading for Arabic Sentiment Analysis by Mohamed Zouidine, Mohammed Khalil

“…To empirically evaluate the proposed method, experiments were carried out on three Arabic sentiment analysis datasets: Large Arabic Book Reviews (LABR), Hotels Arabic Reviews Data (HARD), and Arabic Sentiment Tweets Dataset (ASTD). …”
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Fog Service Placement Optimization: A Survey of State-of-the-Art Strategies and Techniques by Hemant Kumar Apat, Veena Goswami, Bibhudatta Sahoo, Rabindra K. Barik, Manob Jyoti Saikia

“…The fog computing paradigm complements the existing cloud computing services at the edge of the network to facilitate the various services without sending the data to a centralized cloud server. …”
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Comprehensive Analysis of Breast Cancer Cell Lines: Genome-wide Insights from ChIP-seq Analysis by Tanishq Sahu, Ruchi Yadav

“…Four samples of FASTQ files were used and analyzed for the genome-wide analysis. Materials and Methods: Galaxy server (https://usegalaxy.org/) was used for complete ChIP-seq data analysis; different tools such as fast-quality control (QC), multi-QC, Bowtie2, model-based analysis of ChIP-sequencing, and ChIPseeker tools were used for motif enrichment and functional analysis. …”
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Bioinformatic Analysis of WNT Family Proteins by Konstantin Midlovets, Natalia Volkova, Mykyta Peka

“…Furthermore, in silico modeling and molecular dynamics simulations were employed to study the tertiary structure of WNT2B and to assess the significance of O-acylation at serine for the behavior of the mature protein in an aqueous environment. Thus, using computational approaches, new data were obtained on the structural features and dynamic properties of this group of regulatory proteins.…”
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Weakly Profiling Side-channel Analysis by Lichao Wu, Guilherme Perin, Stjepan Picek

“… Profiling side-channel analysis, recognized for its robust attack performance in worst-case scenarios, necessitates adversaries to have a cloned device for profiling measurements and secret information for data labeling. …”
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Development of an artificial intelligence model for wire electrical discharge machining of Inconel 625 in biomedical applications by Pasupuleti Thejasree, Natarajan Manikandan, Neeraj Sunheriya, Jayant Giri, Rajkumar Chadge, T. Sathish, Ajay Kumar, Muhammad Imam Ammarullah

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Introducing the UK Crop Microbiome Cryobank data resource, AgMicrobiomeBase, with case studies and methods on metabarcoding analyses by Payton To Yau, Rodrigo G. Taketani, J. Miguel Bonnin, Helen Stewart, Catriona M.A. Thompson, Ian M. Clark, Tim H. Mauchline, Jacob G. Malone, Matthew J. Ryan, Susan Jones, Nicola Holden

“…Three case studies illustrate how different biological questions in phytobiome research can be addressed using this data resource. The three case studies illustrate how to (1) determine the impact of soil texture and location on microbiome composition, (2) determine a core microbiome for a single crop across different soil types, and (3) analyse a single genus, Fusarium within a single crop microbiome. …”
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3D Urban Digital Twinning on the Web with Low-Cost Technology: 3D Geospatial Data and IoT Integration for Wellness Monitoring by Marcello La Guardia

“…Recent advances in computer science and geomatics have enabled the digitalization of complex two-dimensional and three-dimensional spatial environments and the sharing of geospatial data on the web. …”
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Forecasting of Inflation Based on Univariate and Multivariate Time Series Models: An Empirical Application by Hasnain Iftikhar, Faridoon Khan, Paulo Canas Rodrigues, Abdulmajeed Atiah Alharbi, Jeza Allohibi

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Quantitative Identification of Detrital Minerals by Mineral Characteristic Automatic Analysis System and Error Analysis with Traditional Microscopic Identification by Ze NING, Lei XU, Xuehui LIN, Yuanyuan ZHANG, Yong ZHANG

“…However, for a long time, the acquisition of detrital mineral data has relied mainly on optical microscopes as tools and manual identification, which is labor-intensive and inefficient. …”
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Analysis of Gas‐Bearing Properties of Shale Reservoirs: A Comprehensive Analysis Based on Field Interpretation and Logging Interpretation by Jin Pang, Tongtong Wu, Xinan Yu, Chunxi Zhou, Pei Yu, Haotian Chen, Jiaao Gao

“…Core analysis experiments are conducted to obtain microscopic gas content data, and well logging parameters are calibrated and extended to reveal vertical and lateral distribution characteristics of the reservoir. …”
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Reduced dose helical CT scout imaging on next generation wide volume CT system decreases scan length and overall radiation exposure by Alexa E. Golbus, John L. Schuzer, Chloe Steveson, Shirley F. Rollison, James Matthews, Joseph Henry-Ellis, Marco Razeto, Marcus Y. Chen

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Using the Active Faults of Eurasia Database for solving tectonic problems by D. M. Bachmanov, E. A. Zelenin, A. I. Kozhurin, V. G. Trifonov

“…As an exam‐ ple, we consider processing of the data on several tectonic regions of the Alpine‐Himalayan mobile belt and construction of rose‐diagrams of faults for a comparative analysis of their Late Cenozoic kinematics. …”
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State of health estimation of lithium-ion batteries based on data-driven methods with a selected charging voltage interval by Junguang Sun, Xiaodong Zhang, Wenrui Cao, Lili Bo, Changhai Liu, Bin Wang

“…Accurate state of health (SOH) estimation of lithium-ion batteries is of great importance for achieving efficient energy management in the overall battery energy storage system. Traditional data-driven methods for the SOH estimation of lithium-ion batteries usually require an enormous amount of data from the whole charging phase, which leads to poor performance in both computational efficiency and computational cost. …”
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MapReduce based big data framework using associative Kruskal poly Kernel classifier for diabetic disease prediction by R. Ramani, S. Edwin Raja, D. Dhinakaran, S. Jagan, G. Prabaharan

“…The proposed AKW-MRPK framework achieves up to 92 % accuracy, reduces computational time to as low as 0.875 ms for 25 patients, and demonstrates superior speedup efficiency with a value of 1.9 ms using two computational nodes, consistently outperforming supervised machine learning algorithms and Hadoop-based clusters across these critical metrics. • The AKW-MRPK method selects attributes and accelerates computations for predictions. • Parallelizing polynomial kernels improves accuracy and speed in healthcare data analysis.…”
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Dynamics of Phyto- and Bacterioplankton in Southern Baikal and Irkutsk Reservoir During the Open Water Period of 2023 According to Metabarcoding Data by Yuri Galachyants, Darya Petrova, Artem Marchenkov, Maria Nalimova, Yelena Likhoshway

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Associations Among Diet, Health, Lifestyle, and Gut Microbiota Composition in the General French Population: Protocol for the Le French Gut – Le Microbiote Français Study by Chloe Connan, Sébastien Fromentin, Mourad Benallaoua, Anne-Sophie Alvarez, Nicolas Pons, Benoît Quinquis, Christian Morabito, Julie-Anne Nazare, Elise Borezée-Durant, Florence Haimet, Stanislav Dusko Ehrlich, Karine Valeille, Alexandre Cavezza, Hervé Blottière, Patrick Veiga, Mathieu Almeida, Joël Doré, Robert Benamouzig

“…This is a home-based trial in which volunteers complete a questionnaire with insights about their health and habits, and in which stool samples are self-collected. Data analysis is structured into 6 work packages, each focusing on a specific aspect of the gut microbiome, including its composition and associations with lifestyle, quality of life, and health. …”
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Enhancing software development effort estimation with a cloud-based data framework using use case points, fuzzy logic, and machine learning by Ritu, Pankaj Bhambri

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Design of an Improved Model for Gear Fault Diagnosis Using Acoustic Data and EfficientNet-Based Deep Learning Process by Bundele Shubham, Kane P.V.

“…It is then used to train deep learning models, EfficientNetB0 and EfficientNetB3 that are superior on feature-extraction and computational efficiency. The comparative analysis indicates that EfficientNetB3 outperforms EfficientNetB0 based on all four metrics of accuracy, precision, recall, and overall classification performance. …”
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