Search Results - data processing methods

IDBR: Interaction-Aware Dual-Granularity Learning for Bundle Recommendation by Jinqing Wang, Yuan Cao, Fan Zhang, Feifei Kou, Kaimin Wei, Jinghui Zhang, Jinpeng Chen

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Building Trust in Discontinuous Engagements: Researcher’s Adaptive Self-Presentation in Fieldwork Toward Deeper Penetration in Dispersed Participants by Guannan Zou

“…Fieldwork with participatory methods often emphasizes continuous engagement with well-defined participant groups, in which researchers can foster trust and collect deep data from specified informants. …”
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Capacity Building and Creating Supportive Environments for Inclusive Community-Based Participatory Research: A Case Study of the Overdose Prevention Peer Research Assistant (OPPRA)... by Jeffrey Morgan, Tara Taylor, Seonaid Nolan, Alison Blais, Samuel Landon Cywink, Jean Mark Duguay, Thomas A. Gibbons, Scott Maxwell, Samona Marsh, Melanie Pratt, Stephanie Glegg

“…Further, we outline innovative and collaborative methods for data gathering and analysis developed to involve peers at every step of the research process and share the results of this original research. …”
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BiciZen: Lessons in the Development of a Crowdsourcing Mobile App to Make Cities More Bikeable by Jordi Honey-Rosés, Luca Liebscht, Paulo Batista, Boualem Benatallah, Mark Brussel, Johannes Flacke, Jouni Häkli, Kirsi Pauliina Kallio, Theo Lynn, Miki Mäkelä, Lourenço Melo, Gemma Simón-i-Mas, Fernando Vilariño

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The role of religiosity, product knowledge and product assessment on digital purchasing of halal products by Darsita Suparno, Ahmad Satori Ismail, Nadra Nadra, Ita Fitriana

“…The population of this study is consumers of halal products and the sample of this study is 564 consumers of halal products determined by the simple random sampling method. The research analysis uses the structural equation modeling partial least squares (SEM-PLS) method and uses data processing tools with SmartP LS 4.0 software. …”
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Optimized YOLO Model for Accurate and Real-Time Detection of Machinery Around Shovels in Copper Mining by Mohaddeseh Ghiasi, MasoudReza Aghabozorgi

“…The proposed method was evaluated on a new dataset of shovels under real working conditions. …”
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Permafrost–wildfire interactions: active layer thickness estimates for paired burned and unburned sites in northern high latitudes by A. C. Talucci, M. M. Loranty, J. E. Holloway, B. M. Rogers, H. D. Alexander, N. Baillargeon, J. L. Baltzer, L. T. Berner, A. Breen, L. Brodt, B. Buma, B. Buma, J. Dean, C. J. F. Delcourt, L. R. Diaz, C. M. Dieleman, T. A. Douglas, G. V. Frost, B. V. Gaglioti, R. E. Hewitt, T. Hollingsworth, T. Hollingsworth, M. T. Jorgenson, M. J. Lara, R. A. Loehman, M. C. Mack, K. L. Manies, C. Minions, S. M. Natali, J. A. O'Donnell, D. Olefeldt, A. K. Paulson, A. V. Rocha, L. B. Saperstein, T. A. Shestakova, T. A. Shestakova, T. A. Shestakova, S. Sistla, O. Sizov, A. Soromotin, M. R. Turetsky, S. Veraverbeke, M. A. Walvoord

“…Here, we describe our methods for collecting and quality-checking the data, estimating ALT, the data structure, strengths and limitations, and future research opportunities. …”
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ResDecode: Accelerating Large Language Models Inference via Residual Decoding Heads by Ziqian Zeng, Jiahong Yu, Qianshi Pang, Zihao Wang, Huiping Zhuang, Fan Yu, Hongen Shao, Xiaofeng Zou

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Training Large Models on Heterogeneous and Geo-Distributed Resource with Constricted Networks by Zan Zong, Minkun Guo, Mingshu Zhai, Yinan Tang, Jianjiang Li, Jidong Zhai

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No-Code Edge Artificial Intelligence Frameworks Comparison Using a Multi-Sensor Predictive Maintenance Dataset by Juan M. Montes-Sánchez, Plácido Fernández-Cuevas, Francisco Luna-Perejón, Saturnino Vicente-Diaz, Ángel Jiménez-Fernández

“…Magnetometer data were used for test 1, and accelerometer data were used for test 2. …”
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The role of digital green accounting and environment performance on forest sustainable development goals: A case study on customary forest in Papu by Otniel Safkaur, Bill Pangayow, Halomoan Hutajulu, Lediana Hanasbe

“…The population of this research is indigenous community leaders and the sample of respondents for this research is 432 indigenous community leaders determined using a simple random sampling method. Data analysis for this research uses structural equation modelling (SEM) partial least squares (PLS) with data processing tools using SmartPLS 4.0 software. …”
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Smoothed monthly Greenland ice sheet elevation changes during 2003–2023 by S. A. Khan, H. Seroussi, M. Morlighem, W. Colgan, V. Helm, G. Cheng, D. Berg, V. R. Barletta, N. K. Larsen, W. Kochtitzky, M. van den Broeke, K. H. Kjær, A. Aschwanden, B. Noël, J. E. Box, J. A. MacGregor, R. S. Fausto, K. D. Mankoff, K. D. Mankoff, I. M. Howat, K. Oniszk, D. Fahrner, A. Løkkegaard, E. Y. H. Lippert, A. Bråtner, J. Hassan

“…To estimate elevation changes of the Greenland Ice Sheet (GIS), we utilize radar altimetry data from CryoSat-2 and EnviSat, laser altimetry data from the ICESat and ICESat-2, and laser altimetry data from NASA's Operation IceBridge Airborne Topographic Mapper. …”
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ABERT: Adapting BERT model for efficient detection of human and AI-generated fake news by Jawaher Alghamdi, Yuqing Lin, Suhuai Luo

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Multiple Regression with Transformations and Variable Selection at the Industrial Scale by Gus Greivel, Soutir Bandyopadhyay, Alexandra M. Newman, Brian G. Thomas

“…This article explores the development of a statistical model to predict roll forces during hot rolling in a commercial steel mill based on a dataset from an industrial-scale operation; this dataset consists of 2255 individual coils, processed through five roll stands in the mill, for a total of 11,275 distinct data points. …”
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Can digital transformation improve the transparency and accountability of Indonesian public governance? by M. Irwan Tahir, Ani Martini, Anak Agung Ngurah Gunawan, Yogi Makbul, Nirma Yossa, Wisber Wiryanto, Moch Fahrudin, Rahmat Ilya

“…The respondents of this research were 478 government public service office employees who were determined using a simple random sampling method. Research data analysis uses structural equation modelling (SEM) partial least squares (PLS) with research data analysis tools using SmartPLS 3.0 software. …”
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Fitting of nonnegative physical models based on statistical divergence: application to thermally stimulated depolarization currents by Yasunobu Ando, Shusuke Kasamatsu, Suguru Iwasaki, Yumi Tanaka

“…We propose a theoretical formalism for inferring the parameters of non-negative physical models via statistical divergence to generalise the fitting process beyond conventional methods. For example, we show that minimising L2 and Kullback–Leibler divergence is equivalent to least squares and maximum likelihood estimation, respectively, for the parameters of non-negative physical models like a probability distribution. …”
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Less-supervised learning with knowledge distillation for sperm morphology analysis by Ali Nabipour, Mohammad Javad Shams Nejati, Yasaman Boreshban, Seyed Abolghasem Mirroshandel

“…Our aim is to train an Anomaly Detection model using a dataset comprising unclear images and limited samples, without direct exposure to abnormal data. Our method achieves Receiver ROC/AUC scores of 70.4%, 87.6%, and 71.1% for head, vacuole, and acrosome, respectively, our method matches traditional DL model performance with less than 70% of the data. …”
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Applicability of Artificial Intelligence in Smart Healthcare Systems for Automatic Detection of Parkinson’s Disease by Harikumar Pallathadka, S.J.R.K. Padminivalli V., M. Vasavi, P. Nancy, Mohd Naved, Harish Kumar, Samrat Ray

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Production monitoring and machine tracking in underground mines based on a collision avoidance system: A case study by Artur Skoczylas, Natalia Duda-Mróz, Wioletta Koperska, Paweł Stefaniak, Paweł Śliwiński

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Integrative in Silico modeling for mTOR inhibition: From ridge classifiers to descriptor-free deep neural networks by Seyed Alireza Khanghahi, Hadi Kamkar, Seyedehsamaneh Shojaeilangari, Abdollah Allahverdi, Parviz Abdolmaleki

“…The GRU-QSAR and LSTM-QSAR models, which utilized tokenized SMILES, achieved accuracies of 0.9002 and 0.9021, F1-scores of 0.8595 and 0.8603, and AUCs of 0.9270 and 0.9529, respectively, leveraging their ability to process sequential data.…”
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