Relevant bibliographies by topics / DATA BALANCING TECHNIQUE

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'DATA BALANCING TECHNIQUE'

Author: Grafiati
Published: 11 September 2023
Last updated: 31 July 2025

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Journal articles on the topic "DATA BALANCING TECHNIQUE"

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Febi Nur Salisah, Inggih Permana, Sanusi, and Shir Li Wang. "Evaluating the Impact of Data Balancing Techniques on the k-Nearest Neighbors Algorithm for Microarray Data Classification." Jurnal Inotera 10, no. 2 (2025): 261–71. https://doi.org/10.31572/inotera.vol10.iss2.2025.id497.

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Microarray data classification poses significant challenges in bioinformatics due to the nature of the data, which has a very high number of features but a limited number of samples, and an unbalanced class distribution. This condition can cause a decrease in the performance of classification models, including k-Nearest Neighbor (kNN). This study aims to evaluate the performance of the kNN algorithm in classifying unbalanced and balanced data. The balancing techniques used are Random Undersampling (RUS), Random Oversampling (ROS), and Synthetic Minority Over-sampling Technique (SMOTE). The dat
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Leah.W.Chege, Hellen.W.Waititu, and Cornelious.O.Nyakundi. "Comparative Analysis of K-Means and Divisive Clustering Techniques on Balanced Mental Health Data." Asian Journal of Probability and Statistics 26, no. 10 (2024): 64–79. http://dx.doi.org/10.9734/ajpas/2024/v26i10659.

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Effective Clustering of mental health data can provide significant insights into patterns and relationships that are critical for understanding mental health conditions. This study investigated various clustering techniques applied to balanced mental health data to avoid biases associated with an imbalanced data. Clustering of the balanced mental health data was done with respect to the area of Residence feature. Firstly, Random undersampling and SMOTE techniques were incorporated to the imbalanced data set as balancing techniques so as to improve model performance. Random Undersampling Techni
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Pranav, Bhokardankar* Harshal Sharma. "PROPOSED ALGORITHM FOR LOAD BALANCING ON CLOUD DATA CENTER." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 12 (2016): 702–5. https://doi.org/10.5281/zenodo.218895.

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Cloud servers are basically developed for supporting the efficient communication and the computation remotely. Thus a number of different time zone machines are able to find the services of resources and the computation. Thus each and every time these machines are working in order to resolve the end client request. But sometime these machines are loaded more then their capacity and does not perform as desired and some of the available processing units are considered to be free. This results in the performance loss in the computational servers. In order to find the optimum performance there is
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Economou, Nikos, and Antonis Vafidis. "Spectral balancing GPR data using time-variant bandwidth in the t-f domain." GEOPHYSICS 75, no. 3 (2010): J19—J27. http://dx.doi.org/10.1190/1.3374464.

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Ground-penetrating radar (GPR) sections encounter a resolution reduction with depth because, for electromagnetic (EM) waves propagating in the subsurface, attenuation is typically more pronounced at higher frequencies. To correct for these effects, we have applied a spectral balancing technique, using the S-transform (ST). This signal-processing technique avoids the drawbacks of inverse [Formula: see text] filtering techniques, namely, the need for estimation of the attenuation factor [Formula: see text] from the GPR section and instability caused by scattering effects that result from methods
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Pattanapairoj, Sirorat, Danaipong Chetchotsak, and Banchar Arnonkijpanich. "Hybrid Balancing Technique Using GRSOM and Bootstrap Algorithms for Classifiers with Imbalanced Data." Advanced Materials Research 931-932 (May 2014): 1375–81. http://dx.doi.org/10.4028/www.scientific.net/amr.931-932.1375.

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To deal with imbalanced data, this paper proposes a hybrid data balancing technique which incorporates both over and under-sampling approaches. This technique determines how much minority data should be grown as well as how much majority data should be reduced. In this manner, noise introduced to the data due to excessive over-sampling could be avoided. On top of that, the proposed data balancing technique helps to determine the appropriate size of the balanced data and thus computation time required for construction of classifiers would be more efficient. The data balancing technique over sam
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Muszala, S. P., D. A. Connors, J. J. Hack, and G. Alaghband. "The Promise of Load Balancing the Parameterization of Moist Convection Using a Model Data Load Index." Journal of Atmospheric and Oceanic Technology 23, no. 4 (2006): 525–37. http://dx.doi.org/10.1175/jtech1865.1.

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Abstract The parameterization of physical processes in atmospheric general circulation models contributes to load imbalances among individual processors of message-passing distributed-multiprocessor systems. Load imbalances increase the overall time to completion of a model run and should be eliminated or reduced as much as possible. Presented herein is a new technique that shows promise for load balancing the parameterization of moist convection found in the Community Climate System Model's (CCSM's) Community Atmosphere Model version 3 (CAM3). At the heart of this technique is a load index th
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Chetchotsak, Danaipong, Sirorat Pattanapairoj, and Banchar Arnonkijpanich. "Integrating new data balancing technique with committee networks for imbalanced data: GRSOM approach." Cognitive Neurodynamics 9, no. 6 (2015): 627–38. http://dx.doi.org/10.1007/s11571-015-9350-4.

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Muktar Yahuza, Yamani Idna Bin Idris, Ainuddin Wahid Bin Abdul Wahab, Mahdi A. Musa, and Adamu Abdullahi Garba. "A LIGHTWEIGHT AUTHENTICATION TECHNIQUE FOR SECURE COMMUNICATION OF EDGE/FOG DATA-CENTERS." Science Proceedings Series 2, no. 1 (2020): 76–81. http://dx.doi.org/10.31580/sps.v2i1.1319.

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Edge computing has significantly enhanced the capabilities of cloud computing. Edge data-centres are used for storing data of the end-user devices. Secure communication between the legitimate edge data-centres during the load balancing process has attracted industrial and academic researchers. Recently, Puthal et al. have proposed a technique for authenticating edge datacenters to enable secure load balancing. However, the resource-constraint nature of the edge data-centres is ignored. The scheme is characterized by complex computation and memory intensive cryptographic protocol. It is also vu
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ALANAZI, Fayez, and Aminu SULEIMAN. "Impact of Data Balancing and Feature Engineering on Accident Severity Models." Promet - Traffic&Transportation 37, no. 3 (2025): 665–90. https://doi.org/10.7307/ptt.v37i3.856.

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This study investigates the impacts of feature engineering techniques, including Clustering, Target Encoding and Anomaly Detection, in conjunction with data balancing methods, on the efficacy of machine learning models for predicting road accident severity. Automated Machine Learning (AutoML), Distributed Random Forest (DRF), Boosted Regression Trees (BRT) and Deep Learning models were evaluated on datasets that were balanced using the SMOTE (Synthetic Minority Over-Sampling Technique) and ADASYN (Adaptive Synthetic Sampling) techniques. Evaluation metrics such as Mean Squared Error (MSE), Roo
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Thilagavathi, N., D. Divya Dharani, R. Sasilekha, Vasundhara Suruliandi, and V. Rhymend Uthariaraj. "Energy Efficient Load Balancing in Cloud Data Center Using Clustering Technique." International Journal of Intelligent Information Technologies 15, no. 1 (2019): 84–100. http://dx.doi.org/10.4018/ijiit.2019010104.

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Cloud computing has seen tremendous growth in recent days. As a result of this, there has been a great increase in the growth of data centers all over the world. These data centers consume a lot of energy, resulting in high operating costs. The imbalance in load distribution among the servers in the data center results in increased energy consumption. Server consolidation can be handled by migrating all virtual machines in those underutilized servers. Migration causes performance degradation of the job, based on the migration time and number of migrations. Considering these aspects, the propos
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Dissertations / Theses on the topic "DATA BALANCING TECHNIQUE"

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Lee, Young-Jun. "Routing and Efficient Evaluation Techniques for Multi-hop Mobile Wireless Networks." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7455.

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In this dissertation, routing protocols, load-balancing protocols, and efficient evaluation techniques for multi-hop mobile wireless networks are explored. With the advancements made in wireless communication and computer technologies, a new type of mobile wireless network, known as a mobile ad hoc network (MANET), has drawn constant attention. In recent years, several routing protocols for MANETs have been proposed. However, there still remains the need for mechanisms for better scalability support with respect to network size, traffic volume, and mobility. To address this issue, a new metho
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Sonnert, Adrian. "Predicting inter-frequency measurements in an LTE network using supervised machine learning : a comparative study of learning algorithms and data processing techniques." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148553.

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With increasing demands on network reliability and speed, network suppliers need to effectivize their communications algorithms. Frequency measurements are a core part of mobile network communications, increasing their effectiveness would increase the effectiveness of many network processes such as handovers, load balancing, and carrier aggregation. This study examines the possibility of using supervised learning to predict the signal of inter-frequency measurements by investigating various learning algorithms and pre-processing techniques. We found that random forests have the highest predict
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JAWA, MISHA. "COMPARISION OF ENSEMBLE LEARNING MODELS AND IMPACT OF DATA BALANCING TECHNIQUE FOR SOFTWARE EFFORT ESTIMATION." Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19229.

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Project management is a critical component of every software project's success. Estimating the cost and effort of software development at the outset of the project is one of the most important responsibilities in software project management. Estimating effort allows project managers to more effectively manage resources and activities. The primary purpose of this study was to construct and compare the usage of two common ensemble approaches (bagging and boosting) to improve estimator accuracy and to study the impact of Synthetic Minority Over-Sampling Technique for Regression (SMO
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Books on the topic "DATA BALANCING TECHNIQUE"

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1961-, Choudhary Alok N., and United States. National Aeronautics and Space Administration., eds. Novel techniques for data decomposition and load balancing for parallel processing of vision systems: Implementation and evaluation using a motion estimation system. Coordinated Science Laboratory, College of Engineering, University of Illinois at Urbana-Champaign, 1990.

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Book chapters on the topic "DATA BALANCING TECHNIQUE"

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Vani, K. A., J. Prathima Mabel, and K. N. Rama Mohan Babu. "A Switch-Prioritized Load-Balancing Technique in SDN." In Data Analytics and Learning. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2514-4_4.

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Sharma, Deepak Kumar, Kartik Kwatra, Manan Manwani, Nimit Arora, and Aarti Goel. "Optimized Resource Allocation Technique Using Self-balancing Fast MinMin Algorithm." In Data Analytics and Management. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8335-3_36.

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Bano, Hamida, Nadeem Javaid, Komal Tehreem, Kainat Ansar, Maheen Zahid, and Tooba Nazar. "Cloud Computing Based Resource Allocation by Random Load Balancing Technique." In Lecture Notes on Data Engineering and Communications Technologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02613-4_3.

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Mohammed, Rafiq Ahmed, Kok Wai Wong, Mohd Fairuz Shiratuddin, and Xuequn Wang. "Classification of Multi-class Imbalanced Data Streams Using a Dynamic Data-Balancing Technique." In Communications in Computer and Information Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63823-8_33.

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Ansar, Kainat, Nadeem Javaid, Maheen Zahid, Komal Tehreem, Hamida Bano, and Momina Waheed. "A Hybrid HS-Mean Technique for Efficient Load Balancing in Cloud Computing." In Lecture Notes on Data Engineering and Communications Technologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02613-4_4.

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Xiong, Xuanrui, Yufan Zhang, Huijun Zhang, et al. "Data Balancing Technique Based on AE-Flow Model for Network Instrusion Detection." In Communications and Networking. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34790-0_14.

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de Hollander, J. A. "Application of a metabolic balancing technique to the analysis of microbial fermentation data." In Quantitative Aspects of Growth and Metabolism of Microorganisms. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2446-1_11.

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Rahman, Md Hasibur, Chowdhury Rafeed Rahman, Ruhul Amin, Md Habibur Rahman Sifat, and Afra Anika. "A Hybrid Approach Towards Two Stage Bengali Question Classification Utilizing Smart Data Balancing Technique." In Cyber Security and Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52856-0_36.

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Tripathy, Niva, Subhranshu Sekhar Tripathy, and Satyananda Champati Rai. "Machine learning-based multi-objective optimization technique for load balancing in integrated fog-cloud environment." In Optimization and Computing using Intelligent Data-Driven Approaches for Decision-Making. CRC Press, 2024. http://dx.doi.org/10.1201/9781003503057-6.

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Jin, Ying, and Yanming Chen. "Credit Card Fraud Prediction Based on the Improved Data Balancing Technique and the Gradient Boosting Algorithm." In Proceedings of the 2023 5th International Conference on Economic Management and Cultural Industry (ICEMCI 2023). Atlantis Press International BV, 2024. http://dx.doi.org/10.2991/978-94-6463-368-9_74.

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Conference papers on the topic "DATA BALANCING TECHNIQUE"

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Agazy, Anas Putra, Hindayati Mustafidah, Maulida Ayu Fitriani, and Abid Yanuar Badharudin. "Air Quality Analysis Using Random Forest Algorithm and SMOTE Data Balancing Technique." In 2024 7th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). IEEE, 2024. https://doi.org/10.1109/isriti64779.2024.10963451.

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Bhairavi, R., M. Thiruveni, Anita Christaline J, N. Ashokkumar, I. Chandra, and P. Nagarajan. "Improved Energy Balancing based Data Forwarding incorporating Whale optimization Technique in Underwater Acoustic Sensor Network." In 2024 Third International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT). IEEE, 2024. http://dx.doi.org/10.1109/iceeict61591.2024.10718405.

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T, Divya, and Ramadevi R. "Cosine Similarity and Data Balancing Technique for the Augmentation of ML Models in the Identification of Cerebro Vascular Accident." In 2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS). IEEE, 2024. https://doi.org/10.1109/icicnis64247.2024.10823348.

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Procopio, Leo, and Laura Vielhauer. "Balancing Act: Principles of Design and Formulation for Waterborne Acrylic DTM Coatings." In SSPC 2017 Greencoat. SSPC, 2017. https://doi.org/10.5006/s2017-00057.

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Abstract Achieving high performance with waterborne acrylic latex DTM coatings often requires a balancing act to be performed by both the polymer scientist designing the latex polymer and the coating scientist formulating the coating. A successful coating is defined by a set of critical properties while in the paint can, during application, and ultimately in its performance throughout its service life. It is often found, particularly with single component waterborne acrylic latex DTM coatings, that realizing certain combinations of key properties is difficult because the polymer feature or for
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Kaiser, Tanveer, Avishek Chowdhury, Umme Humyra Nabila, Pantha Singha, Mohammad Hasan, and M. A. Rasel. "Credit Card Fraud Detection: Data Balancing Techniques and Model Interpretability." In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2025. https://doi.org/10.1109/ecce64574.2025.11013141.

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Aby, Aswathy Elma, Salaji S, Anilkumar K K, and Tintu Rajan. "Optimizing Acute Myeloid Leukemia Subtype Classification Through Data Balancing Techniques." In 2025 2nd International Conference on Trends in Engineering Systems and Technologies (ICTEST). IEEE, 2025. https://doi.org/10.1109/ictest64710.2025.11042354.

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Coviello, Antonio, Rafael Sobral Augusto, Davide Gottardello, et al. "Strategies for Efficient ENG Signal Classification Using Data Augmentation and Data Balancing Techniques." In 2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE). IEEE, 2024. https://doi.org/10.1109/metroxraine62247.2024.10796410.

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Kacenjar, Steve, Ronald A. Neely, Philip Feldman, and Aaron Dant. "Enhanced self-directed training (ESDT): dynamic data balancing in regression models." In Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications III, edited by Keith F. Prussing, Kimberly E. Manser, Celso De Melo, Raghuveer M. Rao, and Christopher L. Howell. SPIE, 2025. https://doi.org/10.1117/12.3066302.

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Lao, Yanming. "Deep learning in diabetes prediction: the impact of data balancing techniques." In 2025 2nd International Conference on Algorithms, Software Engineering and Network Security (ASENS). IEEE, 2025. https://doi.org/10.1109/asens64990.2025.11011098.

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Al-Hammouri, Mohammad F., Ziad Akram Ali Hammouri, Islam T. Almalkawi, and Ansam Lafee. "Optimizing Multi-Class Classification in Educational Data with Ensemble Learning and Data Balancing Techniques." In 2024 Fifth International Conference on Intelligent Data Science Technologies and Applications (IDSTA). IEEE, 2024. http://dx.doi.org/10.1109/idsta62194.2024.10746987.

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Reports on the topic "DATA BALANCING TECHNIQUE"

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McDougall, Robert. Entropy Theory and RAS are Friends. GTAP Working Paper, 2000. http://dx.doi.org/10.21642/gtap.wp06.

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Presented at the Second Annual Conference on Global Economic Analysis Recent research in applications of entropy theory to matrix balancing problems in eco-nomics has put powerful new tools in the hands of data base developers, but overshadowed some previous findings. We recall earlier findings that the RAS is an entropy-theoretic model. Investigating the properties of a more recently proposed entropy-theoretic model, we find that in general the RAS remains preferable. We show further that the RAS can be obtained also as a generalised cross-entropy model. Finally, we present examples illus-tra
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Brunner, Huschenbett, and Beshouri. PR-336-06206-R01 Engine Control for Legacy Engines - Cylinder and Cycle Level Control. Pipeline Research Council International, Inc. (PRCI), 2014. http://dx.doi.org/10.55274/r0010041.

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The reduction or even elimination of cylinder to cylinder combustion variation and cycle-to-cycle combustion instability may result in further nitrous oxides (NOx) reductions at the same engine average air/fuel ratio (and hence turbocharger operating point) or allow operation at a leaner air/fuel ratio before the onset of instability. Reductions in imbalance and instability can also reduce carbon pollutant emissions, reduce break specific fuel consumption (BSFC) and improve engine operability. Achieving these goals requires a better understanding of the sources and impacts of that imbalance an
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