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Journal articles on the topic 'Augmentation of Resources'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Zahedi, Sanam, Ellen Hancock, Samee Hameed, Linda G. Phillips, and Clayton L. Moliver. "Social Media’s Influence on Breast Augmentation." Aesthetic Surgery Journal 40, no. 8 (2019): 917–25. http://dx.doi.org/10.1093/asj/sjz253.

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Abstract Background The purpose of this study is to evaluate if and what social media use influences our patients’ decisions to undergo breast augmentation. Objectives A single-institution study was designed to evaluate women who underwent elective breast augmentation from 2017 to 2018. Methods Patients were contacted via validated, prompted telephone survey. Data regarding their demographic information and social media utilization pertaining to breast augmentation were collected after obtaining verbal consent. Results Inclusion criteria were met by 180 patients, of which 69% participated in t

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Shen, Junxin, Yi Yang, and Fanghao Xiao. "Small-Sample Data Pricing Based on Data Augmentation and Meta-Learning." Electronics 13, no. 21 (2024): 4150. http://dx.doi.org/10.3390/electronics13214150.

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Data trading platforms play a crucial role in facilitating data circulation and promoting the sustainable allocation of data resources. Establishing a transparent, fair, and efficient pricing mechanism is key to ensuring the long-term stability and development of such platforms. However, these platforms face challenges in pricing due to the small sample problem, as traditional machine learning methods typically rely on large amounts of data. To address this issue, this paper proposes a data resource pricing model that combines WGAN-GP data augmentation and the Reptile algorithm. Data augmentat

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Zubari, Waleed K., and Ibrahim J. Lori. "Management and sustainability of groundwater resources in Bahrain." Water Policy 8, no. 2 (2006): 127–45. http://dx.doi.org/10.2166/wp.2006.0009.

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In Bahrain, groundwater in the Dammam aquifer is the only natural source of relatively freshwater to meet the increasing demands for water. Heavy reliance on groundwater, particularly by the agricultural and municipal sectors, and its prolonged overexploitation over the last four decades have led to severe deterioration in its water quality. Currently, most of the original groundwater reservoir has been lost to salinization. In the past, groundwater management efforts were concentrated principally on supply management, where great efforts have been made to develop additional non-conventional a

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Maanju, S. K., K. Saha, and R. K. Verma. "Augmentation of Groundwater Resources through Watershed Management: An Overview." IOSR Journal of Applied Geology and Geophysics 2, no. 1 (2014): 47–50. http://dx.doi.org/10.9790/0990-02114750.

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Amarù, Sofia, Davide Marelli, Gianluigi Ciocca, and Raimondo Schettini. "DALib: A Curated Repository of Libraries for Data Augmentation in Computer Vision." Journal of Imaging 9, no. 10 (2023): 232. http://dx.doi.org/10.3390/jimaging9100232.

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Data augmentation is a fundamental technique in machine learning that plays a crucial role in expanding the size of training datasets. By applying various transformations or modifications to existing data, data augmentation enhances the generalization and robustness of machine learning models. In recent years, the development of several libraries has simplified the utilization of diverse data augmentation strategies across different tasks. This paper focuses on the exploration of the most widely adopted libraries specifically designed for data augmentation in computer vision tasks. Here, we ai

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Wang, Zirui, Jingjing Yang, Haonan Jiang, and Xueling Fan. "CNN Training with Twenty Samples for Crack Detection via Data Augmentation." Sensors 20, no. 17 (2020): 4849. http://dx.doi.org/10.3390/s20174849.

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The excellent generalization ability of deep learning methods, e.g., convolutional neural networks (CNNs), depends on a large amount of training data, which is difficult to obtain in industrial practices. Data augmentation is regarded commonly as an effective strategy to address this problem. In this paper, we attempt to construct a crack detector based on CNN with twenty images via a two-stage data augmentation method. In detail, nine data augmentation methods are compared for crack detection in the model training, respectively. As a result, the rotation method outperforms these methods for a

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Silva, Kanishka, Burcu Can, Raheem Sarwar, Frederic Blain, and Ruslan Mitkov. "Text Data Augmentation Using Generative Adversarial Networks – A Systematic Review." Journal of Computational and Applied Linguistics 1 (July 18, 2023): 6–38. https://doi.org/10.33919/jcal.23.1.1.

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Insufficient data is one of the main drawbacks in natural language processing tasks, and the most prevalent solution is to collect a decent amount of data that will be enough for the optimisation of the model. However, recent research directions are strategically moving towards increasing training examples due to the nature of the data-hungry neural models. Data augmentation is an emerging area that aims to ensure the diversity of data without attempting to collect new data exclusively to boost a model’s performance. Limitations in data augmentation, especially for textual data, are mainly due

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Ricci, Joseph A., Christina R. Vargas, Danielle J. Chuang, Samuel J. Lin, and Bernard T. Lee. "Readability Assessment of Online Patient Resources for Breast Augmentation Surgery." Plastic and Reconstructive Surgery 135, no. 6 (2015): 1573–79. http://dx.doi.org/10.1097/prs.0000000000001230.

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Kim, Taeyoon, ChanHo Park, Mansur Mukimbekov, et al. "FusionFlow: Accelerating Data Preprocessing for Machine Learning with CPU-GPU Cooperation." Proceedings of the VLDB Endowment 17, no. 4 (2023): 863–76. http://dx.doi.org/10.14778/3636218.3636238.

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Data augmentation enhances the accuracy of DL models by diversifying training samples through a sequence of data transformations. While recent advancements in data augmentation have demonstrated remarkable efficacy, they often rely on computationally expensive and dynamic algorithms. Unfortunately, current system optimizations, primarily designed to leverage CPUs, cannot effectively support these methods due to costs and limited resource availability. To address these issues, we introduce FusionFlow, a system that cooperatively utilizes both CPUs and GPUs to accelerate the data preprocessing s

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Dubey, Anupama, Basvaraj Bagade, Sanjit Kumar, and Kushala Devi. "Impact of Precipitation Variability on Ground Water Resources: A Comparative Assessment of Indian States." Geo-Eye 10, no. 1 (2021): 5–12. https://doi.org/10.53989/bu.ge.v10i1.2.

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In recent years, many countries are witnessing climate change in the form of precipitation variability. Increasing demand of water because of growing consumption of water in various sectors in India is mounting enormous pressure on water resource management. Not only this, changes in cropping pattern and land-use pattern, over-exploitation of water storage and changes in irrigation and drainage are modifying the hydrological cycle in many climate regions and river basins of India. Therefore, this study aims to assess the precipitation variability and impact of climate change on ground water re

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Zhang, Yonglin, Haibin Wang, Chao Li, and Fabrice Meriaudeau. "Data augmentation aided complex-valued network for channel estimation in underwater acoustic orthogonal frequency division multiplexing system." Journal of the Acoustical Society of America 151, no. 6 (2022): 4150–64. http://dx.doi.org/10.1121/10.0011674.

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In this paper, a data augmentation aided complex-valued network is proposed for underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) channel estimations, wherein empirical mode decomposition based data augmentation is proposed to solve the current dilemma in the deep learning embedded UWA-OFDM communications: data scarcity and data-sampling difficulties in real-world applications. In addition, the significance of high-frequency component augmentation for the UWA channel and how it positively influences the following model training are discussed in detail and demonstrated

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P., M. Suresh Kumar. "Adoption of Technology and Augmentation of Resources for Teaching-Learning in Higher Education." International Journal of Management, Technology, and Social Sciences (IJMTS) 1, no. 1 (2017): 93–98. https://doi.org/10.5281/zenodo.800707.

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Learner centred education through appropriate methodologies facilitates effective learning as teaching-learning modalities of higher education are considered to be relevant to the learner group. Curriculum delivery and pedagogy should incorporate multitude of learning experiences and innovative learning methodologies through adoption of technology. Plenty of resources external to the curriculum come into use, which offer valuable learning experiences. Augmentation of resources for teaching-learning process would enhance the quality of learning. The role of the institution is to identify and pr

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P., M. Suresh Kumar. "Adoption of Technology and Augmentation of Resources for Teaching-Learning in Higher Education." International Journal of Applied Engineering and Management Letters (IJAEML) 1, no. 1 (2017): 63–69. https://doi.org/10.5281/zenodo.821352.

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Learner centred education through appropriate methodologies facilitates effective learning as teaching-learning modalities of higher education are considered to be relevant to the learner group. Curriculum delivery and pedagogy should incorporate multitude of learning experiences and innovative learning methodologies through adoption of technology. Plenty of resources external to the curriculum come into use, which offer valuable learning experiences. Augmentation of resources for teaching-learning process would enhance the quality of learning. The role of the institution is to identify and pr

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Abidin, Hipni Mubarok, Endang Rochyadi, and Neng Tria Sutriani. "Implementation of Early Intervention with Family Resourced to Improve Development of Expressive Language in Children with Cerebral Palsy." Indonesian Journal of Community and Special Needs Education 1, no. 2 (2021): 63–70. http://dx.doi.org/10.17509/ijcsne.v1i2.33417.

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The purpose of this study was to determine the implementation of early intervention with family resources to improve expressive language development in children with cerebral palsy. Early intervention with family resources is carried out on children with cerebral palsy who have problems in the development of their expressive language, so the communication method used is augmentation using alternative and augmentative communication tools called ASIK (I'm Ready to Communicate). This research method uses experiments, starting from modelling, transfer by parents, so that parents can independently

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Kartheeswari, S. "Competence Augmentation Drives Workforce Efficiency." Shanlax International Journal of Arts, Science and Humanities 12, no. 1 (2024): 70–78. http://dx.doi.org/10.34293/sijash.v12i1.7824.

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Skill Development refers to the process of acquiring and enhancing competencies, knowledge and abilities that enable workers to perform specific tasks effectively. It helps personnel adapt to changing job requirements with innovation and contributes to organizational success. It facilitates a cycle of high productivity, increased employment opportunities, improved job satisfaction, enhanced adaptability to new technologies, income growth, and development. In this juncture, employee retention rates also uplifted. A healthy working climate viz. openness, trust, and collaboration is indeed need f

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JIANG, YIWEI, AN ZHANG, and JUELIANG HU. "OPTIMAL ONLINE ALGORITHMS ON TWO HIERARCHICAL MACHINES WITH RESOURCE AUGMENTATION." Discrete Mathematics, Algorithms and Applications 04, no. 01 (2012): 1250012. http://dx.doi.org/10.1142/s1793830912500127.

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This paper investigates an online hierarchical scheduling problem with resource augmentation, i.e., the resources of the online algorithms are different from those of the offline algorithms. The machines are provided with different capacity according to their hierarchies. One with the hierarchy 1 has a speed of s(q) in the online (offline) algorithms and can process all the jobs. The other with hierarchy 2 has a speed of 1 in the online/offline algorithms and can only process partial jobs. The objective is to minimize makespan. For any 0 < q, s < ∞, we present optimal online algorithms w

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Jiang, Lanlan, Xingguo Qin, Jingwei Zhang, and Jun Li. "Multimodal Seed Data Augmentation for Low-Resource Audio Latin Cuengh Language." Applied Sciences 14, no. 20 (2024): 9533. http://dx.doi.org/10.3390/app14209533.

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Latin Cuengh is a low-resource dialect that is prevalent in select ethnic minority regions in China. This language presents unique challenges for intelligent research and preservation efforts, primarily due to its oral tradition and the limited availability of textual resources. Prior research has sought to bolster intelligent processing capabilities with regard to Latin Cuengh through data augmentation techniques leveraging scarce textual data, with modest success. In this study, we introduce an innovative multimodal seed data augmentation model designed to significantly enhance the intellige

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Mondal, Biswajit, Alka Singh, S. D. Singh, et al. "Augmentation of Water Resources Potential and Cropping Intensification Through Watershed Programs." Water Environment Research 90, no. 2 (2018): 101–9. http://dx.doi.org/10.2175/106143017x14902968254700.

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Bolgov, M. V., A. L. Buber, A. A. Komarovsky, and A. V. Lotov. "Compromise solutions for planning and managing augmentation to downstream water of Volgograd hydropower installation. 2nd Tactic planning and operating department." Водные ресурсы 46, no. 3 (2019): 333–44. http://dx.doi.org/10.31857/s0321-0596463333-344.

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The discussion on developing new computer technologies for problems analysis and support of decision-making is continued regarding water resources available for the users of the water utilization system of the Lower Volga and the entire Volga–Kama reservoirs cascade. Thus, this second part examines the objectives of the tactic planning to determine augmentation to the downstream water of Volgograd hydropower installation (special augmentation) and develops an algorithm for defining special augmentation based on the inflow forecast in the spring high water (April–June). This algorithm will aid

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Ordóñez-Morales, Esteban Fernando, Martín López-Nores, Yolanda Blanco-Fernández, et al. "Sporadic Cloud-Based Mobile Augmentation on the Top of a Virtualization Layer: A Case Study of Collaborative Downloads in VANETs." Journal of Advanced Transportation 2019 (March 21, 2019): 1–21. http://dx.doi.org/10.1155/2019/3548213.

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Current approaches to Cloud-based Mobile Augmentation (CMA) leverage (cloud-based) resources to meet the requirements of rich mobile applications, so that a terminal (the so-called application node or AppN) can borrow resources lent by a set of collaborator nodes (CNs). In the most sophisticated approaches proposed for vehicular scenarios, the collaborators are nearby vehicles that must remain together near the application node because the augmentation service is interrupted when they move apart. This leads to disruption in the execution of the applications and consequently impoverishes the mo

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Gusarova, Nataliya, Artem Lobantsev, Aleksandra Vatian, et al. "Generative augmentation to improve lung nodules detection in resource-limited settings." Information and Control Systems, no. 6 (December 15, 2020): 60–69. http://dx.doi.org/10.31799/1684-8853-2020-6-60-69.

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Introduction: Lung cancer is one of the most formidable cancers. The use of neural networks technologies in its diagnostics is promising, but the datasets collected from real clinical practice cannot cover a variety of lung cancer manifestations. Purpose: Assessment of the possibility of improving the classification of pulmonary nodules by means of generative augmentation of available datasets under resource constraints. Methods: We used part of LIDC-IDRI dataset, the StyleGAN architecture for generating artificial lung nodules and the VGG11 model as a classifier. We generated pulmonary nodule

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Chandra, Vaddiraju Shiva. "Effects of Urbanization on Rainfall, Surface and Groundwater Resources of Outskirts of Hyderabad City Using Geospatial Techniques." Ecology, Environment and Conservation 31, Suppl (2025): S351—S358. https://doi.org/10.53550/eec.2025.v31i02s.062.

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Unplanned urbanization and tremendous pressure of increased human activities is often resulting in more stress on urban surface water resources and augmentation of ground water resources and subsequent dependence on transporting of water from large distances for day-to-day needs. In the present study the spatial and temporal variation of urbanization and its subsequent impact on existing water resources which can be used for domestic needs after proper treatment and augmentation of groundwater is analyzed using Geomatics. This paper covers the impact of urban growth on the existing water resou

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Pavlovskiy, D. A., and J. V. Zvorykina. "Transformation of approaches to mineral resource auditing for augmentation of Russia’s wealth in the current political situation." Proceedings of higher educational establishments. Geology and Exploration, no. 6 (January 1, 2024): 79–90. http://dx.doi.org/10.32454/0016-7762-2023-65-6-79-90.

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Background. The mineral resource base is a strategic component of Russia’s economic security and a critical tool for strengthening the country’s position in the international arena. At the same time, economic turmoil and geopolitical tensions negatively affect the quality of resource auditing in the oil and gas and other extractive industries of the Russian Federation. This have an effect on the investment attractiveness of exploration projects, which are highly important for the continuous reproduction of the country’s mineral resource base.Aim and objectives. In order to consider the potenti

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Rahma, Iftitah Athiyyah, and Lya Hulliyyatus Suadaa. "Automated Indonesian Text Augmentation with Web-Based Application Using Flask Framework." Proceedings of The International Conference on Data Science and Official Statistics 2023, no. 1 (2023): 96–108. http://dx.doi.org/10.34123/icdsos.v2023i1.324.

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Text classification is one of the fundamental tasks in natural language processing (NLP). In the real world, data and resources available for text classification are limited. One of the issues with labeled data is imbalanced data. The problem of imbalanced data affects the performance and accuracy of the model because the model only focuses on data with majority labels. This impacts the model performance, which tends to classify correctly for the majority label only. Meanwhile, in some cases, it is more important for the minority label to be predicted correctly. Therefore, the measure of model

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Xu, Fan, Yangjie Dan, Keyu Yan, Yong Ma, and Mingwen Wang. "Low-Resource Language Discrimination toward Chinese Dialects with Transfer Learning and Data Augmentation." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 2 (2022): 1–21. http://dx.doi.org/10.1145/3473499.

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Chinese dialects discrimination is a challenging natural language processing task due to scarce annotation resource. In this article, we develop a novel Chinese dialects discrimination framework with transfer learning and data augmentation (CDDTLDA) in order to overcome the shortage of resources. To be more specific, we first use a relatively larger Chinese dialects corpus to train a source-side automatic speech recognition (ASR) model. Then, we adopt a simple but effective data augmentation method (i.e., speed, pitch, and noise disturbance) to augment the target-side low-resource Chinese dial

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Baporikar, Neeta. "ICT Adoption Implications for SME Innovation and Augmentation." International Journal of Innovation in the Digital Economy 13, no. 1 (2022): 1–20. http://dx.doi.org/10.4018/ijide.292488.

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The adoption of information communication technology (ICT) by small and medium enterprises (SMEs) plays a critical role in their development, growth, and expansion. To achieve a competitive advantage, an enterprise needs to have the resources and capabilities to operate efficiently. The fast improvement of ICT is rapidly shifting the conventional way of performance in all facets of SMEs. Today, all kinds of establishments are accepting the implementation and usage of ICT to advance administrative efficiency, outputs, and improved communication for strengthening their competitiveness. SMEs need

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Cui, Xiaohui, Yu Li, Zheng Xie, Hanzhang Liu, Shijie Yang, and Chao Mou. "ADQE: Obtain Better Deep Learning Models by Evaluating the Augmented Data Quality Using Information Entropy." Electronics 12, no. 19 (2023): 4077. http://dx.doi.org/10.3390/electronics12194077.

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Data augmentation, as a common technique in deep learning training, is primarily used to mitigate overfitting problems, especially with small-scale datasets. However, it is difficult for us to evaluate whether the augmented dataset truly benefits the performance of the model. If the training model is relied upon in each case to validate the quality of the data augmentation and the dataset, it will take a lot of time and resources. This article proposes a simple and practical approach to evaluate the quality of data augmentation for image classification tasks, enriching the theoretical research

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Forattini Jansen, Victor, Emanuel Teixeira Martins, Yasmin Souza Lima, Flavio De Oliveira Silva, Rodrigo Moreira, and Larissa Ferreira Rodrigues Moreira. "Data Augmentation and Convolutional Network Architecture Influence on Distributed Learning." Revista de Informática Teórica e Aplicada 32, no. 1 (2025): 54–60. https://doi.org/10.22456/2175-2745.143508.

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Convolutional Neural Networks (CNNs) have proven to be highly effective in solving a broad spectrum of computer vision tasks, such as classification, identification, and segmentation. These methods can be deployed in both centralized and distributed environments, depending on the computational demands of the task. While much of the literature has focused on the explainability of CNNs, which is essential for building trust and confidence in their predictions, there remains a gap in understanding their impact on computational resources, particularly in distributed training contexts. In this stud

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Khoirunnisa, Emila, Farrikh Alzami, Ricardus Anggi Pramunendar, et al. "Enhanced Semarang Batik Classification using MobileNetV2 and Data Augmentation." sinkron 9, no. 1 (2025): 43–54. https://doi.org/10.33395/sinkron.v9i1.14308.

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Batik, an Indonesian cultural heritage recognized by UNESCO, faces challenges in pattern identification and documentation, particularly for the younger generation. Previous studies on batik classification have shown limitations in handling small datasets and maintaining accuracy with limited computational resources. This research proposes an enhanced classification approach for Semarang Batik motifs using MobileNetV2 architecture combined with strategic data augmentation techniques. The study utilizes a dataset of 3,020 images comprising 10 distinct Semarang Batik motifs, implementing horizont

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Pandey, Deepa, and Mayur Kevat. "A Critical Review on Performance Enhancement of Passive Solar Stills Augmented with Phase Change Materials." Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 132, no. 2 (2025): 149–71. https://doi.org/10.37934/arfmts.132.2.149171.

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Water is one of the most important life resources and potable water is a very precious resource. Clean water is one of the most important challenges in modern world and one of the key goals of sustainable development. Use of solar energy to generate clean water through solar stills is a cost effective and highly sustainable method. The only drawback of solar stills in generating clean distilled water is its low output. A number of methods have been tried to increase the distillate output of the solar still. Augmentation with Phase Change Materials (PCMs) is one of the popular methods for incre

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Camp, Jeremy V., Briana Spruill-Harrell, Robert D. Owen, et al. "Mixed Effects of Habitat Degradation and Resources on Hantaviruses in Sympatric Wild Rodent Reservoirs within a Neotropical Forest." Viruses 13, no. 1 (2021): 85. http://dx.doi.org/10.3390/v13010085.

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Understanding the ecology of rodent-borne hantaviruses is critical to assessing the risk of spillover to humans. Longitudinal surveys have suggested that hantaviral prevalence in a given host population is tightly linked to rodent ecology and correlates with changes in the species composition of a rodent community over time and/or habitat composition. We tested two hypotheses to identify whether resource addition and/or habitat composition may affect hantavirus prevalence among two sympatric reservoir hosts in a neotropical forest: (i) increased food resources will alter the rodent community a

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Camp, Jeremy V., Briana Spruill-Harrell, Robert D. Owen, et al. "Mixed Effects of Habitat Degradation and Resources on Hantaviruses in Sympatric Wild Rodent Reservoirs within a Neotropical Forest." Viruses 13, no. 1 (2021): 85. http://dx.doi.org/10.3390/v13010085.

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Understanding the ecology of rodent-borne hantaviruses is critical to assessing the risk of spillover to humans. Longitudinal surveys have suggested that hantaviral prevalence in a given host population is tightly linked to rodent ecology and correlates with changes in the species composition of a rodent community over time and/or habitat composition. We tested two hypotheses to identify whether resource addition and/or habitat composition may affect hantavirus prevalence among two sympatric reservoir hosts in a neotropical forest: (i) increased food resources will alter the rodent community a

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Qi, Yingmei, Heming Huang, and Huiyun Zhang. "Research on Speech Emotion Recognition Method Based A-CapsNet." Applied Sciences 12, no. 24 (2022): 12983. http://dx.doi.org/10.3390/app122412983.

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Speech emotion recognition is a crucial work direction in speech recognition. To increase the performance of speech emotion detection, researchers have worked relentlessly to improve data augmentation, feature extraction, and pattern formation. To address the concerns of limited speech data resources and model training overfitting, A-CapsNet, a neural network model based on data augmentation methodologies, is proposed in this research. In order to solve the issue of data scarcity and achieve the goal of data augmentation, the noise from the Noisex-92 database is first combined with four differ

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García, E., R. Pizá, X. Benavides, E. Quiles, A. Correcher, and F. Morant. "Mechanical Augmentation Channel Design for Turbine Current Generators." Advances in Mechanical Engineering 6 (January 1, 2014): 650131. http://dx.doi.org/10.1155/2014/650131.

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We present the design and analysis of augmentation channels to increase the efficiency of shrouded marine current turbines in conditions of low intensity flows. These turbines are part of a prototype of a floating device composed of wind and marine current generators for generating renewable energy. It intends to exploit renewable energy resources in an integrated manner using wind and current turbines in offshore plants optimizing the economic investment.

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Buss, Christopher, Jasmin Mousavi, Mikhail Tokarev, Arash Termehchy, David Maier, and Stefan Lee. "Effective Entity Augmentation by Querying External Data Sources." Proceedings of the VLDB Endowment 16, no. 11 (2023): 3404–17. http://dx.doi.org/10.14778/3611479.3611535.

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Users often want to augment and enrich entities in their datasets with relevant information from external data sources. As many external sources are accessible only via keyword-search interfaces, a user usually has to manually formulate a keyword query that extract relevant information for each entity. This approach is challenging as many data sources contain numerous tuples, only a small fraction of which may contain entity-relevant information. Furthermore, different datasets may represent the same information in distinct forms and under different terms (e.g., different data source may use d

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Salgotra, Romesh K., and Charles Neal Stewart. "Genetic Augmentation of Legume Crops Using Genomic Resources and Genotyping Platforms for Nutritional Food Security." Plants 11, no. 14 (2022): 1866. http://dx.doi.org/10.3390/plants11141866.

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Recent advances in next generation sequencing (NGS) technologies have led the surge of genomic resources for the improvement legume crops. Advances in high throughput genotyping (HTG) and high throughput phenotyping (HTP) enable legume breeders to improve legume crops more precisely and efficiently. Now, the legume breeder can reshuffle the natural gene combinations of their choice to enhance the genetic potential of crops. These genomic resources are efficiently deployed through molecular breeding approaches for genetic augmentation of important legume crops, such as chickpea, cowpea, pigeonp

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Yavitt, Joseph B., and S. Joseph Wright. "Seedling growth responses to water and nutrient augmentation in the understorey of a lowland moist forest, Panama." Journal of Tropical Ecology 24, no. 1 (2008): 19–26. http://dx.doi.org/10.1017/s0266467407004713.

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Abstract:We irrigated and fertilized (with nutrients) seedlings of Doliocarpus olivaceus (Dilleniaceae, a shade-tolerant liana), Faramea occidentalis (Rubiaceae, a shade-tolerant understorey tree) and Tetragastris panamensis (Burseraceae, a shade-tolerant canopy tree) growing in the understorey of an old-growth tropical moist forest in Panama to assess the impact of seasonal water availability and nutrient-poor soils on seedling growth rates. In control plots, height growth rates were greater in the dry season than in the wet season for Doliocarpus (21%) and for Faramea (89%), whereas Tetragas

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Ashit Kumar Singh and Dr. Shammi Kesh Roy. "A Study the Role of Water and the Sustainable Development." Journal of Advances and Scholarly Researches in Allied Education 21, no. 1 (2024): 270–76. http://dx.doi.org/10.29070/vm4ym611.

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Water is a crucial natural resource that serves as a primary source of water for irrigation, drinking, and industrial use in India. Despite its importance, Water resources in India are rapidly depleting due to increasing demand, climate change, and unsustainable management practices. Sustainable development, which aims to harmonize economic growth, social inclusion, and environmental sustainability, is paramount in addressing these water challenges. In this context, it is crucial to revisit the Water laws in India to ensure sustainable resource management. The current legal framework for Water

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Caniglia, Joanne, and Michelle Meadows. "Pre-Service Mathematics Teachers’ Use of Web Resources." International Journal for Technology in Mathematics Education 25, no. 3 (2018): 17–34. http://dx.doi.org/10.1564/tme_v25.3.02.

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The internet is used by teachers to help find resources to integrate technology into their classrooms in a variety of ways (Handal, Campbell, Cavanagh, Petocz, & Kelly, 2013). The purpose of this study was to investigate the websites preservice teachers (PSTs) used during their field experiences in secondary mathematics. To address the purpose of this study, the researchers collected survey data, lesson plans, and PST’s work. The Technological, Pedagogical, Content Knowledge (TPACK) Framework and the Substitution, Augmentation, Modification, and Redefinition (SAMR) model both served import

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Mi, Chenggang, Shaolin Zhu, and Rui Nie. "Improving Loanword Identification in Low-Resource Language with Data Augmentation and Multiple Feature Fusion." Computational Intelligence and Neuroscience 2021 (April 8, 2021): 1–9. http://dx.doi.org/10.1155/2021/9975078.

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Loanword identification is studied in recent years to alleviate data sparseness in several natural language processing (NLP) tasks, such as machine translation, cross-lingual information retrieval, and so on. However, recent studies on this topic usually put efforts on high-resource languages (such as Chinese, English, and Russian); for low-resource languages, such as Uyghur and Mongolian, due to the limitation of resources and lack of annotated data, loanword identification on these languages tends to have lower performance. To overcome this problem, we first propose a lexical constraint-base

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Dou, Abdelbasset, and Khalida KISSI. "Building the Oranian-English Parallel Corpus: Methodology and Compilation Process." Journal of Languages and Translation 4, no. 2 (2024): 161–74. http://dx.doi.org/10.70204/jlt.v4i2.358.

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The scarcity of linguistic resources poses a major challenge for automated translation and processing of dialects. These resources are crucial for natural language processing experts conducting research on dialect recognition, processing, and machine translation. This paper describes the compilation of a dataset for an Algerian low-resource language as it emphasizes the importance of developing resources for Algerian dialects. It examines existing relevant corpora and details the creation process and unique features of the pioneering Oranian-English Parallel Corpus (OEPC). OEPC is the first pa

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CHIN, BYUNGSUN, and TSUTOMU NODA. "Black rockfish: Stock enhancement for augmentation of fisheries resources and a future view." NIPPON SUISAN GAKKAISHI 76, no. 2 (2010): 254–55. http://dx.doi.org/10.2331/suisan.76.254.

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Abhishek, GJ, Shridhar Ragi, Kailash Chandra Bhatt, and Rajneesh Kumar. "Comprehensive Overview of Plant Germplasm Augmentation: Strategies, Techniques and Applications." Journal of Advances in Biology & Biotechnology 27, no. 9 (2024): 1009–18. http://dx.doi.org/10.9734/jabb/2024/v27i91371.

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Plant genetic resources (PGR) are fundamental for crop improvement and conservation, encompassing a wide range of genetic materials including land races, modern varieties, and wild relatives. Effective germplasm exploration is essential for advancing plant genetic resources and ensuring agricultural sustainability. Traditional germplasm collection faces challenges such as remote locations, limited material availability, and transport issues. This review explores various strategies to address these challenges, including coarse-grid and fine-grid survey methods, multi-species versus species-spec

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Bassano, Clara, Francesco Caputo, Paolo Barile, and Paolo Piciocchi. "Empowering service systems through Intelligence Augmentation (IA) in digital society." ITM Web of Conferences 62 (2024): 05003. http://dx.doi.org/10.1051/itmconf/20246205003.

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The paper explores the interpretative significance of relevance in the systemic qualification of “intelligence,” particularly in the context of Intelligence Augmentation (IA). Using the Viable System Approach (VSA) matrix, it delves into Informational Variety and Systemic Relevance to understand the incremental impact of AI in complex decision-making. The study reveals that, methodologically, the relevance of the viable system explains the synergistic collaboration between humans and machines, considering technology not just as an influential resource but also possessing structural criticality

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Ma, Jingchao, Chenfei Hu, Peng Zhou, Fangfang Jin, Xu Wang, and Haiyou Huang. "Review of Image Augmentation Used in Deep Learning-Based Material Microscopic Image Segmentation." Applied Sciences 13, no. 11 (2023): 6478. http://dx.doi.org/10.3390/app13116478.

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The deep learning-based image segmentation approach has evolved into the mainstream of target detection and shape characterization in microscopic image analysis. However, the accuracy and generalizability of deep learning approaches are still hindered by the insufficient data problem that results from the high expense of human and material resources for microscopic image acquisition and annotation. Generally, image augmentation can increase the amount of data in a short time by means of mathematical simulation, and has become a necessary module for deep learning-based material microscopic imag

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Prabowo, Dwi Puji, Muhammad Syaifur Rohman, Rama Aria Megantara, et al. "Adaptive Inertia Weight Particle Swarm Optimization for Augmentation Selection in Coral Reef Classification with Convolutional Neural Networks." JOIV : International Journal on Informatics Visualization 9, no. 1 (2025): 216. https://doi.org/10.62527/joiv.9.1.2726.

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Indonesia possesses the world's largest aquatic resources, with 17,504 islands and 6.49 million square kilometers of sea. Located in the coral triangle, Indonesia is home to diverse marine life, including vital coral reefs. However, these reefs face threats from climate change, pollution, and human activities, endangering biodiversity and coastal communities. Therefore, monitoring and preservation are crucial. This study evaluates various augmentation methods for classifying underwater coral reef images using Convolutional Neural Networks (CNNs). Effective augmentation methods are essential du

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Gordienko, Nikita, Yuri Gordienko, and Sergii Stirenko. "Synchronized Multi-Augmentation with Multi-Backbone Ensembling for Enhancing Deep Learning Performance." Applied System Innovation 8, no. 1 (2025): 18. https://doi.org/10.3390/asi8010018.

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This study introduces a novel technique called Synchronized Multi-Augmentation (SMA) combined with multi-backbone (MB) ensembling to enhance model performance and generalization in deep learning (DL) tasks in real-world scenarios. SMA utilizes synchronously augmented input data for training across multiple backbones, improving the overall feature extraction process. The outputs from these backbones are fused using two distinct strategies: the averaging fusion method, which averages predictions, and the dense fusion method, which averages features through a fully connected network. These method

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Ziyaden, Atabay, Amir Yelenov, Fuad Hajiyev, Samir Rustamov, and Alexandr Pak. "Text data augmentation and pre-trained Language Model for enhancing text classification of low-resource languages." PeerJ Computer Science 10 (March 29, 2024): e1974. http://dx.doi.org/10.7717/peerj-cs.1974.

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Background In the domain of natural language processing (NLP), the development and success of advanced language models are predominantly anchored in the richness of available linguistic resources. Languages such as Azerbaijani, which is classified as a low-resource, often face challenges arising from limited labeled datasets, consequently hindering effective model training. Methodology The primary objective of this study was to enhance the effectiveness and generalization capabilities of news text classification models using text augmentation techniques. In this study, we solve the problem of

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Sheibanian, M., Z. S. Khabadze, Yu A. Bakaev, N. N. Fedotova, and M. M. Borlakova. "Early and late complications arising from autologous soft tissue augmentation in dental practice: systematic review." Endodontics Today 20, no. 3 (2022): 230–33. http://dx.doi.org/10.36377/1726-7242-2022-20-3-230-233.

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Aim. To consider and analyze early and late complications that occur during autologous soft tissue augmentation in dental practice, since knowledge of complications helps the doctor to choose the right approach and materials for the treatment of any disease.Materials and methods. The publications in the electronic databases PubMed , Google Scholar and other reliable resources, were studied during a systematic review of the literature. the author has selected articles whose content concerns the study of complications arising from autologous soft tissue augmentation in outpatient dental practice

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Badea, Dorel, Olga BucoveȚchi, and Daniel Roman. "Knowledge Augmentation in the Organizational Field through Managerial Behavioral Models." International conference KNOWLEDGE-BASED ORGANIZATION 27, no. 1 (2021): 168–72. http://dx.doi.org/10.2478/kbo-2021-0026.

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Abstract Organizational transformation based on capabilities and, implicitly, the transfer of experience and expertise between different generations are valuable and interesting organizational processes to increase organizational performance, regardless of the type of organization. In the field of human resources, the temporary succession of different typologies of generations (Z, Millennials, etc.) has brought to the attention of theorists and practitioners the need to reconsider practices to reach the audience with the most appropriate message, in terms of gaining the most appropriate resour

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