Relevant bibliographies by topics / Traditional Datasets

Contents

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

Academic literature on the topic 'Traditional Datasets'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Traditional Datasets"

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Ma, Dong, and Haoyang Song. "Performance analysis and comparison of cat and dog image classification based on different models." Applied and Computational Engineering 41, no. 1 (2024): 197–201. http://dx.doi.org/10.54254/2755-2721/41/20230743.

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Image classification has widespread applications in computer vision, with significant advancements in performance due to deep learning models. Cat and dog image classification, as a classic problem, has attracted considerable research interest. This study aims to conduct a comprehensive analysis and comparison of deep learning models, including LeNet, ResNet, and VGG, in the context of cat and dog image classification. This paper employed two datasets: traditional cat and dog images and non-traditional, diverse images. Data preprocessing and augmentation were applied, and various model archite
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Masello, Leandro, Barry Sheehan, Finbarr Murphy, German Castignani, Kevin McDonnell, and Cian Ryan. "From Traditional to Autonomous Vehicles: A Systematic Review of Data Availability." Transportation Research Record: Journal of the Transportation Research Board 2676, no. 4 (2021): 161–93. http://dx.doi.org/10.1177/03611981211057532.

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The increasing accessibility of mobility datasets has enabled research in green mobility, road safety, vehicular automation, and transportation planning and optimization. Many stakeholders have leveraged vehicular datasets to study conventional driving characteristics and self-driving tasks. Notably, many of these datasets have been made publicly available, fostering collaboration, scientific comparability, and replication. As these datasets encompass several study domains and contain distinctive characteristics, selecting the appropriate dataset to investigate driving aspects might be challen
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Abu, Sarwar Zamani, Mobin Akhtar Md., and Ahamad Danish. "Concealment Conserving the Data Mining of Groups & Individual." Journal of Information Sciences and Computing Technologies 7, no. 1 (2018): 648–53. https://doi.org/10.5281/zenodo.3968147.

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We present an overview of privacy preserving data mining, one of the most popular directions in the data mining research community. In the first part of the chapter, we presented approaches that have been proposed for the protection of either the sensitive data itself in the course of data mining or the sensitive data mining results, in the context of traditional (relational) datasets. Following that, in the second part of the chapter, we focused our attention on one of the most recent as well as prominent directions in privacy preserving data mining: the mining of user mobility data. Although
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Sarma, Moumita Sen, Kaushik Deb, Pranab Kumar Dhar, and Takeshi Koshiba. "Traditional Bangladeshi Sports Video Classification Using Deep Learning Method." Applied Sciences 11, no. 5 (2021): 2149. http://dx.doi.org/10.3390/app11052149.

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Sports activities play a crucial role in preserving our health and mind. Due to the rapid growth of sports video repositories, automatized classification has become essential for easy access and retrieval, content-based recommendations, contextual advertising, etc. Traditional Bangladeshi sport is a genre of sports that bears the cultural significance of Bangladesh. Classification of this genre can act as a catalyst in reviving their lost dignity. In this paper, the Deep Learning method is utilized to classify traditional Bangladeshi sports videos by extracting both the spatial and temporal fe
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Seong, Teh Boon, Vasaki Ponnusamy, Noor Zaman Jhanjhi, Robithoh Annur, and M. N. Talib. "A comparative analysis on traditional wired datasets and the need for wireless datasets for IoT wireless intrusion detection." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (2021): 1165. http://dx.doi.org/10.11591/ijeecs.v22.i2.pp1165-1176.

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<span>IoT networks mostly rely on wireless mediums for communication, and due to that, they are very susceptible to intrusions. And due to the tiny nature, processing complexity, and limited storage capacities, IoT networks require very reliable intrusion detection systems (IDS). Although there are many IDS types of research available in the literature, most of these systems are suitable for wired network environments, and the benchmark datasets used for these research works are mostly relying on wired datasets such as KDD Cup’99 and NSL-KDD. IoT and wireless networks are distinct in nat
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Seong, Teh Boon, Vasaki Ponnusamy, NZ Jhanjhi, Robithoh Annur, and M. N. Talib. "A comparative analysis on traditional wired datasets and the need for wireless datasets for IoT wireless intrusion detection." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (2021): 1165–76. https://doi.org/10.11591/ijeecs.v22.i2.pp1165-1176.

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IoT networks mostly rely on wireless mediums for communication, and due to that, they are very susceptible to intrusions. And due to the tiny nature, processing complexity, and limited storage capacities, IoT networks require very reliable intrusion detection systems (IDS). Although there are many IDS types of research available in the literature, most of these systems are suitable for wired network environments, and the benchmark datasets used for these research works are mostly relying on wired datasets such as KDD Cup’99 and NSL-KDD. IoT and wireless networks are distinct in nature as
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Wang, Hanqiu, Aybek Rehmetulla, Shanshan Guo, et al. "Machine learning based on structural and FTIR spectroscopic datasets for seed autoclassification." RSC Advances 12, no. 18 (2022): 11413–19. http://dx.doi.org/10.1039/d2ra00239f.

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Nassiwa, Faith, and Jiahui Zeng. "Evaluating Traditional Machine Learning Models for Predicting Diabetes Onset Using the Pima Indians Dataset." Annals of Medical and Health Sciences Research 14, no. 7 (2024): 6. https://doi.org/10.5281/zenodo.14505301.

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Diabetes is a leading disease in the world. With the seriousness of diabetes and its complexity in diagnosis, we aimed to produce a model to help with prediction of onset of diabetes. Three models, logistic regression, gradient boosting and random forest were performed and evaluated to predict the onset of diabetes. A dataset of size 768 that includes information about some indian population were used. the population are specific to indian women that are at least 21 years old and of Pima Indian Heritage. Methods of standardizing including Synthetic Minority Oversampling Technique (SMOTE) and h
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Zou, Yajie. "Modeling highly dispersed crash data with sichel GAMLSS: An alternative approach to traditional methods." Multidisciplinary Science Journal 7, no. 8 (2025): 2025392. https://doi.org/10.31893/multiscience.2025392.

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This research examines the application of Sichel (SI) generalized additive models for location, scale, and shape (GAMLSS) in addressing the challenge of modeling highly dispersed crash data. The Sichel distribution, which combines the Poisson distribution with the generalized inverse Gaussian distribution, is particularly suited for modeling data with significant dispersion, where traditional models often prove inadequate. The primary objective of this study was to assess the performance of the Sichel GAMLSS in comparison with the widely-used Negative Binomial (NB) generalized linear model (GL
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J. Thaker, Dhaval, Hitesh R. Raval, and Juhi Khengar. "Leveraging AI for Enhanced Dataset Usability: Intelligent Summarization and Labeling for Academic-Industry Collaboration." Cuestiones de Fisioterapia 54, no. 2 (2025): 3867–77. https://doi.org/10.48047/3xjtdx63.

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In the era of digital transformation, artificial intelligence (AI) and cloud-based technologies arerevolutionizing university-industry collaboration by enhancing data accessibility, organization, andusability. Traditional data management approaches often suffer from inefficiencies, leading to fragmented,underutilized datasets. This research proposes an AI-powered framework that integrates intelligent labeling,automated dataset summarization, and vector-based retrieval to optimize dataset management. The systemefficiently categorizes and summarizes datasets by leveraging natural language proces
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Dissertations / Theses on the topic "Traditional Datasets"

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Hult, Jim, and Pontus Pihl. "Inspecting product quality with computer vision techniques : Comparing traditional image processingmethodswith deep learning methodson small datasets in finding surface defects." Thesis, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-54056.

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Quality control is an important part of any production line. It can be done manually but is most efficient if automated. Inspecting qualitycan include many different processes but this thesisisfocusedon the visual inspection for cracks and scratches. The best way of doingthis at the time of writing is with the help of Artificial Intelligence (AI), more specifically Deep Learning (DL).However, these need a training datasetbeforehand to train on and for some smaller companies, this mightnotbean option. This study triesto find an alternative visual inspection method,that does notrelyon atrained d
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McBee, Matthew T. "Nomination and identification of traditionally underrepresented students for gifted programs insights from a population dataset /." 2006. http://purl.galileo.usg.edu/uga%5Fetd/mcbee%5Fmatthew%5F200605%5Fphd.

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Thesis (Ph. D.)--University of Georgia, 2006.<br>Directed by Thomas Hébert. Includes an article accepted by The journal of secondary gifted education and an article submitted to Journal of educational psychology. Includes bibliographical references.
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Books on the topic "Traditional Datasets"

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Jemielniak, Dariusz. Thick Big Data. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198839705.001.0001.

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The social sciences are becoming datafied. The questions that have been considered the domain of sociologists, now are answered by data scientists, operating on large datasets, and breaking with the methodological tradition for better or worse. The traditional social sciences, such as sociology or anthropology, are thus under the double threat of becoming marginalized or even irrelevant; both because of the new methods of research, which require more computational skills, and because of the increasing competition from the corporate world, which gains an additional advantage based on data acces
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Peng, Handie. Economic Theories and Empirics on the Sex Market. Edited by Scott Cunningham and Manisha Shah. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199915248.013.2.

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This article presents a number of testable predictions from Edlund and Korn’s (2002) theoretical model. In their seminal study, Edlund and Korn propose a model that sees prostitution as an alternative to marriage. According to the them, women can only choose between marriage and prostitution, and “prostitution is low-skill, labor intensive, female, and well paid.” Because prostitution has such an unusual combination of attributes, traditional labor theories might not be able to explain the wage differential of this profession. The Edlund and Korn (EK) model offers “a marriage market explanatio
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Duckworth, Chloë N., and Andrew Wilson, eds. Recycling and Reuse in the Roman Economy. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198860846.001.0001.

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The recycling and reuse of materials and objects were extensive in the past, but have rarely been embedded into models of the economy; even more rarely has any attempt been made to assess the scale of these practices. Recent developments, including the use of large datasets, computational modelling, and high-resolution analytical chemistry, are increasingly offering the means to reconstruct recycling and reuse, and even to approach the thorny matter of quantification. Growing scholarly interest in the topic has also led to an increasing recognition of these practices from those employing more
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Busemeyer, Marius R. Public Opinion and the Politics of Social Investment. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198790488.003.0033.

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Existing survey data on the public’s opinion on education policy usually finds large majorities in support of more educational investments. And yet, in many countries, actual levels of public spending on education remain stagnant. Making use of a new and original dataset on public opinion in eight European countries, this chapter provides a partial answer to this puzzle. In particular, it finds that popular support for more education spending drops significantly once citizens are confronted with the necessity of cutbacks in other parts of the welfare state (such as pensions or unemployment ben
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Edge, M. D. Statistical Thinking from Scratch. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198827627.001.0001.

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In virtually every field, researchers find themselves navigating tremendous amounts of new data. Making sense of this flood of information requires much more than the rote application of traditional statistical methods. This book will train researchers to be creative and confident users of statistics by thinking hard about the application of simple methods to a small dataset. In particular, this book focuses on simple linear regression—a method with strong connections to the most important tools in applied statistics—using it as a detailed case study for teaching resampling-based, likelihood-b
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Veen, Christel. Roman Period Statuettes in the Netherlands and beyond. Amsterdam University Press, 2023. http://dx.doi.org/10.5117/9789463729383.

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The subject of this study is a relatively rare category of artefacts, bronze and terracotta statuettes that represent deities, human figures and animals. They were introduced in the northwestern provinces by Roman troops from the end of the 1st century BCE onwards. The statuettes have been recovered from military and non-military settlements, the surrounding landscape and, to a far lesser extent, from sanctuaries and graves. Until now, their meaning and function have seldom been analysed in relation to their find-spots. Contrary to traditional studies, they have been examined as one separate c
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Book chapters on the topic "Traditional Datasets"

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Kluwak, Konrad, Ryszard Klempous, Atsushi Ito, et al. "Reference Datasets for Analysis of Traditional Japanese and German Martial Arts." In Computer Aided Systems Theory – EUROCAST 2022. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-25312-6_59.

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Kopeć, Jakub. "Evaluating Methods of Transferring Large Datasets." In Supercomputing Frontiers. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10419-0_7.

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AbstractOur society critically depends on data, Big Data. The humanity generates and moves data volumes larger than ever before and their increase is continuously accelerating. The goal of this research is to evaluate tools used for the transfer of large volumes of data. Bulk data transfer is a complex endeavour that requires not only sufficient network infrastructure, but also appropriate software, computing power and storage resources. We report on the series of storage benchmarks conducted using recently developed elbencho tool. The tests were conducted with an objective to understand and a
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Yamamoto, Shuhei, and Noriko Kando. "Temporal Closeness for Enhanced Cross-Modal Retrieval of Sensor and Image Data." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2071-5_13.

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AbstractThis paper presents a new approach to dense retrieval across multiple modalities, emphasizing the integration of images and sensor data. Traditional cross-modal retrieval techniques face significant challenges, particularly in processing non-linguistic modalities and creating effective training datasets. To address these issues, we propose a method that uses a shared vector space, optimized with contrastive loss, to enable efficient and accurate retrieval across diverse modalities. A key innovation of our approach is the introduction of a temporal closeness metric, which evaluates the
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Molinero-Parejo, Ramón. "Geographically Weighted Methods to Validate Land Use Cover Maps." In Land Use Cover Datasets and Validation Tools. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90998-7_13.

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AbstractOne of the most commonly used techniques for validating Land Use Cover (LUC) maps are the accuracy assessment statistics derived from the cross-tabulation matrix. However, although these accuracy metrics are applied to spatial data, this does not mean that they produce spatial results. The overall, user’s and producer’s accuracy metrics provide global information for the entire area analysed, but shed no light on possible variations in accuracy at different points within this area, a shortcoming that has been widely criticized. To address this issue, a series of techniques have been de
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Spinde, Timo. "Feature-based Media Bias Detection." In Automated Detection of Media Bias. Springer Fachmedien Wiesbaden, 2025. https://doi.org/10.1007/978-3-658-47798-1_5.

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Abstract Thus far, we have presented a comprehensive literature review on media bias in Chap. 2, evaluated reliable measures for understanding media bias perception in Chap. 3, and introduced our two new datasets, MBIC and BABE, in Chap. 4. We now turn our attention to the design and implementation of automated bias classification systems. This chapter centers on a traditional machine-learning approach grounded on linguistic features.
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Baboo, Gautam Kumar, Shraddha Dubey, and Veeky Baths. "Comparative Study of Neural Networks (G/C/RNN) and Traditional Machine Learning Models on EEG Datasets." In Proceedings of the International Conference on Cognitive and Intelligent Computing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-2358-6_17.

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Röchert, Daniel, German Neubaum, and Stefan Stieglitz. "Identifying Political Sentiments on YouTube: A Systematic Comparison Regarding the Accuracy of Recurrent Neural Network and Machine Learning Models." In Disinformation in Open Online Media. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61841-4_8.

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Abstract Since social media have increasingly become forums to exchange personal opinions, more and more approaches have been suggested to analyze those sentiments automatically. Neural networks and traditional machine learning methods allow individual adaption by training the data, tailoring the algorithm to the particular topic that is discussed. Still, a great number of methodological combinations involving algorithms (e.g., recurrent neural networks (RNN)), techniques (e.g., word2vec), and methods (e.g., Skip-Gram) are possible. This work offers a systematic comparison of sentiment analyti
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Wang, Haoran, Zhiyu Chen, and Gang Liu. "An Improved Chicken Swarm Optimization Algorithm for Feature Selection." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_19.

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AbstractIn recent years, feature selection is becoming more and more important in data mining. Its target is that reduce the dimensionality of the datasets while at least maintaining the classification accuracy. There are some researches about chicken swarm optimization algorithm (CSO) applied to feature selection, the effect is extraordinary compared with traditional swarm intelligence algorithms. However, there is a complex search space in the challenging task feature selection, the CSO algorithm still has a default that quickly gets stuck in the local minimum problem. An improved chicken sw
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Modak, Sourav, and Anthony Stein. "Enhancing Weed Detection Performance by Means of GenAI-Based Image Augmentation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-91835-3_17.

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Abstract Precise weed management is essential for sustaining crop productivity and ecological balance. Traditional herbicide applications face economic and environmental challenges, emphasizing the need for intelligent weed control systems powered by deep learning. These systems require vast amounts of high-quality training data. The reality of scarcity of well-annotated training data, however, is often addressed through generating more data using data augmentation. Nevertheless, conventional augmentation techniques such as random flipping, color changes, and blurring lack sufficient fidelity
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Jones, Rebet Keith. "Beyond Traditional Learning." In Multisector Insights in Healthcare, Social Sciences, Society, and Technology. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-3226-9.ch012.

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This study explores the efficacy of the bidirectional encoder representations from transformers (BERT) model in the domain of Android malware detection, comparing its performance against traditional machine learning models such as convolutional neural networks (CNNs) and long short-term memory (LSTMs). Employing a comprehensive methodology, the research utilizes two significant datasets, the Drebin dataset and the CIC AndMal2017 dataset, known for their extensive collection of Android malware and benign applications. The models are evaluated based on accuracy, precision, recall, and F1 score.
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Conference papers on the topic "Traditional Datasets"

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Mosca, Nicola, Maria di Summa, Moh Rafik, Vittorio Bianco, Daniele Pirone, and Ettore Stella. "Immersive Exploration of Tomographic Datasets: Virtual Reality Revolutionizing Microscopic Analysis." In Digital Holography and Three-Dimensional Imaging. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/dh.2024.w2b.5.

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Traditional microscopy has limitations. On the contrary, tomographic methods provide non-destructive 3D visualization. This paper proposes immersive exploration via virtual reality, revolutionizing scientific analysis by bridging traditional techniques with advanced methodologies for enhanced research insights.
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Zedan, Mohammad, and Yury S. Bekhtin. "Performance Comparison of Autoencoders and Traditional Denoising Methods Across Four Image Datasets: Metric-Based Optimization." In 2025 7th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE). IEEE, 2025. https://doi.org/10.1109/reepe63962.2025.10970812.

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K�ksal, Ece S., Erdal Aydin, and Metin T�rkay. "An Efficient Convex Training Algorithm for Artificial Neural Networks by Utilizing Piecewise Linear Approximations and Semi-Continuous Formulations." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.125995.

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Artificial neural networks are widely used as data-driven models for capturing complex, nonlinear systems. However, suboptimal training remains a significant challenge due to the nonlinearity of activation functions and the reliance on local solvers, which makes achieving global solutions difficult. One solution involves reformulating activation functions as piecewise linear approximations to convexify the problem, though this approach often requires substantial CPU time. This study demonstrates that a tailored branch-and-bound algorithm can effectively address these challenges by efficiently
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Song, Jihun, Yunhak Noh, and Seungjun Kim. "Enhanced structural health monitoring for large-scale offshore photovoltaic systems based on response pattern recognition." In IABSE Symposium, Tokyo 2025: Environmentally Friendly Technologies and Structures: Focusing on Sustainable Approaches. International Association for Bridge and Structural Engineering (IABSE), 2025. https://doi.org/10.2749/tokyo.2025.3150.

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&lt;p&gt;Offshore floating photovoltaic systems are becoming increasingly prominent due to their energy efficiency and operational reliability. Ensuring their structural integrity, particularly the connections between floating modules, is essential to prevent instability and failures. Traditional inspection methods are impractical for large-scale installations with multiple connections and numerous connecting structures. To address this challenge, we propose a data-driven structural health monitoring approach using artificial neural networks (ANN). In this paper, we introduce an anomaly identi
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MATHRE, NEELA, THAINE H. ASSUMPÇÃO, IOANA POPESCU, ANDREJA JONOSKI, and BORJANA BOGATINOSKA. "IMPORTANCE OF CROWDSOURCING TO AUGMENT TRADITIONAL DATASETS IN HYDRODYNAMIC MODELS." In 38th IAHR World Congress. The International Association for Hydro-Environment Engineering and Research (IAHR), 2019. http://dx.doi.org/10.3850/38wc092019-0619.

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Coco, Marco Del, Pierluigi Carcagni, Marco Leo, Pier Luigi Mazzeo, Paolo Spagnolo, and Cosimo Distante. "Assessment of deep learning for gender classification on traditional datasets." In 2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2016. http://dx.doi.org/10.1109/avss.2016.7738061.

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Yingst, R. "INCORPORATING MULTIPLE DATASETS INTO TRADITIONAL GEOMORPHIC MAPS: THE VESTA EXAMPLE." In GSA Connects 2024 Meeting in Anaheim, California. Geological Society of America, 2024. http://dx.doi.org/10.1130/abs/2024am-401445.

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Monko, Gloriana Joseph, and Masaomi Kimura. "Optimized DBSCAN Parameter Selection: Stratified Sampling for Epsilon and Gridsearch for Minimum Samples." In 2nd International Conference on Software Engineering and Automation. Academy & Industry Research Collaboration Center, 2023. http://dx.doi.org/10.5121/csit.2023.132004.

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This research presents an advanced methodology for estimating the epsilon and minimum samples parameters in the DBSCAN clustering algorithm using a Stratified Sampling and Grid-Search approach. Our method showcased notable improvement in eps estimation precision across nine diverse datasets compared to conventional techniques. By accounting for dataset variations in structure and density, stratified sampling leads to superior cluster formations. The k-nearest distance graph further refines these relationships, ensuring a comprehensive understanding of data densities. Additionally, our method u
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Albuquerque, Renan, and Guilherme Barreto. "Unsupervised Time Series Novelty Detection Using Clustering-based Local Autoencoders." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2024. http://dx.doi.org/10.21528/cbic2023-172.

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Novelty detection, also known as anomaly detection, plays a crucial role in identifying new or abnormal instances within a dataset. Traditional autoencoder models have been effective in learning compact representations of data, but they often struggle with capturing fine-grained local variations in complex and high-dimensional datasets. To address this limitation, we propose a novel learning method called Local Autoencoders (LAEs) for novelty detection. LAEs incorporate local information into the encoding and decoding processes, enabling more precise and detailed reconstructions. In this paper
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Diri, Grace, Philip Obiorah, and Hongbo Du. "Comparative Study of Sentiment Analysis Techniques: Traditional Machine Learning vs. Deep Learning Approaches." In InSITE 2025: Informing Science + IT Education Conferences: Hiroshima. Informing Science Institute, 2025. https://doi.org/10.28945/5490.

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Aim/Purpose This paper presents a comparative analysis of sentiment classification models, focusing on the performance differences between lexicon-based sentiment analysis, traditional machine learning techniques, and transformer-based deep learning approaches across multiple benchmark datasets. Background While numerous methods exist for sentiment analysis, a systematic comparison across modeling paradigms and datasets remains limited. This study addresses this gap by evaluating representative models on IMDB, Yelp Polarity, and Amazon Polarity datasets using unified metrics and preprocessing
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Reports on the topic "Traditional Datasets"

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Marra de Artiñano, Ignacio, Franco Riottini Depetris, and Christian Volpe Martincus. Automatic Product Classification in International Trade: Machine Learning and Large Language Models. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0005012.

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Accurately classifying products is essential in international trade. Virtually all countries categorize products into tariff lines using the Harmonized System (HS) nomenclature for both statistical and duty collection purposes. In this paper, we apply and assess several different algorithms to automatically classify products based on text descriptions. To do so, we use agricultural product descriptions from several public agencies, including customs authorities and the United States Department of Agriculture (USDA). We find that while traditional machine learning (ML) models tend to perform we
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Mosalam, Khalid, Issac Pang, and Selim Gunay. Towards Deep Learning-Based Structural Response Prediction and Ground Motion Reconstruction. Pacific Earthquake Engineering Research Center, 2025. https://doi.org/10.55461/ipos1888.

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This research presents a novel methodology that uses Temporal Convolutional Networks (TCNs), a state-of-the-art deep learning architecture, for predicting the time history of structural responses to seismic events. By leveraging accelerometer data from instrumented buildings, the proposed approach complements traditional structural analysis models, offering a computationally efficient alternative to nonlinear time history analysis. The methodology is validated across a broad spectrum of structural scenarios, including buildings with pronounced higher-mode effects and those exhibiting both line
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Redmond, Paul, Lorcan Kelly, and Elisa Staffa. A study of minimum wage employment in Ireland using online job vacancy data. ESRI, 2024. https://doi.org/10.26504/rs209.

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In recent years, research opportunities in labour economics have expanded, due to the availability of large datasets capturing online job vacancies. While not a replacement for well-established labour market surveys, online job vacancy data have several advantages. They contain information that may be otherwise unavailable from traditional survey data; moreover, this information can be available in real time, thus removing any time lag in data availability that can be a feature of traditional labour market surveys. In this report, we use data from the near universe of online job vacancies in I
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Sava, Elena, Guido Cervone, and Alfred Kalyanapu. Multiscale observation product (MOP) for temporal flood inundation mapping of the 2015 Dallas Texas Flood. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48713.

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This paper presents a new data fusion multiscale observation product (MOP) for flood emergencies. The MOP was created by integrating multiple sources of contributed open-source data with traditional spaceborne remote sensing imagery to provide a sequence of high spatial and temporal resolution flood inundation maps. The study focuses on the 2015 Memorial Day floods that caused up to US$61 million of damage. The Hydraulic Engineering Center River Analysis System (HEC-RAS) model was used to simulate water surfaces for the northern part of the Trinity River in Dallas, using reservoir surcharge re
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Kerr, D. E., A. Plouffe, J. E. Campbell, and I. McMartin. Status of surficial geology mapping in the North. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330334.

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The GEM program has facilitated the availability of new and converted surficial geology maps and associated digital datasets for large sectors of northern Canada, leading to about 70% of the north being mapped and digitally available. Development of the Surficial Data Model (SDM) and Canadian Geoscience Map (CGM) series have streamlined the publication process and created a common standard digital map format and geodatabase. Based on traditional and more recent remote predictive mapping methodologies, there are now three types of surficial geology CGM maps produced: Surficial Geology, Reconnai
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Juvik, John A., Avri Bar Zur, and Torbert R. Rocheford. Breeding for Quality in Vegetable Maize Using Linked Molecular Markers. United States Department of Agriculture, 1993. http://dx.doi.org/10.32747/1993.7568764.bard.

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Recently, the vegetable corn industry has shifted from the use of traditional cultivars with the sugary1 (su1) endosperm mutation to newer hybrids homozygous for the shrunken2 (sh2) or sugary enhancer1 (se1) genes. With greater kernel sucrose content, these hybrids are preferred by consumers and retain sugar for longer post harvest periods, providing the industry with more time to marker products with superior quality. Commercialization has been hindered, however, by reduced field emergence, and the establishment of stands with heterogeneous uniformity and maturities. This investigation was co
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Bond, W., Maria Seale, and Jeffrey Hensley. A dynamic hyperbolic surface model for responsive data mining. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/43886.

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Data management systems impose structure on data via a static representation schema or data structure. Information from the data is extracted by executing queries based on predefined operators. This paradigm restricts the searchability of the data to concepts and relationships that are known or assumed to exist among the objects. While this is an effective and efficient means of retrieving simple information, we propose that such a structure severely limits the ability to derive breakthrough knowledge that exists in data under the guise of “unknown unknowns.” A dynamic system will alleviate th
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Ziegler, Nancy, Nicholas Webb, Adrian Chappell, and Sandra LeGrand. Scale invariance of albedo-based wind friction velocity. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40499.

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Obtaining reliable estimates of aerodynamic roughness is necessary to interpret and accurately predict aeolian sediment transport dynamics. However, inherent uncertainties in field measurements and models of surface aerodynamic properties continue to undermine aeolian research, monitoring, and dust modeling. A new relation between aerodynamic shelter and land surface shadow has been established at the wind tunnel scale, enabling the potential for estimates of wind erosion and dust emission to be obtained across scales from albedo data. Here, we compare estimates of wind friction velocity (u*)
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Knight, R. D., and B. A. Kjarsgaard. Comparative pXRF and Lab ICP-ES/MS methods for mineral resource assessment, Northwest Territories. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331239.

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The Geological Survey of Canada undertook a mineral resource assessment for a proposed national park in northern Canada (~ 33,500 km2) spanning the transition from boreal forest to barren lands tundra. Bedrock geology of this region is complex and includes the Archean Slave Craton, the Archean and Paleoproterozoic Rae domain of the Churchill Province, the Paleoproterozoic Thelon and Taltson magmatic-tectonic zones, and the Paleoproterozoic East Arm sedimentary basin. The area has variable mineral potential for lode gold, kimberlite-hosted diamonds, VMS, vein uranium and copper, SEDEX, as well
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Khan, Mahreen. Evaluating External Government Audit. Institute of Development Studies, 2022. http://dx.doi.org/10.19088/k4d.2022.140.

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This rapid literature review of primary and grey sources found substantial evidence of the merits of donor support to Public Financial Management (PFM) initiatives but no specific evidence assessing donor support for external government audit, such as Supreme Audit Institutions (SAIs). PFM reforms are established as being generally beneficial, assist in reducing or preventing corruption, increasing transparency and accountability, as well as improving service delivery quality, although the exact impacts are difficult to measure. Performance auditing has recently attracted more attention than t
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