Relevant bibliographies by topics / Training and Testing Dataset

Contents

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

Academic literature on the topic 'Training and Testing Dataset'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Training and Testing Dataset"

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Lo, Jui-En, Eugene Yu-Chuan Kang, Yun-Nung Chen, et al. "Data Homogeneity Effect in Deep Learning-Based Prediction of Type 1 Diabetic Retinopathy." Journal of Diabetes Research 2021 (December 28, 2021): 1–9. http://dx.doi.org/10.1155/2021/2751695.

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This study is aimed at evaluating a deep transfer learning-based model for identifying diabetic retinopathy (DR) that was trained using a dataset with high variability and predominant type 2 diabetes (T2D) and comparing model performance with that in patients with type 1 diabetes (T1D). The Kaggle dataset, which is a publicly available dataset, was divided into training and testing Kaggle datasets. In the comparison dataset, we collected retinal fundus images of T1D patients at Chang Gung Memorial Hospital in Taiwan from 2013 to 2020, and the images were divided into training and testing T1D d
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Oyegoke, Temitayo O., Kehinde K. Akomolede, Adesola G. Aderounmu, and Emmanuel R. Adagunodo. "A Multi-Layer Perceptron Model for Classification of E-mail Fraud." European Journal of Information Technologies and Computer Science 1, no. 5 (2021): 16–22. http://dx.doi.org/10.24018/compute.2021.1.5.24.

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This study was developed an e-mail classification model to preempt fraudulent activities. The e-mail has such a predominant nature that makes it suitable for adoption by cyber-fraudsters. This research used a combination of two databases: CLAIR fraudulent and Spambase datasets for creating the training and testing dataset. The CLAIR dataset consists of raw e-mails from users’ inbox which were pre-processed into structured form using Natural Language Processing (NLP) techniques. This dataset was then consolidated with the Spambase dataset as a single dataset. The study deployed the Multi-Layer
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An, Chansik, Yae Won Park, Sung Soo Ahn, Kyunghwa Han, Hwiyoung Kim, and Seung-Koo Lee. "Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results." PLOS ONE 16, no. 8 (2021): e0256152. http://dx.doi.org/10.1371/journal.pone.0256152.

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This study aims to determine how randomly splitting a dataset into training and test sets affects the estimated performance of a machine learning model and its gap from the test performance under different conditions, using real-world brain tumor radiomics data. We conducted two classification tasks of different difficulty levels with magnetic resonance imaging (MRI) radiomics features: (1) “Simple” task, glioblastomas [n = 109] vs. brain metastasis [n = 58] and (2) “difficult” task, low- [n = 163] vs. high-grade [n = 95] meningiomas. Additionally, two undersampled datasets were created by ran
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Mabuni, D., and S. Aquter Babu. "High Accurate and a Variant of k-fold Cross Validation Technique for Predicting the Decision Tree Classifier Accuracy." International Journal of Innovative Technology and Exploring Engineering 10, no. 2 (2021): 105–10. http://dx.doi.org/10.35940/ijitee.c8403.0110321.

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In machine learning data usage is the most important criterion than the logic of the program. With very big and moderate sized datasets it is possible to obtain robust and high classification accuracies but not with small and very small sized datasets. In particular only large training datasets are potential datasets for producing robust decision tree classification results. The classification results obtained by using only one training and one testing dataset pair are not reliable. Cross validation technique uses many random folds of the same dataset for training and validation. In order to o
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D., Mabuni, and Aquter Babu S. "High Accurate and a Variant of k-fold Cross Validation Technique for Predicting the Decision Tree Classifier Accuracy." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 3 (2021): 105–10. https://doi.org/10.35940/ijitee.C8403.0110321.

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In machine learning data usage is the most important criterion than the logic of the program. With very big and moderate sized datasets it is possible to obtain robust and high classification accuracies but not with small and very small sized datasets. In particular only large training datasets are potential datasets for producing robust decision tree classification results. The classification results obtained by using only one training and one testing dataset pair are not reliable. Cross validation technique uses many random folds of the same dataset for training and validation. In order to o
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Lee, Yongju, Sungjun Jang, Han Byeol Bae, Taejae Jeon, and Sangyoun Lee. "Multitask Learning Strategy with Pseudo-Labeling: Face Recognition, Facial Landmark Detection, and Head Pose Estimation." Sensors 24, no. 10 (2024): 3212. http://dx.doi.org/10.3390/s24103212.

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Most facial analysis methods perform well in standardized testing but not in real-world testing. The main reason is that training models cannot easily learn various human features and background noise, especially for facial landmark detection and head pose estimation tasks with limited and noisy training datasets. To alleviate the gap between standardized and real-world testing, we propose a pseudo-labeling technique using a face recognition dataset consisting of various people and background noise. The use of our pseudo-labeled training dataset can help to overcome the lack of diversity among
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Apeināns, Ilmars. "OPTIMAL SIZE OF AGRICULTURAL DATASET FOR YOLOV8 TRAINING." ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference 2 (June 22, 2024): 38–42. http://dx.doi.org/10.17770/etr2024vol2.8041.

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The smart farming solutions are mainly based on the application of convolutional neural networks for object detection tasks. The number of open datasets is restricted in the agricultural domain. Therefore, it is required to find the answer to the question: how big a dataset must be collected to train a convolutional neural network for object detection tasks? To solve this task, the YOLOv8 framework was selected for the experiment. Three datasets were prepared: MinneApples, PFruitlets640 and mosaic dataset using both previously named datasets. 100 images were selected for testing. Other images
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Murugesan, S., R. S. Bhuvaneswaran, H. Khanna Nehemiah, S. Keerthana Sankari, and Y. Nancy Jane. "Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner." Computational and Mathematical Methods in Medicine 2021 (May 17, 2021): 1–18. http://dx.doi.org/10.1155/2021/6662420.

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A computer-aided diagnosis (CAD) system that employs a super learner to diagnose the presence or absence of a disease has been developed. Each clinical dataset is preprocessed and split into training set (60%) and testing set (40%). A wrapper approach that uses three bioinspired algorithms, namely, cat swarm optimization (CSO), krill herd (KH) ,and bacterial foraging optimization (BFO) with the classification accuracy of support vector machine (SVM) as the fitness function has been used for feature selection. The selected features of each bioinspired algorithm are stored in three separate data
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Chua, Tuan-Hong, and Iftekhar Salam. "Evaluation of Machine Learning Algorithms in Network-Based Intrusion Detection Using Progressive Dataset." Symmetry 15, no. 6 (2023): 1251. http://dx.doi.org/10.3390/sym15061251.

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Cybersecurity has become one of the focuses of organisations. The number of cyberattacks keeps increasing as Internet usage continues to grow. As new types of cyberattacks continue to emerge, researchers focus on developing machine learning (ML)-based intrusion detection systems (IDS) to detect zero-day attacks. They usually remove some or all attack samples from the training dataset and only include them in the testing dataset when evaluating the performance. This method may detect unknown attacks; however, it does not reflect the long-term performance of the IDS as it only shows the changes
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Sheshkus, A., A. Chirvonaya, and V. L. Arlazarov. "Tiny CNN for feature point description for document analysis: approach and dataset." Computer Optics 46, no. 3 (2022): 429–35. http://dx.doi.org/10.18287/2412-6179-co-1016.

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In this paper, we study the problem of feature points description in the context of document analysis and template matching. Our study shows that specific training data is required for the task especially if we are to train a lightweight neural network that will be usable on devices with limited computational resources. In this paper, we construct and provide a dataset of photo and synthetically generated images and a method of training patches generation from it. We prove the effectiveness of this data by training a lightweight neural network and show how it performs in both general and docum
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Dissertations / Theses on the topic "Training and Testing Dataset"

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Jonas, Mario Ricardo Edward. "High performance computing and algorithm development: application of dataset development to algorithm parameterization." Thesis, University of the Western Cape, 2006. http://etd.uwc.ac.za/index.php?module=etd&amp.

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A number of technologies exist that captures data from biological systems. In addition, several computational tools, which aim to organize the data resulting from these technologies, have been created. The ability of these tools to organize the information into biologically meaningful results, however, needs to be stringently tested. The research contained herein focuses on data produced by technology that records short Expressed Sequence Tags (EST's).
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Štarha, Dominik. "Meření podobnosti obrazů s pomocí hlubokého učení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-377018.

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This master´s thesis deals with the reseach of technologies using deep learning method, being able to use when processing image data. Specific focus of the work is to evaluate the suitability and effectiveness of deep learning when comparing two image input data. The first – theoretical – part consists of the introduction to neural networks and deep learning. Also, it contains a description of available methods, their benefits and principles, used for processing image data. The second - practical - part of the thesis contains a proposal a appropriate model of Siamese networks to solve the prob
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Oppon, Ekow CruickShank. "Synergistic use of promoter prediction algorithms: a choice of small training dataset?" Thesis, University of the Western Cape, 2000. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_8222_1185436339.

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<p>Promoter detection, especially in prokaryotes, has always been an uphill task and may remain so, because of the many varieties of sigma factors employed by various organisms in transcription. The situation is made more complex by the fact, that any seemingly unimportant sequence segment may be turned into a promoter sequence by an activator or repressor (if the actual promoter sequence is made unavailable). Nevertheless, a computational approach to promoter detection has to be performed due to number of reasons. The obvious that comes to mind is the long and tedious process involved in eluc
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Tambay, Alain Alimou. "Testing Fuzzy Extractors for Face Biometrics: Generating Deep Datasets." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41429.

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Biometrics can provide alternative methods for security than conventional authentication methods. There has been much research done in the field of biometrics, and efforts have been made to make them more easily usable in practice. The initial application for our work is a proof of concept for a system that would expedite some low-risk travellers’ arrival into the country while preserving the user’s privacy. This thesis focuses on the subset of problems related to the generation of cryptographic keys from noisy data, biometrics in our case. This thesis was built in two parts. In the first,
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Bruin, Gerrit. "Limits of training and testing in horses." [Maastricht : Maastricht : Universiteit Maastricht] ; University Library, Maastricht University [Host], 1996. http://arno.unimaas.nl/show.cgi?fid=6700.

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Wilkins, Luke. "Vision testing and visual training in sport." Thesis, University of Birmingham, 2015. http://etheses.bham.ac.uk//id/eprint/6313/.

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This thesis examines vision testing and visual training in sport. Through four related studies, the predictive ability of visual and perceptual tests was examined in a range of activities including driving and one-handed ball catching. The potential benefits of visual training methods were investigated (with particular emphasis on stroboscopic training), as well as the mechanisms that may underpin any changes. A key theme throughout the thesis was that of task representativeness; a concept by which it is believed the more a study design reflects the environment it is meant to predict, the more
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Whittemore, Tom. "INTEGRATED TESTING AND TRAINING INSTRUMENTATION, A REALITY." International Foundation for Telemetering, 1996. http://hdl.handle.net/10150/609831.

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International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California<br>Historically, requirements for instrumentation that supports testing and training have diverged, for a variety of reasons. In general, testing evaluates how well the system or product meets stated operational or contractual requirements, while training evaluates how well the user operates the system in the battlefield environment. Developmental testing evaluates specific system performance characteristics, both to ensure that requirements are met and
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Wissinger, John W. (John Weakley). "Distributed nonparametric training algorithms for hypothesis testing networks." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/12006.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.<br>Includes bibliographical references (p. 495-502).<br>by John W. Wissinger.<br>Ph.D.
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V, Kusyk A. "PRELIMINARY PILOT TESTING." Thesis, ПОЛІТ.Сучасні проблеми науки.Гуманітарні науки:тези доповідей XVII Міжнародної науково-практичної конференції молодих учених і студентів:[y 2-x т.].Т.2(м.Київ,4-7 квітня 2017 р.)/[ред.кол.:В.М.Ісаєнко та ін.]; Національний авіаційний університет.-К.:НАУ,2017.-374 с, 2017. http://er.nau.edu.ua/handle/NAU/27741.

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The work of pilot is one of the most difficult activity, that why the training is difficult as well. The process of professional training includes a lot of instruments and devices. The level of training should mainly guarantee safety. Accident analysis and preconditions shows that factors such as the mistakes in flight operations, errors in piloting techniques and operation of aviation equipment determines the overall accident rate. This causes the need to improve the organization of flight training for flight crews.
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Dalen, Jan van. "Communication skills teaching, testing and learning /." Maastricht : Maastricht : Universitaire Pers Maastricht ; University Library, Maastricht University [Host], 2001. http://arno.unimaas.nl/show.cgi?fid=7619.

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Books on the topic "Training and Testing Dataset"

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Morin, Jean-Benoit, and Pierre Samozino, eds. Biomechanics of Training and Testing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-05633-3.

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World Health Organization. Maternal Health and Safe Motherhood Programme. Division of Family Health., ed. Midwifery training: Field testing version. WHO, 1994.

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World Health Organization. Maternal Health and Safe Motherhood Programme. Division of Family Health., ed. Midwifery training: Field testing version. WHO, 1994.

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World Health Organization. Maternal Health and Safe Motherhood Programme. Division of Family Health., ed. Midwifery training: Field testing version. WHO, 1994.

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Marks, Paul T. Ultrasonic testing classroom training book. American Society for Nondestructive Testing, 2005.

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Stanley, Hoffman, ed. Supervisor's guide to training & testing. Transport Law Research, 1992.

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World Health Organization. Maternal Health and Safe Motherhood Programme. Division of Family Health., ed. Midwifery training: Field testing version. WHO, 1994.

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Association, International City/County Management, ed. Fire personnel testing and training. International City/County Management Association, 1992.

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World Health Organization. Maternal Health and Safe Motherhood Programme. Division of Family Health., ed. Midwifery training: Field testing version. WHO, 1994.

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American Society for Nondestructive Testing., ed. Electromagnetic testing classroom training book. American Society for Nondestructive Testing, 2006.

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Book chapters on the topic "Training and Testing Dataset"

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Husni, Nyayu Latifah, Ade Silvia Handayani, Rossi Passarella, Akhmad Bastari, and Marlina Sylvia. "Datasets Training and Testing in Littering Activity Classification." In Atlantis Highlights in Engineering. Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-118-0_53.

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Zhao, Wenning, Xin Yao, Bixin Wang, et al. "A Visual Detection Method for Train Couplers Based on YOLOv8 Model." In Lecture Notes in Mechanical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1876-4_44.

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AbstractAccurately identifying the coupler operating handle during the operation of the hook-picking robot has a significant impact on production activities. This article is based on the YOLOv8 model. Due to the limited variety of on-site coupler operating handles and working environment, it is difficult to ensure the richness of image categories in the dataset. Before the experiment, a series of expansion operations were performed on the dataset, such as rotation, translation, and brightness adjustment. Use the expanded images to simulate the images detected by the hook-picking robot in harsh
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Anwar, Suzan, Mardin Anwer, and Daniah Al-Nadawi. "DeepFake Technology for Breast Cancer Dataset Generation Using Autoencoders and Deep Neural Networks." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88220-3_1.

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Abstract In the emerging field of radiogenomics, the primary challenge is the high cost of genetic testing, which restricts access to large, paired datasets of imaging and genetic information. Such datasets are essential for the effective training of machine learning algorithms in radiogenomic analyses. This research aims to bridge the gap between gene expression in tumors and their morphological representation in MRI scans of breast cancer patients. In this work an advanced autoencoder for processing gene expression data, and the derived weights from this autoencoder utilized were then employ
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Masood, Muhammad Arslan, Tianyu Cui, and Samuel Kaski. "Deep Bayesian Experimental Design for Drug Discovery." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72381-0_12.

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AbstractIn drug discovery, prioritizing compounds for testing is an important task. Active learning can assist in this endeavor by prioritizing molecules for label acquisition based on their estimated potential to enhance in-silico models. However, in specialized cases like toxicity modeling, limited dataset sizes can hinder effective training of modern neural networks for representation learning and to perform active learning. In this study, we leverage a transformer-based BERT model pretrained on millions of SMILES to perform active learning. Additionally, we explore different acquisition fu
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Spreeuwers, Luuk, Maikel Schils, Raymond Veldhuis, and Una Kelly. "Practical Evaluation of Face Morphing Attack Detection Methods." In Handbook of Digital Face Manipulation and Detection. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_16.

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AbstractFace morphing is a technique to combine facial images of two (or more) subjects such that the result resembles both subjects. In a morphing attack, this is exploited by, e.g., applying for a passport with the morphed image. Both subjects who contributed to the morphed image can then travel using this passport. Many state-of-the-art face recognition systems are vulnerable to morphing attacks. Morphing attack detection (MAD) methods are developed to mitigate this threat. MAD methods published in literature are often trained on a limited number of or even a single dataset where all morphe
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Li, Xiaodong, Song Chai, Liwei Wang, and Hua Wang. "A Configurable and Automated Testing Framework for Hardware Trojan Detection in FPGAs." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2409-6_31.

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Abstract With the widespread adoption of FPGA, the inevitable rise of security threats, including Hardware Trojan, poses significant risks. Detecting such trojan is crucial as they can lead to severe consequences. Consequently, various detection methods have been proposed. However, conventional approaches typically require extensive datasets for model training. To tackle this issue, this paper presents a customizable Hardware Trojan detection framework. This framework is designed to generate ample samples for FPGA hardware security testing, ensuring robust detection capabilities.
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Rizvi, Syed Zeeshan, Muhammad Umar Farooq, and Rana Hammad Raza. "Performance Comparison of Deep Residual Networks-Based Super Resolution Algorithms Using Thermal Images: Case Study of Crowd Counting." In Digital Interaction and Machine Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11432-8_7.

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AbstractHumans are able to perceive objects only in the visible spectrum range which limits the perception abilities in poor weather or low illumination conditions. The limitations are usually handled through technological advancements in thermographic imaging. However, thermal cameras have poor spatial resolutions compared to RGB cameras. Super-resolution (SR) techniques are commonly used to improve the overall quality of low-resolution images. There has been a major shift of research among the Computer Vision researchers towards SR techniques particularly aimed for thermal images. This paper
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Brito-Pacheco, Daniel, Riad Ibadulla, Ximena Fernández, Panos Giannopoulos, and Constantino Carlos Reyes-Aldasoro. "Persistent Homology and Gabor Features Reveal Inconsistencies Between Widely Used Colorectal Cancer Training and Testing Datasets." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-98688-8_7.

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Richards, Bryn, and Nwabueze Emekwuru. "Using Machine Learning to Predict Synthetic Fuel Spray Penetration from Limited Experimental Data Without Computational Fluid Dynamics." In Springer Proceedings in Energy. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30960-1_6.

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AbstractMachine Learning (ML) is increasingly used to predict fuel spray characteristics, but ML conventionally requires large datasets for training. There is a problem of limited training data in the field of synthetic fuel sprays. One solution is to reproduce experimental results using Computational Fluid Dynamics (CFD) and then to augment or replace experimental data with more abundant CFD output data. However, this approach can obscure the relationship of the neural network to the training data by introducing new factors, such as CFD grid design, turbulence model, near-wall treatment, and
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Mol, Frank N., Luuk van der Hoek, Baoqiang Ma, et al. "MRI-Based Head and Neck Tumor Segmentation Using nnU-Net with 15-Fold Cross-Validation Ensemble." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83274-1_13.

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Abstract The superior soft tissue differentiation provided by MRI may enable more accurate tumor segmentation compared to CT and PET, potentially enhancing adaptive radiotherapy treatment planning. The Head and Neck Tumor Segmentation for MR-Guided Applications challenge (HNTSMRG-24) comprises two tasks: segmentation of primary gross tumor volume (GTVp) and metastatic lymph nodes (GTVn) on T2-weighted MRI volumes obtained at (1) pre-radiotherapy (pre-RT) and (2) mid-radiotherapy (mid-RT). The training dataset consists of data from 150 patients, including MRI volumes of pre-RT, mid-RT, and pre-
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Conference papers on the topic "Training and Testing Dataset"

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Shahhoseyni, Shabnam, Arijit Chakraborty, Mohammad Reza Boskabadi, Venkat Venkatasubramanian, and Seyed Soheil Mansouri. "Hybrid machine-learning for dynamic plant-wide biomanufacturing." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.174465.

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This study focuses on biomanufacturing case study, i.e. Lovastatin production, employing a hybrid modeling framework that combines mechanistic and data-driven approaches. A time-series dataset was generated using the KT-Biologics I (KTB1) plantwide model, a dynamic simulation of continuous biomanufacturing. The dataset captures critical parameters such as nutrient concentrations and API production. The AI-DARWIN framework was used to develop interpretable machine learning models with constrained functional forms, ensuring both accuracy and clarity. The resulting polynomial-based models reveal
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Kubicka, Matej, Arben Cela, Philippe Moulin, Hugues Mounier, and S. I. Niculescu. "Dataset for testing and training of map-matching algorithms." In 2015 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2015. http://dx.doi.org/10.1109/ivs.2015.7225829.

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Saeed, Khalida A., and Wasfi T. Kahwachi. "Standard Training Dataset vs. Different Testing Dataset to Compare Deep Learning Architectures Models in Diagnosing COVID-19." In 2023 9th International Conference on Smart Structures and Systems (ICSSS). IEEE, 2023. http://dx.doi.org/10.1109/icsss58085.2023.10407120.

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Gao, Xuanqi, Juan Zhai, Shiqing Ma, Chao Shen, Yufei Chen, and Shiwei Wang. "CILIATE: Towards Fairer Class-Based Incremental Learning by Dataset and Training Refinement." In ISSTA '23: 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis. ACM, 2023. http://dx.doi.org/10.1145/3597926.3598071.

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Rose, Ralph L., Naho Orita, Ayaka Sugawara, and Qiao Wang. "Evaluation Dataset of Multiple-Choice Cloze Items for Vocabulary Training and Testing." In UbiComp/ISWC '22: The 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 2022. http://dx.doi.org/10.1145/3544793.3560378.

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Ryan, Keyanna, Bassam Bahhur, Mark Jeiran, and Bryan I. Vogel. "Evaluation of augmented training datasets." In Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXXII, edited by Gerald C. Holst and David P. Haefner. SPIE, 2021. http://dx.doi.org/10.1117/12.2587177.

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Ren, G., O. Talabi, V. Kumar, et al. "Trapped and Movable CO2 in Geologic Carbon Storage: Deep-Learning Forecasting and Generalization Study." In International Petroleum Technology Conference. IPTC, 2025. https://doi.org/10.2523/iptc-24804-ms.

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Abstract Geological carbon storage (GCS) is crucial for reducing greenhouse gases and mitigating global warming. Deep saline aquifers are regarded as optimal sites for implementing GCS. This paper proposes an LSTM-based deep learning model that can rapid forecast the temporal evolution of the trapped and movable CO2 in the subsurface aquifer during a GCS process. Rapid forecasting enables agile decision-making by providing timely insights into rapidly changing environments. The training and testing datasets consist of 1,600 simulations modeling the evolution of CO2 in saline aquifers under var
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Lew, C. L., C. MacBeth, A. ElSheikh, M. S. Jaya, and M. I. Ahmad Fuad. "Improving Lateral Continuity in Direct Petrophysical Inversion from Seismic Using Deep Learning." In ADIPEC. SPE, 2024. http://dx.doi.org/10.2118/222530-ms.

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Abstract Estimating petrophysical properties directly from measured seismic using multi-realisation of 1D training synthetic database for deep learning training resulted in ‘jittery’ artifact. The 1D training datasets has random geological scenarios, where each realisation is independent and spatially uncorrelatable. A method is developed to generate realistic 2D training database that provides flexibility for the network in analysing neighbouring traces. The process of building realistic 2D training data involves the utilization of the estimated porosity, Vclay and hydrocarbon saturation (Shc
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Larocque-Villiers, Justin, and Patrick Dumond. "Towards Generalization of Intelligent Fault Detection for Roller Element Bearings via Distinct Dataset Transfer Learning." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-67773.

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Abstract Through the intelligent classification of bearing faults, predictive maintenance provides for the possibility of service schedule, inventory, maintenance, and safety optimization. However, real-world rotating machinery undergo a variety of operating conditions, fault conditions, and noise. Due to these factors, it is often required that a fault detection algorithm perform accurately even on data outside its trained domain. Although open-source datasets offer an incredible opportunity to advance the performance of predictive maintenance technology and methods, more research is required
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Lew, C. L., M. I. Ahmad Fuad, M. S. Jaya, A. Trianto, and C. MacBeth. "Estimating Petrophysical Properties Directly from Seismic: A Deep Learning Application to Carbonate Field for CO2 Storage Potential." In SPE Annual Technical Conference and Exhibition. SPE, 2024. http://dx.doi.org/10.2118/220847-ms.

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Abstract Geological carbon capture and storage is vital for reducing carbon dioxide (CO2) emissions. Carbonate Field 1 in Luconia Province, offshore Sarawak is a potential CO2 storage site. Porosity and clay volume (Vclay) estimation from seismic provide valuable spatial and temporal information in characterizing reservoir distribution and overburden for assessing containment integrity and storage capacity. A deep learning inversion method for simultaneous estimation of porosity and Vclay was applied and tested in Carbonate Field 1. UNet architecture, chosen for its ability to preserve spatial
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Reports on the topic "Training and Testing Dataset"

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Rosenblat, Sruly, Tim O'Reilly, and Ilan Strauss. Beyond Public Access in LLM Pre-Training Data: Non-public book content in OpenAI’s Models. AI Disclosures Project, Social Science Research Council, 2025. https://doi.org/10.35650/aidp.4111.d.2025.

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Using a legally obtained dataset of 34 copyrighted O’Reilly Media books, we apply the DE-COP membership inference attack method to investigate whether OpenAI’s large language models were trained on copyrighted content without consent. Our AUROC scores show that GPT-4o, OpenAI’s more recent and capable model, demonstrates strong recognition of paywalled O’Reilly book content (AUROC = 82%), compared to OpenAI’s earlier model GPT-3.5 Turbo. In contrast, GPT-3.5 Turbo shows greater relative recognition of publicly accessible O’Reilly book samples. GPT-4o Mini, as a much smaller model, shows no kno
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Johra, Hicham, Martin Veit, Mathias Østergaard Poulsen, et al. Training and testing labelled image and video datasets of human faces for different indoor visual comfort and glare visual discomfort situations. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau542153983.

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The aim of this technical report is to provide a description and access to labelled image and video datasets of human faces that have been generated for different indoor visual comfort and glare visual discomfort situations. These datasets have been used to train and test a computer-vision artificial neural network detecting glare discomfort from images of human faces.
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Sayre, Amanda M., and Jarrod R. Olson. Development of a SPARK Training Dataset. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1228354.

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Busso, Matías, Julian P. Cristia, and Julián Messina. SkillsBank Methodology Note: Adult Training Methodology. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0004472.

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After determining the pool of studies to be analyzed from the search protocol, we dropped all those papers that appeared twice in the dataset. Then, we focused on the title, abstract and the whole study in that order. The filters we analyzed here timing and language, population, intervention, methods, and measures, in that order.
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Young, Scott W. H. Improving Library User Experience with A/B Testing: Principles and Process [dataset]. Montana State University ScholarWorks, 2014. http://dx.doi.org/10.15788/m2rp42.

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Wiederhold, Mark D. Physiological Monitoring During Simulation Training and Testing. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada436158.

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Garvin, John R., and Peter H. Christensen. USMC Information Assurance Operational Testing and Training Strategy. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada399993.

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Westervelt, James, and Bruce MacAllister. Quick Prediction of Future Training/Testing Opportunities Using mLEAM. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada477943.

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gahl, john. Electromagnetic Radioisotope Separator for Methods Development, Testing, and Training. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2441142.

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Lamontagne, Colette, Janet Mahannah, Kristin Jasinkiewicz, and Kimberly Hogrelius. Strategy to Minimize Energetics Contamination at Military Testing/Training Ranges. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada438602.

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