Relevant bibliographies by topics / Evaluation of extreme classifiers

Contents

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

Academic literature on the topic 'Evaluation of extreme classifiers'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Evaluation of extreme classifiers"

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Balasubramanian, Kishore, and N. P. Ananthamoorthy. "Analysis of hybrid statistical textural and intensity features to discriminate retinal abnormalities through classifiers." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 233, no. 5 (2019): 506–14. http://dx.doi.org/10.1177/0954411919835856.

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Retinal image analysis relies on the effectiveness of computational techniques to discriminate various abnormalities in the eye like diabetic retinopathy, macular degeneration and glaucoma. The onset of the disease is often unnoticed in case of glaucoma, the effect of which is felt only at a later stage. Diagnosis of such degenerative diseases warrants early diagnosis and treatment. In this work, performance of statistical and textural features in retinal vessel segmentation is evaluated through classifiers like extreme learning machine, support vector machine and Random Forest. The fundus ima
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Michau, Gabriel, Yang Hu, Thomas Palmé, and Olga Fink. "Feature learning for fault detection in high-dimensional condition monitoring signals." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 234, no. 1 (2019): 104–15. http://dx.doi.org/10.1177/1748006x19868335.

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Complex industrial systems are continuously monitored by a large number of heterogeneous sensors. The diversity of their operating conditions and the possible fault types make it impossible to collect enough data for learning all the possible fault patterns. This article proposes an integrated automatic unsupervised feature learning and one-class classification for fault detection that uses data on healthy conditions only for its training. The approach is based on stacked extreme learning machines (namely hierarchical extreme learning machines) and comprises an autoencoder, performing unsuperv
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Afolabi, Hassan A., and Abdurazzag A. Aburas. "Statistical performance assessment of supervised machine learning algorithms for intrusion detection system." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 266–77. https://doi.org/10.11591/ijai.v13.i1.pp266-277.

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Several studies have shown that an ensemble classifier's effectiveness is directly correlated with the diversity of its members. However, the algorithms used to build the base learners are one of the issues encountered when using a stacking ensemble. Given the number of options, choosing the best ones might be challenging. In this study, we selected some of the most extensively applied supervised machine learning algorithms and performed a performance evaluation in terms of well-known metrics and validation methods using two internet of things (IoT) intrusion detection datasets, namely network
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Afolabi, Hassan A., and Aburas A. Abdurazzag. "Statistical performance assessment of supervised machine learning algorithms for intrusion detection system." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 266. http://dx.doi.org/10.11591/ijai.v13.i1.pp266-277.

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<span lang="EN-US">Several studies have shown that an ensemble classifier's effectiveness is directly correlated with the diversity of its members. However, the algorithms used to build the base learners are one of the issues encountered when using a stacking ensemble. Given the number of options, choosing the best ones might be challenging. In this study, we selected some of the most extensively applied supervised machine learning algorithms and performed a performance evaluation in terms of well-known metrics and validation methods using two internet of things (IoT) intrusion detection
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Raza, Ali, Furqan Rustam, Hafeez Ur Rehman Siddiqui, et al. "Predicting Genetic Disorder and Types of Disorder Using Chain Classifier Approach." Genes 14, no. 1 (2022): 71. http://dx.doi.org/10.3390/genes14010071.

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Genetic disorders are the result of mutation in the deoxyribonucleic acid (DNA) sequence which can be developed or inherited from parents. Such mutations may lead to fatal diseases such as Alzheimer’s, cancer, Hemochromatosis, etc. Recently, the use of artificial intelligence-based methods has shown superb success in the prediction and prognosis of different diseases. The potential of such methods can be utilized to predict genetic disorders at an early stage using the genome data for timely treatment. This study focuses on the multi-label multi-class problem and makes two major contributions
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Thiamchoo, Nantarika, and Pornchai Phukpattaranont. "Evaluation of feature projection techniques in object grasp classification using electromyogram signals from different limb positions." PeerJ Computer Science 8 (May 6, 2022): e949. http://dx.doi.org/10.7717/peerj-cs.949.

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A myoelectric prosthesis is manipulated using electromyogram (EMG) signals from the existing muscles for performing the activities of daily living. A feature vector that is formed by concatenating data from many EMG channels may result in a high dimensional space, which may cause prolonged computation time, redundancy, and irrelevant information. We evaluated feature projection techniques, namely principal component analysis (PCA), linear discriminant analysis (LDA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and spectral regression extreme learning machine (SRELM), applied to object
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Nateghi, Masoud, Mahdi Rahbar Alam, Hossein Amiri, Samaneh Nasiri, and Reza Sameni. "Model-Based Electroencephalogram Instantaneous Frequency Tracking: Application in Automated Sleep–Wake Stage Classification." Sensors 24, no. 24 (2024): 7881. https://doi.org/10.3390/s24247881.

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Understanding sleep stages is crucial for diagnosing sleep disorders, developing treatments, and studying sleep’s impact on overall health. With the growing availability of affordable brain monitoring devices, the volume of collected brain data has increased significantly. However, analyzing these data, particularly when using the gold standard multi-lead electroencephalogram (EEG), remains resource-intensive and time-consuming. To address this challenge, automated brain monitoring has emerged as a crucial solution for cost-effective and efficient EEG data analysis. A critical component of sle
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Tian, Zhang, Chen, Geng, and Wang. "Selective Ensemble Based on Extreme Learning Machine for Sensor-Based Human Activity Recognition." Sensors 19, no. 16 (2019): 3468. http://dx.doi.org/10.3390/s19163468.

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Sensor-based human activity recognition (HAR) has attracted interest both in academic and applied fields, and can be utilized in health-related areas, fitness, sports training, etc. With a view to improving the performance of sensor-based HAR and optimizing the generalizability and diversity of the base classifier of the ensemble system, a novel HAR approach (pairwise diversity measure and glowworm swarm optimization-based selective ensemble learning, DMGSOSEN) that utilizes ensemble learning with differentiated extreme learning machines (ELMs) is proposed in this paper. Firstly, the bootstrap
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Peng, Sizhong, Congjun Feng, Zhen Qiu, et al. "Prediction of Lithofacies in Heterogeneous Shale Reservoirs Based on a Robust Stacking Machine Learning Model." Minerals 15, no. 3 (2025): 240. https://doi.org/10.3390/min15030240.

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The lithofacies of a reservoir contain key information such as rock lithology, sedimentary structures, and mineral composition. Accurate prediction of shale reservoir lithofacies is crucial for identifying sweet spots for oil and gas development. However, obtaining shale lithofacies through core sampling during drilling is challenging, and the accuracy of traditional logging curve intersection methods is insufficient. To efficiently and accurately predict shale lithofacies, this study proposes a hybrid model called Stacking, which combines four classifiers: Random Forest, HistGradient Boosting
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Tariq, Muhammad Arham, Allah Bux Sargano, Muhammad Aksam Iftikhar, and Zulfiqar Habib. "Comparing Different Oversampling Methods in Predicting Multi-Class Educational Datasets Using Machine Learning Techniques." Cybernetics and Information Technologies 23, no. 4 (2023): 199–212. http://dx.doi.org/10.2478/cait-2023-0044.

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Abstract Predicting students’ academic performance is a critical research area, yet imbalanced educational datasets, characterized by unequal academic-level representation, present challenges for classifiers. While prior research has addressed the imbalance in binary-class datasets, this study focuses on multi-class datasets. A comparison of ten resampling methods (SMOTE, Adasyn, Distance SMOTE, BorderLineSMOTE, KmeansSMOTE, SVMSMOTE, LN SMOTE, MWSMOTE, Safe Level SMOTE, and SMOTETomek) is conducted alongside nine classification models: K-Nearest Neighbors (KNN), Linear Discriminant Analysis (
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Dissertations / Theses on the topic "Evaluation of extreme classifiers"

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Legrand, Juliette. "Simulation and assessment of multivariate extreme models for environmental data." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASJ015.

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L'estimation précise des probabilités d'occurrence des événements extrêmes environnementaux est une préoccupation majeure dans l'évaluation des risques. Pour l'ingénierie côtière par exemple, le dimensionnement de structures implantées sur ou à proximité des côtes doit être tel qu'elles résistent aux événements les plus sévères qu'elles puissent rencontrer au cours de leur vie. Cette thèse porte sur la simulation d'événements extrêmes multivariés, motivée par des applications aux hauteurs significatives de vagues, et sur l'évaluation de modèles de prédiction d'occurrence d'événements extrêmes.
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Lavesson, Niklas. "Evaluation and Analysis of Supervised Learning Algorithms and Classifiers." Licentiate thesis, Karlskrona : Blekinge Institute of Technology, 2006. http://www.bth.se/fou/Forskinfo.nsf/allfirst2/c655a0b1f9f88d16c125714c00355e5d?OpenDocument.

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Nygren, Rasmus. "Evaluation of hyperparameter optimization methods for Random Forest classifiers." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301739.

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In order to create a machine learning model, one is often tasked with selecting certain hyperparameters which configure the behavior of the model. The performance of the model can vary greatly depending on how these hyperparameters are selected, thus making it relevant to investigate the effects of hyperparameter optimization on the classification accuracy of a machine learning model. In this study, we train and evaluate a Random Forest classifier whose hyperparameters are set to default values and compare its classification accuracy to another classifier whose hyperparameters are obtained thr
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Dang, Robin, and Anders Nilsson. "Evaluation of Machine Learning classifiers for Breast Cancer Classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280349.

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Breast cancer is a common and fatal disease among women globally, where early detection is vital to improve the prognosis of patients. In today’s digital society, computers and complex algorithms can evaluate and diagnose diseases more efficiently and with greater certainty than experienced doctors. Several studies have been conducted to automate medical imaging techniques, by utilizing machine learning techniques, to predict and detect breast cancer. In this report, the suitability of using machine learning to classify whether breast cancer is of benign or malignant characteristic is evaluate
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Fischer, Manfred M., Sucharita Gopal, Petra Staufer-Steinnocher, and Klaus Steinocher. "Evaluation of Neural Pattern Classifiers for a Remote Sensing Application." WU Vienna University of Economics and Business, 1995. http://epub.wu.ac.at/4184/1/WSG_DP_4695.pdf.

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This paper evaluates the classification accuracy of three neural network classifiers on a satellite image-based pattern classification problem. The neural network classifiers used include two types of the Multi-Layer-Perceptron (MLP) and the Radial Basis Function Network. A normal (conventional) classifier is used as a benchmark to evaluate the performance of neural network classifiers. The satellite image consists of 2,460 pixels selected from a section (270 x 360) of a Landsat-5 TM scene from the city of Vienna and its northern surroundings. In addition to evaluation of classification
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Alorf, Abdulaziz Abdullah. "Primary/Soft Biometrics: Performance Evaluation and Novel Real-Time Classifiers." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/96942.

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The relevance of faces in our daily lives is indisputable. We learn to recognize faces as newborns, and faces play a major role in interpersonal communication. The spectrum of computer vision research about face analysis includes, but is not limited to, face detection and facial attribute classification, which are the focus of this dissertation. The face is a primary biometric because by itself revels the subject's identity, while facial attributes (such as hair color and eye state) are soft biometrics because by themselves they do not reveal the subject's identity. In this dissertation, we p
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Ayhan, Tezer Bahar. "Damage evaluation of civil engineering structures under extreme loadings." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2013. http://tel.archives-ouvertes.fr/tel-00975488.

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In many industrial and scientific domains, especially in civil engineering and mechanical engineering fields, materials that can be used on the microstructure scale, are highly heterogeneous by comparison to the nature of mechanical behavior. This feature can make the prediction of the behavior of the structure subjected to various loading types, necessary for sustainable design, difficult enough. The construction of civil engineering structures is regulated all over the world: the standards are more stringent and taken into account, up to a limit state, due to different loadings, for example
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Zuzáková, Barbora. "Exchange market pressure: an evaluation using extreme value theory." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-199589.

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This thesis discusses the phenomenon of currency crises, in particular it is devoted to empirical identification of crisis periods. As a crisis indicator, we aim to utilize an exchange market pressure index which has been revealed as a very powerful tool for the exchange market pressure quantification. Since enumeration of the exchange market pressure index is crucial for further analysis, we pay special attention to different approaches of its construction. In the majority of existing literature on exchange market pressure models, a currency crisis is defined as a period of time when the exch
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Buolamwini, Joy Adowaa. "Gender shades : intersectional phenotypic and demographic evaluation of face datasets and gender classifiers." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/114068.

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Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 103-116).<br>This thesis (1) characterizes the gender and skin type distribution of IJB-A, a government facial recognition benchmark, and Adience, a gender classification benchmark, (2) outlines an approach for capturing images with more diverse skin types which is then applied to develop the Pilot Parliaments Benchmark (PPB), and (3) uses PPB to assess the classification accura
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Pydipati, Rajesh. "Evaluation of classifiers for automatic disease detection in citrus leaves using machine vision." [Gainesville, Fla.] : University of Florida, 2004. http://purl.fcla.edu/fcla/etd/UFE0006991.

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Books on the topic "Evaluation of extreme classifiers"

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Margineantu, Dragos D. Bootstrap methods for the cost-sensitive evaluation of classifiers. Oregon State University, Dept. of Computer Science, 2000.

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Research, United States Office of Federal Coordinator for Meteorological Services and Supporting. Report on wind chill temperature and extreme heat indices: Evaluation and improvement projects. U.S. Department of Commerce, National Oceanic and Atmospheric Administration, Office of the Federal Coordinator for Meteorological Services and Supporting Research, 2003.

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Matin, M. A. Risk assessment and evaluation of probability of extreme hydrological events: Case study from Noakhali Sadar and Subarnachar Upazilas. IUCN Bangladesh Country Office, 2008.

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Matin, M. A. Risk assessment and evaluation of probability of extreme hydrological events: Case study from Noakhali Sadar and Subarnachar Upazilas. IUCN Bangladesh Country Office, 2008.

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The Evaluation of Competing Classifiers. Storming Media, 2000.

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Extreme Government Makeover: Increasing Our Capacity to Do More Good. Governing Books, 2011.

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Meacham, Brian J. Extreme Event Mitigation in Buildings; Analysis and Design. National Fire Protection Association, 2006.

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Bush, Kenneth, and Colleen Duggan. Evaluation in the Extreme: Research, Impact and Politics in Violently Divided Societies. SAGE Publications India Pvt, Ltd., 2015.

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Bush, Kenneth, and Colleen Duggan. Evaluation in the Extreme: Research, Impact and Politics in Violently Divided Societies. SAGE Publications India Pvt, Ltd., 2021.

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Evaluation in the Extreme: Research, Impact and Politics in Violently Divided Societies. SAGE Publications India Pvt, Ltd., 2015.

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Book chapters on the topic "Evaluation of extreme classifiers"

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Seewald, Alexander K., and Johannes Fürnkranz. "An Evaluation of Grading Classifiers." In Advances in Intelligent Data Analysis. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44816-0_12.

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Alonzo, Todd A., and Margaret Sullivan Pepe. "Development and Evaluation of Classifiers." In Topics in Biostatistics. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-530-5_6.

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Lóczy, Dénes. "Evaluation of Geomorphological Impact." In Geomorphological impacts of extreme weather. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6301-2_23.

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Ashok, Pranav, Tomáš Brázdil, Krishnendu Chatterjee, Jan Křetínský, Christoph H. Lampert, and Viktor Toman. "Strategy Representation by Decision Trees with Linear Classifiers." In Quantitative Evaluation of Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30281-8_7.

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Torzilli, Guido, Guido Costa, Fabio Procopio, Luca Viganó, and Matteo Donadon. "Intraoperative Evaluation of Resectability." In Extreme Hepatic Surgery and Other Strategies. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-13896-1_11.

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Szadkowski, Rudolf, Jan Drchal, and Jan Faigl. "Basic Evaluation Scenarios for Incrementally Trained Classifiers." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30484-3_41.

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Viechnicki, Peter. "A performance evaluation of automatic survey classifiers." In Grammatical Inference. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0054080.

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Cieslak, Kasia P., Roelof J. Bennink, and Thomas M. van Gulik. "Preoperative Evaluation of Liver Function." In Extreme Hepatic Surgery and Other Strategies. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-13896-1_3.

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Chao, J. Carlos Aguado. "Artificial Intelligence Classifiers and Their Social Impact." In Soft Computing for Risk Evaluation and Management. Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-7908-1814-7_11.

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Nirkhi, Smita. "Evaluation of Classifiers for Detection of Authorship Attribution." In Computational Intelligence: Theories, Applications and Future Directions - Volume I. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1132-1_18.

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Conference papers on the topic "Evaluation of extreme classifiers"

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Ostapuk, Natalia, Ljiljana Dolamic, Alain Mermoud, and Philippe Cudré-Mauroux. "Leveraging Pre-Trained Extreme Multi-Label Classifiers for Zero-Shot Learning." In 2024 11th IEEE Swiss Conference on Data Science (SDS). IEEE, 2024. http://dx.doi.org/10.1109/sds60720.2024.00041.

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Kostko, Oleg, Maximillian Mueller, and Patrick Naulleau. "Electron blur evaluation for different electron energies." In International Conference on Extreme Ultraviolet Lithography 2024, edited by Joern-Holger Franke, Kurt G. Ronse, Paolo A. Gargini, Patrick P. Naulleau, and Toshiro Itani. SPIE, 2024. http://dx.doi.org/10.1117/12.3034739.

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Hamdan, S., S. Wagle, S. Poudel, Y. Zhou, and K. Poudel. "Assessing Algorithmic Bias in Machine Learning Classifiers: A Fairness Evaluation." In 2024 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE, 2024. https://doi.org/10.1109/spmb62441.2024.10842230.

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Hosoda, Kazuki, Takashi Namikawa, Shinji Yamakawa, Tetsuo Harada, and Takeo Watanabe. "Outgas evaluation of cable materials for EUV lithography system." In International Conference on Extreme Ultraviolet Lithography 2024, edited by Joern-Holger Franke, Kurt G. Ronse, Paolo A. Gargini, Patrick P. Naulleau, and Toshiro Itani. SPIE, 2024. http://dx.doi.org/10.1117/12.3033877.

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Britto, Larissa, and Luciano Pacífico. "Classificação de Espécies de Plantas Usando Extreme Learning Machine." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/eniac.2019.9268.

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Plants play an important role in nature, but correct plant species identification is still a challenging task for non-specialized people. Many works have been proposed towards the development of automatic plant species recognition systems through Machine Learning methods, but most of them lack the proper experimental analysis. In this work, we evaluate the performance of a general-purpose Artificial Neural Network to perform plant classification task: the Extreme Learning Machine (ELM).We compare ELM with several classifiers from plant recognition literature by means of three real-world data s
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Flores, Christian, Christian Fonseca, David Achanccaray, and Javier Andreu-Perez. "Performance Evaluation of a P300 Brain-Computer Interface Using a Kernel Extreme Learning Machine Classifier." In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2018. http://dx.doi.org/10.1109/smc.2018.00629.

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Itikawa, M. A., V. R. R. Ahón, T. A. Souza, et al. "Automatic Cement Evaluation Using Machine Learning." In Offshore Technology Conference Brasil. OTC, 2023. http://dx.doi.org/10.4043/32961-ms.

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Abstract Cementing is an extremely important step in the well construction process. It has important objectives such as hydraulic sealing to prevent migration of undesired fluids from the formations and their collapse. One of the methods to verify the quality of cementat jobs is running acoustic logging tools such as CBL/VDL and ultrasonic and inferring zonal isolation by the interpretation of such data. This study aims to use machine learning techniques for automatic cement logs interpration. Cement logs of 25 wells were used as database. The logs responses have been classified in five classe
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Gautam, Chandan, Aruna Tiwari, and Sriram Ravindran. "Construction of multi-class classifiers by Extreme Learning Machine based one-class classifiers." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727445.

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Fein-Ashley, Jacob, Tian Ye, Rajgopal Kannan, Viktor Prasanna, and Carl Busart. "Benchmarking Deep Learning Classifiers for SAR Automatic Target Recognition." In 2023 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, 2023. http://dx.doi.org/10.1109/hpec58863.2023.10363455.

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Sivaguru, Raaghavi, Chhaya Choudhary, Bin Yu, Vadym Tymchenko, Anderson Nascimento, and Martine De Cock. "An Evaluation of DGA Classifiers." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8621875.

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Reports on the topic "Evaluation of extreme classifiers"

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KAB LABS INC SAN DIEGO CA. Feature Set Evaluation for Classifiers. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada226903.

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KAB LABS INC SAN DIEGO CA. Feature Set Evaluation for Classifiers. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada226905.

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Liguori, Giovanni, and Nadia Pinardi. Evaluation of Extreme Forecast Indices (WP5+6). EuroSea, 2023. http://dx.doi.org/10.3289/eurosea_d4.11.

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While originally developed for weather forecasting, the Extreme Forecast index (EFI) concept has found utility in diverse fields. This study marks the inaugural application of EFI principles to numerical ocean forecasting. EFI offers a metric to gauge the forecast's deviation from historical norms specific to the location and time of year. A heightened EFI value signifies that the forecast falls beyond the usual range of variability, signifying a higher probability of extreme conditions. This novel use of EFI stands to benefit oceanographers by identifying significant oceanic events, aiding de
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Asenath-Smith, Emily, Terry Melendy, Amelia Menke, Andrew Bernier, and George Blaisdell. Evaluation of airfield damage repair methods for extreme cold temperatures. Engineer Research and Development Center (U.S.), 2019. http://dx.doi.org/10.21079/11681/32298.

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Ruby, Brent C. Evaluation of the Human/Extreme Environment Interaction: Implications for Enhancing Operational Performance and Recovery. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada592672.

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Ruby, Brent C. Evaluation of the Human/Extreme Environment Interaction: Implications for Enhancing Operational Performance and Recovery. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada592673.

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Ruby, Brent C. Evaluation of the Human/Extreme Environment Interaction: Implications for Enhancing Operational Performance and Recovery. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada600954.

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Bäumler, Maximilian, and Matthias Lehmann. Generating representative test scenarios: The FUSE for Representativity (fuse4rep) process model for collecting and analysing traffic observation data. TU Dresden, 2024. http://dx.doi.org/10.26128/2024.2.

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Scenario-based testing is a pillar of assessing the effectiveness of automated driving systems (ADSs). For data-driven scenario-based testing, representative traffic scenarios need to describe real road traffic situations in compressed form and, as such, cover normal driving along with critical and accident situations originating from different data sources. Nevertheless, in the choice of data sources, a conflict often arises between sample quality and depth of information. Police accident data (PD) covering accident situations, for example, represent a full survey and thus have high sample qu
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Truffer-Moudra, Dana, Sarah Azmi-Wendler, Robbin Garber-Slaght, Prateek Shrestha, Qwerty Mackey, and Conor Dennehy. Performance Evaluation and Costs of a Combined Ground Source Heat Pump and Solar Photovoltaic Storage System in an Extreme Cold Climate. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1986504.

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Huntington, Dale. Anti-trafficking programs in South Asia: Appropriate activities, indicators and evaluation methodologies. Population Council, 2002. http://dx.doi.org/10.31899/rh2002.1019.

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Throughout South Asia, men, women, boys, and girls are trafficked within their own countries and across international borders against their wills in what is essentially a clandestine slave trade. The Congressional Research Service and the U.S. State Department estimate that between 1 to 2 million people are trafficked each year worldwide with the majority originating in Asia. Root causes include extreme disparities of wealth, increased awareness of job opportunities far from home, pervasive inequality due to caste, class, and gender bias, lack of transparency in regulations governing labor mig
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