Relevant bibliographies by topics / Prediction

Contents

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

Academic literature on the topic 'Prediction'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Prediction"

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Burbey, Ingrid, and Thomas L. Martin. "A survey on predicting personal mobility." International Journal of Pervasive Computing and Communications 8, no. 1 (2012): 5–22. http://dx.doi.org/10.1108/17427371211221063.

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PurposeLocation‐prediction enables the next generation of location‐based applications. The purpose of this paper is to provide a historical summary of research in personal location‐prediction. Location‐prediction began as a tool for network management, predicting the load on particular cellular towers or WiFi access points. With the increasing popularity of mobile devices, location‐prediction turned personal, predicting individuals' next locations given their current locations.Design/methodology/approachThis paper includes an overview of prediction techniques and reviews several location‐predi
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Hanappi, Hardy. "Predictions and Hopes: Global Political Economy Dynamics of the Next Ten Years." Advances in Social Sciences Research Journal 11, no. 8 (2024): 66–87. http://dx.doi.org/10.14738/assrj.118.17381.

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Predictions and hopes are different things. Predictions are based on past empirical observations. They single out what seem to be essential variables and the relationships between them and assume that their importance will prevail in the future. Hopes add a component to a prediction, namely an evaluation, which refers back to the entity that produces the prediction. More favourable predictions are hoped to become a reality while others, which would see the entity in a worse position, are not hoped for. A closer look reveals that with a consideration of what predictions are used for by an entit
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Verhun, Volodymyr, and Mykhailo Granchak. "M&A PREDICTIONS: RECONSIDERING THEIR VALUE, END-USERS, AND METHODOLOGIES." Actual Problems of International Relations, no. 160 (2024): 138–51. http://dx.doi.org/10.17721/apmv.2024.160.1.138-151.

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The article explores market participants who may benefit from M&A predictions and how their goals may impact the requirements for M&A predictions. These participants (also called end-users of M&A predictions) are company shareholders considering selling their business, shareholders and company management considering acquiring one or a few other companies, shareholders and company management competing with potential M&A targets or buyers, and advisory firms providing investment banking services in the industries where M&A deals occur. Analyzing their goals while applying M&a
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Sun, Zhaoyue, Jiazheng Li, Gabriele Pergola, and Yulan He. "ExDDI: Explaining Drug-Drug Interaction Predictions with Natural Language." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 24 (2025): 25228–36. https://doi.org/10.1609/aaai.v39i24.34709.

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Predicting unknown drug-drug interactions (DDIs) is crucial for improving medication safety. Previous efforts in DDI prediction have typically focused on binary classification or predicting DDI categories, with the absence of explanatory insights that could enhance trust in these predictions. In this work, we propose to generate natural language explanations for DDI predictions, enabling the model to reveal the underlying pharmacodynamics and pharmacokinetics mechanisms simultaneously as making the prediction. To do this, we have collected DDI explanations from DDInter and DrugBank and develop
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Tang, Li, Ping He Pan, and Yong Yi Yao. "EPAK: A Computational Intelligence Model for 2-level Prediction of Stock Indices." International Journal of Computers Communications & Control 13, no. 2 (2018): 268–79. http://dx.doi.org/10.15837/ijccc.2018.2.3187.

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This paper proposes a new computational intelligence model for predicting univariate time series, called EPAK, and a complex prediction model for stock market index synthesizing all the sector index predictions using EPAK as a kernel. The EPAK model uses a complex nonlinear feature extraction procedure integrating a forward rolling Empirical Mode Decomposition (EMD) for financial time series signal analysis and Principal Component Analysis (PCA) for dimension reduction to generate information-rich features as input to a new two-layer K-Nearest Neighbor (KNN) with Affinity Propagation (AP) clus
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Yan, Xiaohui, Tianqi Zhang, Wenying Du, Qingjia Meng, Xinghan Xu, and Xiang Zhao. "A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years." Journal of Marine Science and Engineering 12, no. 1 (2024): 159. http://dx.doi.org/10.3390/jmse12010159.

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Water quality prediction, a well-established field with broad implications across various sectors, is thoroughly examined in this comprehensive review. Through an exhaustive analysis of over 170 studies conducted in the last five years, we focus on the application of machine learning for predicting water quality. The review begins by presenting the latest methodologies for acquiring water quality data. Categorizing machine learning-based predictions for water quality into two primary segments—indicator prediction and water quality index prediction—further distinguishes between single-indicator
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Zhang, Chenglong, and Hyunchul Ahn. "E-Learning at-Risk Group Prediction Considering the Semester and Realistic Factors." Education Sciences 13, no. 11 (2023): 1130. http://dx.doi.org/10.3390/educsci13111130.

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This study focused on predicting at-risk groups of students at the Open University (OU), a UK university that offers distance-learning courses and adult education. The research was conducted by drawing on publicly available data provided by the Open University for the year 2013–2014. The semester’s time series was considered, and data from previous semesters were used to predict the current semester’s results. Each course was predicted separately so that the research reflected reality as closely as possible. Three different methods for selecting training data were listed. Since the at-risk pre
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Zhuang, Wei, Zhiheng Li, Ying Wang, Qingyu Xi, and Min Xia. "GCN–Informer: A Novel Framework for Mid-Term Photovoltaic Power Forecasting." Applied Sciences 14, no. 5 (2024): 2181. http://dx.doi.org/10.3390/app14052181.

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Predicting photovoltaic (PV) power generation is a crucial task in the field of clean energy. Achieving high-accuracy PV power prediction requires addressing two challenges in current deep learning methods: (1) In photovoltaic power generation prediction, traditional deep learning methods often generate predictions for long sequences one by one, significantly impacting the efficiency of model predictions. As the scale of photovoltaic power stations expands and the demand for predictions increases, this sequential prediction approach may lead to slow prediction speeds, making it difficult to me
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Rather, Akhter Mohiuddin. "A Hybrid Intelligent Method of Predicting Stock Returns." Advances in Artificial Neural Systems 2014 (September 7, 2014): 1–7. http://dx.doi.org/10.1155/2014/246487.

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This paper proposes a novel method for predicting stock returns by means of a hybrid intelligent model. Initially predictions are obtained by a linear model, and thereby prediction errors are collected and fed into a recurrent neural network which is actually an autoregressive moving reference neural network. Recurrent neural network results in minimized prediction errors because of nonlinear processing and also because of its configuration. These prediction errors are used to obtain final predictions by summation method as well as by multiplication method. The proposed model is thus hybrid of
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Harahap, Rahma Sari, Iskandar Muda, and Rina Br Bukit. "Analisis penggunaan metode Altman Z-Score dan Springate untuk mengetahui potensi terjadinya Financial Distress pada perusahaan manufaktur sektor industri dasar dan kimia Sub Sektor semen yang terdaftar di Bursa Efek Indonesia 2000-2020." Owner 6, no. 4 (2022): 4315–25. http://dx.doi.org/10.33395/owner.v6i4.1576.

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The objective of the research is to find out the result of predicting bankruptcy, using Altman Z-Score and Springate methods in the manufacturing companies of basic industrial and chemistry sectors, cement sub-sector listed on BEI (Indonesia Stock Exchange) in the period of 2000-2020 and to determine the most accurate predicting method of bankruptcy to be applied in the manufacturing companies in basic industrial and chemistry sectors, cement sub-sector. The research employs descriptive quantitative method. The samples are taken by using purposive sampling method with three manufacture compani
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Dissertations / Theses on the topic "Prediction"

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Carrión, Brännström Robin. "Aggregating predictions using Non-Disclosed Conformal Prediction." Thesis, Uppsala universitet, Statistiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385098.

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When data are stored in different locations and pooling of such data is not allowed, there is an informational loss when doing predictive modeling. In this thesis, a new method called Non-Disclosed Conformal Prediction (NDCP) is adapted into a regression setting, such that predictions and prediction intervals can be aggregated from different data sources without interchanging any data. The method is built upon the Conformal Prediction framework, which produces predictions with confidence measures on top of any machine learning method. The method is evaluated on regression benchmark data sets u
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Miller, Mark Daniel. "Entangled predictive brain : emotion, prediction and embodied cognition." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33218.

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How does the living body impact, and perhaps even help constitute, the thinking, reasoning, feeling agent? This is the guiding question that the following work seeks to answer. The subtitle of this project is emotion, prediction and embodied cognition for good reason: these are the three closely related themes that tie together the various chapters of the following thesis. The central claim is that a better understanding of the nature of emotion offers valuable insight for understanding the nature of the so called 'predictive mind', including a powerful new way to think about the mind as embod
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Björsell, Joachim. "Long Range Channel Predictions for Broadband Systems : Predictor antenna experiments and interpolation of Kalman predictions." Thesis, Uppsala universitet, Signaler och System, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-281058.

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The field of wireless communication is under massive development and the demands on the cellular system, especially, are constantly increasing as the utilizing devices are increasing in number and diversity. A key component of wireless communication is the knowledge of the channel, i.e, how the signal is affected when sent over the wireless medium. Channel prediction is one concept which can improve current techniques or enable new ones in order to increase the performance of the cellular system. Firstly, this report will investigate the concept of a predictor antenna on new, extensive measure
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Bramlet, John. "Earthquake prediction and earthquake damage prediction /." Connect to resource, 1996. http://hdl.handle.net/1811/31764.

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Greco, Antonino. "The role of task relevance in the modulation of brain dynamics during sensory predictions." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/307050.

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Associative learning is a fundamental ability biological systems possess in order to adapt to a nonstationary environment. One of the core aspects of associative learning theoretical frameworks is that surprising events drive learning by signalling the need to update the system’s beliefs about the probability structure governing stimuli associations. Specifically, the central neural system generates internal predictions to anticipate the causes of its perceptual experience and compute a prediction error to update its generative model of the environment, an idea generally known as the predictiv
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Kock, Peter. "Prediction and predictive control for economic optimisation of vehicle operation." Thesis, Kingston University, 2013. http://eprints.kingston.ac.uk/35861/.

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Truck manufacturers are currently under pressure to reduce pollution and cost of transportation. The cost efficient way to reduce CO[sub]2 and cost is to reduce fuel consumption by adaptation of the vehicle speed to the driving conditions - by heuristic knowledge or mathematical optimisation. Due to their experience, professional drivers are capable of driving with great efficiency in terms of fuel consumption. The key research question addressed in this work is the comparison of the fuel efficiency for an unassisted drive by an experienced professional driver versus an enhanced drive using dr
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Andeta, Jemal Ahmed. "Road-traffic accident prediction model : Predicting the Number of Casualties." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20146.

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Efficient and effective road traffic prediction and management techniques are crucial in intelligent transportation systems. It can positively influence road advancement, safety enhancement, regulation formulation, and route planning to save living things in advance from road traffic accidents. This thesis considers road safety by predicting the number of casualties if an accident occurs using multiple traffic accident attributes. It helps individuals (drivers) or traffic offices to adjust and control their contributions for the occurrence of an accident before emerging it. Three candidate alg
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Peterson, Ashley Thomas. "Cavitation prediction." Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612813.

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Åkermark, Alexander, and Mattias Hallefält. "Churn Prediction." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-41236.

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Churn analysis is an important tool for companies as it can reduce the costs that are related to customer churn. Churn prediction is the process of identifying users before they churn, this is done by implementing methods on collected data in order to find patterns that can be helpful when predicting new churners in the future.The objective of this report is to identify churners with the use of surveys collected from different golfclubs, their members and guests. This was accomplished by testing several different supervised machine learning algorithms in order to find the different classes and to s
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Jahedpari, Fatemeh. "Artificial prediction markets for online prediction of continuous variables." Thesis, University of Bath, 2016. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.690730.

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In this dissertation, we propose an online machine learning technique – named Artificial Continuous Prediction Market (ACPM) – to predict the value of a continuous variable by (i) integrating a set of data streams from heterogeneous sources with time varying compositions such as changing the quality of data streams, (ii) integrating the results of several analysis models for each data source when the most suitable model for a given data source is not known a priori, (iii) dynamically weighting the prediction of each analysis model and data source to form the system prediction. We adapt the con
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Books on the topic "Prediction"

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Kanjilal, P. P. Adaptive prediction and predictive control. P. Peregrinus on behalf of Institution of Electrical Engineers, 1995.

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Engineers, Institution of Electrical, ed. Adaptive prediction and predictive control. P. Peregrinus on behalf of Institution of Electrical Engineers, 1995.

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Manski, Charles F. Interpreting the predictions of prediction markets. National Bureau of Economic Research, 2004.

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Ma, Zongjin, Zhengxiang Fu, Yingzhen Zhang, Chengmin Wang, Guomin Zhang, and Defu Liu. Earthquake Prediction. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-61269-5.

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Peijnenburg, Willie J. G. M., and Jirí Damborský, eds. Biodegradability Prediction. Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-011-5686-8.

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Kollmar, Martin, ed. Gene Prediction. Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9173-0.

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Shimazaki, Kunihiko, and William Stuart, eds. Earthquake Prediction. Birkhäuser Basel, 1985. http://dx.doi.org/10.1007/978-3-0348-6245-5.

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Wyatt, Ray. Plan Prediction. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46430-5.

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Luckner, Stefan, Jan Schröder, Christian Slamka, et al. Prediction Markets. Gabler Verlag, 2012. http://dx.doi.org/10.1007/978-3-8349-7085-5.

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Wolfers, Justin. Prediction markets. National Bureau of Economic Research, 2004.

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Book chapters on the topic "Prediction"

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Pourbafrani, Mahsa, Shreya Kar, Sebastian Kaiser, and Wil M. P. van der Aalst. "Remaining Time Prediction for Processes with Inter-case Dynamics." In Lecture Notes in Business Information Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_11.

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AbstractProcess mining techniques use event data to describe business processes, where the provided insights are used for predicting processes’ future states (Predictive Process Monitoring). Remaining Time Prediction of process instances is an important task in the field of Predictive Process Monitoring (PPM). Existing approaches have two key limitations in developing Remaining Time Prediction Models (RTM): (1) The features used for predictions lack process context, and the created models are black-boxes. (2) The process instances are considered to be in isolation, despite the fact that proces
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Fani Sani, Mohammadreza, Mozhgan Vazifehdoostirani, Gyunam Park, Marco Pegoraro, Sebastiaan J. van Zelst, and Wil M. P. van der Aalst. "Event Log Sampling for Predictive Monitoring." In Lecture Notes in Business Information Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_12.

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AbstractPredictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. This paper proposes an instance selection procedure that allows sampling training process instances for prediction models. We show that our sampling method allows for a significant increase of training speed for next activity predictio
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Liu, Wendi, Léan E. Garland, Jesus Ochoa, and Michael J. Pyrcz. "A Geostatistical Heterogeneity Metric for Spatial Feature Engineering." In Springer Proceedings in Earth and Environmental Sciences. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19845-8_1.

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AbstractHeterogeneity is a vital spatial feature for subsurface resource recovery predictions, such as mining grade tonnage functions, hydrocarbon recovery factor, and water aquifer draw-down predictions. Feature engineering presents the opportunity to integrate heterogeneity information, but traditional heterogeneity engineered features like Dykstra-Parsons and Lorenz coefficients ignore the spatial context; therefore, are not sufficient to quantify the heterogeneity over multiple scales of spatial intervals to inform predictive machine learning models. We propose a novel use of dispersion va
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Spenrath, Yorick, Marwan Hassani, and Boudewijn F. van Dongen. "Online Prediction of Aggregated Retailer Consumer Behaviour." In Lecture Notes in Business Information Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_16.

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AbstractPredicting the behaviour of consumers provides valuable information for retailers, such as the expected spend of a consumer or the total turnover of the retailer. The ability to make predictions on an individual level is useful, as it allows retailers to accurately perform targeted marketing. However, with the expected large number of consumers and their diverse behaviour, making accurate predictions on an individual consumer level is difficult. In this paper we present a framework that focuses on this trade-off in an online setting. By making predictions on a larger number of consumer
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Luckner, Stefan, Jan Schröder, Christian Slamka, et al. "Introduction." In Prediction Markets. Gabler Verlag, 2012. http://dx.doi.org/10.1007/978-3-8349-7085-5_1.

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Luckner, Stefan, Jan Schröder, Christian Slamka, et al. "Fundamentals of Prediction Markets." In Prediction Markets. Gabler Verlag, 2012. http://dx.doi.org/10.1007/978-3-8349-7085-5_2.

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Luckner, Stefan, Jan Schröder, Christian Slamka, et al. "Key Design Elements of Prediction Markets." In Prediction Markets. Gabler Verlag, 2012. http://dx.doi.org/10.1007/978-3-8349-7085-5_3.

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Luckner, Stefan, Jan Schröder, Christian Slamka, et al. "Applications of Prediction Markets." In Prediction Markets. Gabler Verlag, 2012. http://dx.doi.org/10.1007/978-3-8349-7085-5_4.

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Luckner, Stefan, Jan Schröder, Christian Slamka, et al. "Conclusion." In Prediction Markets. Gabler Verlag, 2012. http://dx.doi.org/10.1007/978-3-8349-7085-5_5.

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Peijnenburg, W. J. G. M., and J. Damborský. "Introduction, Main Conclusions and Recommendations of The Workshop “QSAR Biodegradation II”." In Biodegradability Prediction. Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-011-5686-8_1.

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Conference papers on the topic "Prediction"

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Santana, Vinicius V., Carine M. Rebello, Erbet A. Costa, et al. "Recurrent Deep Learning Models for Multi-step Ahead Prediction: Comparison and Evaluation for Real Electrical Submersible Pump (ESP) System." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.107762.

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Predicting processes� future behavior based on past data is vital for automatic control and dynamic optimization in engineering. Recent advances in deep learning, particularly Artificial Neural Networks, have improved predictions in various engineering fields. Recurrent Neural Networks (RNNs) are well-suited for time series data, as they naturally evolve through dynamic systems with recurrent updates. Despite their high predictive power, RNNs may underperform if their training ignores the model's future application. In Model Predictive Control, for example, the model evolves over time using on
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John, Aastha Mariam, Prakash Duraisamy, and Tushar Sandan. "A robust roof segmentation using noise-resilient convolutional neural network." In Pattern Recognition and Prediction XXXVI, edited by Mohammad S. Alam and Vijayan K. Asari. SPIE, 2025. https://doi.org/10.1117/12.3054394.

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Manno, Michael, and Daqing Hou. "Secure data collection for keystroke dynamics." In Pattern Recognition and Prediction XXXVI, edited by Mohammad S. Alam and Vijayan K. Asari. SPIE, 2025. https://doi.org/10.1117/12.3061295.

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Sultana, Aqsa, Shaik Nordin Abouzahra, Vijayan K. Asari, et al. "Ultralight VisionMamba UNet: a segmentation architecture for meltpond region localization." In Pattern Recognition and Prediction XXXVI, edited by Mohammad S. Alam and Vijayan K. Asari. SPIE, 2025. https://doi.org/10.1117/12.3054674.

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Küçükateş Yalçın, Büşra, Duygu Selin Ak, and Şükrücan Taylan Işıkoğlu. "Generic object detection via image style transfer technique for x-ray baggage scanning devices." In Pattern Recognition and Prediction XXXVI, edited by Mohammad S. Alam and Vijayan K. Asari. SPIE, 2025. https://doi.org/10.1117/12.3052234.

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Suer, Sait, Arif Karakas, and Mahmut Karakaya. "Image quality-based collaborative learning for diabetic retinopathy detection." In Pattern Recognition and Prediction XXXVI, edited by Mohammad S. Alam and Vijayan K. Asari. SPIE, 2025. https://doi.org/10.1117/12.3055313.

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Abouzahra, Shaik N., Alison Hardie, Theus H. Aspiras, Vijayan K. Asari, Andrew J. Stokes, and Brett L. Keaffaber. "Enhancing point cloud labeling: a scalable superpoint-based approach for efficient data annotation and analysis." In Pattern Recognition and Prediction XXXVI, edited by Mohammad S. Alam and Vijayan K. Asari. SPIE, 2025. https://doi.org/10.1117/12.3055042.

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Saeed, Amir K., Anthony Trautman, Chris Windle, and Benjamin M. Rodriguez. "A systematic framework for design of experiments in system design: establishing order and relevance in first-order analysis." In Pattern Recognition and Prediction XXXVI, edited by Mohammad S. Alam and Vijayan K. Asari. SPIE, 2025. https://doi.org/10.1117/12.3053946.

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Tebor, Daniel, and Mahmut Karakaya. "Triplet loss-based deep learning frameworks for off-angle iris recognition." In Pattern Recognition and Prediction XXXVI, edited by Mohammad S. Alam and Vijayan K. Asari. SPIE, 2025. https://doi.org/10.1117/12.3055314.

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Akintan, Oreofeoluwa A., and Daniel D. Uyeh. "Can we determine water activity in heterogeneous materials: a computer vision approach." In Pattern Recognition and Prediction XXXVI, edited by Mohammad S. Alam and Vijayan K. Asari. SPIE, 2025. https://doi.org/10.1117/12.3053225.

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Reports on the topic "Prediction"

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Manski, Charles. Interpreting the Predictions of Prediction Markets. National Bureau of Economic Research, 2004. http://dx.doi.org/10.3386/w10359.

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Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera, and Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2320.

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Nearly 5,000 people are killed and more than 418,000 are injured in weather-related traffic incidents each year. Assessments of the effectiveness of statistical models applied to crash severity prediction compared to machine learning (ML) and deep learning techniques (DL) help researchers and practitioners know what models are most effective under specific conditions. Given the class imbalance in crash data, the synthetic minority over-sampling technique for nominal (SMOTE-N) data was employed to generate synthetic samples for the minority class. The ordered logit model (OLM) and the ordered p
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Wolfers, Justin, and Eric Zitzewitz. Prediction Markets. National Bureau of Economic Research, 2004. http://dx.doi.org/10.3386/w10504.

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Rumelhart, D. E., P. G. Skokowski, and B. O. Martin. Word prediction. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/123254.

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Cerulli, Giovanni. Non-Parametric Regression for Prediction and Scenario Analysis. Instats Inc., 2024. http://dx.doi.org/10.61700/h03w8dvg3h26b767.

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This one-day workshop, led by Giovanni Cerulli from the Research Institute on Sustainable Economic Growth, provides a comprehensive understanding of non-parametric regression for prediction and 'scenario analysis' to project the results of policies and interventions. Participants, ranging from PhD students to professional researchers across various disciplines, will gain practical skills in applying non-parametric regression using Stata, enabling them to make accurate predictions and develop scenarios in their own research.
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Buchanan, Randy, Christina Rinaudo, George Gallarno, and M. Lagarde. Early life-cycle prediction of reliability. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/46919.

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The intent of this project is to investigate a variety of approaches for the development of a basic model for the early life-cycle prediction of reliability (pre-Milestone A). The United States Department of Defense (DoD) currently utilizes an acquisition framework in which system development advances through a series of checkpoints known as milestones. Each milestone represents a decision point, with Milestone A being the earliest in the life cycle. At Milestone A, also known as the risk-reduction decision, the DoD evaluates design concepts while also committing funds to the maturation of tec
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Busby, Ryan, Wade Wall, and Lauren Bosche. Remote detection of soil shear strength in Arctic and subarctic environments. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49815.

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Soil shear strength affects many military activities and is affected significantly by plant roots. Unfortunately, root contribution to soil shear strength is difficult to measure and predict. In the boreal forest ecosystem, soil and hydrologic dynamics make soil shear strength less predictable, while the need for prediction grows due to the rapid changes occurring in this environment. Our current study objectives are to (1) observe possible aboveground vegetation indicators of soil shear strength variation across soils and other environmental heterogeneity, (2) observe possible image-based ind
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McKay, M. D. Evaluating prediction uncertainty. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/29432.

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Henney, Carl J., Richard Radick, Donald C. Norquist, et al. Space Weather Prediction. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada612376.

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Bust, Gary S. Mesoscale Ionospheric Prediction. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada631417.

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