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Relevant bibliographies by topics / Prediction and analysis

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Academic literature on the topic 'Prediction and analysis'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Prediction and analysis"

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Gaur, Varun, Sharad Bhardwaj, Utsav Gaur, and Sushant Gupta. "Stock Market Prediction & Analysis." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 4404–8. http://dx.doi.org/10.22214/ijraset.2022.43403.

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Abstract: Stock trading is one of the most essential activities in the financial sector. The act of attempting to anticipate the future value of a stock or other financial instrument is known as stock market prediction. A financial exchange-traded instrument. This document illustrates how Machine Learning is used to predict a stock. The time series analysis or technical and fundamental analysis is used most stockbrokers use when deciding on a stock predictions. To forecast the outcome, the computer language is employed. Python is a stock market that uses machine learning. This paper is about We suggest a Machine Learning (ML) strategy that will be cost-effective. taught from publicly available stock data and intelligence and then applies what they've learned to make an accurate prediction. This work use machine learning in this setting. Support Vector Machine (SVM) is a technology for predicting Stock prices for large and small cap companies, as well as in the three different markets, using daily and weekly pricing Frequencies that are up to date. Keywords: Support Vector Machine, Stock Market, Machine Learning, Predictions

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Carlsson, Leo S., Mikael Vejdemo-Johansson, Gunnar Carlsson, and Pär G. Jönsson. "Fibers of Failure: Classifying Errors in Predictive Processes." Algorithms 13, no. 6 (June 23, 2020): 150. http://dx.doi.org/10.3390/a13060150.

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Predictive models are used in many different fields of science and engineering and are always prone to make faulty predictions. These faulty predictions can be more or less malignant depending on the model application. We describe fibers of failure (FiFa), a method to classify failure modes of predictive processes. Our method uses Mapper, an algorithm from topological data analysis (TDA), to build a graphical model of input data stratified by prediction errors. We demonstrate two ways to use the failure mode groupings: either to produce a correction layer that adjusts predictions by similarity to the failure modes; or to inspect members of the failure modes to illustrate and investigate what characterizes each failure mode. We demonstrate FiFa on two scenarios: a convolutional neural network (CNN) predicting MNIST images with added noise, and an artificial neural network (ANN) predicting the electrical energy consumption of an electric arc furnace (EAF). The correction layer on the CNN model improved its prediction accuracy significantly while the inspection of failure modes for the EAF model provided guiding insights into the domain-specific reasons behind several high-error regions.

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Kim, Jae Kwon, and Sanggil Kang. "Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis." Journal of Healthcare Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/2780501.

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Background. Of the machine learning techniques used in predicting coronary heart disease (CHD), neural network (NN) is popularly used to improve performance accuracy. Objective. Even though NN-based systems provide meaningful results based on clinical experiments, medical experts are not satisfied with their predictive performances because NN is trained in a “black-box” style. Method. We sought to devise an NN-based prediction of CHD risk using feature correlation analysis (NN-FCA) using two stages. First, the feature selection stage, which makes features acceding to the importance in predicting CHD risk, is ranked, and second, the feature correlation analysis stage, during which one learns about the existence of correlations between feature relations and the data of each NN predictor output, is determined. Result. Of the 4146 individuals in the Korean dataset evaluated, 3031 had low CHD risk and 1115 had CHD high risk. The area under the receiver operating characteristic (ROC) curve of the proposed model (0.749 ± 0.010) was larger than the Framingham risk score (FRS) (0.393 ± 0.010). Conclusions. The proposed NN-FCA, which utilizes feature correlation analysis, was found to be better than FRS in terms of CHD risk prediction. Furthermore, the proposed model resulted in a larger ROC curve and more accurate predictions of CHD risk in the Korean population than the FRS.

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Wade, Bruce A., Krishnendu Ghosh, and Peter J. Tonellato. "Optimization of a Gene Analysis Application." Computing Letters 2, no. 1-2 (March 6, 2006): 81–88. http://dx.doi.org/10.1163/157404006777491927.

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MetaGene is a software environment for gene analysis developed at the Bioinformatics Research Center, Medical College of Wisconsin. In this work, a new neural network optimization module is developed to enhance the prediction of gene features developed by MetaGene. The input of the neural network consists of gene feature predictions from several gene analysis engines used by MetaGene. When compared, these predictions are often in conflict. The output from the neural net is a synthesis of these individual predictions taking into account the degree of conflict detected. This optimized prediction provides a more accurate answer when compared to the default prediction of MetaGene or any single prediction engine’s solution.

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Dall’Aglio, John. "Sex and Prediction Error, Part 3: Provoking Prediction Error." Journal of the American Psychoanalytic Association 69, no. 4 (August 2021): 743–65. http://dx.doi.org/10.1177/00030651211042059.

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In parts 1 and 2 of this Lacanian neuropsychoanalytic series, surplus prediction error was presented as a neural correlate of the Lacanian concept of jouissance. Affective consciousness (a key source of prediction error in the brain) impels the work of cognition, the predictive work of explaining what is foreign and surprising. Yet this arousal is the necessary bedrock of all consciousness. Although the brain’s predictive model strives for homeostatic explanation of prediction error, jouissance “drives a hole” in the work of homeostasis. Some residual prediction error always remains. Lacanian clinical technique attends to this surplus and the failed predictions to which this jouissance “sticks.” Rather than striving to eliminate prediction error, clinical practice seeks its metabolization. Analysis targets one’s mode of jouissance to create a space for the subject to enjoy in some other way. This entails working with prediction error, not removing or tolerating it. Analysis aims to shake the very core of the subject by provoking prediction error—this drives clinical change. Brief clinical examples illustrate this view.

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Yin, Tao, and Yiming Wang. "Nonlinear analysis and prediction of soybean futures." Agricultural Economics (Zemědělská ekonomika) 67, No. 5 (May 20, 2021): 200–207. http://dx.doi.org/10.17221/480/2020-agricecon.

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We use chaotic artificial neural network (CANN) technology to predict the price of the most widely traded agricultural futures – soybean futures. The nonlinear existence test results show that the time series of soybean futures have multifractal dynamics, long-range dependence, self similarity, and chaos characteristics. This also provides a basis for the construction of a CANN model. Compared with the artificial neural network (ANN) structure as our benchmark system, the predictability of CANN is much higher. The ANN is based on Gaussian kernel function and is only suitable for local approximation of nonstationary signals, so it cannot approach the global nonlinear chaotical hidden pattern. Improving the prediction accuracy of soybean futures prices is of great significance for investors, soybean producers, and decision makers.

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Panchal, D. S., M. B. Shelke, S. S. Kawathekar, and S. N. Deshmukh. "Prediction of Healthcare Quality Using Sentiment Analysis." Indian Journal Of Science And Technology 16, no. 21 (June 3, 2023): 1603–13. http://dx.doi.org/10.17485/ijst/v16i21.2506.

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N, Sathyanarayana, Anjani Lahoty, Anubhav ., Archana S, and Dhanush Rao H S. "PREDICTIVE ANALYSIS OF SPORTS DATA USING MACHINE LEARNING." International Research Journal of Computer Science 9, no. 8 (August 13, 2022): 240–44. http://dx.doi.org/10.26562/irjcs.2022.v0908.17.

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There are numerous methods for making sports predictions, and data analysis is crucial to predicting. Previous attempts in sports data analysis have resulted in the prediction of sports such as football, tennis next shot location prediction, Olympic athlete performance, basketball slam dunk shot frequency, and many more. Cricket prediction is tough due to the numerous variables that might affect the result or outcome of a cricket match. Previously, simple cricket match prediction systems focused on the venue, ignoring aspects such as weather, stadium size, captaincy, etc. Factors such as the match's location, pitch, weather conditions, first-pitch batting, and fielding all play a role in forecasting the match's outcome. To predict, suitable models are required, and data mining allows the required information to be extracted from data sets. This paper is a review of techniques used for predicting the winners of three different games. In order to anticipate various facts linked to a certain match, such as the outcome of the match, an injured player's performance in the match, the discovery of new talents in the game, etc., various machine learning algorithms can be used to exploit the statistical data of the game. The objective is to correctly forecast the outcome of a specific game.

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Zain, Zuhaira Muhammad, Mona Alshenaifi, Abeer Aljaloud, Tamadhur Albednah, Reham Alghanim, Alanoud Alqifari, and Amal Alqahtani. "Predicting breast cancer recurrence using principal component analysis as feature extraction: an unbiased comparative analysis." International Journal of Advances in Intelligent Informatics 6, no. 3 (November 6, 2020): 313. http://dx.doi.org/10.26555/ijain.v6i3.462.

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Breast cancer recurrence is among the most noteworthy fears faced by women. Nevertheless, with modern innovations in data mining technology, early recurrence prediction can help relieve these fears. Although medical information is typically complicated, and simplifying searches to the most relevant input is challenging, new sophisticated data mining techniques promise accurate predictions from high-dimensional data. In this study, the performances of three established data mining algorithms: Naïve Bayes (NB), k-nearest neighbor (KNN), and fast decision tree (REPTree), adopting the feature extraction algorithm, principal component analysis (PCA), for predicting breast cancer recurrence were contrasted. The comparison was conducted between models built in the absence and presence of PCA. The results showed that KNN produced better prediction without PCA (F-measure = 72.1%), whereas the other two techniques: NB and REPTree, improved when used with PCA (F-measure = 76.1% and 72.8%, respectively). This study can benefit the healthcare industry in assisting physicians in predicting breast cancer recurrence precisely.

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Prahmana, I. Gusti, and Kristina Annatasia Br Sitepu. "Knearst Algorithm Analysis – Neighbor Breast Cancer Prediction Coimbra." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 1, no. 3 (June 15, 2022): 226–30. http://dx.doi.org/10.59934/jaiea.v1i3.97.

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A process to explain the results of the KNN algorithm analysis with the prediction of Breast Cancer Coimbra disease (Breast Cancer). The prediction output of the KNN algorithm will be added with the Simple Linear Regression algorithm modeling to measure the predictive data through a straight line as an illustration of the correlation relationship between 2 or more variables. Linear regression prediction is used as a technique for the relationship between variables in the prediction process of the Breast Cancer Coimbra data set (Breast Cancer). for the value of K in analyzing the KNN algorithm, take the nearest neighbor with the ranking results with K = 5 nearest neighbors which are taken in the KNN calculation. Which is where the output of the KNN algorithm classification will be analyzed with the Simple Linear Regression algorithm with Dependent (Cause) and Independent (effect) variables. The test results determine that the patient has breast cancer and the number of predictions based on age with glucose means that the patient is predicted to have breast cancer. analyze the KNN algorithm with Simple Liner Regression modeling with Python programming language.

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Dissertations / Theses on the topic "Prediction and analysis"

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Ratti, Carlo. "Urban analysis for environmental prediction." Thesis, University of Cambridge, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.421692.

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Vlasák, Pavel. "Exhange Rates Prediction." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-76388.

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The aim of this thesis is to examine the dependence of the exchange rate movement on the core fundamentals of the economy in the long term, as well as to test the validity of selected indicators of technical analysis in the short term. The dependence of the exchange rate will be examined using correlation and the discussed fundamentals are the main macroeconomic indicators, such as GDP, short-term interest rates and money base M2. In the part, which deals with the technical analysis, I will test the two groups of indicators, namely trend indicators and oscillators. From the first group it will be simple moving average (SMA), Exponential Moving Average (EMA), the weighted moving average (WMA), the triangular moving average (TMA) and MACD. From the group of oscillators I will test the relative strength index (RSI). All these indicators will be first described in the theoretical part of this thesis. The thesis is divided into two parts - theoretical and practical. The theoretical part includes two chapters which deals with the analysis of the Forex market. The first chapter deals with fundamental analysis. The second chapter deals with technical analysis. In the third chapter I will discuss both methods in practice, with emphasis on technical analysis.

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Iqbal, Ammar Tanange Rakesh Virk Shafqat. "Vehicle fault prediction analysis : a health prediction tool for heavy vehicles /." Göteborg : IT-universitetet, Chalmers tekniska högskola och Göteborgs universitet, 2006. http://www.ituniv.se/w/index.php?option=com_itu_thesis&Itemid=319.

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Lidholm, Tomas. "Knock prediction with reduced reaction analysis." Thesis, Linköping University, Department of Electrical Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1928.

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In the report a model using a reduced reaction analysis has been used to see if it is possible to predict knock. The model is based on n-heptane combustion, but it is used for iso-octane. The model was supposed to be able to adapt to different fuels, but it is shown to be unable to do so. Further, the model has been compared to an existing method for predicting knock, known as knock index, to see if any improvements could be made. When comparing the model to the knock index, it has shown that no big advantages can be found using the new model. It is more time consuming and is not able to work with simulated input, instead of measured. It can however predict if knock occurs with a good reliability, but compared to the knock index it is not an improvement.

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Copley, Richard Robertson. "Analysis and prediction of protein structure." Thesis, University of Oxford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361954.

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Boscott, Paul Edmond. "Sequence analysis in protein structure prediction." Thesis, University of Oxford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386870.

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Marsden, Russell Leonard. "Analysis and prediction of protein domains." Thesis, University College London (University of London), 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.408035.

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Ahmed, Ikhlaaq. "Meta-analysis of risk prediction studies." Thesis, University of Birmingham, 2015. http://etheses.bham.ac.uk//id/eprint/6376/.

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This thesis identifies and demonstrates the methodological challenges of meta-analysing risk prediction models using either aggregate data or individual patient data (IPD). Firstly, a systematic review of published breast cancer models is performed, to summarise their content and performance using aggregate data. It is found that models were not available for comparison. To address this issue, a systematic review is performed to examine articles that develop and/or validate a risk prediction model using IPD from multiple studies. This identifies that most articles only use the IPD for model development, and thus ignore external validation, and also ignore clustering of patients within studies. In response to these issues, IPD is obtained from an article which uses parathyroid hormone (PTH) assay (a continuous variable) to predict postoperative hypocalcaemia after thyroidectomy. It is shown that ignoring clustering is inappropriate, as it ignores potential between-study heterogeneity in discrimination and calibration performance. This dataset was also used to evaluate an imputation method for dealing with missing thresholds when IPD are unavailable, and the simulation results indicate the approach performs well, though further research is required. This thesis therefore makes a positive contribution towards meta-analysis of risk prediction models to improve clinical practice.

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Ellis, Daniel Patrick Whittlesey. "Prediction-driven computational auditory scene analysis." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/11006.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.
Includes bibliographical references (p. 173-180).
by Daniel P.W. Ellis.
Ph.D.

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Elliott, Craig Julian. "Analysis and prediction of protein structure." Thesis, University of York, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284165.

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Books on the topic "Prediction and analysis"

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1948-, Procházka A., ed. Signal analysis and prediction. Boston: Birkhauser, 1998.

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Procházka, Ales, Jan Uhlíř, P. W. J. Rayner, and N. G. Kingsbury, eds. Signal Analysis and Prediction. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-1768-8.

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Melchers, Robert E., and André T. Beck, eds. Structural Reliability Analysis and Prediction. Chichester, UK: John Wiley & Sons Ltd, 2017. http://dx.doi.org/10.1002/9781119266105.

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M, Clark Robert. Intelligence analysis: Estimation and prediction. Baltimore, Md: American Literary Press, 1996.

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service), SpringerLink (Online, ed. Designing Quantitative Experiments: Prediction Analysis. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.

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Causation, prediction, and legal analysis. New York: Quorum Books, 1986.

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United States. National Aeronautics and Space Administration., ed. Solar prediction analysis: Final report. [Washington, DC: National Aeronautics and Space Administration, 1992.

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Tadros, Angela. Prediction in text. Birmingham: University of Birmingham, English Language Research, 1985.

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United States. National Aeronautics and Space Administration., ed. Measurement-based reliability prediction methodology. [Urbana, IL]: Center for Reliable and High-Performance Computing, Coordinated Science Laboratory, College of Engineering, University of Illinois at Urbana-Champaign, 1991.

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United States. National Aeronautics and Space Administration., ed. Measurement-based reliability prediction methodology. [Urbana, IL]: Center for Reliable and High-Performance Computing, Coordinated Science Laboratory, College of Engineering, University of Illinois at Urbana-Champaign, 1991.

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Book chapters on the topic "Prediction and analysis"

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Elsner, James B., and Anastasios A. Tsonis. "Prediction." In Singular Spectrum Analysis, 133–41. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-2514-8_9.

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Wolberg, Emeritus John. "Prediction Analysis." In Designing Quantitative Experiments, 90–127. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11589-9_4.

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Dodla, Venkata Bhaskar Rao. "Objective Analysis." In Numerical Weather Prediction, 125–60. London: CRC Press, 2022. http://dx.doi.org/10.1201/9781003354017-4.

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Beran, Jan. "Prediction." In Mathematical Foundations of Time Series Analysis, 223–39. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-74380-6_8.

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Johnson, Wesley O. "Survival Analysis for Interval Data." In Diagnosis and Prediction, 75–90. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1540-0_5.

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Ramanathan, Jayakumar. "Prediction Theory." In Methods of Applied Fourier Analysis, 63–86. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-1756-5_3.

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Whiteman, Charles H., and Kurt F. Lewis. "Prediction formulas." In Macroeconometrics and Time Series Analysis, 178–92. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230280830_21.

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Luo, Tiejian, Su Chen, Guandong Xu, and Jia Zhou. "Sentiment Analysis." In Trust-based Collective View Prediction, 53–68. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7202-5_4.

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Hanson, R. Karl. "Meta-analysis." In Prediction statistics for psychological assessment., 261–82. Washington: American Psychological Association, 2022. http://dx.doi.org/10.1037/0000275-014.

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Niazi, Mansour. "Regression Analysis of Reported Earthquake Precursors. I. Presentation of Data." In Earthquake Prediction, 966–81. Basel: Birkhäuser Basel, 1985. http://dx.doi.org/10.1007/978-3-0348-6245-5_15.

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Conference papers on the topic "Prediction and analysis"

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Ben-Haim, Yakov, and Franc¸ois M. Hemez. "Robustness, Fidelity and Prediction-Looseness of Models." In ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58008.

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Assessment of the credibility of a mathematical or numerical model of an engineering system must combine three components: (1) The fidelity of the model to test data. (2) The robustness, of model fidelity, to lack of understanding of the underlying processes. (3) The prediction looseness of the model. ‘Prediction looseness’ is the range of predictions of models which are equivalent in terms of fidelity. The main result of this paper is that high fidelity, high robustness, and small prediction looseness are mutually incompatible. A model with high fidelity to data and high robustness to imperfect understanding of the process, will have low predictive focus. Our analysis is based on info-gap models of uncertainty.

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Hutton, Mike. "Session details: Expanding Rentian Analysis." In SLIP02: System Level Interconnect Prediction Workshop. New York, NY, USA: ACM, 2002. http://dx.doi.org/10.1145/3245930.

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Dhakksinesh, A., Olivia R. Katherine, and V. S. Pooja. "Crime Analysis and Prediction Based on Machine Learning Algorithm." In International Research Conference on IOT, Cloud and Data Science. Switzerland: Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-y21866.

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Crime prediction is a unique approach to identify and to find pattern trends of crime. Prediction means, using analysis and learning techniques, to find predictive actions of a specific activity and this is found to be effective in doing predictive analysis for various tasks such as crime prediction. The aim of this paper is to implement an approach for the problem in predicting the number of cases of crime happening in different parts of India. During the research we considered the machine learning model Random Forest and used the same for the prediction for crime. The prediction metrics used in this model are taken from feature selection technique. This technique increases the efficiency and accuracy of the prediction and also to avoid the model from over fitting. This model was tested on the crime data of India.

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Al Salmi, H. "Pore-pressure Prediction Using Multiresolution Analysis." In Second EAGE Workshop on Pore Pressure Prediction. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201900502.

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Zarkesh-Ha, Payman. "Session details: Power Grid and Signal Integrity Analysis." In SLIP02: System Level Interconnect Prediction Workshop. New York, NY, USA: ACM, 2002. http://dx.doi.org/10.1145/3245933.

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Maruyama, William Takahiro, and Luciano Antonio Digiampietri. "Co-authorship prediction in academic social network." In Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/brasnam.2016.6445.

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The prediction of relationships in a social network is a complex and extremely useful task to enhance or maximize collaborations by indicating the most promising partnerships. In academic social networks, prediction of relationships is typically used to try to identify potential partners in the development of a project and/or co-authors for publishing papers. This paper presents an approach to predict coauthorships combining artificial intelligence techniques with the state-of-the-art metrics for link predicting in social networks.

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Seasly, Elaine, Gugu Rutherford, and Walter Wrigglesworth. "Analysis of spacecraft contaminants with portable Raman spectroscopy." In Systems Contamination: Prediction, Control, and Performance 2018, edited by Carlos E. Soares, Eve M. Wooldridge, and Bruce A. Matheson. SPIE, 2018. http://dx.doi.org/10.1117/12.2320653.

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Hsu, Po-Ya, Chun-Han Yao, Yuwei Wang, and Chung-Kuan Cheng. "Adaptive sensitivity analysis with nonlinear power load modeling." In SLIP '18: System Level Interconnect Prediction Workshop. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3225209.3225211.

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O'Connor, S. A., and R. H. Oughton. "The Analysis of Meaningful Uncertainty in Pore Pressure Prediction." In First EAGE Workshop on Pore Pressure Prediction. Netherlands: EAGE Publications BV, 2017. http://dx.doi.org/10.3997/2214-4609.201700079.

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Kang, Ilgweon, Dongwon Park, Changho Han, and Chung-Kuan Cheng. "Fast and precise routability analysis with conditional design rules." In SLIP '18: System Level Interconnect Prediction Workshop. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3225209.3225210.

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Reports on the topic "Prediction and analysis"

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Simpson, William R., John H. Bailey, Katherine B. Barto, and Eugene Esker. Prediction and Analysis of Testability Attributes: Organizational-Level Testability Prediction. Fort Belvoir, VA: Defense Technical Information Center, February 1986. http://dx.doi.org/10.21236/ada167957.

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Harstad, Ethan. Analysis of HAB Flight Prediction Methods. Ames (Iowa): Iowa State University. Library. Digital Press, January 2011. http://dx.doi.org/10.31274/ahac.8128.

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Harstad, Ethan. Analysis of Balloon Trajectory Prediction Methods. Ames (Iowa): Iowa State University. Library. Digital Press, January 2012. http://dx.doi.org/10.31274/ahac.8334.

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YEON, Yeong Kwang, Jong Gyu HAN, and Hye Ja HYUN. Spatial Prediction Analysis Using a Neural Network. Cogeo@oeaw-giscience, September 2011. http://dx.doi.org/10.5242/iamg.2011.0058.

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Richter, Juergen H. Coastal Variability Analysis, Measurement, and Prediction (COVAMP). Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada629294.

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Paulus, Richard A., Douglas Jensen, Kenneth L. Davidson, Kenneth D. Anderson, and L. T. Rogers. Coastal Variability Analysis, Measurement, and Prediction (COVAMP). Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada630122.

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Poulain, Pierre-Marie. Lagrangian Data Analysis in Mesoscale Prediction Studies. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada636799.

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Paulus, Richard A. Coastal Variability Analysis, Measurement, and Prediction (COVAMP). Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada625674.

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Lewis, James K., and Peter J. Stein. The Coupled Oceanographic-Tomographic Analysis and Prediction System. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada533104.

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Hurley, Morgan J., and Alex Munguia. Analysis of FDS thermal detector response prediction capability. Gaithersburg, MD: National Institute of Standards and Technology, 2009. http://dx.doi.org/10.6028/nist.gcr.09-921.

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