Готові списки джерел за темами / Prediction algoritm

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Добірка наукової літератури з теми "Prediction algoritm"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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Статті в журналах з теми "Prediction algoritm"

Cherepanov, Anatoliy, and Stepan Manuylov. "ALGORITM STAGED LIFE PREDICTION OF VESSEL." Modern Technologies and Scientific and Technological Progress 2020, no. 1 (2020): 87–88. http://dx.doi.org/10.36629/2686-9896-2020-1-87-88.

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The forecasting algorithm is considered using the method of step-by-step determination of the initial resource during manufacture, and then-the remaining resource after a certain period of operation of pressure vessel

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Aqham, Ahmad Ashifuddin, and Kristoko Dwi Hartomo. "Data Mining untuk Nasabah Bank Telemarketing Menggunakan kombinasi Algoritm Naïve Bayes Dan Algoritma Genetik." InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan) 4, no. 1 (2019): 47–56. http://dx.doi.org/10.30743/infotekjar.v4i1.1574.

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The strategy used for telemarketing by conducting promotional media, this strategy is a marketing method used by banks, in offering products to customers, banks, one of the products that will be offered is time deposits, the bank has difficulty in knowing the obstacles experienced by customers in making a decision to make deposits against the bank, so that later it will have the effect of a financial crisis at the bank. Telemarketing banks must have targets for customers, where customers have the potential to join one of the bank's products, namely deposits by looking at existing customer data

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Suyash S. Tambe, Vivek Ambetkar, and Prathamesh Ambovkar. "Potential Customer Prediction." International Journal of Engineering and Management Research 11, no. 2 (2021): 100–102. http://dx.doi.org/10.31033/ijemr.11.2.14.

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Major industries today are dealing with large amount of data even small shops are no oblivion for the huge pile of customer data. To gain profit in competitive marketing is imperative that the useful information shall be extracted out of this data. The processing of this huge pile of data becomes monotonous task and the different types of software and algorithms are developed to process and acquire result out of this data. This project deals with same kind of problem of dealing with data. Taking customer purchase history as an input our system using "Apriori Algoritm" classifies these customer

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Komara, Eki, Muhammad Faiz Nugraha, Widya Utama, and Sherly Ardhya Garini. "Lithology Prediction Using Convolutional Neural Network Algoritm Study Case Poseidon Field Australia Basin." IOP Conference Series: Earth and Environmental Science 1458, no. 1 (2025): 012038. https://doi.org/10.1088/1755-1315/1458/1/012038.

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Abstract Convolutional Neural Network (CNN) method is one of a machine learning algorithm that is adapted from the the way of human brain works and is used to facilitate the processing of large amounts of data. The CNN method predicts the lithology of a well with well log data, there are Gamma-Ray Logs, Density Logs, Neutron Porosity Logs, and DT Logs. The CNN model is formed by several layers, such as 2 Convolutional Layers, 2 Dense Layers, 0.5 Dropout Layers, and 256 Dense Nodes. Contrary outcomes are observed in predictions that were previously generated utilizing 10 input parameters from P

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Fahraini, Bacharuddin, Wuryanto Hadi, Yuliza, and Nugraha Beny. "Optimum Work Frequency for Marine Monitoring Based on Genetic Algorithm." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 4 (2018): 1551–59. https://doi.org/10.12928/TELKOMNIKA.v16i4.7328.

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The communication using of HF (High Frequency) is a system that depends on wave propagation using sky waves reflected by the earth's ionosphere layer so that it is highly effective for long distance communication, but highly dependent on varying ionospheric conditions from day and night (time after time) as well as the location of the transmitter and receiver radio. Currently, there is only one main frequency channel and one reserve frequency channel so that there are frequency constraints unable to communicate due to ionosphere changes. This research will predicted allocation of HF freque

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Chen, Zhenpeng, Yuanjie Zheng, Xiaojie Li, et al. "Interactive Trimap Generation for Digital Matting Based on Single-Sample Learning." Electronics 9, no. 4 (2020): 659. http://dx.doi.org/10.3390/electronics9040659.

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Image matting refers to the task of estimating the foreground of images, which is an important problem in image processing. Recently, trimap generation has attracted considerable attention because designing a trimap for every image is labor-intensive. In this paper, a two-step algorithm is proposed to generate trimaps. To use the proposed algorithm, users must only provide some clicks (foreground clicks and background clicks), which are employed as the input to generate a binary mask. One-shot learning technique achieves remarkable progress on semantic segmentation, we extend this technique to

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Herdatullah, Rizki, Syaiful Bukhori, and Windi Eka Yulia Retnani. "OPTIMASI PERSEDIAAN MATERIAL TRANSFORMATOR MENGGUNAKAN METODE JARINGAN SYARAF TIRUAN DAN ANT COLONY OPTIMIZATION DI PT. PLN (PERSERO) AREA JEMBER." INFORMAL: Informatics Journal 4, no. 1 (2019): 25. http://dx.doi.org/10.19184/isj.v4i1.12892.

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   Optimization comes from basic words optimal which mean the best, highest, most beneficial, make the best, and do optimizing (make the best, highest, etc.). Forecasting is an attempt to predict the future. Prediction can be done by studying the pattern of historical data to find a model that can show future data. This methoed is called time series data forecasting. One of many algorithm that can builds model from historical data is Artificial Neural Networks (ANN). The algoritm mimics the human neuron system so that is can solve non-linear problems, such as the forecasting

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Nilson, Tiit, Mattias Rennel, Andres Luhamaa, Maris Hordo, Aire Olesk, and Mait Lang. "MERIS GPP/NPP product for Estonia: I. Algorithm and preliminary results of simulation / MERIS’e GPP/NPP tulem Eesti jaoks: I. Algoritm ja mudelarvutuste esialgsed tulemused." Forestry Studies 56, no. 1 (2012): 56–78. http://dx.doi.org/10.2478/v10132-012-0005-5.

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Abstract. A light use efficiency (LUE) type model named EST_PP to simulate the yearly gross primary production (GPP) and net primary production (NPP) of Estonian land on a 1 km2 grid is described. The model is based on MERIS (MEdium Resolution Imaging Spectrometer) satellite images to describe the fraction of photosynthetically active radiation (fAPAR) and leaf area index (LAI) as well as meteorological reanalysis datasets on 11 km2 grid produced by Estonian Meteorological Institute (EMHI) and Tartu University (TU) by means of the HIRLAM (High Resolution Limited Area Model) numerical weather p

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Dmytro, Zubov. "BRAIN Journal - Early Warning of Heat/Cold Waves as a Smart City Subsystem: A Retrospective Case Study of Non-anticipative Analog Methodology." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 6, no. 1-2 (2015): 43–53. https://doi.org/10.5281/zenodo.1044160.

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ABSTRACT In this paper, a forecasting of the heat/cold waves is discussed as a subsystem of the smart city concept using the non-anticipative analog method. The prediction algorithm is described by two paradigms. First one (short range) uses quantum computing formalism. D-Wave adiabatic quantum computing Ising model is employed and evaluated for the forecasting of positive extremes of daily mean air temperature. Forecast models are designed with two to five qubits, which represent 2-, 3-, 4-, and 5-day historical data, respectively. Ising model’s real-valued weights and dimensionless coefficie

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Ahmadi, Farrokh, Abbas Toloie Eshlaghi, and Reza Radfar. "Examining and Comparing the Efficiency of MLP and SimpleRNN Algorithms in Cryptocurrency Price Prediction." Management Strategies and Engineering Sciences 6, no. 3 (2024): 121–37. https://doi.org/10.61838/msesj.6.3.12.

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Cryptocurrencies have been widely identified and established as a new form of electronic currency exchange, carrying significant implications for emerging economies and the global economy. This research focused on the "examination and comparison of the efficiency of MLP and SimpleRNN algorithms in predicting cryptocurrency prices" using the Python programming language. Price predictions for Bitcoin, Ethereum, Binance Coin, Cardano, and Ripple were made using two deep learning algorithms (including the MLP algorithm and the SimpleRNN algorithm) over the period from 2017 to 2023. The results of

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Дисертації з теми "Prediction algoritm"

Chen, Yutao. "Algorithms and Applications for Nonlinear Model Predictive Control with Long Prediction Horizon." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3421957.

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Fast implementations of NMPC are important when addressing real-time control of systems exhibiting features like fast dynamics, large dimension, and long prediction horizon, as in such situations the computational burden of the NMPC may limit the achievable control bandwidth. For that purpose, this thesis addresses both algorithms and applications. First, fast NMPC algorithms for controlling continuous-time dynamic systems using a long prediction horizon have been developed. A bridge between linear and nonlinear MPC is built using partial linearizations or sensitivity update. In order to

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Barrueta, Meza Renzo André, and Villarreal Edgar Jean Paul Castillo. "Modelo de análisis predictivo para determinar clientes con tendencia a la deserción en bancos peruanos." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2018. http://hdl.handle.net/10757/626023.

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En la actualidad, el rol que cumplen los bancos en la economía del país y el impacto que tienen en las diferentes clases sociales es cada vez más importante. Estos siempre han sido un mercado que históricamente ha recibido un gran número de quejas y reclamaciones. Es por ello que, un mal servicio por parte del proveedor, una deficiente calidad de los productos y un precio fuera de mercado son las principales razones por las que los clientes abandonan una entidad bancaria. Esta situación va aumentando cada vez más y los bancos muestran su preocupación por este problema intentando implementa

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Altmisdort, F. Nadir. "Development of a new prediction algorithm and a simulator for the Predictive Read Cache (PRC)." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1996. http://handle.dtic.mil/100.2/ADA322724.

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Thesis (M.S. in Electrical Engineering) Naval Postgraduate School, September 1996.<br>Thesis advisor(s): Douglas J. Fouts. "September 1996." Includes bibliographical references (p. 127-128). Also available online.

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Alkindy, Bassam. "Combining approaches for predicting genomic evolution." Thesis, Besançon, 2015. http://www.theses.fr/2015BESA2012/document.

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En bio-informatique, comprendre comment les molécules d’ADN ont évolué au cours du temps reste un problème ouvert etcomplexe. Des algorithmes ont été proposés pour résoudre ce problème, mais ils se limitent soit à l’évolution d’un caractèredonné (par exemple, un nucléotide précis), ou se focalisent a contrario sur de gros génomes nucléaires (plusieurs milliardsde paires de base), ces derniers ayant connus de multiples événements de recombinaison – le problème étant NP completquand on considère l’ensemble de toutes les opérations possibles sur ces séquences, aucune solution n’existe à l’heureac

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Losik, Len. "Using Oracol® for Predicting Long-Term Telemetry Behavior for Earth and Lunar Orbiting and Interplanetary Spacecraft." International Foundation for Telemetering, 2010. http://hdl.handle.net/10150/604280.

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ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California<br>Providing normal telemetry behavior predictions prior to and post launch will help to stop surprise catastrophic satellite and spacecraft equipment failures. In-orbit spacecraft fail from surprise equipment failures that can result from not having normal telemetry behavior available for comparison with actual behavior catching satellite engineers by surprise. Some surprise equipm

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ITC/USA 2009 Conference Proceedings / The Forty-Fifth Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2009 / Riviera Hotel & Convention Center, Las Vegas, Nevada<br>Providing normal telemetry behavior predictions prior to and post launch will help to stop surprise catastrophic satellite and spacecraft equipment failures. In-orbit spacecraft fail from surprise equipment failures that can result from not having normal telemetry behavior available for comparison with actual behavior catching satellite engineers by surprise. Some surprise equipment failures l

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Truong, Ngoc Cuong. "Algorithms for appliance usage prediction." Thesis, University of Southampton, 2014. https://eprints.soton.ac.uk/367540/.

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Demand-Side Management (DSM) is one of the key elements of future Smart Electricity Grids. DSM involves mechanisms to reduce or shift the consumption of electricity in an attempt to minimise peaks. By so doing it is possible to avoid using expensive peaking plants that are also highly carbon emitting. A key challenge in DSM, however, is the need to predict energy usage from specific home appliances accurately so that consumers can be notified to shift or reduce the use of high energy-consuming appliances. In some cases, such notifications may be also need to be given at very short notice. Henc

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Harparan, Dhindsa, and Jonathan Malmström. "Predictive Motion Cueing Algorithm." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230145.

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Advancements in vehicle development has made motion-based driving simulators an impor- tant and cost-e↵ective tool when validating and designing vehicles. Although one-to-one rendition of the real world is not a possibility due to hard limitations set by the rig, motion cueing algorithms intend to improve the perception of reality by mimicking the movements of a real vehicle into the movement of a motion platform. To further yield a greater motion envelope, prepositioning can be applied beforehand to gradually counteract the intended motion of a predetermined track model. The purpose of this t

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Hanittinan, Wichai. "Resilient modulus prediction using neural network algorithm." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1190140082.

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Abraham, Nathan Luke. "A genetic algorithm for crystal structure prediction." Thesis, University of York, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.444727.

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Книги з теми "Prediction algoritm"

Altmisdort, F. Nadir. Development of a new prediction algorithm and a simulator for the Predictive Read Cache (PRC). Naval Postgraduate School, 1996.

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Smith, Richard E. Genetic algorithms for share price prediction. University of Manchester, Department of Computer Science, 1997.

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Sigg, Stephan. Development of a novel context prediction algorithm and analysis of context prediction schemes. Kassel Univ. Press, 2008.

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Nowozin, Sebastian. Advanced structured prediction. The MIT Press, 2014.

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Urazaev, R. P. Metody genera͡tsii algoritmov prognozirovani͡ia pri pomoshchi opera͡tsiĭ nad bazovymi algoritmami. Vychislitelʹnyĭ ͡tsentr AN SSSR, 1988.

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Rangwala, Huzefa. Introduction to protein structure prediction: Methods and algorithms. Wiley, 2010.

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Rangwala, Huzefa, G. Karypis, and G. Karypis. Introduction to protein structure prediction: Methods and algorithms. Wiley, 2010.

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Ławryńczuk, Maciej. Computationally Efficient Model Predictive Control Algorithms. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04229-9.

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Rosário Lucas, Luís Filipe, Eduardo Antônio Barros da Silva, Sérgio Manuel Maciel de Faria, Nuno Miguel Morais Rodrigues, and Carla Liberal Pagliari. Efficient Predictive Algorithms for Image Compression. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51180-1.

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Chen, S. A prediction-error estimation algorithm for nonlinear output-affine systems. University of Sheffield, Dept. of Control Engineering, 1987.

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Частини книг з теми "Prediction algoritm"

Kjeldsberg, Fabian, Ziaul Haque Munim, Morten Bustgaard, Sahil Bhagat, Emilia Lindroos, and Per Haavardtun. "Sensitivity of Predictive Performance Assessment Accuracy in Varying k-fold Cross Validation." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-84170-5_7.

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Abstract In machine learning (ML) applications, cross-validation (CV) allows greater generalizability of a trained algorithm over out-of-sample or new data. This study explores the accuracy of trained ML algorithms in predicting student performance in a maritime simulator exercise scenario in four different k-fold CVs. Three, five, eight, and ten-fold CVs were trained using a cloud-ML platform. Three top-performing ML algorithms were evaluated considering log loss, accuracy, and area under the curve (AUC). The results indicate higher predictive accuracy with increasing k in CV folds. Consideri

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Bautista-Hernández, Jorge, and María Ángeles Martín-Prats. "Monte Carlo Simulation Applicable for Predictive Algorithm Analysis in Aerospace." In Technological Innovation for Connected Cyber Physical Spaces. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36007-7_18.

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AbstractSafety investigations about electrical wiring harness caused by failures in electrical systems establish that origin of these accidents are related to electrical installation. Predictive techniques which mitigate and reduce risk of the occurrence of errors to enhance safety shall be considered. The development of machine learning has evolved towards the creation of innovative predictive algorithms which show high performance in data analysis and making predictions in the context of artificial intelligence. The Monte Carlo approach is used to validate the model performance. In this pape

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Qian, Shenghua. "Vehicle Collision Prediction Model on the Internet of Vehicles." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_53.

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AbstractAn active collision prediction model on the Internet of Vehicles is proposed. Through big data calculation on the cloud computing platform, the model predicts whether the vehicles may collide and the time of the collision, so the server actively sends warning signals to the vehicles that may collide. Firstly, the vehicle collision prediction model preprocesses the data set, and then constructs a new feature set through feature engineering. For the imbalance of the data set, which affects predictive results, SMOTE algorithm is proposed to generate new samples. Then, the LightGBM algorit

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Khekare, Ganesh, Anil V. Turukmane, Chetan Dhule, Pooja Sharma, and Lokesh Kumar Bramhane. "Experimental Performance Analysis of Machine Learning Algorithms." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_104.

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AbstractMachine Learning models and algorithms have become quite common these days. Deep Learning and Machine Learning algorithms are utilized in various projects, and now, it has opened the door to several opportunities in various fields of research and business. However, identifying the appropriate algorithm for a particular program has always been an enigma, and that necessitates to be solved ere the development of any machine learning system. Let’s take the example of the Stock Price Prediction system, it is used to identify the future asset prediction of a industry or other financial aspe

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Aljebreen, Abdulaziz, Allan Pang, Marc de Kamps, and Owen Johnson. "Predicting Unplanned Hospital Readmissions Using Outcome-Oriented Predictive Process Mining." In Lecture Notes in Business Information Processing. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82225-4_31.

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Abstract Many hospitals in the world are under pressure to improve their efficiency and effectiveness so that they can achieve better health outcomes with limited resources. One common measure of performance is the rate of unplanned hospital readmissions (UHRs) within 30-days. Emergency readmissions for the same disease can be assumed to indicate inappropriate discharge or poor planning, are costly, increase patients’ mortality risks and put additional pressure on bed capacity. Data Mining (DM) techniques have been used to predict UHRs based on clinical and demographic features, but these igno

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Asesh, Aishwarya. "Predicting Music Using Machine Learning." In Digital Interaction and Machine Intelligence. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37649-8_3.

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AbstractThe intricate temporally prolonged sequences seen in music make it a perfect environment for the study of prediction. Melody, harmony, and rhythm are three examples of the structural elements found in music. This research incorporates music excerpts prediction by understanding structural details using Markov chain and LSTM models. The novel approach compares to state-of-the-art algorithms by predicting how a musical excerpt would continue after being given as input. To compare the variations in prediction and learning, different learning models with different input feature representati

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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Linear Mixed Models." In Multivariate Statistical Machine Learning Methods for Genomic Prediction. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_5.

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AbstractThe linear mixed model framework is explained in detail in this chapter. We explore three methods of parameter estimation (maximum likelihood, EM algorithm, and REML) and illustrate how genomic-enabled predictions are performed under this framework. We illustrate the use of linear mixed models by using the predictor several components such as environments, genotypes, and genotype × environment interaction. Also, the linear mixed model is illustrated under a multi-trait framework that is important in the prediction performance when the degree of correlation between traits is moderate or

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Yang, Kaifeng, and Michael Affenzeller. "Surrogate-assisted Multi-objective Optimization via Genetic Programming Based Symbolic Regression." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27250-9_13.

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AbstractSurrogate-assisted optimization algorithms are a commonly used technique to solve expensive-evaluation problems, in which a regression model is built to replace an expensive function. In some acquisition functions, the only requirement for a regression model is the predictions. However, some other acquisition functions also require a regression model to estimate the “uncertainty” of the prediction, instead of merely providing predictions. Unfortunately, very few statistical modeling techniques can achieve this, such as Kriging/Gaussian processes, and recently proposed genetic programmi

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Zhang, Yuetong, Wuwei Zhu, Shenglong Jin, et al. "A Prediction Model for Loess Collapse Coefficient Based on Genetic Algorithm Back Propagation Neural Network." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5814-2_38.

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AbstractAs an important property of loess, the collapsibility coefficient is commonly used in engineering to evaluate the collapsibility of loess. The main methods for predicting the collapsibility coefficient of loess currently include fuzzy algorithm, principal component analysis, data mining, etc. Due to the numerous indicators that affect the collapsibility of loess and their mutual influence, current research has problems such as incomplete consideration of indicators or insufficient data used to fit prediction models, resulting in insufficient prediction accuracy. Using factor analysis m

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Starbuck, Craig. "Predictive Modeling." In The Fundamentals of People Analytics. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28674-2_13.

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AbstractThis chapter progresses from explanatory to predictive models. Topics include cross-validation, model performance metrics, prediction intervals, bias–variance tradeoff, tree-based algorithms, and various types of models with utility in classification and forecasting applications.

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Тези доповідей конференцій з теми "Prediction algoritm"

Hornyák, Olivér. "Predicting Remaining Useful Life Using AdaBoost Algorithm." In 10th International Scientific Conference on Advances in Mechanical Engineering. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-3vqcf6.

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Predicting the Remaining Useful Life (RUL) of machinery and critical components is crucial for proactive maintenance and operational efficiency in industrial settings. This paper presents an approach to RUL prediction using the AdaBoost algorithm, a technique that iteratively improves prediction accuracy by focusing on difficult-to-predict cases. The AdaBoost algorithm will be extended to handle both binary and multi-class classification, enabling it to distinguish between various stages of degradation. By providing more granular insights into the health status of components, this approach enh

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Panda, Ayush, Ayush Suman, Nilamadhab Mishra, et al. "Alzheimer's Disease Prediction using Advanced Predictive Intelligence Model." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721920.

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Kere, Kiswendsida Jules, and Qindan Huang. "Development of Probabilistic Models of Defect Interaction Identification and Failure Pressure for Pipelines with Colony of Corrosion Defects." In CONFERENCE 2023. AMPP, 2023. https://doi.org/10.5006/c2023-19404.

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Abstract The interaction of defects in a defect colony has a higher pipeline failure risk than the case when such interaction is not considered. The goal of this study is to develop probabilistic interaction rule and predictive failure pressure model for pipelines with colony of corrosion defects. The proposed interaction rule is developed based on the logistic regression algorithm by considering pipe properties and colony configurations as the independent variables. A performance comparison with the existing interaction rules shows that the proposed interaction rule has the most accurate pred

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Setiyanti, Michelle, Genrawan Hoendarto, and Jimmy Tjen. "Enhancing Water Potability Identification through Random Forest Regression and Genetic Algorithm Optimization." In INTERNATIONAL CONFERENCE ON APPLIED TECHNOLOGY 2024. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-2fikqf.

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Water quality is important for both environmental sustainability and public health. This research introduces an innovative method for forecasting water quality using Random Forest Regression, optimized through Genetic Algorithm (GA) techniques. The goal is to enhance prediction accuracy and offer meaningful insights for better water resource management. The study employed the “Water Quality Data” dataset, encompassing 11 essential water quality parameters from different locations. After thorough data preprocessing, the Random Forest model, refined with GA optimization, achieved a Mean Squared

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Hånde, Bjørn M. "A New Matching Procedure for Multishaft Gas Turbines Modelled With the Mean Line Prediction Method." In ASME 1994 International Gas Turbine and Aeroengine Congress and Exposition. American Society of Mechanical Engineers, 1994. http://dx.doi.org/10.1115/94-gt-493.

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A new matching procedure adapted for multishaft gas turbines modelled with the mean line prediction method, is presented. Mass flow and pressure convergence is achieved with stage by stage marching in the flow direction where the high pressure and low pressure units are treated together. Independent iteration on the power balance gives a stable procedure that can be applied in static as well as dynamic simulations. The procedure can be accelerated with an algoritm combining an advanced version of the Ellipse equation with the mean line prediction method.

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Adeeyo, Yisa. "Random Forest Ensemble Model for Reservoir Fluid Property Prediction." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/212044-ms.

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Abstract Reservoir fluid PVT properties are measured in the laboratory for various use in reservoir engineering evaluation and estimation. Despite the indispensability of these PVT parameters, PVT lab data are seldomly available and if available may be unreliable. Instead, various empirical models have been developed and used in the industry. These empirical models are inherently inaccurate when used to predict PVT properties of fluid from different geological region with different depositional environment and fingerprint. Artificial Intelligence (AI) has evolved over the years and provided so

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Zhu, Shaopeng, Yuanning Tang, Bing Li, Xiaoliang Wang, and Huipeng Chen. "Angle-Weighted Time-to-Collision Algorithm for Enhanced Obstacle Avoidance in Autonomous Driving." In SAE 2024 Vehicle Powertrain Diversification Technology Forum. SAE International, 2025. https://doi.org/10.4271/2025-01-7022.

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<div class="section abstract"><div class="htmlview paragraph">To tackle the challenge of accurately predicting collision times for autonomous vehicles navigating complex dynamic obstacles, this paper proposes an innovative Angle-Weighted Time-to-Collision (AW-TTC) algorithm. Traditional TTC algorithms are known for their computational simplicity and strong real-time performance, making them widely applicable across various driving scenarios. However, they often struggle with predictive accuracy when encountering obstacles moving at angles, which can delay vehicle response and compr

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Hu, Chao, Byeng D. Youn, and Taejin Kim. "Semi-Supervised Learning With Co-Training for Data-Driven Prognostics." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48302.

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Traditional data-driven prognostics often requires a large amount of failure data for the offline training in order to achieve good accuracy for the online prediction. However, in many engineered systems, failure data are fairly expensive and time-consuming to obtain while suspension data are readily available. In such cases, it becomes essentially critical to utilize suspension data, which may carry rich information regarding the degradation trend and help achieve more accurate remaining useful life (RUL) prediction. To this end, this paper proposes a co-training-based data-driven prognostic

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Omotosho, Temitope James. "Oil Production Prediction Using Time Series Forecasting and Machine Learning Techniques." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2024. http://dx.doi.org/10.2118/221728-ms.

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Abstract Prediction of oil production is critical for the oil and gas industry, as it helps production engineers plan and execute strategic decisions. In the past, various empirical correlations and mathematical models have been utilized for this purpose. However, with the advent of data-driven techniques, machine learning algorithms such as Random Forest (RF), Artificial Neural Network (ANN), Long Short-Term Memory neural network (LSTM), Recurrent Neural Network (RNN), DeepAR, and others have been adopted for predicting oil production. This paper presents a comparative analysis of time series

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Vidal, JR, and OR Vidal. "EVALUATION OF THE EFFICACY OF AN ARTIFICIAL INTELLIGENCE ALGORITHM FOR PREDICTING THE ESTIMATED VALUE OF PLATELET CONCENTRATE DURING APHERESIS." In Resumos do 55º Congresso Brasileiro de Patologia Clínica/Medicina Laboratorial. Zeppelini Editorial e Comunicação, 2023. http://dx.doi.org/10.5327/1516-3180.141s2.8789.

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Objective: Artificial intelligence (AI) can enhance human health by providing predictions and recommendations based on clinical data. The aim of this study was to develop and evaluate the effectiveness of an AI algorithm for predicting the estimated value of platelet concentrate (PC) obtained by apheresis. Method: This was an applied study using data from 30 patients, including age, BMI, gender, complete blood count (hematocrit, blood type, platelets), number of cycles, apheresis time, processed blood volume, and platelet concentrate value. Linear regression in Python was used for platelet con

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Звіти організацій з теми "Prediction algoritm"

Johansen, Richard A., Christina L. Saltus, Molly K. Reif, and Kaytee L. Pokrzywinski. A Review of Empirical Algorithms for the Detection and Quantification of Harmful Algal Blooms Using Satellite-Borne Remote Sensing. U.S. Army Engineer Research and Development Center, 2022. http://dx.doi.org/10.21079/11681/44523.

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Harmful Algal Blooms (HABs) continue to be a global concern, especially since predicting bloom events including the intensity, extent, and geographic location, remain difficult. However, remote sensing platforms are useful tools for monitoring HABs across space and time. The main objective of this review was to explore the scientific literature to develop a near-comprehensive list of spectrally derived empirical algorithms for satellite imagers commonly utilized for the detection and quantification HABs and water quality indicators. This review identified the 29 WorldView-2 MSI algorithms, 25

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Poehls, Kenneth A., David M. Crandall, Kevin O'Rourke, and Kenneth E. Heikes. Worldwide Cloud Prediction Algorithms. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada359020.

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Wright, Adam, Marija Milacic, Karen Rothfels, et al. Evaluating the Predictive Accuracy of Reactome's Curated Biological Pathways. Reactome, 2022. http://dx.doi.org/10.3180/poster/20221109wright.

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Reactome is a database of human biological pathways manually curated from the primary literature and peer-reviewed by experts. To evaluate the utility of Reactome pathways for predicting functional consequences of genetic perturbations, we compared predictions of perturbation effects based on Reactome pathways against published empirical observations. Ten cancer-relevant Reactome pathways, representing diverse biological processes such as signal transduction, cell division, DNA repair, and transcriptional regulation, were selected for testing. For each pathway, root input nodes and key pathway

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Idakwo, Gabriel, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang, and Ping Gong. Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41302.

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Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented. To further explore the advantages of DL over shallow learning, we conducted this case study using two cell-based androgen receptor (AR) activity datasets with 10K chemicals generated from the Tox21 program. A nested double-loop cross-validation approach was adopted along with a stratified sampling strategy for partitioning chemicals of multiple AR

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Fluhr, Robert, and Volker Brendel. Harnessing the genetic diversity engendered by alternative gene splicing. United States Department of Agriculture, 2005. http://dx.doi.org/10.32747/2005.7696517.bard.

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Our original objectives were to assess the unexplored dimension of alternative splicing as a source of genetic variation. In particular, we sought to initially establish an alternative splicing database for Arabidopsis, the only plant for which a near-complete genome has been assembled. Our goal was to then use the database, in part, to advance plant gene prediction programs that are currently a limiting factor in annotating genomic sequence data and thus will facilitate the exploitation of the ever increasing quantity of raw genomic data accumulating for plants. Additionally, the database was

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Соловйов, В. М., та В. В. Соловйова. Моделювання мультиплексних мереж. Видавець Ткачук О.В., 2016. http://dx.doi.org/10.31812/0564/1253.

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From the standpoint of interdisciplinary self-organization theories and synergetics analyzes current approaches to modeling socio-economic systems. It is shown that the complex network paradigm is the foundation on which to build predictive models of complex systems. We consider two algorithms to transform time series or a set of time series to the network: recurrent and graph visibility. For the received network designed dynamic spectral, topological and multiplex measures of complexity. For example, the daily values the stock indices show that most of the complexity measures behaving in a ch

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Arhin, Stephen, Babin Manandhar, Hamdiat Baba Adam, and Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.1943.

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Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automati

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Chen, Chong. Ternary alloy material prediction using genetic algorithm and cluster expansion. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1334533.

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Mahajan, Aprajit, Shekhar Mittal, Ofir Reich, and Taha Barwahwala. Using Machine Learning to Catch Bogus Firms. Institute of Development Studies, 2024. http://dx.doi.org/10.19088/ictd.2024.050.

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We investigate the use of a machine learning algorithm to identify non-existent(fraudulent) firms that are used for tax evasion. Using a rich dataset of tax returns in an Indian state over several years, we train a machine learning-based model to predict fraudulent firms. We then use the model predictions to carry out field inspections of firms identified as suspicious by the machine learning tool. We find that the machine learning model is accurate in both simulated and field settings in identifying non-existent firms. Withholding a randomly selected group of firms from inspection, we estimat

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Sikora, Jerome P., and Nathan B. Klontz. Seaway Load Prediction Algorithms for High-Speed Hull Forms. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada425380.

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