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Journal articles on the topic 'Prediction models'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Ansah, Kwabena, Ismail Wafaa Denwar, and Justice Kwame Appati. "Intelligent Models for Stock Price Prediction." Journal of Information Technology Research 15, no. 1 (2022): 1–17. http://dx.doi.org/10.4018/jitr.298616.

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Prediction of the stock price is a crucial task as predicting it may lead to profits. Stock price prediction is a challenge owing to non-stationary and chaotic data. Thus, the projection becomes challenging among the investors and shareholders to invest the money to make profits. This paper is a review of stock price prediction, focusing on metrics, models, and datasets. It presents a detailed review of 30 research papers suggesting the methodologies, such as Support Vector Machine Random Forest, Linear Regression, Recursive Neural Network, and Long Short-Term Movement based on the stock price

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Fang, Yiheng. "Prediction of the Ammonia Nitrogen Content with Improved Grey Model by Markov Chain." Highlights in Science, Engineering and Technology 88 (March 29, 2024): 156–61. http://dx.doi.org/10.54097/zee1cd17.

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Water pollution prediction plays a crucial role in environmental protection and sustainable development. This study proposes an innovative approach to enhance the accuracy of water pollution prediction by combining the grey prediction model (GM) with Markov chain analysis. This research focuses on predicting the concentration of ammonia nitrogen (NH3-N) in Dongting Lake, a significant water body. Grey prediction models (GM) are utilized to forecast NH3-N content, addressing the challenge posed by incomplete or insufficient data. However, due to the dynamic nature of water quality indicators, G

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Siemens, Angela, Spencer J. Anderson, S. Rod Rassekh, Colin J. D. Ross, and Bruce C. Carleton. "A Systematic Review of Polygenic Models for Predicting Drug Outcomes." Journal of Personalized Medicine 12, no. 9 (2022): 1394. http://dx.doi.org/10.3390/jpm12091394.

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Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understand how polygenic models incorporating pharmacogenetic variants are being used in the prediction of drug outcomes. A systematic review was conducted with the aim of gaining insights into the methods used to construct polygenic models, as well as their performance in drug outcome prediction. The searc

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Ben Shoham, Ofir, and Nadav Rappoport. "CPLLM: Clinical prediction with large language models." PLOS Digital Health 3, no. 12 (2024): e0000680. https://doi.org/10.1371/journal.pdig.0000680.

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We present Clinical Prediction with Large Language Models (CPLLM), a method that involves fine-tuning a pre-trained Large Language Model (LLM) for predicting clinical disease and readmission. We utilized quantization and fine-tuned the LLM using prompts. For diagnostic predictions, we predicted whether patients would be diagnosed with a target disease during their next visit or in the subsequent diagnosis, leveraging their historical medical records. We compared our results to various baselines, including Retain and Med-BERT, the latter of which is the current state-of-the-art model for diseas

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Ikenna, Ukabuiro, and Stella Agomah. "Prediction Models for Forex Data Exchange System." Prediction Models for Forex Data Exchange System 8, no. 12 (2024): 4. https://doi.org/10.5281/zenodo.10453255.

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Foreign exchange prediction is of important interest to investors and individual traders in financial industries in other to maximize profits and reduces  losses. However owing to some factors and the non- linearity of the FX markets especially in a developing  economy like Nigeria, generating suitable, accurate and appropriate FX predictions becomes difficult for the traders of the market. This study utilized models that include various machine learning algorithm over a trend analysis and pattern of its prediction. The model results on the currency pair of United States(USD) over Ni

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Busari, Ibrahim, Debabrata Sahoo, R. Daren Harmel, and Brian E. Haggard. "A Review of Machine Learning Models for Harmful Algal Bloom Monitoring in Freshwater Systems." Journal of Natural Resources and Agricultural Ecosystems 1, no. 2 (2023): 63–76. http://dx.doi.org/10.13031/jnrae.15647.

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Highlights Machine Learning (ML) models are identified, reviewed, and analyzed for HAB predictions. Data preprocessing is vital for efficient ML model development. ML models for toxin production and monitoring are limited. Abstract. Harmful algal blooms (HABs) are detrimental to livestock, humans, pets, the environment, and the global economy, which calls for a robust approach to their management. While process-based models can inform practitioners about HAB enabling conditions, they have inherent limitations in accurately predicting harmful algal blooms. To address these limitations, Machine

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Afshartous, David, and Jan de Leeuw. "Prediction in Multilevel Models." Journal of Educational and Behavioral Statistics 30, no. 2 (2005): 109–39. http://dx.doi.org/10.3102/10769986030002109.

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Multilevel modeling is an increasingly popular technique for analyzing hierarchical data. This article addresses the problem of predicting a future observable y*j in thej th group of a hierarchical data set. Three prediction rules are considered and several analytical results on the relative performance of these prediction rules are demonstrated. In addition, the prediction rules are assessed by means of a Monte Carlo study that extensively covers both the sample size and parameter space. Specifically, the sample size space concerns the various combinations of Level 1 (individual) and Level 2

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Kappen, Teus H., and Linda M. Peelen. "Prediction models." Current Opinion in Anaesthesiology 29, no. 6 (2016): 717–26. http://dx.doi.org/10.1097/aco.0000000000000386.

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Lekea, Angella, and Wynand J. vdM Steyn. "Performance of Pavement Temperature Prediction Models." Applied Sciences 13, no. 7 (2023): 4164. http://dx.doi.org/10.3390/app13074164.

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Appropriate asphalt binder selection is dependent on the correct determination of maximum and minimum pavement temperatures. Temperature prediction models have been developed to determine pavement design temperatures. Accordingly, accurate temperature prediction is necessary to ensure the correct design of climate-resilient pavements and for suitable pavement overlay design. Research has shown that the complexity of the model, input variables, geographical location among others affect the accuracy of temperature prediction models. Calibration has also proved to improve the accuracy of the pred

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10

Geweke, John, and Gianni Amisano. "Prediction with Misspecified Models." American Economic Review 102, no. 3 (2012): 482–86. http://dx.doi.org/10.1257/aer.102.3.482.

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The assumption that one of a set of prediction models is a literal description of reality formally underlies many formal econometric methods, including Bayesian model averaging and most approaches to model selection. Prediction pooling does not invoke this assumption and leads to predictions that improve on those based on Bayesian model averaging, as assessed by the log predictive score. The paper shows that the improvement is substantial using a pool consisting of a dynamic stochastic general equilibrium model, a vector autoregression, and a dynamic factor model, in conjunction with standard

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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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Chauhan, Kushal, Rishabh Tiwari, Jan Freyberg, Pradeep Shenoy, and Krishnamurthy Dvijotham. "Interactive Concept Bottleneck Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 5 (2023): 5948–55. http://dx.doi.org/10.1609/aaai.v37i5.25736.

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Concept bottleneck models (CBMs) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and then predict the final label based on the concept label predictions. We extend CBMs to interactive prediction settings where the model can query a human collaborator for the label to some concepts. We develop an interaction policy that, at prediction time, chooses which concepts to request a label for so as to maximally improve the final prediction. We demonstrate that a simple policy combining concept prediction uncertainty and infl

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Hu, Guang, and Yue Tang. "GERPM: A Geographically Weighted Stacking Ensemble Learning-Based Urban Residential Rents Prediction Model." Mathematics 11, no. 14 (2023): 3160. http://dx.doi.org/10.3390/math11143160.

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Accurate prediction of urban residential rents is of great importance for landlords, tenants, and investors. However, existing rents prediction models face challenges in meeting practical demands due to their limited perspectives and inadequate prediction performance. The existing individual prediction models often lack satisfactory accuracy, while ensemble learning models that combine multiple individual models to improve prediction results often overlook the impact of spatial heterogeneity on residential rents. To address these issues, this paper proposes a novel prediction model called GERP

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Masdiantini, Putu Riesty, and Ni Made Sindy Warasniasih. "Laporan Keuangan dan Prediksi Kebangkrutan Perusahaan." Jurnal Ilmiah Akuntansi 5, no. 1 (2020): 196. http://dx.doi.org/10.23887/jia.v5i1.25119.

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This study aims to determine differences in bankruptcy predictions at company’s sub-sector of cosmetics and household listed on the Indonesia Stock Exchange (IDX) using the Altman model, Springate model, Zmijewski model, Taffler model, and Fulmer model, and to determine the bankruptcy prediction model that is the most accurate of the five bankruptcy prediction models. This study uses secondary data in the form of company financial statements for the period 2014-2018. Data analysis techniques in this study used the Kruskal-Wallis test. The results showed there were differences in bankruptcy pre

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Raghuram, Shree R., and Bhawna Rathi Dr. "Prediction Models and Analysis for Diabetes Prediction." Journal Of Scientific Research And Technology (JSRT) 1, no. 2 (2023): 101–15. https://doi.org/10.5281/zenodo.7980387.

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Domain knowledge is an important part of data science. Data science requires domain expertise, which is the familiarity with and comprehension of the context in which data science is used. Acquiring domain-specific knowledge entails familiarity with important ideas, terms, and procedures. The ability to read and understand the data being worked with is essential for data scientists in order to create relevant and accurate models and analyses. Data science is being utilized across many different sectors, including finance, military, telecommunications, healthcare, and many more, all of which pl

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Rejovitzky, Elisha, and Eli Altus. "On single damage variable models for fatigue." International Journal of Damage Mechanics 22, no. 2 (2012): 268–84. http://dx.doi.org/10.1177/1056789512443902.

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This study focuses on an analytical investigation of the common characteristics of fatigue models based on a single damage variable. The general single damage variable constitutive equation is used to extract several fundamental properties. It is shown that at constant amplitude loads, damage evolution results are sufficient for predicting fatigue life under any load history. Two-level fatigue envelopes constitute an indirect measure of the damage evolution and form an alternative basis for life prediction. In addition, high-to-low and low-to-high envelopes are anti-symmetrical with respect to

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17

Lu, Zenan. "Comparison of stock price prediction models for linear models, random forest and LSTM." Applied and Computational Engineering 54, no. 1 (2024): 226–33. http://dx.doi.org/10.54254/2755-2721/54/20241598.

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With the rapid development of financial markets, accurate stock price prediction is significant to investors and financial institutions. Many researchers proposed stock price prediction models, including linear models, random forests, and LSTMs. However, few studies have comprehensively compared the three models. This study aims to fill this gap by analysing the forecasting effectiveness of different models through empirical studies. This research is to explore the application of linear models, random forests, and LSTM models in predicting stock prices and analyse and compare the principles, a

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Merrill, Zachary, Subashan Perera, and Rakié Cham. "Torso Segment Parameter Prediction in Working Adults." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (2018): 1257–61. http://dx.doi.org/10.1177/1541931218621289.

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Body segment parameters (BSPs) such as segment mass, center of mass, and radius of gyration are used as inputs in static and dynamic ergonomic and biomechanical models used to predict joint and muscle forces, and related risks of musculoskeletal injury. Because these models are sensitive to BSP values, accurate and representative parameters are necessary for injury risk prediction. While previous studies have determined segment parameters in the general population, as well as the impact of age and obesity levels on these parameters, estimated errors in the prediction of BSPs can be as large as

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19

Salehi, Mahdi, Mahmoud Lari Dashtbayaz, and Masomeh Heydari. "Audit fees prediction using fuzzy models." Problems and Perspectives in Management 14, no. 2 (2016): 104–17. http://dx.doi.org/10.21511/ppm.14(2).2016.11.

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The current study aims to predict the optimal amount of independent audit fees based on the factors influencing audit fees. To identify the factors influencing audit fees, the stakeholders of 30 auditing firms, members of the Iranian Association of Certified Public Accountants in Tehran selected randomly, were interviewed. Finally, the linear programming model for audit fees and its determinants is defined and sum of squared error is used to solve the function with minimum. Also, given that the data are quantitative and comparative and normally distributed, Pearson’s correlation coefficient is

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Jeong, Jiseok, and Changwan Kim. "Comparison of Machine Learning Approaches for Medium-to-Long-Term Financial Distress Predictions in the Construction Industry." Buildings 12, no. 10 (2022): 1759. http://dx.doi.org/10.3390/buildings12101759.

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A method for predicting the financial status of construction companies after a medium-to-long-term period can help stakeholders in large construction projects make decisions to select an appropriate company for the project. This study compares the performances of various prediction models. It proposes an appropriate model for predicting the financial distress of construction companies considering three, five, and seven years ahead of the prediction point. To establish the prediction model, a financial ratio was selected, which was adopted in existing studies on medium-to-long-term predictions

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Archer, Graeme, Michael Balls, Leon H. Bruner, et al. "The Validation of Toxicological Prediction Models." Alternatives to Laboratory Animals 25, no. 5 (1997): 505–16. http://dx.doi.org/10.1177/026119299702500507.

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An alternative method is shown to consist of two parts: the test system itself; and a prediction model for converting in vitro endpoints into predictions of in vivo toxicity. For the alternative method to be relevant and reliable, it is important that its prediction model component is of high predictive power and is sufficiently robust against sources of data variability. In other words, the prediction model must be subjected to criticism, leading successful models to the state of confirmation. It is shown that there are certain circumstances in which a new prediction model may be introduced w

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Hier Majumder, C. A., E. Bélanger, S. DeRosier, D. A. Yuen, and A. P. Vincent. "Data assimilation for plume models." Nonlinear Processes in Geophysics 12, no. 2 (2005): 257–67. http://dx.doi.org/10.5194/npg-12-257-2005.

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Abstract. We use a four-dimensional variational data assimilation (4D-VAR) algorithm to observe the growth of 2-D plumes from a point heat source. In order to test the predictability of the 4D-VAR technique for 2-D plumes, we perturb the initial conditions and compare the resulting predictions to the predictions given by a direct numerical simulation (DNS) without any 4D-VAR correction. We have studied plumes in fluids with Rayleigh numbers between 106 and 107 and Prandtl numbers between 0.7 and 70, and we find the quality of the prediction to have a definite dependence on both the Rayleigh an

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Je-Gal, Hong, Young-Seo Park, Seong-Ho Park, et al. "Time-Series Explanatory Fault Prediction Framework for Marine Main Engine Using Explainable Artificial Intelligence." Journal of Marine Science and Engineering 12, no. 8 (2024): 1296. http://dx.doi.org/10.3390/jmse12081296.

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As engine monitoring data has become more complex with an increasing number of sensors, fault prediction based on artificial intelligence (AI) has emerged. Existing fault prediction models using AI significantly improve the accuracy of predictions by effectively handling such complex data, but at the same time, the problem arises that the AI-based models cannot explain the rationale of their predictions to users. To address this issue, we propose a time-series explanatory fault prediction framework to provide an explainability even when using AI-based fault prediction models. It consists of a

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Karpac, Dusan, and Viera Bartosova. "The verification of prediction and classification ability of selected Slovak prediction models and their emplacement in forecasts of financial health of a company in aspect of globalization." SHS Web of Conferences 74 (2020): 06010. http://dx.doi.org/10.1051/shsconf/20207406010.

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Predicting financial health of a company is in this global world necessary for each business entity, especially for the international ones, as it´s very important to know financial stability. Forecasting business failure is a worldwide known term, in a global notion, and there is a lot of prediction models constructed to compute financial health of a company and, by that, state whether a company inclines to financial boom or bankruptcy. Globalized prediction models compute financial health of companies, but the vast majority of models predicting business failure are constructed solely for the

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Kim, Hyeongjun, Hoon Cho, and Doojin Ryu. "Corporate Default Predictions Using Machine Learning: Literature Review." Sustainability 12, no. 16 (2020): 6325. http://dx.doi.org/10.3390/su12166325.

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Corporate default predictions play an essential role in each sector of the economy, as highlighted by the global financial crisis and the increase in credit risk. This study reviews the corporate default prediction literature from the perspectives of financial engineering and machine learning. We define three generations of statistical models: discriminant analyses, binary response models, and hazard models. In addition, we introduce three representative machine learning methodologies: support vector machines, decision trees, and artificial neural network algorithms. For both the statistical m

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Mujahidin, Irfan, and Fikri Arif Rakhman. "Application of Optimization Algorithm to Machine Learning Model for Solar Panel Output Power Prediction: A Review." Jurnal Informatika: Jurnal Pengembangan IT 9, no. 2 (2024): 180–87. http://dx.doi.org/10.30591/jpit.v9i2.7051.

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Solar panels have become a popular source of renewable energy due to their sustainability and environmental friendliness. Accurate predictions of solar panel output are crucial for various applications, such as energy system optimization, power grid management, and economic planning. Many important factors pose challenges in predicting the output of solar panels, such as weather conditions that can change at any time, geographical factors, data quality, and the duration of data collection. Machine learning (ML) models show promising performance in this prediction; there are many types of machi

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Lin, Fucheng, Longfei Xie, Yuanshuo Hao, Zheng Miao, and Lihu Dong. "Comparison of Modeling Approaches for the Height–Diameter Relationship: An Example with Planted Mongolian Pine (Pinus sylvestris var. mongolica) Trees in Northeast China." Forests 13, no. 8 (2022): 1168. http://dx.doi.org/10.3390/f13081168.

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In the process of modeling height–diameter models for Mongolian pine (Pinus sylvestris var. mongolica), the fitting abilities of six models were compared: (1) a basic model with only diameter at breast height (D) as a predictor (BM); (2) a plot-level basic mixed-effects model (BMM); (3) quantile regression with nine quantiles based on BM (BQR); (4) a generalized model with stand or competition covariates (GM); (5) a plot-level generalized mixed-effects model (GMM); and (6) quantile regression with nine quantiles based on GM (GQR). The prediction bias of the developed models was assessed in cas

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Lema, Guillermo. "Risk Prediction Models." Mayo Clinic Proceedings 96, no. 4 (2021): 1095. http://dx.doi.org/10.1016/j.mayocp.2021.02.001.

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Hadayeghi, Alireza, Amer S. Shalaby, and Bhagwant N. Persaud. "Safety Prediction Models." Transportation Research Record: Journal of the Transportation Research Board 2019, no. 1 (2007): 225–36. http://dx.doi.org/10.3141/2019-27.

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DETSKY, ALLAN S. "Clinical prediction models." Acta Anaesthesiologica Scandinavica 39 (June 1995): 134–35. http://dx.doi.org/10.1111/j.1399-6576.1995.tb04295.x.

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31

Steyerberg, Ewout W., and Hester F. Lingsma. "Validating prediction models." BMJ 336, no. 7648 (2008): 789.2–789. http://dx.doi.org/10.1136/bmj.39542.610000.3a.

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Liao, Lawrence, and Daniel B. Mark. "Clinical prediction models." Journal of the American College of Cardiology 42, no. 5 (2003): 851–53. http://dx.doi.org/10.1016/s0735-1097(03)00836-2.

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Tripepi, G., G. Heinze, K. J. Jager, V. S. Stel, F. W. Dekker, and C. Zoccali. "Risk prediction models." Nephrology Dialysis Transplantation 28, no. 8 (2013): 1975–80. http://dx.doi.org/10.1093/ndt/gft095.

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Ranstam, J., J. A. Cook, and G. S. Collins. "Clinical prediction models." British Journal of Surgery 103, no. 13 (2016): 1886. http://dx.doi.org/10.1002/bjs.10242.

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Mijderwijk, Hendrik-Jan, Thomas Beez, Daniel Hänggi, and Daan Nieboer. "Clinical prediction models." Child's Nervous System 36, no. 5 (2020): 895–97. http://dx.doi.org/10.1007/s00381-020-04577-8.

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Möst, Lisa, Matthias Schmid, Florian Faschingbauer, and Torsten Hothorn. "Predicting birth weight with conditionally linear transformation models." Statistical Methods in Medical Research 25, no. 6 (2016): 2781–810. http://dx.doi.org/10.1177/0962280214532745.

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Low and high birth weight (BW) are important risk factors for neonatal morbidity and mortality. Gynecologists must therefore accurately predict BW before delivery. Most prediction formulas for BW are based on prenatal ultrasound measurements carried out within one week prior to birth. Although successfully used in clinical practice, these formulas focus on point predictions of BW but do not systematically quantify uncertainty of the predictions, i.e. they result in estimates of the conditional mean of BW but do not deliver prediction intervals. To overcome this problem, we introduce conditiona

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Vidyadevi, G.Biradar. "A Study on Prediction Models." Journal of Advancement in Parallel Computing 5, no. 1 (2022): 1–14. https://doi.org/10.5281/zenodo.6471760.

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<em>Defect-prone code development may be improved by focusing on recent research on software defect prediction. This improves software quality and saves resources. Many approaches, datasets, and frameworks for predicting software defects have been published, but it is difficult to get an overall sense of where defect prediction research is now at. In this research article, we have tried to understand the relationships between various variables which are important for IT SME’s. The study is carried out with the help of a well-structured questionnaire using IBM SPSS tool for data analysis

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Carballo, Alba, María Durbán, and Dae-Jin Lee. "Out-of-Sample Prediction in Multidimensional P-Spline Models." Mathematics 9, no. 15 (2021): 1761. http://dx.doi.org/10.3390/math9151761.

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The prediction of out-of-sample values is an interesting problem in any regression model. In the context of penalized smoothing using a mixed-model reparameterization, a general framework has been proposed for predicting in additive models but without interaction terms. The aim of this paper is to generalize this work, extending the methodology proposed in the multidimensional case, to models that include interaction terms, i.e., when prediction is carried out in a multidimensional setting. Our method fits the data, predicts new observations at the same time, and uses constraints to ensure a c

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Xiao, Yang. "Individual Stock Price Prediction Using Stacking Method." Advances in Economics, Management and Political Sciences 99, no. 1 (2024): 17–22. http://dx.doi.org/10.54254/2754-1169/99/2024ox0212.

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This paper explores the application of stacking in machine learning to predict the price of a single stock. The complexity of financial markets and the high noise in data make stock price prediction a challenging task. To improve prediction accuracy, this paper combines multiple machine learning models, including linear regression, decision trees, and random forests, using stacking to integrate the predictions of these base models. Experimental results indicate that the stacking model performs exceptionally well in predicting the stock price of Apple Inc. (AAPL), significantly outperforming in

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Fang, Lili. "Prediction of Rainfall-Induced Landslide-Mudslide Hazard Chain Using Coupled TRIGRS and RAMMS Models." International Journal of Information System Modeling and Design 16, no. 1 (2025): 1–23. https://doi.org/10.4018/ijismd.370964.

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This study develops an effective prediction method for rainfall-induced landslide-mudslide disaster chains by integrating the TRIGRS landslide prediction model with the RAMMS mudslide simulation model. Using a mountainous area prone to such disasters as a case study, it first applies the TRIGRS model to assess slope stability under rainfall conditions to identify potential landslide hazards. The identified landslide predictions serve as input for the RAMMS model to simulate the subsequent mudslide processes, predicting their paths, velocities, impact forces, and accumulation areas. Through ite

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Dhakal, Rabin, Ashish Sedai, Suhas Pol, Siva Parameswaran, Ali Nejat, and Hanna Moussa. "A Novel Hybrid Method for Short-Term Wind Speed Prediction Based on Wind Probability Distribution Function and Machine Learning Models." Applied Sciences 12, no. 18 (2022): 9038. http://dx.doi.org/10.3390/app12189038.

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The need to deliver accurate predictions of renewable energy generation has long been recognized by stakeholders in the field and has propelled recent improvements in more precise wind speed prediction (WSP) methods. Models such as Weibull-probability-density-based WSP (WEB), Rayleigh-probability-density-based WSP (RYM), autoregressive integrated moving average (ARIMA), Kalman filter and support vector machines (SVR), artificial neural network (ANN), and hybrid models have been used for accurate prediction of wind speed with various forecast horizons. This study intends to incorporate all thes

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Wu, Peng, and Yongze Song. "Land Use Quantile Regression Modeling of Fine Particulate Matter in Australia." Remote Sensing 14, no. 6 (2022): 1370. http://dx.doi.org/10.3390/rs14061370.

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Small data samples are still a critical challenge for spatial predictions. Land use regression (LUR) is a widely used model for spatial predictions with observations at a limited number of locations. Studies have demonstrated that LUR models can overcome the limitation exhibited by other spatial prediction models which usually require greater spatial densities of observations. However, the prediction accuracy and robustness of LUR models still need to be improved due to the linear regression within the LUR model. To improve LUR models, this study develops a land use quantile regression (LUQR)

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Sandhu, Karansher Singh, Meriem Aoun, Craig F. Morris, and Arron H. Carter. "Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models." Biology 10, no. 7 (2021): 689. http://dx.doi.org/10.3390/biology10070689.

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Breeding for grain yield, biotic and abiotic stress resistance, and end-use quality are important goals of wheat breeding programs. Screening for end-use quality traits is usually secondary to grain yield due to high labor needs, cost of testing, and large seed requirements for phenotyping. Genomic selection provides an alternative to predict performance using genome-wide markers under forward and across location predictions, where a previous year’s dataset can be used to build the models. Due to large datasets in breeding programs, we explored the potential of the machine and deep learning mo

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Srinath, M. "Vehicular Traffic Flow Prediction Model Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (2023): 109–12. http://dx.doi.org/10.22214/ijraset.2023.54576.

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Abstract: Efficient traffic flow prediction is crucial for effective traffic management and congestion reduction in urban areas. However, traditional statistical models often struggle to accurately capture the intricate dynamics of vehicular traffic flow, particularly under dynamic conditions. In this research project, we propose a novel approach that leverages deep learning techniques, specifically Long Short-Term Memory (LSTM) neural networks, AdaBoost, and gradient descent, to enhance the accuracy of traffic flow predictions .By harnessing historical traffic data, our model generates precis

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Khaokaew, Yonchanok, Hao Xue, and Flora D. Salim. "MAPLE." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 8, no. 1 (2024): 1–25. http://dx.doi.org/10.1145/3643514.

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In recent years, predicting mobile app usage has become increasingly important for areas like app recommendation, user behaviour analysis, and mobile resource management. Existing models, however, struggle with the heterogeneous nature of contextual data and the user cold start problem. This study introduces a novel prediction model, Mobile App Prediction Leveraging Large Language Model Embeddings (MAPLE), which employs Large Language Models (LLMs) and installed app similarity to overcome these challenges. MAPLE utilises the power of LLMs to process contextual data and discern intricate relati

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Sudriyanto, Sudriyanto, Fatimatus Syahro, and Novi Fitriani. "PERBANDINGAN PERFORMA MODEL MACHINE LEARNING SUPPORT VECTOR MACHINE, NEURAL NETWORK, DAN K-NEAREST NEIGHBORS DALAM PREDIKSI HARGA SAHAM." Jurnal Advanced Research Informatika 2, no. 1 (2023): 13–21. http://dx.doi.org/10.24929/jars.v2i1.2983.

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This study aims to analyze the performance of three prediction models, namely K-Nearest Neighbors (K-NN), Neural Network (NN), and Support Vector Machine (SVM), in predicting the stock price of PT Astra International Tbk (ASII.JK). The research encompasses the initial stages through evaluation using optimal parameters for these three algorithms. The research findings reveal that the K-NN prediction model has the lowest Root Mean Square Error (RMSE) value, with a value of 0.037, indicating the most accurate prediction compared to the other models. Despite the NN model having an RMSE of 0.048, w

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Siagian, Ruben Cornelius, Lulut Alfaris, Ghulab Nabi Ahmad, et al. "Relationship between Solar Flux and Sunspot Activity Using Several Regression Models." JURNAL ILMU FISIKA | UNIVERSITAS ANDALAS 15, no. 2 (2023): 146–65. http://dx.doi.org/10.25077/jif.15.2.146-165.2023.

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This study examines the correlation and prediction between sunspots and solar flux, two closely related factors associated with solar activity, covering the period from 2005 to 2022. The study utilizes a combination of linear regression analysis and the ARIMA prediction method to analyze the relationship between these factors and forecast their values. The analysis results reveal a significant positive correlation between sunspots and solar flux. Additionally, the ARIMA prediction method suggests that the SARIMA model can effectively forecast the values of both sunspots and solar flux for a 12

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Tao, Jiahui, Yicheng Gu, Xin Yin, Junlai Chen, Tianqi Ao, and Jianyun Zhang. "Coupling SWAT and Transformer Models for Enhanced Monthly Streamflow Prediction." Sustainability 16, no. 19 (2024): 8699. http://dx.doi.org/10.3390/su16198699.

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The establishment of an accurate and reliable predictive model is essential for water resources planning and management. Standalone models, such as physics-based hydrological models or data-driven hydrological models, have their specific applications, strengths, and limitations. In this study, a hybrid model (namely SWAT-Transformer) was developed by coupling the physics-based Soil and Water Assessment Tool (SWAT) with the data-driven Transformer to enhance monthly streamflow prediction accuracy. SWAT is first constructed and calibrated, and then its outputs are used as part of the inputs to T

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Gauthier, Gilles, Guillaume Péron, Jean-Dominique Lebreton, Patrick Grenier, and Louise van Oudenhove. "Partitioning prediction uncertainty in climate-dependent population models." Proceedings of the Royal Society B: Biological Sciences 283, no. 1845 (2016): 20162353. http://dx.doi.org/10.1098/rspb.2016.2353.

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The science of complex systems is increasingly asked to forecast the consequences of climate change. As a result, scientists are now engaged in making predictions about an uncertain future, which entails the efficient communication of this uncertainty. Here we show the benefits of hierarchically decomposing the uncertainty in predicted changes in animal population size into its components due to structural uncertainty in climate scenarios (greenhouse gas emissions and global circulation models), structural uncertainty in the demographic model, climatic stochasticity, environmental stochasticit

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Lei, Xiangdong, Changhui Peng, Haiyan Wang, and Xiaolu Zhou. "Individual height–diameter models for young black spruce (Picea mariana) and jack pine (Pinus banksiana) plantations in New Brunswick, Canada." Forestry Chronicle 85, no. 1 (2009): 43–56. http://dx.doi.org/10.5558/tfc85043-1.

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Historically, height–diameter models have mainly been developed for mature trees; consequently, few height–diameter models have been calibrated for young forest stands. In order to develop equations predicting the height of trees with small diameters, 46 individual height–diameter models were fitted and tested in young black spruce (Picea mariana) and jack pine (Pinus banksiana) plantations between the ages of 4 to 8 years, measured from 182 plots in New Brunswick, Canada. The models were divided into 2 groups: a diameter group and a second group applying both diameter and additional stand- or

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