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Journal articles on the topic 'Prediction Accuracy'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Wadi, Faska Aris Y. K., Putu Sugiartawan, Ni Nengah Dita Adriani, and Ni Nengah Dita Adriani. "Analisa Prediksi Time Series Jumlah Kasus Covid-19 Dengan Metode BPNN Di Bali." Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) 4, no. 1 (2022): 24–33. http://dx.doi.org/10.33173/jsikti.124.

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The COVID-19 pandemic has not yet subsided. This epidemic has spread to almost all countries in the world. In Indonesia, especially in the province of Bali, which experienced a large number of additional positive cases, recoveries and deaths from COVID-19, an analysis was carried out. The purpose of this analysis is to be able to obtain accuracy in predicting the addition of COVID-19 cases, recoveries and deaths in the province of Bali, predictions are made using the covid-19 time series data used in making predictions. what was done obtained the best and not good prediction accuracy, predicti

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Jierula, Alipujiang, Shuhong Wang, Tae-Min OH, and Pengyu Wang. "Study on Accuracy Metrics for Evaluating the Predictions of Damage Locations in Deep Piles Using Artificial Neural Networks with Acoustic Emission Data." Applied Sciences 11, no. 5 (2021): 2314. http://dx.doi.org/10.3390/app11052314.

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Accuracy metrics have been widely used for the evaluation of predictions in machine learning. However, the selection of an appropriate accuracy metric for the evaluation of a specific prediction has not yet been specified. In this study, seven of the most used accuracy metrics in machine learning were summarized, and both their advantages and disadvantages were studied. To achieve this, the acoustic emission data of damage locations were collected from a pile hit test. A backpropagation artificial neural network prediction model for damage locations was trained with acoustic emission data usin

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Cheng, Dehe, Jinlong Li, Shuwei Guo, et al. "Genomic Prediction for Germplasm Improvement Through Inter-Heterotic-Group Line Crossing in Maize." International Journal of Molecular Sciences 26, no. 6 (2025): 2662. https://doi.org/10.3390/ijms26062662.

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Germplasm improvement is essential for maize breeding. Currently, intra-heterotic-group crossing is the major method for germplasm improvement, while inter-heterotic-group crossing is also used in breeding but not in a systematic way. In this study, five inbred lines from four heterotic groups were used to develop a connected segregating population through inter-heterotic-group line crossing (CSPIC), which comprised 5 subpopulations with 535 doubled haploid (DH) lines and 15 related test-cross populations including 1568 hybrids. Significant genetic variation was observed in most subpopulations

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Malkin, Zinovy. "On Estimate of Real Accuracy of EOP Prediction." International Astronomical Union Colloquium 178 (2000): 505–10. http://dx.doi.org/10.1017/s0252921100061674.

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AbstractTo estimate the real accuracy of EOP predictions, real-time predictions made by the IERS Subbureau for Rapid Service and Prediction (USNO) and at the IAA EOP Service are analyzed. Methods of estimating prediction accuracy are discussed.

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Zhang, Chenglong, and Hyunchul Ahn. "E-Learning at-Risk Group Prediction Considering the Semester and Realistic Factors." Education Sciences 13, no. 11 (2023): 1130. http://dx.doi.org/10.3390/educsci13111130.

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This study focused on predicting at-risk groups of students at the Open University (OU), a UK university that offers distance-learning courses and adult education. The research was conducted by drawing on publicly available data provided by the Open University for the year 2013–2014. The semester’s time series was considered, and data from previous semesters were used to predict the current semester’s results. Each course was predicted separately so that the research reflected reality as closely as possible. Three different methods for selecting training data were listed. Since the at-risk pre

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Harahap, Rahma Sari, Iskandar Muda, and Rina Br Bukit. "Analisis penggunaan metode Altman Z-Score dan Springate untuk mengetahui potensi terjadinya Financial Distress pada perusahaan manufaktur sektor industri dasar dan kimia Sub Sektor semen yang terdaftar di Bursa Efek Indonesia 2000-2020." Owner 6, no. 4 (2022): 4315–25. http://dx.doi.org/10.33395/owner.v6i4.1576.

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

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Abanades, Brennan, Guy Georges, Alexander Bujotzek, and Charlotte M. Deane. "ABlooper: fast accurate antibody CDR loop structure prediction with accuracy estimation." Bioinformatics 38, no. 7 (2022): 1877–80. http://dx.doi.org/10.1093/bioinformatics/btac016.

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Abstract Motivation Antibodies are a key component of the immune system and have been extensively used as biotherapeutics. Accurate knowledge of their structure is central to understanding their antigen-binding function. The key area for antigen binding and the main area of structural variation in antibodies are concentrated in the six complementarity determining regions (CDRs), with the most important for binding and most variable being the CDR-H3 loop. The sequence and structural variability of CDR-H3 make it particularly challenging to model. Recently deep learning methods have offered a st

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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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Wu, Menglong, Yicheng Ye, Nanyan Hu, Qihu Wang, Huimin Jiang, and Wen Li. "EMD-GM-ARMA Model for Mining Safety Production Situation Prediction." Complexity 2020 (June 8, 2020): 1–14. http://dx.doi.org/10.1155/2020/1341047.

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In order to improve the prediction accuracy of mining safety production situation and remove the difficulty of model selection for nonstationary time series, a grey (GM) autoregressive moving average (ARMA) model based on the empirical mode decomposition (EMD) is proposed. First of all, according to the nonstationary characteristics of the mining safety accident time series, nonstationary original time series were decomposed into high- and low-frequency signals using the EMD algorithm, which represents the overall trend and random disturbances, respectively. Subsequently, the GM model was used

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Francisco, Micanaldo Ernesto, Thaddeus M. Carvajal, and Kozo Watanabe. "Hybrid Machine Learning Approach to Zero-Inflated Data Improves Accuracy of Dengue Prediction." PLOS Neglected Tropical Diseases 18, no. 10 (2024): e0012599. http://dx.doi.org/10.1371/journal.pntd.0012599.

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Background Spatiotemporal dengue forecasting using machine learning (ML) can contribute to the development of prevention and control strategies for impending dengue outbreaks. However, training data for dengue incidence may be inflated with frequent zero values because of the rarity of cases, which lowers the prediction accuracy. This study aimed to understand the influence of spatiotemporal resolutions of data on the accuracy of dengue incidence prediction using ML models, to understand how the influence of spatiotemporal resolution differs between quantitative and qualitative predictions of

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de Zarzà, I., J. de Curtò, Enrique Hernández-Orallo, and Carlos T. Calafate. "Cascading and Ensemble Techniques in Deep Learning." Electronics 12, no. 15 (2023): 3354. http://dx.doi.org/10.3390/electronics12153354.

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In this study, we explore the integration of cascading and ensemble techniques in Deep Learning (DL) to improve prediction accuracy on diabetes data. The primary approach involves creating multiple Neural Networks (NNs), each predicting the outcome independently, and then feeding these initial predictions into another set of NN. Our exploration starts from an initial preliminary study and extends to various ensemble techniques including bagging, stacking, and finally cascading. The cascading ensemble involves training a second layer of models on the predictions of the first. This cascading str

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Zhuang, Wei, Zhiheng Li, Ying Wang, Qingyu Xi, and Min Xia. "GCN–Informer: A Novel Framework for Mid-Term Photovoltaic Power Forecasting." Applied Sciences 14, no. 5 (2024): 2181. http://dx.doi.org/10.3390/app14052181.

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Predicting photovoltaic (PV) power generation is a crucial task in the field of clean energy. Achieving high-accuracy PV power prediction requires addressing two challenges in current deep learning methods: (1) In photovoltaic power generation prediction, traditional deep learning methods often generate predictions for long sequences one by one, significantly impacting the efficiency of model predictions. As the scale of photovoltaic power stations expands and the demand for predictions increases, this sequential prediction approach may lead to slow prediction speeds, making it difficult to me

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Rasero, Javier, Amy Isabella Sentis, Fang-Cheng Yeh, and Timothy Verstynen. "Integrating across neuroimaging modalities boosts prediction accuracy of cognitive ability." PLOS Computational Biology 17, no. 3 (2021): e1008347. http://dx.doi.org/10.1371/journal.pcbi.1008347.

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Variation in cognitive ability arises from subtle differences in underlying neural architecture. Understanding and predicting individual variability in cognition from the differences in brain networks requires harnessing the unique variance captured by different neuroimaging modalities. Here we adopted a multi-level machine learning approach that combines diffusion, functional, and structural MRI data from the Human Connectome Project (N = 1050) to provide unitary prediction models of various cognitive abilities: global cognitive function, fluid intelligence, crystallized intelligence, impulsi

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Wangeci, Alex, Daniel Adén, Thomas Nikolajsen, Mogens H. Greve, and Maria Knadel. "Combining Laser-Induced Breakdown Spectroscopy and Visible Near-Infrared Spectroscopy for Predicting Soil Organic Carbon and Texture: A Danish National-Scale Study." Sensors 24, no. 14 (2024): 4464. http://dx.doi.org/10.3390/s24144464.

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Laser-induced breakdown spectroscopy (LIBS) and visible near-infrared spectroscopy (vis-NIRS) are spectroscopic techniques that offer promising alternatives to traditional laboratory methods for the rapid and cost-effective determination of soil properties on a large scale. Despite their individual limitations, combining LIBS and vis-NIRS has been shown to enhance the prediction accuracy for the determination of soil properties compared to single-sensor approaches. In this study, we used a comprehensive Danish national-scale soil dataset encompassing mostly sandy soils collected from various l

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Quinsey, Vernon L. "Improving decision accuracy where base rates matter: The prediction of violent recidivism." Behavioral and Brain Sciences 19, no. 1 (1996): 37–38. http://dx.doi.org/10.1017/s0140525x0004139x.

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AbstractBase rates are vital in predicting violent criminal recidivism. However, both lay people given simulated prediction tasks and professionals milking real life predictions appear insensitive to variations in the base rate of violent recidivism. Although there are techniques to help decision makers attend to base rates, increased decision accuracy is better sought in improved actuarial models as opposed to improved clinicians.

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Pinsky, Eugene, and Siddhant Shah. "Estimating the Accuracy of a Bagged Ensemble." Machine Learning and Applications: An International Journal 12, no. 1 (2025): 89–105. https://doi.org/10.5121/mlaij.2025.12106.

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In ensemble machine learning, we combine the decisions of weak learners to derive a decision that is, hopefully, better than the individual ones. The combination of these learners can be aggregated by a majority vote or simple averaging, or it can be more complicated and involve multiple steps such as in boosting. In this paper, we consider the question of predicting the accuracy of an ensemble created with bagging for a given number of weak learners. We achieve a low relative error on our predictions and can make this prediction in a shorter time, as compared to training the ensemble over var

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Arnesen, Sveinung, and Ole Bergfjord. "Prediction markets vs polls – an examination of accuracy for the 2008 and 2012 elections." Journal of Prediction Markets 8, no. 3 (2015): 24–33. http://dx.doi.org/10.5750/jpm.v8i3.981.

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Which technique is more accurate in predicting the outcome of U.S. presidential elections, polls or prediction markets? Several studies on this have been conducted in the past. We use market data and poll numbers, included adjusted version of the poll numbers, to reexamine this question based on the two last American presidential elections, in 2008 and 2012. We find that the market predictions outperformed the polls for these elections, and that adjusting the polls makes them less accurate relative to prediction markets, if anything.

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An, Zhongqi. "Real-Time Football Match Prediction Platform." ITM Web of Conferences 70 (2025): 04003. https://doi.org/10.1051/itmconf/20257004003.

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The integration of real-time data into sports analytics has significantly enhanced the accuracy of football match predictions, which is vital for team management, tactical planning, and commercial applications *such as sports betting. This paper presents a Python-based platform for predicting football match outcomes by collecting and processing real-time data from the SofaScore website. The platform employs machine learning models, including Random Forest, Support Vector Machines (SVM), and Neural Networks, combined with feature engineering techniques, to generate accurate predictions. A user-

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Madsen, Jens Koed. "Goal-line oracles: Exploring accuracy of wisdom of the crowd for football predictions." PLOS ONE 20, no. 1 (2025): e0312487. https://doi.org/10.1371/journal.pone.0312487.

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Football’s inherent volatility and low-scoring nature present unique challenges for predicting outcomes. This study investigates the efficacy of Wisdom of the Crowd in forecasting football match outcomes as well as expected goals (XG) across a Premier League season. Participants predicted team goal counts, which were then compared to actual expected goals (XG) and match results. Results across 760 team predictions reveal that while Wisdom of the Crowd accurately predicts XG on average, it overestimates ’big-6’ teams and underestimates others, hinting at inherent biases. Notably, however, colle

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Ge, Shaojia, Erkki Tomppo, Yrjö Rauste, et al. "Sentinel-1 Time Series for Predicting Growing Stock Volume of Boreal Forest: Multitemporal Analysis and Feature Selection." Remote Sensing 15, no. 14 (2023): 3489. http://dx.doi.org/10.3390/rs15143489.

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Copernicus Sentinel-1 images are widely used for forest mapping and predicting forest growing stock volume (GSV) due to their accessibility. However, certain important aspects related to the use of Sentinel-1 time series have not been thoroughly explored in the literature. These include the impact of image time series length on prediction accuracy, the optimal feature selection approaches, and the best prediction methods. In this study, we conduct an in-depth exploration of the potential of long time series of Sentinel-1 SAR data to predict forest GSV and evaluate the temporal dynamics of the

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Sonali, Sonali, and Sonali Ridhorkar. "HI2NN: Heuristic Intelligence towards Enhancing Rainfall Prediction with Improved Artificial Neural Networks." Fusion: Practice and Applications 19, no. 2 (2025): 288–303. https://doi.org/10.54216/fpa.190221.

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Predicting rainfall proves critical for businesses to organize their water resources, make agricultural choices, and prevent disasters. Therefore, proposed model presents a novel approach, namely Heuristic Intelligence towards Enhancing Rainfall Prediction with Artificial Neural Networks (HI2NN) to enhance rainfall prediction by designing heuristic Intelligence combined with Improved Artificial Neural Networks (IANNs). The proposed HI2NN framework leverages heuristic optimization techniques to fine-tune ANN parameters to improve prediction accuracy. Prediction accuracy is computed through our

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Yolanda, Anggie. "Prediksi Pergerakan Harga Saham Menggunakan Support Vector Machines Di Indonesia." BUDGETING : Journal of Business, Management and Accounting 5, no. 2 (2024): 1175–88. http://dx.doi.org/10.31539/budgeting.v5i2.9829.

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Predicting stock movements is challenging due to their dynamic nature and influence from various factors. One of these factors is the lack of research considering the use of fundamental analysis regarding currency exchange rates and the use of foreign stock index movements related to technical analysis. This research aims to predict stock price movements in Indonesia based on sentiment analysis, technical analysis, and fundamental analysis using Support Vector Machine. The results obtained have an average prediction accuracy rate of 65.33%. The inclusion of currency exchange rates and foreign

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Aiyegbeni, Gifty, Yang Li, Joseph Annan, and Funminiyi Adebayo. "Credit Rating Prediction Using Different Machine Learning Techniques." International Journal of Data Science and Advanced Analytics 5, no. 5 (2023): 219–38. http://dx.doi.org/10.69511/ijdsaa.v5i5.193.

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Credit rating prediction is a crucial task in the banking and financial industry. Financial firms want to identify the likelihood of customers repaying loans or credit. With the advent of machine learning algorithms and big data analytics, it is now possible to automate and improve the accuracy of credit rating prediction. In this research, we aim to develop a machine learning-based approach for customer credit rating prediction. Machine learning algorithms, including decision trees, random forests, support vector machines, and logistic regression, were evaluated and compared in terms of accur

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Sajjadian, Mehri, Raymond W. Lam, Roumen Milev, et al. "Machine learning in the prediction of depression treatment outcomes: a systematic review and meta-analysis." Psychological Medicine 51, no. 16 (2021): 2742–51. http://dx.doi.org/10.1017/s0033291721003871.

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AbstractBackgroundMultiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes.MethodsOf 7732 non-duplicate records identified through literature search, we retained 59 eligible reports and extracted data on sample, treatment, predictors, machine learning method, and treatment outcome pred

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Agarwal, Sanskar, Debolina Das, Affan Hasnuddin Sayed, Rangappa Gari Narayanappa, Sachin Sharma, and Vasana Vijayan. "Enhanced Accuracy of Stock Market Prediction with ANN Algorithm." International Journal of Research Publication and Reviews 5, no. 3 (2024): 3358–62. http://dx.doi.org/10.55248/gengpi.5.0324.0759.

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Šaur, David, and Lukáš Pavlík. "Comparison of accuracy of forecasting methods of convective precipitation." MATEC Web of Conferences 210 (2018): 04035. http://dx.doi.org/10.1051/matecconf/201821004035.

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This article is focused on the comparison of the accuracy of quantitative, numerical, statistical and nowcasting forecasting methods of convective precipitation including three flood events that occurred in the Zlin region in the years 2015 - 2017. Quantitative prediction is applied to the Algorithm of Storm Prediction for outputs “The probability of convective precipitation and The statistical forecast of convective precipitation”. The quantitative prediction of the probability of convective precipitation is primarily compared with the precipitation forecasts calculated by publicly available

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Wilson, Stewart W. "Classifier Fitness Based on Accuracy." Evolutionary Computation 3, no. 2 (1995): 149–75. http://dx.doi.org/10.1162/evco.1995.3.2.149.

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In many classifier systems, the classifier strength parameter serves as a predictor of future payoff and as the classifier's fitness for the genetic algorithm. We investigate a classifier system, XCS, in which each classifier maintains a prediction of expected payoff, but the classifier's fitness is given by a measure of the prediction's accuracy. The system executes the genetic algorithm in niches defined by the match sets, instead of panmictically. These aspects of XCS result in its population tending to form a complete and accurate mapping X × A → P from inputs and actions to payoff predict

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Alpackaya, Irina, Mohammed Hussein Fallah, Nomula Mounika, et al. "Renewable Energy Forecasting using Deep Learning Techniques." E3S Web of Conferences 581 (2024): 01011. http://dx.doi.org/10.1051/e3sconf/202458101011.

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A detailed research on deep learning in renewable energy forecasting shows how sophisticated algorithms may improve prediction accuracy. The research explores deep learning models and finds intriguing aspects that improve predictions. Long Short-Term Memory (LSTM) networks can capture temporal relationships in energy data, making them successful in predicting short-term variations with a prediction accuracy boost of 18.18% over ARIMA. Convolutional Neural Networks (CNNs) capture spatial correlations in huge datasets with up to 13% accuracy. With its capacity to analyze sequential data, Recurre

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Zhou, Jiahao, Wenyu Jiang, Fei Wang, Yuming Qiao, and Qingxiang Meng. "Comparing Accuracy of Wildfire Spread Prediction Models under Different Data Deficiency Conditions." Fire 7, no. 4 (2024): 141. http://dx.doi.org/10.3390/fire7040141.

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Wildfire is one of the most severe natural disasters globally, profoundly affecting natural ecology, economy, and health and safety. Precisely predicting the spread of wildfires has become an important research topic. Current fire spread prediction models depend on inputs from a variety of geographical and environmental variables. However, unlike the ideal conditions simulated in the laboratory, data gaps often occur in real wildfire scenarios, posing challenges to the accuracy and robustness of predictions. It is necessary to explore the extent to which different missing items affect predicti

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Zhang, Zhimei, and Xiaobo Wang. "Fatigue Life Prediction of FRP-Strengthened Reinforced Concrete Beams Based on Soft Computing Techniques." Materials 18, no. 2 (2025): 230. https://doi.org/10.3390/ma18020230.

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This paper establishes fatigue life prediction models using the soft computing method to address insufficient parameter consideration and limited computational accuracy in predicting the fatigue life of fiber-reinforced polymer (FRP) strengthened concrete beams. Five different input forms were proposed by collecting 117 sets of fatigue test data of FRP-strengthened concrete beams from the existing literature and integrating the outcomes from Pearson correlation analysis and significance testing. Using Gene Expression Programming (GEP), the effects of various input configurations on the accurac

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Priambodo, Bagus, Ruci Meiyanti, Samidi Samidi, Gushelmi Gushelmi, Rabiah Abdul Kadir, and Azlina Ahmad. "Integrating Fibonacci Retracement To Improve Accuracy of Time Series Prediction of Gold Prices." Journal of Applied Engineering and Technological Science (JAETS) 6, no. 2 (2025): 1268–79. https://doi.org/10.37385/jaets.v6i2.6073.

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The prediction of gold prices is crucial for investors and policymakers due to its significant impact on global financial markets. Machine learning and deep learning have been used for predicting gold prices on time series data. This study employs MLR, SVM and CNN LSTM with Fibonacci retracement levels to forecast gold prices based on time series data. The experiment results demonstrate that combining Fibonacci retracement with model prediction significantly enhances predictive performance compared to prediction without Fibonacci. The use of Fibonacci levels has resulted in a higher R² score a

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Fauser, Daniel V., and Andreas Gruener. "Corporate Social Irresponsibility and Credit Risk Prediction: A Machine Learning Approach." Credit and Capital Markets – Kredit und Kapital: Volume 53, Issue 4 53, no. 4 (2020): 513–54. http://dx.doi.org/10.3790/ccm.53.4.513.

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This paper examines the prediction accuracy of various machine learning (ML) algorithms for firm credit risk. It marks the first attempt to leverage data on corporate social irresponsibility (CSI) to better predict credit risk in an ML context. Even though the literature on default and credit risk is vast, the potential explanatory power of CSI for firm credit risk prediction remains unexplored. Previous research has shown that CSI may jeopardize firm survival and thus potentially comes into play in predicting credit risk. We find that prediction accuracy varies considerably between algorithms

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Diqi, Mohammad, and Hamzah Hamzah. "Improving Stock Price Prediction Accuracy with StacBi LSTM." JISKA (Jurnal Informatika Sunan Kalijaga) 9, no. 1 (2024): 10–26. http://dx.doi.org/10.14421/jiska.2024.9.1.10-26.

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This research aimed to enhance stock price prediction accuracy using the Stacked Bidirectional Long Short-Term Memory (StacBi LSTM) model. The study addressed the challenge of capturing long-term dependencies and temporal patterns inherent in stock price data. The research objectives were to evaluate the model's performance across different input sequence lengths and identify the optimal length for prediction. Leveraging a dataset from the Indonesian Stock Exchange, the model's predictions were evaluated using key metrics such as RMSE, MAE, MAPE, and R2. Results indicated that the StacBi LSTM

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López de Frutos, Laura, Jorge J. Cebolla, Pilar Irún, Ralf Köhler, and Pilar Giraldo. "Web-Based Bioinformatics Predictors: Recommendations to Assess Lysosomal Cholesterol Trafficking Diseases-Related Genes." Methods of Information in Medicine 58, no. 01 (2019): 050–59. http://dx.doi.org/10.1055/s-0039-1692463.

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Introduction The growing number of genetic variants of unknown significance (VUS) and availability of several in silico prediction tools make the evaluation of potentially deleterious gene variants challenging. Materials and Methods We evaluated several programs and software to determine the one that can predict the impact of genetic variants found in lysosomal storage disorders (LSDs) caused by defects in cholesterol trafficking best. We evaluated the sensitivity, specificity, accuracy, precision, and Matthew's correlation coefficient of the most common software. Results Our findings showed t

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Zhang, Hanlin, Zheng Tang, Haowei Zheng, and Kai Wang. "Research on Multi-scale Photovoltaic Output Prediction Method Based on GAN-Informer." Journal of Physics: Conference Series 2806, no. 1 (2024): 012015. http://dx.doi.org/10.1088/1742-6596/2806/1/012015.

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Abstract The challenge of predicting photovoltaic power (PV) output is addressed in this paper, with a particular focus on enhancing prediction accuracy. A novel multi-scale prediction method for PV output is introduced, based on the integration of Generative Adversarial Networks (GANs) with the Informer model. The study’s content revolves around the leveraging of GANs for feature extraction and the development of the GAN-Informer model to forecast PV power outputs in national grid systems. Empirical investigations were conducted to validate the superior performance of the model. The outcomes

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Dai, Yaoda, Mingzhang Liao, and Zewei Li. "Navigating Complexity: GPT-4's Performance in Predicting Earnings and Stock Returns in China's A-Share Market." Highlights in Business, Economics and Management 42 (November 19, 2024): 189–203. http://dx.doi.org/10.54097/4rwdat95.

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This study investigates the application of GPT-4, a large language model, in predicting earnings changes and stock returns within China's A-share market from 2000 to 2023. We evaluate the model's performance using various metrics, including prediction accuracy, F1 score, stock returns, Sharpe ratio, and alpha. Our findings reveal significant fluctuations in the model's predictive accuracy, ranging from 10.62% to 48.67%, with an average F1 score of 0.30. Despite inconsistent accuracy, the model maintained high prediction confidence levels between 75% and 90%. Stock returns associated with the m

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Mahmudi, Bambang, and Enis Khaerunnisa. "BANKRUPTCY PREDICTION ANALYSIS USING THE ALTMAN Z-SCORE AND SPRINGATE MODELS IN INSURANCE COMPANIES WHICH GO PUBLIC IN THE INDONESIA STOCK EXCHANGE." Management Science Research Journal 2, no. 1 (2023): 28–45. http://dx.doi.org/10.56548/msr.v2i1.45.

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The research aims to analyze the potential bankruptcy using Altman Z-Score and the Springate model also to find out the level of accuracy prediction model in insurance companies go public on the Indonesia Stock Exchange. The population used in this research all insurance companies listed on the Indonesia Stock Exchange in the period 2016-2019 with sample of 12 companies. The research method employs descriptive analysis using secondary data and Paired t test. The results of the research on the prediction of bankruptcy using the Altman Z-Score model showed that 11 insurance companies were in the

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Chen, Zixuan, Guojie Wang, Xikun Wei, et al. "Basin-Scale Daily Drought Prediction Using Convolutional Neural Networks in Fenhe River Basin, China." Atmosphere 15, no. 2 (2024): 155. http://dx.doi.org/10.3390/atmos15020155.

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Drought is a natural disaster that occurs globally and can damage the environment, disrupt agricultural production and cause large economic losses. The accurate prediction of drought can effectively reduce the impacts of droughts. Deep learning methods have shown promise in drought prediction, with convolutional neural networks (CNNs) being particularly effective in handling spatial information. In this study, we employed a deep learning approach to predict drought in the Fenhe River (FHR) basin, taking into account the meteorological conditions of surrounding regions. We used the daily SAPEI

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Monroe, Lyman K., Duc P. Truong, Jacob C. Miner, et al. "Conotoxin Prediction: New Features to Increase Prediction Accuracy." Toxins 15, no. 11 (2023): 641. http://dx.doi.org/10.3390/toxins15110641.

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Conotoxins are toxic, disulfide-bond-rich peptides from cone snail venom that target a wide range of receptors and ion channels with multiple pathophysiological effects. Conotoxins have extraordinary potential for medical therapeutics that include cancer, microbial infections, epilepsy, autoimmune diseases, neurological conditions, and cardiovascular disorders. Despite the potential for these compounds in novel therapeutic treatment development, the process of identifying and characterizing the toxicities of conotoxins is difficult, costly, and time-consuming. This challenge requires a series

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Nurhabidah, Fauziah. "Analysis of the Accuracy Level of Using the Monte Carlo Method in Predicting the Number of Dengue Fever Sufferers." Journal of Mathematics and Scientific Computing With Applications 5, no. 2 (2024): 45–51. https://doi.org/10.53806/jmscowa.v5i2.984.

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Dengue fever is an infectious disease that continues to be a threat to public health in Indonesia. The increasing number of sufferers every year requires accurate prediction strategies to support effective prevention and control policies. This study aims to analyze the level of accuracy of the Monte Carlo method in predicting the number of dengue fever sufferers in Indonesia, especially in North Sumatra Province. The data used in this research is data on dengue fever cases from 2021 to 2023 obtained from the Central Statistics Agency (BPS) and the Health Service. The prediction process is carr

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Alanazi, Wafa, Di Meng, and Gianluca Pollastri. "PaleAle 6.0: Prediction of Protein Relative Solvent Accessibility by Leveraging Pre-Trained Language Models (PLMs)." Biomolecules 15, no. 1 (2025): 49. https://doi.org/10.3390/biom15010049.

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Predicting the relative solvent accessibility (RSA) of a protein is critical to understanding its 3D structure and biological function. RSA prediction, especially when homology transfer cannot provide information about a protein’s structure, is a significant step toward addressing the protein structure prediction challenge. Today, deep learning is arguably the most powerful method for predicting RSA and other structural features of proteins. In particular, recent breakthroughs in deep learning—driven by the integration of natural language processing (NLP) algorithms—have significantly advanced

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Pintelas, Emmanuel, Meletis Liaskos, Ioannis E. Livieris, Sotiris Kotsiantis, and Panagiotis Pintelas. "Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction." Journal of Imaging 6, no. 6 (2020): 37. http://dx.doi.org/10.3390/jimaging6060037.

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Image classification is a very popular machine learning domain in which deep convolutional neural networks have mainly emerged on such applications. These networks manage to achieve remarkable performance in terms of prediction accuracy but they are considered as black box models since they lack the ability to interpret their inner working mechanism and explain the main reasoning of their predictions. There is a variety of real world tasks, such as medical applications, in which interpretability and explainability play a significant role. Making decisions on critical issues such as cancer pred

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Mendoza, Nadia B., Chii-Dean Lin, Susan M. Kiene, et al. "Evaluating Imputation Methods to Improve Prediction Accuracy for an HIV Study in Uganda." Stats 7, no. 4 (2024): 1405–20. http://dx.doi.org/10.3390/stats7040082.

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Standard statistical analyses often exclude incomplete observations, which can be particularly problematic when predicting rare outcomes, such as HIV positivity. In the linkage to the HIV care dataset, there were initially 553 complete HIV positive cases, with an additional 554 cases added through imputation. Imputation methods amelia, hmisc, mice and missForest were evaluated. Simulations were conducted across various scenarios using the complete data to guide imputation for the full dataset. A random forest model was used to predict HIV status, assessing imputation precision, overall predict

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Nisa, Ehairun, Adriyansyah, and Izma Fahria. "Prediction of Water Availability in Kolong ST 12 Reservoir as a Raw Water Source using Singular Spectrum Analysis." Bentang : Jurnal Teoritis dan Terapan Bidang Rekayasa Sipil 13, no. 2 (2025): 261–75. https://doi.org/10.33558/bentang.v13i2.10605.

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Water is a fundamental human need that must be available to support daily activities. However, ensuring the availability of clean water remains a significant challenge for both communities and water providers, whether managed by the private sector or the government. One effective approach to ensuring the availability of clean water is accurately predicting the water discharge from raw water sources. The results of these predictions can be used to map the potential for water availability and identify areas with water deficits. This study focuses on predicting water discharge in the Kolong ST 12

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Li, Junyi, Yuntian Xu, Zizheng Xu, Luca Pan, and Shubo Zhang. "Research on NBA winning percentage prediction." Theoretical and Natural Science 34, no. 1 (2024): 298–305. http://dx.doi.org/10.54254/2753-8818/34/20240714.

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The National Basketball Association, also known as the NBA, is a professional basketball league composed of 30 professional teams in North America and one of the four major professional sports leagues in the United States. This research delves into predicting winning percentages in NBA games using a data-driven approach. It utilizes three years worth of comprehensive game data from 30 NBA teams (2014-2016), including actual outcomes and expert predictions. The study aims to acquire valuable teamwork and data research skills while contributing to sports analytics. Data preparation involves clea

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Labaybi, Outmane, Mohamed Bennani Taj, Khalid El Fahssi, Said El Garouani, Mohamed Lamrini, and Mohamed El Far. "Boosting stroke prediction with ensemble learning on imbalanced healthcare data." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 2 (2025): 1137. https://doi.org/10.11591/ijeecs.v38.i2.pp1137-1148.

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Detecting strokes at the early day is crucial for preventing health issues and potentially saving lives. Predicting strokes accurately can be challenging, especially when working with unbalanced healthcare datasets. In this article, we suggest a thorough method combining machine learning (ML) algorithms and ensemble learning techniques to improve the accuracy of predicting strokes. Our approach includes using preprocessing methods for tackling imbalanced data, feature engineering for extracting key information, and utilizing different ML algorithms such as random forests (RF), decision trees (

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Outmane, Labaybi Mohamed Bennani Taj Khalid El Fahssi Said El Garouani Mohamed Lamrini Mohamed El Far. "Boosting stroke prediction with ensemble learning on imbalanced healthcare data." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 2 (2025): 1137–48. https://doi.org/10.11591/ijeecs.v38.i2.pp1137-1148.

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Detecting strokes at the early day is crucial for preventing health issues and potentially saving lives. Predicting strokes accurately can be challenging, especially when working with unbalanced healthcare datasets. In this article, we suggest a thorough method combining machine learning (ML) algorithms and ensemble learning techniques to improve the accuracy of predicting strokes. Our approach includes using preprocessing methods for tackling imbalanced data, feature engineering for extracting key information, and utilizing different ML algorithms such as random forests (RF), decision trees (

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Turck-Chièze, S. "On the Accuracy of Solar Modelling." International Astronomical Union Colloquium 121 (1990): 125–32. http://dx.doi.org/10.1017/s0252921100067877.

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AbstractThe confrontation between theoretical predictions and observations requires an estimate of the uncertainties of these predictions. Recent results on nuclear reaction rates and photospheric abundances are analysed, in a classical model framework. The role of opacities in the determination of the helium content, the neutrino fluxes prediction and the adiabatic sound speed is discussed. Comments on extra phenomena as mass loss, turbulent mixing and WIMPS are also presented, in the light of very recent seismological results.

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Fayaz, Sheikh Amir Fayaz, Majid Zaman, Sameer Kaul, and Waseem Jeelani Bakshi. "Optimizing Cardiovascular Disease Prediction: A Synergistic Approach of Grey Wolf Levenberg Model and Neural Networks." Journal of Information Systems Engineering and Business Intelligence 9, no. 2 (2023): 119–35. http://dx.doi.org/10.20473/jisebi.9.2.119-135.

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Background: One of the latest issues in predicting cardiovascular disease is the limited performance of current risk prediction models. Although several models have been developed, they often fail to identify a significant proportion of individuals who go on to develop the disease. This highlights the need for more accurate and personalized prediction models. Objective: This study aims to investigate the effectiveness of the Grey Wolf Levenberg Model and Neural Networks in predicting cardiovascular diseases. The objective is to identify a synergistic approach that can improve the accuracy of p

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Bhonde, Kanchan A., Snehal Yeole, Kartik Jawanjal, Mayank Modi, Aditya Panchwagh, and Aditya Paprunia. "Assessment of Accuracy of Indian Almanac for Daily Rainfall Prediction." Indian Journal Of Science And Technology 15, no. 32 (2022): 1548–60. http://dx.doi.org/10.17485/ijst/v15i32.1743.

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