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1
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 (March 5, 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, impulsivity, spatial orientation, verbal episodic memory and sustained attention. Out-of-sample predictions of each cognitive score were first generated using a sparsity-constrained principal component regression on individual neuroimaging modalities. These individual predictions were then aggregated and submitted to a LASSO estimator that removed redundant variability across channels. This stacked prediction led to a significant improvement in accuracy, relative to the best single modality predictions (approximately 1% to more than 3% boost in variance explained), across a majority of the cognitive abilities tested. Further analysis found that diffusion and brain surface properties contribute the most to the predictive power. Our findings establish a lower bound to predict individual differences in cognition using multiple neuroimaging measures of brain architecture, both structural and functional, quantify the relative predictive power of the different imaging modalities, and reveal how each modality provides unique and complementary information about individual differences in cognitive function.
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Yang, Ke. "Predicting Student Performance Using Artificial Neural Networks." Journal of Arts, Society, and Education Studies 6, no. 1 (May 15, 2024): 45–77. http://dx.doi.org/10.69610/j.ases.20240515.
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<p class="MsoNormal" style="text-align: justify;"><span style="font-family: Times New Roman;">This paper explores machine learning approaches to predicting student performance using artificial neural networks. By employing educational data mining and predictive modeling techniques, accurate predictions of student outcomes were achieved. The results indicate that artificial neural networks exhibit high accuracy and reliability in forecasting student academic performance. Through comprehensive analysis and empirical testing, this approach significantly enhances the effectiveness of student performance predictions. Future research directions may include further optimization of the model's algorithms and expansion of the data sample size to improve prediction accuracy and applicability. The method demonstrated exceptional performance in predicting student outcomes, offering high accuracy and efficacy. By mining and analyzing extensive educational data, a predictive model was established and validated through experiments. We introduce a novel predictive model to the field of education, providing robust support for student learning and educational decision-making. Future enhancements can optimize the model, increase prediction precision, and expand application fields to better serve the development of educational endeavors.</span></p>
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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 (January 25, 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 model excelled in capturing stock price trends and demonstrated strengths over traditional methods. The optimal input sequence length was identified, balancing computational efficiency and prediction accuracy. This research contributes valuable insights into improving stock price prediction techniques and offers practical implications for traders and investors. Future research directions encompass hybrid models and integrating external factors to enhance predictive capabilities further.
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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 model's predictions varied widely, from -4.86% to 13.59%, showing no consistent correlation with prediction accuracy. The study highlights the challenges of applying AI models to financial analysis in emerging markets, particularly given the unique characteristics of China's A-share market, such as frequent policy interventions and a high proportion of retail investors. We discuss the implications of these findings for the future of AI-driven financial analysis, emphasizing the need for improved model calibration, ethical considerations, and regulatory frameworks.
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Mendoza, Nadia B., Chii-Dean Lin, Susan M. Kiene, Nicolas A. Menzies, Rhoda K. Wanyenze, Katherine A. Schmarje, Rose Naigino, Michael Ediau, Seth C. Kalichman, and Barbara A. Bailey. "Evaluating Imputation Methods to Improve Prediction Accuracy for an HIV Study in Uganda." Stats 7, no. 4 (November 24, 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 prediction accuracy, and sensitivity. While missForest produced imputed values closer to the observed ones, this did not translate into better predictive models. Hmisc and mice imputations led to higher prediction accuracy and sensitivity, with median accuracy increasing from 64% to 76% and median sensitivity rising from 0.4 to 0.75. Hmisc and amelia were the fastest imputation methods. Additionally, oversampling the minority class combined with undersampling the majority class did not improve predictions of new HIV positive cases using only the complete observations. However, increasing the minority class information through imputation enhanced sensitivity for predicting cases in this class.
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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 (January 14, 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, prediction using one input and one output obtained the best precision model accuracy of 72% and for poor accuracy using three inputs and one output with a prediction model accuracy of 33% in the process Covid-19 predictions in Bali.
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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 (March 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 using six different training algorithms, and the prediction accuracies of six algorithms were evaluated using seven different accuracy metrics. Test results showed that the training algorithm of “TRAINGLM” exhibited the best performance for predicting damage locations in deep piles. Subsequently, the artificial neural networks were trained using three different datasets collected from three acoustic emission sensor groups, and the prediction accuracies of three models were evaluated with the seven different accuracy metrics. The test results showed that the dataset collected from the pile body-installed sensors group exhibited the highest accuracy for predicting damage locations in deep piles. Subsequently, the correlations between the seven accuracy metrics and the sensitivity of each accuracy metrics were discussed based on the analysis results. Eventually, a novel selection method for an appropriate accuracy metric to evaluate the accuracy of specific predictions was proposed. This novel method is useful to select an appropriate accuracy metric for wide predictions, especially in the engineering field.
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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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Xiao, Yang. "Individual Stock Price Prediction Using Stacking Method." Advances in Economics, Management and Political Sciences 99, no. 1 (September 10, 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 individual models. This paper evaluates the model using mean squared error (MSE) and root mean squared error (RMSE) and demonstrated the models prediction accuracy and robustness. The findings demonstrate that the stacking model not only reduces prediction errors but also enhances robustness against the volatile nature of stock prices. This study underscores the high potential of stacking in financial time series prediction, providing valuable insights and references for investment decisions. By integrating multiple predictive models, stacking offers a powerful tool for navigating the complexities of financial markets and making informed investment choices.
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Grover, Arman, Debajyoti Roy Burman, Priyansh Kapaida, and Neelamani Samal. "Stock Market Price Prediction." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (December 31, 2023): 591–95. http://dx.doi.org/10.22214/ijraset.2023.57184.
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Abstract: Investing in the stock market can be a convoluted and refined method of conducting business. Stock prediction is an extremely difficult and complex endeavor since stock values can fluctuate abruptly owing to a variety of reasons, making the stock market incredibly unpredictable.This paper explores predictive models for the stock market, aiming to forecast stock prices using machine learning algorithms. By analyzing historical market data and employing various predictive techniques, thestudy aims to enhance accuracy in predicting future stock movements. this paper contributes understanding into the potential of LSTM models for enhancing stock market prediction accuracy and reliability.
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Lyu, Xiaozhong, Cuiqing Jiang, Yong Ding, Zhao Wang, and Yao Liu. "Sales Prediction by Integrating the Heat and Sentiments of Product Dimensions." Sustainability 11, no. 3 (February 11, 2019): 913. http://dx.doi.org/10.3390/su11030913.
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Online word-of-mouth (eWOM) disseminated on social media contains a considerable amount of important information that can predict sales. However, the accuracy of sales prediction models using big data on eWOM is still unsatisfactory. We argue that eWOM contains the heat and sentiments of product dimensions, which can improve the accuracy of prediction models based on multiattribute attitude theory. In this paper, we propose a dynamic topic analysis (DTA) framework to extract the heat and sentiments of product dimensions from big data on eWOM. Ultimately, we propose an autoregressive heat-sentiment (ARHS) model that integrates the heat and sentiments of dimensions into the benchmark predictive model to forecast daily sales. We conduct an empirical study of the movie industry and confirm that the ARHS model is better than other models in predicting movie box-office revenues. The robustness check with regard to predicting opening-week revenues based on a back-propagation neural network also suggests that the heat and sentiments of dimensions can improve the accuracy of sales predictions when the machine-learning method is used.
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Ma, Haoran, Hongwei Li, Fei Ge, Huqiong Zhao, Bo Zhu, Lupei Zhang, Huijiang Gao, Lingyang Xu, Junya Li, and Zezhao Wang. "Improving Genomic Predictions in Multi-Breed Cattle Populations: A Comparative Analysis of BayesR and GBLUP Models." Genes 15, no. 2 (February 18, 2024): 253. http://dx.doi.org/10.3390/genes15020253.
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Numerous studies have shown that combining populations from similar or closely related genetic breeds improves the accuracy of genomic predictions (GP). Extensive experimentation with diverse Bayesian and genomic best linear unbiased prediction (GBLUP) models have been developed to explore multi-breed genomic selection (GS) in livestock, ultimately establishing them as successful approaches for predicting genomic estimated breeding value (GEBV). This study aimed to assess the effectiveness of using BayesR and GBLUP models with linkage disequilibrium (LD)-weighted genomic relationship matrices (GRMs) for genomic prediction in three different beef cattle breeds to identify the best approach for enhancing the accuracy of multi-breed genomic selection in beef cattle. Additionally, a comparison was conducted to evaluate the predictive precision of different marker densities and genetic correlations among the three breeds of beef cattle. The GRM between Yunling cattle (YL) and other breeds demonstrated modest affinity and highlighted a notable genetic concordance of 0.87 between Chinese Wagyu (WG) and Huaxi (HX) cattle. In the within-breed GS, BayesR demonstrated an advantage over GBLUP. The prediction accuracies for HX cattle using the BayesR model were 0.52 with BovineHD BeadChip data (HD) and 0.46 with whole-genome sequencing data (WGS). In comparison to the GBLUP model, the accuracy increased by 26.8% for HD data and 9.5% for WGS data. For WG and YL, BayesR doubled the within-breed prediction accuracy to 14.3% from 7.1%, outperforming GBLUP across both HD and WGS datasets. Moreover, analyzing multiple breeds using genomic selection showed that BayesR consistently outperformed GBLUP in terms of predictive accuracy, especially when using WGS. For instance, in a mixed reference population of HX and WG, BayesR achieved a significant accuracy of 0.53 using WGS for HX, which was a substantial enhancement over the accuracies obtained with GBLUP models. The research further highlights the benefit of including various breeds in the reference group, leading to enhanced accuracy in predictions and emphasizing the importance of comprehensive genomic selection methods. Our research findings indicate that BayesR exhibits superior performance compared to GBLUP in multi-breed genomic prediction accuracy, achieving a maximum improvement of 33.3%, especially in genetically diverse breeds. The improvement can be attributed to the effective utilization of higher single nucleotide polymorphism (SNP) marker density by BayesR, resulting in enhanced prediction accuracy. This evidence conclusively demonstrates the significant impact of BayesR on enhancing genomic predictions in diverse cattle populations, underscoring the crucial role of genetic relatedness in selection methodologies. In parallel, subsequent studies should focus on refining GRM and exploring alternative models for GP.
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Liang, Qi. "Research on Software Defect Prediction Model based on Deep Learning." Highlights in Science, Engineering and Technology 122 (December 15, 2024): 23–29. https://doi.org/10.54097/y0w76b47.
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As software systems grow in complexity and scale, detecting and predicting defects has become crucial for ensuring software quality and enhancing development efficiency. Traditional approaches to software defect prediction rely heavily on manual feature extraction and statistical models, which often struggle to handle intricate defect patterns and large-scale datasets. Recently, deep learning has demonstrated significant promise in software defect prediction, primarily due to its ability to automatically extract features and its strong pattern recognition capabilities. To enhance both the accuracy of defect predictions and the interpretability of the models, this paper proposes a deep learning-based prediction model. The focus is placed on the stages of data preprocessing, model selection, parameter tuning, and model assessment. The experimental outcomes indicate that the deep learning approach consistently outperforms conventional methods across various public datasets, achieving superior predictive accuracy and robustness. This study offers theoretical insights and practical evidence for further advancing the effectiveness of software defect prediction techniques.
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Obodoekwe, Ekene, Xianwen Fang, and Ke Lu. "Convolutional Neural Networks in Process Mining and Data Analytics for Prediction Accuracy." Electronics 11, no. 14 (July 7, 2022): 2128. http://dx.doi.org/10.3390/electronics11142128.
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For the reliable prediction and analysis of large amounts of data, big data analytics may be applied in many disciplines. They facilitate the discovery of important information in large amounts of data that would otherwise be obscured. Almost all organizations stored their data in the cloud as event logs over the last few decades. These data can be utilized to extract useful information, which can be used to boost an organization’s productivity and effectiveness by identifying, monitoring, and optimizing its processes. Supporting operations, recognizing faults in running processes, predicting event length, and predicting the next activity are all ways of accomplishing this. As part of our strategy, we provide a data collection and machine learning technique. Process mining can help you achieve these objectives. The major enabler of data-driven approaches in process mining is predictive process monitoring. Deep learning has been used in the realm of predictive monitoring to provide accurate future activity predictions in a running trace by analyzing data from previous events. Using image-based data engineering and convolutional neural networks, the next activity in a business process has been forecast in this paper (CNN). The use of CNN in process mining and data analytics guarantees that the proposed system has high accuracy in predicting the next activity in a business process. The experimental evaluation shows that the proposed CNN algorithm is faster at training and inference than the Long Short-Term Memory (LSTM) approach, even when the process has longer traces.
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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 (January 31, 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 step change in our ability to predict protein structures. Results In this work, we present ABlooper, an end-to-end equivariant deep learning-based CDR loop structure prediction tool. ABlooper rapidly predicts the structure of CDR loops with high accuracy and provides a confidence estimate for each of its predictions. On the models of the Rosetta Antibody Benchmark, ABlooper makes predictions with an average CDR-H3 RMSD of 2.49 Å, which drops to 2.05 Å when considering only its 75% most confident predictions. Availability and implementation https://github.com/oxpig/ABlooper. Supplementary information Supplementary data are available at Bioinformatics online.
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Sabarinath U S and Ashly Mathew. "Medical Insurance Cost Prediction." Indian Journal of Data Communication and Networking 4, no. 4 (June 30, 2024): 1–4. http://dx.doi.org/10.54105/ijdcn.d5037.04040624.
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This is a medical insurance cost prediction model that uses a linear regression algorithm to predict the medical insurance charges of a person based on the given data. To predict things that have never been so easy. In this project used to predict values that wonder how Insurance amount is normally charged. This is a medical insurance cost prediction model that uses a linear regression algorithm to predict the medical insurance charges of a person based on the given data. This project on predicting medical insurance costs can serve various purposes and address several needs that are Accurate Pricing Insurance companies need accurate predictions of medical insurance costs to set appropriate premiums for policyholders. Predictive models can analyse historical data and various factors such as age, gender, pre-existing conditions, lifestyle habits, and geographic location to estimate future healthcare expenses accurately. This Prediction model achieves three regression methods accuracy that the linear regression gets an accuracy of 74.45 %, whereas Ridge regression and Support Vector Regression gets 82.59% word-level state-of-the-art accuracy. The Medical Insurance Cost Prediction project, proposes a comprehensive approach to predict the medical cost, aiming to develop a robust and accurate system capable of predicting the accurate cost for a particular individual. Leveraging linear regression, our proposed system builds upon the successes of existing models like different types of regressions like linear regression, Ridge regression and Support Vector regression. We will put the Regression algorithm into practice and evaluate how it performs in comparison to the other three algorithms. By comparing the performance of these three methodologies, this project aims to identify the most effective approach for medical insurance cost prediction. Through rigorous evaluation and validation processes, the selected model will provide valuable insights for insurance companies, policymakers, and individuals seeking to optimize healthcare resource allocation and financial planning strategies.
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Nakashima, Toshihisa, Takayuki Ohno, Keiichi Koido, Hironobu Hashimoto, and Hiroyuki Terakado. "Accuracy of predicting the vancomycin concentration in Japanese cancer patients by the Sawchuk–Zaske method or Bayesian method." Journal of Oncology Pharmacy Practice 26, no. 3 (May 29, 2019): 543–48. http://dx.doi.org/10.1177/1078155219851834.
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Background In cancer patients treated with vancomycin, therapeutic drug monitoring is currently performed by the Bayesian method that involves estimating individual pharmacokinetics from population pharmacokinetic parameters and trough concentrations rather than the Sawchuk–Zaske method using peak and trough concentrations. Although the presence of malignancy influences the pharmacokinetic parameters of vancomycin, it is unclear whether cancer patients were included in the Japanese patient populations employed to estimate population pharmacokinetic parameters for this drug. The difference of predictive accuracy between the Sawchuk–Zaske and Bayesian methods in Japanese cancer patients is not completely understood. Objective To retrospectively compare the accuracy of predicting vancomycin concentrations between the Sawchuk–Zaske method and the Bayesian method in Japanese cancer patients. Methods Using data from 48 patients with various malignancies, the predictive accuracy (bias) and precision of the two methods were assessed by calculating the mean prediction error, the mean absolute prediction error, and the root mean squared prediction error. Results Prediction of the trough and peak vancomycin concentrations by the Sawchuk–Zaske method and the peak concentration by the Bayesian method showed a bias toward low values according to the mean prediction error. However, there were no significant differences between the two methods with regard to the changes of the mean prediction error, mean absolute prediction error, and root mean squared prediction error. Conclusion The Sawchuk–Zaske method and Bayesian method showed similar accuracy for predicting vancomycin concentrations in Japanese cancer patients.
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Iftikhar, Taqdees, Naila Qazi, Naushaba Malik, Nida Hamid, Nadia Gul, and Razia Rauf. "Prediction of Successful Induction of Labour jointly using Bishop Score and Transvaginal Sonography in Primigravida Women in Pakistan." Annals of PIMS-Shaheed Zulfiqar Ali Bhutto Medical University 19, no. 2 (May 31, 2023): 141–46. http://dx.doi.org/10.48036/apims.v19i2.783.
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Objective: To assess the diagnostic efficacy of the Bishop Score and Transvaginal Ultrasonography (TVS) in predicting successful labor induction in primigravida women in a peri-urban population in Islamabad. Additionally, the study aimed to evaluate the effectiveness of combining the predictions from both methods to enhance accuracy in predicting successful labor induction. Methodology: A prospective comparative study was conducted at the Departments of Obstetrics and Gynecology, Rawal Institute of Health Sciences, and Farooq Hospital, Islamabad, from December 2021 to December 2022. A total of 520 pregnant, primigravida women undergoing labor induction were included, and they were randomly divided into two groups for assessment using either the Bishop Score or Transvaginal ultrasonography. The outcome of interest was documented as the initiation of active labor within 24 hours. The efficacy of each method was validated separately and jointly, and the predictive accuracy of all three predictors was compared. Results: The two groups demonstrated that both TVS and the Bishop Score were individually effective at predicting successful labor induction (p<0.00001 for both methods). TVS outperformed the Bishop Score in several key predictive measures, such as accuracy and the F1 Score. However, combining predictions from both the Bishop Score and TVS significantly improved both positive and negative predictive values (by more than 10% for each metric), resulting in a more reliable prediction. Conclusion: Both the Bishop Score and TVS are effective methods for predicting successful labor induction in the peri-urban population of Islamabad, Pakistan. While TVS showed significant quantitative advantages over the Bishop Score, combining both predictors yielded even better performance, suggesting that using both methods together should be prioritized for prediction.
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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 (November 13, 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 prediction results needed to be provided to the instructor every week, four representative time points during the semester were chosen to assess the predictions. Furthermore, we used eight single and three integrated machine-learning algorithms to compare the prediction results. The results show that using the same semester code course data for training saved prediction calculation time and improved the prediction accuracy at all time points. In week 16, predictions using the algorithms with the voting classifier method showed higher prediction accuracy and were more stable than predictions using a single algorithm. The prediction accuracy of this model reached 81.2% for the midterm predictions and 84% for the end-of-semester predictions. Finally, the study used the Shapley additive explanation values to explore the main predictor variables of the prediction model.
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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 (October 14, 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 companies in basic industrial and chemistry sectors and cement sub-sector. The data are analyzed by using the accuracy and error levels in each predicting method of bankruptcy, and each method shows different prediction. The result of financial distress prediction, using Altman Z-Score shows that there are 19 financial distress predictions, 26 non-financial distress predictions, and 18 gray area predictions. The result of financial distress prediction, using Springate method shows that there are 22 financial distress predictions and 41 non-financial distress predictions, the result of the calculation in accuracy and error levels, using Springate method, shows that Springate method is the most accurate with the accuracy level of 65.08% and the error level of 34.92% .
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Yoo, Jang, Jaeho Lee, Miju Cheon, Sang-Keun Woo, Myung-Ju Ahn, Hong Ryull Pyo, Yong Soo Choi, Joung Ho Han, and Joon Young Choi. "Predictive Value of 18F-FDG PET/CT Using Machine Learning for Pathological Response to Neoadjuvant Concurrent Chemoradiotherapy in Patients with Stage III Non-Small Cell Lung Cancer." Cancers 14, no. 8 (April 14, 2022): 1987. http://dx.doi.org/10.3390/cancers14081987.
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We investigated predictions from 18F-FDG PET/CT using machine learning (ML) to assess the neoadjuvant CCRT response of patients with stage III non-small cell lung cancer (NSCLC) and compared them with predictions from conventional PET parameters and from physicians. A retrospective study was conducted of 430 patients. They underwent 18F-FDG PET/CT before initial treatment and after neoadjuvant CCRT followed by curative surgery. We analyzed texture features from segmented tumors and reviewed the pathologic response. The ML model employed a random forest and was used to classify the binary outcome of the pathological complete response (pCR). The predictive accuracy of the ML model for the pCR was 93.4%. The accuracy of predicting pCR using the conventional PET parameters was up to 70.9%, and the accuracy of the physicians’ assessment was 80.5%. The accuracy of the prediction from the ML model was significantly higher than those derived from conventional PET parameters and provided by physicians (p < 0.05). The ML model is useful for predicting pCR after neoadjuvant CCRT, which showed a higher predictive accuracy than those achieved from conventional PET parameters and from physicians.
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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, GM models may have limitations in terms of accuracy. To overcome this issue, this study introduces the concept of Markov chains, incorporating historical state transitions into prediction models to achieve more precise forecasts. The research demonstrates a novel method for water pollution prediction that integrates GM models with Markov chain analysis, resulting in improved accuracy when predicting NH3-N concentrations. A comparison with traditional GM predictions highlights the effectiveness of this approach. The model's performance was evaluated using actual data from the China Automated Water Quality Monitoring Report. Combining grey prediction models with Markov chains outperforms traditional methods when it comes to predicting water pollution levels. The result contributes to advancing the field of water pollution forecasting by enhancing forecasting accuracy and providing informed decision support for environmental protection and management purposes.
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Juneja, Dr Sonia. "House Price Prediction Using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 3156–64. http://dx.doi.org/10.22214/ijraset.2023.54259.
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Abstract: House price prediction is the process of using learning based techniques to predict the future sale price of a house. It explores the use of predictive models to accurately forecast house prices. It also examines the effectiveness of using machine learning algorithms to predict house prices. In particular, our research investigates the impact of data such as location, duration of house, dimension of house on the accuracy of the predictions. Finally, a discussion on the implications of using machine learning algorithms for predicting price for consumers and real estate professionals is presented
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Widhi, Oktavandi, Maria Safitri, Usman Usman, and Amalia Nur Chasanah. "Comparation Of Bankruptcy Prediction At Retail Companies In Indonesia Using Altman, Zmijewski and Springate Methods." Finance : International Journal of Management Finance 2, no. 2 (December 23, 2024): 1–10. https://doi.org/10.62017/finance.v2i2.55.
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The Indonesian retail sector, a significant contributor to the nation's GDP, faces challenges due to digital transformation, shifting consumer behavior, and increased competition from e-commerce platforms, leading to potential bankruptcy risks among traditional retailers. This study aims to compare the Altman, Zmijewski, and Springate models in predicting bankruptcy of retail companies listed on the Indonesia Stock Exchange from 2019 to 2023. Using financial data from 11 retail companies, the study calculated bankruptcy predictions using the three models and performed statistical tests (Kolmogorov-Smirnov and Kruskal-Wallis tests) to compare their predictive accuracy. The results indicated that the Zmijewski model had the highest prediction accuracy at 90.91%, followed by the Springate model at 81.82%, and the Altman model at 45.45%. The Zmijewski model predicted one company as potentially bankrupt, while the other models provided varying predictions. The study concludes that the Zmijewski model is highest prediction rate in predicting bankruptcy in Indonesian retail companies. This has implications for investors and stakeholders in assessing financial health and making informed decisions
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Drisya, G. V., D. C. Kiplangat, K. Asokan, and K. Satheesh Kumar. "Deterministic prediction of surface wind speed variations." Annales Geophysicae 32, no. 11 (November 19, 2014): 1415–25. http://dx.doi.org/10.5194/angeo-32-1415-2014.
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Abstract. Accurate prediction of wind speed is an important aspect of various tasks related to wind energy management such as wind turbine predictive control and wind power scheduling. The most typical characteristic of wind speed data is its persistent temporal variations. Most of the techniques reported in the literature for prediction of wind speed and power are based on statistical methods or probabilistic distribution of wind speed data. In this paper we demonstrate that deterministic forecasting methods can make accurate short-term predictions of wind speed using past data, at locations where the wind dynamics exhibit chaotic behaviour. The predictions are remarkably accurate up to 1 h with a normalised RMSE (root mean square error) of less than 0.02 and reasonably accurate up to 3 h with an error of less than 0.06. Repeated application of these methods at 234 different geographical locations for predicting wind speeds at 30-day intervals for 3 years reveals that the accuracy of prediction is more or less the same across all locations and time periods. Comparison of the results with f-ARIMA model predictions shows that the deterministic models with suitable parameters are capable of returning improved prediction accuracy and capturing the dynamical variations of the actual time series more faithfully. These methods are simple and computationally efficient and require only records of past data for making short-term wind speed forecasts within practically tolerable margin of errors.
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Wang, Mingliang, Jagtar Bhatti, Yonghe Wang, and Thierry Varem-Sanders. "Examining the Gain in Model Prediction Accuracy Using Serial Autocorrelation for Dominant Height Prediction." Forest Science 57, no. 3 (June 1, 2011): 241–51. http://dx.doi.org/10.1093/forestscience/57.3.241.
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Abstract Within-subject serial correlation (autocorrelation) has long been a concern in forest growth and yield modeling but has been ignored for predictive purposes in most studies. In this study, we used linear prediction theory combined with linearized (with respect to random effects) nonlinear mixed models to investigate the improvement in model prediction achieved with autocorrelation. In this setting, predictions rely on estimates of common parameters obtained from a set of previous growth series and prior observations of new growth series, allowing the response variable for the new series to be projected either backward or forward in time. The prediction gains associated with using autocorrelation were evaluated using stem analysis data sets for black spruce (Picea mariana [Mill.] BSP) and red alder (Alnus rubra Bong.). The evaluations involved splitting the data and comparing models with one or more random parameters, with and without use of autocorrelation. Autocorrelation improved the projection of dominant height (site index) over short ranges (10–20 years), but the gain was trivial for the long range (>20 years). Consequently, in cases of dominant height projection based on one single observation, for practical purposes, autocorrelation can be ignored in both model-fitting and prediction stages. Cross-comparison between models with different random effects indicated that simple models with one random effect had the best predictive performance. Rather than excluding such models solely on the basis of certain fit statistics, it is recommended that the predictive abilities of models with a single random effect be evaluated, with and without correlated errors, relative to their counterparts with more random effects.
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Guo, Shengnan, and Jianqiu Xu. "CPRQ: Cost Prediction for Range Queries in Moving Object Databases." ISPRS International Journal of Geo-Information 10, no. 7 (July 8, 2021): 468. http://dx.doi.org/10.3390/ijgi10070468.
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Predicting query cost plays an important role in moving object databases. Accurate predictions help database administrators effectively schedule workloads and achieve optimal resource allocation strategies. There are some works focusing on query cost prediction, but most of them employ analytical methods to obtain an index-based cost prediction model. The accuracy can be seriously challenged as the workload of the database management system becomes more and more complex. Differing from the previous work, this paper proposes a method called CPRQ (Cost Prediction of Range Query) which is based on machine-learning techniques. The proposed method contains four learning models: the polynomial regression model, the decision tree regression model, the random forest regression model, and the KNN (k-Nearest Neighbor) regression model. Using R-squared and MSE (Mean Squared Error) as measurements, we perform an extensive experimental evaluation. The results demonstrate that CPRQ achieves high accuracy and the random forest regression model obtains the best predictive performance (R-squared is 0.9695 and MSE is 0.154).
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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 to predict high-frequency signal sequence, while the ARMA model was used to predict low-frequency signal sequence. Finally, aiming to predict the mining safety production situation, the EMD-GM-ARMA model was constructed via superimposing the prediction results of each subsequence, thereby compared to the ARIMA model, wavelet neural network model, and PSO-SVM model. The results demonstrated that the EMD-GM-ARMA model and the PSO-SVM model hold the highest prediction accuracy in the short-term prediction, and the wavelet neural network has the lowest prediction accuracy. The PSO-SVM model’s prediction accuracy decreases in medium- and long-term predictions while the EMD-GM-ARMA model still can maintain high prediction accuracy. Moreover, the relative error fluctuations of the EMD-GM-ARMA model are relatively stable in both short-term and medium-term predictions. This shows that the EMD-GM-ARMA model can provide high-precision predictions with high stability, proving the model to be feasible and effective in predicting the mining safety production situation.
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Takahashi, K., R. Ooka, and S. Ikeda. "Anomaly detection and missing data imputation in building energy data for automated data pre-processing." Journal of Physics: Conference Series 2069, no. 1 (November 1, 2021): 012144. http://dx.doi.org/10.1088/1742-6596/2069/1/012144.
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Abstract A new trend in building automation is the implementation of smart energy management systems to measure and control building systems without a need for decision-making by human operators. Artificial intelligence can optimize these systems by predicting future demand to make informed decisions about how to efficiently operate individual equipment. These machine learning algorithms use historical data to learn demand trends and require high quality datasets in order to make accurate predictions. But because of issues with data transmission or sensor errors, real world datasets often contain outliers or have data missing. In most research settings, these values can be simply omitted, but in practice, anomalies compromise the automation system’s prediction accuracy, rendering it unable to maximize energy savings. This study explores different machine learning algorithms for anomaly detection for automatically pre-processing incoming data using a case study on an actual electrical demand in a hospital building in Japan, namely cluster-based techniques such as k-means clustering and neural network-based approaches such as the autoencoder. Once anomalies were identified, the missing data was filled with prediction values from a deep neural network model. The newly composed data was then evaluated based on detection accuracy, prediction accuracy and training time. The proposed method of processing anomaly values allows the prediction model to process collected data without interruption, and shows similar predictive accuracy as manually processing the data. These predictions allow energy systems to optimize HVAC equipment control, increasing energy savings and reducing peak building loads.
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Ji, Jung-Hwan, Sung-Gwe Ahn, Youngbum Yoo, Shin-Young Park, Joo-Heung Kim, Ji-Yeong Jeong, Seho Park, and Ilkyun Lee. "Prediction of a Multi-Gene Assay (Oncotype DX and Mammaprint) Recurrence Risk Group Using Machine Learning in Estrogen Receptor-Positive, HER2-Negative Breast Cancer—The BRAIN Study." Cancers 16, no. 4 (February 13, 2024): 774. http://dx.doi.org/10.3390/cancers16040774.
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This study aimed to develop a machine learning-based prediction model for predicting multi-gene assay (MGA) risk categories. Patients with estrogen receptor-positive (ER+)/HER2− breast cancer who had undergone Oncotype DX (ODX) or MammaPrint (MMP) were used to develop the prediction model. The development cohort consisted of a total of 2565 patients including 2039 patients tested with ODX and 526 patients tested with MMP. The MMP risk prediction model utilized a single XGBoost model, and the ODX risk prediction model utilized combined LightGBM, CatBoost, and XGBoost models through soft voting. Additionally, the ensemble (MMP + ODX) model combining MMP and ODX utilized CatBoost and XGBoost through soft voting. Ten random samples, corresponding to 10% of the modeling dataset, were extracted, and cross-validation was performed to evaluate the accuracy on each validation set. The accuracy of our predictive models was 84.8% for MMP, 87.9% for ODX, and 86.8% for the ensemble model. In the ensemble cohort, the sensitivity, specificity, and precision for predicting the low-risk category were 0.91, 0.66, and 0.92, respectively. The prediction accuracy exceeded 90% in several subgroups, with the highest prediction accuracy of 95.7% in the subgroup that met Ki-67 <20 and HG 1~2 and premenopausal status. Our machine learning-based predictive model has the potential to complement existing MGAs in ER+/HER2− breast cancer.
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Sifat, Isteaq Kabir, and Md Kaderi Kibria. "Optimizing hypertension prediction using ensemble learning approaches." PLOS ONE 19, no. 12 (December 23, 2024): e0315865. https://doi.org/10.1371/journal.pone.0315865.
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Hypertension (HTN) prediction is critical for effective preventive healthcare strategies. This study investigates how well ensemble learning techniques work to increase the accuracy of HTN prediction models. Utilizing a dataset of 612 participants from Ethiopia, which includes 27 features potentially associated with HTN risk, we aimed to enhance predictive performance over traditional single-model methods. A multi-faceted feature selection approach was employed, incorporating Boruta, Lasso Regression, Forward and Backward Selection, and Random Forest feature importance, and found 13 common features that were considered for prediction. Five machine learning (ML) models such as logistic regression (LR), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and a stacking ensemble model were trained using selected features to predict HTN. The models’ performance on the testing set was evaluated using accuracy, precision, recall, F1-score, and area under the curve (AUC). Additionally, SHapley Additive exPlanations (SHAP) was utilized to examine the impact of individual features on the models’ predictions and identify the most important risk factors for HTN. The stacking ensemble model emerged as the most effective approach for predicting HTN risk, achieving an accuracy of 96.32%, precision of 95.48%, recall of 97.51%, F1-score of 96.48%, and an AUC of 0.971. SHAP analysis of the stacking model identified weight, drinking habits, history of hypertension, salt intake, age, diabetes, BMI, and fat intake as the most significant and interpretable risk factors for HTN. Our results demonstrate significant advancements in predictive accuracy and robustness, highlighting the potential of ensemble learning as a pivotal tool in healthcare analytics. This research contributes to ongoing efforts to optimize HTN prediction models, ultimately supporting early intervention and personalized healthcare management.
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Timonidis, Nestor, Rembrandt Bakker, and Paul Tiesinga. "Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data." Neuroinformatics 18, no. 4 (May 24, 2020): 611–26. http://dx.doi.org/10.1007/s12021-020-09471-x.
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Abstract Reconstructing brain connectivity at sufficient resolution for computational models designed to study the biophysical mechanisms underlying cognitive processes is extremely challenging. For such a purpose, a mesoconnectome that includes laminar and cell-class specificity would be a major step forward. We analyzed the ability of gene expression patterns to predict cell-class and layer-specific projection patterns and assessed the functional annotations of the most predictive groups of genes. To achieve our goal we used publicly available volumetric gene expression and connectivity data and we trained computational models to learn and predict cell-class and layer-specific axonal projections using gene expression data. Predictions were done in two ways, namely predicting projection strengths using the expression of individual genes and using the co-expression of genes organized in spatial modules, as well as predicting binary forms of projection. For predicting the strength of projections, we found that ridge (L2-regularized) regression had the highest cross-validated accuracy with a median r2 score of 0.54 which corresponded for binarized predictions to a median area under the ROC value of 0.89. Next, we identified 200 spatial gene modules using a dictionary learning and sparse coding approach. We found that these modules yielded predictions of comparable accuracy, with a median r2 score of 0.51. Finally, a gene ontology enrichment analysis of the most predictive gene groups resulted in significant annotations related to postsynaptic function. Taken together, we have demonstrated a prediction workflow that can be used to perform multimodal data integration to improve the accuracy of the predicted mesoconnectome and support other neuroscience use cases.
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Li, Fanghong, Norliza Abdul Majid, and Shuo Ding. "Unlocking the potential of LSTM for accurate salary prediction with MLE, Jeffreys prior, and advanced risk functions." PeerJ Computer Science 10 (February 22, 2024): e1875. http://dx.doi.org/10.7717/peerj-cs.1875.
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This article aims to address the challenge of predicting the salaries of college graduates, a subject of significant practical value in the fields of human resources and career planning. Traditional prediction models often overlook diverse influencing factors and complex data distributions, limiting the accuracy and reliability of their predictions. Against this backdrop, we propose a novel prediction model that integrates maximum likelihood estimation (MLE), Jeffreys priors, Kullback-Leibler risk function, and Gaussian mixture models to optimize LSTM models in deep learning. Compared to existing research, our approach has multiple innovations: First, we successfully improve the model’s predictive accuracy through the use of MLE. Second, we reduce the model’s complexity and enhance its interpretability by applying Jeffreys priors. Lastly, we employ the Kullback-Leibler risk function for model selection and optimization, while the Gaussian mixture models further refine the capture of complex characteristics of salary distribution. To validate the effectiveness and robustness of our model, we conducted experiments on two different datasets. The results show significant improvements in prediction accuracy, model complexity, and risk performance. This study not only provides an efficient and reliable tool for predicting the salaries of college graduates but also offers robust theoretical and empirical foundations for future research in this field.
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Rau, Cheng-Shyuan, Shao-Chun Wu, Jung-Fang Chuang, Chun-Ying Huang, Hang-Tsung Liu, Peng-Chen Chien, and Ching-Hua Hsieh. "Machine Learning Models of Survival Prediction in Trauma Patients." Journal of Clinical Medicine 8, no. 6 (June 5, 2019): 799. http://dx.doi.org/10.3390/jcm8060799.
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Background: We aimed to build a model using machine learning for the prediction of survival in trauma patients and compared these model predictions to those predicted by the most commonly used algorithm, the Trauma and Injury Severity Score (TRISS). Methods: Enrolled hospitalized trauma patients from 2009 to 2016 were divided into a training dataset (70% of the original data set) for generation of a plausible model under supervised classification, and a test dataset (30% of the original data set) to test the performance of the model. The training and test datasets comprised 13,208 (12,871 survival and 337 mortality) and 5603 (5473 survival and 130 mortality) patients, respectively. With the provision of additional information such as pre-existing comorbidity status or laboratory data, logistic regression (LR), support vector machine (SVM), and neural network (NN) (with the Stuttgart Neural Network Simulator (RSNNS)) were used to build models of survival prediction and compared to the predictive performance of TRISS. Predictive performance was evaluated by accuracy, sensitivity, and specificity, as well as by area under the curve (AUC) measures of receiver operating characteristic curves. Results: In the validation dataset, NN and the TRISS presented the highest score (82.0%) for balanced accuracy, followed by SVM (75.2%) and LR (71.8%) models. In the test dataset, NN had the highest balanced accuracy (75.1%), followed by the TRISS (70.2%), SVM (70.6%), and LR (68.9%) models. All four models (LR, SVM, NN, and TRISS) exhibited a high accuracy of more than 97.5% and a sensitivity of more than 98.6%. However, NN exhibited the highest specificity (51.5%), followed by the TRISS (41.5%), SVM (40.8%), and LR (38.5%) models. Conclusions: These four models (LR, SVM, NN, and TRISS) exhibited a similar high accuracy and sensitivity in predicting the survival of the trauma patients. In the test dataset, the NN model had the highest balanced accuracy and predictive specificity.
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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 (October 21, 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 dengue incidence, and to improve the accuracy of dengue incidence prediction with zero-inflated data. Methodology We predicted dengue incidence at six spatiotemporal resolutions and compared their prediction accuracy. Six ML algorithms were compared: generalized additive models, random forests, conditional inference forest, artificial neural networks, support vector machines and regression, and extreme gradient boosting. Data from 2009 to 2012 were used for training, and data from 2013 were used for model validation with quantitative and qualitative dengue variables. To address the inaccuracy in the quantitative prediction of dengue incidence due to zero-inflated data at fine spatiotemporal scales, we developed a hybrid approach in which the second-stage quantitative prediction is performed only when/where the first-stage qualitative model predicts the occurrence of dengue cases. Principal findings At higher resolutions, the dengue incidence data were zero-inflated, which was insufficient for quantitative pattern extraction of relationships between dengue incidence and environmental variables by ML. Qualitative models, used as binary variables, eased the effect of data distribution. Our novel hybrid approach of combining qualitative and quantitative predictions demonstrated high potential for predicting zero-inflated or rare phenomena, such as dengue. Significance Our research contributes valuable insights to the field of spatiotemporal dengue prediction and provides an alternative solution to enhance prediction accuracy in zero-inflated data where hurdle or zero-inflated models cannot be applied.
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Dashti, Mahmood, Farshad Khosraviani, Tara Azimi, Mohammad Soroush Sehat, Ehsan Alekajbaf, Amir Fahimipour, and Niusha Zare. "Predicting Mandibular Bone Growth Using Artificial Intelligence and Machine Learning: A Systematic Review." Advances in Artificial Intelligence and Machine Learning 04, no. 03 (2024): 2731–45. http://dx.doi.org/10.54364/aaiml.2024.43159.
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Introduction The accurate prediction of mandibular bone growth is crucial in orthodontics and maxillofacial surgery, impacting treatment planning and patient outcomes. Traditional methods often fall short due to their reliance on linear models and clinician expertise, which are prone to human error and variability. Artificial intelligence (AI) and machine learning (ML) offer advanced alternatives, capable of processing complex datasets to provide more accurate predictions. This systematic review examines the efficacy of AI and ML models in predicting mandibular growth compared to traditional methods. Method. A systematic review was conducted following the PRISMA guidelines, focusing on studies published up to July 2024. Databases searched included PubMed, Embase, Scopus, and Web of Science. Studies were selected based on their use of AI and ML algorithms for predicting mandibular growth. A total of 31 studies were identified, with 6 meeting the inclusion criteria. Data were extracted on study characteristics, AI models used, and prediction accuracy. The risk of bias was assessed using the QUADAS-2 tool. Results. The review found that AI and ML models generally provided high accuracy in predicting mandibular growth. For instance, the LASSO model achieved an average error of 1.41 mm for predicting skeletal landmarks. However, not all AI models outperformed traditional methods; in some cases, deep learning models were less accurate than conventional growth prediction models. Discussion. The variability in datasets and study designs across the included studies posed challenges for comparing AI models’ effectiveness. Additionally, the complexity of AI models may limit their clinical applicability. Despite these challenges, AI and ML show significant promise in enhancing predictive accuracy for mandibular growth. Conclusion. AI and ML models have the potential to revolutionize mandibular growth prediction, offering greater accuracy and reliability than traditional methods. However, further research is needed to standardize methodologies, expand datasets, and improve model interpretability for clinical integration.
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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 (August 5, 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 structure, combined with ensemble voting for the final prediction, aims to exploit the strengths of multiple models while mitigating their individual weaknesses. Our results demonstrate significant improvement in prediction accuracy, providing a compelling case for the potential utility of these techniques in healthcare applications, specifically for prediction of diabetes where we achieve compelling model accuracy of 91.5% on the test set on a particular challenging dataset, where we compare thoroughly against many other methodologies.
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Ding, Zizhou, and Ahmed Elkady. "Accuracy assessment of predictive models for semirigid extended end‐plate connections." ce/papers 6, no. 3-4 (September 2023): 1263–68. http://dx.doi.org/10.1002/cepa.2242.
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AbstractTaking advantage of semi‐rigid connections' inherent stiffness and strength can highly benefit the steel industry, since this can lead to efficient designs and consequently to lower construction costs and carbon emissions. This requires the ability to accurately predict the connection's moment‐rotation response. National standards and research studies proposed a number of predictive models to do so, including analytical, mechanical, and empirical models. This applies to the popular bolted extended end‐plate connections. A number of studies have indicated that such models have limitations and may provide inaccurate predictions. In this study, a recently compiled database, comprising more than 750 test specimens, is used to assess the accuracy of several models in predicting key response quantities related to stiffness, strength, and ductility. The mean and standard deviation of the prediction error is quantified while highlighting the advantages and drawbacks of each model. The results aim to shed light on the limitations of existing predictive models and offer recommendations for improved future models.
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Uppalaiah, Bandu, D. Mallikarjuna Reddy, Vediyappan Govindan, and Haewon Byeon. "Optimizing quality of service forecasting in mobile networks through modified walrus optimization and multivariate approaches." Edelweiss Applied Science and Technology 8, no. 6 (December 16, 2024): 7878–901. https://doi.org/10.55214/25768484.v8i6.3717.
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This paper presents Ensemble-based Service Quality Prediction (EAQP), an automated method for predicting service quality under changing mobile network conditions. EAQP incorporates data preparation methods such as transformation, purification, & imputation, and then performs feature extraction utilizing statistical, geographical, as well as temporal approaches. An improved feature selection method, using a unique weighting approach and optimized by a modified Walrus Optimization Algorithm, improves the accuracy of predictions. EAQP utilizes a variety of prediction models such as support vector regression, recurrent neural network models, bi-directional short-term long-term memory networks, extreme learning machines, along with multi-layer perceptron neural networks to enhance predictive accuracy. EAQP uses complex optimization algorithms and ensemble learning approaches to provide precise and dependable predictions about service quality in real-time. This helps in proactive network management as well as improvement. This comprehensive approach shows potential for boosting network efficiency, optimizing the distribution of resources, and enhancing the end-user experience when using mobile communications systems.
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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 (March 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 meet real-time prediction requirements. (2) Feature extraction is a crucial step in photovoltaic power generation prediction. However, traditional feature extraction methods often focus solely on surface features, and fail to capture the inherent relationships between various influencing factors in photovoltaic power generation data, such as light intensity, temperature, and more. To overcome these limitations, this paper proposes a mid-term PV power prediction model that combines Graph Convolutional Network (GCN) and Informer models. This fusion model leverages the multi-output capability of the Informer model to ensure the timely generation of predictions for long sequences. Additionally, it harnesses the feature extraction ability of the GCN model from nodes, utilizing graph convolutional modules to extract feature information from the ‘query’ and ‘key’ components within the attention mechanism. This approach provides more reliable feature information for mid-term PV power prediction, thereby ensuring the accuracy of long sequence predictions. Results demonstrate that the GCN–Informer model significantly reduces prediction errors while improving the precision of power generation forecasting compared to the original Informer model. Overall, this research enhances the prediction accuracy of PV power generation and contributes to advancing the field of clean energy.
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Chiang, Johannes K., and Renhe Chi. "A Novel Stock Price Prediction and Trading Methodology Based on Active Learning Surrogated with CycleGAN and Deep Learning and System Engineering Integration: A Case Study on TSMC Stock Data." FinTech 3, no. 3 (September 18, 2024): 427–59. http://dx.doi.org/10.3390/fintech3030024.
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Technical analysis, reliant on statistics and charting tools, is a predominant method for predicting stock prices. However, given the impact of the joint effect of stock price and trading volume, analyses focusing solely on single factors at isolated time points often yield partial or inaccurate results. This study introduces the application of Cycle Generative Adversarial Network (CycleGAN) alongside Deep Learning (DL) models, such as Residual Neural Network (ResNet) and Long Short-Term Memory (LSTM), to assess the joint effects of stock price and trading volume on prediction accuracy. By incorporating these models into system engineering (SE), the research aims to decode short-term stock market trends and improve investment decisions through the integration of predicted stock prices with Bollinger Bands. Thereby, active learning (AL) is employed to avoid over-and under-fitting and find the hyperparameters for the overall system model. Focusing on TSMC’s stock price prediction, the use of CycleGAN for analyzing 30-day stock data showcases the capability of ResNet and LSTM models in achieving high accuracy and F-1 scores for a five-day prediction period. Further analysis reveals that combining DL predictions with SE principles leads to more precise short-term forecasts. Additionally, integrating these predictions with Bollinger Bands demonstrates a decrease in trading frequency and a significant 30% increase in average Return on Investment (ROI). This innovative approach marks a first in the field of stock market prediction, offering a comprehensive framework for enhancing predictive accuracy and investment outcomes.
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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 (March 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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Olusanya, Micheal O., Ropo Ebenezer Ogunsakin, Meenu Ghai, and Matthew Adekunle Adeleke. "Accuracy of Machine Learning Classification Models for the Prediction of Type 2 Diabetes Mellitus: A Systematic Survey and Meta-Analysis Approach." International Journal of Environmental Research and Public Health 19, no. 21 (November 1, 2022): 14280. http://dx.doi.org/10.3390/ijerph192114280.
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Soft-computing and statistical learning models have gained substantial momentum in predicting type 2 diabetes mellitus (T2DM) disease. This paper reviews recent soft-computing and statistical learning models in T2DM using a meta-analysis approach. We searched for papers using soft-computing and statistical learning models focused on T2DM published between 2010 and 2021 on three different search engines. Of 1215 studies identified, 34 with 136952 patients met our inclusion criteria. The pooled algorithm’s performance was able to predict T2DM with an overall accuracy of 0.86 (95% confidence interval [CI] of [0.82, 0.89]). The classification of diabetes prediction was significantly greater in models with a screening and diagnosis (pooled proportion [95% CI] = 0.91 [0.74, 0.97]) when compared to models with nephropathy (pooled proportion = 0.48 [0.76, 0.89] to 0.88 [0.83, 0.91]). For the prediction of T2DM, the decision trees (DT) models had a pooled accuracy of 0.88 [95% CI: 0.82, 0.92], and the neural network (NN) models had a pooled accuracy of 0.85 [95% CI: 0.79, 0.89]. Meta-regression did not provide any statistically significant findings for the heterogeneous accuracy in studies with different diabetes predictions, sample sizes, and impact factors. Additionally, ML models showed high accuracy for the prediction of T2DM. The predictive accuracy of ML algorithms in T2DM is promising, mainly through DT and NN models. However, there is heterogeneity among ML models. We compared the results and models and concluded that this evidence might help clinicians interpret data and implement optimum models for their dataset for T2DM prediction.
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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 (July 10, 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 land uses and soil depths to evaluate the performance of LIBS and vis-NIRS, as well as their combined spectra, in predicting soil organic carbon (SOC) and texture. Firstly, partial least squares regression (PLSR) models were developed to correlate both LIBS and vis-NIRS spectra with the reference data. Subsequently, we merged LIBS and vis-NIRS data and developed PLSR models for the combined spectra. Finally, interval partial least squares regression (iPLSR) models were applied to assess the impact of variable selection on prediction accuracy for both LIBS and vis-NIRS. Despite being fundamentally different techniques, LIBS and vis-NIRS displayed comparable prediction performance for the investigated soil properties. LIBS achieved a root mean square error of prediction (RMSEP) of <7% for texture and 0.5% for SOC, while vis-NIRS achieved an RMSEP of <8% for texture and 0.5% for SOC. Combining LIBS and vis-NIRS spectra improved the prediction accuracy by 16% for clay, 6% for silt and sand, and 2% for SOC compared to single-sensor LIBS predictions. On the other hand, vis-NIRS single-sensor predictions were improved by 10% for clay, 17% for silt, 16% for sand, and 4% for SOC. Furthermore, applying iPLSR for variable selection improved prediction accuracy for both LIBS and vis-NIRS. Compared to LIBS PLSR predictions, iPLSR achieved reductions of 27% and 17% in RMSEP for clay and sand prediction, respectively, and an 8% reduction for silt and SOC prediction. Similarly, vis-NIRS iPLSR models demonstrated reductions of 6% and 4% in RMSEP for clay and SOC, respectively, and a 3% reduction for silt and sand. Interestingly, LIBS iPLSR models outperformed combined LIBS-vis-NIRS models in terms of prediction accuracy. Although combining LIBS and vis-NIRS improved the prediction accuracy of texture and SOC, LIBS coupled with variable selection had a greater benefit in terms of prediction accuracy. Future studies should investigate the influence of reference method uncertainty on prediction accuracy.
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Gruca, Thomas S., and Joyce E. Berg. "PUBLIC INFORMATION BIAS AND PREDICTION MARKET ACCURACY." Journal of Prediction Markets 1, no. 3 (December 14, 2012): 219–31. http://dx.doi.org/10.5750/jpm.v1i3.430.
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How do prediction markets achieve high levels of accuracy? We propose that, in some situations, traders in prediction markets improve upon publicly available information. Specifically, when there is a known bias in publicly available information, markets provide an incentive for traders to “de-bias” this information. In such a situation, a prediction market will provide a more accurate forecast than the public information available to traders. We test our conjecture using real-money prediction markets for seven local elections in the United States. We find that the prediction market forecasts are significantly more accurate than those generated using the pre-election polls.Previous versions of this paper were presented at the Workshop on The Growth of Gambling and Prediction Markets, the ISBM Conference on Prediction Markets in Marketing: Issues, Challenges and Research Opportunities, the DIMACS Workshop on Markets as Predictive Devices, and the 17th Annual AMA Advanced Research Techniques Forum. The authors thank the participants for their insights which have helped improve this research
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Sallam, Ahmad H., Emily Conley, Dzianis Prakapenka, Yang Da, and James A. Anderson. "Improving Prediction Accuracy Using Multi-allelic Haplotype Prediction and Training Population Optimization in Wheat." G3: Genes|Genomes|Genetics 10, no. 7 (May 5, 2020): 2265–73. http://dx.doi.org/10.1534/g3.120.401165.
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The use of haplotypes may improve the accuracy of genomic prediction over single SNPs because haplotypes can better capture linkage disequilibrium and genomic similarity in different lines and may capture local high-order allelic interactions. Additionally, prediction accuracy could be improved by portraying population structure in the calibration set. A set of 383 advanced lines and cultivars that represent the diversity of the University of Minnesota wheat breeding program was phenotyped for yield, test weight, and protein content and genotyped using the Illumina 90K SNP Assay. Population structure was confirmed using single SNPs. Haplotype blocks of 5, 10, 15, and 20 adjacent markers were constructed for all chromosomes. A multi-allelic haplotype prediction algorithm was implemented and compared with single SNPs using both k-fold cross validation and stratified sampling optimization. After confirming population structure, the stratified sampling improved the predictive ability compared with k-fold cross validation for yield and protein content, but reduced the predictive ability for test weight. In all cases, haplotype predictions outperformed single SNPs. Haplotypes of 15 adjacent markers showed the best improvement in accuracy for all traits; however, this was more pronounced in yield and protein content. The combined use of haplotypes of 15 adjacent markers and training population optimization significantly improved the predictive ability for yield and protein content by 14.3 (four percentage points) and 16.8% (seven percentage points), respectively, compared with using single SNPs and k-fold cross validation. These results emphasize the effectiveness of using haplotypes in genomic selection to increase genetic gain in self-fertilized crops.
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Lu, Di. "Prediction-based early warning system for overflow of people in metro stations." Applied and Computational Engineering 30, no. 1 (January 22, 2024): 179–88. http://dx.doi.org/10.54254/2755-2721/30/20230095.
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As cities rapidly urbanize, handling the management of crowded transportation hubs, notably metro stations, has become an immediate concern. High passenger traffic can lead to severe risks, including stampedes. While many past passenger flow forecasting systems aim to enhance prediction accuracy, the inherently noisy nature of passenger flow data makes it challenging for existing technologies to provide stable and precise predictions, making passenger flow management based solely on these models risky. This paper introduces a novel system that mitigates this risk by integrating a predictive model with managerial methods. The proposed management framework formulates a unique model for each station, determining a risk deviation coefficient grounded in the station's historical prediction accuracy. Station management is then holistically executed based on this coefficient and the predictive model. This paper employs the LSTM model for station-specific passenger flow prediction and defines the risk-related parameter , taking prediction accuracy into account. This adjusted LSTM prediction is then utilized to proactively streamline resource allocation, targeting improved passenger safety and overall station experience.
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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 (March 9, 2024): 3358–62. http://dx.doi.org/10.55248/gengpi.5.0324.0759.
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Zhao, Nana, Wenming Lu, and Zhongyuan Zhang. "The Application of Time Series Models in the Prediction of Hand-Foot-Mouth Disease Incidence." International Journal of Biology and Life Sciences 6, no. 3 (July 30, 2024): 24–29. http://dx.doi.org/10.54097/jq7es934.
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Hand-Foot-Mouth Disease (HFMD) is a contagious illness predominantly affecting infants and children under five years old, caused by human enteroviruses. Over the past five decades, HFMD has rapidly spread across the Asia-Pacific region, gradually evolving into a significant public health challenge for many countries within this area. Currently, HFMD has emerged as an increasingly severe public health issue in our country. Therefore, analyzing the influencing factors of HFMD and predicting its incidence trends are of paramount importance for the prevention of the disease. With the rapid advancement of artificial intelligence technology, predictive models employing deep learning techniques have demonstrated superior performance among various infectious disease prediction models. This paper aims to construct a predictive model using deep learning methods to further enhance the accuracy of HFMD incidence predictions. We compared the effectiveness of Long Short-Term Memory (LSTM) networks, Transformer, and Informer models in HFMD prediction. The research findings indicate that the Informer model, by utilizing self-attention mechanisms and convolutional neural networks, can more effectively address long-term dependencies in time series data, thereby showing better performance in HFMD prediction compared to the LSTM and Transformer models. This has led to improvements in prediction accuracy and generalization capability.
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Li, Lu, Yingying Dong, Yingxin Xiao, Linyi Liu, Xing Zhao, and Wenjiang Huang. "Combining Disease Mechanism and Machine Learning to Predict Wheat Fusarium Head Blight." Remote Sensing 14, no. 12 (June 7, 2022): 2732. http://dx.doi.org/10.3390/rs14122732.
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Wheat Fusarium head blight (FHB) can be effectively controlled through prediction. To address the low accuracy and poor stability of model predictions of wheat FHB, a prediction method of wheat FHB that couples a logistic regression mechanism-based model and k-nearest neighbours (KNN) model is proposed in this paper. First, we selected predictive factors, including remote sensing-based and meteorological factors. Then, we quantitatively expressed the factor weights of the disease occurrence and development mechanisms in the disease prediction model by using a logistic model. Subsequently, we integrated the obtained factor weights into the predictive factors and input the predictive factors with weights into the KNN model to predict the incidence of wheat FHB. Finally, the accuracy and generalizability of the models were evaluated. Wheat fields in Changfeng, Dingyuan, Fengyuan, and Feidong counties, Anhui Province, where wheat FHB often occurs, were used as the study area. The incidences of wheat FHB on 29 April and 10 May 2021 were predicted. Compared with a model that did not consider disease mechanism, the accuracy of our model increased by approximately 13%. The overall accuracies of the models for the two dates were 0.88 and 0.92, and the F1 index was 0.86 and 0.94, respectively. The results show that the predictions made with the logistic-KNN model had higher accuracy and better stability than those made with the KNN model, thus achieving remote sensing-based high-precision prediction of wheat FHB.