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Journal articles on the topic 'Prediction of Accuracy result'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

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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TJ, Mr GOWTHAM. "ELECTION RESULT PREDICTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem36223.

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This study utilizes Text Blob for sentiment analysis of social media and news data to predict election results. By analyzing sentiment polarity, a comprehensive understanding of public opinion towards political entities is obtained. Machine learning algorithms are employed to build predictive models using historical election data. Results demonstrate the effectiveness of sentiment analysis in enhancing prediction accuracy compared to traditional methods. This research has implications for political campaigns and policymakers in gauging public sentiment and anticipating electoral outcomes. Keyw

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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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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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Assrani, Dwika, Pahala Sirait, and Andri Andri. "Pembobotan Kriteria Dalam Prediksi Meningitis Tuberkulosis Menggunakan Metode SWARA dan Nearest Neighbor." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 4 (2021): 1453. http://dx.doi.org/10.30865/mib.v5i4.3276.

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Weights greatly affect the value and results of decisions or predictions of a test data, a problem that often occurs in the results of the prediction process is the weighting of symptom attributes which is less certain of the value of the weight, thus affecting the prediction results and the level of accuracy of a prediction itself. This study predicts a data using the Nearest Neighbor method where in the process of predicting the attribute weight value does not yet have a definite value for testing. Then we need an attribute weighting for each test attribute to get a definite weight value res

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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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Tong, Tingting, and Zhen Li. "Predicting learning achievement using ensemble learning with result explanation." PLOS ONE 20, no. 1 (2025): e0312124. https://doi.org/10.1371/journal.pone.0312124.

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Predicting learning achievement is a crucial strategy to address high dropout rates. However, existing prediction models often exhibit biases, limiting their accuracy. Moreover, the lack of interpretability in current machine learning methods restricts their practical application in education. To overcome these challenges, this research combines the strengths of various machine learning algorithms to design a robust model that performs well across multiple metrics, and uses interpretability analysis to elucidate the prediction results. This study introduces a predictive framework for learning

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Liu, Keqian, Ang Li, Xinran Lin, Zhuobin Mao, and Weiyang Zhang. "Empirical study on the performance of various machine learning models in predicting stock price movements as a binary classification task." Applied and Computational Engineering 55, no. 1 (2024): 129–44. http://dx.doi.org/10.54254/2755-2721/55/20241403.

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This paper examines the accuracy of stock price rise-or-fall predictions of seven different machine learning algorithms, including support vector machines and random forests, for three industry types: securities, banks, and Internet companies. The purpose of the research is to explore the effects of different models in the stock market, so as to help people choose the optimal machine learning model in predicting different types of stocks. The study produced nine features based on the study by Patel et al for prediction. By collecting 9 types of stock data from companies in different industries

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Alamer, Latifah. "Intelligent Health Risk and Disease Prediction Using Optimized Naive Bayes Classifier." Journal of Internet Services and Information Security 13, no. 1 (2023): 01–10. http://dx.doi.org/10.58346/jisis.2023.i1.001.

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Machine learning is the subset of Artificial Intelligence and it is used for prediction various real time data analytics applications. Health care monitoring is the major area to analyse the result and make effective decisions. We need intelligent and automated process for predicting diseases using medical dataset. Machine learning methods are proposed to handle the dataset. Smart healthcare prediction is proposed to identify the user or patient information or symptoms as an input. Our system has forecasting accuracy index based on likelihood of the disease and health information. We use Naive

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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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-, Zulfauzi, Budi Santoso, M. Agus Syamsul Arifin, and Siti Nuraisyah. "IMPLEMENTATION OF ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) METHOD ON RICE PRICE PREDICTION IN LUBUKLINGGAU CITY." JURNAL TEKNOLOGI DAN OPEN SOURCE 4, no. 2 (2021): 260–69. http://dx.doi.org/10.36378/jtos.v4i2.1847.

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The problem behind this research is the imbalance between the capacity offered and the capacity demanded by the community, resulting in uncontrolled rice prices, so it is necessary to predict rice price in the future to monitor the stability of rice prices in the Lubuklinggau City area. In this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) method was used to predict future rice prices. The sample used in this study is data on rice price in Lubuklinggau City from January 2016 to December 2020. The result of the prediction of rice price in the Lubuklinggau City area for the next five

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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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Malkin, Z. M. "Analysis of progress in improving the prediction accuracy of Celestial Pole coordinates over the past 16 years." Publications of the Pulkovo Observatory 232 (March 2024): 49–53. http://dx.doi.org/10.31725/0367-7966-2024-232-49-53.

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The prediction of the Earth’s rotation parameters, including the coordinates of the celestial pole (precession-nutation angles), is necessary for many practical applications. This work is devoted to the study of changes in the accuracy of the prediction of precession-nutation angles over time over the past 16 years. This study was conducted on the basis of real predictions computed in 2007–2022 at the U.S. Naval Observatory, which serves as the Center of Rapid Service and Predictions of the International Earth Rotation and Reference Systems Service (IERS), and at the Pulkovo Observatory. As a

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Okunlola, O. A., and adebola Ojo. "Performance Evaluation of Classification Algorithms on Academic Performance of Postgraduate Students." International Journal of Computer Applications (IJCA) 184, no. 45 (2023): 13–16. https://doi.org/10.5120/ijca2023922522.

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Educational data mininghas contributed to enhancing student academic performance by way of enabling stakeholders in academic institutions to have a pre-knowledge of the risks and dangers ahead and how to mitigate them. Prediction algorithms perform differently on dataset, and so, the need to develop models using different prediction algorithms and evaluating the result of such predictions is very important in order to be sure the best algorithm for a particular dataset is used.This work employed four classifiers: K-Nearest-Neighbour, Neural Network, Naïve Bayes and Decision Tree to m

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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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Alimkhan, A. M. "Прогнозирование результатов игры в баскетбол с использованием алгоритмов глубокого обучения". INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGIES, № 6(6) (14 березня 2022): 112–19. http://dx.doi.org/10.54309/ijict.2022.2.6.015.

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With the development of information technology and an ever-expanding statistical base, the possibilities for forecasting are expanding, and the dependencies of the calculated indicators on the result are considered. In this article we compare 3 most widely spread game result prediction models, and namely, Support Vector Regression, K-Nearest Neighbor model and the Linear Regression model in terms of their prediction accuracy and experimentally demonstrate the advantages of the latter.

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Lu, Yi. "Heart Disease Prediction Model based on Prophet." Highlights in Science, Engineering and Technology 39 (April 1, 2023): 1035–40. http://dx.doi.org/10.54097/hset.v39i.6700.

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Heart disease is one of the major causes of death for people of all races, genders, and nationalities. In the United States, for instance, heart disease causes more than 600,000 deaths every year and is the largest leading cause of death in 2020. A reliable heart diseases mortality prediction model could acknowledge the patients’ medical professionals that the heart disease risk level of the specific group. This approach is significant in preventing further increases in heart disease mortality rates worldwide. Nowadays, multiple Machine Learning (ML) models, including hybrid models produced im

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B., Senthil Kumar, and Gunavathi R. "Early prediction of diabetes using Feature Transformation and hybrid Random Forest Algorithm." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 787–91. https://doi.org/10.35940/ijeat.E9836.069520.

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Diabetes is the most common chronic disease among the world. Early prediction of these will assist the physicians to provide the improved treatment. Machine learning approaches are widely used for predicting the disease at the earlier stage. However the selecting the significant features and the suitable classifier are still reduces the diagnosis accuracy. In this paper the PCA based feature transformation and the hybrid random forest classifier is utilized for diabetes prediction. PCA attempt to identify the best subset of transformed components that greatly improves the classification result

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Bhushan, Megha, Utkarsh Verma, Chetna Garg, and Arun Negi. "Machine Learning-Based Academic Result Prediction System." International Journal of Software Innovation 12, no. 1 (2023): 1–14. http://dx.doi.org/10.4018/ijsi.334715.

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Students' academic performance is a critical issue as it decides his/her career. It is pivotal for the educational institutes to track the performance record because it can help to enhance the standard of their quality education. Thus, the role of the academic result prediction system comes into existence which uses semester grade point average (SGPA) as a metric. The proposed work aims to create a model that can forecast the SGPA of students based on certain traits. It predicts the result in the form of SGPA of computer science students considering their past academic performance, study, and

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Yu, Feng Ming, Xi Cang Li, Jin Hua Song, Chun Xiang Gao, and Chun Long Jiang. "Research on Wind Power Prediction by Combining Mesoscale Numerical Model with Neural Network Model." Advanced Materials Research 512-515 (May 2012): 771–77. http://dx.doi.org/10.4028/www.scientific.net/amr.512-515.771.

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Effective wind power prediction on wind farm can not only guarantee safe operation of wind farm, but also increase wind power storage and utilization efficiency. This research combines mesoscale numerical weather prediction model with BP neural network model for the use of wind power prediction. WRF model is used to recalculate the meteorological elements of trial wind farm from Jun. 2008 to Jun. 2009, and the accuracy check result shows that the correlation coefficient between predicted value and corresponding measured value of wind speed reaches 0.72. Predictions accuracy of wind direction,

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Hosseini, Seyed Amirhossein, and Omar Smadi. "How Prediction Accuracy Can Affect the Decision-Making Process in Pavement Management System." Infrastructures 6, no. 2 (2021): 28. http://dx.doi.org/10.3390/infrastructures6020028.

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One of the most important components of pavement management systems is predicting the deterioration of the network through performance models. The accuracy of the prediction model is important for prioritizing maintenance action. This paper describes how the accuracy of prediction models can have an effect on the decision-making process in terms of the cost of maintenance and rehabilitation activities. The process is simulating the propagation of the error between the actual and predicted values of pavement performance indicators. Different rate of error (10%, 30%, 50%, 70%, and 90%) was added

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Assegie, Tsehay Admassu. "Support Vector Machine And K-Nearest Neighbor Based Liver Disease Classification Model." Indonesian Journal of electronics, electromedical engineering, and medical informatics 3, no. 1 (2021): 9–14. http://dx.doi.org/10.35882/ijeeemi.v3i1.2.

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Machine-learning approaches have become greatly applicable in disease diagnosis and prediction process. This is because of the accuracy and better precision of the machine learning models in disease prediction. However, different machine learning models have different accuracy and precision on disease prediction. Selecting the better model that would result in better disease prediction accuracy and precision is an open research problem. In this study, we have proposed machine learning model for liver disease prediction using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) learning a

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Pavithraa, G., and S. Sivaprasad. "Analysis And Comparison Of Prediction Of Heart Disease Using Novel Random Forest And Naive Bayes Algorithm." CARDIOMETRY, no. 25 (February 14, 2023): 788–93. http://dx.doi.org/10.18137/cardiometry.2022.25.788793.

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Aim : Prediction of heart disease using Novel Random Forest and comparing its accuracy with Naive Bayes algorithm. Materials and methods: Two groups are proposed for predicting the accuracy (%) of heart disease. Namely, the Novel Random Forest and Naive Bayes algorithm. Here we take 20 samples each for evaluation and compared. The sample size was calculated using G power with pretest power at 80% and the alpha of 0.05 value. Result : The Novel Random Forest gives better accuracy (86.40%) compared to the Naive Bayes accuracy (80.08%). Therefore the statistical significance of Novel Random Fores

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Teja, P. P. S., and T. Veeramani. "Improving the Efficiency of Heart Disease Prediction Using Novel Random Forest Classifier Over Support Vector Machine Algorithm." CARDIOMETRY, no. 25 (February 14, 2023): 1468–76. http://dx.doi.org/10.18137/cardiometry.2022.25.14681476.

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Aim: The Aim of the research study is to see how accurate Novel Random Forest (RF) and Support Vector Machine (SVM) classification algorithms were in predicting heart disease.Materials and Methods: The RF Classifier is used to a 304-Record dataset with heart disease.A paradigm for heart disease prediction in the medical field has been presented and developed, comparing Novel Random Forest with SVM classifiers. The total number of images in the sample was 42, with 21 in each test group. Result:-The classifiers were evaluated, predictions and accuracy were supplied. Based on the information prov

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Muslim, Fitri, and Julie Dewi Barliana. "Prediction Errors and Accuracy of Intraocular Lens (IOL) Calculation Formulas in Pediatric Eyes." Ophthalmologica Indonesiana 46, no. 2 (2020): 175. http://dx.doi.org/10.35749/journal.v46i2.100096.

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   Background: Acquiring an accurate intraocular (IOL) power in children undergoing cataract surgery is challenging. Different IOL calculation formulas in children have been previously studied to achieve a precise prediction of the IOL power. Larger errors in IOL formula predictions have shown in several studies on children as future growth of the eye affects the keratometry readings and axial length. Prediction error (PE) and absolute prediction error (APE) can be effective indicators in assessing the accuracy of IOL power calculation formulas. Therefore, this review aims to

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Huang, Xing, Ru Ming Wei, Ling Yuan, and Xiao Qing Li. "Research on the Prediction of High Embankment Settlement Based on the Real-Time Monitoring On-Site." Applied Mechanics and Materials 256-259 (December 2012): 1754–60. http://dx.doi.org/10.4028/www.scientific.net/amm.256-259.1754.

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Based on the real-time settlement monitoring of embankment on-site, Hyperbola method and Index curve method are used in the prediction of embankment settlement and the prediction results were compared to verify the feasibility and the potential engineering value of the prediction of embankment settlement. The calculation results showed that the relative error of the results calculated by the above methods was controlled within 2%. The accuracy of early settlement prediction of Index curve method was lower than the accuracy of later settlement prediction, but the predicting result met the needs

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Rajam, Francis Julee, and Britto Ramesh Kumar Swakkin. "MAPLE: A Novel Processing Technique for Adult Autism Prediction." Engineering, Technology & Applied Science Research 15, no. 3 (2025): 23901–6. https://doi.org/10.48084/etasr.10864.

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Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that affects individuals throughout their lives. Predicting autism in adults is a critical challenge with significant implications for early intervention and support. Current autism prediction systems in adults frequently encounter difficulties arising from incomplete or noisy data, which can affect the accuracy of prediction. This paper presents MAPLE (Missing data imputation and Anomaly removal for Preprocessing with Learning Enhancement), a novel algorithm designed to address these challenges and improve data quality in

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Christanto, Febrian Wahyu, Victor Gayuh Utomo, Rastri Prathivi, and Christine Dewi. "The Impact of Financial Statement Integration in Machine Learning for Stock Price Prediction." International Journal of Information Technology and Computer Science 16, no. 1 (2024): 35–42. http://dx.doi.org/10.5815/ijitcs.2024.01.04.

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In the capital market, there are two methods used by investors to make stock price predictions, namely fundamental analysis, and technical analysis. In computer science, it is possible to make prediction, including stock price prediction, use Machine Learning (ML). While there is research result that said both fundamental and technical parameter should give an optimum prediction there is lack of confirmation in Machine Learning to this result. This research conducts experiment using Support Vector Regression (SVR) and Support Vector Machine (SVM) as ML method to predict stock price. Further, t

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Thon Nguyen, Da, Hanh T Tan, and Duy Hoang Pham. "Improving Webpage Access Predictions Based on Sequence Prediction and PageRank Algorithm." Interdisciplinary Journal of Information, Knowledge, and Management 14 (2019): 027–44. http://dx.doi.org/10.28945/4176.

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Aim/Purpose: In this article, we provide a better solution to Webpage access prediction. In particularly, our core proposed approach is to increase accuracy and efficiency by reducing the sequence space with integration of PageRank into CPT+. Background: The problem of predicting the next page on a web site has become significant because of the non-stop growth of Internet in terms of the volume of contents and the mass of users. The webpage prediction is complex because we should consider multiple kinds of information such as the webpage name, the contents of the webpage, the user profile, the

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Zhang, Xinchen, Linghao Zhang, Qincheng Zhou, and Xu Jin. "A Novel Bitcoin and Gold Prices Prediction Method Using an LSTM-P Neural Network Model." Computational Intelligence and Neuroscience 2022 (May 5, 2022): 1–12. http://dx.doi.org/10.1155/2022/1643413.

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As a result of the fast growth of financial technology and artificial intelligence around the world, quantitative algorithms are now being employed in many classic futures and stock trading, as well as hot digital currency trades, among other applications today. Using the historical price series of Bitcoin and gold from 9/11/2016 to 9/10/2021, we investigate an LSTM-P neural network model for predicting the values of Bitcoin and gold in this research. We first employ a noise reduction approach based on the wavelet transform to smooth the fluctuations of the price data, which has been shown to

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Chiang, Jie Lun, and Yu Shiue Tsai. "Reservoir Drought Prediction Using Two-Stage SVM." Applied Mechanics and Materials 284-287 (January 2013): 1473–77. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.1473.

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The support vector machine (SVM) has been applied to drought prediction and it typically yields good performance on overall accuracy. However, the prediction accuracy of the drought category is much lower than that of the non-drought and severe drought categories. In this study, a two-stage approach was used to improve the SVM to increase the drought prediction accuracy. Four features, (1) reservoir storage, (2) inflows, (3) critical limit of operation rule curves, and (4) the Nth ten-day in a year, were used as input data to predict reservoir drought. We used these features as input data beca

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Si, Zhan Jun, Yuan Hu, Jia Wang, and Yu Li. "Study on Color Spectrum Prediction Accuracy of CYNSN Model." Applied Mechanics and Materials 262 (December 2012): 3–8. http://dx.doi.org/10.4028/www.scientific.net/amm.262.3.

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Based on comparison of spectrum models, this paper chose CYNSN spectrum model as the study object, spectrum prediction as the main study content, the best model parameters’ acquisition as the aim, RRMS and GFC as the evaluate parameters of prediction accuracy. This paper designed the experiment, training samples and test samples to measure and calculate the parameters by programming: Ai(effective dot coverage ratio of the i-th Neugebauer primary), Rλ, i(spectral reflectivity of the i-th Neugebauer primary), n (Yule-Nielsen coefficient) and cell (cell number). The result of experiment and calcu

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Goud, P. Abhishek, and Dr M. Arathi. "Machine Learning Algorithms for COPD Patients Readmission Prediction." International Journal for Research in Applied Science and Engineering Technology 11, no. 9 (2023): 988–93. http://dx.doi.org/10.22214/ijraset.2023.55770.

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Abstract: Patients' readmission might be seen as a crucial aspect in lowering costs while maintaining high-quality patient care. As a result, anticipating and reducing readmission rates for patients will considerably enhance healthcare delivery. The goal of this research is to use machine learning algorithms to predict readmission of COPD (Chronic Obstructive Pulmonary Disease) patients. The major metrics for measuring models' prediction capability in each time frame were Area under Curve (AUC) and Accuracy (ACC). Then, the factors' relevance for each result was clearly recognized, and specifi

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Lee, Sungjin, Soo Cho, Seo-Hoon Kim, et al. "Deep Neural Network Approach for Prediction of Heating Energy Consumption in Old Houses." Energies 14, no. 1 (2020): 122. http://dx.doi.org/10.3390/en14010122.

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Neural network models are data-driven and are effective for predicting and interpreting nonlinear or unexplainable physical phenomena. This study collected building information and heating energy consumption data from 16,158 old houses, selected key input variables that affect the heating energy consumption based on the collected datasets, and developed a deep neural network (DNN) model that showed the highest accuracy for the prediction of heating energy consumption in an old house. As a result, 11 key input variables were selected, and an optimal DNN model was developed. This optimal DNN mod

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Guhdar, Mohammed, Amera Ismail Melhum, and Alaa Luqman Ibrahim. "Optimizing Accuracy of Stroke Prediction Using Logistic Regression." Journal of Technology and Informatics (JoTI) 4, no. 2 (2023): 41–47. http://dx.doi.org/10.37802/joti.v4i2.278.

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An unexpected limitation of blood supply to the brain and heart causes the majority of strokes. Stroke severity can be reduced by being aware of the many stroke warning signs in advance. A stroke may result if the flow of blood to a portion of the brain stops suddenly. In this research, we present a strategy for predicting the early start of stroke disease by using Logistic Regression (LR) algorithms. To improve the performance of the model, preprocessing techniques including SMOTE, feature selection and outlier handling were applied to the dataset. This method helped in achieving a balance of

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Ramya, S., and Dr D. Kalaivani. "Machine Learning Approach for Diabetes Prediction." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 4444–48. http://dx.doi.org/10.22214/ijraset.2022.46012.

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Abstract: Diabetes is an illness caused because of high glucose level in a human body. Diabetes should not be ignored if it is untreated then Diabetes may cause some major issues in a person like: heart related problems, kidney problem, blood pressure, eye damage and it can also affects other organs of human body. Diabetes can be controlled if it is predicted earlier. To achieve this goal this project work we will do early prediction of Diabetes in a human body or a patient for a higher accuracy through applying, Various Machine Learning Techniques. Machine learning techniques Provide better r

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Zou, Jiang Ping, Bi De Zhang, and Yuan Tian. "Short Term Wind Speed Prediction Based on Linear Combination and Error Correction." Applied Mechanics and Materials 521 (February 2014): 135–42. http://dx.doi.org/10.4028/www.scientific.net/amm.521.135.

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In order to improve the accuracy of wind speed prediction, a model based on linear combination and error correction is proposed. Firstly, sustainability model, grey verhulst model and weibull model are modified to obtain three predictions; secondly, the three predictions are matrix empowering analyzed based on the proximity to the ideal value to gain weights and linearly combined based on weights to gain the combination result; finally, the error between the actual value and the combination value is predicted by ARMA model, to correct the prediction wind speed to improve accuracy. The wind spe

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Ismaya, Fikri, Windu Gata, Muhammad Romadhona Kusuma, Dedi Dwi Saputra, and Sigit Kurniawan. "Classification of Software Defect Prediction for Bisnissyariah.co.id Media Portal using Machine Learning Technology." Journal of Innovation and Computer Science (JICS) 1, no. 2 (2025): 61–83. https://doi.org/10.57053/jics.v1i2.96.

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This research was conducted to develop software defect prediction using a dataset from Bisnissyariah, a forum website with up-to-date news related to Islamic business. The study employed a straightforward research design to ensure easy comprehension for the readers. Machine learning models, including Random Forest, Gradient Boosting, and Support Vector Machine, were utilized in this research—the application of these models aimed to evaluate and compare the accuracy of software defect predictions. The research findings indicated that the Random Forest model outperformed the others, achieving an

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Li, Haijun, Yongpeng Zhao, Changxi Ma, Ke Wang, Xiaoting Huang, and Wentao Zhang. "Short-Term Passenger Flow Prediction of Urban Rail Transit Based on SDS-SSA-LSTM." Journal of Advanced Transportation 2022 (September 21, 2022): 1–11. http://dx.doi.org/10.1155/2022/2589681.

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Predicting rail transit passenger flow is crucial for modifying the metro schedule. To increase prediction accuracy, a model is proposed that combines long short-term memory (LSTM) with single spectrum analysis (SSA). Firstly, a stepwise decomposition sampling (SDS) strategy based on SSA progressive decomposition is proposed as a solution to the data leaking issue in traditional sequence decomposition. Then, based on this strategy, the passenger flow time series with complex features is decomposed into a relatively single trend and fluctuation component. Finally, the LSTM network is employed t

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Liu, Yang, Hongyu Chen, Limao Zhang, and Xianjia Wang. "RISK PREDICTION AND DIAGNOSIS OF WATER SEEPAGE IN OPERATIONAL SHIELD TUNNELS BASED ON RANDOM FOREST." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 27, no. 7 (2021): 539–52. http://dx.doi.org/10.3846/jcem.2021.14901.

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Water seepage (WS) is a paramount defect during tunnel operation and directly affects the operational safety of tunnels. Effectively predicting and diagnosing WS are problems that urgently need to be solved. This paper presents a standard and an evaluation index system for WS grades and constructs a sample dataset from monitoring recoreds for demonstration purposes. First, we use bootstrap resampling to build a random forest (RF) seepage risk prediction model. Second, the optimal branch and parameters are selected by the 5-fold cross-validation method to establish the RF prediction training mo

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Wang, Xianhe, Ying Li, Qian Qiao, Adriano Tavares, and Yanchun Liang. "Water Quality Prediction Based on Machine Learning and Comprehensive Weighting Methods." Entropy 25, no. 8 (2023): 1186. http://dx.doi.org/10.3390/e25081186.

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In the context of escalating global environmental concerns, the importance of preserving water resources and upholding ecological equilibrium has become increasingly apparent. As a result, the monitoring and prediction of water quality have emerged as vital tasks in achieving these objectives. However, ensuring the accuracy and dependability of water quality prediction has proven to be a challenging endeavor. To address this issue, this study proposes a comprehensive weight-based approach that combines entropy weighting with the Pearson correlation coefficient to select crucial features in wat

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Utomo, Victor Gayuh, Tirta Yurista Kumkamdhani, and Galih Setiarso. "Anti-Corruption Disclosure Prediction Using Deep Learning." Jurnal Online Informatika 7, no. 2 (2022): 168–76. http://dx.doi.org/10.15575/join.v7i2.840.

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Corruption gives major problem to many countries. It gives negative impact to a nation economy. People also realized that corruption comes from two sides, demand from the authority and supply from corporate. On that regard, corporates may have their part in fight against corruption in the form of anti- corruption disclosure (ACD). This study proposes new method of ACD prediction in corporate using deep learning. The data in this study are taken from every companies listed in Indonesia Stock Exchange (IDX) from the year 2017 to 2019. The companies can be categorized in 9 categories and the data

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Takeda, Norio, and Tomohiro Naruse. "Accurate Prediction of Fatigue Life under Random Loading." Advanced Materials Research 891-892 (March 2014): 1347–52. http://dx.doi.org/10.4028/www.scientific.net/amr.891-892.1347.

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This study focuses on the method of predicting the fatigue life of materials subjected to random loading. Since random stress caused by random loading is rigorously expressed in the frequency domain as stress power spectral density (PSD), fatigue life should be predicted using stress PSD. We propose two adjustment methods of improving the accuracy of fatigue life prediction using stress PSD in the frequency domain. The method proposed by Dirlik is widely used for predicting the fatigue life in the frequency domain; however, it overestimates fatigue damage caused by large stress amplitude when

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Dr, A. R. JayaSudha, and M. Kathiravan. "Rainfall Prediction." Research and Applications of Web Development and Design 6, no. 1 (2023): 44–52. https://doi.org/10.5281/zenodo.7797731.

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<em>Predicting rainfall is essential because intense rainfall can cause a wide variety of natural catastrophes. People will be able to take preventative measures as a result of the projection, and it is important that the prediction is accurate. There are two different kinds of weather forecasting: short-term weather forecasting and long-term weather forecasting. The accuracy of our results can largely be determined through projection, particularly short-term prediction. Building a model capable of predicting long-term rainfall is the primary obstacle to overcome. Due to the fact that it is in

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Lee, Won-Chan, You-Boo Jeon, Seong-Soo Han, and Chang-Sung Jeong. "Position Prediction in Space System for Vehicles Using Artificial Intelligence." Symmetry 14, no. 6 (2022): 1151. http://dx.doi.org/10.3390/sym14061151.

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This paper deals with the prediction of the future location of vehicles, which is attracting attention in the era of the fourth industrial revolution and is required in various fields, such as autonomous vehicles and smart city traffic management systems. Currently, vehicle traffic prediction models and accident prediction models are being tested in various places, and considerable progress is being made. However, there are always errors in positioning when using wireless sensors due to various variables, such as the appearance of various substances (water, metal) that occur in the space where

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Sabri, Norlina Mohd, and Siti Fatimah Azzahra Hamrizan. "Prediction of MUET Results Based on K-Nearest Neighbour Algorithm." Annals of Emerging Technologies in Computing 7, no. 5 (2023): 50–59. http://dx.doi.org/10.33166/aetic.2023.05.005.

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The machine learning based prediction has been applied in various fields to solve different kind of problems. In education, the research on the predictions of examination results is gaining more attentions among the researchers. The adaptation of machine learning for the prediction of students’ achievement enables the educational institutions to identify the high failure rate, learning problems, and reasons for low student performance. This research is proposing the prediction of the Malaysian University English Test (MUET) results based on the K-Nearest Neighbour Algorithm (KNN). KNN is a pow

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Providence, Alimasi Mongo, and Chaoyu Yang. "Gas concentration level prediction with neural network model in multiple coal mine stations." Molecular & Cellular Biomechanics 21 (August 19, 2024): 118. http://dx.doi.org/10.62617/mcb.v21.118.

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Gas concentration level prediction in coal mines is a challenging task due to the complex environment and the high risk of gas explosion. Traditional gas concentration level prediction methods rely on manual monitoring and experience, which may result in inaccurate predictions and even accidents. In recent years, neural network (NN) models have been applied in gas concentration level prediction, showing promising results. This paper aims to investigate the effectiveness of NN models in gas concentration level prediction in multiple coal mine stations. A dataset of gas concentration level measu

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Wang, You, Zhan Wang, Junwei Diao, Xiyang Sun, Zhiyuan Luo, and Guang Li. "Discrimination of Different Species of Dendrobium with an Electronic Nose Using Aggregated Conformal Predictor." Sensors 19, no. 4 (2019): 964. http://dx.doi.org/10.3390/s19040964.

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A method using electronic nose to discriminate 10 different species of dendrobium, which is a kind of precious herb with medicinal application, was developed with high efficiency and low cost. A framework named aggregated conformal prediction was applied to make predictions with accuracy and reliability for E-nose detection. This method achieved a classification accuracy close to 80% with an average improvement of 6.2% when compared with the results obtained by using traditional inductive conformal prediction. It also provided reliability assessment to show more comprehensive information for e

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Allemar Jhone P. Delima. "An Enhanced K-Nearest Neighbor Predictive Model through Metaheuristic Optimization." International Journal of Engineering and Technology Innovation 10, no. 4 (2020): 280–92. http://dx.doi.org/10.46604/ijeti.2020.4646.

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The k-nearest neighbor (KNN) algorithm is vulnerable to noise, which is rooted in the dataset and has negative effects on its accuracy. Hence, various researchers employ variable minimization techniques before predicting the KNN in the quest so as to improve its predictive capability. The genetic algorithm (GA) is the most widely used metaheuristics for such purpose; however, the GA suffers a problem that its mating scheme is bounded on its crossover operator. Thus, the use of the novel inversed bi-segmented average crossover (IBAX) is observed. In the present work, the crossover improve

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Qin, Wenxi. "Predictive Analysis of AAPL Stock Trend by Random Forest and K-NN Classifier." Highlights in Business, Economics and Management 24 (January 22, 2024): 1418–22. http://dx.doi.org/10.54097/pgh78d83.

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While perfection in stock market prediction is impossible to accomplish, minimizing the investment risk utilizing data mining classifier to forecast the Stock Market trend becomes one of the most popular fields to research. This paper analysis and forecast AAPL ’s daily stock trends by utilize two most frequently use data mining algorithm K-Nearest Neighbor and Random Forest Classifier. By comparing the accuracy of the two model to finds out which model would best estimate the AAPL’s stock price. Both Algorithm utilize simple rolling average as the predictors to increase the accuracy of the pr

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