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Journal articles on the topic 'Predictive model accuracy'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Zhang, Binzhe, Min Duan, Yufan Sun, Yatong Lyu, Yali Hou, and Tao Tan. "Air Quality Index Prediction in Six Major Chinese Urban Agglomerations: A Comparative Study of Single Machine Learning Model, Ensemble Model, and Hybrid Model." Atmosphere 14, no. 10 (2023): 1478. http://dx.doi.org/10.3390/atmos14101478.

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Air pollution is a hotspot of wide concern in Chinese cities. With the worsening of air pollution, urban agglomerations face an increasingly complex environment for air quality monitoring, hindering sustainable and high-quality development in China. More effective methods for predicting air quality are urgently needed. In this study, we employed seven single models and ensemble learning algorithms and constructed a hybrid learning algorithm, the LSTM-SVR model, totaling eight machine learning algorithms, to predict the Air Quality Index in six major urban agglomerations in China. We comprehens

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Pali, Prof Pankaj, and Prof Saurabh Verma. "High-Accuracy Machine Learning Model for Predicting Diabetes Mellitus Progression." International Journal of Innovative Research in Computer and Communication Engineering 12, no. 06 (2024): 9101–9. http://dx.doi.org/10.15680/ijircce.2024.1206106.

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Diabetes mellitus, a chronic metabolic disorder marked by persistent hyperglycemia, presents a major global health challenge, affecting over 463 million adults worldwide. Timely and accurate prediction of disease progression is crucial for mitigating complications and improving patient outcomes. This research paper details the development and validation of an advanced machine learning model designed to predict diabetes progression. The proposed model integrates various machine learning algorithms, such as regression analysis, decision trees, and neural networks, to enhance predictive accuracy.

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Brunello, Gabriel Hideki Vatanabe, and Eduardo Yoshio Nakano. "A Bayesian Measure of Model Accuracy." Entropy 26, no. 6 (2024): 510. http://dx.doi.org/10.3390/e26060510.

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Ensuring that the proposed probabilistic model accurately represents the problem is a critical step in statistical modeling, as choosing a poorly fitting model can have significant repercussions on the decision-making process. The primary objective of statistical modeling often revolves around predicting new observations, highlighting the importance of assessing the model’s accuracy. However, current methods for evaluating predictive ability typically involve model comparison, which may not guarantee a good model selection. This work presents an accuracy measure designed for evaluating a model

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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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Jiang, Min Lan, Xiao Dong Wang, and Xiu Hui He. "Dynamic Accuracy Loss Prediction Model Based on BPNN." Advanced Materials Research 108-111 (May 2010): 795–98. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.795.

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In this paper, study the dynamic accuracy loss prediction model of measurement system, using measurement standard deviation as the systemic accuracy, the change of measurement standard deviation in different time as the systemic accuracy loss. Using BPNN build the dynamic accuracy loss prediction model about the practical measurement, model precision reach , realized real-time prediction of systemic accuracy loss , the establishment of predictive models to the system Real-time error correction and compensation to provide a theoretical basis, and that can cost-effectively improve the system of

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Han, Runxing, Ruixuan Meng, and Qianwei Zhu. "Predictive Analytics in Heart Disease: Leveraging LightGBM for Improved Diagnostic Accuracy." Applied and Computational Engineering 112, no. 1 (2024): 210–17. https://doi.org/10.54254/2755-2721/2025.18135.

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The prevalence of heart disease is significant, making it a prominent health concern, and early prediction and diagnosis are critical. The application of artificial intelligence algorithms in heart disease prediction is highly promising. This study utilizes the LightGBM algorithm for the predictive modelling of a heart disease dataset containing 1025 records and 14 health indicators. Predictive models were effectively built through data preprocessing, feature selection, and model training. The results show that the LightGBM model got 98.54% accuracy in the test set is better than the model of

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Yang, Ke. "Predicting Student Performance Using Artificial Neural Networks." Journal of Arts, Society, and Education Studies 6, no. 1 (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 s

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Wei, Li-Li, Yue-Shuai Pan, Yan Zhang, Kai Chen, Hao-Yu Wang, and Jing-Yuan Wang. "Application of machine learning algorithm for predicting gestational diabetes mellitus in early pregnancy†." Frontiers of Nursing 8, no. 3 (2021): 209–21. http://dx.doi.org/10.2478/fon-2021-0022.

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Abstract Objective To study the application of a machine learning algorithm for predicting gestational diabetes mellitus (GDM) in early pregnancy. Methods This study identified indicators related to GDM through a literature review and expert discussion. Pregnant women who had attended medical institutions for an antenatal examination from November 2017 to August 2018 were selected for analysis, and the collected indicators were retrospectively analyzed. Based on Python, the indicators were classified and modeled using a random forest regression algorithm, and the performance of the prediction

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Ji, Jung-Hwan, Sung-Gwe Ahn, Youngbum Yoo, et al. "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 (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.

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Maju, Sonam V., and Gnana Prakasi Oliver Sirya Pushpam. "A novel two-tier feature selection model for Alzheimer's disease prediction." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 1 (2024): 227–35. https://doi.org/10.11591/ijeecs.v33.i1.pp227-235.

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The interdisciplinary research studies of artificial intelligence in health sector is bringing drastic life saving changes in the healthcare domain. One such aspect is the early disease prediction using machine learning and regression algorithms. The purpose of this research is to improve the prediction accuracy of Alzheimer ’s disease by analysing the correlation of unexplored Alzheimer causing diseases. The work proposes Chi square-lasso ridge linear (Chi-LRL) model, a new two-tier feature ranking model which recognizes the significance of including diabetes, blood pressure and body ma

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Cao, Siyuan, Ying Yuan, Xiaodong Sun, et al. "The Debris Flow Risk Prediction Model Based on PCA-Elman." Applied Sciences 14, no. 24 (2024): 11960. https://doi.org/10.3390/app142411960.

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Accurate prediction of the risk levels of debris flows is crucial for devising effective disaster prevention and mitigation strategies. This study, based on debris flow sample data from Yunnan Province, initially employs Principal Component Analysis to reduce the dimensionality of the raw data, extracting key features and minimizing data dimensions. Subsequently, a 5-fold cross-validation method is utilized to segment the dataset into training and testing sets, and a predictive model integrating Principal Component Analysis with an Elman Neural Network (PCA-Elman) is constructed. The study inv

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Florence, David Onyirimba, Deme Abraham, Dadik Bibu Gideon, et al. "Performance Evaluation of Machine Learning Models For Cervical Cancer Prediction." RA JOURNAL OF APPLIED RESEARCH 08, no. 11 (2022): 821–28. https://doi.org/10.5281/zenodo.7359642.

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ABSTRACT   Cervical cancer is exclusively an anatomy of the female genitals involving the cervix and is the common cancer type that appears in all age women groups and the most common cause of death associated with cancer in gynecological practice, yet it is almost completely preventable if precancerous lesions are identified and treated promptly. The need to develop a quick, cheap and efficient method to diagnose a precursor lesion in an environment with high burden of the diseases with a view of reducing the burden of the disease motivated the need to apply Machine Learning (ML) techniq

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Husam Kadhim Gharkan, Mustafa Jawad Radif. "Predicting Student Performance Using a Hybrid Model Based on Machine Learning and Feature Selection Techniques." Journal of Information Systems Engineering and Management 10, no. 4 (2025): 192–99. https://doi.org/10.52783/jisem.v10i4.8921.

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Accurately predicting student performance plays a critical role in modern educational institutions. It enables targeted interventions and enhances educational outcomes. This paper proposes a hybrid predictive model for predicting student performance employing feature selection based on standard deviation filtering, coupled with machine learning techniques. In the machine learning phase used Decision Tree (DT), Random Forest (RF), K-Nearest Neighbours (KNN), and Support Vector Machines (SVM) were used. The proposed model is tested and evaluated over the Student Performance Prediction—Multiclass

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Heagerty, Patrick J., and Yingye Zheng. "Survival Model Predictive Accuracy and ROC Curves." Biometrics 61, no. 1 (2005): 92–105. http://dx.doi.org/10.1111/j.0006-341x.2005.030814.x.

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Liu, Mingkai, Yanming Feng, Shanshan Yang, and Huaizhi Su. "Dam Deformation Prediction Considering the Seasonal Fluctuations Using Ensemble Learning Algorithm." Buildings 14, no. 7 (2024): 2163. http://dx.doi.org/10.3390/buildings14072163.

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Dam deformation is the most visual and relevant monitoring quantity that reflects the operational condition of a concrete dam. The seasonal variations in the external environment can induce seasonal fluctuations in the deformation of concrete dams. Hence, preprocessing the deformation monitoring series to identify seasonal fluctuations within the series can effectively enhance the accuracy of the predictive model. Firstly, the dam deformation time series are decomposed into the seasonal and non-seasonal components based on the seasonal decomposition technique. The advanced ensemble learning al

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Wiyono, Slamet, and Taufiq Abidin. "COMPARATIVE STUDY OF MACHINE LEARNING KNN, SVM, AND DECISION TREE ALGORITHM TO PREDICT STUDENT’S PERFORMANCE." International Journal of Research -GRANTHAALAYAH 7, no. 1 (2019): 190–96. http://dx.doi.org/10.29121/granthaalayah.v7.i1.2019.1048.

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Students who are not-active will affect the number of students who graduate on time. Prevention of not-active students can be done by predicting student performance. The study was conducted by comparing the KNN, SVM, and Decision Tree algorithms to get the best predictive model. The model making process was carried out by steps; data collecting, pre-processing, model building, comparison of models, and evaluation. The results show that the SVM algorithm has the best accuracy in predicting with a precision value of 95%. The Decision Tree algorithm has a prediction accuracy of 93% and the KNN al

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Slamet, Wiyono, and Abidin Taufiq. "COMPARATIVE STUDY OF MACHINE LEARNING KNN, SVM, AND DECISION TREE ALGORITHM TO PREDICT STUDENT'S PERFORMANCE." International Journal of Research - Granthaalayah 7, no. 1 (2019): 190–96. https://doi.org/10.5281/zenodo.2550651.

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Students who are not-active will affect the number of students who graduate on time. Prevention of not-active students can be done by predicting student performance. The study was conducted by comparing the KNN, SVM, and Decision Tree algorithms to get the best predictive model. The model making process was carried out by steps; data collecting, pre-processing, model building, comparison of models, and evaluation. The results show that the SVM algorithm has the best accuracy in predicting with a precision value of 95%. The Decision Tree algorithm has a prediction accuracy of 93% and the KNN al

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Duong, Xuan-Lam, and Shu-Yi Liaw. "Comparative Analysis of Data Mining Classification Techniques for Prediction of Problematic Internet Shopping." International Journal of Applied Sciences & Development 3 (June 14, 2024): 82–88. http://dx.doi.org/10.37394/232029.2024.3.7.

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As online shopping has surged, so do disorders on internet purchasing. This study aims to develop and compare predictive models that use data mining methods to predict problematic internet shopping. We used the Artificial Neural Network (ANN), CHAID with bagging, and C5.0 and compared them with traditional logistic regression to construct predictive models on a training cohort of 858 shoppers. Another cohort of 368 buyers was utilized to confirm the accuracy of the predictive model. The accuracy, sensitivity, specificity, and the ROC-AUC were used to assess the predictive performance. The C5.0

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Kaftanov, A. N., A. E. Andreychenko, A. D. Ermak, D. V. Gavrilov, A. V. Gusev, and R. E. Novitskiy. "A model for predicting death in adult patients within 10 years." Public Health 5, no. 2 (2025): 4–16. https://doi.org/10.21045/2782-1676-2025-5-2-4-16.

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Introduction. The identification of risk factors and the prediction of mortality from various causes are important issues in medicine. From a preventive perspective, it is crucial to identify patients at high risk of death, as early detection and treatment of diseases effectively increase life expectancy. The purpose of the study: to develop a universal model for predicting death in adult patients within 10 years and to compare the predictive ability of predicting death in a large contemporary cohort of the machine learning model (decision trees) with a Cox regression. Materials and methods. T

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Krstić, Marija, and Lazar Krstić. "A logistic regression-based model for predicting heart failure mortality." Journal of Engineering Management and Competitiveness 15, no. 1 (2025): 57–64. https://doi.org/10.5937/jemc2501057k.

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Recent trends in evaluating World Wide Web data include the use of traditional data mining techniques, such as regression, clustering, and classification. This paper aims to develop a model for predicting heart failure mortality based on a publicly available online dataset containing medical records of 299 patients. Since the prediction outcome can have only one of two possible values, the binary logistic regression technique was applied. Research shows that the predictive model created using logistic regression can accurately predict patient mortality based on their clinical characteristics a

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Surus, Robert, Mateusz Tejer, and Tomasz Tarczewski. "An Impact of Switching Frequency and Model Accuracy on Model Predictive Current Control Performance for Reluctance Synchronous Motors." Power Electronics and Drives 9, no. 1 (2024): 176–90. http://dx.doi.org/10.2478/pead-2024-0012.

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Abstract The present paper investigates the feasibility of utilizing the simplified prediction model for finite control set model predictive current control (FCS-MPCC) applied to reluctance synchronous motors (RSMs). The FCS-MPCC exhibits torque and current ripples, and a crucial consideration is the reduction of these ripples by increasing the switching frequency. The algorithm’s computational complexity is tied to the accuracy of the adopted model. Two approaches with varying levels of accuracy in predicting current dependencies concerning changes in the inductance of the RSM are investigate

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Jin, Bing Jie, Bu Han Zhang, Chang Hong Deng, and Jun Li Wu. "Wavelet-ARMA Model Revised by Neural Network to Predict Wind Power." Advanced Materials Research 724-725 (August 2013): 669–74. http://dx.doi.org/10.4028/www.scientific.net/amr.724-725.669.

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Research of wind power prediction has great significance for power balance and economic operation. This paper combined the ARMA model and the neural network model to establish a wavelet-ARMA model revised by neural network. After decomposing the historical wind power data by wavelet analysis, the high frequency components and low frequency components were separately predicted by ARMA. The reconstructed predictive outcomes were revised by BP neural network. To improve the accuracy, the predictive values of predictive moments were added to the BP neural network. This paper gave a 96 points rolli

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Mebarkia, Mohamed, Asma Abdelmalek, Zoubir Aoulmi, Messaoud Louafi, Abdelhak Tabet, and Aissa Benselhoub. "Synergistic prediction of penetration rate in Boukhadhra mining using regression, design of experiments, fuzzy logic, and artificial neural networks." Technology audit and production reserves 4, no. 1(78) (2024): 32–42. http://dx.doi.org/10.15587/2706-5448.2024.309965.

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The comparative analysis of predictive methodologies highlights the original contribution of this study in optimizing the prediction of Rate of Penetration (ROP) in mining drilling operations. The emphasis on employing advanced Artificial Neural Networks (ANN), fuzzy logic, and linear regression models provides new insights into enhancing predictive accuracy and operational efficiency in mining practices. This study aims to quantify the effects of three pivotal drilling parameters: compressive strength, rotational pressure, and thrust pressure on the rate of penetration, a critical performance

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Liu, Yan, Bo Yin, and Yanping Cong. "The Probability of Ischaemic Stroke Prediction with a Multi-Neural-Network Model." Sensors 20, no. 17 (2020): 4995. http://dx.doi.org/10.3390/s20174995.

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As is known, cerebral stroke has become one of the main diseases endangering people’s health; ischaemic strokes accounts for approximately 85% of cerebral strokes. According to research, early prediction and prevention can effectively reduce the incidence rate of the disease. However, it is difficult to predict the ischaemic stroke because the data related to the disease are multi-modal. To achieve high accuracy of prediction and combine the stroke risk predictors obtained by previous researchers, a method for predicting the probability of stroke occurrence based on a multi-model fusion convol

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Srinivasappa, K. V., Swamy Arpitha, and V. Nandini. "Enhancing Software Quality with Ensemble Machine Learning and Evolutionary Approaches." Journal of Scholastic Engineering Science and Management (JSESM), A Peer Reviewed Universities Refereed Multidisciplinary Research Journal 3, no. 8 (2024): 7–21. https://doi.org/10.5281/zenodo.14912826.

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Software engineering faces significant challenges in ensuring high-quality software design amidst increasing competition.  A key approach to achieving this is the reliable and effective prediction of software defects during quality checks.  Existing defect prediction methodologies have been reviewed and found to have shortcomings.  Therefore, this paper proposes a novel, simplified, and flexible predictive architecture for software defect assessment.  Leveraging ensemble machine learning, the model employs a neuro-genetic feature selection method to enhance software quality

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Nordin, Noratikah, Zurinahni Zainol, Mohd Halim Mohd Noor, and Chan Lai Fong. "A comparative study of machine learning techniques for suicide attempts predictive model." Health Informatics Journal 27, no. 1 (2021): 146045822198939. http://dx.doi.org/10.1177/1460458221989395.

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Current suicide risk assessments for predicting suicide attempts are time consuming, of low predictive value and have inadequate reliability. This paper aims to develop a predictive model for suicide attempts among patients with depression using machine learning algorithms as well as presents a comparative study on single predictive models with ensemble predictive models for differentiating depressed patients with suicide attempts from non-suicide attempters. We applied and trained eight different machine learning algorithms using a dataset that consists of 75 patients diagnosed with a depress

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Assegie, Tsehay Admassu, R. Lakshmi Tulasi, and N. Komal Kumar. "Breast cancer prediction model with decision tree and adaptive boosting." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (2021): 184. http://dx.doi.org/10.11591/ijai.v10.i1.pp184-190.

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In this study, breast cancer prediction model is proposed with decision tree and adaptive boosting (Adboost). Furthermore, an extensive experimental evaluation of the predictive performance of the proposed model is conducted. The study is conducted on breast cancer dataset collected form the kaggle data repository. The dataset consists of 569 observations of which the 212 or 37.25% are benign or breast cancer negative and 62.74% are malignant or breast cancer positive. The class distribution shows that, the dataset is highly imbalanced and a learning algorithm such as decision tree is biased t

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Tsehay, Admassu Assegie, Lakshmi Tulasi R., and Komal Kumar N. "Breast cancer prediction model with decision tree and adaptive boosting." International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (2021): 184–90. https://doi.org/10.11591/ijai.v10.i1.pp184-190.

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In this study, breast cancer prediction model is proposed with decision tree and adaptive boosting (Adboost). Furthermore, an extensive experimental evaluation of the predictive performance of the proposed model is conducted. The study is conducted on breast cancer dataset collected form the kaggle data repository. The dataset consists of 569 observations of which the 212 or 37.25% are benign or breast cancer negative and 62.74% are malignant or breast cancer positive. The class distribution shows that, the dataset is highly imbalanced and a learning algorithm such as decision tree is biased t

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Liu, Yong, Cheng Liu, Xianguo Tuo, and Xiang He. "Application of BITCN-BIGRU Neural Network Based on ICPO Optimization in Pit Deformation Prediction." Buildings 15, no. 11 (2025): 1956. https://doi.org/10.3390/buildings15111956.

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Predicting pit deformation to prevent safety accidents is the primary objective of pit deformation forecasting. A reliable predictive model enhances the ability to accurately monitor future deformation trends in pits. To enhance the prediction of pit deformation and improve accuracy and precision, an Improved Crown Porcupine Optimization Algorithm (ICPO) based on a Bidirectional Time Convolution Network–Bidirectional Gated Recirculation Unit (BITCN-BIGRU) is developed. This model is utilized to forecast the future deformation trends of the pit. Utilizing site data from a metro station pit proj

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Nguyen, Quoc Giang, Linh Hoang Nguyen, Md Monir Hosen, et al. "Enhancing Credit Risk Management with Machine Learning: A Comparative Study of Predictive Models for Credit Default Prediction." American Journal of Applied Sciences 07, no. 01 (2025): 21–30. https://doi.org/10.37547/tajas/volume07issue01-04.

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This study investigates the application of machine learning algorithms for predictive analytics in credit risk management, aiming to enhance the accuracy of predicting credit defaults. The research compares multiple machine learning models, including logistic regression, decision trees, random forests, gradient boosting, XGBoost, and LightGBM, using a real-world credit risk dataset. The study focuses on evaluating the models' performance based on metrics such as accuracy, precision, recall, and F1-score. The results show that ensemble models, particularly XGBoost and LightGBM, outperform tradi

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Wang, Bo, Muhammad Shahzad, Xianglin Zhu, Khalil Ur Rehman, and Saad Uddin. "A Non-linear Model Predictive Control Based on Grey-Wolf Optimization Using Least-Square Support Vector Machine for Product Concentration Control in l-Lysine Fermentation." Sensors 20, no. 11 (2020): 3335. http://dx.doi.org/10.3390/s20113335.

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l-Lysine is produced by a complex non-linear fermentation process. A non-linear model predictive control (NMPC) scheme is proposed to control product concentration in real time for enhancing production. However, product concentration cannot be directly measured in real time. Least-square support vector machine (LSSVM) is used to predict product concentration in real time. Grey-Wolf Optimization (GWO) algorithm is used to optimize the key model parameters (penalty factor and kernel width) of LSSVM for increasing its prediction accuracy (GWO-LSSVM). The proposed optimal prediction model is used

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Mathivathanaraj, M., A. Deena Dayalan, U. Rajarethinam, A. Ajay, and N. Pazhaniraja. "Vehicle CO2 Emission Prediction Using MAWRF-AESL Model." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44137.

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Over the past few decades, rising CO₂ emissions from vehicles have become a major environmental concern, necessitating accurate predictive models for sustainable solutions. This study proposes a novel MAWRF-AESL hybrid model, integrating Modified Adaptive Weighted Random Forest (MAWRF) with Attention-Enhanced Sequential Learning (AESL) to enhance prediction accuracy. The MAWRF model efficiently extracts relevant features using an adaptive weighted tree-based approach, while AESL refines the predictions through LSTM networks with an attention mechanism. By sequentially processing the data—first

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Abdul-Kareem, Ahmed Amer, Zaki T. Fayed, Sherine Rady, Salsabil Amin El-Regaily, and Bashar M. Nema. "Forecasting Financial Investment Firms’ Insolvencies Empowered with Enhanced Predictive Modeling." Journal of Risk and Financial Management 17, no. 9 (2024): 424. http://dx.doi.org/10.3390/jrfm17090424.

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In the realm of financial decision-making, it is crucial to consider multiple factors, among which lies the pivotal concern of a firm’s potential insolvency. Numerous insolvency prediction models utilize machine learning techniques try to solve this critical aspect. This paper aims to assess the financial performance of financial investment firms listed on the Iraq Stock Exchange (ISX) from 2012 to 2022. A Multi-Layer Perceptron predicting model with a parameter optimizer is proposed integrating an additional feature selection process. For this latter process, three methods are proposed and co

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Yoo, Jang, Jaeho Lee, Miju Cheon, et al. "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 (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 outco

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Wang, Huilong, Daran Mai, Qian Li, and Zhikun Ding. "Evaluating Machine Learning Models for HVAC Demand Response: The Impact of Prediction Accuracy on Model Predictive Control Performance." Buildings 14, no. 7 (2024): 2212. http://dx.doi.org/10.3390/buildings14072212.

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Heating, ventilation, and air-conditioning systems (HVAC) have significant potential to support demand response programs within power grids. Model Predictive Control (MPC) is an effective technique for utilizing the flexibility of HVAC systems to achieve this support. In this study, to identify a proper prediction model in the MPC controller, four machine learning models (i.e., SVM, ANN, XGBoost, LightGBM) are compared in terms of prediction accuracy, prediction time, and training time. The impact of model prediction accuracy on the performance of MPC for HVAC demand response is also systemati

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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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Shan, Kun, Liaoyuan Zhang, Bo Tan, et al. "Prediction Model for Material Removal Rate of TC4 Titanium Alloy Processed by Vertical Vibratory Finishing." Coatings 15, no. 3 (2025): 286. https://doi.org/10.3390/coatings15030286.

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To establish a high-precision prediction model for the material removal rate (MRR) of TC4 titanium alloy material in vertical vibratory finishing equipment, an orthogonal experiment was conducted using TC4 titanium alloy plate as the experimental specimen. We performed variance analysis of the impact of vibration frequency, the phase difference, the mass of upper eccentric block, and the mass of lower eccentric block on the MRR. We then drew the main effect diagram and analyzed the influence of various process parameters on the MRR. Mathematical regression and a neural network were used to con

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Ling, Chaofan, Junpei Zhong, and Weihua Li. "Pyramidal Predictive Network: A Model for Visual-Frame Prediction Based on Predictive Coding Theory." Electronics 11, no. 18 (2022): 2969. http://dx.doi.org/10.3390/electronics11182969.

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Visual-frame prediction is a pixel-dense prediction task that infers future frames from past frames. A lack of appearance details, low prediction accuracy and a high computational overhead are still major problems associated with current models or methods. In this paper, we propose a novel neural network model inspired by the well-known predictive coding theory to deal with these problems. Predictive coding provides an interesting and reliable computational framework. We combined this approach with other theories, such as the theory that the cerebral cortex oscillates at different frequencies

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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 (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 limitatio

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Spolidoro, Giulia C. I., Veronica D’Oria, Valentina De Cosmi, et al. "Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children." Nutrients 13, no. 11 (2021): 3797. http://dx.doi.org/10.3390/nu13113797.

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Introduction: Accurate assessment of resting energy expenditure (REE) can guide optimal nutritional prescription in critically ill children. Indirect calorimetry (IC) is the gold standard for REE measurement, but its use is limited. Alternatively, REE estimates by predictive equations/formulae are often inaccurate. Recently, predicting REE with artificial neural networks (ANN) was found to be accurate in healthy children. We aimed to investigate the role of ANN in predicting REE in critically ill children and to compare the accuracy with common equations/formulae. Study methods: We enrolled 25

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Xin, Ruo Bo, Zhi Fang Jiang, Ning Li, and Lu Jian Hou. "An Air Quality Predictive Model of Licang of Qingdao City Based on BP Neural Network." Advanced Materials Research 756-759 (September 2013): 3366–71. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3366.

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In order to obtain high precision results of urban air quality forecast, we propose a short-term predictive model of air quality in this paper, which is on the basis of the ambient air quality monitoring data and relevant meteorological data of a monitoring site in Licang district of Qingdao city in recent three years. The predictive model is based on BP neural network and used to predict the ambient air quality in the next some day or within a certain period of hours. In the design of the predictive model, we apply LM algorithm, Simulated Annealing algorithm and Early Stopping algorithm into

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C M, Likith Kumar, Dr S. Ajitha, and Ms Reshma K J. "Predictive Analysis of Employee Turnover: A Comprehensive Model for Attrition Forecasting." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–6. https://doi.org/10.55041/ijsrem39823.

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Employee attrition has become a critical concern for organizations seeking to maintain a stable workforce and minimize turnover-related costs. This study aims to develop a predictive model to forecast employee attrition by analyzing key factors influencing turnover. The research investigates a range of variables, including push factors (e.g., job dissatisfaction, workplace conflict), pull factors (e.g., external job opportunities), organizational factors (e.g., leadership, culture), and external factors (e.g., economic conditions). The data is collected via a structured questionnaire through G

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Dagogo-George, Tamunopriye Ene, Hammed Adeleye Mojeed, Abdulateef Oluwagbemiga Balogun, Modinat Abolore Mabayoje, and Shakirat Aderonke Salihu. "Tree-based homogeneous ensemble model with feature selection for diabetic retinopathy prediction." Jurnal Teknologi dan Sistem Komputer 8, no. 4 (2020): 297–303. http://dx.doi.org/10.14710/jtsiskom.2020.13669.

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Diabetic Retinopathy (DR) is a condition that emerges from prolonged diabetes, causing severe damages to the eyes. Early diagnosis of this disease is highly imperative as late diagnosis may be fatal. Existing studies employed machine learning approaches with Support Vector Machines (SVM) having the highest performance on most analyses and Decision Trees (DT) having the lowest. However, SVM has been known to suffer from parameter and kernel selection problems, which undermine its predictive capability. Hence, this study presents homogenous ensemble classification methods with DT as the base cla

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Xu, Fengming, Qing Feng, Jixing Yi та ін. "α- and β-Genotyping of Thalassemia Patients Based on a Multimodal Liver MRI Radiomics Model: A Preliminary Study in Two Centers". Diagnostics 13, № 5 (2023): 958. http://dx.doi.org/10.3390/diagnostics13050958.

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Background: So far, there is no non-invasive method that can popularize the genetic testing of thalassemia (TM) patients on a large scale. The purpose of the study was to investigate the value of predicting the α- and β- genotypes of TM patients based on a liver MRI radiomics model. Methods: Radiomics features of liver MRI image data and clinical data of 175 TM patients were extracted using Analysis Kinetics (AK) software. The radiomics model with optimal predictive performance was combined with the clinical model to construct a joint model. The predictive performance of the model was evaluate

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Yan, Yan, Jingjing Lei, and Yuqing Huang. "Forest Aboveground Biomass Estimation Based on Unmanned Aerial Vehicle–Light Detection and Ranging and Machine Learning." Sensors 24, no. 21 (2024): 7071. http://dx.doi.org/10.3390/s24217071.

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Eucalyptus is a widely planted species in plantation forests because of its outstanding characteristics, such as fast growth rate and high adaptability. Accurate and rapid prediction of Eucalyptus biomass is important for plantation forest management and the prediction of carbon stock in terrestrial ecosystems. In this study, the performance of predictive biomass regression equations and machine learning algorithms, including multivariate linear stepwise regression (MLSR), support vector machine regression (SVR), and k-nearest neighbor (KNN) for constructing a predictive forest AGB model was a

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Zhu, Difeng, Guojiang Shen, Duanyang Liu, Jingjing Chen, and Yijiang Zhang. "FCG-ASpredictor: An Approach for the Prediction of Average Speed of Road Segments with Floating Car GPS Data." Sensors 19, no. 22 (2019): 4967. http://dx.doi.org/10.3390/s19224967.

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The average speed (AS) of a road segment is an important factor for predicting traffic congestion, because the accuracy of AS can directly affect the implementation of traffic management. The traffic environment, spatiotemporal information, and the dynamic interaction between these two factors impact the predictive accuracy of AS in the existing literature, and floating car data comprehensively reflect the operation of urban road vehicles. In this paper, we proposed a novel road segment AS predictive model, which is based on floating car data. First, the impact of historical AS, weather, and d

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Xu, Qi, Ge Sun, Song Zhang, et al. "Prediction of hypertensive disorders in pregnancy based on placental growth factor." Technology and Health Care 29 (March 25, 2021): 165–70. http://dx.doi.org/10.3233/thc-218017.

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BACKGROUND: The prediction of hypertensive disorders in pregnancy (HDP) mainly involves various aspects such as maternal characteristics and biomarkers. OBJECTIVE: We aimed to study the effect of the HDP prediction model with or without placental growth factor (PlGF). METHODS: This study used maternal factors and PlGF, and standardized the data uniformly. At 12–20 weeks, the comprehensive comparison of model quality with or without PlGF was conducted by logistic regression. RESULTS: The area under curve and the model accuracy of the model with PlGF were higher than those of the model without P

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Ismail, Ahmed G., Sayed H. A. Elbanna, and Hassan S. Mohamed. "Prediction of electrical load demand using combined LHS with ANFIS." PLOS One 20, no. 6 (2025): e0325747. https://doi.org/10.1371/journal.pone.0325747.

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Enhancement prediction of load demand is crucial for effective energy management and resource allocation in modern power systems and especially in medical segment. Proposed method leverages strengths of ANFIS in learning complex nonlinear relationships inherent in load demand data. To evaluate the effectiveness of the proposed approach, researchers conducted hybrid methodology combine LHS with ANFIS, using actual load demand readings. Comparative analysis investigates performing various machine learning models, including Adaptive Neuro-Fuzzy Inference Systems (ANFIS) alone, and ANFIS combined

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Huo, Tao Ran, and Shun Guo Bai. "Permanent Settlement Prediction of Composite Foundation Improved by Cement-Soil Piles under Cycle Loading." Advanced Materials Research 482-484 (February 2012): 1205–8. http://dx.doi.org/10.4028/www.scientific.net/amr.482-484.1205.

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Through large-scale model tests on composite foundation of cement-soil piles under cyclic loading, a program was compiled based on the platform of MATLAB 7.0, considering the effects of cement mixing ratio, replacement ratio, cyclic stress ratio and number of cyclic on the permanent settlement of composite ground. A back-propagation neural network model to predict the settlement was established, and its prediction accuracy was inspected. Meanwhile, an predicting model of multivariable linear regression analysis was established .To compare the prediction accuracy of the two methods, and the res

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Liu, Haijun, Yang Wu, Dongqing Tan, Yi Chen, and Haoran Wang. "CGAOA-AttBiGRU: A Novel Deep Learning Framework for Forecasting CO2 Emissions." Mathematics 12, no. 18 (2024): 2956. http://dx.doi.org/10.3390/math12182956.

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Accurately predicting carbon dioxide (CO2) emissions is crucial for environmental protection. Currently, there are two main issues with predicting CO2 emissions: (1) existing CO2 emission prediction models mainly rely on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) models, which can only model unidirectional temporal features, resulting in insufficient accuracy: (2) existing research on CO2 emissions mainly focuses on designing predictive models, without paying attention to model optimization, resulting in models being unable to achieve their optimal performance. To address thes

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