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检索条件"主题词=prediction algorithms"

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Road Accident prediction in Highways using Machine Learning algorithms

Road Accident Prediction in Highways using Machine Learning ...

引用

Smart Electronics and Communication (ICOSEC), International Conference on

作者： Jambula Snehith Reddy M. R. Rashmi Lee Hoong Pin Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Faculty of Engineering and QS INTI International University Nilai Malaysia

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Road accidents are increasing day by day. In India, every year nearly 33% of road accidents take place in national highways and 23% in state highways. This paper focuses on predicting the road accidents in national and state highways in India using machine learning algorithms. The data is curated using annual reports published on road accidents in India by ministry of road transport & highways also, the reports on the compensation to the victims by motor vehicle accident fund trust. Road accident details are captured from the year 1990-2022. This covers the information about monthwise, state-wise accidents due to all types of vehicles, road environment, type of traffic control, vehicle condition, weather condition, rural/urban areas and so on. The data contains information about total number vehicles registered, road length, number of accidents in a year, number of people killed and the number of people injured. The road accidents and the number of vehicles causing accidents are predicted using machine learning algorithms viz., Linear Regression, Ridge Regression, Decision Tree, K-Nearest Neighbour (KNN) and Multilayer Perceptron algorithms. Multilayer perceptron model outperformed all other prediction models with highest R2-Score of 0.93278 and Mean Squared Error (MSE) of 0.277503 and Root Mean Squared Error (RMSE) of 0.387880 for five-fold cross validation. prediction helps in identifying the critical regions/roads where the number of accidents are more. This helps in implementing accident preventive measures like restricting speed limit in such highways.

关键词： Road accidents Machine learning algorithms Roads Multilayer perceptrons Predictive models Nearest neighbor methods Traffic control prediction algorithms Real-time systems Regression tree analysis

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Risk prediction of Investment System Under Big Data Statistical algorithms

Risk Prediction of Investment System Under Big Data Statisti...

引用

Artificial Intelligence and Autonomous Robot Systems (AIARS), International Conference on

作者： Yue Zhao College of Finance and Economics Liaoning Vocational University of Technology Jinzhou Liaoning

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

With the rapid growth of China's economy and the rapid development of China's high-tech industry, the number of high-tech enterprises in China would continue to increase, and the requirements for investment risks would also continue to increase. The purpose of developing an investment risk assessment system is to provide information technology services for the daily work and business of the unit. This article constructs an investment risk prediction model based on big data statistical algorithms, and applies the investment risk prediction model to the investment risk prediction system. In the system functional improvement test, when the investment amount is 10 million yuan, the investment return is 5 times. When the investment amount is 8 million, the return on investment is 3.5 times. Through the full load working intensity test of 10×24 hours, this method has achieved good results in practical application (response time less than 0.6 seconds). This article helps to optimize the ecological environment of entrepreneurial capital and is of great significance for promoting the healthy development of investment risks.

关键词： Biological system modeling Predictive models Big Data prediction algorithms Data models Risk management Time factors Information technology Investment Load modeling

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prediction of Student Performance in Learning Online Courses using Ensemble Learning algorithms

Prediction of Student Performance in Learning Online Courses...

引用

Expert Clouds and Applications (ICOECA), International Conference on

作者： Jetti Pavithra V. SriHarsha P. Hussain Basha T. Kishore Babu Department Of Computer Science & Engineering Pace Institute Of Technology Sciences Ongole AP Department of CSE Pace Institute of Technology Sciences Ongole AP

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

Understanding and forecasting student performance has become essential for educators and administrators due to the widespread use of online learning platforms. This research study examines how well ensemble learning algorithms predict the performance of students enrolled in distance learning programs. Ensemble learning, which combines several base learners to increase generalization and prediction accuracy, presents a viable method for handling the challenging nature of student performance prediction problems. This research examines an ensemble learning method that analyses student data from online learning platforms using Hidden Markov Models (HMM) and XGBoost. The data gathered from multiple web sources was trained using the Decision Trees (DT) training technique. The comprehensive feature collection, comprising engagement metrics, demographic data, and past academic achievements, was obtained from student interactions through Ensemble feature extraction. The ensemble models use these features as inputs and are trained to predict student outcomes like retention, grades, and course completion. Through thorough testing and assessment of real-world datasets, it has been shown that ensemble learning successfully predicts student performance in various online courses and academic fields. When comparing the performance of ensemble models to conventional single-model methods, it is possible to see considerable gains in prediction resilience and accuracy. Lastly, the interpretability of ensemble models to learn more about the elements influencing how well students succeed in virtual learning environments. Several functional patterns and connections are identified that can guide intervention tactics, instructional design, and personalized learning experiences by examining feature importance and model explanations.

关键词： Accuracy Uncertainty Heuristic algorithms Hidden Markov models Predictive models Feature extraction prediction algorithms

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Categorical prediction and Design Implementation for Triple Marker Test Using Clustering algorithms and Validations

Categorical Prediction and Design Implementation for Triple ...

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： P. Nagaraj Ravinuthala Dhanush Babu T. Marimuthu Shaik Shaestha Saloni A. M. Gurusigaamani Tanuja Sri Kiran K Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India

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A WebApp Framework for the prediction of e-GFR value and CKD stage using Regression-based Machine Learning algorithms

A WebApp Framework for the prediction of e-GFR value and CKD...

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India Council International Subsections Conference (INDISCON), IEEE

作者： M Gayathri Hegde Priyanka S Marellavar G Sunil Kumar P Deepa Shenoy K R Venugopal C Arvind Department of CSE University Visvesvaraya College Of Engineering Bangalore Department of ISE JSS Academy of Technical Education Bangalore Nephrologist The Bangalore Hospital Bangalore

ISBN: (数字)9798350376753

ISBN: (纸本)9798350376760

Chronic Kidney Disease(CKD) refers to a slow descent in kidney function and has become a serious health concern. Early detection and proper treatment are essential as CKD remains irreversible and unnoticeable until it progresses to advanced stages. Progression of CKD to five different stages depends on Glomeluar Filration Rate(GFR). Hence, timely intervention is crucial for a favorable prognosis and extended lifespan. In this regard, Machine Learning(ML) algorithms have proven to be capable and point in the direction of future disease diagnosis. This paper offers a methodology that uses a range of Regression-based ML algorithms on a real-time CKD dataset with 399 examples to forecast the state of CKD. The work is carried out in two distinct phases. During the initial phase various regression-based ML algorithms like Linear Regression(LR), Lasso Regression, Ridge Regression, K-Nearest Neighbors, Random Forest, and XGBOOST are employed in the CKD dataset to predict the estimated-GFR(e-GFR). In the subsequent phase, the most effective ML algorithm identified from initial phase is intergrated with Flask web framework. This integration enables the prediction of the e-GFR and the CKD stage displayed on the web page. The experimental result showed that the XGBOOST regression method outperformed with an accuracy of 99.6% compared to other regression-based ML algorithms.

关键词： Machine learning algorithms Web pages Predictive models prediction algorithms Chronic kidney disease Real-time systems Servers Prognostics and health management Random forests Diseases

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Enhanced Fuzzy Inference System for $P M_{10}$ Concentration prediction Using Genetic algorithms

Enhanced Fuzzy Inference System for $P M_{10}$ Concentration...

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International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)

作者： Francisco-Javier Moreno-Vazquez Felipe Trujillo-Romero Amanda Enriqueta Violante-Gavira Mechanics Engineer Department University of Guanajuato Salamanca Mexico Electronics Engineer Department University of Guanajuato Salamanca Mexico

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

This research introduces an enhanced Fuzzy Inference System (FIS) by integrating Genetic algorithms (GA) for optimizing the rule set. The proposed GA+FIS model is applied to forecast $\boldsymbol{P}\boldsymbol{M}_{\boldsymbol{1}\boldsymbolprediction}$ concentrations in the Metropolitan Area of the Valley of Mexico (ZMVM). To validate its effectiveness, the GA+FIS model is compared with two robust machine learning models, XGBoost and LightGBM, regarding their performance in $\boldsymbol{P}\boldsymbol{M}_{\boldsymbol{1}\boldsymbolprediction}$ concentration prediction. The results demonstrate that the GA+FIS model surpassed both XGBoost and LightGBM models, achieving an RMSE of 3.500632, R2 of 0.888926, and MAE of 2.759147. Notably, the GA+FIS model exhibited the closest alignment between predicted and actual $\boldsymbol{P}\boldsymbol{M}_{\boldsymbol{1}\boldsymbolprediction}$ concentrations, with an ideal slope of 0.97, indicating superior accuracy compared to LightGBM (slope = 0.80) and XGBoost (slope = 0.75). Across sampled instances, the GA+FIS model consistently aligned well with observed data points, underscoring its robustness in capturing complex relationships within the dataset.

关键词： Fuzzy logic Computational modeling Urban areas Machine learning Predictive models Electrical engineering computing prediction algorithms Robustness Data models Genetic algorithms

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A Performance prediction Model for Newly Enrolled University Students Based on The Fusion of K-Means and LightGBM algorithms

A Performance Prediction Model for Newly Enrolled University...

引用

Cloud Computing, Big Data Applications and Software Engineering (CBASE), International Conference on

作者： Tianze Shen Yuming Zhang School of Humanities Jinan University Zhuhai China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

Accurately predicting the academic performance (GPA) of newly admitted university students is crucial for educational management and resource allocation. To improve prediction accuracy, this study proposes a fusion algorithm that combines K-means clustering and LightGBM regression. The model utilizes multi-dimensional student features, including province, gender, region, entrance exam scores, and high school subject selection, to predict their GPA. First, the K-Means algorithm is applied to cluster the student population, grouping students into several clusters to capture heterogeneity among them. Second, the resulting clusters are then integrated into the LightGBM model as new features for regression-based performance prediction, further enhancing accuracy. Experimental results show that the fusion model of K-means and LightGBM performs well across different values of K, with optimal results achieved at $\mathrm{K}=5$ . This approach improves the model's generalization and applicability, providing educational administrators with a basis for personalized interventions for new students.

关键词： Measurement Accuracy Computational modeling Decision making Clustering algorithms Predictive models prediction algorithms Data models Resource management Software engineering

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Optimisation algorithms in Multi-Step prediction Horizon MPC for Two-Level Inverters

Optimisation Algorithms in Multi-Step Prediction Horizon MPC...

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Electrical, Electronics and Information Engineering (ISEEIE), International Symposium on

作者： Joseph O. Akinwumi Yuan Gao George T. West Harold S. Ruiz School of Engineering University of Leicester Leicester United Kingdom

ISBN: (数字)9798350355772

ISBN: (纸本)9798350355789

This paper presents a detailed comparison of three algorithms used in Finite Control Set Model Predictive Control (FCS-MPC) for two-level inverters: the standard Sphere Decoding Algorithm (SDA), the K-best SDA, and the Exhaustive Search Algorithm (ESA). ESA was implemented in two frameworks: Firstly, by listing all control sequences and secondly, by exploring all nodes in a search tree. The study focuses on a traditional three-phase two-level Voltage Source Inverter (VSI), using Matlab/Simulink for benchmarking the different simulation models. Our results demonstrate a reduction in the number of iterations and computational complexity with the SDA and K-best SDA, while maintaining similar control performance with ESA. The 3-phase load has a significant influence on the standard SDA, with the number of iterations varying for linear and non-linear loads. These findings suggest that the K-best SDA is a practical and efficient alternative to ESA for FCS- MPC applications, especially in scenarios of longer prediction horizons.

关键词： Voltage source inverters Benchmark testing prediction algorithms Timing Computational complexity MATLAB Standards Optimization Predictive control Load modeling

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Optimizing Gradient Boosting algorithms for Obesity Risk prediction: A Comparative Analysis of XGBoost, LightGBM, and CatBoost Models

Optimizing Gradient Boosting Algorithms for Obesity Risk Pre...

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Cybernation and Computation (CYBERCOM), International Conference on

作者： Eshika Jain Amanveer Singh Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

The importance of should be given to the detection of obesity risk. In the context of this study, the comparison of three machine learning models to the detection of obesity risk demonstrates that the most accurate model is an XGBoost classifier with an accuracy of 91%, while LightGBM and CatBoost models were slightly less accurate, with the accuracy of 90%. These models may become significant tools in recognizing people at risk of obesity to promote preventive and personalized healthcare that could address the alarming rates of obesity-related complications. Since the models drive on extensive datasets of various demographic and clinical characteristics, including the following features and variable importance plot, Weight 0.38, Height 0.11, Age 0.10, Frequency of Consumption of Vegetables FCVC 0.098, Gender 0.06, Time Using Technology TUE 0.045, Consumption of Water Daily CH2O 0.045, Physical Activity Frequency FAF 0.036, Number of main meals NCP 0.031, Family history with overweight 0.025; CAEC 0.023; CALC 0.021; Mode of transport MTRANS 0.013; Frequency of Consumption of High Caloric Food FAVC 0.011; Type of Caloric Beverage SCC 0.005, they have the capacity to identify subtle patterns and trends that may indicate obesity risk. These pieces of knowledge can empower practitioners to implement individualized approaches to obesity prevention and control strategies. The accurate identification of people at risk of obesity may be a breakthrough discovery to promote public health. Person-time detection can help identify at-risk people for targeted preventive efforts, starting with lifestyle modification and patient education and extending to pharmacological and technological means.

关键词： Obesity Time-frequency analysis Accuracy Machine learning algorithms Computational modeling Predictive models prediction algorithms Boosting Data models Context modeling

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A Comparative Analysis of Machine Learning Classification algorithms for Cardiovascular Disease prediction

A Comparative Analysis of Machine Learning Classification Al...

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International Conference on Adaptive Science & Technology (ICAST)

作者： Elliot Kojo Attipoe Alimatu Saadia Yussiff Maame Gyamfua Asante-Mensah Dept. of Computer Science and Information Technology University of Cape Coast Cape Coast Ghana

ISBN: (数字)9798350385403

ISBN: (纸本)9798350385410

Cardiovascular disease is a life-threatening condition accounting for over 17 million deaths globally every year. Several studies have revealed the advancement of machine learning algorithms in predictive modeling and risk assessment, leading to improved chronic disease management and preventive measures. The algorithms analyze complex medical datasets to uncover hidden patterns, trends, and correlations that might be difficult for the medical practitioner, thereby improving the efficiency and accuracy of the medical decision-making process. This study utilizes a cardiovascular disease dataset comprising 70,000 instances to compare the performance of ten classification algorithms in predicting cardiovascular diseases. A new machine learning model is proposed, which performs better than all existing models using the same dataset. The results show the XGBoost classifier as the best model for predicting cardiovascular diseases, with an accuracy of 73.3% and an AUC score of 80%.

关键词： Training Machine learning algorithms Accuracy Stacking Predictive models prediction algorithms Classification algorithms Cardiovascular diseases Tuning Testing

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