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检索条件"主题词=Machine Learning Algorithms"

共 25877 条记录，以下是571-580 订阅

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Epileptic State Prediction using Phase Space Domain and machine learning algorithms

Epileptic State Prediction using Phase Space Domain and Mach...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Boluwatife Faremi Yedukondala Rao Veeranki Hugo F. Posada-Quintero Biomedical Engineering University of Connecticut Storrs USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Epilepsy is a disease of the brain that causes unprovoked or reflex seizures that affects millions of individuals worldwide. Traditionally, identifying epileptic states involves assessing neuroimaging scans or brain electrical signals recorded by EEG devices. However, due to the complex nature of these signals, there are growing demands for developing predictive systems that can improve the detection of this brain condition through unseen discriminating features. This study investigates predicting and detecting epileptic states by transforming 2-dimensional EEG time series data to the Phase space domain. The angular distance and probability density function between phase vectors were computed in the new domain to extract features. Renyi and Tsallis complex features were mainly extracted to train probabilistic, discriminatory, tree, and kernel-based models. The performance of the learning algorithms was evaluated using leaveone-subject-out cross-validation. Results revealed that the probabilistic models combined with complex features from the phase domain had a 91.5% accuracy compared to other algorithms. This result indicates the efficacy of the phase space domain for detecting and predicting epilepsy states.

关键词： machine learning algorithms Accuracy Computational modeling Epilepsy Feature extraction Brain modeling Probabilistic logic Prediction algorithms Electroencephalography Vectors

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Prediction of Voltage Discharge for Electric Tricycle Using machine learning algorithms

Prediction of Voltage Discharge for Electric Tricycle Using ...

引用

Cyber Physical Systems, Power Electronics and Electric Vehicles (ICPEEV), International Conference on

作者： Salava V Satyanarayana Pillalamarri Madhavi Vadla NavaKishore Pedda Suresh Ogeti Electrical and Electronics Engineering Hyderabad Institute of Technology and Management Hyderabad India Computer Science and Data Science Hyderabad Institute of Technology and Management Hyderabad India

ISBN: (数字)9798350388664

ISBN: (纸本)9798350388671

The application of Supervised machine learning algorithms for the prediction of Voltage discharge in Electric Tricycles will be explored in this paper. This research investigates the effectiveness of machine learning in predicting the voltage discharge based on the various tricycle operational data. This includes collecting data on discharge voltage, potentially alongside factors like Motor temperature, power, and terrain. machine learning algorithms will predict the voltage discharge by training with data. This will ultimately enhance the user experience for electric tricycle riders by providing them with a reliable indication of their remaining range.

关键词： Training Temperature distribution machine learning algorithms Voltage Discharges (electric) User experience Reliability Forecasting Regression tree analysis Random forests

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EV Speed Tracking Using the Regression-Based Supervised machine learning algorithms

EV Speed Tracking Using the Regression-Based Supervised Mach...

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Women in Computing (InCoWoCo), International Conference for

作者： Mary Ann George Anna Merine George Dattaguru V. Kamath Ciji Pearl Kurian Department of E&C Manipal Academy of Higher Education Dubai University of Wollongong Dubai Department of E&C BMS Institute of Technology & Management Bengaluru India Department of Mechatronics MIT Manipal Academy of Higher Education (MAHE) India Department of E&E MIT Manipal Academy of Higher Education (MAHE) India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Electric vehicle (EV) technology is an emerging eco-friendly solution that has reshaped the transportation sector. This paper aims to design a controller for EV speed tracking using three regression-based supervised machine learning (ML) algorithms. Regression techniques such as Ensemble learning (EL), Support Vector Regression (SVR), and Gaussian Process Regression (GPR) are used to develop ML-based controllers to predict the EV speed. The training data set for the ML models, including the predictors and response data, is extracted from the fuzzy-based controller scheme. Statistical measurement metrics, including the correlation coefficient (R), Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE), are used to evaluate the performance of the models. The simulation of the proposed controllers is carried out in a MATLAB-Simulink environment. Simulation results demonstrate the GPR model's advantage over other ML models for tracking EV speed. From the statistical performance of the EL, GPR, and SVR models, it is observed that GPR model with Bayesian optimization gives the best performance with the lowest RMSE of 3.31 and MAE of 0.0026 compared to other models.

关键词： machine learning algorithms Biological system modeling Predictive models Prediction algorithms Electric vehicles Data models Real-time systems Bayes methods Data mining Optimization

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Soil Monitoring Using machine learning algorithms

Soil Monitoring Using Machine Learning Algorithms

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Smart Devices (ICSD), International Conference on

作者： Jyoti Agarwal Ishita Bhatt Sahaj Goyal Prabhdeep Singh Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350394412

ISBN: (纸本)9798350394429

Soil monitoring is important aspect of agriculture to grow good quality food. It is crucial aspect for famers and the environmental scientists as well. If soil is monitored properly then chances of good quality crop increased which reduces farmer’s tension and increase their income. This research paper is a step towards applying machine learning algorithms for soil monitoring. Decision Tree Regression, Random Forest Regression, Gradient Boosting Regressor and Stacking Regressor are such algorithms. Comparison analysis of these techniques are also done in this paper. This work will be helpful for farmers and land managers to accurately predict about the soil fertility which will further help them to take decision which crop should be grow as per the soil condition.

关键词： machine learning algorithms Stacking Crops Soil Prediction algorithms Vectors Smart devices Regression tree analysis Monitoring Random forests

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Sentiment Analysis of machine learning algorithms: A Transformer-Based Approach

Sentiment Analysis of Machine Learning Algorithms: A Transfo...

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Intelligent Systems and machine learning Conference (ISML)

作者： Nadimpallli Madana Kailash Varma Sudabathula Vijay Sai Kumar Sri Harsh Mattaparty Shifa Ismail Marisetti Harshini Syed Ahmeduddin Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Hyderabad Telangana India

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

machine learning algorithms have become pervasive in diverse applications, revolutionizing various domains. However, the abundance of algorithms, each designed for specific purposes, poses a challenge for both novice users and experts in selecting the most suitable model. This research addresses this issue through a comprehensive analysis, leveraging Natural Language Processing (NLP) techniques and the powerful Transformers library. Researcher’s comments from published papers were analyzed using the Transformers library, presenting a novel approach that maps algorithm scores based on adjectives. Results indicate that Neural Networks consistently outperform other algorithms, providing valuable insights for practitioners. Our research contributes to a systematic evaluation framework, aiding researchers in algorithm selection.

关键词： Support vector machines Sentiment analysis machine learning algorithms Systematics Neural networks Transformers Libraries Vectors Robustness Splines (mathematics)

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Automated Land Use Identification Using Hyper Spectral Data and machine learning algorithms

Automated Land Use Identification Using Hyper Spectral Data ...

引用

Artificial Intelligence and machine learning Applications Theme: Healthcare and Internet of Things (AIMLA), International Conference on

作者： Govind Shay Sharma Lakshman K Arvind Kumar Pandey Department of Mathematics Vivekananda Global University Jaipur India Department of Management School of Mangement - UG JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science & IT ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

this paper discusses the usage of devices, gaining knowledge of algorithms and hyper spectral facts to discover land use routinely. Hyper spectral information is used to broaden land use classifiers with the use of numerous machine-gaining knowledge techniques, which include aid Vector device (SVM) and Random forest (RF). These land use classifiers are then used to identify land use sorts from hyper spectral photographs. It is miles proven that the land use classifiers that are developed with the use of those gadget mastering algorithms provide correct and efficient identification of land use sorts with excessive type accuracy while compared to different conventional methods. The outcomes reveal the potential of the machine learning algorithms for automatic land use identity from hyper spectral pictures.

关键词： Support vector machines Radio frequency machine learning algorithms Medical services Vectors Classification algorithms Object recognition

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Run Time and Accuracy of machine learning algorithms for Speaker Identification

Run Time and Accuracy of Machine Learning Algorithms for Spe...

引用

2024 International Symposium on Computer Science and Educational Technology (ISCSET)

作者： Stephan Lede René Schmidt Wolfram Hardt Department of Computer Engineering Chemnitz University of Technology Chemnitz Germany

ISBN: (数字)9798350390803

ISBN: (纸本)9798350390810

The efficiency and accuracy of machine learning algorithms in embedded systems are paramount for real-time processing tasks such as identifying an intended speaker out of potential sound sources. This paper presents an evaluation of support vector machines (SVMs) and feed-forward neural networks (FFNNs) for the regression problem of speaker identification, with a focus on constant calculation time and the influence of the use of different features. Utilizing a dataset comprising different speaker configurations and combinations of features were examined to assess their influence on accuracy and runtime. Results indicate that SVMs with linear, polynomial, and RBF kernels, as well as FFNNs, demonstrate effectiveness in solving the identification problem by providing real-time capabilities.

关键词： Support vector machines Accuracy machine learning algorithms Embedded systems Runtime Educational technology Real-time systems Polynomials Feedforward neural networks Kernel

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Advancements in Heart Disease Detection: Integrating machine learning algorithms and Clinical Insights

Advancements in Heart Disease Detection: Integrating Machine...

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Sagar Sharma Ramandeep Sandhu Manik Rakhra School of Computer Science and Engineering Lovely Professional University Phagwara Jalandhar Punjab India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

This study evaluates various machine learning algorithms, including deep learning, ensemble methods, and hybrid approaches, for classifying heart rhythms. It emphasizes the need for a detailed comparative analysis to understand each algorithm's strengths and weaknesses. The research highlights the importance of feature selection in enhancing predictive capabilities but notes the absence of detailed evaluation metrics such as precision, recall, F1 score, and ROC-AUC. Additionally, the study identifies gaps in understanding the significance of specific features in heart disease prediction, suggesting further investigation into feature relevance. Ethical concerns, such as data privacy, algorithmic bias, and decision-making transparency, are underscored as critical issues that need addressing to ensure the responsible and equitable deployment of predictive models in healthcare. The study advocates for a holistic approach, combining technical and ethical considerations, to advance heart disease prediction technology. Using KNN within the CDSS and a hybrid algorithm combining SVM and Random Forest, the study seeks to find the best predictive model for heart disease.

关键词： Heart Support vector machines Measurement Ethics machine learning algorithms Predictive models Prediction algorithms Feature extraction Random forests Diseases

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Interpretable machine learning algorithms for Interpretation of Clinical Data

Interpretable Machine Learning Algorithms for Interpretation...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Gautham Krishna Manoj Kumar Pathak Meenakshi Dheer Department of Civil Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of English ARKA JAIN University Jamshedpur Jharkhand India Department of Agriculture Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Interpretable machine learning (IML) algorithms use mathematical principles of machine learning to assist know-how, perception, and manipulation of scientific facts. Those algorithms aim to bridge the gap between clinicians’ knowledge of the facts and the output of traditional predictive analytics. IML algorithms have proved suitable for diverse obligations related to records analysis and interpretation of scientific facts, inclusive of predicting medical responses to remedy, exploring medical pathways, predicting future medical outcomes, and clustering affected person populations. Numerous techniques may be employed while using IML algorithms, which include linear fashions, choice tree models, help vector machines, neural networks, and Bayesian networks. Those fashions are mixed with specific parameters, including feature selection and weighting that can be used to evaluate the importance of every characteristic in predictive models. Moreover, those models can be implemented for non-linear facts by using regularization techniques. One essential advantage of IML over traditional devices in getting to know algorithms is the capacity to interpret the version outputs. Through using interpretable facts visualizations consisting of bar plots, scatter plots, and heat maps, clinicians can take advantage of insights into the relationships among features and goal variables.

关键词： Knowledge engineering machine learning algorithms Neural networks Clustering algorithms machine learning Predictive models Prediction algorithms Vectors Reliability Predictive analytics

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Plant Diseases Recognition Using machine learning algorithms

Plant Diseases Recognition Using Machine Learning Algorithms

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IEEE/ACM International Symposium on Big Data Computing (BDC)

作者： S. Alketbi T. Bonny Department of Computer Engineering University of Sharjah Sharjah UAE

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

This report explores the critical field of plant disease recognition through the use of various machine learning algorithms. As agriculture faces increasing challenges from pathogens and diseases, the rapid and accurate identification of plant afflictions is essential for effective crop management and improved yields. We analyzed a range of machine learning techniques, from traditional classifiers to advanced deep learning models, focusing on their ability to classify and diagnose plant diseases using symptomatology and image analysis. Our study evaluates the strengths and limitations of these approaches, providing insights into their technical details and practical applicability in agriculture. Through comprehensive experimentation and performance comparisons, we highlight the efficacy and robustness of different algorithms in plant disease management. The findings offer valuable guidance on how these methodologies can contribute to agricultural sustainability, helping stakeholders refine disease management strategies, optimize resources, and enhance resilience against environmental and pathogenic challenges. In summary, this report underscores the potential of diverse machine learning algorithms in advancing plant disease recognition, aiming to support effective and sustainable agricultural practices.

关键词： Plant diseases machine learning algorithms Image analysis Plants (biology) Feature extraction Agriculture Data models Sustainable development Monitoring Resilience

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