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检索条件"主题词=CLASSIFICATION algorithms"

共 26309 条记录，以下是81-90 订阅

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classification algorithms for weather radar

Classification algorithms for weather radar

引用

International Conference on Mathematical Methods in Electromagnetic Theory (MMET)

作者： Felix Yanovsky Vitaly Marchuk Yaroslav Ostrovsky Yulia Averyanova National Aviation University Kiev Ukraine

Theory, measurements, and signal processing applying to the radar remote sensing of weather objects are considered. algorithms for hydrometeor type and turbulence intensity recognition are developed and analyzed. Part... 详细信息

关键词： Artificial neural networks Radar classification algorithms Rain Meteorological radar Radar measurements Doppler effect

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classification algorithms Research on Facial Expression Recognition

Classification Algorithms Research on Facial Expression Reco...

引用

2012 International Conference on Solid State Devices and Materials Science(SSDMS 2012)

作者： Jun Ou Department of Network Engineering Hainan Software Profession Institute

In order to improve the reliability of facial expression recognition system, and reduce the chance of false positives caused by error, classification strategy is important in recognition process. In the process, the k-nearest neighbor algorithm is improved. Experimental results show that the performance of the proposed method is excellent when it is applied to facial expression recognition system.

关键词： classification algorithms Facial expression recognition Feature characterization

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classification algorithms of Trojan Horse Detection Based on Behavior

Classification Algorithms of Trojan Horse Detection Based on...

引用

International Conference on Multimedia Information Networking and Security (MINES)

作者： Qin-Zhang Chen Rong Cheng Yu-Jie Gu Department of Computer Zhejiang University of Technology HangZhou China

Current anti-Trojan is almost signature-based strategies, which cannot detect new one. Behavior analysis, with the ability to detect Trojans with unknown signatures, is a technique of initiative defense. However, current behavior analysis based anti-Trojan strategies have the following problems: high false or failure alarm rate, poor efficiency, and poor user-friendly interface design, etc. The paper works on the design of an anti-Trojan oriented algorithm based on behavior analysis. And we construct a standard of anti-Trojan algorithm system and point the up-limit of the precision. We propose an improved hierarchical fuzzy classification algorithm which is specifically designed for anti-Trojan. Finally, we organize the experiment to get the results. The results show high classification accuracy using our algorithm. Compared to Bayesian algorithm, our algorithm have better performance.

关键词： classification algorithms Invasive software Algorithm design and analysis Information security Failure analysis Feature extraction Law Legal factors Computer security Computer networks

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classification algorithms for SIFT-MS Medical Diagnosis

Classification Algorithms for SIFT-MS Medical Diagnosis

引用

29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS 2007), vol.13

作者： K. Moorhead D. Lee J. G. Chase A. Moot K. Ledingham J. Scotter R. Allardyce S. Senthilmohan Z. Endre Department of Mechanical Engineering University of Canterbury Christchurch New Zealand

Selected Ion Flow Tube -^sMass Spectrometry (SIFT-MS) is an analytical technique for the real-time quantification of trace gases in air or breath samples. The SIFT-MS system can potentially offer unique capability in the early and rapid detection of a wide variety of diseases, infectious bacteria and patient conditions, by using a classifier to differentiate between control and test groups. By identifying which masses and Volatile Organic Compounds (VOCs) contribute most strongly towards a successful classification, biomarkers for a particular disease state may be discovered. A classification method is presented and validated in a simple study in which saturated nitrogen in tedlar bags was differentiated from dry nitrogen in tedlar bags. Several biomarkers were identified, with the most reliable being N{sub}2H{sup}+·H{sub}2O, and isotopes and water clusters of H{sub}3O{sup}+, as expected. The classifier was then applied in a clinical setting to differentiate between patient breath samples after one and four hours of dialysis treatment. Biomarkers for classification were ammonia, acetaldehyde, ethanol, isoprene and acetone. The model classifies significantly better than random, with an ROC area of 0.89.

关键词： classification algorithms Medical diagnosis Biomarkers Diseases Nitrogen Spectroscopy Gases Microorganisms Control systems System testing

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Research on computer image recognition and classification algorithms for complex scenes 2

Research on computer image recognition and classification al...

引用

2nd IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2024

作者： Zhou, Ziyao Tianjin University of Science and Technology College of Artificial Intelligence Tianjin China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368208

Image recognition and classification algorithms are more and more widely used in various complex scenes. However, the problems of diverse visual features, occlusion phenomenon, illumination variation and background interference in complex scenes bring great challenges to the existing algorithms. To address the challenges of image recognition and classification in complex scenarios, this paper thoroughly investigates existing mainstream image processing algorithms and introduces an enhanced model that leverages deep learning and multi-scale feature extraction. By integrating convolutional neural network (CNN) and attention mechanism, the model realizes efficient extraction of detailed features in images and global perception of scene information. Experimental results show that the proposed algorithm is superior to traditional algorithms in image recognition accuracy and classification performance in complex scenes, and has high robustness and generalization ability, providing effective technical support for applications in related fields. © 2024 IEEE.

关键词： Visualization Image recognition Attention mechanisms Accuracy Computational modeling Feature extraction Robustness classification algorithms Convolutional neural networks Image classification

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classification algorithms with a Complex Structure in Pattern Recognition

Classification Algorithms with a Complex Structure in Patter...

引用

Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Akram Kh. Nishanov Oybek Allamov Askar T. Rakhmanov Janar Yusupova Ortik B. Ruzibaev Department of Systematic and Practical Programming Technical Sciences Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Department of Software Engineering Urgench Branch of Tashkent University of Information Technology named after Muhammad al Khwarizmi Urgench Uzbekistan Department of systematic and practical programming Tashkent university of information technologies named after muhammad al-Khwarizmi Tashkent Uzbekistan Faculty of Software Engineering Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

The article considers in the intellectual processing of information, moving from a large unit of measurement to a smaller, more important unit of measurement, selecting sets of informative symbols and classifying symbols, developing modern recognition systems that help specialists in the field, as well as evaluating the states of objects to be classified, developing a classification algorithm for multi-class cases of educational samples. The development of a multi-class algorithm for symbol classification serves to develop the theory of symbol recognition. It deals with the construction of a decision rule construction algorithm. Its main purpose is to determine which of the given classes the recognized object belongs to. The complexity or simplicity of the proposed algorithm depends on the number of classes participating in the study sample. If the number of classes is two, then the formulation of decision rules is simple. That is why the classification algorithms proposed by most authors are solved for cases where the number of classes is three or more, the decisive rule in the matter of classification of objects is solved by making two classes. The construction of a multi-class decision rule for classification, which takes into account all classes at the same time, is a complex and urgent issue. In the article, in the classification of objects, a multi-level complex structured decision rule was developed based on special inequalities, and two-, three- and multi-class classification algorithms were developed.

关键词： Training Productivity Measurement units Atmospheric measurements Symbols Particle measurements classification algorithms

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Predicting Board Performance Using classification algorithms and Time Series Analysis 7

Predicting Board Performance Using Classification Algorithms...

引用

7th International Conference on Informatics and Computational Sciences, ICICoS 2024

作者： Velasco, Marienel N. Victoriano, Jayson M. Polytechnic University of the Philippines Batangas Philippines Bulacan State University Philippines Bulacan Philippines

ISBN: (纸本)9798350375886

Higher Education Institutions are increasingly recognizing the value of Educational Data Mining (EDM), a method that collects and analyzes educational data. EDM can employ machine learning techniques to assess and predict students' academic outcomes. This paper aims to apply EDM, integrating time series analysis and classification algorithms, to assist a university in the Philippines in improving its students' performance on the Licensure Examination for Teachers. The study involved an in-depth literature reviews and applied feature selection techniques to determine the significance of specific attributes. Subsequently, a comparative analysis was undertaken among three classification algorithms to ascertain the most accurate model in predicting student's examination performance. Various validation metrics were used to validate the results. Finally, the most optimal model was selected and integrated into the system. Moreover, the Rapid Application Development (RAD) model was employed to develop the 'Board Prophet' system, which offers various functionalities. These includes an online dashboard for accessing historical data, time series analysis capabilities for predicting the university's board performance, individual predictions of a students' success or failure in the licensure examination, and proposed intervention plans based on the system's predictions. Furthermore, the study's findings revealed that the student's General Weighted Average and Review Center were the two most significant attributes, with Decision tree outperforming other classification algorithms. The system features were assessed using ISO/IEC 25010 standards and the Technology Acceptance Model (TAM), with users showing high acceptance. These findings will empower the university make better decisions that will improve their students' licensure examination performance. © 2024 IEEE.

关键词： classification algorithms educational data mining machine learning time series analysis

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Evaluating the Performance of classification algorithms for Land-Cover classification 22

Evaluating the Performance of Classification Algorithms for ...

引用

22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Pastorini, Marcos Gorgoglione, Angela Rodriguez, Rafael Etcheverry, Lorena Castro, Alberto Universidad de la República Department of Computer Science Uruguay Universidad de la República Department of Fluid Mechanics and Environmental Engineering Uruguay Universidad de la República Department of Electrical Engineering Uruguay

ISBN: (纸本)9798350345346

Over the past few decades, the growing population in developing countries has significantly impacted land use and land cover (LULC), resulting in a threat to natural resources. Therefore, monitoring LULC changes in critical areas for effective land-use planning and policy-making is crucial. Google Earth Engine (GEE) cloud computing is a new platform that processes geospatial data and classifies LULC over vast areas utilizing machine-learning classification algorithms. In this study, we tested several classification models using Python and GEE to evaluate their accuracy and reliability in reproducing the LULC of a watershed located in Uruguay. We aimed to address the limited availability of GEE models. Our findings indicated that the Histogram-based Gradient Boosting Classifier outperforms the other models and delivers an improved performance of 21% compared to the model implemented in GEE. © 2023 IEEE.

关键词： classification algorithms Google Earth Engine Land-Cover Map Machine Learning

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classification algorithms and Deep Embedded Learning for Solving Sparse Inverse Linear Problem in Massive MIMO Systems

Classification Algorithms and Deep Embedded Learning for Sol...

引用

Sustainable Energy, Signal Processing and Cyber Security (iSSSC), IEEE International Symposium on

作者： Yasser Saeid Thomas Kopinski Engineering and economics South Westphalia University of Applied Sciences Meschede Germany

ISBN: (数字)9781728188805

ISBN: (纸本)9781728188812

In massive MIMO systems, base stations (BSs) may deploy a large number of antennas. When the system operates in a frequency-division-duplex (FDD) mode, the extensive number of antennas poses a significant challenge to the channel estimation in the downlink of the communication system. One option is to explore the possible underlying channel structure whereby the high-dimensional channel vector admits a low-dimensional (sparse) representation in some basis or dictionary. In this paper, we investigate the possibility to provide compressed (sparse) channel representation by performing clustering techniques for complex data elements. For this purpose we evaluate the performance of two algorithms: The first one is an adaptation of the classic k-means algorithm. The second one is a non-parametric method based on deep embedded clustering. The compressed channel representation may be seen as an alternative to methods based on compressed channel feedback, with the difference that the channel compression takes place directly in the domain of the low-dimensional channel representation.

关键词： Signal processing algorithms Clustering algorithms Channel estimation Massive MIMO Downlink classification algorithms Antennas

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classification algorithms Using Ensemble Methods

Classification Algorithms Using Ensemble Methods

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International Conference on Future Internet of Things and Cloud (FiCloud)

作者： Aishat Ganiyu Iman Darvishi Ronald Addo-Quaye Abel Yeboah-Ofori Bismark Tei Asare Oluwole Oguntoyinbo School of Eng. Phys. and Math. Sc. Royal Holloway University of London London United Kingdom School of Computing and Engineering University of West London London United Kingdom School of Business and Law Central Queensland University Brisbane Australia School of Arts Hum. and Social Sci. University of Roehampton London United Kingdom

ISBN: (数字)9798331527198

ISBN: (纸本)9798331527204

Machine Learning techniques are the backbone of successful Artificial Intelligence (AI) applications as they empower AI systems to make quality predictions and provide valuable insights from data that will aid decision-making within various industries. Ensemble method is a machine learning technique that helps to determine the suitable model for a dataset while limiting bias and variance; it is a technique used to obtain predictions by conducting maximum votes or averaging. This paper explores various Ensemble methods such as Bagging/Bootstrap Aggregation, Random Forest, and Boosting alongside their underlying algorithm Decision Tree for Regression and classification cases. The contribution of this paper is threefold. The foremost objective is to perform a theoretical analysis of various Ensemble methods with sample pseudocodes to aid the implementation process. Secondly, we implement Ensemble method algorithms using basic Python packages such as numpy, pandas, math, matplotlib and dataset installation packages for performance evaluations. Finally, we applied the developed code to real-world datasets across various industries, including healthcare, manufacturing, and real estate. The results highlight the different parameters and their performance on the algorithms while observing the proposed Ensemble program for performance evaluation.

关键词： Performance evaluation Industries Radio frequency Medical services Predictive models Prediction algorithms classification algorithms Manufacturing Ensemble learning Python

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