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检索条件"主题词=Classification Algorithms"

共 26309 条记录，以下是171-180 订阅

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Pixel classification algorithms for noise removal and signal preservation in low-pass filtering for contrast enhancement

Pixel classification algorithms for noise removal and signal...

引用

International Conference on Digital Signal Processing (DSP)

作者： Chunyan Wang Sha Gong Department of Electrical and Computer engineering Concordia University Montreal Canada Montreal Canada

With a view to obtaining a high quality contrast enhancement, low-pass filters are used to remove the noise generated in a high-gain histogram equalization process. To preserve signal variations, the LP operation applied to the pixels in non-homogeneous regions should have less smoothing strength than that in homogeneous regions. The pixel classification according to the gray level homogeneity is thus a critical part in the LP filtering. In this paper, two algorithms for pixel classification according to the gray level homogeneity of their regions are proposed. In each of them, image pixels are grouped in such a way that, in the same group, pixels in homogeneous regions can be easily distinguished from those in non-homogeneous regions by a simple gradient thresholding, despite the complexity of signal gradient degradation in images. The two proposed classification algorithms are very simple, requiring very small quantity of computation. Their effectiveness has been proven by the simulation results.

关键词： Histograms classification algorithms Signal processing algorithms Noise Filtering Digital signal processing Algorithm design and analysis

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Performance Analysis of Keystroke Dynamics Using classification algorithms

Performance Analysis of Keystroke Dynamics Using Classificat...

引用

International Conference on Information and Computer Technologies (ICICT)

作者： Alaa Darabseh Doyel Pal Department of Mathematics Engineering and Computer Science LaGuardia Community College CUNY Long Island City NY USA

ISBN: (数字)9781728172835

ISBN: (纸本)9781728172842

Authentication is the process of verifying the identity of a user. Biometric authentication assures user identity by identifying users physiological or behavioral traits. Keystroke dynamics is a behavioral biometric based on users typing pattern. It can be used to authenticate legitimate users based on their unique typing style on the keyboard. From a pattern recognition point of view, user authentication using keystroke dynamics is a challenging task. It can be accomplished by using classification algorithms - two-class and one-class classification algorithms. In this paper, we study and evaluate the effectiveness of using the one-class classification algorithms over the two-class classification algorithms for keystroke dynamics authentication system. We implemented and evaluated 18 classification algorithms (both two-class and one-class) from the literature of keystroke dynamics and pattern recognition. The result of our experiments is evaluated using 28 subjects with the total of 378 unique comparisons for each classifier. Our results show that the top-performing classifiers of one-class are not very different from two-class classifiers and can be considered to use in the real-world authentication systems.

关键词： classification algorithms Authentication Heuristic algorithms Machine learning algorithms Detection algorithms Feature extraction Machine learning

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Side Scan Sonar Object classification algorithms

Side Scan Sonar Object Classification Algorithms

引用

International Symposium on Unmanned Untethered Submersible Technology

作者： M.F. Doherty J.G. Landowski P.F. Maynard G.T. Uber D.W. Fries F.H. Maltz Astronautics Division-Marine Systems Lockheed Missiles and Space Company Inc. Santa Clara CA USA

Autonomous underwater vehicles require the capability to understand their environment. This understanding, coupled with the operational goals of the vehicle, determines the subsequent actions of the vehicle. Environmental understanding is realized through the vehicle's sensors and a priori knowledge. This paper focuses on our investigation of automatic interpretation of side scan sonar data for the purpose of detecting and classifying undersea mines. The test data set is a series of side scan sonar images taken from a U. S. Navy acoustic sensor under optimal conditions (flat, sandy bottom). Groundtruth is available for acoustic images with eight unique types of mine targets. The interpretation of the data is per formed in two stages. The first stage, preprocessing and target detection, uses an adaptive thresholding algorithm coupled with an adaptive averaging technique to locate objects of interest in the sonar image. The second stage, classification, performs a binary classification of whether each detected object is, or is not, a mine. The classification is achieved using an attribute-based decision tree. An approach for a third stage, identification of the mark and mod of the classified bottom mines, is also presented. The results to date, as well as plans for the future, are discussed.

关键词： classification algorithms Sonar detection Acoustic signal detection Remotely operated vehicles Object detection Acoustic sensors Image sensors Acoustic testing Underwater acoustics Mobile robots

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Underwater threat recognition: Are automatic target classification algorithms going to replace expert human operators in the near future?

Underwater threat recognition: Are automatic target classifi...

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OCEANS - Europe

作者： Olga Lopera Tellez Signal and Image Centre Royal Military Academy Brussels Belgium

In this paper, different human/machine strategies are tested in order to evaluate their performance in underwater threat recognition. Sonar images collected using synthetic aperture sonar (SAS) and side scan sonar (SSS) during real mine countermeasures exercises are used. Data are collected over a test area on the Belgian Continental Shelf, where several targets were deployed. Image resolution is divided in three categories: (1) up to 5cm pixel size, (2) between 5cm and 10cm pixel size, (3) larger than 10cm pixel size. Soil complexity is also evaluated and used to build up different strategies. Results demonstrate the utility of considering the human operator as an integral part of the automatic underwater object recognition process, as well as how automated algorithms can extend and complement human performances.

关键词： classification algorithms Synthetic aperture sonar Sea floor Soil Imaging Databases

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Application of Data Mining classification algorithms in Customer Membership Card classification Model

Application of Data Mining Classification Algorithms in Cust...

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International Conference on Information Management, Innovation Management and Industrial Engineering, ICIII

作者： Lin Zhang Yan Chen Yan Liang Nan Li College of Transportation and Management Dalian Maritime University Dalian Liaoning China

This paper uses data mining classification algorithms--C5.0 and CART algorithms to get useful information to decision-making out of customerspsila transaction behaviors. Firstly, by business understanding, data understanding and data preparing, modeling and evaluating we get the results of the two algorithms and by comparing the results ,we know that the two algorithms can both be applied in the customer membership card classification model and can obtain a quite accurate result. Then we introduce the application of this model. Through analysis, we get to know customerspsila income level and children number are the two main factors to affect them to choose cards. Knowing that, enterprises can take corresponding measures, such as dividing customers into different groups and then recommending the corresponding card to the customer who has the similar characteristics. By this means, enterprises can provide special service to different card rank users in order to attract more and more customers.

关键词： Data mining classification algorithms classification tree analysis Databases Innovation management Data analysis Decision trees Information management Industrial engineering Educational institutions

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A survey and taxonomy of various packet classification algorithms

A survey and taxonomy of various packet classification algor...

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International Conference on Advances in Computer Engineering and Applications (ICACEA)

作者： Bharti Nagpal Nanhay Singh Naresh Chauhan Radhika Murari CSE Deptt. AIACT&R Delhi CSE Deptt. YMCAUST Faridabad Haryana

Routers can also function as firewalls and perform various operations on the incoming and outgoing packets. In case when all the packets share common header characteristics, it is termed as a packet flow. In order to classify a packet, routers perform a lookup on a classifier table using one or more fields from the packet header to classify the packet into its corresponding flow. A classifier is a set of rules which identify each flow and the appropriate actions to be taken for any packet belonging to that flow. The paper examines the problem of packet classification and various proposed techniques for the same.

关键词： classification algorithms Memory management Algorithm design and analysis Hardware Decision trees Data structures Taxonomy

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Performance Comparison of classification algorithms for EEG-based remote epileptic seizure detection in Wireless Sensor Networks

Performance Comparison of classification algorithms for EEG-...

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ACS/IEEE International Conference on Computer Systems and Applications

作者： Khalid Abualsaud Massudi Mahmuddin Mohammad Saleh Amr Mohamed Department of Computer Science & Engineering Qatar University Doha Qatar Computer Science Dept. Graduate School of Computing University Utara Malaysia Sintok Kedah

Identification of epileptic seizure remotely by analyzing the electroencephalography (EEG) signal is very important for scalable sensor-based health systems. classification is the most important technique for wide-ranging applications to categorize the items according to its features with respect to predefined set of classes. In this paper, we conduct a performance evaluation based on the noiseless and noisy EEG-based epileptic seizure data using various classification algorithms including BayesNet, DecisionTable, IBK, J48/C4.5, and VFI. The reconstructed and noisy EEG data are decomposed with discrete cosine transform into several sub-bands. In addition, some of statistical features are extracted from the wavelet coefficients to represent the whole EEG data inputs into the classifiers. Benchmark on widely used dataset is utilized for automatic epileptic seizure detection including both normal and epileptic EEG datasets. The classification accuracy results confirm that the selected classifiers have greater potentiality to identify the noisy epileptic disorders.

关键词： Electroencephalography Accuracy classification algorithms Noise measurement Feature extraction Wireless communication Epilepsy

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Analyzing the Impact of Preprocessing Techniques on the Efficiency and Accuracy of Sentiment classification algorithms

Analyzing the Impact of Preprocessing Techniques on the Effi...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Sai Nikhila Kanigiri Chachi Mekuriyaw Garrett Goodman Michail S. Alexiou Department of Computer Science Kennesaw State University Marietta GA USA Computer Science and Software Engineering Miami University Oxford OH USA

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

In the era of data-driven decision-making, the ability to rapidly and accurately analyze vast amounts of textual data is essential. This paper examines the role of text pre-processing techniques in enhancing the effectiveness of sentiment classification algorithms. Such algorithms are pivotal in interpreting the emotional tone of texts and serving as a preliminary step towards reverse-engineering complex data systems. Through comprehensive analysis, we assess the impact of various preprocessing strategies such as keyword removal, lemmatization, and stemming and their combination on the performance of both traditional and advanced machine learning models, including Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Bidirectional Encoder Representations from Transformers (BERT). The current study evaluates the balance between efficiency and accuracy in sentiment analysis algorithms, illustrating the impact of preprocessing techniques. In addition, it aims to serve as a guideline for selecting preprocessing methods that optimize model performance in both sentiment analysis and textual data reverse-engineering.

关键词： Support vector machines Sentiment analysis Analytical models Machine learning algorithms Accuracy Transformers Encoding classification algorithms Random forests Guidelines

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Prediction of Students Final Project Values Based on Errors in Scientific Writing Using Data Mining classification algorithms

Prediction of Students Final Project Values Based on Errors ...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Taqwa Hariguna Berlilana Andhika Rafi Hananto Information Systems Departement Universitas Amikom Purwokerto Purwokerto Indonesia

College databases are quickly amassing more and more data. Some data, which has not been used to raise the caliber of student performance, includes secret information regarding student performance. Data mining is used to examine accessible educational data and reveal any hidden information that may be there. This study uses a data mining classification model to develop rules that can forecast the final project value of program students' information management based on course values that aid in the final project's preparation. The study's analysis of student performance in courses that help them prepare for their final projects will also be included. According to the subject that underpins their final project, this prediction is supposed to assist in determining grades. Predictive analysis employing ID3 has an accuracy of 65.54%, CHAID 70.67%, and Nave Bayes 73.13%, According to research that has been done, the competition from the Naive Bayes algorithm has succeeded in being slightly superior to other algorithms.

关键词： Data mining classification algorithms Data models Prediction algorithms Decision trees Predictive models Databases

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Comparative Analysis of classification algorithms for Landuse / Landcover Change Over A Part of The East Coast Region of Tamil Nadu And Its Environs

Comparative Analysis of Classification Algorithms for Landus...

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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Jannath Firthouse Mohammed Yashin Aarthi Deivanayagam Abdul Rahaman Sheik Mohideen Jegankumar Rajagopal Bharathidasan University Tiruchirappalli Tamil Nadu India

ISBN: (数字)9781728131146

ISBN: (纸本)9781728131153

The Landuse/Landcover (LULC) changes become more intense in this era due to rapid urbanization, industrialization and over utilization of agricultural land for human wellbeing. This study is an attempt to find an effective approach among various classifiers for the evaluation of spatio-temporal variations in LULC over a part of the East coastal region of Tamil Nadu for the period of 30 years. High and low resolution remote sensing data are used to perform five different LULC classification algorithms: K-means, IsoData, Maximum Likelihood (ML), Parallelepiped (PP) and Support Vector Machine (SVM). The experimental outcomes conclude that the Support vector machine classifier comparatively shows high accuracy and classification performance than others.

关键词： Support vector machines Earth Satellites Image resolution Vegetation mapping classification algorithms Remote sensing

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