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检索条件"主题词=Classification Algorithms"

共 26309 条记录，以下是151-160 订阅

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Integration of K-Means Clustering and Naïve Bayes classification algorithms for Smart AC Monitoring and Control in WSAN

Integration of K-Means Clustering and Naïve Bayes Classific...

引用

International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Ryan Putranda Kristianto Banu Santoso Marti Widya Sari Informatics Department Universitas Amikom Yogyakarta Jl. Ring Road Utara Condong Catur Sleman Yogyakarta Indonesia Computer Engineering Department Universitas Amikom Yogyakarta Jl. Ring Road Utara Condong Catur Sleman Yogyakarta Indonesia Informatics Department Universitas PGRI Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9781728151182

ISBN: (纸本)9781728151199

There is still a lot of excessive use of lamps, televisions, air conditioners (AC) and other electronic goods, resulting in a surge in electricity bills charged by electricity users due to neglect and waste of electrical energy. Based on these problems a system that is needed not only to monitor but also to control electrical equipment remotely so that electricity consumption can be controlled. Wireless Sensor and Actuator Network (WSAN) technology can monitor the physical condition of the environment which is widely applied in intelligent environments. WSAN is placed at certain regional points that will be observed the physical condition of the environment, each WSAN can use several sensors and actuators which will later be sent to the server via a wireless connection. In this research, we will test by making Smart AC (Air Conditioner) where at every point where there is AC will be installed WSAN. Data from several sensors generated from WSAN will be sent to the server to be observed and processed using intelligent computing and machine learning (K-Means and Naïve Bayes) so that the AC can turn on and off according to the physical conditions in the place. We evaluate our model too by using Confusion Matrix and obtain the score for accuracy 90%, precision 83%, recall 100% and error rate 10%, So our model can be called good model.

关键词： classification algorithms Clustering algorithms Actuators Monitoring Servers Temperature sensors Prediction algorithms

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Automatic artifact identification in image communication using watermarking and classification algorithms

Automatic artifact identification in image communication usi...

引用

International Symposium on Telecommunications, IST

作者： Shabnam Sodagari G. Hossein Hajimirsadeghi Alireza Nasiri Avanaki Control and Intelligent Processing Center of Excellence School of ECE University of Tehran Tehran Iran

We present a novel scheme, which enables the receiver to automatically identify the channel noise type in image communication. The method is based on embedding the image histogram, and deriving its specific statistics as descriptive features of the original image, through a robust watermarking algorithm within itself at the transmitter and extracting the hidden data at the receiver, for comparing with the corresponding features of the noise-distorted image. Then, by using a classifier, we are able to distinguish the channel noise type. Implementation results prove the efficiency of our proposed system in capably recognizing the noise type for common image transmission noise varieties.

关键词： classification algorithms Noise Histograms Support vector machines Principal component analysis Watermarking Training

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Evaluating the Performance of Different classification algorithms for Fabricated Semiconductor Wafers

Evaluating the Performance of Different Classification Algor...

引用

IEEE International Workshop on Electronic Design, Test and Applications (DELTA)

作者： Jian Wei Cheng Melanie Po-Leen Ooi Chris Chan Ye Chow Kuang Serge Demidenko School of Engineering Monash University Malaysia Freescale Semiconductor Inc. Selangor Darul Ehsan Malaysia

ISBN: (纸本)9781424460250;9780769539782

Defect detection and classification is crucial in ensuring product quality and reliability. classification provides information on problems related to the detected defects which can then be used to perform yield prediction, fault diagnosis, correcting manufacturing issues and process control. Accurate classification requires good selection of features to help distinguish between different cluster types. This research investigates the use of two features for classification: Polar Fourier Transform (PFT) and image Rotational Moment Invariant (RMI). It provides a comprehensive critical evaluation of several classification schemes in terms of performance and accuracy based on these features. It concludes by discussing the suitability of each classifier for classifying different types of defect clusters on fabricated semiconductor wafers.

关键词： classification algorithms Manufacturing processes Data mining Artificial neural networks classification tree analysis Fault diagnosis Semiconductor device manufacture Semiconductor device testing Semiconductor device reliability Process control

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Sensitivity of hyperspectral classification algorithms to training sample size

Sensitivity of hyperspectral classification algorithms to tr...

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Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS

作者： Matthew A. Lee Saurabh Prasad Lori Mann Bruce Terrance R. West Daniel Reynolds Trent Irby Hemanth Kalluri Electrical and Computer Engineering Department Mississippi State University Mississippi Geosystems Research Institute Mississippi State University Mississippi USA Electrical and Computer Engineering Department Mississippi State University Mississippi USA Plant and Soil Science Department Mississippi State University Mississippi USA

algorithms that exploit hyperspectral imagery often encounter problems related to the high dimensionality of the data, particularly when the amount of training data is limited. Recently, two algorithms were proposed to alleviate the small sample size problem - one is based on employing a Multi-Classifier Decision Fusion (MCDF) in the raw reflectance domain, and the other employed the MCDF framework in the Discrete Wavelet Transform domain (DWT-MCDF). This paper investigates the sensitivity of conventional single classifier based classification approaches, as well as MCDF and DWT-MCDF to variations in the amount of data employed for training the classification system. The hyperspectral data in this experiment was obtained using an airborne hyperspectral imager used by SpecTIRtrade. The results of the experimental analysis show that for the given application, the MCDF and DWT-MCDF algorithms are significantly less sensitive than the conventional algorithms to limited training data. PCA consistently results in overall accuracies of about 35%. LDA accuracies are very high, about 75%, when there is an abundance of training data - about 10X (i.e. number of training samples is 10 times the number of spectral bands); remains above 60% for training data abundances of 2X and higher; but dramatically decreases to ~20% for abundances of 1X. MCDF results in accuracies ranging between 65% and 75% for training data abundance of 3X and higher, but the accuracies drop to ~60% for 2X and ~55% for 1X. DWT-MCDF results in high accuracies with the least sensitivity to training data abundance. Its accuracies range between ~60-65% for abundances of 1X to 10X.

关键词： Hyperspectral sensors classification algorithms Training data Discrete wavelet transforms Hyperspectral imaging Reflectivity Wavelet domain Algorithm design and analysis Principal component analysis Linear discriminant analysis

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New classification algorithms for Developing Online Program Recommendation Systems

New Classification Algorithms for Developing Online Program ...

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International Conference on Mobile, Hybrid, and On-line Learning (ELML)

作者： Thomas Meller Eric Wang Fuhua Lin Chunsheng Yang Centre of Educational & Information Technology Douglas College New Westminster Canada School of Business Athabasca University Athabasca Canada School of Computing and Information Systems Athabasca University Athabasca Canada Institute of Information Technology National Research Council Canada Ottawa Canada

This paper presents two novel nearest-neighbor-like classification algorithms for program recommendation in a Web-based system, which provides a program planning service to academic advisors and students of post-secondary institutions. To evaluate the accuracy of classification for program recommendations generated by our algorithm, a statistical study was conducted through comparing our algorithm against two well-known classification algorithms, the Naive Bayes algorithm and the J48 algorithm, for making recommendations to students based on their academic history. The study shows that our proposed nearest-neighbor-like algorithms outperform the two well-known classification algorithms in terms of student classification success rate when there is uncertainty present in the data.

关键词： classification algorithms Inference algorithms Educational institutions History Data mining Nearest neighbor searches Information technology Technology planning Bayesian methods Mobile computing

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Optimization Research on Deep Learning-Based Image classification algorithms in Computer Vision

Optimization Research on Deep Learning-Based Image Classific...

引用

Artificial Intelligence and Computer Engineering (ICAICE), International Conference on

作者： Tianyi Sun Zhejiang Gongshang University School of Information and Electronic Engineering Hangzhou Zhejiang

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

Aiming at the problems of high computational complexity and insufficient accuracy of traditional deep learning image classification algorithms, a novel network structure called DARN (Dual Attention ResNet) is designed. This method embeds channel attention and spatial attention modules in the residual blocks, optimizes the training strategy, and reduces the algorithm complexity. On the ImageNet dataset, the classification accuracy reaches 78.6%, which is 2.3% higher than the original ResNet50, with a 20.5% reduction in parameters and a 31% increase in inference speed, achieving a balance between model performance and computational efficiency.

关键词： Deep learning Training Accuracy Computational modeling Inference algorithms classification algorithms Computational efficiency Optimization Image classification Residual neural networks

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Mobile Terminal Session SIR Prediction Method Based on Clustering and classification algorithms

Mobile Terminal Session SIR Prediction Method Based on Clust...

引用

IEEE Conference on Wireless Communications and Networking

作者： David Martín-Sacristán Jose F. Monserrat Daniel Calabuig Narcis Cardona ITEAM-Institute of Telecommunications and Multimedia Applications Universidad Politecnica de Valencia Spain Spanish Ministry of Education and Science iTEAM - Institute of Telecommunications and Multimedia Applications Spain

ISBN: (纸本)9781424463961;9781424463985

In this paper, a novel method for mobile terminal session signal to interference ratio (SIR) prediction is presented. The method is based on clustering and classification algorithms configuring a fully-automatic process, highly adaptable to the deployment scenario. It is focused on the prediction of the session SIR probability density function (pdf) of each user whose knowledge will allow for the application of advanced radio resource management (RRM) techniques. The proposed method has been applied in simulated and real scenarios showing its validity in both cases.

关键词： classification algorithms 3G mobile communication Clustering algorithms Training Prototypes Ground penetrating radar

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An empirical comparison of ensemble classification algorithms with support vector machines

An empirical comparison of ensemble classification algorithm...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Zhong-Hui Hu Yuan-Gui Li Yun-Ze Cai Xiao-Ming Xu Department of Automation Shanghai Jiaotong University Shanghai China

ISBN: (纸本)0780384032

An ensemble classifier often has better performance than any of the single learned classifiers in the ensemble. In this paper, the trained support vector machine (SVM) classifiers are used as basic classifiers. The ensemble methods for creating ensemble classifier, such as bagging and boosting, etc., are evaluated on two data sets. Some conclusions are obtained. Bagging with SVM can stably improve classification accuracy, while the improvement obtained by boosting with SVM is not obvious. These two methods largely increase space complexity and time complexity. Comparatively, the multiple SVM decision model, training individual SVM classifiers using training subsets obtained by partitioning the original training set, has a better trade-off between the classification accuracy and efficiency.

关键词： classification algorithms Support vector machines Support vector machine classification Bagging Boosting Machine learning classification tree analysis Decision trees Artificial neural networks Machine learning algorithms

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Enhanced Intrusion Detection in Cloud Security by Optimizing classification algorithms

Enhanced Intrusion Detection in Cloud Security by Optimizing...

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

作者： Mohammad Ziad Mizher Ali Bou Nassif Department of Computer Engineering University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

The primary threat to network security is detecting intrusive activities over the network. Because network models and bandwidths are rapidly evolving, the effectiveness of Intrusion Detection Systems (IDSs) requires intuitive Artificial Intelligence (AI). Furthermore, studies in recent years have shown that parsing network traffic data into an IDS is a productive method for detecting security breaches. Detecting malicious assaults on any system is the first step in securing it. To detect whether a network traffic is intrusive or normal, classification algorithms support the IDSs in classifying network traffic types. In this paper, we display the comparative results obtained from conducting the CSE-CICIDS2018 dataset base, which represents in information security research the most reliable and up-to-date dataset. Applying the new AI trends to evaluate different feature selections and machine learning techniques. The classification algorithms such as Multilayer Perceptron (MLP), Naïve Bayes, Radial Basis Neural Network (RBF), Support Vector Machine (SVM), k-Nearest Neighbours (KNN), and Decision Tree (J48) algorithms come up to show the unwanted features to be removed from nonbehavioral intrusions and identify the most beneficial combination of qualities for a specific statistical report depending on the accuracy, precision, recall, and ROC curve, leading to the best model used with clarification. Results figured that the best classification algorithm is KNN with an accuracy of 100%.

关键词： Support vector machines Accuracy Machine learning algorithms Intrusion detection Telecommunication traffic Machine learning Nearest neighbor methods Feature extraction classification algorithms Decision trees

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Effective classification algorithms and Feature Selection for Bio-Medical Data using IoT

Effective Classification Algorithms and Feature Selection fo...

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HCT Information Technology Trends (ITT)

作者： Junaid Masood Muhammad Shahzad Zahoor Ali Khan Vishesh Akre Amala Rajan Sheeraz Ahmed Fawad Masood Iqra National University Peshawar Pakistan MNS University of Engineering and Technology Multanr Pakistan Career Dynamics Research Centre Peshawar Pakistan CIS Higher Colleges of Technology Fujairah UAE Higher Colleges of Technology Dubai UAE Institute of Space Technology Islamabad Pakistan

ISBN: (数字)9781728183794

ISBN: (纸本)9781728183800

Data mining is the process to predict future trends. Data mining involving searching of patterns whose general purpose is to extract information using intelligent methods from a set of data and information turn it into an understandable structure for later use. Data mining using IoT becomes one of the leading providers applicable in several areas. The method of extracting usable data from larger set of arbitrary raw data is called data mining. This includes the analysis of sample data in large amounts of data with one or more programs. Concentrate on large amounts of data and databases to analyze, we use predictive analysis in the field of medicine. In this study, we examine the performance of algorithms along with the use of IoT that analyses large amounts of scattered information to make sense of it and turn it into knowledge. For this we used feature selection and classification algorithm on hepatitis data to get best result with minimum error rate and compare feature selection with classification algorithm so that to check which method is best according to results.

关键词： classification algorithms Feature extraction Data mining Error analysis Software Prediction algorithms Liver

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