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检索条件"主题词=CLASSIFICATION algorithms"

共 26309 条记录，以下是121-130 订阅

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A comparison of different classification algorithms for determining the depth of anesthesia level on a new set of attributes

A comparison of different classification algorithms for dete...

引用

IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)

作者： Ayşe Arslan Baha Şen Fatih V. Çelebi Musa Peker Abdulkadir But Department of Computer Engineering Yildmm Beyazrt University Ankara Turkey Ankara Yildirim Beyazit Universitesi Ankara Ankara TR Department of Information Technology Samandira Vocational and Technical Anatolian High School Istanbul Turkey Department of Anesthesiology and Reanimation Yildirim Beyazit University Ankara Turkey

The effect of anesthesia on patient is expressed as the depth of anesthesia. The detection of appropriate depth of anesthesia is a matter of great importance in surgery. Too deep or too little anesthesia implementation may lead to many psychological and physical disorders on patients. Therefore it is necessary to keep the patient at the most appropriate level of anesthesia. This process is important and challenging operation. In this study, a system is proposed which can be used to determine the depth of anesthesia in order to assist physician. Anesthetic substances significantly affect the cortex of the brain. There are studies for determination of depth of anesthesia by monitoring of brain activity. In this study, EEG signals that reflect the brain activity are utilized to measure the depth of anesthesia. The study consists of feature extraction and classification stages of the EEG signal. In the feature extraction stage, a new attribute set consisting of 44 attributes in different categories was obtained. In this way, it is aimed to create an effective set of attributes that can represent EEG signals. The obtained attributes were used as input parameters for classification algorithms. In classification stage, the classification problem is classified by seven different classification algorithms. In this way, comparison of calculation time and accuracy for obtained results in different classification algorithms was provided. With the proposed method for the determination of different depth of anesthesia, 98.169% classification accuracy was achieved.

关键词： Anesthesia Electroencephalography classification algorithms Feature extraction Indexes Entropy Band-pass filters

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Sentiment Analysis of Mobile Phone Products Reviews Using classification algorithms

Sentiment Analysis of Mobile Phone Products Reviews Using Cl...

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International Conference on Promising Electronic Technologies (ICPET)

作者： Mohammed A. M. Salem Ashraf Y. A. Maghari Faculty of Information Technology Islamic University of Gaza Gaza Palestine

ISBN: (数字)9780738111391

ISBN: (纸本)9781665403818

Sentiment analysis or opinion mining is a process of analyzing opinions and emotions to infer the tendencies and impressions shown on the analyzed data and classify them as positive or negative. Sentiment analysis is extremely important because it helps companies and institutions to measure the scope of their customer's satisfaction with a specific product based on reviews in a very fast way. As the manual analysis of these reviews is very difficult because of the increase in the numbers of reviews on products day after day. This paper proposes a sentiment analysis model to classify product reviews as positive, neutral and negative. It applies five popular machine learning classifiers namely: Naive Bayes, Support Vector Machine, Decision Tree, K-Nearest Neighbor and Maximum Entropy with the aim to come up with the most efficient classifier. The dataset used consists of 82,815 reviews about mobile phone products, collected from Kaggle website. In order to evaluate the five classifiers, we used recall, precision, F1-mesaure and accuracy to measure the performance of each algorithm. The results show that Maximum Entropy and Naïve Bayes outperforms the other classifiers in term of accuracy in all experiments. Decision Tree algorithm gave the lowest results across all experiments in terms of accuracy.

关键词： Sentiment analysis Support vector machine classification Entropy Mobile handsets classification algorithms Decision trees Velocity measurement

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Evaluation of three-phase voltage dips classification algorithms based on computational intelligence

Evaluation of three-phase voltage dips classification algori...

引用

Workshop on Information Processing and Control (RPIC)

作者： J. Strack I. Carugati C. Orallo P. Donato S. Maestri J. Suárez Universidad Nacional de Mar del Plata Mar del Plata AR

ISBN: (纸本)9781467384674

In this paper are evaluated three different three-phase voltage dip classification algorithms developed by the authors. All them are based in the information provided by a sequence detector developed in a previuos work. The first algorithm is a logical classifier, the second it is based on fuzzy logic, and the third algorithm is a neural network adequately trained for this purpose. This evaluation is part of a wider work, and it will be used to the potential development of a power quality monitor. The performance of the algorithms under test in the classification of voltage dips, in ideal conditions and with variations in the phase and frequency, and high harmonic content is assessed. The conclusions of this analysis are the basis for the development and implementation of some of the functions of this instrument.

关键词： classification algorithms Voltage fluctuations Detectors Fuzzy logic Neural networks Electric potential Harmonic analysis

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Multiple classification algorithms for the BCI P300 speller diagram using ensemble of SVMs

Multiple classification algorithms for the BCI P300 speller ...

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IEEE GCC Conference & Exhibition

作者： Hend El Dabbagh Waleed Fakhr Arab Academy for Science Technology and Maritime Transport Egypt Arab Academy for Science Technology and Maritime Transport

Brain computer interface is one of the most recent and controversial field in Computer Science which emerged in order to help some handicapped people. This paper investigates different classification algorithms dealing with the BCI P300 speller diagram. The system used is composed of an ensemble of Support vector machines. Three different methods are used namely weighted ensemble of SVM, row & column based SVM ensemble and channel selection with optimized SVM's. Experimental results show that proposed methods obtain better results than published results of competition III dataset II.

关键词： Support vector machines Training Electroencephalography Feature extraction classification algorithms Continuous wavelet transforms Training data

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Comparative Study of classification algorithms to Classify the Restoration Base on Burn Severity Level

Comparative Study of Classification Algorithms to Classify t...

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Computer Science and Engineering (IC2SE), International Conference on

作者： Septiyani Septiyani Susilowati Susilowati Indra Ranggadara Saruni Dwiasnati Wawan Gunawan Faculty of Computer Science Mercu Buana University Jakarta Indonesia

ISBN: (纸本)9781665400473

The land fire that occurred in September 2019 in Katingan Regency, Central Kalimantan Province, covering an area of 970.44 hectares, caused pollution losses and environmental damage. Estimating the size damaged by fire is essential to identify areas that need restoration and implement an effective fire management plan. In this study, fire severity data were obtained from Landsat 8 OLI images from July 2019 to December 2019, which were then clipped and stacked as preprocessing and processed with the RdNBR extraction feature to identify the density of the Spatio-temporal hotspot based on the burn severity index. Furthermore, the feature extraction results are modeled using two classification algorithms, namely the Random Forest Classifier and the ANFIS algorithm. The comparison results show that the value of precision, recall, and accuracy for the ANFIS algorithm is higher than the RF classification. So it can be concluded that the ANFIS algorithm shows a more accurate performance than the RF classification in classifying burned areas in Katingan Regency, Central Kalimantan.

关键词： Pollution Humidity Feature extraction classification algorithms Image restoration Sensors Pollution measurement

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An Effective Design of Intrusion Detection System With classification algorithms And Feature Reduction In Machine Learning

An Effective Design of Intrusion Detection System With Class...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： S.Rajarajeswari Madhur Grover Yashoda L Piyush Mathurkar D. Bhanu Manpreet Singh Department of S&H Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement - UG Management JAIN (Deemed to be University) Karnataka India E&TC Vishwakarma Institute of Technology Pune India Department of Information Technology Karpagam Institute of Technology Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

To safeguard networks from outside attacks, the Intrusion Detection System (IDS) has become an essential tool in the modern realm. With the proliferation of data-generating and -sharing tools like Big Data, CC, and the IoT, it has become more challenging to isolate characteristics that contribute to effective intrusion detection systems (IDS). This problem has been addressed by using feature selection techniques (FSA) to filter out superfluous characteristics and isolate critical ones from network data. This has resulted in the creation of intrusion detection system models that are suitable for large-scale networks and have reduced costs while producing greater performance. A number of FSA classifiers have been investigated and tested in an effort to identify the best one for intrusion detection system capture. On real-time datasets, the top-performing classifier utilizing an appropriate feature reduction approach (FST) has been tested. Separately, we compared the efficacy and performance of FSA classifiers to those that included all characteristics. The proposed model (DT + RFE) is said to enhance IDS performance on the NSL-KDD, CICDDoS2019, and CICIDS2017 datasets, achieving 99.21%, 99.97%, and 99.94% average accuracy, respectively, while decreasing computation cost.

关键词： Training Costs Accuracy Computational modeling Intrusion detection Signal processing algorithms Feature extraction NSL-KDD Real-time systems classification algorithms

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Comparative Study of classification algorithms for Big Data in VANET

Comparative Study of Classification Algorithms for Big Data ...

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International Conference on Advances in Computing and Communication Engineering (ICACCE)

作者： Bahije Najat EL Hadaj Salah Laboratory of Applied Mathematics and Computer Science Cadi Ayyad University Marrakech Morocco

classification is a method used to predict membership of specific instance into a group of data. It uses a supervised learning method. Various of classification algorithm are available to process data, but the issue is which one is preferred taking consideration of the different input parameters. In this paper, we choose the several major supervised algorithms: K-NN (K Nearest Neighbors), NB (Naive Bays) and DT (Decision Tree). We use Vehicle Ad Hoc Network (VANET) real time data. This paper focuses on study of effectiveness measures in terms of accuracy and runtime.

关键词： classification algorithms Decision trees Big Data Vehicular ad hoc networks Runtime Safety

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Analysis of classification algorithms applied to road surface recognition

Analysis of classification algorithms applied to road surfac...

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IEEE National Conference on Radar

作者： Aleksandr Bystrov Mohammad Abbas Edward Hoare Thuy-Yung Tran Nigel Clarke Marina Gashinova Mikhail Cherniakov University of Birmingham Birmingham UK Jaguar Land Rover Research Department Coventry UK

ISBN: (纸本)9781479982332

The development of remote surface recognition systems is an important step in ensuring road safety. This paper examines the performance of surface classification algorithms, used for the analysis of backscattered microwave and ultrasonic signals. The novelty of our research is the joint use of data obtained from sonar and multifrequency polarimetric radar. The results demonstrate the feasibility of reliable surface classification using the proposed methodology.

关键词： Asphalt Sonar Radar Roads Acoustics classification algorithms Microwave theory and techniques

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A Comparable Study Employing WEKA Clustering/classification algorithms for Web Page classification

A Comparable Study Employing WEKA Clustering/Classification ...

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Panhellenic Conference on Informatics (PCI)

作者： Ioannis Charalampopoulos Ioannis Anagnostopoulos Department of Information and Communication Systems Engineering University of the Aegean Karlovassi Greece Department of Computer Science and Biomedical Informatics University of Central Greece Lamia Greece

Documents and web pages share many similarities. Thus classification methods used in documents can be applied to advanced web content, with or even without modifications. algorithms for document and web classification are presented as an introduction. One out of many tools that can be used in method evaluation, application and modification is WEKA (Waikato Environment for Knowledge Analysis). Testing results and conclusions strengthen the principles and bases of classification, while demonstrating the need for a new interlayer in the evaluation of classification methods.

关键词： classification algorithms Algorithm design and analysis Web pages Clustering algorithms Indexing Internet Machine learning

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Manufacturing Test Data Analysis-On Comparing Different classification algorithms

Manufacturing Test Data Analysis-On Comparing Different Clas...

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International Symposium on Information Technology Convergence (ISITC)

作者： Jia Keat Lee Somnuk Phon-Amnuaisuk Huat Chin Chew Chin Kuan Ho Faculty of Information Technology Multimedia University Cyberjaya Malaysia Bayan Lepas FIZ Intel슮 Corporation Penang Malaysia

Due to the circuit complexity and the number of parameters involved, test relationship of a Test Program (TP) might not be fully discovered. Traditionally, TP setup are defined based on the domain expertise and gathered experience of an engineer. Such judgment is time consuming and could be inefficient especially when new products and technologies are rapidly developed for the competing market. If the complexity of a TP increases, the undetected interrelationship among tests in a TP will also increase. In this paper, inferences are performed to a huge and complex TP using different classification algorithms, with the primary goal to discover potential test relationships in a fast and efficient way. The mining output can be used as a reference and basis for test engineers to improve TP setup or to reprogram test machine to replace current exhaustive test policy.

关键词： Manufacturing Data analysis Algorithm design and analysis classification algorithms Circuit testing Performance evaluation Information technology Complexity theory Process design Inference algorithms

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