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IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW)

作者： Hladek, Daniel Stas, Jan Pleva, Matus Su, Ming-Hsiang Tech Univ Kosice Fac Elect Engn & Informat Dept Elect & Multimedia Commun Kosice Slovakia Soochow Univ Sch Big Data Management Dept Data Sci Suzhou Peoples R China

ISBN: (数字)9781665470506

ISBN: (纸本)9781665470506

This paper compares the statistical classification methods with deep learning methods for the document classification for documents in the Slovak language. The experiments evaluate the classification accuracy and times of execution. This study can help with selecting a natural language processing system for the Slovak language for devices with constrained memory and computational capabilities, especially cell phones. The classification algorithms are not constrained to Slovak languages. Adaptation of the algorithms to other languages requires only small adjustments.

关键词： Deep learning Power demand Cellular phones Neural networks Natural language processing classification algorithms Task analysis

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fuzzy-rough-learn 0.2: a Python library for fuzzy rough set algorithms and one-class classification

fuzzy-rough-learn 0.2: a Python library for fuzzy rough set ...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Lenz, Oliver Urs Cornelis, Chris Peraltat, Daniel Univ Ghent Dept Appl Math Comp Sci & Stat Computat Web Intelligence Ghent Belgium Univ Ghent IMEC Dept Informat Technol IDLab Ghent Belgium

ISBN: (数字)9781665467100

ISBN: (纸本)9781665467100

We have expanded the scope of fuzzy-rough-learn 0.2 from fuzzy rough sets to also cover one-class classification, facilitating the exploration of practical and conceptual connections between these two areas of machine learning. The new algorithms for one-class classification consist of nine data descriptors and one feature preprocessor. In addition, we have added Fuzzy Rough Nearest Neighbour regression and a number of preprocessors for feature scaling. Apart from these new core algorithms, we have rewritten fuzzy-rough-learn from the ground up, and included a large number of utility functions, to allow users to easily adapt any of the algorithms to their use case.

关键词： Machine learning algorithms Rough sets Machine learning Libraries classification algorithms Fuzzy systems

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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...

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

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

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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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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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Creation of an Adaptive Classifier to enhance the classification accuracy of existing classification algorithms in the field of Medical Data Mining

Creation of an Adaptive Classifier to enhance the classifica...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Sneha Chandra Maneet Kaur School of Computer Science & Engineering Lovely Professional University Phagwara Punjab India

Data Mining is the process of discovering interesting patterns and knowledge from large amounts of data. One of the most important techniques of Data Mining is classification which is used for prediction purposes. In this paper, we present a novel classifier for classification in the field of Medical Data Mining. The idea is to apply the Adaptive Classifier on the sample medical dataset, and compare its results with the results obtained through the individual classification techniques. This approach has been implemented and tested to show higher classification accuracy for the Adaptive Classifier. The results obtained have lit a great spark for future investigation.

关键词： Diseases Accuracy Diabetes Data mining classification algorithms Medical diagnostic imaging Bayes methods

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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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Detection and classification of Conflict Flows in SDN Using Machine Learning algorithms

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IEEE ACCESS 2021年 9卷 76024-76037页

作者： Khairi, Mutaz Hamed Hussien Ariffin, Sharifah Hafizah Syed Latiff, Nurul Mu'Azzah Abdul Yusof, Kamaludin Mohamad Hassan, Mohamed Khalafalla Al-Dhief, Fahad Taha Hamdan, Mosab Khan, Suleman Hamzah, Muzaffar Univ Teknol Malaysia UTM Sch Elect Engn Fac Engn Johor Baharu 81310 Malaysia Future Univ Fac Engn Khartoum 10553 Sudan Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England Univ Malaysia Sabah Fac Comp & Informat Kota Kinabalu 88400 Sabah Malaysia

Software-Defined Networking (SDN) is a new type of technology that embraces high flexibility and adaptability. The applications in SDN have the ability to manage and control networks while ensuring load balancing, access control, and routing. These are considered the most significant benefits of SDN. However, SDN can be influenced by several types of conflicting flows which may lead to deterioration in network performance in terms of efficiency and optimisation. Besides, SDN conflicts occur due to the impact and adjustment of certain features such as priority and action. Moreover, applying machine learning algorithms in the identification and classification of conflicting flows has limitations. As a result, this paper presents several machine learning algorithms that include Decision Tree (DT), Support Vector Machine (SVM), Extremely Fast Decision Tree (EFDT) and Hybrid (DT-SVM) for detecting and classifying conflicting flows in SDNs. The EFDT and hybrid DT-SVM algorithms were designed and deployed based on DT and SVM algorithms to achieve improved performance. Using a range flows from 1000 to 100000 with an increment of 10000 flows per step in two network topologies namely, Fat Tree and Simple Tree Topologies, that were created using the Mininet simulator and connected to the Ryu controller, the performance of the proposed algorithms was evaluated for efficiency and effectiveness across a variety of evaluation metrics. The experimental results of the detection of conflict flows show that the DT and SVM algorithms achieve accuracies of 99.27% and 98.53% respectively while the EFDT and hybrid DT-SVM algorithms achieve respective accuracies of 99.49% and 99.27%. In addition, the proposed EFDT algorithm achieves 95.73% accuracy on the task of classification between conflict flow types. The proposed EFDT and hybrid DT-SVM algorithms show a high capability of SDN applications to offer fast detection and classification of conflict flows.

关键词： Machine learning algorithms classification algorithms Support vector machines Control systems Decision trees Software algorithms Computer networks Software-defined network conflict flows detection flow classification machine learning algorithms

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A comparative study of classification algorithms for risk prediction in pregnancy

A comparative study of classification algorithms for risk pr...

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IEEE Region 10 International Conference TENCON

作者： Lakshmi B.N. Indumathi T.S. Nandini Ravi PG Research Centre Visvesvaraya Technological University Bangalore Karanataka India Obstetrics and Gynaecology Bangalore Karnataka India

ISBN: (纸本)9781479986422

Pregnancy is one of the most delicate stages in every woman's life requiring increased medical care and attention. Pregnancy induces a variety of abnormalities which may lead to severe complications if unnoticed or neglected. Pregnancy complications are health problems that are caused due to changes in physiological parameters during the period of gestation. Pregnancy complications can lead to severe maternal illness by women during pregnancy, at delivery and after delivery. The aim of this paper is to predict the present complications in the health of a pregnant woman using two classification algorithms namely C4.5 decision tree classification algorithm and Naive Bayes classification Algorithm. The selected algorithms are powerful and popular tools used for the tasks of classification and prediction in Data mining. These two algorithms use pregnancy data collected from pregnant women in different stages of pregnancy to predict their present health state and the associated health complications. This study focuses on identifying the best algorithm among the two classification algorithms to predict the health status of each pregnant woman and its associated complication. Applying these classification techniques on pregnancy related data the status of risk in the health of any pregnant women can be identified and maternal and fetal morality rate can be reduced.

关键词： Pregnancy classification algorithms Decision trees Prediction algorithms Algorithm design and analysis Pediatrics Blood pressure

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Physical activity recognition using inertial wearable sensors — A review of supervised classification algorithms

Physical activity recognition using inertial wearable sensor...

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International Conference on Advances in Biomedical Engineering (ICABME)

作者： Khaled Safi Ferhat Attal Samer Mohammed Mohamad Khalil Yacine Amirat Laboratoire LISSI Paris-Est University France AZM center EDST Lebanese university Lebanon

The goal of this paper is to compare the performances of various supervised algorithms in classifying physical daily living activities. Six healthy subjects were asked to perform twelve static and dynamic activities such as walking, running, sitting down, standing-up, lying, climbing stairs, etc. Three triaxial accelerometers are used to measure the human movements resulting from each activity. Seven supervised classification techniques are used: K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Random Forest (RF), Multi-Layer Perceptron (MLP), classification And Regression Tree (CART), Naive Bayes (NB), and Gaussian Mixture Model (GMM). These methods are compared in terms of correct classification rate (Accuracy), Recall, Precision, F-measure and execution time. The 10-fold cross validation is used as a validation procedure. The obtained results show that K-NN gives the best results with 96.26 % of correct classification rate.

关键词： Radio frequency Support vector machines Legged locomotion Yttrium Europe classification algorithms Conferences

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