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IEEE International Conference on Systems, Man and Cybernetics

作者： Cinar, Eyup Sahin, Ferat Electrical Engineering Department Rochester Institute of Technology USA

ISBN: (纸本)9781424465880

This paper analyzes the application of different classification techniques for Electroencephalography (EEG) signals. Fuzzy Functions Support Vector Classifier (FFSVC), Improved Fuzzy Functions Support Vector Classifier (IFFSVC) and a novel hybrid technique that has been designed utilizing Particle Swarm Optimization and Radial Basis Function Networks (PSO-RBFN) have been studied. The classification performance of the techniques is compared on the same standard datasets that are publicly available and used by many Brain Computer Interface (BCI) researchers. Results show that proposed classifiers might reach the classification performance of state of the art classifiers and might be used as alternative techniques in the classification applications of EEG signals.

关键词： Brain Computer Interface classification algorithms FFSVC IFFSVC and PSO-RBF

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Automatic Artifact Identification in Image Communication Using Watermarking and classification algorithms

Automatic Artifact Identification in Image Communication Usi...

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International Symposium on Telecommunications

作者： Sodagari, Shabnam Hajimirsadeghi, G. Hossein Avanaki, Alireza Nasiri Univ Tehran Control & Intelligent Proc Ctr Excellence Sch ECE Tehran Iran

ISBN: (纸本)9781424427505

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： Image communication noise classification algorithms watermarking

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Use of collaboration techniques and classification algorithms in personal healthcare

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HEALTH AND TECHNOLOGY 2012年第1期2卷 43-55页

作者： Trajkovik, Vladimir Vlahu-Gjorgievska, Elena Kulev, Igor Univ Ss Cyril & Metodius Fac Comp Sci & Engn Rugjer Boshkovikj 16POB 393 Skopje 1000 Macedonia Univ St Kliment Ohridski Fac Adm & Informat Syst Management Bitola 7000 Macedonia

Adoption of mobile devices and technology in the field of medical monitoring and personal health care systems is very important nowadays, especially when it comes to certain categories of people with chronicle diseases who need 24 hour access to medical care. The collaborative Information system model we present in this paper, gives a new dimension in the usage of novel technologies in healthcare. Using mobile, web and broadband technologies enable the citizens to have ubiquity of support services where ever they may be. The model incorporates collaboration techniques and classification algorithms in order to generate recommendations and suggestions for preventive intervention. In addition, the system enables the patient (system user) to contact other people with similar condition and exchange their experience. This system improves the terms of home care treatment of the patient and allows the user to adapt his/her physical activities to improve own health condition.

关键词： Personal health care system Social network Collaborative techniques classification algorithms

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classification algorithms in the personalisation of educational portals

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INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG LEARNING 2016年第1期26卷 105-116页

作者： Debska, Barbara Kubacka, Agnieszka Rzeszow Univ Technol Dept Comp Chem Powstancow Warszawy 6 PL-35959 Rzeszow Poland State Higher Vocat Sch Krosno Dept Informat Inst Technol Wyspianskiego 20 PL-38400 Krosno Poland

Education by means of the e-learning method is becoming more and more popular nowadays and a rapid development of information technologies makes traditional, static websites used for online education being replaced by interactive, intelligent portals. In spite of the rapid advances in informatics, there is still no software which would meet the needs of all learners. Some personalisation features characterise the e-student portal which is addressed to the students of the Informatics Department at a Stanislaw Pigon Higher Vocational State School in Krosno. This paper will present the structure of the portal and also describe how to use it for the personalised online education system.

关键词： blended learning classification algorithms cluster analysis distance education e-learning portals individual learning paths k-means method personalisation of e-learning portal preliminary classification

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Comparative Analysis of Different classification algorithms for Prediction of Diabetes Disease 17

Comparative Analysis of Different Classification Algorithms ...

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2nd International Conference on Internet of things, Data and Cloud Computing (ICC)

作者： AlThunayan, Lujain AlSahdi, Nahed Syed, Liyakathunisa Prince Sultan Univ Coll Comp & Informat Sci Dept Software Engn Riyadh Saudi Arabia Prince Sultan Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh Saudi Arabia

ISBN: (纸本)9781450347747

Diabetes Mellitus is fast becoming an endemic in the world, especially in developing countries. An efficient prediction methodology is needed to diagnose the diabetes disease, which can be helpful for health care professionals. Data mining techniques have been widely used in healthcare to mine knowledgeable information from medical data. Data mining is the process of analyzing data based on different perspectives and summarizing it into useful information. Data mining techniques are proven forearly prediction of several diseases with higher accuracy and lower error rate and cost. classification is one of the generally used techniques in medical data mining. In this paper, we intend to explore various data mining techniques to show the comparison of different classification algorithms using Waikato Environment for Knowledge Analysis (WEKA) and analyze the results in order to find the best suitable classification algorithm for prediction of diabetes diseases. Various performance measures metrics such as sensitivity, specificity, accuracy and error rate are used for finding the accuracy of the classifier.

关键词： Data Mining Diabetes classification algorithms WEKA

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Meta-Heuristic Supported Feature Selection in classification algorithms for Diabetes Diagnosis 32

Meta-Heuristic Supported Feature Selection in Classification...

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32nd IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Alp, Gozde Soygazi, Fatih Aydin Adnan Menderes Univ Muhendislik Fak Bilgisayar Muhendisligi Bolumu Aydin Turkiye

ISBN: (纸本)9798350388978;9798350388961

Diabetes is a health problem that occurs when blood sugar levels rise above normal as a result of the body's inability to produce sufficient amounts of insulin or to use it effectively. Early diagnosis of this disease is vital to prevent complications and improve quality of life. However, since the traditional diagnosis process is challenging, the development of an intelligent decision support system utilizing machine learning techniques can facilitate the early diagnosis of diabetes. This paper presents classification studies on the Pima Indian Diabetes dataset using different machine learning techniques for early diagnosis of diabetes. Genetic Algorithm (GA) and Simulated Annealing (SA) meta-heuristic algorithms were utilized in the feature selection phase to increase the accuracy. Within the scope of the research, linear regression, random forest, support vector machines, XGBoost and LightGBM algorithms were tested without and with GA and SA support and the most successful result was determined. The success of each classification process is reported with accuracy, precision, recall and F1 score performance metrics. It was found that the highest accuracy value among the proposed methods was obtained from the LightGBM+GA model as 86% and the feature selection improved the result in all models, especially LightGBM and XGBoost.

关键词： feature selection genetic algorithm simulated annealing classification algorithms diabetes dataset machine learning gradient boosting

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Assessment of Heart Patients Dataset Through Regression and classification algorithms 3rd

Assessment of Heart Patients Dataset Through Regression and ...

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3rd EAI International Conference on Data and Information in Online Environments (DIONE)

作者： Somavilla, Eliane de Araujo, Gustavo Medeiros Univ Fed Santa Catarina PGCIN Florianopolis Brazil

ISBN: (纸本)9783031223235;9783031223242

This study focused on the survival analysis of patientswith heart failure whowere admitted to the Institute of Cardiology and Allied Hospital of Faisalabad-Pakistan during April and December 2015. All patients had left ventricular systolic dysfunction, belonging to classes III and IV of the classification carried out by the New York Heart Association. Several Machine Learning algorithms capable of analyzing data through regression and classification techniques were used to predict the mortality rate of future patients with similar problems. Characteristics such as age, ejection fraction, serum creatinine, serum sodium, anemia, platelets, creatinine phosphokinase, blood pressure, diabetes and smoking were considered as potential contributors to mortality. All characteristics were analyzed in order to identify the minimum set of information necessary for a quick and efficient diagnosis of heart failure.

关键词： Heart patients Regression algorithms classification algorithms Feature select

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A Simple Study of Webpage Text classification algorithms for Arabic and English Languages

A Simple Study of Webpage Text Classification Algorithms for...

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3rd International Conference on IT Convergence and Security (ICITCS)

作者： AL-Ghuribi, Sumaia Mohammed Alshomrani, Saleh King Abdulaziz Univ Fac Comp & Informat Technol Jeddah 21413 Saudi Arabia

ISBN: (纸本)9781479928453

Webpage text classification is an important problem that has been studied through different approaches and algorithms. It aims to assign a predefined category to a Webpage based on its content and linguistic features. It has many applications such as word sense disambiguation, document indexing, text filtering, Webpages hierarchical categorization and document organization. This study is a part of a work in progress, in which we are targeting to develop Bi-languages algorithm for classifying Arabic and English Webpage text and can perform accurate and efficient in both languages. It aims at providing a simple overview of many approaches that constructed for classifying Arabic and English Webpage documents. In this survey, the widely used algorithms for text classification are given with a comparison of the recent researches in classification field for Arabic and English languages to conclude which is the best algorithm that we can apply for both Arabic and English Languages.

关键词： Webpage text classification linguistic features Arabic language classification algorithms

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On the Performance of classification algorithms for Learning Pareto-Dominance Relations

On the Performance of Classification Algorithms for Learning...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Bandaru, Sunith Ng, Amos H. C. Deb, Kalyanmoy Univ Skovde Virtual Syst Res Ctr Skovde Sweden Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA

ISBN: (纸本)9781479914883

Multi-objective evolutionary algorithms (MOEAs) are often criticized for their high-computational costs. This becomes especially relevant in simulation-based optimization where the objectives lack a closed form and are expensive to evaluate. Over the years, meta-modeling or surrogate modeling techniques have been used to build inexpensive approximations of the objective functions which reduce the overall number of function evaluations (simulations). Some recent studies however, have pointed out that accurate models of the objective functions may not be required at all since evolutionary algorithms only rely on the relative ranking of candidate solutions. Extending this notion to MOEAs, algorithms which can 'learn' Pareto-dominance relations can be used to compare candidate solutions under multiple objectives. With this goal in mind, in this paper, we study the performance of ten different off-the-shelf classification algorithms for learning Pareto-dominance relations in the ZDT test suite of benchmark problems. We consider prediction accuracy and training time as performance measures with respect to dimensionality and skewness of the training data. Being a preliminary study, this paper does not include results of integrating the classifiers into the search process of MOEAs.

关键词： Meta-modeling Multi-objective optimization classification algorithms Pareto-dominance Machine learning

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A review and comparison of classification algorithms for medical decision making

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Health Policy 2005年第3期71卷 315-331页

作者： Harper, Paul R. School of Mathematics University of Southampton SO17 1BJ Southampton United Kingdom

Within a health care setting, it is often desirable from both clinical and operational perspective to capture the uncertainty and variability amongst a patient population, for example to predict individual patient outcomes, risks or resource needs. Homogeneity brings the benefits of increased certainty in individual patient needs and resource utilisation, thus providing an opportunity for both improved clinical diagnosis and more efficient planning and management of health care resources. A number of classification algorithms are considered and evaluated for their relative performances and practical usefulness on different types of health care datasets. The algorithms are evaluated using four criteria: accuracy, computational time, comprehensibility of the results and ease of use of the algorithm to relatively statistically naive medical users. The research has shown that there is not necessarily a single best classification tool, but instead the best performing algorithm will depend on the features of the dataset to be analysed, with particular emphasis on health care data, which are discussed in the paper. © 2004 Elsevier Ireland Ltd. All rights reserved.

关键词： CART classification algorithms Clinical decision making Neural networks

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