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A New Method Based on Adaptive Discrete Wavelet Entropy Energy and neural network classifier (ADWEENN) for Recognition of Urine Cells from Microscopic Images Independent of Rotation and Scaling

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JOURNAL OF MEDICAL SYSTEMS 2014年第2期38卷 1-9页

作者： Avci, Derya Leblebicioglu, Mehmet Kemal Poyraz, Mustafa Dogantekin, Esin Firat Univ Fac Engn Dept Elect Elect Engn TR-23119 Elazig Turkey Midle East Tech Univ Fac Engn Dept Elect Elect Engn Ankara Turkey Zirve Univ Emine Bahaeddin Nakiboglu Med Fac Gaziantep Turkey

So far, analysis and classification of urine cells number has become an important topic for medical diagnosis of some diseases. Therefore, in this study, we suggest a new technique based on Adaptive Discrete Wavelet Entropy Energy and neural network classifier (ADWEENN) for Recognition of Urine Cells from Microscopic Images Independent of Rotation and Scaling. Some digital image processing methods such as noise reduction, contrast enhancement, segmentation, and morphological process are used for feature extraction stage of this ADWEENN in this study. Nowadays, the image processing and pattern recognition topics have come into prominence. The image processing concludes operation and design of systems that recognize patterns in data sets. In the past years, very difficulty in classification of microscopic images was the deficiency of enough methods to characterize. Lately, it is seen that, multi-resolution image analysis methods such as Gabor filters, discrete wavelet decompositions are superior to other classic methods for analysis of these microscopic images. In this study, the structure of the ADWEENN method composes of four stages. These are preprocessing stage, feature extraction stage, classification stage and testing stage. The Discrete Wavelet Transform (DWT) and adaptive wavelet entropy and energy is used for adaptive feature extraction in feature extraction stage to strengthen the premium features of the artificial neural network (ANN) classifier in this study. Efficiency of the developed ADWEENN method was tested showing that an avarage of 97.58 % recognition succes was obtained.

关键词： Urine cells recognition Image processing Feature extraction Discrete wavelet transform Microscopic images artificial neural network classifier

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Material discrimination using cosmic ray muon scattering tomography with an artificial neural network

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Radiation Detection Technology and Methods 2022年第2期6卷 254-261页

作者： Weibo He Dingyue Chang Rengang Shi Maobing Shuai Yingru Li Sa Xiao Institute of Materials China Academy of Engineering PhysicsJiangyou 621907China Key Laboratory of Green Manufacturing of Superlight Elastomer Materials of State Ethnic Affairs Commission Hubei Minzu UniversityEnshi 445000China

Introduction Muon scattering tomography(MST)can be employed to scan cargo containers and vehicles for special nuclear materials by using cosmic ***,the flux of cosmic ray muons is relatively low for direct ***,the detection has to be done in a short timescale with small numbers of muons to satisfy the demands of practical *** In this paper,we propose an artificial neural network(ANN)algorithm for material discrimination using *** muon scattering angles were simulated using Geant4 to formulate the training set,and the muon scatter angles were measured by Micromegas detection system to create the test *** The ANN-based algorithm presented here ensures a discrimination accuracy of 98.0%between aluminum,copper and tungsten in a 5 min measurement of 4×4×4 cm^(3)blocks.

关键词： Muon scattering tomography Cargo container inspection Material discrimination artificial neural network classifier

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Minimizing Sensors for System Monitoring - A Case Study with EEG Signals 2

Minimizing Sensors for System Monitoring - A Case Study with...

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IEEE 2nd International Conference on Cybernetics (CYBCONF)

作者： Chakraborty, Goutam Horie, Shigeki Yokoha, Hikaru Kokosinski, Zbigniew Iwate Prefectural Univ Dept Software & Informat Sci Takizawa Iwate Japan Iwate Prefectural Univ Grad Sch Software & Informat Sci Takizawa Iwate Japan Cracow Univ Technol Fac ECE Dept Automat Control & Informat Technol PL-31155 Krakow Poland

ISBN: (纸本)9781479983223

For monitoring any system, be it a chemical plant, a nuclear power station, or a human heart or brain, we need to attach sensors and analyze the multivariate time-series data collected by those sensors. If the system has two states, say state (good) and state 2 (bad), we need to infer which state the system is, by classifying the collected time-series signals. To make this work efficiently, it is important to search the least number of probes that would give best classification result. It is a multi-objective optimization problem. The proposed approach works in two steps. We start with a large number of probes. As the first step, we cluster the time-series signals. and choose a representative one from each cluster. Next, we run pareto GA to select the smallest set of probes (from cluster representatives), that would give the highest classification result. Depending on the nature of the signals, and the target application, appropriate signal-features, clustering and classification algorithms will be different, but the basic principle is applicable to any system. In this paper, we tested the effectiveness of our algorithm with EEG signals, to detect the presence or absence of ERP 300. Improved results with less number of probes compared with previous works validated the approach.

关键词： Ward's Hierarchical Clustering Dendrogram Electroencephalogram (EEG) Brain Computer Interface (BCI) Pareto GA artificial neural network classifier

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Stuck around the Stadium? An Approach to Identify Road Segments Affected by Planned Special Events 18

Stuck around the Stadium? An Approach to Identify Road Segme...

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18th IEEE International Conference on Intelligent Transportation Systems

作者： Kwoczek, Simon Di Martino, Sergio Nejdl, Wolfgang Volkswagen AG Res Grp Wolfsburg Germany Univ Naples Federico II DIETI Naples Italy Leibniz Univ Hannover Res Ctr L3S Hannover Germany

ISBN: (纸本)9781467365963

The recent availability of large amount of mobility data has fostered many research efforts to improve mobility prediction. Lots of these studies are focused on learning the impact of influencing factors on traffic, such as rush hour or accidents. Nevertheless, only very few have investigated the impact of Planned Special Events (PSEs), such as concerts, soccer games, etc., despite their well-known influence on traffic. In this paper we present an automatic solution to model the impact of PSEs on traffic around the venue of the events. In particular, we answer the question of "which road segments are affected by PSEs?" by identifying which roads show an event specific behavior that can identify the happening of a PSE reliably. For that, we propose a solution based on an artificial neural network (ANN) classifier that is trained on traffic data on event and non-event days for each road. The proposed approach has been evaluated on two different venues in Germany with a leave-one-out cross-validation performed on all the soccer matches played in those locations during the season 2013/14 of the German First League. Results show that the approach can reliably identify road segments affected by PSEs, with an F-Measure up to 0.97.

关键词： intelligent transportation systems neural nets pattern classification road traffic ANN classifier German First League soccer matches Germany PSEs artificial neural network classifier mobility prediction planned special events road segments artificial neural networks Cities and towns Correlation Delays Games Reliability Roads

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A Coronavirus Herd Immunity Optimizer For Intrusion Detection System 29

A Coronavirus Herd Immunity Optimizer For Intrusion Detectio...

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29th Iranian Conference on Electrical Engineering (ICEE)

作者： Mahboob, Amir Soltany Shahhoseini, Hadi Shahriar Moghaddam, Mohammad Reza Ostadi Yousefi, Shima Iran Univ Sci & Technol Sch Elect Engn Tehran Iran

ISBN: (纸本)9781665433655

Intrusion Detection System (IDS) is considered as one of the essential components of a secure network. Due to the high number of network packet features, one of the major problems of IDS is false intrusion alerts and low intrusion detection rates. Feature selection removes all redundant or irrelevant features among the various features of network packets. For this reason, it plays a pivotal role in overcoming the mentioned problems and can improve the accuracy of intrusion detection system. In this paper, a new human-inspired optimization algorithm called coronavirus herd immunity optimizer (CHIO) is proposed for feature selection in IDS. CHIO is able to select the optimal subset of features from numerous features without affecting system performance. In order to select the feature, two types of classifiers, K-Nearest Neighbor (KNN) and artificial neural network (ANN), are used to obtain the accuracy of intrusion detection. In addition, the ANN classifier is trained with the classic Gradient Descent ( GD) method as well as the two intelligent methods artificial Bee Colony (ABC) and Harmony Search (HS). In order to demonstrate the performance, our method is tested on 20% of NSL-KDD and its results are reported and compared to other studies. The proposed method has been able to achieve better performance in terms of intrusion detection accuracy and number of features compared to similar works.

关键词： Intrusion detection system feature selection Coronavirus herd immunity optimizer artificial neural network classifier K-Nearest Neighbor classifier

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neural network-Based Brain Tissue Segmentation in MR Images Using Extracted Features from Intraframe Coding in H.264

Neural Network-Based Brain Tissue Segmentation in MR Images ...

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4th International Conference on Machine Vision (ICMV) - Machine Vision, Image Processing and Pattern Analysis

作者： Jafari, Mehdi Kasaei, Shohreh Islamic Azad Univ Dept Elect Engn Kerman Branch Kerman Iran

ISBN: (纸本)9780819490254

Automatic brain tissue segmentation is a crucial task in diagnosis and treatment of medical images. This paper presents a new algorithm to segment different brain tissues, such as white matter (WM), gray matter (GM), cerebral spinal fluid (CSF), background (BKG), and tumor tissues. The proposed technique uses the modified intraframe coding yielded from H.264/(AVC), for feature extraction. Extracted features are then imposed to an artificial back propagation neural network (BPN) classifier to assign each block to its appropriate class. Since the newest coding standard, H.264/AVC, has the highest compression ratio, it decreases the dimension of extracted features and thus yields to a more accurate classifier with low computational complexity. The performance of the BPN classifier is evaluated using the classification accuracy and computational complexity terms. The results show that the proposed technique is more robust and effective with low computational complexity compared to other recent works.

关键词： H.264/AVC feature extraction brain tissue segmentation artificial neural network classifier

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Combining Fuzzy Clustering with ANN classifier for Categorical Data

Combining Fuzzy Clustering with ANN Classifier for Categoric...

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IEEE International Advance Computing Conference

作者： Saha, Indrajit Mukhopadhyay, Anirban Maulik, Ujjwal

ISBN: (纸本)9781424429271

This article deals with the development of an improved clustering technique for categorical data that is based on the identification of points having significant membership to multiple classes. Cluster assignments of such points are difficult, and they often affect the actual partitioning of the data. As a consequence, it may be more effective if the points that are associated with maximum confusion regarding their cluster assignments are first identified and excluded from consideration at the first stage of algorithm and these points may be assigned to one of the identified clusters based on an ANN classifier at the second stage of this algorithm. At the first stage of this algorithm we are using our developed genetic algorithm and simulated annealing based fuzzy clustering and well known Fuzzy C-Medoids algorithm when the number of clusters is known a priori. The performance of the proposed clustering algorithms has been compared with the average linkage hierarchical clustering algorithm, in addition to the genetic algorithm based fuzzy clustering, simulated annealing based fuzzy clustering and Fuzzy C-Medoids with ANN for a variety of artificial and real life categorical data sets. Also statistical significance test have been performed to establish the superiority of the proposed algorithm.

关键词： artificial neural network classifier Fuzzy clustering Genetic algorithm Simulated annealing Multi-class membership Statistical significance test

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Intelligent classification of ECG signals to distinguish between pre and on-music states 7

Intelligent classification of ECG signals to distinguish bet...

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2015 7th Conference on Information and Knowledge Technology (IKT)

作者： Hajizadeh, Soheila Abbasi, Ataollah Goshvarpour, Atefeh Sahand Univ Technol Dept Biomed Engn Tabriz Iran

ISBN: (纸本)9781467374859

In this work, the classification of heart signals affected by music is investigated. The nonlinear and chaotic nature of ECG signals makes it desirable to develop and apply an intelligent mechanism for efficient signal classification. Afterwards, extracting the recognizable and functional features plays a significant role in classification accuracy. Empirical mode decomposition (EMD), as an adaptive mathematical analysis is applied to decompose the signals into a sum of components each called an intrinsic mode function (IMF). IMF values are applied to determine whether the changes in signal features are experimentally significant due to the music. The performance of two practical classification methods is reported to determine the most efficient input-output relationship between music and heart signals. Experimental results over 62 cases, validates the generalization capability of the proposed method and perform acceptable values of MSE for the classification process. Elman recurrent neural network (ERNN) performed most effectively in classifying the maximum frequency (MaxFreq) and sample entropy (SampEn) of IMF (2). However, results reflect the considerable potential of feed-forward neural network (FFNN) for the classification of maximum amplitude of FFT (MaxFFT) and MaxFreq of IMF (1).

关键词： electrocardiogram (ECG) empirical mode decomposition (EMD) fast fourier transform (FFT) Iranian music artificial neural network classifier sample entropy (SampEn)

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Automated detection of atrial fibrillation in ECG signals based on wavelet packet transform and correlation function of random process

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2020年第0期55卷 101662-000页

作者： Wang, Jibin Wang, Ping Wang, Suping Tianjin Univ Sch Math Tianjin 300354 Peoples R China Tianjin Univ Visual Pattern Anal Res Lab Tianjin 300072 Peoples R China

Atrial fibrillation (AF) is a common cardiac arrhythmia in clinic. The traditional AF detection using visual inspection by trained physicians is an inefficient and burdensome task. In this paper, we introduce a novel method for the automated AF detection using two-lead electrocardiogram (ECG) signals. We use the wavelet packet transform (WPT) and the correlation function of random process theory to devise an efficient feature extraction strategy for physiological signal analysis, and construct the corresponding histogram. Then, multivariate statistical features based on the correlation among wavelet coefficient series are extracted from the corresponding histogram as the feature set, which is the input to artificial neural network (ANN) classifier for the detection. Moreover, various statistical analyses are performed and some parameter tuning strategies are formulated by fitting the receiver operating characteristic (ROC) curve to ensure the reliability and robustness of the work. To evaluate the classification performance of the algorithm, 10-fold cross-validation is implemented on the MIT-BIH AF database. Compared with some state-of-the-art algorithms, the numerical results prove that our proposed strategy yields superior classification performance. To the best of our knowledge, this is also the first application of random process theory for AF detection, providing great potential in medical diagnosis. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Electrocardiogram Atrial fibrillation Wavelet packet transform Correlation function artificial neural network classifier

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Capillary Sensor with Disposable Optrode for Diesel Fuel Quality Testing

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SENSORS 2019年第9期19卷 1980.-1980.页

作者： Borecki, Michal Prus, Przemyslaw Korwin-Pawlowski, Michael L. Warsaw Univ Technol Inst Microelect & Optoelect PL-00662 Warsaw Poland Blue Oak Invent PL-56400 Wroclaw Poland Univ Quebec Outaouais Dept Informat Ingn Gatineau PQ J8X 3X7 Canada

Diesel fuel quality can be considered from many different points of view. Fuel producers, fuel consumers, and ecologists have their own ideas. In this paper, a sensor of diesel fuel quality type, and fuel condition that is oriented to the fuel's consumers, is presented. The fuel quality types include premium, standard, and full bio-diesel classes. The fuel conditions include fuel fit for use and fuel degraded classes. The classes of fuel are connected with characteristics of engine operation. The presented sensor uses signal processing of an optoelectronic device monitoring fuel samples that are locally heated to the first step of boiling. Compared to previous works which consider diesel fuel quality sensing with disposable optrodes which use a more complex construction, the sensor now consists only of a capillary probe and advanced signal processing. The signal processing addresses automatic conversion of the data series to form a data pattern, estimates the measurement uncertainty, eliminates outlier data, and determines the fuel quality with an intelligent artificial neural network classifier. The sensor allows the quality classification of different unknown diesel fuel samples in less than a few minutes with the measurement costs of a single disposable capillary probe and two plugs.

关键词： capillary sensor diesel fuel quality diesel fuel user outlier data feature vector of diesel fuel sensor automation artificial neural network classifier

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