Machine learning approaches are generally adopted i
n many fields including data mining, image
processing, intelligent fault diagnosis etc. As a c
lassic unsupervised learning technology, fuzzy C-me
ans
cluster analysis plays a vital role in machine lear
ning based intelligent fault diagnosis. With the ra
pid
development of science and technology, the monitori
ng signal data is numerous and keeps growing fast.
Only typical fault samples can be obtained and labe
led. Thus, how to apply semi-supervised learning
technology in fault diagnosis is significant for gu
aranteeing the equipment safety. According to this,
a novel
fault diagnosis method based on semi-supervised fuz
zy C-means(SFCM) cluster analysis is proposed.
Experimental results on Iris data set and the steel
plates faults data set show that this method is su
perior to
traditional fuzzy C-means clustering analysis
Improving face recognition by artificial neural network using principal compo...TELKOMNIKA JOURNAL
The face-recognition system is among the most effective pattern recognition and image analysis techniques. This technique has met great attention from academic and industrial fields because of its extensive use in detecting the identity of individuals for monitoring systems, security and many other practical fields. In this paper, an effective method of face recognition was proposed. Ten person's faces images were selected from ORL dataset, for each person (42) image with total of (420) images as dataset. Features are extracted using principle component analysis PCA to reduce the dimensionality of the face images. Four models where created, the first one was trained using feed forward back propagation learning (FFBBL) with 40 features, the second was trained using 50 features with FFBBL, the third was trained using the same features but using Elman Neural Network. For each person (24) image used as training set for the neural networks, while the remaining images used as testing set. The results showed that the proposed method was effective and highly accurate. FFBBL give accuracy of (98.33,97.14) with (40, 50) features respectively, while Elman gives (98.33, 98.80) for with (40, 50) features respectively.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Face Recognition Based Intelligent Door Control Systemijtsrd
This paper presents the intelligent door control system based on face detection and recognition. This system can avoid the need to control by persons with the use of keys, security cards, password or pattern to open the door. The main objective is to develop a simple and fast recognition system for personal identification and face recognition to provide the security system. Face is a complex multidimensional structure and needs good computing techniques for recognition. The system is composed of two main parts face recognition and automatic door access control. It needs to detect the face before recognizing the face of the person. In face detection step, Viola Jones face detection algorithm is applied to detect the human face. Face recognition is implemented by using the Principal Component Analysis PCA and Neural Network. Image processing toolbox which is in MATLAB 2013a is used for the recognition process in this research. The PIC microcontroller is used to automatic door access control system by programming MikroC language. The door is opened automatically for the known person according to the result of verification in the MATLAB. On the other hand, the door remains closed for the unknown person. San San Naing | Thiri Oo Kywe | Ni Ni San Hlaing ""Face Recognition Based Intelligent Door Control System"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23893.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/23893/face-recognition-based-intelligent-door-control-system/san-san-naing
Improving face recognition by artificial neural network using principal compo...TELKOMNIKA JOURNAL
The face-recognition system is among the most effective pattern recognition and image analysis techniques. This technique has met great attention from academic and industrial fields because of its extensive use in detecting the identity of individuals for monitoring systems, security and many other practical fields. In this paper, an effective method of face recognition was proposed. Ten person's faces images were selected from ORL dataset, for each person (42) image with total of (420) images as dataset. Features are extracted using principle component analysis PCA to reduce the dimensionality of the face images. Four models where created, the first one was trained using feed forward back propagation learning (FFBBL) with 40 features, the second was trained using 50 features with FFBBL, the third was trained using the same features but using Elman Neural Network. For each person (24) image used as training set for the neural networks, while the remaining images used as testing set. The results showed that the proposed method was effective and highly accurate. FFBBL give accuracy of (98.33,97.14) with (40, 50) features respectively, while Elman gives (98.33, 98.80) for with (40, 50) features respectively.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Face Recognition Based Intelligent Door Control Systemijtsrd
This paper presents the intelligent door control system based on face detection and recognition. This system can avoid the need to control by persons with the use of keys, security cards, password or pattern to open the door. The main objective is to develop a simple and fast recognition system for personal identification and face recognition to provide the security system. Face is a complex multidimensional structure and needs good computing techniques for recognition. The system is composed of two main parts face recognition and automatic door access control. It needs to detect the face before recognizing the face of the person. In face detection step, Viola Jones face detection algorithm is applied to detect the human face. Face recognition is implemented by using the Principal Component Analysis PCA and Neural Network. Image processing toolbox which is in MATLAB 2013a is used for the recognition process in this research. The PIC microcontroller is used to automatic door access control system by programming MikroC language. The door is opened automatically for the known person according to the result of verification in the MATLAB. On the other hand, the door remains closed for the unknown person. San San Naing | Thiri Oo Kywe | Ni Ni San Hlaing ""Face Recognition Based Intelligent Door Control System"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23893.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/23893/face-recognition-based-intelligent-door-control-system/san-san-naing
COMPARISON OF ANFIS AND ANN TECHNIQUES IN THE SIMULATION OF A TYPICAL AIRCRAF...ijaia
The performance of an aircraft can be improved by predicting the possible complications associated with the system. Prognostics and Health Management (PHM) methodology includes fault detection, diagnosis, and prognosis. In this paper, a comparison of Adaptive Neuro-Fuzzy Inference System (ANFIS) with Artificial Neural Network (ANN) based fault prognosis tool for a typical aircraft fuel system is proposed.
The ANFIS is an expert system which works on logical rules. The inputs of both ANFIS and ANN are trained by considering the same input data and generate the corresponding control signal. These methods identify the presence of faults and mitigate them to maintain a proper fuel flow to the engine. Overlooking the presence of any faults in time could potentially be catastrophic which can lead to possible loss of lives and the aircraft as well. These proposed tools work on the logical rules developed as per the engine’s fuel consumption and quantity of fuel flow from the tanks. The results are compared and analyzed which demonstrate the superiority of ANFIS tool compared to ANN.
Computer aided classification of Bascal cell carcinoma using adaptive Neuro-f...Editor IJMTER
For skin lesion detection pathologists examine biopsies to make diagnostic
assessment largely based on cell anatomy and tissue distribution. However in many
instances it is subjective and often leads to considerable variability. Whereas computer
diagnostic tools enable objective judgments by making use of quantitative measures.
Paper presents a diagnosis system based on an adaptive Neuro-fuzzy inference system
for effective classification of Bascal cell carcinoma images from the given set of all types
of skin lesions. System divide in three parts. Image Processing, Feature Extraction, and
classification. First part deals with the noise reduction and artifacts removing from the
set of images. Second part deals with extracting variety of features of Bascal Cell
Carcinoma using the Greedy feature flip algorithm (G-flip), and classification method
using ANFIS algorithm and finally Part three deals with the results that is classification
of BCC images from the variety of pre-cancerous stage images that is Actinic Keratosis
and also other images called psoriasis which looks as cancer images at a first look . The
results confirmed that the proposed ANFIS model has potential in classifying the skin
cancer diagnosis.
Microarray Data Classification Using Support Vector MachineCSCJournals
DNA microarrays allow biologist to measure the expression of thousands of genes simultaneously on a small chip. These microarrays generate huge amount of data and new methods are needed to analyse them. In this paper, a new classification method based on support vector machine is proposed. The proposed method is used to classify gene expression data recorded on DNA microarrays. It is found that the proposed method is faster than neural network and the classification performance is not less than neural network.
Detection and classification of brain tumor are very important because it provides anatomical information of normal and abnormal tissues which helps in early treatment planning and patient's case follow-up. There is a number of techniques for medical image classification. We used PNN (Probabilistic Neural Network Algorithm) for image classification technique based on Genetic Algorithm (GA) and K-Nearest Neighbor (K-NN) classifier for feature selection is proposed in this paper. The searching capabilities of genetic algorithms are explored for appropriate selection of features from input data and to obtain an optimal classification. The method is implemented to classify and label brain MRI images into seven tumor types. A number of texture features (Gray Level Co-occurrence Matrix (GLCM)) can be extracted from an image, so choosing the best features to avoid poor generalization and over specialization is of paramount importance then the classification of the image and compare results based on the PNN algorithm.
The Technology Research of Camera Calibration Based On LabVIEWIJRES Journal
The technology of camera calibration is most important part for machine vision detection and
location, the accuracy of calibration directly determines the processing accuracy of machine vision systems. In
this paper, we use LabVIEW and MATLAB to calibrate the internal and external parameters of the camera, at
the same time, we use dot calibration board, the circle edge is detected by Canny operator, then with the method
of circle fitting based on subpixel edge extraction, the information of dots image coordinate is extracted. The
present method reduces the difficulty of camera calibration and shortens the software development cycle, the
most important is that it has a high calibration accuracy, which can meet the actual industrial detection accuracy,
the results of experimental show that the method is feasible.
MALIGNANT AND BENIGN BRAIN TUMOR SEGMENTATION AND CLASSIFICATION USING SVM WI...sipij
In this article a method is proposed for segmentation and classification of benign and malignant tumor slices in brain Computed Tomography (CT) images. In this study image noises are removed using median and wiener filter and brain tumors are segmented using Support Vector Machine (SVM). Then a two-level discrete wavelet decomposition of tumor image is performed and the approximation at the second level is obtained to replace the original image to be used for texture analysis. Here, 17 features are extracted that 6 of them are selected using Student’s t-test. Dominant gray level run length and gray level co-occurrence texture features are used for SVM training. Malignant and benign tumors are classified using SVM with kernel width and Weighted kernel width (WSVM) and k-Nearest Neighbors (k-NN) classifier. Classification accuracy of classifiers are evaluated using 10 fold cross validation method. The segmentation results are
also compared with the experienced radiologist ground truth. The experimental results show that the proposed WSVM classifier is able to achieve high classification accuracy effectiveness as measured by sensitivity and specificity.
BEARINGS PROGNOSTIC USING MIXTURE OF GAUSSIANS HIDDEN MARKOV MODEL AND SUPPOR...IJNSA Journal
Prognostic of future health state relies on the estimation of the Remaining Useful Life (RUL) of physical
systems or components based on their current health state. RUL can be estimated by using three main
approaches: model-based, experience-based and data-driven approaches. This paper deals with a datadriven
prognostics method which is based on the transformation of the data provided by the sensors into
models that are able to characterize the behavior of the degradation of bearings.
For this purpose, we used Support Vector Machine (SVM) as modeling tool. The experiments on the
recently published data base taken from the platform PRONOSTIA clearly show the superiority of the
proposed approach compared to well established method in literature like Mixture of Gaussian Hidden
Markov Models (MoG-HMMs).
COMPARISON OF ANFIS AND ANN TECHNIQUES IN THE SIMULATION OF A TYPICAL AIRCRAF...ijaia
The performance of an aircraft can be improved by predicting the possible complications associated with the system. Prognostics and Health Management (PHM) methodology includes fault detection, diagnosis, and prognosis. In this paper, a comparison of Adaptive Neuro-Fuzzy Inference System (ANFIS) with Artificial Neural Network (ANN) based fault prognosis tool for a typical aircraft fuel system is proposed.
The ANFIS is an expert system which works on logical rules. The inputs of both ANFIS and ANN are trained by considering the same input data and generate the corresponding control signal. These methods identify the presence of faults and mitigate them to maintain a proper fuel flow to the engine. Overlooking the presence of any faults in time could potentially be catastrophic which can lead to possible loss of lives and the aircraft as well. These proposed tools work on the logical rules developed as per the engine’s fuel consumption and quantity of fuel flow from the tanks. The results are compared and analyzed which demonstrate the superiority of ANFIS tool compared to ANN.
Computer aided classification of Bascal cell carcinoma using adaptive Neuro-f...Editor IJMTER
For skin lesion detection pathologists examine biopsies to make diagnostic
assessment largely based on cell anatomy and tissue distribution. However in many
instances it is subjective and often leads to considerable variability. Whereas computer
diagnostic tools enable objective judgments by making use of quantitative measures.
Paper presents a diagnosis system based on an adaptive Neuro-fuzzy inference system
for effective classification of Bascal cell carcinoma images from the given set of all types
of skin lesions. System divide in three parts. Image Processing, Feature Extraction, and
classification. First part deals with the noise reduction and artifacts removing from the
set of images. Second part deals with extracting variety of features of Bascal Cell
Carcinoma using the Greedy feature flip algorithm (G-flip), and classification method
using ANFIS algorithm and finally Part three deals with the results that is classification
of BCC images from the variety of pre-cancerous stage images that is Actinic Keratosis
and also other images called psoriasis which looks as cancer images at a first look . The
results confirmed that the proposed ANFIS model has potential in classifying the skin
cancer diagnosis.
Microarray Data Classification Using Support Vector MachineCSCJournals
DNA microarrays allow biologist to measure the expression of thousands of genes simultaneously on a small chip. These microarrays generate huge amount of data and new methods are needed to analyse them. In this paper, a new classification method based on support vector machine is proposed. The proposed method is used to classify gene expression data recorded on DNA microarrays. It is found that the proposed method is faster than neural network and the classification performance is not less than neural network.
Detection and classification of brain tumor are very important because it provides anatomical information of normal and abnormal tissues which helps in early treatment planning and patient's case follow-up. There is a number of techniques for medical image classification. We used PNN (Probabilistic Neural Network Algorithm) for image classification technique based on Genetic Algorithm (GA) and K-Nearest Neighbor (K-NN) classifier for feature selection is proposed in this paper. The searching capabilities of genetic algorithms are explored for appropriate selection of features from input data and to obtain an optimal classification. The method is implemented to classify and label brain MRI images into seven tumor types. A number of texture features (Gray Level Co-occurrence Matrix (GLCM)) can be extracted from an image, so choosing the best features to avoid poor generalization and over specialization is of paramount importance then the classification of the image and compare results based on the PNN algorithm.
The Technology Research of Camera Calibration Based On LabVIEWIJRES Journal
The technology of camera calibration is most important part for machine vision detection and
location, the accuracy of calibration directly determines the processing accuracy of machine vision systems. In
this paper, we use LabVIEW and MATLAB to calibrate the internal and external parameters of the camera, at
the same time, we use dot calibration board, the circle edge is detected by Canny operator, then with the method
of circle fitting based on subpixel edge extraction, the information of dots image coordinate is extracted. The
present method reduces the difficulty of camera calibration and shortens the software development cycle, the
most important is that it has a high calibration accuracy, which can meet the actual industrial detection accuracy,
the results of experimental show that the method is feasible.
MALIGNANT AND BENIGN BRAIN TUMOR SEGMENTATION AND CLASSIFICATION USING SVM WI...sipij
In this article a method is proposed for segmentation and classification of benign and malignant tumor slices in brain Computed Tomography (CT) images. In this study image noises are removed using median and wiener filter and brain tumors are segmented using Support Vector Machine (SVM). Then a two-level discrete wavelet decomposition of tumor image is performed and the approximation at the second level is obtained to replace the original image to be used for texture analysis. Here, 17 features are extracted that 6 of them are selected using Student’s t-test. Dominant gray level run length and gray level co-occurrence texture features are used for SVM training. Malignant and benign tumors are classified using SVM with kernel width and Weighted kernel width (WSVM) and k-Nearest Neighbors (k-NN) classifier. Classification accuracy of classifiers are evaluated using 10 fold cross validation method. The segmentation results are
also compared with the experienced radiologist ground truth. The experimental results show that the proposed WSVM classifier is able to achieve high classification accuracy effectiveness as measured by sensitivity and specificity.
BEARINGS PROGNOSTIC USING MIXTURE OF GAUSSIANS HIDDEN MARKOV MODEL AND SUPPOR...IJNSA Journal
Prognostic of future health state relies on the estimation of the Remaining Useful Life (RUL) of physical
systems or components based on their current health state. RUL can be estimated by using three main
approaches: model-based, experience-based and data-driven approaches. This paper deals with a datadriven
prognostics method which is based on the transformation of the data provided by the sensors into
models that are able to characterize the behavior of the degradation of bearings.
For this purpose, we used Support Vector Machine (SVM) as modeling tool. The experiments on the
recently published data base taken from the platform PRONOSTIA clearly show the superiority of the
proposed approach compared to well established method in literature like Mixture of Gaussian Hidden
Markov Models (MoG-HMMs).
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Kids Math is a Smartphone game aimed at perfecting kids learning to recognize, write, and count math digits in a highly interactive fashion. It offers 3 major absorbing educational levels stages of Park, Forest and a Zoo to kids which further gives thorough experience of knowing about and writing digits, knowledge about geometric shapes and drawing them, animals and counting them.
C LUSTERING B ASED A TTRIBUTE S UBSET S ELECTION U SING F AST A LGORITHmIJCI JOURNAL
In machine learning and data mining, attribute sel
ect is the practice of selecting a subset o
f most
consequential attributes for utilize in model const
ruction. Using an attribute select method is that t
he data
encloses many redundant or extraneous attributes. W
here redundant attributes are those which sup
ply
no supplemental information than the presently
selected attributes, and impertinent attribut
es offer
no valuable information in any context
E FFICIENT E NERGY U TILIZATION P ATH A LGORITHM I N W IRELESS S ENSOR...IJCI JOURNAL
With limited amount of energy, nodes are powered by
batteries in wireless networks. Increasing the lif
e
span of the network and reducing the usage of energ
y are two severe problems in Wireless Sensor
Networks. A small number of energy utilization path
algorithms like minimum spanning tree reduces tota
l
energy consumption of a Wireless Sensor Network, ho
wever very heavy load of sending data packets on
many key nodes is used with the intention that the
nodes quickly consumes battery energy, by raising t
he
life span of the network reduced. Our proposal work
aimed on presenting an Energy Conserved Fast and
Secure Data Aggregation Scheme for WSN in time and
security logic occurrence data collection
application. To begin with, initially the goal is m
ade on energy preservation of sensed data gathering
from
event identified sensor nodes to destination. Inven
tion is finished on Energy Efficient Utilization Pa
th
Algorithm (EEUPA), to extend the lifespan by proces
sing the collecting series with path mediators
depending on gene characteristics sequencing of nod
e energy drain rate, energy consumption rate, and
message overhead together with extended network lif
e span. Additionally, a mathematical programming
technique is designed to improve the lifespan of th
e network. Simulation experiments carried out among
different relating conditions of wireless sensor ne
twork by different path algorithms to analyze the
efficiency and effectiveness of planned Efficient E
nergy Utilization Path Algorithm in wireless sensor
network (EEUPA)
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUEScscpconf
This paper presents a new idea for fault detection and isolation (FDI) technique which is applied to industrial system. This technique is based on Neural Networks fault-free and Faulty
behaviours Models (NNFMs). NNFMs are used for residual generation, while decision tree architecture is used for residual evaluation. The decision tree is realized with data collected
from the NNFM’s outputs and is used to isolate detectable faults depending on computed threshold. Each part of the tree corresponds to specific residual. With the decision tree, it
becomes possible to take the appropriate decision regarding the actual process behaviour by evaluating few numbers of residuals. In comparison to usual systematic evaluation of all
residuals, the proposed technique requires less computational effort and can be used for on line diagnosis. An application example is presented to illustrate and confirm the effectiveness and the accuracy of the proposed approach.
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUEScsitconf
This paper presents a new idea for fault detection and isolation (FDI) technique which is
applied to industrial system. This technique is based on Neural Networks fault-free and Faulty
behaviours Models (NNFMs). NNFMs are used for residual generation, while decision tree
architecture is used for residual evaluation. The decision tree is realized with data collected
from the NNFM’s outputs and is used to isolate detectable faults depending on computed
threshold. Each part of the tree corresponds to specific residual. With the decision tree, it
becomes possible to take the appropriate decision regarding the actual process behaviour by
evaluating few numbers of residuals. In comparison to usual systematic evaluation of all
residuals, the proposed technique requires less computational effort and can be used for on line
diagnosis. An application example is presented to illustrate and confirm the effectiveness and
the accuracy of the proposed approach.
EFFICIENT USE OF HYBRID ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM COMBINED WITH N...csandit
This research study proposes a novel method for automatic fault prediction from foundry data
introducing the so-called Meta Prediction Function (MPF). Kernel Principal Component
Analysis (KPCA) is used for dimension reduction. Different algorithms are used for building the
MPF such as Multiple Linear Regression (MLR), Adaptive Neuro Fuzzy Inference System
(ANFIS), Support Vector Machine (SVM) and Neural Network (NN). We used classical
machine learning methods such as ANFIS, SVM and NN for comparison with our proposed
MPF. Our empirical results show that the MPF consistently outperform the classical methods.
Failure prediction of e-banking application system using adaptive neuro fuzzy...IJECEIAES
Problems often faced by IT operation unit is the difficulty in determining the cause of the failure of an incident such as slowing access to the internet banking url, non-functioning of some features of m-banking or even the cessation of the entire e-banking service. The proposed method to modify ANFIS with Fuzzy C-Means Clustering (FCM) approach is applied to detect four typical kinds of faults that may happen in the e-banking system, which are application response times, transaction per second, server utilization and network performance. Input data is obtained from the e-banking monitoring results throughout 2017 that become data training and data testing. The study shows that an ANFIS modeling with FCM optimized input has a RMSE 0.006 and increased accuracy by 1.27% compared to ANFIS without FCM optimization.
Prediction of dementia using machine learning model and performance improvem...IJECEIAES
Dementia is a brain disease that stays in the seventh position of death rate as per the report of the World Health Organization (WHO). Among the various types of dementia, Alzheimer’s disease has more than 70% of cases of dementia. The objective is to predict dementia disease from the open access series of imaging studies (OASIS) dataset using machine learning techniques. Also, the performance of the machine learning model is analyzed to improve the performance of the model using the cuckoo algorithm. In this paper, feature engineering has been focused and the prediction of dementia has been done using the OASIS dataset with the help of data mining techniques. Feature engineering is followed by prediction using the machine learning model Gaussian naïve Bayes (NB), support vector machine, and linear regression. Also, the best prediction model has been selected and done the validation. The evaluation metrics considered for validating the models are accuracy, precision, recall, and F1-Score and the highest values are 95%, 97%, 95%, and 95%. The Gaussian NB has been given these best results. The accuracy of the machine learning models has been increased by eliminating the factors which affect the performance of the models using the cuckoo algorithm
AN ANN APPROACH FOR NETWORK INTRUSION DETECTION USING ENTROPY BASED FEATURE S...IJNSA Journal
With the increase in Internet users the number of malicious users are also growing day-by-day posing a serious problem in distinguishing between normal and abnormal behavior of users in the network. This has led to the research area of intrusion detection which essentially analyzes the network traffic and tries to determine normal and abnormal patterns of behavior.In this paper, we have analyzed the standard NSL-KDD intrusion dataset using some neural network based techniques for predicting possible intrusions. Four most effective classification methods, namely, Radial Basis Function Network, SelfOrganizing Map, Sequential Minimal Optimization, and Projective Adaptive Resonance Theory have been applied. In order to enhance the performance of the classifiers, three entropy based feature selection methods have been applied as preprocessing of data. Performances of different combinations of classifiers and attribute reduction methods have also been compared.
Extended Fuzzy C-Means with Random Sampling Techniques for Clustering Large DataAM Publications
Big data are any data that you cannot load into your computer’s primary memory. Clustering is a primary
task in pattern recognition and data mining. We need algorithms that scale well with the data size. The former
implementation, literal Fuzzy C-Means is linear or serialized. FCM algorithm attempts to partition a finite collection
of n elements into collection of c fuzzy clusters. So, given a finite set of data, this algorithm returns a list of c cluster
centers. However it doesn't scale well and slows down with increase in the size of data and is thus impractical and
sometimes undesirable. In this paper, we propose an extended version of fuzzy c-means clustering algorithm by means of various random sampling techniques to study which method scales well for large or very large data.
With the increase in Internet users the number of malicious users are also growing day-by-day posing a
serious problem in distinguishing between normal and abnormal behavior of users in the network. This
has led to the research area of intrusion detection which essentially analyzes the network traffic and tries
to determine normal and abnormal patterns of behavior.In this paper, we have analyzed the standard
NSL-KDD intrusion dataset using some neural network based techniques for predicting possible
intrusions. Four most effective classification methods, namely, Radial Basis Function Network, Self-
Organizing Map, Sequential Minimal Optimization, and Projective Adaptive Resonance Theory have been
applied. In order to enhance the performance of the classifiers, three entropy based feature selection
methods have been applied as preprocessing of data. Performances of different combinations of classifiers
and attribute reduction methods have also been compared.
Cerebral infarction classification using multiple support vector machine with...journalBEEI
Stroke ranks the third leading cause of death in the world after heart disease and cancer. It also occupies the first position as a disease that causes both mild and severe disability. The most common type of stroke is cerebral infarction, which increases every year in Indonesia. This disease does not only occur in the elderly, but in young and productive people which makes early detection very important. Although there are varied of medical methods used to classify cerebral infarction, this study uses a multiple support vector machine with information gain feature selection (MSVM-IG). MSVM-IG is a modification among IG Feature Selection and SVM, where SVM conducted doubly in the process of classification which utilizes the support vector as a new dataset. The data obtained from Cipto Mangunkusumo Hospital, Jakarta. Based on the results, the proposed method was able to achieve an accuracy value of 81%, therefore, this method can be considered to use for better classification result.
Ensemble learning approach for multi-class classification of Alzheimer’s stag...TELKOMNIKA JOURNAL
Alzheimer’s disease (AD) is a gradually progressing neurodegenerative irreversible disorder. Mild cognitive impairment convertible (MCIc) is the clinical forerunner of AD. Precise diagnosis of MCIc is essential for effective treatments to reduce the progressing rate of the disease. The other cognitive states included in this study are mild cognitive impairment non-convertible (MCInc) and cognitively normal (CN). MCInc is a stage in which aged people suffer from memory problems, but the stage will not lead to AD. The classification between MCIc and MCInc is crucial for the early detection of AD. In this work, an algorithm is proposed which concatenates the output layers of Xception, InceptionV3, and MobileNet pre-trained models. The algorithm is tested on the baseline T1-weighted structural magnetic resonance imaging (MRI) images obtained from Alzheimer’s disease neuroimaging initiative database. The proposed algorithm provided multi-class classification accuracy of 85%. Also, the proposed algorithm gave an accuracy of 85% for classifying MCIc vs MCInc, an accuracy of 94% for classifying AD vs CN, and an accuracy of 92% for classifying MCIc vs CN. The results exhibit that the proposed algorithm outruns other state-of-the-art methods for the multi-class classification and classification between MCIc and MCInc.
An intrusion detection algorithm for amiIJCI JOURNAL
Nowadays, using the smart metering devices for energy users to manage a wide variety of subscribers,
reading devices for measuring, billing, disconnection and connection of subscribers’ connection
management is an important issue. The performance of these intelligent systems is based on information
transfer in the context of information technology, so reported data from network should be managed to
avoid the malicious activities that including the issues that could affect the quality of service the system. In
this paper for control of the reported data and to ensure the veracity of the obtained information, using
intrusion detection system is proposed based on the support vector machine and principle component
analysis (PCA) to recognize and identify the intrusions and attacks in the smart grid. Here, the operation of
intrusion detection systems for different kernel of SVM when using support vector machine (SVM) and PCA
simultaneously is studied. To evaluate the algorithm, based on data KDD99, numerical simulation is done
on five different kernels for an intrusion detection system using support vector machine with PCA
simultaneously. Also comparison analysis is investigated for presented intrusion detection algorithm in
terms of time - response, rate of increase network efficiency and increase system error and differences in
the use or lack of use PCA. The results indicate that correct detection rate and the rate of attack error
detection have best value when PCA is used, and when the core of algorithm is radial type, in SVM
algorithm reduces the time for data analysis and enhances performance of intrusion detection.
Design of Kalman filter for Airborne ApplicationsIJERA Editor
Today multiple multi-sensor airborne surveillance systems are available which comprises of primary radar and
secondary surveillance radar as the active sensor on board. The electronics and communication support measure
system (ECSMS) will aid in identification, detection and classification of targets. These systems will detect,
identify, classify the different threats present in the surveillance area and supports defense operation. These
systems contain multiple functional operations as detection of air borne and surface target, tracking, and Multisensor
data fusion. This paper presents the multi-sensor data fusion technique and how to detect and track
moving target in the surveillance area.
Product defect detection based on convolutional autoencoder and one-class cla...IAESIJAI
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Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
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A F AULT D IAGNOSIS M ETHOD BASED ON S EMI - S UPERVISED F UZZY C-M EANS C LUSTER A NALYSIS
1. International Journal on Cybernetics & Informatics (IJCI) Vol. 4, No. 2, April 2015
DOI: 10.5121/ijci.2015.4227 281
A FAULT DIAGNOSIS METHOD BASED ON SEMI-
SUPERVISED FUZZY C-MEANS CLUSTER ANALYSIS
Su-Qun Cao1, 2, 3
, Xinggang Ma2
, Youfu Zhang4
, Limin Luo1
, Fupeng Yi3
1
School of Computer Science and Engineering, Southeast University, Nanjing, China
2
The affiliated Huai’an Hospital of Xuzhou Medical College, Huai’an, China
3
Faculty of Mechanical Engineering, Huaiyin Institute of Technology, Huai’an, China
4
School of Information and Communication Engineering, Beijing University of Posts and
Telecommunications, Beijing, China
ABSTRACT
Machine learning approaches are generally adopted in many fields including data mining, image
processing, intelligent fault diagnosis etc. As a classic unsupervised learning technology, fuzzy C-means
cluster analysis plays a vital role in machine learning based intelligent fault diagnosis. With the rapid
development of science and technology, the monitoring signal data is numerous and keeps growing fast.
Only typical fault samples can be obtained and labeled. Thus, how to apply semi-supervised learning
technology in fault diagnosis is significant for guaranteeing the equipment safety. According to this, a novel
fault diagnosis method based on semi-supervised fuzzy C-means(SFCM) cluster analysis is proposed.
Experimental results on Iris data set and the steel plates faults data set show that this method is superior to
traditional fuzzy C-means clustering analysis.
KEYWORDS
Fault Diagnosis, Unsupervised pattern, Cluster Analysis, Semi-supervised Learning
1. INTRODUCTION
With the widespread use of modern manufacturing systems, machinery is expected to have stable
performance for fully meeting customer requirements. Obviously, unexpected downtime due to
machine failure leads to be more costly than before. Therefore, fault diagnosis of machinery is of
great practical significance in guaranteeing machinery safety and keeping away from the losses.
Traditional intelligent fault diagnosis based on expert system is over-reliance on empirical
knowledge, and it is hard for getting knowledge automatically [1]. With the development of
pattern recognition and data mining, machine learning based methods have been a research focus
gradually in the field of intelligent fault diagnosis.
Many machine learning techniques have been proposed in recent years. Most of them use
supervised learning methods such as neural network[2], support vector machine[3] et al. to solve
fault diagnosis problems. Jaroslaw Kurek et al. [4] presents an automatic computerized system for
the diagnosis of the rotor bars of the induction electrical motor by applying the support vector
machine. Yi Lu Murphey et al. [5] present a model-based fault diagnostics system developed
using a machine learning technology for detecting and locating multiple classes of faults in an
electric drive. Huo-Ching Sun et al. [6] proposed an enhanced particle swarm optimization
(EPSO)-based support vector classifier (SVC) that extracts the support vector from databases, in
2. International Journal on Cybernetics & Informatics (IJCI) Vol. 4, No. 2, April 2015
282
order to diagnose vibration faults in steam turbine-generator sets. Miguel Delgado Prieto et al. [7]
presents a novel monitoring scheme applied to diagnose bearing faults. The development of
diagnosis methodologies considering both kinds of bearing faults is, nowadays, subject of
concern in fault diagnosis of electrical machines. A novel neural network structure is adopted to
perform the classification stage. Kaveh Bastani et al. [8] proposed a fault diagnosis methodology
by integrating the state space model with the enhanced relevance vector machine (RVM) to
identify the process faults through the sparse estimate of the variance change of the process
errors. Muhammad Amar et al.[9] presented a novel vibration spectrum imaging (VSI) feature
enhancement procedure for low SNR conditions. An artificial neural network (ANN) has been
used as a fault classifier using these enhanced features of the faults. It provides enhanced spectral
images for ANN training and thus leads to a highly robust fault classifier.
Some apply unsupervised learning methods such as cluster analysis [10] to detect and diagnose
the failure data. Wentao Sui et al. [11] proposed a new method of bearing fault diagnosis based on
feature weighted FCM. G.Yu et al. [12] presented a cluster-based feature extraction from the
coefficients of discrete wavelet transform for machine fault diagnosis. Qing Yang et al. [13]
proposed an ensemble fault diagnosis algorithm based on fuzzy c-means algorithm (FCM) with
the Optimal Number of Clusters (ONC) and probabilistic neural network (PNN), called FCM-
ONC-PNN. Abdenour Soualhi et al. [14] presented a new approach for fault detection and
diagnosis of IMs using signal-based method based on signal processing and an unsupervised
classification technique called the artificial ant clustering. Debin Zhao et al. [15] presented a
novel intelligent fault diagnosis method based on ant colony clustering analysis and it is verified
by vibration signals acquired from a rotor test bed. Yaguo Lei et al. [16] presented a new hybrid
clustering algorithm based on a three-layer feed forward neural network (FFNN), a distribution
density function, and a cluster validity index to solve fault diagnosis problem of rotating
machinery. Hesam Komari Alaei et al. [17] proposed a new on-line fuzzy clustering-based
algorithm which is developed using integration of an adaptive principal component analysis
approach with a weighted fuzzy C-means (WFCM) methodology for process fault detection and
diagnosis (FDD) applications.
But because the monitoring signal data is numerous, only typical fault samples can be labeled.
Thus, how to apply semi-supervised learning technology in fault diagnosis is important for
guaranteeing the equipment safety. According to this, a novel fault diagnosis method based on
semi-supervised fuzzy C-means(SFCM) cluster analysis is proposed in this paper.
2. FUZZY C-MEANS CLUSTERING ALGORITHM
FCM algorithm was introduced by J.C.Bezdek[18]. It is intended to obtain clustering result of a
data set by minimizing of the basic c-means objective function:
2
1 1
( ; , )
c N
m
ik ik
i k
J Z U V u D
= =
= ∑∑
Where:
[ ]iku=U is a fuzzy partition matrix of Z;
[ ]1 2, , , , n
c i= ∈V v v v v RK is a vector of cluster prototypes;
ik k iD = −z v is Euclidean distance between the sample kz and the center iv of the cluster i;
( )1,m∈ ∞ is a parameter which decides the fuzziness of the resulting clusters.
3. International Journal on Cybernetics & Informatics (IJCI) Vol. 4, No. 2, April 2015
283
The minimization of ( ; , )J Z U V , under the constraint
1
1
c
ik
i
u
=
=∑ , [0,1]iku ∈ , leads to the iteration of
the following steps:
1
1
( )
( )
N
m
ik k
k
i N
m
ik
k
u
u
=
=
=
∑
∑
z
v , 1 i c≤ ≤
and
2 ( 1)
1
1
( / )
ik c
m
ik jk
j
u
D D −
=
=
∑
3. SEMI-SUPERVISED FUZZY C-MEANS CLUSTERING ALGORITHM (SFCM)
The semi-supervised fuzzy c-means (SFCM) algorithm was first introduced by Pedrycz [19]. The
objective function is shown as follows.
2
1 1
22
1 1
2
2 )(),;,( ik
c
i
n
k
ikikik
c
i
n
k
ik
s
dfuduFXVUJ ∑∑∑∑ = == =
−+=
Pedrycz and Waletzky introduced a binary vector b ( kb is equal to 1 if the sample kx has been
already labeled and 0=kb otherwise) to distinguish whether the sample is supervised and
proposed an improved semi-supervised FCM algorithm, which has the following objective
function:
2
1 1
2
1 1
2
)(),;,( ik
c
i
n
k
m
kikikik
c
i
n
k
ik
s
am dbfuduFXVUJ ∑∑∑∑ = == =
⋅ −+= α
Here, α is used to maintain a balance between supervised and unsupervised optimization
mechanism and is proportional to the ratio of the number of all samples and the number of labeled
samples.
4. EXPERIMENTS
4.1 Experiment on Iris data set
In this experiment, the UCI data set [20], Iris, is used to do clustering by FCM and SFCM. The
following Rand index [21] is adopted to evaluate the clustering effectiveness of these two
methods.
2/)1(
),( 21
−×
+
=
nn
ba
PPRand
Where 1P means the clustering result of Iris data set, 2P means the standard clustering result
obtained by the labels of Iris data set, a denotes the number of any two samples in Iris data set
belonging to the same cluster in 21, PP , b denotes the number of any two samples in Iris data set
belonging to two different clusters in 21, PP , and n is the number of all samples. Obviously,
4. International Journal on Cybernetics & Informatics (IJCI) Vol. 4, No. 2, April 2015
284
]1,0[),( 21 ∈PPRand . And 1),( 21 =PPRand when 1P is the same as 2P . The smaller
),( 21 PPRand is, the bigger the difference between 1P and 2P .
The Iris data set consists of 150 samples which are belong to three classes of Iris such as Iris
setosa, Iris virginica and Iris versicolor. Each sample is measured by four features: the length and
the width of the sepals and petals. Table 1 illustrates the basic information of the data set.
FCM is applied in Iris data set and the clustering result plotted by first two features is shown in
Figure 1. The Rand index of FCM clustering result is 0.9495. We set 30 samples as labeled
samples and then apply SFCM in Iris data set. The clustering result plotted by first two features is
shown in Figure 2. The Rand index of SFCM clustering result is 0.9575. This means that SFCM
has better clustering result than FCM.
Table 1. Class distribution and features of Iris data set
Class
Number of
Samples
Features
No. Name
Class 1:Iris setosa 50 1 Sepal length
Class 2:Iris virginica 50 2 Sepal width
Class 3:Iris versicolor 50 3 Petal length
4 Petal width
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
Figure 1. FCM Clustering on Iris data set
5. International Journal on Cybernetics & Informatics (IJCI) Vol. 4, No. 2, April 2015
285
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
Figure 2. SFCM Clustering on Iris data set
4.2. Experiment on the steel plates faults data set
The Steel Plates Faults Data Set was given by Research Center of Sciences of Communication,
Rome, Italy [22]. There are 7 different steel plates’ faults in this data set: Pastry, Z_Scratch,
K_Scatch, Stains, Dirtiness, Bumps and Other_Faults. It includes 1941 samples and each sample
is described by 27 independent features.
Table 2 shows the basic information of the data set. 348 samples which belong to Pastry and
Z_Scratch faults are chosen as testing data set.
FCM is applied in the steel plates faults data set and the clustering result plotted by first two
features is shown in Figure 3. The Rand index of FCM clustering result is 0.5066. We set 80
samples as labeled samples and then apply SFCM in this data set. The clustering result plotted by
first two features is shown in Figure 4. The Rand index of SFCM clustering result is 0.5200. This
means that SFCM has better clustering result than FCM.
5. CONCLUSIONS
Because the monitoring signal data is numerous, only typical fault samples can be labeled. Thus,
how to apply semi-supervised learning technology in fault diagnosis is important for guaranteeing
the equipment safety. This paper presents a novel fault diagnosis method based on Semi-
supervised Fuzzy C-Means(SFCM) clustering algorithm. It use labeled samples to guide the
clustering process. Experimental results show that it is more efficient than traditional Fuzzy C-
Means(FCM) clustering algorithm. In the future, we will make a deep research about how to do
fault diagnosis by semi-supervised clustering based on the kernel function[23, 24].
6. International Journal on Cybernetics & Informatics (IJCI) Vol. 4, No. 2, April 2015
286
Table 2. Class distribution and features of Steel Plates data set
Class
Number
of
Samples
Features
No. Name No. Name
Pastry 158 1 X_Minimum 15 Edges_Index
Z_Scratch 190 2 X_Maximum 16 Empty_Index
K_Scatch 391 3 Y_Minimum 17 Square_Index
Stains 72 4 Y_Maximum 18 Outside_X_Index
Dirtiness 55 5 Pixels_Areas 19 Edges_X_Index
Bumps 402 6 X_Perimeter 20 Edges_Y_Index
Other_Faults 673 7 Y_Perimeter 21 Outside_Global_Index
8 Sum_of_Luminosity 22 LogOfAreas
9 Minimum_of_Luminosity 23 Log_X_Index
10 Maximum_of_Luminosity 24 Log_Y_Index
11 Length_of_Conveyer 25 Orientation_Index
12 TypeOfSteel_A300 26 Luminosity_Index
13 TypeOfSteel_A400 27 SigmoidOfAreas
14 Steel_Plate_Thickness
0 200 400 600 800 1000 1200 1400 1600 1800
0
200
400
600
800
1000
1200
1400
1600
1800
Figure 3. FCM Clustering on the steel plates faults data set
7. International Journal on Cybernetics & Informatics (IJCI) Vol. 4, No. 2, April 2015
287
0 200 400 600 800 1000 1200 1400 1600 1800
0
200
400
600
800
1000
1200
1400
1600
1800
Figure 4. SFCM Clustering on the steel plates faults data set
ACKNOWLEDGEMENTS
This work is supported by National Natural Science Foundation of China (61402192), the
National Sparking Plan Project, China (2013GA690404), the Major Program of the Natural
Science Foundation of the Jiangsu Higher Education Institutions of China (11KJA460001,
13KJA460001), the Open Project from the Key Laboratory of Digital Manufacture Technology at
Jiangsu Province in China (HGDML-1005), Technology Innovation Project of Science and
Technology Enterprises at Jiangsu Province in China (BC2012429), Huaian 533 Talents Project,
Huaian International Science and Technology Cooperation Project (HG201308), and Jiangsu
Overseas Research & Training Program for University Prominent Young & Middle-Aged
Teachers and Presidents, China. Xinggang Ma is the corresponding author(e-mail:
hamxg@sina.com).
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AUTHORS
Su-Qun Cao was born in Huai’an City, Jiangsu Prov., China in 1976. He received the
B.E. in Mechanical Engineering from Jilin University of Technology, China, in 1997,
the second B.E. in Software Engineering from Tsinghua Uni versity, China, in 2002, and
the Ph.D. degree in Computer Engineering from Jiangnan University, China, in 2009.
Since 2010 he has been an associate professor at the Faculty of Mechanical Engineering,
Huaiyin Institute of Technology, China. From 2012 to 2013, he was a visiting scholar at
the University of Melbourne, Australia. Now he is a post-doctor at Southeast University,
C hina. His current r esearch interests include machine learning, pattern recognition and fault diagnosis.
Xinggang Ma is a professor and supervisor of postgraduate at Xuzhou Medical College.He
is also a chief physician at the affiliated Huaian Hospital of Xuzhou Medical College. His
current research interests include gastroenterology and medical informatization.
Youfu Zhang was born in Huai'an City, Jiangsu Province, China in 1994. From September
2012, He began studying for an undergraduate degree in Information Engineering at
Beijing University of Posts and Telecommunications in Beijing. His current research
interests include information system and software development.