Artificial neural networks (ANN) consider classification as one of the most dynamic research and
application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by
back propagation algorithm. The different combinations of functions and its effect while using ANN as a
classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The
back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification
with suitable combination of training, learning and transfer functions. When the maximum likelihood
method was compared with backpropagation neural network method, the BPNN was more accurate than
maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible.
Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to
be more effective than other classification algorithms.
Brain Image Fusion using DWT and Laplacian Pyramid Approach and Tumor Detecti...INFOGAIN PUBLICATION
Image fusion is the process of combining important information from two or more images into a single image. The resulting image will be more enhanced than any of the input pictures. The idea of combining multiple image modalities to furnish a single, more enhanced image is well established, special fusion methods have been proposed in literature. This paper is based on image fusion using laplacian pyramid and Discreet Wavelet Transform (DWT) methods. This system uses an easy and effective algorithm for multi-focus image fusion which uses fusion rules to create fused image. Subsequently, the fused image is obtained by applying inverse discreet wavelet transform. After fused image is obtained, watershed segmentation algorithm is applied to detect the tumor part in fused image.
AN EFFICIENT PSO BASED ENSEMBLE CLASSIFICATION MODEL ON HIGH DIMENSIONAL DATA...ijsc
As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the
availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection
based ensemble learning models is to classify the high dimensional data with high computational efficiency
and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray
datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.
AN ANN BASED BRAIN ABNORMALITY DETECTION USING MR IMAGEScscpconf
The Main purpose of this paper is to design, implement and evaluate a strong automatic diagnostic system that increases the accuracy of tumor diagnosis in brain using MR images.This presented work classifies the brain tissues as normal or abnormal automatically, usingcomputer vision. This saves lot of radiologist time to carryout monotonous repeated job. The
acquired MR images are processed using image preprocessing techniques. The preprocessed images are then segmented, and the various features are extracted. The extracted features are
fed to the artificial neural network as input that trains the network using error back propagation algorithm for correct decision making.
ENHANCED SYSTEM FOR COMPUTER-AIDED DETECTION OF MRI BRAIN TUMORSsipij
The brain images are indicating what condition the brain has. The objective of this research is to design a software that will automatically classifies the brain images to their associated disorders. In order to achieve the objective of this research, a database for training and testing the software of brain images must to be found. In this research we have 105 number of images in data set. In order to differentiate between the classes of those brain images, features had to be extracted from the images. Then, images will be classified into two classes normal and abnormal by using SVM and KNN classifier. The features that were extracted were used in the classification process. The classifiers performed really well, whereas the SVM classifier performed better since its accuracy is 100% on testing set. In the end, the software was successful in separating the two classes.
MRI Image Segmentation Using Gradient Based Watershed Transform In Level Set ...IJERA Editor
Brain image classification is one of the utmost imperative parts of clinical investigative tools. Brain images
typically comprise noise, inhomogeneity and sometimes deviation. Therefore, precise segmentation of brain
images is a very challenging task. Nevertheless, the process of perfect segmentation of these images is very
important and crucial for a spot-on diagnosis by clinical tools. Also, intensity inhomogeneity often arises in realworld
images, which presents a substantial challenge in image segmentation. The most extensively used image
segmentation algorithms are region-based and usually rely on the homogeneousness of the image intensities in
the sections of interest, which often fail to afford precise segmentation results due to the intensity
inhomogeneity. This Research presents a more accurate segmentation using Gradient Based watershed
transform in level set method for a medical diagnosis system. Experimental results proved that our method
validating a much better rate of segmentation accuracy as compare to the traditional approaches, results are also
validated in terms of certain Measure properties of image regions like eccentricity, perimeter etc.
Brain Image Fusion using DWT and Laplacian Pyramid Approach and Tumor Detecti...INFOGAIN PUBLICATION
Image fusion is the process of combining important information from two or more images into a single image. The resulting image will be more enhanced than any of the input pictures. The idea of combining multiple image modalities to furnish a single, more enhanced image is well established, special fusion methods have been proposed in literature. This paper is based on image fusion using laplacian pyramid and Discreet Wavelet Transform (DWT) methods. This system uses an easy and effective algorithm for multi-focus image fusion which uses fusion rules to create fused image. Subsequently, the fused image is obtained by applying inverse discreet wavelet transform. After fused image is obtained, watershed segmentation algorithm is applied to detect the tumor part in fused image.
AN EFFICIENT PSO BASED ENSEMBLE CLASSIFICATION MODEL ON HIGH DIMENSIONAL DATA...ijsc
As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the
availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection
based ensemble learning models is to classify the high dimensional data with high computational efficiency
and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray
datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.
AN ANN BASED BRAIN ABNORMALITY DETECTION USING MR IMAGEScscpconf
The Main purpose of this paper is to design, implement and evaluate a strong automatic diagnostic system that increases the accuracy of tumor diagnosis in brain using MR images.This presented work classifies the brain tissues as normal or abnormal automatically, usingcomputer vision. This saves lot of radiologist time to carryout monotonous repeated job. The
acquired MR images are processed using image preprocessing techniques. The preprocessed images are then segmented, and the various features are extracted. The extracted features are
fed to the artificial neural network as input that trains the network using error back propagation algorithm for correct decision making.
ENHANCED SYSTEM FOR COMPUTER-AIDED DETECTION OF MRI BRAIN TUMORSsipij
The brain images are indicating what condition the brain has. The objective of this research is to design a software that will automatically classifies the brain images to their associated disorders. In order to achieve the objective of this research, a database for training and testing the software of brain images must to be found. In this research we have 105 number of images in data set. In order to differentiate between the classes of those brain images, features had to be extracted from the images. Then, images will be classified into two classes normal and abnormal by using SVM and KNN classifier. The features that were extracted were used in the classification process. The classifiers performed really well, whereas the SVM classifier performed better since its accuracy is 100% on testing set. In the end, the software was successful in separating the two classes.
MRI Image Segmentation Using Gradient Based Watershed Transform In Level Set ...IJERA Editor
Brain image classification is one of the utmost imperative parts of clinical investigative tools. Brain images
typically comprise noise, inhomogeneity and sometimes deviation. Therefore, precise segmentation of brain
images is a very challenging task. Nevertheless, the process of perfect segmentation of these images is very
important and crucial for a spot-on diagnosis by clinical tools. Also, intensity inhomogeneity often arises in realworld
images, which presents a substantial challenge in image segmentation. The most extensively used image
segmentation algorithms are region-based and usually rely on the homogeneousness of the image intensities in
the sections of interest, which often fail to afford precise segmentation results due to the intensity
inhomogeneity. This Research presents a more accurate segmentation using Gradient Based watershed
transform in level set method for a medical diagnosis system. Experimental results proved that our method
validating a much better rate of segmentation accuracy as compare to the traditional approaches, results are also
validated in terms of certain Measure properties of image regions like eccentricity, perimeter etc.
An Artificial Neural Network Model for Neonatal Disease DiagnosisWaqas Tariq
The significance of disease diagnosis by artificial intelligence is not obscure now days. The increasing demand of Artificial Neural Network application for predicting the disease shows better performance in the field of medical decision making. This paper represents the use of artificial neural networks in predicting neonatal disease diagnosis. The proposed technique involves training a Multi Layer Perceptron with a BP learning algorithm to recognize a pattern for the diagnosing and prediction of neonatal diseases. A comparative study of using different training algorithm of MLP, Quick Propagation, Conjugate Gradient Descent, shows the higher prediction accuracy. The Backpropogation algorithm was used to train the ANN architecture and the same has been tested for the various categories of neonatal disease. About 94 cases of different sign and symptoms parameter have been tested in this model. This study exhibits ANN based prediction of neonatal disease and improves the diagnosis accuracy of 75% with higher stability. Key words: Artificial Intelligence, Multi Layer Perceptron, Neural Network, Neonate
A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS FOR EEG SIGNAL CLASSIFICATIONsipij
In this paper, different machine learning algorithms such as Linear Discriminant Analysis, Support vector
machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour, and Autoencoder with SVM
have been compared. This comparison was conducted to seek a robust method that would produce good
classification accuracy. To this end, a robust method of classifying raw Electroencephalography (EEG)
signals associated with imagined movement of the right hand and relaxation state, namely Autoencoder
with SVM has been proposed. The EEG dataset used in this research was created by the University of
Tubingen, Germany. The best classification accuracy achieved was 70.4% with SVM through feature
engineering. However, our prosed method of autoencoder in combination with SVM produced a similar
accuracy of 65% without using any feature engineering technique. This research shows that this system of
classification of motor movements can be used in a Brain-Computer Interface system (BCI) to mentally
control a robotic device or an exoskeleton.
CLASSIFICATION OF OCT IMAGES FOR DETECTING DIABETIC RETINOPATHY DISEASE USING...sipij
Optical Coherence Tomography (OCT) imaging aids in retinal abnormality detection by showing the
tomographic retinal layers. OCT images are a useful tool for detecting Diabetic Retinopathy (DR) disease
because of their capability to capture micrometer-resolution. An automated technique was introduced to
differentiate DR images from normal ones. 214 images were subjected to the experiment, of which 160
images were used for classifiers’ training, and 54 images were used for testing. Different features were
extracted to feed our classifiers, including statistical features and local binary pattern (LBP) features. The
experimental results demonstrated that our classifiers were able to discriminate DR retina from the normal
retina with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 100%. The retinal
OCT images have common texture patterns and using a powerful tool for pattern analysis like LBP
features has a significant impact on the achieved results. The result has better performance than previously
proposed methods in the literature.
Comparison of Image Segmentation Algorithms for Brain Tumor DetectionIJMTST Journal
This paper deals with the implementation of Simple Algorithms for detection of size and shape of tumor in brain using MRI images. Generally, CT scan or MRI that is directed into intracranial cavity produces a complete image of brain. This image is visually examined by the physician for detection & diagnosis of brain tumor. However this method of detection resists the accurate determination of stage & size of tumor. To avoid that, this project uses computer aided method for segmentation (detection) of brain tumor by applying Fuzzy C-Means, K-Means, Gaussian Kernel and Pillar K-means algorithms. This segmentation process includes a new mechanism for clustering the elements of high-resolution images in order to improve precision and reduce computation time. The system applies FCM, Gaussian kernel and K-means clustering to the image later optimized by Pillar Algorithm. It designates the initial centroids’ positions by calculating the Euclidian distance metric between each data point and all previous centroids. Then it selects data points which have the maximum distance as new initial centroids. This algorithm distributes all initial centroids according to the maximum accumulated distance metric. In addition, it also reduces the time for analysis. At the end of the process the tumor is extracted from the MRI image and its exact position and the shape is also determined. This paper evaluates the proposed approach for Brain tumor detection by comparing with K-means, Fuzzy C means, Gaussian Kernel and manually segmented algorithms. The experimental results clarify the effectiveness of proposed approach to improve the segmentation quality in aspects of precision and computational time.
Classification of physiological diseases using eeg signals and machine learni...eSAT Journals
Abstract
In rural areas providing advanced diagnostics for various health disorders is not possible in countries like India. With latest technological breakthrough, brain signals (EEG signal) capturing devices are available at rate less 50$. If these brain signals can be used to predict any Physiological disorders like heart problem, kidney problems etc., then these EEG devices can be provided to rural health care centre for preliminary investigation and on diagnosis the patient can move to city hospitals for diagnostics and treatment. In this project, we provide a solution of identifying physiological problems using EEG signals and use machine learning techniques for diagnosis.
Keywords: EEG Signals, EEG Frame, Feature Extraction
Classification Of Iris Plant Using Feedforward Neural Networkirjes
The classification and recognition of type on the basis of individual features and behaviors constitute
a preliminary measure and is an important target in the behavioral sciences. Current statistical methods do not
always yield satisfactory answers. A Feed Forward Artificial Neural Network is the computer model inspired by
the structure of the Human Brain. It views as in the set of artificial nerve cells that are interconnected with the
other neurons. The primary aim of this paper is to demonstrate the process of developing the Artificial Neural
network based classifier which classifies the Iris database. The problem concerns the identification of Iris plant
species on the basis of plant attribute measurements. This paper is related to the use of feed forward neural
networks towards the identification of iris plants on the basis of the following measurements: sepal length, sepal
width, petal length, and petal width. Using this data set a Neural Network (NN) is used for the classification of
iris data set. The EBPA is used for training of this ANN. The results of simulations illustrate the effectiveness of
the neural system in iris class identification.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
USING ARTIFICIAL NEURAL NETWORK IN DIAGNOSIS OF THYROID DISEASE: A CASE STUDYijcsa
Nowadays, one of the main issues to create challenges in medicine sciences by developing technology is the
disease diagnosis with high accuracy. In the recent decades, Artificial Neural Networks (ANNs) are considered as the best solutions to achieve this goal and involve in widespread researches to diagnose the diseases. In this paper, we consider a Multi-layer Perceptron (MLP) ANN using back propagation learning algorithm to classify Thyroid disease. It consists of an input layer with 5 neurons, a hidden layer with 6 neurons and an output layer with just 1 neuron. The suitable selection of activation function and the number of neurons in the hidden layer and also the number of layers are achieved using test and error method. Our simulation results indicate that the performed optimization in MLP ANNs can be reached the accuracy level to 98.6%.
This paper presents a discrete wavelet transform and neural network approach to fault
detection and classification and location in transmission lines. The fault detection is carried out by
using energy of the detail coefficients of the phase signals and artificial neutral network algorithm
used for fault type classification and fault distance location for all the types of faults for 220 KV
transmission line. The energies of the all three phases A, B, C and ground phase are given in put to
the neural network for the fault classification. For each type of fault separate neural network is
prepared for finding out the fault location. An improved performance is obtained once the neutral
network is trained suitably, thus performance correctly when faced with different system parameters
and conditions.
An Artificial Neural Network Model for Neonatal Disease DiagnosisWaqas Tariq
The significance of disease diagnosis by artificial intelligence is not obscure now days. The increasing demand of Artificial Neural Network application for predicting the disease shows better performance in the field of medical decision making. This paper represents the use of artificial neural networks in predicting neonatal disease diagnosis. The proposed technique involves training a Multi Layer Perceptron with a BP learning algorithm to recognize a pattern for the diagnosing and prediction of neonatal diseases. A comparative study of using different training algorithm of MLP, Quick Propagation, Conjugate Gradient Descent, shows the higher prediction accuracy. The Backpropogation algorithm was used to train the ANN architecture and the same has been tested for the various categories of neonatal disease. About 94 cases of different sign and symptoms parameter have been tested in this model. This study exhibits ANN based prediction of neonatal disease and improves the diagnosis accuracy of 75% with higher stability. Key words: Artificial Intelligence, Multi Layer Perceptron, Neural Network, Neonate
A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS FOR EEG SIGNAL CLASSIFICATIONsipij
In this paper, different machine learning algorithms such as Linear Discriminant Analysis, Support vector
machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour, and Autoencoder with SVM
have been compared. This comparison was conducted to seek a robust method that would produce good
classification accuracy. To this end, a robust method of classifying raw Electroencephalography (EEG)
signals associated with imagined movement of the right hand and relaxation state, namely Autoencoder
with SVM has been proposed. The EEG dataset used in this research was created by the University of
Tubingen, Germany. The best classification accuracy achieved was 70.4% with SVM through feature
engineering. However, our prosed method of autoencoder in combination with SVM produced a similar
accuracy of 65% without using any feature engineering technique. This research shows that this system of
classification of motor movements can be used in a Brain-Computer Interface system (BCI) to mentally
control a robotic device or an exoskeleton.
CLASSIFICATION OF OCT IMAGES FOR DETECTING DIABETIC RETINOPATHY DISEASE USING...sipij
Optical Coherence Tomography (OCT) imaging aids in retinal abnormality detection by showing the
tomographic retinal layers. OCT images are a useful tool for detecting Diabetic Retinopathy (DR) disease
because of their capability to capture micrometer-resolution. An automated technique was introduced to
differentiate DR images from normal ones. 214 images were subjected to the experiment, of which 160
images were used for classifiers’ training, and 54 images were used for testing. Different features were
extracted to feed our classifiers, including statistical features and local binary pattern (LBP) features. The
experimental results demonstrated that our classifiers were able to discriminate DR retina from the normal
retina with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 100%. The retinal
OCT images have common texture patterns and using a powerful tool for pattern analysis like LBP
features has a significant impact on the achieved results. The result has better performance than previously
proposed methods in the literature.
Comparison of Image Segmentation Algorithms for Brain Tumor DetectionIJMTST Journal
This paper deals with the implementation of Simple Algorithms for detection of size and shape of tumor in brain using MRI images. Generally, CT scan or MRI that is directed into intracranial cavity produces a complete image of brain. This image is visually examined by the physician for detection & diagnosis of brain tumor. However this method of detection resists the accurate determination of stage & size of tumor. To avoid that, this project uses computer aided method for segmentation (detection) of brain tumor by applying Fuzzy C-Means, K-Means, Gaussian Kernel and Pillar K-means algorithms. This segmentation process includes a new mechanism for clustering the elements of high-resolution images in order to improve precision and reduce computation time. The system applies FCM, Gaussian kernel and K-means clustering to the image later optimized by Pillar Algorithm. It designates the initial centroids’ positions by calculating the Euclidian distance metric between each data point and all previous centroids. Then it selects data points which have the maximum distance as new initial centroids. This algorithm distributes all initial centroids according to the maximum accumulated distance metric. In addition, it also reduces the time for analysis. At the end of the process the tumor is extracted from the MRI image and its exact position and the shape is also determined. This paper evaluates the proposed approach for Brain tumor detection by comparing with K-means, Fuzzy C means, Gaussian Kernel and manually segmented algorithms. The experimental results clarify the effectiveness of proposed approach to improve the segmentation quality in aspects of precision and computational time.
Classification of physiological diseases using eeg signals and machine learni...eSAT Journals
Abstract
In rural areas providing advanced diagnostics for various health disorders is not possible in countries like India. With latest technological breakthrough, brain signals (EEG signal) capturing devices are available at rate less 50$. If these brain signals can be used to predict any Physiological disorders like heart problem, kidney problems etc., then these EEG devices can be provided to rural health care centre for preliminary investigation and on diagnosis the patient can move to city hospitals for diagnostics and treatment. In this project, we provide a solution of identifying physiological problems using EEG signals and use machine learning techniques for diagnosis.
Keywords: EEG Signals, EEG Frame, Feature Extraction
Classification Of Iris Plant Using Feedforward Neural Networkirjes
The classification and recognition of type on the basis of individual features and behaviors constitute
a preliminary measure and is an important target in the behavioral sciences. Current statistical methods do not
always yield satisfactory answers. A Feed Forward Artificial Neural Network is the computer model inspired by
the structure of the Human Brain. It views as in the set of artificial nerve cells that are interconnected with the
other neurons. The primary aim of this paper is to demonstrate the process of developing the Artificial Neural
network based classifier which classifies the Iris database. The problem concerns the identification of Iris plant
species on the basis of plant attribute measurements. This paper is related to the use of feed forward neural
networks towards the identification of iris plants on the basis of the following measurements: sepal length, sepal
width, petal length, and petal width. Using this data set a Neural Network (NN) is used for the classification of
iris data set. The EBPA is used for training of this ANN. The results of simulations illustrate the effectiveness of
the neural system in iris class identification.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
USING ARTIFICIAL NEURAL NETWORK IN DIAGNOSIS OF THYROID DISEASE: A CASE STUDYijcsa
Nowadays, one of the main issues to create challenges in medicine sciences by developing technology is the
disease diagnosis with high accuracy. In the recent decades, Artificial Neural Networks (ANNs) are considered as the best solutions to achieve this goal and involve in widespread researches to diagnose the diseases. In this paper, we consider a Multi-layer Perceptron (MLP) ANN using back propagation learning algorithm to classify Thyroid disease. It consists of an input layer with 5 neurons, a hidden layer with 6 neurons and an output layer with just 1 neuron. The suitable selection of activation function and the number of neurons in the hidden layer and also the number of layers are achieved using test and error method. Our simulation results indicate that the performed optimization in MLP ANNs can be reached the accuracy level to 98.6%.
This paper presents a discrete wavelet transform and neural network approach to fault
detection and classification and location in transmission lines. The fault detection is carried out by
using energy of the detail coefficients of the phase signals and artificial neutral network algorithm
used for fault type classification and fault distance location for all the types of faults for 220 KV
transmission line. The energies of the all three phases A, B, C and ground phase are given in put to
the neural network for the fault classification. For each type of fault separate neural network is
prepared for finding out the fault location. An improved performance is obtained once the neutral
network is trained suitably, thus performance correctly when faced with different system parameters
and conditions.
Fault detection and diagnosis ingears using wavelet enveloped power spectrum ...eSAT Journals
Abstract In this work, automatic detection and diagnosis of gear condition monitoring technique is presented. The vibration signals in time domain wereobtained from a fault simulator apparatus from a healthy gear and an induced faulty gear. These time domain signals were processed using Laplace and Morlet wavelet based enveloped power spectrum to detect the faults in gears. The vibration signals obtained were filtered to enhance the signal components before the application of wavelet analysis. The time and frequency domain features extracted from Laplace wavelet based wavelet transform are used as input to ANN for gear fault classification. Genetic algorithm was used to optimize the wavelet and ANN classification parameters. The result shows the successful classification of ANN test process. Index Terms:Continuous wavelet transform, Envelope power spectrum, Wavelet, Filtering, ANN.
The transmission overhead line is one of the vital elements in the power system for transmitting the electrical energy. In the transmission, the disturbances are often occurred. In the conventional algorithm, alpha and beta (mode) currents generated by Clarke’s transformation are utilized to convert the signal of Discrete Wavelet Transform (DWT) to obtain the Wavelet Transform Coefficient (WTC) and the Wavelet Coefficient Energy (WCE). This study introduces a new algorithm, called Modified Clarke for fault detection and classification using DWT and Back-Propagation Neural Network (BPNN) based on Clarke’s transformation on transmission overhead line by adding gamma current in the system. Daubechies4 (Db4) is used as a mother wavelet to decompose the high frequency components of the signal error. Simulation is performed using PSCAD / EMTDC transmission system modeling and carried out at different locations along the transmission line with different types of fault, fault resistances, fault locations and fault of the initial angle on a given power system model. The simulated fault types are in the study are the Single Line to Ground, the Line To Line, the Double Line to Ground and the Three Phases. There are four statistic methods utilized in the present study to determine the accuracy of detection and classification of faults. The result shows that the best and the worst structures of BPNN occurred on the configuration of 12-24-48-4 and 12-12-6-4, respectively. For instance, the error using Mean Square Error Method. The Error Of Clarke’s, Without Clarke’s and Modified Clarke’s are 0.05862, 0.05513 and 0.03721 which are the best, respectively, whereas, the worst are 0.06387, 0.0753 and 0.052, respectively. This indicates that the Modified Clarke’s result is in the lowest error. The method is successfully implement can be utilized in the detection and classification of fault in transmission line by utilities and power regulation in power system planning and operation.
- This paper proposes a new technique of using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) based on Clarke’s transformation for fault classification and detection on a single circuit transmission line. Simulation and training process for the neural network are done by using PSCAD / EMTDC and MATLAB. Daubechies4 mother wavelet (DB4) is used to decompose the high frequency components of these signals. The wavelet transform coefficients (WTC) and wavelet energy coefficients (WEC) for classification fault and detect patterns used as input for neural network training back-propagation (BPNN). This information is then fed into a neural network to classify the fault condition. A DWT with quasi optimal performance for preprocessing stage are presented. This study also includes a comparison of the results of training BPPN and DWT with and without Clarke’s transformation, where the results show that using Clarke transformation in training will give in a smaller mean square error (MSE) and mean absolute error (MAE). The simulation also shows that the new algorithm is more reliable and accurate.
An Efficient PSO Based Ensemble Classification Model on High Dimensional Data...ijsc
As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to
analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection based ensemble learning models is to classify the high dimensional data with high computational efficiency and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.
Performance analysis of neural network models for oxazolines and oxazoles der...ijistjournal
Neural networks have been used successfully to a br
oad range of areas such as business, data mining, d
rug
discovery and biology. In medicine, neural network
s have been applied widely in medical diagnosis,
detection and evaluation of new drugs and treatment
cost estimation. In addition, neural networks have
begin practice in data mining strategies for the a
im of prediction, knowledge discovery. This paper
will
present the application of neural networks for the
prediction and analysis of antitubercular activity
of
Oxazolines and Oxazoles derivatives. This study pre
sents techniques based on the development of Single
hidden layer neural network (SHLFFNN), Gradient Des
cent Back propagation neural network (GDBPNN),
Gradient Descent Back propagation with momentum neu
ral network (GDBPMNN), Back propagation with
Weight decay neural network (BPWDNN) and Quantile r
egression neural network (QRNN) of artificial
neural network (ANN) models Here, we comparatively
evaluate the performance of five neural network
techniques. The evaluation of the efficiency of eac
h model by ways of benchmark experiments is an
accepted application. Cross-validation and resampli
ng techniques are commonly used to derive point
estimates of the performances which are compared to
identify methods with good properties. Predictiv
e
accuracy was evaluated using the root mean squared
error (RMSE), Coefficient determination(
), mean
absolute error(MAE), mean percentage error(MPE) and
relative square error(RSE). We found that all five
neural network models were able to produce feasible
models. QRNN model is outperforms with all
statistical tests amongst other four models.
PERFORMANCE ANALYSIS OF NEURAL NETWORK MODELS FOR OXAZOLINES AND OXAZOLES DER...ijistjournal
Neural networks have been used successfully to a broad range of areas such as business, data mining, drug discovery and biology. In medicine, neural networks have been applied widely in medical diagnosis, detection and evaluation of new drugs and treatment cost estimation. In addition, neural networks have begin practice in data mining strategies for the aim of prediction, knowledge discovery. This paper will present the application of neural networks for the prediction and analysis of antitubercular activity of Oxazolines and Oxazoles derivatives. This study presents techniques based on the development of Single hidden layer neural network (SHLFFNN), Gradient Descent Back propagation neural network (GDBPNN), Gradient Descent Back propagation with momentum neural network (GDBPMNN), Back propagation with Weight decay neural network (BPWDNN) and Quantile regression neural network (QRNN) of artificial neural network (ANN) models Here, we comparatively evaluate the performance of five neural network techniques. The evaluation of the efficiency of each model by ways of benchmark experiments is an accepted application. Cross-validation and resampling techniques are commonly used to derive point estimates of the performances which are compared to identify methods with good properties. Predictive accuracy was evaluated using the root mean squared error (RMSE), Coefficient determination(R2), mean absolute error(MAE), mean percentage error(MPE) and relative square error(RSE). We found that all five neural network models were able to produce feasible models. QRNN model is outperforms with all statistical tests amongst other four models.
International Journal of Research in Engineering and Science is an open access peer-reviewed international forum for scientists involved in research to publish quality and refereed papers. Papers reporting original research or experimentally proved review work are welcome. Papers for publication are selected through peer review to ensure originality, relevance, and readability.
Human activity recognition with self-attentionIJECEIAES
In this paper, a self-attention based neural network architecture to address human activity recognition is proposed. The dataset used was collected using smartphone. The contribution of this paper is using a multi-layer multi-head self-attention neural network architecture for human activity recognition and compared to two strong baseline architectures, which are convolutional neural network (CNN) and long-short term network (LSTM). The dropout rate, positional encoding and scaling factor are also been investigated to find the best model. The results show that proposed model achieves a test accuracy of 91.75%, which is a comparable result when compared to both the baseline models.
Application of Hybrid Genetic Algorithm Using Artificial Neural Network in Da...IOSRjournaljce
The main purpose of data mining is to extract knowledge from large amount of data. Artificial Neural network (ANN) has already been applied in a variety of domains with remarkable success. This paper presents the application of hybrid model for stroke disease that integrates Genetic algorithm and back propagation algorithm. Selecting a good subset of features, without sacrificing accuracy, is of great importance for neural networks to be successfully applied to the area. In addition the hybrid model that leads to further improvised categorization, accuracy compared to the result produced by genetic algorithm alone. In this study, a new hybrid model of Neural Networks and Genetic Algorithm (GA) to initialize and optimize the connection weights of ANN so as to improve the performance of the ANN and the same has been applied in a medical problem of predicting stroke disease for verification of the results.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Pattern Recognition using Artificial Neural NetworkEditor IJCATR
An artificial neural network (ANN) usually called neural network. It can be considered as a resemblance to a paradigm
which is inspired by biological nervous system. In network the signals are transmitted by the means of connections links. The links
possess an associated way which is multiplied along with the incoming signal. The output signal is obtained by applying activation to
the net input NN are one of the most exciting and challenging research areas. As ANN mature into commercial systems, they are likely
to be implemented in hardware. Their fault tolerance and reliability are therefore vital to the functioning of the system in which they
are embedded. The pattern recognition system is implemented with Back propagation network and Hopfield network to remove the
distortion from the input. The Hopfield network has high fault tolerance which supports this system to get the accurate output.
AN APPROACH FOR IRIS PLANT CLASSIFICATION USING NEURAL NETWORKijsc
Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced. This paper focuses on IRIS plant classification using Neural Network. The problem concerns the identification of IRIS plant species on
the basis of plant attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS plant and how the prediction was made from analyzing the pattern to form the class of IRIS plant. By using this pattern and classification, in future upcoming years
the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, function approximations, optimization, and associative memories. In this work, Multilayer feed- forward networks are trained using back propagation learning
algorithm.
An Approach for IRIS Plant Classification Using Neural Network ijsc
Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced. This paper focuses on IRIS plant classification using Neural Network. The problem concerns the identification of IRIS plant species on the basis of plant attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS plant and how the prediction was made from analyzing the pattern to form the class of IRIS plant. By using this pattern and classification, in future upcoming years the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, function approximations, optimization, and associative memories. In this work, Multilayer feed- forward networks are trained using back propagation learning algorithm.
Wearable sensor-based human activity recognition with ensemble learning: a co...IJECEIAES
The spectacular growth of wearable sensors has provided a key contribution to the field of human activity recognition. Due to its effective and versatile usage and application in various fields such as smart homes and medical areas, human activity recognition has always been an appealing research topic in artificial intelligence. From this perspective, there are a lot of existing works that make use of accelerometer and gyroscope sensor data for recognizing human activities. This paper presents a comparative study of ensemble learning methods for human activity recognition. The methods include random forest, adaptive boosting, gradient boosting, extreme gradient boosting, and light gradient boosting machine (LightGBM). Among the ensemble learning methods in comparison, light gradient boosting machine and random forest demonstrate the best performance. The experimental results revealed that light gradient boosting machine yields the highest accuracy of 94.50% on UCI-HAR dataset and 100% on single accelerometer dataset while random forest records the highest accuracy of 93.41% on motion sense dataset.
Similar to Review on classification based on artificial (20)
A CONCEPTUAL FRAMEWORK OF A DETECTIVE MODEL FOR SOCIAL BOT CLASSIFICATIONijasa
Social media platform has greatly enhanced human interactive activities in the virtual community. Virtual
socialization has positively influenced social bonding among social media users irrespective of one’s
location in the connected global village. Human user and social bot user are the two types of social media
users. While human users personally operate their social media accounts, social bot users are developed
software that manages a social media account for the human user called the botmaster. This botmaster in
most cases are hackers with bad intention of attacking social media users through various attacking mode
using social bots. The aim of this research work is to design an intelligent framework that will prevent
attacks through social bots on social media network platforms.
DESIGN OF A MINIATURE RECTANGULAR PATCH ANTENNA FOR KU BAND APPLICATIONSijasa
A significant portion of communication devices employs microstrip antennas because of their compact size,
low profile, and ability to conform to both planar and non-planar surfaces. To achieve this, we present a
miniature inset-fed rectangular patch antenna using partial ground plane for Ku band applications. The
proposed antenna design used an operating frequency of 15.5 GHz, a FR4 substrate with a dielectric
constant of 4.3, and a thickness of 1.4 mm. It is fed by a 50 Ω inset feedline. Computer simulation
technology (CST) software is used to design, simulate, and analyze. The simulation yields the antenna
performance parameters, including return loss (S11), bandwidth, VSWR, gain, directivity, and radiation
efficiency. The simulation findings revealed that the proposed antenna resonated at 15.5 GHz, with a
return loss of -22.312 dB, a bandwidth of 2.73 GHz (2730 MHz), VSWR of 1.17, a gain of 3.843 dBi, a
directivity of 5.926 dBi, and an antenna efficiency of -2.083 dB (61.901%).
SMART SOUND SYSTEM APPLIED FOR THE EXTENSIVE CARE OF PEOPLE WITH HEARING IMPA...ijasa
We, as normal people, have access to a potent communication tool, which is sound. Although we can continuously gather, analyse, and interpret sounds thanks to our sense of hearing, it can be challenging for people with hearing impairment to perceive their surroundings through sound. Also known as PWHI (People with Hearing Impairment). Auditory/phonic impairment is one of the most prevailing sensory deficits in humans at present. Fortunately, there is room to apply a solution to this issue, given the development of technology. Our project involves capturing ambient sounds from the user’s surroundings and notifying the user through a mobile application using IoT and Deep Learning. Its architecture offers sound recognition using a tool, such as a microphone, to capture sounds from the user's surroundings. These sounds are identified and categorized as ambient sounds, like a doorbell, baby cry, and dog barking; as well as emergency-related sounds, such as alarms, sirens, et
AN INTELLIGENT AND DATA-DRIVEN MOBILE VOLUNTEER EVENT MANAGEMENT PLATFORM USI...ijasa
In Lewis and Clark High School’s Key Club, meetings are always held in a crowded classroom. The
system of event sign-up is inefficient and hinders members from joining events. This has led to students
becoming discouraged from joining Key Club and often resulted in a lack of volunteers for important
events. The club needed a more efficient way of connecting volunteers with volunteering opportunities. To
solve this problem, we developed a VolunteerMatch Mobile application using Dart and Flutter framework
for Key Club to use. The next steps will be to add a volunteer event recommendation and matching feature,
utilizing the results from the research on machine learning models and algorithms in this paper.
A STUDY OF IOT BASED REAL-TIME SOLAR POWER REMOTE MONITORING SYSTEMijasa
We have Developed an IoT-based real-time solar power monitoring system in this paper. It seeks an opensource IoT solution that can collect real-time data and continuously monitor the power output and environmental conditions of a photovoltaic panel.The Objective of this work is to continuously monitor the status of various parameters associated with solar systems through sensors without visiting manually, saving time and ensures efficient power output from PV panels while monitoring for faulty solar panels, weather conditionsand other such issues that affect solar effectiveness.Manually, the user must use a multimeter to determine what value of measurement of the system is appropriate for appliance consumers, which is difficult for the larger System. But the Solar Energy Monitoring system is designed to make it easier for users to use the solar system.This system is comprised of a microcontroller (Node MCU), a PV panel, sensors (INA219 Current Module, Digital Temperature Sensor, LDR), a Battery Charger Module, and a battery. The data from the PV panels and other appliances are sent to the cloud (Thingspeak) via the internet using IoT technology and a Wi-Fi module (NodeMCU). It also allows users in remote areas to monitor the parameters of the solar power plant using connected devices. The user can view the current, previous, and average parameters of the solar PV system, such as voltage, current, temperature, and light intensity using a Graphical User Interface. This will facilitate fault detection and maintenance of the solar power plant easier and saves time.
SENSOR BASED SMART IRRIGATION SYSTEM WITH MONITORING AND CONTROLLING USING IN...ijasa
This paper presents the development of a sensor based smart irrigation system with the capabilities of remote monitoring and controlling of water usage in the agriculture field using Internet of Things (IoT). With the employment of IoT in irrigation system, all agricultural information can be viewed and controlled at the user's fingertips. The system consists of a microcontroller (Node MCU), sensors (soil moisture, DHT11), and irrigation of a water pump with a decision-making system. Sensors are linked to a Wi-Fi module (Node MCU) and are interdependent to provide increased sensitivity to the irrigation system. The data obtained will be uploaded to the cloud (ThingSpeak) and presented in the form of graphs accessible via the website. A web page is used to control the water pump for irrigation purposes. This paper is managed to meet all of its aims to help farmers in terms of time, project cost, labor, water consumption, power consumption, and reliability by implementing the IoT-based smart irrigation system.
COMPARISON OF BIT ERROR RATE PERFORMANCE OF VARIOUS DIGITAL MODULATION SCHEME...ijasa
Digital modulation increases information capacity, data security, and system availability while maintaining high communication quality. As a result, digital modulation techniques are in higher demand than analog modulation techniques due to their ability to transmit larger amounts of data. Amplitude Shift Keying (ASK), Frequency Shift Keying (FSK), Phase Shift Keying (PSK), Differential Phase Shift Keying (DPSK), and Quadrature Amplitude Modulation (QAM) are critical components of current communications systems development, particularly for broadband wireless communications. In this paper, the comparison of bit error rate performance of different modulation schemes (BPSK, QPSK, and16-QAM) and various equalization techniques such as constant modulus algorithm (CMA) and maximum likelihood sequence estimate (MLSE) for the AWGN and Rayleigh fading channels is analyzed using Simulink. BPSK outperforms QPSK and 16-QAM when compared to the other two digital modulation schemes. Among the three digital modulation schemes, BPSK is showing better performance as compared to QPSK and 16- QAM
PERFORMANCE OF CONVOLUTION AND CRC CHANNEL ENCODED V-BLAST 4×4 MIMO MCCDMA WI...ijasa
Wireless communications are among the rapidly growing fields in our current life and have a massive effect on every aspect of our everyday life. In this paper, the performance of the various digital modulation techniques (BPSK, DPSK, QPSK, and QAM) based wireless communication system on the audio signal transmission through the additive Gaussian Noise (AWGN) channel is assessed on the basis of bit error rate (BER) as a function of the signal-to-noise ratio (SNR). Based on the results of this study, BPSK modulation outperforms the DPSK, QPSK, and QAM modulation strategies in the MIMO MC-CDMA VBlast based wireless communication system. The digital modulation of QPSK shows the worst performance in audio signal transmission especially in comparison to other digital modulations. It is clear from the current simulation study based on MATLAB that the V-Blast encoded 4×4 MIMO MC-CDMA wireless system with minimum mean square error (MMSE) signal detection and 1⁄2-rated convolution and cyclic redundancy check (CRC) channel encoding strategies show good performance utilizing BPSK digital modulation in audio signal transmission
A SCRUTINY TO ATTACK ISSUES AND SECURITY CHALLENGES IN CLOUD COMPUTINGijasa
Cloud computing is an anthology in which one or more computers are connected in a network. Cloud
computing is a cluster of lattice computing, autonomic computing and utility computing. Cloud provides an
on demand services to the users. Many numbers of users access the cloud to utilize the cloud resources.
The security is one the major problem in cloud computing. Hence security is a major issue in cloud
computing. Providing security is a major requirement of cloud computing. The study enclose all the
security issues and attack issues in cloud computing.
The International Journal of Ambient Systems and Applications (IJASA) ijasa
The International Journal of Ambient Systems and applications is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of ambient Systems. The journal focuses on all technical and practical aspects of ambient Systems, networks, technologies and applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced ambient Systems and establishing new collaborations in these areas.Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in ambient Systems.
A SCRUTINY TO ATTACK ISSUES AND SECURITY CHALLENGES IN CLOUD COMPUTINGijasa
Cloud computing is an anthology in which one or more computers are connected in a network. Cloud computing is a cluster of lattice computing, autonomic computing and utility computing. Cloud provides an on demand services to the users. Many numbers of users access the cloud to utilize the cloud resources. The security is one the major problem in cloud computing. Hence security is a major issue in cloud computing. Providing security is a major requirement of cloud computing. The study enclose all the security issues and attack issues in cloud computing.
TOWARD ORGANIC COMPUTING APPROACH FOR CYBERNETIC RESPONSIVE ENVIRONMENTijasa
The developpment of the Internet of Things (IoT) concept revives Responsive Environments (RE) technologies. Nowadays, the idea of a permanent connection between physical and digital world is technologically possible. The capillar Internet relates to the Internet extension into daily appliances such as they become actors of Internet like any hu-man. The parallel development of Machine-to-Machine
communications and Arti cial Intelligence (AI) technics start a new area of cybernetic. This paper presents an approach for Cybernetic Organism (Cyborg) for RE based on Organic Computing (OC). In such approach, each appli-ance is a part of an autonomic system in order to control a physical environment.The underlying idea is that such systems must have self-x properties in order to adapt their behavior to
external disturbances with a high-degree of autonomy.
A STUDY ON DEVELOPING A SMART ENVIRONMENT IN AGRICULTURAL IRRIGATION TECHNIQUEijasa
Maintaining a good irrigation system is a necessity in today’s water scarcity environment. This paper describes a new approach for automated Smart Irrigation (SIR) system in agricultural management. Using
various types of sensors in the crop field area, temperature and moisture value of the soil is monitored.Based on the sensed data, SIR will automatically decide about the necessary action for irrigation and also notifies the user. The system will also focus on the reduction of energy consumption by the sensors during communication.
A REVIEW ON DDOS PREVENTION AND DETECTION METHODOLOGYijasa
Denial of Service (DoS) or Distributed-Denial of Service (DDoS) is major threat to network security.
Network is collection of nodes that interconnect with each other for exchange the Information. This
information is required for that node is kept confidentially. Attacker in network computer captures this
information that is confidential and misuse the network. Hence security is one of the major issues. There
are one or many attacks in network. One of the major threats to internet service is DDoS (Distributed
denial of services) attack. DDoS attack is a malicious attempt to suspending or interrupting services to
target node. DDoS or DoS is an attempt to make network resource or the machine is unavailable to its
intended user. Many ideas are developed for avoiding the DDoS or DoS. DDoS happen in two ways
naturally or it may due to some botnets .Various schemes are developed defense against to this attack.
Main idea of this paper is present basis of DDoS attack. DDoS attack types, DDoS attack components,
survey on different mechanism to prevent DDoS
The smart mobile terminal operator platform Android is getting popular all over the world with its wide variety of applications and enormous use in numerous spheres of our daily life. Considering the fact of increasing demand of home security and automation, an Android based control system is presented in this paper where the proposed system can maintain the security of home main entrance and also the car door lock. Another important feature of the designed system is that it can control the overall appliances in a room. The mobile to security system or home automation system interface is established through Bluetooth. The hardware part is designed with the PIC microcontroller.
The World Wide Web is booming and radically vibrant due to the well established standards and widely accountable framework which guarantees the interoperability at various levels of the application and the society as a whole. So far, the web has been functioning at the random rate on the basis of the human intervention and some manual processing but the next generation web which the researchers called semantic web, edging for automatic processing and machine-level understanding. The well set notion, Semantic Web would be turn possible if only there exists the further levels of interoperability prevails among the applications and networks. In achieving this interoperability and greater functionality among the applications, the W3C standardization has already released the well defined standards such as RDF/RDF Schema and OWL. Using XML as a tool for semantic interoperability has not achieved anything effective and failed to bring the interconnection at the larger level. This leads to the further inclusion of inference layer at the top of the web architecture and its paves the way for proposing the common design for encoding the ontology representation languages in the data models such as RDF/RDFS. In this research article, we have given the clear implication of semantic web research roots and its ontological background process which may help to augment the sheer understanding of named entities in the web.
Wireless sensor networks provide ubiquitous computing systems in various open environments. In the
environment, sensor nodes can easily be compromised by adversaries to generate injecting false data
attacks. The injecting false data attack not only consumes unnecessary energy in en-route nodes, but also
causes false alarms at the base station. To detect this type of attack, a bandwidth-efficient cooperative
authentication (BECAN) scheme was proposed to achieve high filtering probability and high reliability
based on random graph characteristics and cooperative bit-compressed authentication techniques. This
scheme may waste energy resources in en-route nodes due to the fixed number of forwarding reports. In
this paper, our proposed method effectively selects a dynamic number of forwarding reports in the source
nodes based on an evaluation function. The experimental results indicate that our proposed method
enhances the energy savings while maintaining security levels as compared to BECAN.
Wireless sensor networks (WSNs) are regularly deployed in harsh and unattended environments, and
sensor nodes are easily exposed to attacks due to the random arrangement of the sensor field. An attacker
can inject fabricated reports from a compromised node with false votes and false vote-based reports. The
false report attacks can waste the energy of the intermediate nodes, shortening the network lifetime.
Furthermore, false votes cause the filtering out of legitimate reports. A probabilistic voting-based filtering
scheme (PVFS) was proposed as a countermeasure against this type of attacks by Li and Wu. PVFS uses a
vote threshold, a security threshold, and a verification node. The scheme does not make additional use
energy or communications resources because the verification node and threshold values are fixed. There
needs to be a verification node selection method that considers the energy resources of the node. In this
paper, we propose a verification path election scheme based on a fuzzy logic system. In the proposed
scheme, one node transmits reports in the node with a strong state through a fuzzy logic system after which
a neighbor is selected out of two from the surroundings. Experimental results show that the proposed
scheme improves energy savings up to maximum 13% relative to the PVFS.
In this paper a novel intelligent soft computing based cryptographic technique based on synchronization of
two chaotic systems (CSCT) between sender and receiver has been proposed to generate session key using
Pecora and Caroll (PC) method. Chaotic system has some unique features like sensitive to initial
conditions, topologically mixing; and dense periodic orbits. By nature, the Lorenz system is very sensitive
to initial conditions meaning that the error between attacker and receiver is going to grow exponentially if
there is a very slight difference between their initial conditions. All these features make chaotic system as
good alternatives for session key generation. In the proposed CSCT few parameters ( , b , r , x1 ,y2 and z2 )
are being exchanged between sender and receiver. Some of the parameter which takes major roles to form
the session key does not get transmitted via public channel, sender keeps these parameters secret. This way
of handling parameter passing mechanism prevents any kind of attacks during exchange of parameters like
sniffing, spoofing or phishing.
A black-hole attack in the Mobile Ad-hoc NETwork (MANET) is an attack occurs due to malicious nodes,
which attracts the data packets by falsely advertising a fresh route to the destination. In this paper, we
present a clustering approach in Ad-hoc On-demand Distance Vector (AODV) routing protocol for the
detection and prevention of black-hole attack in MANETs. In this approach every member of the cluster will
ping once to the cluster head, to detect the peculiar difference between the number of data packets received
and forwarded by the node. If anomalousness is perceived, all the nodes will obscure the malicious nodes
from the network.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
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/
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
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Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Forklift Classes Overview by Intella PartsIntella Parts
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1. International Journal of Ambient Systems and Applications (IJASA) Vol.2, No.4, December 2014
DOI:10.5121/ijasa.2014.2402 11
REVIEW ON CLASSIFICATION BASED ON ARTIFICIAL
NEURAL NETWORKS
Saravanan K1
and S. Sasithra2
1
Assistant Professor/Department of Computer Engineering, Erode Sengunthar
Engineering College, Erode, India
2
Department of Computer Engineering, Erode Sengunthar Engineering College, Erode,
India
ABSTRACT
Artificial neural networks (ANN) consider classification as one of the most dynamic research and
application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by
back propagation algorithm. The different combinations of functions and its effect while using ANN as a
classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The
back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification
with suitable combination of training, learning and transfer functions. When the maximum likelihood
method was compared with backpropagation neural network method, the BPNN was more accurate than
maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible.
Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to
be more effective than other classification algorithms.
KEYWORDS
Back propagation algorithm, Maximum Likelihood method, Multilayer feed-forward neural network.
1. INTRODUCTION
A neural network model which is the branch of artificial intelligence is generally referred to as
artificial neural networks (ANNs). ANN teaches the system to execute task, instead of
programming computational system to do definite tasks. To perform such tasks, Artificial
Intelligence System (AI) is generated. It is a pragmatic model which can quickly and precisely
find the patterns buried in data that replicate useful knowledge. One case of these AI models is
neural networks. AI systems should discover from data on a constant basis. In the areas of
medical diagnosis relationships with dissimilar data, the most available techniques are the
Artificial Intelligence techniques.
An artificial neural network is made up of many artificial neurons which are correlated together in
accordance with explicit network architecture. The objective of the neural network is to convert
the inputs into significant outputs.The teaching mode can be supervised or unsupervised. Neural
Networks learn in the presence of noise.
ANNs found their usage in many areas suc as,
• Bankruptcy prediction
2. International Journal of Ambient Systems and Applications (IJASA) Vol.2, No.4, December 2014
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• Speech recognition
• Product inspection
• Fault detection
Fig1. Artificial Neural Network
2. EXISTING SYSTEM
2.1. Back propagation Neural Network
Artificial neural networks (ANN) consider classification as one of the most dynamic research and
application areas. The major disadvantage in using ANN is to find the most appropriate grouping
of training, learning and transfer function for classifying the data sets with growing number of
features and classified sets. The different combinations of functions and its effect while using
ANN as a classifier is studied and the correctness of these functions are analyzed for various
kinds of datasets.
The real world problems which are represented by multidimensional datasets are taken from
medical background. The classification and clustering of these data sets are significant. The data
set is divided into training set and testing set and it has no usage in the training process. The
results are produced with the help of these datasets and it is used for testing. The training set is
taken from 2/3rd
of the dataset and the remaining has been taken as test set. This is made through
the assessment of the accuracy achieved through testing against these data sets. Then the network
is simulated with the same data.
The back propagation algorithm trains the neural network. Gradient descent method (GDM) was
used to decrease the mean squared error between network output and the actual error rate.The
following parameters are considered to measure the efficiency of the network,
• Rate of convergence
• No of epochs taken to converge the network.
• The calculated Mean Square Error (MSE).
With the appropriate combination of training, learning and transfer functions the dataset
classification uses the most successful tool called back propagation neural network. The
combination of TRAINLM, LEARNGDM and LOGSIG mentioned in the proposed work works
3. International Journal of Ambient Systems and Applications (IJASA) Vol.2, No.4, December 2014
13
better for relatively smaller datasets but the combination TRAINSCG LEARNGDM and
LOGSIG is successful for larger datasets.
E. Hosseini Aria, J. Amini, M.R.Saradjian(2009) proposed a method for Classifying IRS-1D
Satellite Images[4].
The fitness of Back Propagation Neural Network (BPNN) for classification of remote sensing
images based on three steps is proposed. As an initial step, from the measures of first order
histogram measures the features are extracted.In the second step, feature classification based on
BPNN is done, and in the third step the outcomes are compared with the maximum likelihood
classification (MLC) method. The statistical features in this paper depend on the first-order
distribution measure such as mean, standard-deviation, skewness, kurtosis, energy, and entropy.
The network contains 3 layers. The input layer is fed with extracted features which contains 18
neurons.in the classification of IRS-1D satellite images six classes were used and the back
ropagation neural network was trained on these classes. The whole image was classified using
this trained network. The regions of Iran are taken for testing.
The IRS-1D satellite images uses Artificial neural network for the classification of images. The
major problem with the classification of IRS data is to choose a better method for training.
TrainLM method has been implemented on using back propagation neural networks algorithm on
IRS images.
There are various training algorithms for feed forward networks The gradient of the performance
function is used by all the algorithms to find out how to fiddle with the weights to decrease the
performance. The back propagation technique determines the gradient. This gradient performs
computational backwards through the network. When the maximum likelihood method was
compared with backpropagation neural network method, the BPNN was more accurate than
maximum likelihood method.The overall preciseness in MLC method is 75.00% whereas in
BPNN method is 85.19%.
Helena Grip, Fredrik(2003) proposed Whiplash-Associated Disorders by classifying of neck
movement patterns[5].
A novel method for the classification of neck movement patterns related to Whiplash-associated
disorders (WAD) using a flexible back propagation neural network (BPNN) is studied. WAD is a
common diagnosis after neck trauma, mainly caused by rear-ends car accidents. Since physical
injuries cannot be detected with the current imaging techniques, the diagnosis can be complex to
make. The dynamic range of the neck is often visually detected in patients with neck pain, but this
is a biased measure, and a more intentional decision support system, that gives a consistent and
more complete analysis of neck movement pattern, is needed. The estimation of the prognostic
ability of a BPNN, using neck movement variables as input is the main objective of the paper.
The collection of three-dimensional (3-D) neck movement data from 59 subjects with WAD and
56 control subjects is made with a proReflex system. Rotation angle and angular velocity were
measured using the direct helical axis method and motion variables and the results are extracted.
To increase the performance of BPNN a principal component analysis was performed which
reduces data. BPNNs with six hidden nodes had a yield of 0.89, a sensitivity of 0.90 and a
specificity of 0.88, which are very hopeful results. The results were predicted from the neck
movement analysis. The result was combined with a neural network where the origin of decision
support system is constructed, which classifies the suspected WAD.
4. International Journal of Ambient Systems and Applications (IJASA) Vol.2, No.4, December 2014
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The flexible back propagation neural network (BPNN) resulted in a correct calculation for 84
percent of the control subjects and 89 percent of the WAD, showing that a BPNN could be
appropriate for predicting motion characteristics. The presented method is very hopeful as an aid
to determine whether a patient with suspected WAD has a neck movement pattern that deviates
from that found in control subjects. A few perceptive variables seems to amplify the efficiency on
abbreviating the results . A high predictive ability with stable and well functioning BPNN is
presented using early stopping method.
Yu-guo Wang, Hua-peng Li(2010) proposed classification of Remote sensing images using
artificial neural network[6].
Artificial neural network (ANN) is a significant part of artificial intelligence, It has been
extensively used in the research field of remote sensing classification. The wetlands remote
sensing classification based on ANN is complicated, because of the intricate feature of wetlands
areas. The remote sensing image supervised classification is carried out on the training samples.
The clarity is examined and it was found that it is hard to guarantee because it will have an effect
on the classification results.This article proposed a method for sample purification to filter the
training samples based on statistical analysis theory for enhanced wetlands remote sensing
classification based on ANN. The BP ANN with a nonlinear mapping function gives better
classification results for intricate areas. The TM image of Honghe Wetlands National Nature
Reserve is chosen for classification. First, the statistical analysis theory to eradicate noise in
training samples is used. Then the original samples and purified samples are used to train the BP
ANN individually and created two classification maps of TM image based on two trained BP
ANN. At last, the classification accuracy between the two maps is compared. The statistical
analysis method for purifying training samples for remote sensing classification based on BP
ANN is performed. The experiments showed that it was an efficient method to develop image
classification.
Guoqiang Peter Zhang(2000) proposed the use of harmony search and back propagation
based ANN to classify breast cancer data.
Breast cancer is one of the most commonly seen cancer types. Analysis of breast cells
characteristics has great importance in diagnosis, treatment and following of this disease.
Through two different types of ANN algorithm 699 instances of breast cancer data which is
present in UCI are classified. In this study, abundant algorithms are used for training of artificial
neural network. In this study, two algorithms namely harmony search and back propagation
algorithms are used to train feed forward artificial neural network. While classifying the
performance values of classification are found by means of Accuracy/SSE/Regression
parameters. The performance values of back propagation are achieved as 94.1/0.007/0.92 whereas
the results obtained by the harmony search algorithm are 97.57/0.005/0.96 respectively. With the
help of harmony search based ANN algorithm breast cancer data are classified for the first time
with this study.
2.2 MULTILAYER FEEDFORWARD NETWORK
Guoqiang Peter Zhang(2000) proposed Neural Networks for Classification[7].
Classification is one of the most dynamic exploratory and application areas of neural network.
The issues of posterior probability estimation, link between neural and conventional classifiers,
learning and generalization tradeoff in classification, feature variable selection, effect of
misclassification costs are studied.
5. International Journal of Ambient Systems and Applications (IJASA) Vol.2, No.4, December 2014
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Multilayer feed-forward network is used for classification. Although many procedures are
available in traditional statistical classification, the usefulness depends on assumptions and
circumstances. Inorder to overcome the above disadvantage, neural networks are used since they
are,
• self-adaptive methods,
• universal functional approximators and
• non-linear models.
Many problems still stay unsettled even though neural networks have grown rapidly. As
represented prior, many researches should be dedicated to develop more successful and proficient
methods in neural model identification, feature variable selection, classifier combination, and
uneven misclassification treatment. As a practical decision making tool, systematic evaluation
and comparison of neural networks with other traditional classifiers need to be done. The lacks of
comparisons between other classifying methods and neural network have been proposed by some
authors in the existing literature. Due to the lack of comparisons, mixed results are produced and
reported in pragmatic studies.
There are other topics which are related to neural classification. The topics include sample size
issues, Bayesian analysis, wavelet network and network training model design and selection.
Some applications of neural network have been reviewed formerly and the problems mentioned
above are general to all applications. Abundant research opportunities are available in neural
classifiers. More research actions are generated by neural network classification because of its
multidisciplinary nature and brings about more productive outcomes in the future.
Abid Ali, Olaf Magnor and Matthias Schultalbers proposed Neural Network Based Pattern
Recognition Technique which detects the misfire in gasoline engines[9]
The application of ANN to misfire detection in gasoline engines is studied. A feed-forward
multiple layer neural network is used for the classification of firing and misfiring events.
Importance is given on the transaction between performance, computational cost and implement
ability and the system is applied on a production electronic control unit (ECU). The technique is
used to identify misfire events over the whole range of operation defined by official on-board
diagnosis (OBD) regulations and it is applied to a six-cylinder gasoline engine. Experimental
results on a passenger car are presented.
Misfire occurs because of some error in the combustion process. This error occurs because of
some insufficiency in ignition, injection or compression system of the engine or due to a
substandard fuel. In addition to their disagreeable effects on the drive quality, misfires in gasoline
engines amplify pollutants in exhaust gases and causes harm to the components. Misfire due to
some defect in ignition system will enlarge unburned mixture of hydrocarbons and oxygen in the
raw exhausts causing an increased exothermic reaction in the catalyst and increased HC and CO
emissions.
The investigation of realistic application of a neural network based pattern recognition technique
to misfire detection in gasoline engines is done. The difficulty of misfire detection is framed as a
pattern recognition problem. The classification of firing and misfiring events is done with the help
of feed-forward multiple-layer neural network. To detect the misfire events in six-cylinder
gasoline engines, the developed technique is applied over a wide range of operation. The results
6. International Journal of Ambient Systems and Applications (IJASA) Vol.2, No.4, December 2014
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are produced in this section. The results show that the developed technique detects all patters of
misfire.
R. Furferi, L. Governi and Y. Volpe(2011) proposed the classification of car seat fabrics[10].
Car seat fabrics are exclusively shaped textiles. A first-rate car seat fabric needs a little quantity
of spots and discolored areas. There are large and low number of spots in the fabrics which
characterizes them. The above mentioned fabric types are considered to be of poor quality. Most
of them is branded by a sponged-like appearance, characterized by spots and slightly discolored
areas. The human experts with the help of manual inspection and classification perform sponged-
like fabric grading. Though this manual classification and inspection proves to be valuable in
fabric grading, the procedure is skewed and its results may differ depending on the operator skills.
The computer based tool which classifies sponged-like fabrics similar to the classification
performed by skilled operators is shown here. Such a tool which is developed by an oppositely
devised machine vision system, is able to extract a lot of numerical parameters characterizing the
fabric veins and discolored areas. Since the Artificial Neural Network has to be trained, the
parameters are used which classifies the fabrics based on quality. At last, a relationship between
the ANN-based classification and the classification provided by fabric inspectors is done. The
validation set composed by 65 sponged-like fabrics is tested and it shows that it is able to classify
the fabrics into the correct quality class in 93.8% of the cases, similar to the selection provided by
human operators.
With the aim of training a ANN able to correctly classify the fabrics, a series of parameters
describing their appearance have been extracted by means of image processing-based algorithms;
such data are used as a training, validation and testing set for the ANN. In detail, as described
below, three kinds of parameters are extracted: colorimetric data, entropy curve and area
subtended to the entropy curve.
Most of the methods evaluates the classification performance by defining a dimensionless
parameter. A comparison between those methods and the proposed method was provided. For
instance, the correlation between measured and forecasted classifications of colored textiles is
stated to be in the range 0.85 – 0.98. The classification error of textured objects is less than 10%.
These results are comparable with the ones provided by the present work, thus allowing to state
that a satisfactory performance has been obtained.
3. PROBLEMS IN THE EXISTING SYSTEM
The major disadvantage in using ANN is to find the most appropriate grouping of training,
learning and transfer function for classifying the data sets with growing number of features and
classified sets. The classification of IRS-1D satellite images is done using Artificial Neural
Networks. The algorithm is used to categorize the imagery. The major problem with the
classification of IRS data is to choose a better method for training. Abundant algorithms are used
for training of artificial neural network. . Although many procedures are available in traditional
statistical classification, the usefulness depends on assumptions and circumstances. In order to
overcome the above disadvantage, neural networks are used.
7. International Journal of Ambient Systems and Applications (IJASA) Vol.2, No.4, December 2014
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Table1 Comparison of various classification methods based on artificial neural network
SNO Mechanism Method used Author Advantages Disadvantages
1 Neural network
for classification
Probability
estimation for
Posterior
Peter Zhang
Guoqiang
The important
aspects is solving
classification
problems are
discussed
lack of the
rigorous
2 ANN for dataset
classification
Backpropagation
algorithm
Rojalina
Priyadarshini
The best
combination of
training, learning
and transfer
function for
classification of
datasets is found
The combination
doesn’t worked
for larger
datasets
3 Classification of
car seat fabrics
Computer based
tool is used
R.Furferi, L.
Governi
Classified the
fabrics in 93.8%
of the cases
Result varies
from operator to
operator
4 Classification of
IRS-1D satellite
images
Backpropagation
algorithm
E. Hosseini
Aria, J. Amini,
M.R.Saradjian
BPNN is more
accurate
Maximum
likelihood
classification is
less accurate
than BPNN
5 Classification of
neck movement
patterns
Backpropagation
algorithm
Helena Grip,
Fredrik Öhberg
the predictive
ability of a BPNN,
using neck
movement
variables as input
is analysed and it
produced the
result with 90%
accuracy
data
concerning age,
proprioception,
muscle activity
are not discussed
6 Misfire detection
using NN
Feed-forward
multiple layer NN
Abid Ali, Olaf
Magnor
It detects all
patterns of misfire
potential
implementation
factors, like
fixed-point
operations,
quantization are
not discussed
7 Remote sensing
image
classification
Backpropagation
algorithm
Yu-guo Wang,
Hua-peng Li
BP ANN with a
mapping function
is used and the
image
classification is
improved
Not suitable for
wide spectral
distribution
8 Gene Expression
Data
Classification
Samples filtering Wutao
Chen,Huijuan
Lu,Mingyi
Wang
The result is more
accurate and stable
It is unstable for
single neural
network
8. International Journal of Ambient Systems and Applications (IJASA) Vol.2, No.4, December 2014
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4. CONCULSION
Artificial neural networks (ANN) say that the most active research and application an area is
classification.The neural network was trained by back propagation algorithm. The dataset
classification uses the most effective tool called back propagation neural network. The usage of
Back Propagation Neural Network (BPNN) for classifying the images in remote sensing
technology is also examined. When the maximum likelihood method was compared with
backpropagation neural network method, the BPNN was more accurate than maximum likelihood
method. The classification of neck movement patterns related to Whiplash-associated disorders
(WAD) uses a resilient back propagation neural network (BPNN). The resilient back propagation
neural network (BPNN) resulted in a correct prediction for 84 percent of the control subjects and
89 percent of the WAD, showing that a BPNN could be suitable for predicting motion
characteristics. The statistical analysis method for purifying training samples for remote sensing
classification based on BP ANN is performed. The experiments showed that it was an effective
method to improve image classification. Analyses of breast cells characteristics have great
importance in diagnosis, treatment and following of this disease. In this study, classification of
699 instances of breast cancer data that is available in UCI is performed through artificial neural
network algorithm. Multilayer feed-forward neural network algorithm is also used for
classification, application of misfire detection in gasoline engines, classification of car seat
fabrics etc. however BPNN proves to be more effective than other classification algorithms.
REFERENCES
[1] Rojalina Priyadarshini; “Functional Analysis of Artificial Neural Network for Dataset Classification”,
Special Issue of IJCCT Vol. 1 Issue 2, 3, 4; 2010 for International Conference [ACCTA-2010], 3-5
August 2010.
[2] Guoqiang Peter Zhang “Neural Network for Classification- A Survey 2000” IEEE Transactions on
systems, man and cybermetics- part c: applications and reviews, Vol 30,
[3] R.Furferi, L. Governi; “Neural Network based classification of car seat fabrics” International Journal
of Mathematical Models and Methods in Applied Sciences; Issues 3, Vol. 5, 2011.
[4] E. Hosseini Aria, J. Amini, M.R.Saradjian, “Back Propagation Neural Network for Classification of
IRS-1D Satellite Images” Vol.1, Issue.2, 2003
[5] Helena Grip, Fredrik Öhberg, Urban Wiklund, Ylva Sterner, J. Stefan Karlsson, and Björn Gerdle,
“Classification of Neck Movement Patterns Related to Whiplash-Associated Disorders Using Neural
Networks”, IEEE transactions on information technology in biomedicine, Vol.7, Issue.4,2003.
[6] Yu-guo Wang, Hua-peng Li ,”Remote sensing image classification based on artificial neural network
“,International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE)
, Vol.1, Issue.2, 2010.
[7] Guoqiang Peter Zhang, “Classification of Breast Cancer Data with Harmony Search and Back
Propagation Based Artificial Neural Network”, IEEE 22nd Signal Processing and Communications
Applications Conference, 2014.
[8] Guoqiang Peter Zhang , “Neural Networks for Classification: A Survey “,IEEE transactions on
systems, man, and cybernetics , Vol.30, Issue.4, 2000.
[9] Abid Ali, Olaf Magnor and Matthias Schultalbers , “Misfire Detection Using a Neural Network Based
Pattern Recognition “,International Conference on Artificial Intelligence and Computational
Intelligence “, Vol.2, Issue.3, 2009.
[10] R. Furferi, L. Governi and Y. Volpe, “Neural Network based classification of car seat fabrics”,
International journal of mathematical models and methods in applied sciences, Vol.3, Issue.5, 2011.