Future scope lies in the use of former classifiers like SVM with the aim of having multidimensional data and making use of feature reduction algorithms, so that accuracy rate can be enhanced. SVMs bring a unique solution, since the optimality problem is rounded. This is an advantage to Artificial neural network (ANN) which has several solutions related with local minima and for this reason may not be tough over different samples. For optimization algorithms similar to artificial bee colony (ABC) and (PSO) Particle swarm optimization would be used.
Abstract — Epilepsy is a neurological disorder with prevalence of about 1-2% of the world’s population. Epilepsy is a neurological condition in which is due to chronic abnormal bursts of electrical discharge in the brain. Monitoring brain activity through the electroencephalogram (EEG) has become an important tool in the diagnosis of epilepsy. The EEG recordings of patients suffering from epilepsy show two categories of abnormal activity: inter-ictal, abnormal signals recorded between epileptic seizures; and ictal, the activity recorded during an epileptic seizure. The term EEG refers that the brain activity emits the signal from head and being drawn. It is produced by bombardment of neurons within the brain. EEG signal provides valuable information of the brain function and neurobiological disorders as it provides a visual display of the recorded waveform and allows computer aided signal processing techniques to characterize them. This gives a prime motivation to apply the advanced digital signal processing techniques for analysis of EEG signals. The main objective of our research is to analyze the acquired EEG signals using signal processing tools such as wavelet transform and classify them into diﬀerent classes. The features from the EEG are extracted using statistical analysis of parameters obtained by wavelet transform and Auto-Regressive model. Total 300 EEG data subjects were analyzed. These data were grouped in three classes’ i.e, Normal patient class, Epileptic patient class and epileptic patient during non-seizure zone respectively. In order to achieve this we have applied a backpropgation based neural network classifier. After feature extraction secondary goal is to improve the accuracy of classiﬁcation. 100 subjects from each set were analysed for feature extraction and classification and data were divided in training, testing and validation of proposed algorithm.
In this proposed system is hybrid approach for heart disease prediction using Support Vector Machine (SVM) and Artificial Neural Networks (ANN) on UCI heart disease Dataset . The main intention is to obtain high accuracy rate of prediction. This paper is designed as Section I is a brief information about healthcare problem especially in heart disease with data mining use cases are added introduction section, section II is all about existing work. Section III specifies that proposed system architecture and algorithmic introduction. Then next section specifies about the steps required for SVM and ANN algorithm. In section V Implementation details steps are summarized along with dataset in detail. Section VI is about result and conclusion. This paper proposed a new model to boost the accuracy in recognizing the pattern of heart patients. It uses the different algorithm of Classification such as SVM and ANN combined.
Accurate diagnosis of cancer plays an importance role in order to save human life. The results of the diagnosis indicate by the medical experts are mostly differentiated based on the experience of different medical experts. This problem could risk the life of the cancer patients. From the literature, it has been found that Artificial Intelligence (AI) machine learning classifiers such as an Artificial Neural Network (ANN) and Support Vector Machine (SVM) can help doctors in diagnosing cancer more precisely. Both of them have been proven to produce good performance of cancer classification accuracy. The aim of this study is to compare the performance of the ANN and SVM classifiers on four different cancer datasets. For breast cancer and liver cancer dataset, the features of the data are based on the condition of the organs which is also called as standard data while for prostate cancer and ovarian cancer; both of these datasets are in the form of gene expression data. The datasets including benign and malignant tumours is specified to classify with proposed methods. The performance of both classifiers is evaluated using four different measuring tools which are accuracy, sensitivity, specificity and Area under Curve (AUC). This research has shown that the SVM classifier can obtain good performance in classifying cancer data compare to ANN classifier.
In this experiment, our SVM and ANN classifier models was compared with KNN, to verify the performance of the proposed classifier models. Table I provides the classification results of the three methods (our SVM and ANN methodologies and KNN model). It can be seen that KNN classifier model delivered the very best specificity however their sensitivity was low, whereas our methodologies obtained the most effective sensitivity. With regards to sensitivity, the performance of our models was more than that of the compared KNN classifier models. Therefore, with the highest accuracy and the best balance between sensitivity and specificity, our SVM and ANN models greatly outperforms the compared classifier model. In  wherever features are extracted on patch regions, the models are in a position to cope with incomplete lesion objects. In our methodology, new border features are proposed and on that SVM and ANN classification models are designed. As could be seen in Table I, once compared with the system in , by using our methodology the sensitivity and accuracy were greatly improved, that could be a terribly positive outcome. With the most effective accuracy, our methodology is superior to the compared ways. Therefore, our proposed features and designed classifier are additionally highly effective on the datasets.
In the paper of 2015, the authors applied Automatic Modulation Classification (AMC) is the technique for classifying the modulation scheme of an intercepted and possibly noisy signal whose modulation scheme is unknown. Automatic modulation classification of the digital modulation type of a signal has been taking much interest in the communication areas. This is due to the advances in reconfigurable signal processing systems, especially for the application of software radio system. Ten Digitally modulated signals are considered. Channel conditions have been modelled by simulating A WGN and multipath Rayleigh fading effect. Seven key features have been used to develop the classifier. Higher order QAM signals such as 16QAM, 64QAM and 256QAM are classified using higher order statistical parameters such as moments and cumulants. Feature based Decision tree and ANN classifier have been developed and their performances are compared under varying channel conditions for SNR as low as - 5dB.An Automatic Modulation Classifier is a system that automatically identifies the modulation type of the received radio signal given that the signal exists and its parameters lie in a known range.
In  a two-stage SVM based scheme is proposed for recognition of Farsi isolated characters. After binarizing the image, the undersampled bitmaps and chain-code directional frequencies of the contour pixels are used as features. For the first stage the characters are grouped into 8 classes, and then the undersampled bitmaps feature is used to assign an input image to the class that belongs to. In the second stage the classifier are trained on those classes using this time chain-code feature in order to discriminate between the characters belonging to the same class. Using the IFHCDB database, the authors reach a recognition rate of 98% and 97%, respectively for 8-class and 32-class problems.
Both systems were presented with clean and noisy inputs, where the noise was additive Gaussian noise of zero mean and varying standard deviation (noise level),. Fig.8 shows the performance of both systems for varying noise level. As can be seen from Fig.8, the CVQ always outperforms the standard ANN system and produces higher success rates. This is true for levels of contaminating noise.
starts as a tumor but end up spreading all over the body. It uses nutrition of body for its own growth and there is no regulatory mechanism for its growth. Breast cancer has no cure once it progresses to an advanced stage. Therefore, it is very crucial to detect the breast cancer at early stage. An attempt has been made to develop a prediction model for breast cancer to find out the malignancy of a tumor using machine learning algorithms of artificial intelligence. Artificial neural network (ANN), Bayesian classifier and Multiple linear regression (MLR) were used to generate the prediction models using a dataset of 699 patients. Descriptors used to generate prediction models were uniformity of cell size, bare nuclei, bland chromatin and normal nucleoli. The percentage accuracies of generated models for ANN, Bayesian classifier and MLR were found to be 83.73%, 83.53% and 82.14% respectively. These models can potentially be useful for preliminary classification of breast cancer.
The performance of the trained model is shown in Ta- bles 2-4 for the three data sets. In the three cases shown, an overall base-calling average of 98.4% and 98.64% are achieved by ANN and PC, respectively, indicating the flexibility of the designed topologies [19,20]. Moreover, in comparison to ABI and PHRED, the currently most widely used base-calling software, in terms of deletion, insertion and substitution errors, both proposed models achieved a higher accuracy than PHRED and a compara- ble performance to that of ABI. However, ABI and PHRED base-callers were designed using thousands of chromatogram traces while the models designed in this paper used a discrete number of traces for its training and testing. This indicates the high potential of the proposed classifiers as more efficient alternative base-callers. Data acquired were divided into three sets based on the
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In image processing, the evolution of the convolution neural network (CNN) attracts the attention of various researchers. CNN based applications are achieving good results as compared to existing machine learning approaches. Glorot and Bengio  reviews pros and cons of the CNN and ANN. In this study, authors find that ANN requires more effort in training but the CNN does not requires any effort during the training. However, CNN based approach requires specific hardware support such as graphics processing units (GPU) for efficient and faster training. Apart from the technical perspective, the various vehicles have license plate written in multiple lines also. This introduces another challenge for research community to deal with. As most of the character segmentation approaches are indented to segment the single line license plates.
ANN, inspired by the human brain developed, through main links connecting to each other and each of them has its own memory that is information processing elements. ANN are modeled by inspired of biological neural networks but has a simpler structure than them. ANN system modeling is used in classification and interpretation works such as recognition and interpretation of speech recognition , handwriting recognition, fingerprint recognition, physiological signs (heart function, brain function, respiratory function, etc.) In addition to this, ANN is also used to solve complex problems such as business, industry, finance, industry, education and science fields. In this study, one of the most important and widely used models of ANN, that is, Multi-Layered Perceptron (MLP) architecture, supervised error Back-Propagation learning algorithm has been used. In all layers, sigmoid activation function is used to obtain best results. In this study, the learning rate: 0.95 and for momentum coefficient: 0.95 as the best constants were found to be.
The main purpose of ANN is to act as a classifier for various classification algorithms. The basic inspiration behind ANN is the biological neuron system in the brain of humans. How neurons in the brain of human act as a processing units and share information to generate results. Intelligent computer systems can be made on the basic of ANN also by giving sufficient amount of training to the system. The Basic Artificial Model-
In this study, the investigation has done on the student’s placement log collected from heterogeneous data sources. Two major works, such as predicting the interesting pattern for student placement opportunities and analyzing the data set using four prominent classifier models. Analysis carried out in the aspects of analyzing the performance metrics of different classifiers. Upon completion of the analysis, deep insights in classifier accuracy, error rate, validating the accuracy are assessed and presented in the discussion. The study concludes with the classifier multilayer perceptron is an efficient one on the student’s placement log dataset. In the future, the study can be moved towards finding the best optimization technique to improve the classification accuracy by Influencing the parallel processing on MLP.
In a typical Jstacs application for sequence classification, a user first chooses appropriate statis- tical models for the data of the different classes. One then combines these models to a classifier, chooses a learning principle for learning the parameters of this classifier, and learns this classifier on training data. Finally, one uses the classifier for predicting class labels for previously unseen data. For assessing the performance of the classifier, one can choose an evaluation schema like cross-validation and choose different performance measures.
On the other hand, the SVM model tends to perform well in high-dimensioned classification problems that may have over hundreds of thousands of dimensions, which is not the case of this study. In addition, the SVM model does not tend to perform well if the classes of the problem are strongly overlapping. In general, parametric models (e.g., SVM, Bayesian Network) can suffer from remembering local groupings as by their nature they summarize information in some way. ANN can usually outperform other methods if the dataset is very large and if the structure of the data is complex (e.g., they have many layers). This is an advantage for the KNN classifier which makes the least number of assumptions regarding the input data.
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This research aims to propose a hybridization method for the detection of R peaks automatically. R-peaks havethe highest amplitude in other ECG signal waveform . For the hybridization, optimization algorithms, GA and CS are used, and for the classification,SVM and NN are used. ECG signals are pre-processed by utilizing a pre-processing technique that removes unwanted signals. For high- frequency signals and for removing low-frequency signal, use of high pass filter is considered. The feature extraction algorithm is used for the extraction of pre-processed wave . GA and CS are utilized for reducing the undesired features, whereas ANN and SVM are utilized for classification and to find the normal and abnormal RR peaks.
(CAGE) . However, it used DNA sequence features as features not ChIP-Seq datasets in DEEP-FANTOM5. It used SVMs as base classifiers to train datasets from single tissues or cell lines and it used ANN as a main classifier to make the final decision combining the results of the base classifiers. It has been proved that getting the global optimum of ANN is a NP-hard problem. The simple implementing algorithm, back-propagation algorithm, is a heuristic algorithm, which is easy to trap in local optimal solution . Thus, the weakness of DEEP was obvious. In the layer of algorithm, the predicting result was not the global optimum; in the layer of datasets, it didn’t use effect- ive features in predicting eRNAs based enhancers.
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In order to meet the above stated design considerations, the general optimal design for each one of the blocks of the discrete ‗ANN‘ simulation model is done. The general optimal designs of the discrete neural network classifier blocks are realized on the basis of ‗multi-layer perceptron‘ (MLP) and ‗radial basis function‘ (RBF) type of neural network topologies. The comparative performance analysis of the general optimal designs of the discrete neural network classifier blocks based on ‗multi-layer perceptron‘ (MLP) and ‗radial basis function‘ (RBF) type of neural network architectures is done to select appropriate neural network topology. The striking generalizations, which were derived on the basis of the comparative performance analysis further resemble that the general optimum design specifications, which are determined on the basis of ‗MLP‘ network are preferred as an optimum choice over the ‗RBF‘ network for the classification and qualitative assessment task of state of degradation of insulation in three-phase ac induction motor. However, there is a further need to explore the possibility of any reduction in the size of the ‗MLP‘ network.
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Abstract—Amputation is a medical procedure that is required to cut part of or all of the extremity, i.e. upper limbs or lower limbs. In the final phase of the procedure, patients have to adapt to their new condition including the use of prostheses. Nowadays, Prosthetic hand have had a lot of improvements that enable patients to do normal activities by exploiting their myoelectric signal. This study has a goal to produce prosthetic hand that can respond to patient generating myoelectric signal. Three muscle leads (2 on muscle flexor digitorum, 1 on muscle extensor digitorum) were processed by 3 channels surface electromyography (sEMG) that contain of instrument amplifier i.e. high-pass filter, rectifier, and notch filter. Myoelectric signal is processed to extraction feature and classified by artificial neural network (ANN) that had been offline-trained before and had 21 neurons input layer, 10 neurons hidden layer, and 3 neurons output layer to detect 3 hand movements, i.e. grasping, pinch, and open grasp. ANN and prosthetic hand control was embedded on Arduino Due microcontroller so that the system could be used in stand-alone and real time mode. The results of the testing from 4 research subjects shown that the hand prostheses system had success rate of 87% – 91%.