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This article offers a general approach to developing methods of determining operation tolerances for the parameters' values of memristor-based artificial neural networks (ANNM), as a system that constitutes an united physical and informational object implemented by the hardware and software learning facilities. While looking for a solution to the issues of analysis and synthesis of this system's tolerances,...
The growing interests in multi-way data analysis have made the tensor factorization and classification a crucial issue in machine learning for signal processing. Conventional neural network (NN) classifier is estimated from a set of input vectors. The multi-way data are unfolded as high-dimensional vectors for model training. The classification performance is constrained because the neighboring temporal...
Artificial neural networks have been investigated for many years as a technique for automated diagnosis of defects causing partial discharge (PD). While good levels of accuracy have been reported, disadvantages include the difficulty of explaining results, and the need to hand-craft appropriate features for standard two-layer networks. Recent advances in the design and training of deep neural networks,...
Extreme learning machine (ELM) is an efficient learning algorithm which can be easily used with least human intervene. But when ELM is applied as multiclass classifier, the results of some classes are not satisfactory and it's hard to adjust the parameters for these classes without affecting other classes. To overcome these limitations, a novel method is proposed. In proposed approach, binary ELM...
In this paper, a Neural Network Deployment (NND) algorithm is presented to realize and synthesize Multi-Valued Logic (MVL) functions. The algorithm is combined with back-propagation learning capability and MVL operators. The operators are used to synthesize the functions. Consequently the synthesized expressions are applied by the MVL neural operators. The advantages of NND-MVL algorithm are demonstrated...
Remote Sensing is widely used for mapping of land cover and land use. Classification of image satellites is also done by using these mapping. In this paper the classifier proposed is the Probabilistic based Neural Network developed using MATLAB. The data for image classification is acquired over various parts of Mumbai region which is LISS-III. Probabilistic based neural network is a supervised classification...
Text-to-phoneme mapping is a very important preliminary step in any text-to-speech synthesis system. In this paper, we study the performances of the multilayer perceptron (MLP) neural network for the problem of text-to-phoneme mapping. Specifically, we study the influence of the input letter encoding in the conversion accuracy of such system. We show, that for large network complexities the orthogonal...
Anomalous traffic detection on internet is a major issue of security as per the growth of smart devices and this technology. Several attacks are affecting the systems and deteriorate its computing performance. Intrusion detection system is one of the techniques, which helps to determine the system security, by alarming when intrusion is detected. In this paper performance of NSL-KDD dataset is evaluated...
Research into deep learning has demonstrated performance competitive with humans on some visual tasks, however, these systems have been primarily trained through supervised and unsupervised learning algorithms. Alternatively, research is showing that evolution may have a significant role in the development of visual systems. Thus neuroevolution for deep learning is investigated in this paper. In particular,...
The brain-inspired neural networks have demonstrated great potential in big data analysis. The spiking neural network (SNN), which encodes the real world data into spike trains, promises great performance in computational ability and energy efficiency. Moreover, it is much more biologically plausible than the traditional artificial neural network (ANN), which keeps the input data in its original form...
Surgical removal of bladder, i.e. radical cystectomy, is a standard treatment option for muscle invasive bladder cancer. Unfortunately, the treatment is associated with significant morbidities and mortalities. Many studies have been conducted to predict the morbidities and mortalities of radical cystectomy based on statistical analysis. In this paper, an artificial neural network is employed to predict...
Gender recognition has important applications in apparel design, social security, and human-computer interaction systems. In this paper, we investigate gender-recognition technologies using 3-D human body shape. The front and side silhouettes from 459 female subjects and 107 male subjects were extracted and then modeled using normalized Elliptic Fourier descriptors. Principal Component Analysis (PCA)...
The design of Artificial Neural Network (ANN) is a typical task as it is depends on human experience. There are few techniques like the Back-Propagation algorithm and nature inspired meta-heuristic are one of the most widely used and popular technique for optimizing feed forward neural network training. Artificial Bee Colony (ABC) algorithm is nature inspired meta-heuristic approach based on behavior...
In this paper, a new method for music symbol classification named Combined Neural Network (CNN) is proposed. Tests are conducted on more than 9000 music symbols from both real and scanned music sheets, which show that the proposed technique offers superior classification capability. At the same time, the performance of the new network is compared with the single Neural Network (NN) classifier using...
The use of wind energy has developed significantly worldwide. Wind power is the strongest growing form of renewable energy, ideal for a future with pollution-free electric power. But the intermittent nature of wind makes it difficult to forecast. The researchers have embarked on use of the Multi-Layered Perceptrons (MLP) neural networks and other architectures of neural networks for predicting the...
Credit scoring is always a hot topic for the researchers because of its profitability. In this paper, we proposed a novel data-distribution based imbalanced data classification method to construct the credit scoring model using BP neural networks. The method distinguished itself by focusing on the distribution of the data and artificially changes the probabilities of the sampling for the purpose of...
Hybridization of neural networks and fuzzy sets has proved its efficiency in solving different pattern classification tasks, which led to the development of granular neural networks (GNNs). GNN works with the principles of granular computing and basically operates on granules of information. The present paper proposes an efficient multiple classifier system (MCS) framework with different guiding rules...
Classification is a rather omnipresent problem in many of the technological areas ranging from image processing to medical applications. With complex-valued neural network classifiers posing better decision making capabilities due to its orthogonal decision boundaries and it's comparatively better computational capability many complex valued neural network (CVNN) classifiers has been presented in...
Prediction of protein structural class has been a new area of research in the scientific community in the last decade. Various approaches has been adopted and analysed. However representing the raw amino acid sequence to preserve the property of proteins has posed a great challenge. Chou's pseudo amino acid composition feature representation method has fetched wide attention in this regard. In Chou's...
Modeling an ultra-wideband (UWB) channel is an important and challenging task in wireless communications. Modeling a channel in an underground mine environment presents additional challenges and difficulties. Many researchers and techniques have treated this subject. In this paper we will present a new approach in modeling the channel in an underground mine by using artificial neural networks (ANN)...
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