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The technological process of the water distributor flow deployment has mixed features of strong non-linearity, tough interference of multilayer coupling and complex models in Water Injection Well. The traditional model-based control strategies cause many problems (e.g. system instability, high time-consuming deployment, pressure built up in pipeline), which make it difficult to meet the expected requirements...
This paper presents a novel neuro-fuzzy inference system, called RBFuzzy, capable of knowledge extraction and generation of highly interpretable Mamdani-type fuzzy rules. RBFuzzy is a four layer neuro-fuzzy inference system that takes advantage of the functional behavior of Radial Basis Function (RBF) neurons and their relationship with fuzzy inference systems. Inputs are combined in the RBF neurons...
Shadows in most color aerial sensing images with high resolutions are evident, and their existence causes the image degradation and obstructs the image interpretation. In this paper, we propose Pulse Coupled Neural Network (PCNN) algorithm to detect shadows in color aerial images. Experiments and comparisons indicate that the proposed PCNN algorithm is feasible and effective to shadow detection in...
Sign language is important for facilitating communication between hearing impaired and the rest of society. Two approaches have traditionally been used in the literature: image-based and sensor-based systems. Sensor-based systems require the user to wear electronic gloves while performing the signs. The glove includes a number of sensors detecting different hand and finger articulations. Image-based...
We present an architecture of a spike based multiclass classifier using neurons with non-linear dendrites and sparse synaptic connectivity where each synapse takes a binary value. The learning in this model happens not through weight updates but through structural changes, i.e. a change of connectivity between inputs and dendrites. Hence, it is well suited for implementation in neuromorphic systems...
Clustering methods are one of the most important tools used in different areas by researchers. The self-organizing map network is one of the most popular neural networks which was designed for solving problems that involve tasks such as clustering, visualization, and abstraction. Specially, It provides a new strategy of clustering using a competition and co-operation principal. However, the optimal...
A new method combined PCA (Principal Component Analysis) with SOM (Self-Organizing Maps) neural network is presented for clustering analysis of gene expression data. Firstly, the principal components are extracted from the genetic data set by PCA, in order to get a low dimensional data set. These principal components with lower dimension can basically express comprehensive information of original...
The Hierarchical Graph Neuron (HGN) has already been known that, it implements a single-cycle memorization and recall operation. The scheme also utilizes small response time that is insensitive to the increases in the number of stored patterns. In this improved approach, the architecture of multidimensional HGN (mHGN) is developed so, that it is not only suitable for scrutinizing 1D- or 2D-patterns;...
By combining two fast training methods, i.e., the weights-direct-determination (WDD) method and Levenberg-Marquardt method, this paper proposes a novel training algorithm called weights and structure policy (WASP) for the three-layer feedforward neuronet, in addition to the algorithm of weights and structure determination (WASD). Note that the pruning-while-growing and second-pruning techniques are...
Speech signal processing and its recognition system have gained a lot of attention from last few years due to its widespread application. In this study, we have conducted a comparative analysis for effective detection of Parkinson's disease using various machine learning classifiers from voice disorder known as dysphonia. To investigate robust detection process, three independent classifier topologies...
In the present paper we describe a recent approach of probabilistic self-organizing maps (PRSOM). The PRSOM become more and more interesting in many fields such as: pattern recognition, clustering, classification, speech recognition, data compression, medical diagnosis… The PRSOM give an estimation of the density probability function of the data, this density dependent on the parameters of the PRSOM,...
Spirometric pulmonary function test is a wellestablished test in clinical medicine for the assessment of respiratory diseases. It measures the volume of air inhaled or exhaled as a function of time during forced breathing maneuvers and generates large data set. However, spirometric investigation is often prone to incomplete data sets due to inability of the children and patient to perform this test...
Large variations in human actions lead to major challenges in computer vision research. Several algorithms are designed to solve the challenges. Algorithms that stand apart, help in solving the challenge in addition to performing faster and efficient manner. In this paper, we propose a human cognition inspired projection based learning for person-independent human action recognition in the H.264/AVC...
In medical field the disease diagnosis is often made based on the knowledge and experience of the medical practitioner. Due to this there are chances of errors, unwanted biases and also takes longer time in accurate diagnosis of disease. In case of heart disease, its diagnosis is most difficult task. It depends on the careful analysis of different clinical and pathological data of the patient by medical...
In this paper a new conversion technique is proposed for complex-valued neuron (CVN) to convert real value into complex value in order to solve real-valued classification problems & Time series analysis. Previously phase encoding system was used to solve these types of problems. In this proposed encoding system, each real-valued input is converted into complex value according to the input real...
Marathi is one of the ancient Indian languages majorly spoken in the state of Maharashtra. Marathi is one of the Devanagari script and the literals and numerals are almost similar to Hindi. Recognition of handwritten Marathi numerals is quite challenging task because people have the practice of writing these numerals in variant ways. In this work we have presented a method to recognize the handwritten...
Feature selection (FS) of high dimensional electroencephalographic (EEG) data helps to identify and diagnose the brain conditions easily. Features can be selected with different ways where canonical correlation analysis (CCA) is one of them which are a statistical method. We employed neural network (NN) with CCA for salient features extraction of EEG data, called Neural Canonical Correlation Analysis...
In medical field the diagnosis of heart disease is most difficult task. It depends on the careful analysis of different clinical and pathological data of the patient by medical experts, which is complicated process. Due to advancement in machine learning and information technology, the researchers and medical practitioners in large extent are interested in the development of automated system for the...
Many systems have been developed for computer analysis of the lungs in high resolution computed tomography (HRCT) scans for detection and analysis of Interstitial Lung Diseases (ILDs). This paper presents a novel approach for classification of lung tissue patterns affected with Interstitial Lung Diseases (ILDs) in high resolution computed tomography (HRCT) scans. The proposed scheme makes use of texture...
Before the routine anesthesia, an airway examination must be performed during the pre-anesthetic examination for all patients who need a surgical operation in order to decide whether the tracheal intubation is easy or hard. In the field of anesthesia and intensive care, many works have been performed in order to reduce as much as possible the anesthetic risks and the mortality rate as well as to provide...
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