scholarly journals An Overview on Neural Network and Its Application

2021 ◽  
Vol 9 (8) ◽  
pp. 1242-1248
Author(s):  
Subhash Kumar Sharma
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Nervous System ◽  
Hedge Fund ◽  
The Body ◽  
Biological Model ◽  
Axon Terminals ◽  
Marketing Segmentation ◽  
Business Applications ◽  
Network Functions

Abstract: In this paper an overview on neural network and its application is focused. In Real-world business applications for neural networks are booming. In some cases, NNs have already become the method of choice for businesses that use hedge fund analytics, marketing segmentation, and fraud detection. Here are some neural network innovators who are changing the business landscape. Here shown that how the biological model of neural network functions, all mammalian brains consist of interconnected neurons that transmit electrochemical signals. Neurons have several components: the body, which includes a nucleus and dendrites; axons, which connect to other cells; and axon terminals or synapses, which transmit information or stimuli from one neuron to another. Combined, this unit carries out communication and integration functions in the nervous system. Keywords: Neurons, neural network, biological model of neural network functions

Application of various optimization methods in calculating the error of a neural network for diagnosing parasitological diseases of the gastrointestinal tract

2021 ◽  
Author(s):  
N.T. Abdullaev ◽  
U.N. Musevi ◽  
K.S. Pashaeva
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Gastrointestinal Tract ◽  
Optimization Methods ◽  
Optimization Method ◽  
Medical Diagnostics ◽  
The Body ◽  
Conjugate Gradients ◽  
Practical Significance ◽  
Parasitic Diseases

Formulation of the problem. This work is devoted to the use of artificial neural networks for diagnosing the functional state of the gastrointestinal tract caused by the influence of parasites in the body. For the experiment, 24 symptoms were selected, the number of which can be increased, and 9 most common diseases. The coincidence of neural network diagnostics with classical medical diagnostics for a specific disease is shown. The purpose of the work is to compare the neural networks in terms of their performance after describing the methods of preprocessing, isolating symptoms and classifying parasitic diseases of the gastrointestinal tract. Computer implementation of the experiment was carried out in the NeuroPro 0.25 software environment and optimization methods were chosen for training the network: "gradient descent" modified by Par Tan, "conjugate gradients", BFGS. Results. The results of forecasting using a multilayer perceptron using the above optimization methods are presented. To compare optimization methods, we used the values of the minimum and maximum network errors. Comparison of optimization methods using network errors makes it possible to draw the correct conclusion that for the task at hand, the best results were obtained when using the "conjugate gradients" optimization method. Practical significance. The proposed approach facilitates the work of the experimenter-doctor in choosing the optimization method when working with neural networks for the problem of diagnosing parasitic diseases of the gastrointestinal tract from the point of view of assessing the network error.

ELECTROCARDIOGRAM ARRHYTHMIA PATTERN RECOGNITION BASED ON AN IMPROVED WAVELET NEURAL NETWORK

2013 ◽  
Vol 13 (01) ◽  
pp. 1350018 ◽  
Author(s):  
GUANGYING YANG
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Wavelet Neural Network ◽  
The Body ◽  
Support Vector ◽  
Ventricular Premature Beat ◽  
Ecg Signal ◽  
Data Set ◽  
Bundle Branch Block

Electrocardiography (ECG) is a transthoracic interpretation of the electrical activity of the heart over a period of time, as detected by electrodes attached to the outer surface of the skin and recorded by a device external to the body. ECG signal classification is very important for the clinical detection of arrhythmia. This paper presents an application of an improved wavelet neural network structure to the classification of the ECG beats, because of the high precision and fast learning rate. Feature extraction method in this paper is wavelet transform. Our experimental data set is taken from the MIT-BIH arrhythmia database. The correct detection rate of QRS wave is 95% by testing the data of MIT-BIH database. The proposed methods are applied to a large number of ECG signals consisting of 600 training samples and 120 test samples from the MIT-BIH database. The samples equally represent six different ECG signal types, including normal beat, atrial premature beat, ventricular premature beat, left bundle branch block, right bundle branch block and paced beat. In comparison with pattern recognition methods of BP neural networks, RBF neural networks and Support Vector Machines (SVM), the results in this experiment prove that the wavelet neural network method has a better recognition rate when classifying electrocardiogram signals. The experimental results prove that supposed method in this paper is effective for arrhythmia pattern recognition field.

Fuzzy wavelet neural networks applied as inferential sensors of neonatal incubator dynamics

2020 ◽  
Vol 39 (3) ◽  
pp. 2567-2579
Author(s):  
José M. Araújo Júnior ◽  
Leandro L.S. Linhares ◽  
Fábio M.U. Araújo ◽  
Otacílio M. Almeida
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Inference ◽  
Good Health ◽  
Great Difficulty ◽  
Neural System ◽  
Wavelet Neural Network ◽  
The Body ◽  
Inference System ◽  
Health Complications

Newborns with health complications have great difficulty in regulating the body temperature due to distinct factors, which include the high metabolism rate and low weight. In this context, neonatal incubators help maintaining good health conditions because they provide a thermally-neutral environment, which is adequate to ensure the least energy expenditure by the newborn. In the last decades, artificial neural networks (ANNs) have been established as one of the main tools for the identification of nonlinear systems. Among the various approaches used in the identification process, the fuzzy wavelet neural network (FWNN) can be regarded as a prominent technique, consisting of the combination of wavelet neural network (WNN) and adaptive network-based fuzzy inference system (ANFIS). This work proposes the use of FWNN to infer the temperature and humidity values inside the incubator in order to certify the equipment operation. Results obtained with the analyzed neural system have shown the generalization and inference capacities of FWNNs, thus allowing their application to practical tasks aiming to increase the efficiency of incubators.

scholarly journals CONCEPTION OF A HYBRID AD APTIVE PROTECTIONOF INFORMATION SYSTEMS

2014 ◽  
pp. 86-98
Author(s):  
Igor V. Kotenko ◽  
Filipp G. Nesteruk ◽  
Andrey V. Shorov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Nervous System ◽  
Information Systems ◽  
Formation Processes ◽  
Distributed Information ◽  
Telecommunication Systems ◽  
Software Packages ◽  
Processing Information ◽  
Network Software

The paper suggests the conception of a hybrid adaptive protection of information and telecommunication systems which is based on a biometaphor of nervous and neural networks. A top level of a protection system, based on an approach of “nervous system network” is a distributed mechanism for collecting and processing information. We suggest to implement the in formation processes on the low level with the assistance of an “information field” programming. It allows specifying the distributed information fields in the form of neural network software packages.

Non-symbolic approaches to artificial intelligence and the mind

1994 ◽  
Vol 349 (1689) ◽  
pp. 87-102 ◽  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Nervous System ◽  
Computational Neuroscience ◽  
External World ◽  
The Mind ◽  
Symbolic Approach ◽  
Different Levels ◽  
New Algorithms

Current theories of artificial intelligence and the mind are dominated by the notion that thinking involves the manipulation of symbols. The symbols are intended to have a specific semantics in the sense that they represent concepts referring to objects in the external world and they conform to a syntax, being operated on by specific rules. I describe three alternative, non-symbolic approaches, each with a different emphasis but all using the same underlying computational model. This is a network of interacting computing units, a unit representing a nerve cell to a greater or lesser degree of fidelity in the different approaches. Computational neuroscience emphasizes the development and functioning of the nervous system; the approach of neural networks examines new algorithms for specific applications in, for example, pattern recognition and classification; according to the sub-symbolic approach , concepts are built up of entities called sub-symbols, which are the activities of individual processing units in a neural network. A frequently debated question is whether theories formulated at the subsymbolic level are ‘mere implementations’ of symbolic ones. I describe recent work due to Foster, who proposes that it is valid to view a system at many different levels of description and that, whereas any theory may have many different implementations, in general sub-symbolic theories may not be implementations of symbolic ones.

scholarly journals Spatially and Spectrally Concatenated Neural Networks for Efficient Lossless Compression of Hyperspectral Imagery

2020 ◽  
Vol 6 (6) ◽  
pp. 38
Author(s):  
Zhuocheng Jiang ◽  
W. David Pan ◽  
Hongda Shen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
State Of The Art ◽  
Lossless Compression ◽  
Low Complexity ◽  
Hyperspectral Images ◽  
Value Prediction ◽  
Pixel Value ◽  
Network Functions ◽  
Predictive Filtering

To achieve efficient lossless compression of hyperspectral images, we design a concatenated neural network, which is capable of extracting both spatial and spectral correlations for accurate pixel value prediction. Unlike conventional neural network based methods in the literature, the proposed neural network functions as an adaptive filter, thereby eliminating the need for pre-training using decompressed data. To meet the demand for low-complexity onboard processing, we use a shallow network with only two hidden layers for efficient feature extraction and predictive filtering. Extensive simulations on commonly used hyperspectral datasets and the standard CCSDS test datasets show that the proposed approach attains significant improvements over several other state-of-the-art methods, including standard compressors such as ESA, CCSDS-122, and CCSDS-123.

Classification and enhancement of invasive ductal carcinoma samples using convolutional neural networks

2021 ◽  
pp. 1-9
Author(s):  
Edgar E. Sierra-Enriquez ◽  
José E. Valdez-Rodríguez ◽  
Edgardo M. Felipe-Riveró ◽  
Hiram Calvo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma ◽  
The Body ◽  
Carcinoma Cells ◽  
Breast Cancers ◽  
Spatial Dimensions ◽  
Long Time ◽  
Histogram Stretching

In the medical area, the detection of invasive ductal carcinoma is the most common sub-type of all breast cancers; about 80% of all breast cancers are invasive ductal carcinomas. Detection of this type of cancer shows a great challenge for specialist doctors since the digital images of the sample must be analyzed by sections because the spatial dimensions of this kind of image are above 50k × 50k pixels; doing this operation manually takes long time to determine if the patient suffers this type of cancer. Time is essential for the patient because this cancer can invade quickly other parts of the body. Its name reaffirms this characteristic, with the term "invasive" forming part of its name. With the purpose of solving this task, we propose an automatic methodology consisting in improving the performance of a convolutional neural network that classifies images containing invasive ductal carcinoma cells by highlighting cancer cells using several preprocessing methods such as histogram stretching and contrast enhancement. In this way, characteristics of the sub-images are extracted from the panoramic sample and it is possible to learn to classify them in a better way.

Contemporary concept about organization of central nervous system: human connectome and neural networks

2021 ◽  
pp. 42-47
Author(s):  
I. V. Damulin ◽  
A. A. Strutzenko
Keyword(s):  
Neural Networks ◽  
Central Nervous System ◽  
Nervous System ◽  
Functional Neuroimaging ◽  
The State ◽  
The Body ◽  
Adaptive Plasticity ◽  
Functional Connections ◽  
Pathological Conditions ◽  
The Brain

The aim. To systematize contemporary concept about the structural and functional organization of the central nervous system (CNS) and the importance of developing the concept of the human connectome.Main concepts. Signifcant progress in understanding the organization of the CNS in normal and in various pathological conditions was achieved after the introduction of structural and functional neuroimaging methods frst into scientifc and then into clinical practice. Recently, when studying the neuropsychiatric sphere, special attention has been paid to neural networks. One of the achievements in this feld is the construction of the human connectome – a system of structural and functional connections between various cerebral areas, the state of which is assessed using multimodal methods of functional neuroimaging. Thus, the development of brain sciences has reached a completely different level – the level of systemic psychoneurology, when the existing processes are analyzed comprehensively, with the involvement of specialists in various felds – neurology, psychiatry, neuroimaging, mathematics, etc. The human connectome is basically a biological system, therefore, although the analogy with artifcial intelligence can be traced, it does not take the frst place. The functioning of the human connectome is based on the principle of parallel, rather than sequential, information processing. Taking into account the inherent ability of the brain (at least, some of its areas) to generate spontaneous non-rhythmic oscillations, this leads to the implementation of the basic principle of the functioning of the CNS – minimizing energy consumption. In addition, the presence of spontaneous non-rhythmic oscillations (the principle of uncertainty) probably underlies the inherent human ability to intuitively think, develop new ideas. The state of the connectome in a rest is determined by past experience, the duration of external influences, and age. It affects the nature and severity of neuroplastic processes, as well as, in particular, the effectiveness of certain pharmacological drugs in a given individual. At the same time, the fnal result of neuroplastic changes may be of a different nature. It can be favorable for the body (the so-called adaptive plasticity), do not affect the body in any way, or even have a negative result (the so-called maladaptive neuroplasticity). In children, such maladaptive manifestations are less pronounced. Currently, hardware methods of influencing the connectome are being actively studied. For example, it was shown that the structure of the connectome in a rest state can change after transcranial magnetic stimulation. Further studies of this problem will open up new opportunities for studying the activity of such a complexly organized system as the brain – in normal and in various pathological conditions – and to develop more effective methods of neurorehabilitation.

scholarly journals Connections

2020 ◽  
Vol 2 (3(September-December)) ◽  
pp. e642020
Author(s):  
Ricardo Santos De Oliveira
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Nervous System ◽  
Human Brain ◽  
Nerve Cells ◽  
Seer Database ◽  
Biological Neural Network ◽  
Artificial Neural ◽  
The Brain

The human brain contains around 86 billion nerve cells and about as many glial cells [1]. In addition, there are about 100 trillion connections between the nerve cells alone. While mapping all the connections of a human brain remains out of reach, scientists have started to address the problem on a smaller scale. The term artificial neural networks (ANNs or simply neural networks (NNs), encompassing a family of nonlinear computational methods that, at least in the early stage of their development, were inspired by the functioning of the human brain. Indeed, the first ANNs were nothing more than integrated circuits devised to reproduce and understand the transmission of nerve stimuli and signals in the human central nervous system [2]. The correct way of doing it is to the first study human behavior. The human brain has a biological neural network that has billions of interconnections. As the brain learns, these connections are either formed, changed or removed, similar to how an artificial neural network adjusts its weights to account for a new training example. This complexity is the reason why it is said that practice makes one perfect since a greater number of learning instances allow the biological neural network to become better at whatever it is doing. Depending upon the stimulus, only a certain subset of neurons are activated in the nervous system. Recently, Moreau et al., [3] published an interesting paper studying how artificial intelligence can help doctors and patients with meningiomas make better treatment decisions, according to a new study. They demonstrated that their models were capable of predicting meaningful individual-specific clinical outcome variables and show good generalizability across the Surveillance, Epidemiology, and End Results (SEER) database to predict meningioma malignancy and survival after specific treatments. Statistical learning models were trained and validated on 62,844 patients from the SEER database and a model scoring for the malignancy model was performed using a series of metrics. A free smartphone and web application were also provided for readers to access and test the predictive models (www.meningioma.app). The use of artificial intelligence techniques is gradually bringing efficient theoretical solutions to a large number of real-world clinical problems related to the brain (4). Specifically, recently, thanks to the accumulation of relevant data and the development of increasingly effective algorithms, it has been possible to significantly increase the understanding of complex brain mechanisms. The researchers' efforts are creating increasingly sophisticated and interpretable algorithms, which could favor a more intensive use of “intelligent” technologies in practical clinical contexts. Brain and machine working together will improve the power of these methods to make individual-patient predictions could lead to improved diagnosis, patient counseling, and outcomes.

scholarly journals Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: a recurrent neural network solution

PeerJ ◽  
2022 ◽  
Vol 10 ◽  
pp. e12752
Author(s):  
Ryan S. Alcantara ◽  
W. Brent Edwards ◽  
Guillaume Y. Millet ◽  
Alena M. Grabowski
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Recurrent Neural Network ◽  
Human Movement ◽  
The Body ◽  
Ground Reaction Forces ◽  
Accelerometer Data ◽  
Laboratory Environment ◽  
Reaction Forces ◽  
External Forces

Background Ground reaction forces (GRFs) are important for understanding human movement, but their measurement is generally limited to a laboratory environment. Previous studies have used neural networks to predict GRF waveforms during running from wearable device data, but these predictions are limited to the stance phase of level-ground running. A method of predicting the normal (perpendicular to running surface) GRF waveform using wearable devices across a range of running speeds and slopes could allow researchers and clinicians to predict kinetic and kinematic variables outside the laboratory environment. Purpose We sought to develop a recurrent neural network capable of predicting continuous normal (perpendicular to surface) GRFs across a range of running speeds and slopes from accelerometer data. Methods Nineteen subjects ran on a force-measuring treadmill at five slopes (0°, ±5°, ±10°) and three speeds (2.5, 3.33, 4.17 m/s) per slope with sacral- and shoe-mounted accelerometers. We then trained a recurrent neural network to predict normal GRF waveforms frame-by-frame. The predicted versus measured GRF waveforms had an average ± SD RMSE of 0.16 ± 0.04 BW and relative RMSE of 6.4 ± 1.5% across all conditions and subjects. Results The recurrent neural network predicted continuous normal GRF waveforms across a range of running speeds and slopes with greater accuracy than neural networks implemented in previous studies. This approach may facilitate predictions of biomechanical variables outside the laboratory in near real-time and improves the accuracy of quantifying and monitoring external forces experienced by the body when running.

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