E3S Web of Conferences 287, 03001 (2021) https://doi.org/10.1051/e3sconf/202128703001 ICPEAM2020

A View of Artificial Neural Network Models in Different Application Areas

Kumaravel ArulRaj 1,*, Muthu Karthikeyan2, and Deenadayalan Narmatha3

1Department of Mechanical Engineering, Einstein College of Engineering, Tirunelvelli, Tamilnadu, India. 2Department of Mechanical Engineering, Raja Rajeshwari College of Engineering, Bengaluru, Karnataka, India. 3Department of Electronics and Communication Engineering, Einstein College of Engineering, Tirunelvelli, Tamilnadu, India.

Abstract. Neural network is a web of million numbers of inter-connected which executes parallel processing. An Artificial neural network is a nonlinear mapping structure; an information processing pattern is stimulated by the approach as biological nervous system () process the information. It is used as a powerful tool for modeling the data in the application domains where incomplete understanding of the data relationship to be solved with the readily available trained data. The basic element for this processing pattern is the structure of the data which is the collection of densely interconnected neurons to elucidate the problems. A prominent part of these network is their adaptive nature to “learn by example” just like human substitutes “programming” in resolving the problems. Through learning process, neural net is designed for data classification and prediction where statistical techniques and regression model have been employed. This report is an overview of artificial neural networks in different application areas and it also illustrate the architecture structure formed for the applications. It also provides information about the training algorithm used for certain application.

Keywords: Artificial Neural Network and applications, data classification and prediction

1.Introduction furnished with other benefits as fault tolerance, self- organization, real time process and adaptive learning. Artificial neural network (ANN) as a branch of artificial intelligence have established in a variety of problem-solving areas. The rapid growth of research activity in neural network applications has been accompanied by advancement in the commercial use of ANNs. The concept of artificial neural network is familiarized from the biological neural network which plays a vital role in human body. The human brain comprises of million and million number of neurons that performs a task when existed with the stimulus, it produces an output into other neurons Fig.1 Artificial Neural Network Structure through synaptic connection. Similarly the artificial neurons perform a task by adding the inputs with a The main differences between Artificial Neural transfer function in hidden layer and computing the Networks and Biological Neural Networks (BNN) are total value as an output. Based on the structure of the described with some parameters as follows; net, an ANN can be single layer or multilayer that is 1. ANN has few kinds of high-speed input, feedback or hidden and output as shown in processing unit whereas BNN has simple Fig.1. In ANN architecture, training or learning is and more than 100 kinds of low speed termed as the programming for adjusting the weights. biological neurons is shown in Fig2. The learning process can be done by given weights 2. The human brain has distributed memory computed from trained data set or by fine- tuning the then integrated into processor while ANN weights automatically. Trained neural network is having localized memory separated from expertise in analyzing the information has been processor. 3. The computational method in biological

© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/). E3S Web of Conferences 287, 03001 (2021) https://doi.org/10.1051/e3sconf/202128703001 ICPEAM2020

neurons is occur in neurons, dendrites and Dase et al., presented a review of application synapses with self-learning process in a of ANN for the prediction of stock market index distributed method while in neural net which is a challenging task. ANN is capable of computing the data in sequential and calculating precise stock index and it also extract the centralized method. information from the big data thereby it predicts to buy or sell or hold the stock market shares. [4] 4. The reliability of the biological network is Vishnu et al., learned that neural network robust and in ANN is vulnerable. need not be reprogrammed. When the processing 5. The operating environment is element of the neural net fails, the network can poorly defined and unconstrained in continue its function by parallel processing. ANN is biological network and for neural net is well more prevalent in the prediction of outcome from the defined environment. process parameters by appropriately training the data. 6. Error removal capability of human brain is [5] stronger than an ANN by recognizing the Mitali et al., briefly explained the prediction error easily. technique in neural network. This paper reports the 7. ANN knowledge can be replaceable different artificial neural network trainings which whereas BNN knowledge is adaptable. minimize the mean square error and the steps used in Matlab. ANN is used to increase the prediction accuracy with minimal dependent on experimental data and helps in optimal selection of machining parameter for its optimization. [6] Rani Pagariya et al., reported the artificial neural network introduction, explained the types of neural network and describe the benefits of neural network with historical background. The mathematical models for the back-propagation algorithm are presented. This paper also investigates the architecture of the neural network. [7] Foram S. Panchal et al. emphasized that that artificial neural network is more suitable for Fig.2 Biological pattern recognition, signal processing, forecasting, To cover wide area of applications, artificial classification and data analysis. To get proper intelligence will enable by integrating the artificial results, neural network is used. It needs trained data, neural network with the expert systems. An ANN has selected architecture and preprocessed data but still the self-learning capability, the requirement is to the performance is based on the network size. In the provide data and train it; the outcome depends on the neural network, hidden node selection causes structure of the network. The expert system overfitting or underfitting problem. This work finds knowledge is restricted with the human knowledge the solution for the selection of hidden node issue by domain; the outcome of this system is subject to how choosing the optimal number of hidden nodes based the knowledge has been signified. Both systems have on similarities of input data after training the their own characteristics but can cooperate among network with real data. [8] them and for some issues they overlap in use. [1] Hong Hui Tan et al., explained about the second order optimization technique. This technique 2. Literature Review offers additional curvature information which Rouse viewed that neural network is a web reduces the iteration in training the data and attains of programs and structure of data which is similar to quick convergence by optimizing the step length in the human brain function. A neural network training phase of neural network. It also reviews the comprises of densely interconnected processing unit techniques involved in second order optimization as in parallel each with its own domain of knowledge follows, Hessian calculation method is appropriate and data access in its memory. [2] for network training and it is efficient by configuring Kayri observed that in neural network, with conjugate gradient algorithm, Gauss-Newton training algorithm plays an important role in and Levenberg- Marquardt method uses less adjusting the weight and minimizing the mean square memory, with the support of parallel processing error between the experimental data and the predicted computation power per iterations is reduced. The data output. In the trained data, the error can be feasibility and optimization technique performance reduced to exact threshold level. In this paper, Gradient descent algorithm is used to lessen the mean is summarized. [9] square error. [3] Subhadra et al., proposed a system of

2 E3S Web of Conferences 287, 03001 (2021) https://doi.org/10.1051/e3sconf/202128703001 ICPEAM2020 neurons is occur in neurons, dendrites and Dase et al., presented a review of application predicting the heart disease with suitable diagnosis Alcohol abuse, Both right and left Arm and Leg synapses with self-learning process in a of ANN for the prediction of stock market index by using multilayer perceptron neural network. The weakness, Speech Slurring, Giddiness, Headache, distributed method while in neural net which is a challenging task. ANN is capable of data is trained by the use of back propagation Vomiting, Memory Deficits, Swallowing Difficulties, computing the data in sequential and calculating precise stock index and it also extract the algorithm. The inputs of the network are age, sex, Vision loss, isolated vertigo, Hand & leg Numbness centralized method. information from the big data thereby it predicts to chest, resting blood pressure, serum cholesterol, and Dizziness. The network architecture has 20 input buy or sell or hold the stock market shares. [4] fasting blood sugar, resting electrocardiograph, nodes, 10 hidden nodes and 10 output nodes. 4. The reliability of the biological network is Vishnu et al., learned that neural network heart rate, exercise induced angina, sloping rate and robust and in ANN is vulnerable. need not be reprogrammed. When the processing major vessels count. Genetic algorithm, K-mean 5. The operating environment is element of the neural net fails, the network can algorithm are commonly used data mining method poorly defined and unconstrained in continue its function by parallel processing. ANN is biological network and for neural net is well more prevalent in the prediction of outcome from the for predicting the heart disease. The performance defined environment. process parameters by appropriately training the data. metrics such as sensitivity, specificity, precision 6. Error removal capability of human brain is [5] and accuracy are calculated for various approaches stronger than an ANN by recognizing the Mitali et al., briefly explained the prediction and proved that the proposed system is proficient in error easily. technique in neural network. This paper reports the predicting the heart disease. [10] 7. ANN knowledge can be replaceable different artificial neural network trainings which whereas BNN knowledge is adaptable. minimize the mean square error and the steps used in Matlab. ANN is used to increase the prediction 2. Aspects and Applications of accuracy with minimal dependent on experimental data and helps in optimal selection of machining Neural Networking parameter for its optimization. [6] An artificial neural network is an approach Rani Pagariya et al., reported the artificial which provides the nearer results to human neural network introduction, explained the types of perception and recognition than conventional neural network and describe the benefits of neural methods. The distinctive features make neural network with historical background. The networks valuable in a variety of applications. One of mathematical models for the back-propagation the characteristics of ANN is that, for an incomplete algorithm are presented. This paper also investigates input, it can bring forth sensible outcomes. the architecture of the neural network. [7] Foram S. Panchal et al. emphasized that 1. Self-Organizing Traffic Control System that artificial neural network is more suitable for Fig.2 Biological Neuron pattern recognition, signal processing, forecasting, A four-layer ANN is used to approximate To cover wide area of applications, artificial classification and data analysis. To get proper optimal splits of signal phases in traffic control intelligence will enable by integrating the artificial results, neural network is used. It needs trained data, system. The traffic control system is built on two neural network with the expert systems. An ANN has selected architecture and preprocessed data but still conditions as, constant length over the road the self-learning capability, the requirement is to the performance is based on the network size. In the network and no offsets between nearby joints. The provide data and train it; the outcome depends on the neural network, hidden node selection causes inputs are control variables such as signal phases split structure of the network. The expert system overfitting or underfitting problem. This work finds lengths and on inflow links traffic volumes. The Fig.3 ANN Architecture for Stroke disease knowledge is restricted with the human knowledge the solution for the selection of hidden node issue by output from the neural network are queue length and Prediction domain; the outcome of this system is subject to how choosing the optimal number of hidden nodes based performance measures. In the training process, by The types of stroke diseases such as TIA, the knowledge has been signified. Both systems have on similarities of input data after training the altering the synaptic weights, the targeted outcomes Left Hemiplegia, Right Hemiplegia, Dysphasia, their own characteristics but can cooperate among network with real data. [8] were achieved. [11] Monoplegia, Left Hemianopia, Aphasia, Right them and for some issues they overlap in use. [1] Hong Hui Tan et al., explained about the 2. Thrombo Embolic stroke Prediction Hemianthesia, Dysphagia, Quadruplegia are the second order optimization technique. This technique 2. Literature Review output parameters (D1 to D10 in Fig3). It helps the offers additional curvature information which ANN exhibits better performance in the doctors to provide earlier diagnosis and better Rouse viewed that neural network is a web reduces the iteration in training the data and attains stroke disease prediction by using back propagation medication to the patient. Neural network provides of programs and structure of data which is similar to quick convergence by optimizing the step length in algorithm to train the stroke data structure and it has the improved classification or predication of disease the human brain function. A neural network training phase of neural network. It also reviews the been tested for the types of stroke disease viz. outcome with certain probability. [12] comprises of densely interconnected processing unit techniques involved in second order optimization as cerebrovascular accident, ischemic stroke, embolic in parallel each with its own domain of knowledge follows, Hessian calculation method is appropriate stroke, thrombotic stroke and brain attack. Here 3. Shear Wave Velocity Prediction neural network helps the doctor in decision making, and data access in its memory. [2] for network training and it is efficient by configuring Shear wave velocity approximation is the screening and to double check their diagnosis. Kayri observed that in neural network, with conjugate gradient algorithm, Gauss-Newton important parameter in the seismic exploration and Backward stepwise feature selection method is used training algorithm plays an important role in and Levenberg- Marquardt method uses less representation of a carbonate reservoir. To optimize for input feature selection. adjusting the weight and minimizing the mean square the petro physical parameters multiple regression memory, with the support of parallel processing The network structure for the prediction of error between the experimental data and the predicted method is used. The fast training neural network can computation power per iterations is reduced. The stroke disease has the twenty input parameters (as data output. In the trained data, the error can be predict shear wave velocity from carbonate rocks feasibility and optimization technique performance shown in Fig.3 X1 to X20) as Hypertension, Diabetes reduced to exact threshold level. In this paper, porosity, density and compressional wave velocity. is summarized. [9] mellitus, Myocardial infarction, cardiac Failure, Gradient descent algorithm is used to lessen the mean [13] square error. [3] Subhadra et al., proposed a system of Atrial Fibrillation, Smoking, High Blood Cholesterol,

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4. Lung Cancer Diagnosing rainfall, snowmelt, evaporation and temperature. The artificial neural network is the effective tool to In carcinogenesis, neural network has its role handle the complex problem and has the ability to in both pre-clinical and post-clinical diagnosis with a model rainfall runoff in the arid and semiarid areas high precision rate which supports the clinicians to where the uneven downpours occur. The impacts of detect the cancer at its early stage. The back training process and input dimension on rainfall propagation algorithm is used for faster execution and runoff prediction competence of the neural network for greater accuracy rate with the trained and test data were applied in many hydrological fields. [16] set obtained from the Lung cancer patients. [14] 7. Data Mining 5. Cardiac Arrhythmias Detection Data Mining is the process of extracting Cardiac Arrhythmias is the heart disease data from the larger database. Data mining can shows a condition of atypical electrical activity in estimate the upcoming trends and supports for the heart. It can be detected by using decision making. For a data mining task, some of the electrocardiogram signal which is a graphical features like simplicity of the architecture deigning representation of the electrical activity of the heart. and low computational complexity are achieved by The neural network is trained by using Levenberg using the artificial neural network. To improve the Marquardt Back propagation algorithm to identify artificial neural network performance, genetic the disease with the trained dataset comprising the algorithm is used in neural network design and QRS complex features as minimum and maximum implements the Neuro- fuzzy system. [17] QRS width, moderate QRS width and the Heart rate [15]. 8. Image Processing The network architecture for the disease Artificial neural network is used to resolve prediction consists of input layer consists of five the real-world problems in the image processing neurons with tangent sigmoid transfer function and field. In an image processing, the most important output layer comprises of six neurons which step is preprocessing which can be categorized into represent the presence of diseases shown in Fig.4 image construction, restoration and enhancement. with linear transfer function. Here artificial neural Neural network is directly applied to the image network is used to identify and predict features from features and pixel data. Regression feed forward highly abnormal ECG and it provides better network is used in preprocessing to reconstruct the images, to suppress noise in image restoration and proficiency for the heartbeats classification act as an edge detectors in an image enhancement. In according to different arrhythmias. image compression application neural net perform the encoding and in feature extraction applications supervised and unsupervised networks are used to extract the features. The optimization can be done by Hopfield artificial neural network. [18] 9. Weather Prediction Weather forecasting is to determine the state of temperature in future at a given location. For the temperature prediction, three layered back propagation neural network technique is used. It is observed that neural network model produces less error with high efficiency compared with the numerical differentiation method. [19] Agricultural and industrial field requires accurate weather forecasting which also alert about natural disaster. The input data for the weather prediction in the neural network consists of temperature, air pressure, wind direction, cloudiness, humidity and wind speed collected from Fig. 4 Network Architecture meteorological experts. The data are trained using 6. Rainfall-run off Prediction various techniques such as back propagation, optical neural network, radial basis function, regression, and Rainfall-runoff plays a major role in the fuzzy ARTMAP [20] water resource management. The inputs for an ANN model are the meteorological variables include 10. Civil Engineering

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4. Lung Cancer Diagnosing rainfall, snowmelt, evaporation and temperature. The Artificial neural networks have provided an emboli classification based on trans-cranial artificial neural network is the effective tool to effective tool for various engineering applications ultrasound signals, tissue temperature modeling In carcinogenesis, neural network has its role handle the complex problem and has the ability to including wave-induced seabed instability based on imaging transducer’s raw data and in both pre-clinical and post-clinical diagnosis with a model rainfall runoff in the arid and semiarid areas identification, earthquake- induced liquefaction and identification of ischemic cerebral vascular accident high precision rate which supports the clinicians to where the uneven downpours occur. The impacts of tide forecasting. In Tidal level forecasting, to predict areas based on computer tomography images. In all detect the cancer at its early stage. The back training process and input dimension on rainfall the tidal level Ann method is used. Similarly to predict the cases, ANN shows its high performance and propagation algorithm is used for faster execution and runoff prediction competence of the neural network the wave induced and earthquake liquefaction potential makes it useful for easy diagnosis. [24] for greater accuracy rate with the trained and test data ANN produces effective response. [21] were applied in many hydrological fields. [16] set obtained from the Lung cancer patients. [14] In near future ANN approach in medical field 11. Abrasive Wear Behavior Prediction plays a vital role during emergency situations. Due to 7. Data Mining 5. Cardiac Arrhythmias Detection the environmental change, there will be possibility of Artificial neural networks approach for the Data Mining is the process of extracting more diseases. But nowadays technology has its wide Cardiac Arrhythmias is the heart disease prediction of abrasive wear behavior of fabric data from the larger database. Data mining can growth, through that self-diagnosis may applicable. shows a condition of atypical electrical activity in reinforced epoxy composite shows that the data estimate the upcoming trends and supports for Implementing neural networks for medical reasoning the heart. It can be detected by using predictions are effective when compared with the provides solutions for common problems by decision making. For a data mining task, some of the electrocardiogram signal which is a graphical experimental data. To predict the wear properties, back identifying with the symptoms, recognizes the features like simplicity of the architecture deigning representation of the electrical activity of the heart. propagation neural network architecture with different disease and give suggestions for first aid. [25] and low computational complexity are achieved by training algorithm is used and compared the results in The neural network is trained by using Levenberg using the artificial neural network. To improve the which Levenberg Marquardt algorithm provides better ANN has its applications in diagnosis of urinary Marquardt Back propagation algorithm to identify artificial neural network performance, genetic results. system disease. Feed forward back propagation act as the disease with the trained dataset comprising the algorithm is used in neural network design and a classifier to distinguish infected or non-infected QRS complex features as minimum and maximum implements the Neuro- fuzzy system. [17] urinary system disease such as urinary bladder QRS width, moderate QRS width and the Heart rate inflammation and nephritis of renal pelvis. Based on [15]. 8. Image Processing the symptoms, supervised neural networks classify the infected and non-infected person. ANN shows The network architecture for the disease Artificial neural network is used to resolve significant results in diagnosis of the disease. [26] prediction consists of input layer consists of five the real-world problems in the image processing neurons with tangent sigmoid transfer function and field. In an image processing, the most important 4. Conclusion output layer comprises of six neurons which step is preprocessing which can be categorized into represent the presence of diseases shown in Fig.4 image construction, restoration and enhancement. The fundamental concepts of neural with linear transfer function. Here artificial neural Neural network is directly applied to the image network, the similarities and differences of artificial network is used to identify and predict features from features and pixel data. Regression feed forward neural networks with the biological neural network and expert systems were presented. This work reveals highly abnormal ECG and it provides better network is used in preprocessing to reconstruct the images, to suppress noise in image restoration and the appropriateness and applicability of artificial proficiency for the heartbeats classification act as an edge detectors in an image enhancement. In neural network techniques in various fields. Artificial according to different arrhythmias. image compression application neural net perform neural networks are intended to become the powerful the encoding and in feature extraction applications Fig.5 Network Structure for Wear Prediction tool in enhancing the state of artificial intelligence supervised and unsupervised networks are used to applications. It also explains the training algorithm extract the features. The optimization can be done by In figure.5, Filter content, Applied Load and sliding and the network structure for various applications of Hopfield artificial neural network. 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