Applications of Artificial Neural Networks: A Review
28 Dec 2016-Indian journal of science and technology (The Indian Society of Education and Environment)-Vol. 9, Iss: 47, pp 1-8
TL;DR: Different methods related to artificial neural network used for prediction and detection of the lung cancer in its early stages are studied so that survival rate of lung cancer patients can be increased.
Abstract: Objectives: The objective of this research paper is study the different methods related to artificial neural network used for prediction and detection of the lung cancer in its early stages so that survival rate of lung cancer patients can be increased. Methods: Lung cancer is the leading cause of death in India so early detection of lung cancer is very important. The detection and prediction of lung cancer was determined with image prepossessing method where segmentation, smoothing and enhancement steps were processed and features were extracted from images and stages of lung cancer were identified with suitable artificial neural network model and also survival rate of lung cancer patients was determined. Findings: Artificial neural network has a significant role in medical area. In these days most of the disease cure methods are process with the help of artificial intelligence to increase the performance of output. In lung cancer disease the artificial neural network model is very useful because detection of lung cancer in its early stages can be determine and it is very important to cure this disease initially because with the increasing stages of lung cancer it is very difficult to cure this disease and also the survival rate of lung cancer patients in higher stages is very low Improvements: The ultimate goal of this paper is to study different methods of Artificial Neural Networks model that can help for detection, prediction and find the survival rate of lung cancer patients.
Citations
More filters
TL;DR: A review on the application of artificial neural network for the management of environmental odours is presented and this work aims to address the scarcity of information by addressing the gaps from existing studies in terms of the selection of the most suitable configuration.
72 citations
01 Jun 2018
TL;DR: The basic theory of ANNs is explained, followed by a review of different studies related to ANNs used for applications in buildings such as energy management, systems control and energy prediction.
Abstract: Artificial Neural Networks (ANNs) constitute a research area of high interest, for both practitioners and academics, as they are found very useful for solving complex problems that are difficult to solve using known and well developed conventional methods or techniques. They can be used for prediction, control, estimation, data clustering and many other applications that are found in everyday scenarios. This paper explains in brief the basic theory of ANNs, followed by a review of different studies related to ANNs used for applications in buildings such as energy management, systems control and energy prediction. It has been found that applying ANNs in buildings the energy consumption can be reduced, depending on the application. Furthermore, efficient control mechanisms also become possible, leading to the reduction of the energy consumption. Through this review, the reader will be able to recognise the value of ANNs and their big potential in buildings and energy sector, in general. Finally, an ANN-based structure for predicting the local RES generation and the load demand for a building is proposed.
14 citations
Cites methods from "Applications of Artificial Neural N..."
...There are six continuous activation functions f that are commonly used, namely Sigmoid, Hyperbolic Tangent, Inverse Tangent, Threshold, Gaussian Radial basis, Linear and Step/Binary function; for more details and examples see [8], [10]–[12]....
[...]
TL;DR: In this article, a new architecture of Recurrent Neural Network (RNN) was proposed to perform the detection process better than traditional machine learning algorithms, and the experimental results shown that the proposed model has scored 98.58 level of accuracy.
13 citations
01 May 2021
TL;DR: This review paper discussed how to apply the rule of deep learning to various neural networks to obtain better compression in the image with high accuracy and minimize loss and superior visibility of the image.
Abstract: Image compression is an essential technology for encoding and improving various forms of images in the digital era. The inventors have extended the principle of deep learning to the different states of neural networks as one of the most exciting machine learning methods to show that it is the most versatile way to analyze, classify, and compress images. Many neural networks are required for image compressions, such as deep neural networks, artificial neural networks, recurrent neural networks, and convolution neural networks. Therefore, this review paper discussed how to apply the rule of deep learning to various neural networks to obtain better compression in the image with high accuracy and minimize loss and superior visibility of the image. Therefore, deep learning and its application to different types of images in a justified manner with distinct analysis to obtain these things need deep learning.
11 citations
Cites background from "Applications of Artificial Neural N..."
...Between the 1980s and 1990s, neural network research on image coding was a topic of much research [16]....
[...]
TL;DR: In this article , an integrated bubbling fluidized bed gasifier and SOFC model was created to generate data for training the ANN models with Aspen Plus simulation, which can predict the performance parameters in terms of electrical efficiency, net voltage and current density successfully using the varying operating conditions and 30 different biomass types as input parameters.
10 citations
References
More filters
01 Jan 2015
TL;DR: The current use of Artificial Intelligence technologies in the PSS design to damp the power system oscillations caused by interruptions, in Network Intrusion for protecting computer and communication networks from intruders, in the medical area- medicine, to improve hospital inpatient care, and in the application areas of this technology.
Abstract: In the future, intelligent machines will replace or enhance human capabilities in many areas. Artificial intelligence is the intelligence exhibited by machines or software. It is the subfield of computer science. Artificial Intelligence is becoming a popular field in computer science as it has enhanced the human life in many areas. Artificial intelligence in the last two decades has greatly improved performance of the manufacturing and service systems. Study in the area of artificial intelligence has given rise to the rapidly growing technology known as expert system. Application areas of Artificial Intelligence is having a huge impact on various fields of life as expert system is widely used these days to solve the complex problems in various areas as science, engineering, business, medicine, weather forecasting. The areas employing the technology of Artificial Intelligence have seen an increase in the quality and efficiency. This paper gives an overview of this technology and the application areas of this technology. This paper will also explore the current use of Artificial Intelligence technologies in the PSS design to damp the power system oscillations caused by interruptions, in Network Intrusion for protecting computer and communication networks from intruders, in the medical area- medicine, to improve hospital inpatient care, for medical image classification, in the accounting databases to mitigate the problems of it and in the computer games.
117 citations
19 Apr 2011
TL;DR: Two segmentation methods, Hopfield Neural Network (HNN) and a Fuzzy C-Mean (FCM) clustering algorithm, for segmenting sputum color images to detect the lung cancer in its early stages are presented.
Abstract: The early detection of the lung cancer is a challenging problem, due to the structure of the cancer cells. This paper presents two segmentation methods, Hopfield Neural Network (HNN) and a Fuzzy C-Mean (FCM) clustering algorithm, for segmenting sputum color images to detect the lung cancer in its early stages. The manual analysis of the sputum samples is time consuming, inaccurate and requires intensive trained person to avoid diagnostic errors. The segmentation results will be used as a base for a Computer Aided Diagnosis (CAD) system for early detection of lung cancer which will improves the chances of survival for the patient. The two methods are designed to classify the image of N pixels among M classes. In this study, we used 1000 sputum color images to test both methods, and HNN has shown a better classification result than FCM, the HNN succeeded in extracting the nuclei and cytoplasm regions.
71 citations
TL;DR: Though the artificial neural network developed in this study cannot diagnose cancer conclusively, it helps physicians in deciding whether a biopsy is required by providing information about whether the patient has breast cancer or not.
Abstract: In this study, an artificial neural network (ANN) was developed to determine whether patients have breast cancer or not. Whether patients have cancer or not and if they have its type can be determined by using ANN and BI-RADS evaluation and based on the age of the patient, mass shape, mass border and mass density. Though this system cannot diagnose cancer conclusively, it helps physicians in deciding whether a biopsy is required by providing information about whether the patient has breast cancer or not. Data obtained from 800 patients who were diagnosed with cancer definitively through biopsy. The definitive diagnosis corresponding to each patient and the data from ANN model results were investigated using Confusion matrix and ROC analyses. In the test data of the ANN model that was implemented as a result of these analyses, disease prediction rate was 90.5% and the health ratio was 80.9%. It is seen from these high predictive values that the ANN model is fast, reliable and without any risks and therefore can be of great help to physicians.
68 citations
Journal Article•
TL;DR: The present study shows that neural network model is a more powerful statistical tool in predicting the survival rate of the gastric cancer patients compared to Cox proportional hazard regression model.
Abstract: Background : The aim of this study was to predict the survival rate of Iranian gastric cancer patients using the Cox proportional hazard and artificial neural network models as well as comparing the ability of these approaches in predicting the survival of these patients. Methods: In this historical cohort study, the data gathered from 436 registered gastric cancer patients who have had surgery between 2002 and 2007 at the Taleghani Hospital (a referral center for gastrointestinal cancers), Tehran, Iran, to predict the survival time using Cox proportional hazard and artificial neural network techniques. Results: The estimated one-year, two-year, three-year, four-year and five-year survival rates of the patients were 77.9%, 53.1%, 40.8%, 32.0%, and 17.4%, respectively. The Cox regression analysis revealed that the age at diag-nosis, high-risk behaviors, extent of wall penetration, distant metastasis and tumor stage were significantly associated with the survival rate of the patients. The true prediction of neural network was 83.1%, and for Cox regression model, 75.0%. Conclusion: The present study shows that neural network model is a more powerful statistical tool in predicting the survival rate of the gastric cancer patients compared to Cox proportional hazard regression model. Therefore, this model recommended for the predicting the survival rate of these patients.
41 citations
TL;DR: Simulation results show the effectiveness of these methods even with less rules and parameters in performance result and maintain the accuracy of original fuzzy neural system and have high interpretability by human in diagnosis of breast cancer.
Abstract: Breast cancer is the cause of the most common cancer death in women. Early detection of the breast cancer is an effective method to reduce mortality. Fuzzy Neural Networks (FNN) comprises an integration of the merits of neural and fuzzy approaches, enabling one to build more intelligent decision-making systems. But increasing the number of inputs causes exponential growth in the number of parameters in Fuzzy Neural Networks (FNN) and computational complexity increases accordingly. This phenomenon is named as "curse of dimensionality". The Hierarchical Fuzzy Neural Network (HFNN) and the Fuzzy Gaussian Potential Neural Network (FGPNN) are utilized to deal this problem. In this study, the HFNN and FGPNN by using new training algorithm, are applied to the Wisconsin Breast Cancer Database to classify breast cancer into two groups; benign and malignant lesions. The HFNN consists of hierarchically connected low-dimensional fuzzy neural networks. It can use fewer rules and parameters to model nonlinear system. Moreover, the FGPNN consists of Gaussian Potential Function (GPF) used in the antecedent as the membership function. When the number of inputs increases in FGPNN, the number of fuzzy rules does not increase. The performance of HFNN and FGPNN are evaluated and compared with FNN. Simulation results show the effectiveness of these methods even with less rules and parameters in performance result. These methods maintain the accuracy of original fuzzy neural system and have high interpretability by human in diagnosis of breast cancer.
28 citations