A New Approach for Prediction of Lung Carcinoma Using Back Propagation Neural Network with Decision Tree Classifiers
01 Nov 2018-pp 111-115
TL;DR: The principle point of this paper is to give the prior notice to the clients and to quantify the execution investigation of the classification algorithms utilizing WEKA Tool, which will improve the execution of prediction and classification.
Abstract: In this paper an investigation was made to examine the lung tumour expectation utilizing classification algorithms, for example, Back Propagation Neural Network and Decision Tree. At first 20 tumour and non-disease patients' examples information were gathered with 30 qualities, pre-prepared and dissected utilizing classification algorithms and later a similar methodology was actualized on 50 occurrences (50 Cancer patients and 10 non growth patients). The informational indexes utilized as a part of this examination are taken from UCI data sets for patients affected by lung cancer and Michigan Lung Cancer patient's informational index. The principle point of this paper is to give the prior notice to the clients and to quantify the execution investigation of the classification algorithms utilizing WEKA Tool. Test comes about demonstrate that the previously mentioned calculation has promising outcomes for this reason with the general forecast exactness of 94% and 95.4%, separately. Another way to deal with identifies the lungs tumour by Decision tree and BPNN calculation will give viable outcome as contrast with other calculation. The proposed framework will improve the execution of prediction and classification.
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TL;DR: This method is tested on 95 mammograms images collected and classified using SVM and it shows that the proposed method is effectively classify the abnormal classes of mammograms.
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TL;DR: This new approach intensely tested through several steganalysis attacks and shown that the stego image has delivered the strong opposition force against all attacks and the data embedding capacity has attained at an improved level compared with typical methods.
Abstract: The steganography is a graceful tactic to convey the confidential information to an authorized recipient with the most reliable safety measure which leads to avoiding the breaches of data security. Nowadays the significance of taking strong protection measures in data communication medium has a challenging task because of the security related issues which are developed by unauthorized intervention. This presentation intends to provide a new approach based on a combination of Steganography and Cryptography procedure for inserting the hidden data into a cover object and obtain high data embedding capacity with an improved security level. In this approach, the Elliptic Curve Cryptography algorithm is used to encrypt the hidden information and the encrypted data inserted into a cover object by the process of the LSB Inversion algorithm. This blend of technology has successfully reached the benchmark level of some essential properties known as data confidentiality, integrity verification, capacity and robustness which are the evidence to prove the excellent performance and effective implementation of this steganography process. This new approach intensely tested through several steganalysis attacks such as analysis of visual, histogram, and chi-square. The outcome of the experimental result shown that the stego image has delivered the strong opposition force against all attacks. The data embedding capacity has attained at an improved level compared with typical methods.
23 citations
Cites methods from "A New Approach for Prediction of Lu..."
...LSB steganography method is one of the customary strategies which is fit for concealing mystery information in a computerized cover picture without presenting numerous detectable contortions [19]....
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TL;DR: In this article, the Deep Recurrent Neural Network (DRNN) was used to predict online shopping behavior for improving E-business performance during the COVID-19 pandemic.
Abstract: The Covid-19 pandemic caused substantial changes, particularly concerning marketing, which led to high digital use. Social networking enables people to communicate easily with others and provides marketers with many ways to interact with consumers. As a consequence of the lockdown, economic activity is declining dramatically. The response of policymakers, the government, and industry to resolving the harm caused by economic factors and how the marketer can react to changing consumer behavior. This study analyzes the impact of social networks and social behavior on electronic business or E-Business during the COVID-19 pandemic using deep learning techniques. This paper introduces the Deep Recurrent Neural Network (DRNN) to predict online shopping behavior for improving E-business performance. The article utilizes clickstream information to forecast online purchase behavior in real-time and target marketing measures. Measures of profit impact with production from classifier metrics demonstrate the feasibility and the usage of deep recurrent learners in campaign targeting via RNN-based clickstream modeling. The numerical results show that the suggested model enhances the profitability ratio of 98.5%, the performance ratio of 97.5%, the accuracy ratio of 96.7%, the prediction ratio of 97.9%, and less error rate of 11.3% other existing methods.
17 citations
TL;DR: In this article, the authors proposed a novel methodology and algorithm to handle the mining of distributed medical data sources at different sites (hospitals and clinics) using Association Rules, where each distributed data source is represented by an agent and global association rule computation is then performed by the agent either exchanging some minimal summaries with other agents or travelling to all the sites and performing local tasks that can be done at each local site.
Abstract: Electronic Health Records (EHRs) are aggregated, combined and analyzed for suitable treatment planning and safe therapeutic procedures of patients. Integrated EHRs facilitate the examination, diagnosis and treatment of diseases. However, the existing EHRs models are centralized. There are several obstacles that limit the proliferation of centralized EHRs, such as data size, privacy and data ownership consideration. In this paper, we propose a novel methodology and algorithm to handle the mining of distributed medical data sources at different sites (hospitals and clinics) using Association Rules. These medical data resources cannot be moved to other network sites. Therefore, the desired global computation must be decomposed into local computations to match the distribution of data across the network. The capability to decompose computations must be general enough to handle different distributions of data and different participating nodes in each instance of the global computation. In the proposed methodology, each distributed data source is represented by an agent. The global association rule computation is then performed by the agent either exchanging some minimal summaries with other agents or travelling to all the sites and performing local tasks that can be done at each local site. The objective is to perform global tasks with a minimum of communication or travel by participating agents across the network, this will preserve the privacy and the security of the local data. The proposed association rule mining methodology will be used for heart disease prediction using real heart disease data. These real data exist at different clinics and cannot be moved to a central site. The proposed model protects the patient data privacy and achieves the same results as if the data are moved and joined at a central site. We also validate the extracted association rules from all the data providers using an independent test datasets.
13 citations
TL;DR: An improved version of GOA is proposed, called (LAGOA), which uses Learning Automata (LA) for adjusting the parameters of GoA in an adaptive way, and two-phase mutation for enhancing exploitation capability of the algorithm.
Abstract: In predictive modelling it is important to use any feature selection methods as irrelevant features when used with powerful classifiers can lead to over-fitting and thus create models which fail to perform as good as when these features are not used. Particularly it is important in case of disease datasets where various features or attributes are available through the patients’ medical records and many features in these datasets may not be relevant to the diagnosis of some specific disease. Wrong models in this case can be disastrous and lead to wrong diagnosis, and maybe in extreme cases lead to loss of life. To this end, we have used a wrapper based feature selection model for the said purpose. In recent years, Grasshopper Optimization Algorithm (GOA) has proved its superiority over other optimization algorithms in different research areas. In this paper, we propose an improved version of GOA, called (LAGOA), which uses Learning Automata (LA) for adjusting the parameters of GOA in an adaptive way, and two-phase mutation for enhancing exploitation capability of the algorithm. LA is used for adjusting the parameter values of each grasshopper in the population individually. In two-phase mutation the first phase reduces the number of selected features while preserving high classification accuracy, while the second phase adds relevant features which increase the classification accuracy. Proposed method has been applied to Breast Cancer (Wisconsin), Breast Cancer (Diagnosis), Statlog (Heart), Lung Cancer, SpectF Heart and Hepatitis datasets taken from UCI Machine Learning Repository. Experimental results confirm its superiority over state-of-the-art methods considered here for comparison.
10 citations
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TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
Abstract: The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directions.
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TL;DR: It is shown that random forest has comparable performance to other classification methods, including DLDA, KNN, and SVM, and that the new gene selection procedure yields very small sets of genes (often smaller than alternative methods) while preserving predictive accuracy.
Abstract: Selection of relevant genes for sample classification is a common task in most gene expression studies, where researchers try to identify the smallest possible set of genes that can still achieve good predictive performance (for instance, for future use with diagnostic purposes in clinical practice). Many gene selection approaches use univariate (gene-by-gene) rankings of gene relevance and arbitrary thresholds to select the number of genes, can only be applied to two-class problems, and use gene selection ranking criteria unrelated to the classification algorithm. In contrast, random forest is a classification algorithm well suited for microarray data: it shows excellent performance even when most predictive variables are noise, can be used when the number of variables is much larger than the number of observations and in problems involving more than two classes, and returns measures of variable importance. Thus, it is important to understand the performance of random forest with microarray data and its possible use for gene selection. We investigate the use of random forest for classification of microarray data (including multi-class problems) and propose a new method of gene selection in classification problems based on random forest. Using simulated and nine microarray data sets we show that random forest has comparable performance to other classification methods, including DLDA, KNN, and SVM, and that the new gene selection procedure yields very small sets of genes (often smaller than alternative methods) while preserving predictive accuracy. Because of its performance and features, random forest and gene selection using random forest should probably become part of the "standard tool-box" of methods for class prediction and gene selection with microarray data.
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TL;DR: A new systematic approach is used for the diabetes diseases and the related medical data is generated by using the UCI Repository dataset and the medical sensors for predicting the people who has affected with diabetes severely and a new classification algorithm called Fuzzy Rule based Neural Classifier is proposed for diagnosing the disease and the severity.
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