An Intelligent Risk Prediction System for Breast Cancer Using Fuzzy Temporal Rules

Kanimozhi, U.; Ganapathy, Sannasi; Manjula, D.; Kannan, A.

doi:10.1007/s40009-018-0732-0

Cited by 53 publications

(27 citation statements)

References 6 publications

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“…But if the system is dynamic, in order to be able to model the transient and permanent modes of the system well, this dynamic must be provided for the system. In other words, the inputs and outputs of the previous moments must also be used (Kanimozhi 2019 ). There are two ways to do this, known as external dynamics and internal dynamics.…”

Section: The Principles Of System Identificationmentioning

confidence: 99%

“…In (Failed 2012 ), the application of fuzzy rough systems in intrude detection problem is studied and it is shown that by the use of fuzzy logic systems (FLSs) the accuracy is improved and the rate of false alarm is decreased. In (Kanimozhi 2019 ), a fuzzy prediction systems is designed and it is used for cancer prediction. In (Ganapathy, et al 2014 ), a pattern classification methodology is developed using FLSs and it is concluded that FLSs improve the detection accuracy significantly.…”

Section: Introductionmentioning

confidence: 99%

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A review on type-2 fuzzy neural networks for system identification

2021

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In many engineering problems, the systems dynamics are uncertain, and then, the accurate dynamic modeling is required. Type-2 fuzzy neural networks (T2F-NNs) are extensively used in system identification problems, because of their strong estimation capability. In this paper, the application of T2F-NNs is reviewed and classified. First, an introduction to the principles of system identification, including how to extract data from a system, persistency of excitation, preprocessing of information and data, removal of outlier data, and sorting of data to learn the T2F-NNs, is presented. Then, various learning methods for structure and parameters of the T2F-NNs are reviewed and analyzed. A number of different T2F-NNs that have been used to system identification are reviewed, and their disadvantages and advantages are described. Also, their efficiency in different applications is reviewed. Finally, we will look at the horizon ahead in this issue and analyze its challenges.

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Section: The Principles Of System Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A review on type-2 fuzzy neural networks for system identification

2021

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“…Intelligent agents use the effective rules for making effective decision over the critical situations. In addition, many intelligent systems have been proposed by many researchers in the past for detecting intruders [8] [14], secure routing processes [9][10] [16], predicting the cancer disease [11] and analysing the social network users [13]. The various intelligent agents are explained in detail with their functionalities, roles and responsibilities over the detection process [8].…”

Section: Literature Surveymentioning

confidence: 99%

An Intelligent Accident Detection Information System using IoT

Ramasamy

2019

IJEAT

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In the current technology development IOT plays a vital role. By using IOT in this work, the accidents occurring in the roads are detected and the information is passed. As the number of vehicles in road increases the chances of accident also increases gradually. According to WHO (“World Health Organization’s”) for every 4 minutes one person dies in the road accident. The main aim of this work is to save of the life of the persons who are dying in accidents because of not alerting anyone about the accident at the correct time. With help of the arduino uno device in the vehicle the accident is detected. The emergency contact person will be receiving the co-ordinates of the place of the accident occurred and the information is sent to the emergency contact person from the arduino uno using the Ethernet adapter in the device. This device sends message to the respective number which is predefined in the arduino board. By sending the message in right time, the billions of people life is saved.

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“…The various effective Intrusion Detection Systems [35][36][37][38][39] and intelligent data mining systems [40][41][42][43][44][45] are perceived and discussed by eminent educators in this direction. The conception of intrusion detection started with the work by Anderson [5] that described misuse detection is possible through the audit trails.…”

Section: Related Workmentioning

confidence: 99%

“…Automatic feature selection is obtained using the Correlation based feature selection in [22]. In [23] The standard benchmark dataset called KDD'99 Cup dataset has been used for evaluating the intrusion detection system in the various intrusion detection systems [35][36][37][38][39] and the intelligent agents are also used by various researchers in their prediction and detection systems [40][41][42][43][44][45]. These intelligent systems have been achieved better detection accuracy with less false alarm rate.…”

Section: Related Workmentioning

confidence: 99%

Feature Reduction using Lasso Hybrid Algorithm in Wireless Intrusion Detection System

Vijayakumar¹,

Ganapathy²

2019

IJITEE

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To maintain the integrity and protection of networks, intrusion detection systems play a vital role. Growth of wireless networks turned the globe to perform all pecuniary tasks online resulting a lot of security breaches in the network. One of the common breaches happening in network is the intruders who eventually tries to bypass the adopted security framework. Every day new intrusions arises and new solutions as well, however the research in making the intrusion detection system intelligent holds energetic. Today most of the systems are becoming intelligent by adopting machine learning and artificial intelligence algorithms. Success of building an efficient machine learning model to make intelligent intrusion detection system is relied on the effective features considered for classification and prediction. Thus, feature reduction is an integral part for discarding irrelevant and redundant features to produce a computationally decisive system that can identify defects with high accuracy. This implementation is an attempt to identify the smaller feature set possible for the well adopted wireless intrusion detection dataset AWID. Here, we proposed a LASSO based implementation to produce a smaller decisive set of features. Incorporation of Lasso on feature reduction not only provides a smaller set of features, but also allow to adopt prediction algorithms inside Lasso resulting lesser number of false alarms as well

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An Intelligent Risk Prediction System for Breast Cancer Using Fuzzy Temporal Rules

Cited by 53 publications

References 6 publications

A review on type-2 fuzzy neural networks for system identification

A review on type-2 fuzzy neural networks for system identification

An Intelligent Accident Detection Information System using IoT

Feature Reduction using Lasso Hybrid Algorithm in Wireless Intrusion Detection System

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