Neuro-fuzzy rule generation: survey in soft computing framework

Software birthmark is a unique quality of software to detect software theft. Comparing birthmarks of software can tell us whether a program or software is a copy of another. Software theft and piracy are rapidly increasing problems of copying, stealing, and misusing the software without proper permission, as mentioned in the desired license agreement. The estimation of birthmark can play a key role in understanding the effectiveness of a birthmark. In this paper, a new technique is presented to evaluate and estimate software birthmark based on the two most sought-after properties of birthmarks, that is, credibility and resilience. For this purpose, the concept of soft computing such as probabilistic and fuzzy computing has been taken into account and fuzzy logic is used to estimate properties of birthmark. The proposed fuzzy rule based technique is validated through a case study and the results show that the technique is successful in assessing the specified properties of the birthmark, its resilience and credibility. This, in turn, shows how much effort will be required to detect the originality of the software based on its birthmark.

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Fuzzy Rule Generation using Modified PSO for Clustering in Wireless Sensor Networks

IEEE Transactions on Green Communications and Networking ◽

10.1109/tgcn.2021.3060324 ◽

2021 ◽

pp. 1-1

Author(s):

Amruta Lipare ◽

Damodar Reddy Edla ◽

Ramesh Dharavath

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Fuzzy Rule ◽

Wireless Sensor ◽

Rule Generation ◽

Modified Pso

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Human Activities Recognition Based on Neuro-Fuzzy Finite State Machine

Technologies ◽

10.3390/technologies6040110 ◽

2018 ◽

Vol 6 (4) ◽

pp. 110 ◽

Cited By ~ 4

Author(s):

Gadelhag Mohmed ◽

Ahmad Lotfi ◽

Amir Pourabdollah

Keyword(s):

Neural Networks ◽

Human Activities ◽

Finite State Machine ◽

Fuzzy Rule ◽

State Machine ◽

Machine Learning Techniques ◽

Rule Based ◽

Neuro Fuzzy ◽

Finite State ◽

Learning Capabilities

Human activity recognition and modelling comprise an area of research interest that has been tackled by many researchers. The application of different machine learning techniques including regression analysis, deep learning neural networks, and fuzzy rule-based models has already been investigated. In this paper, a novel method based on Fuzzy Finite State Machine (FFSM) integrated with the learning capabilities of Neural Networks (NNs) is proposed to represent human activities in an intelligent environment. The proposed approach, called Neuro-Fuzzy Finite State Machine (N-FFSM), is able to learn the parameters of a rule-based fuzzy system, which processes the numerical input/output data gathered from the sensors and/or human experts’ knowledge. Generating fuzzy rules that represent the transition between states leads to assigning a degree of transition from one state to another. Experimental results are presented to demonstrate the effectiveness of the proposed method. The model is tested and evaluated using a dataset collected from a real home environment. The results show the effectiveness of using this method for modelling the activities of daily living based on ambient sensory datasets. The performance of the proposed method is compared with the standard NNs and FFSM techniques.

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