Confidence value prediction of DNA sequencing with Petri net model

Hamed, Raed I.; Ahson, S. I.

doi:10.1016/j.jksuci.2011.05.004

Cited by 9 publications

(8 citation statements)

References 23 publications

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“…The computational approach described in this paper is Mamdani fuzzy Petri net (MFPN) that is able to overcome the drawbacks specific to pure Petri nets [6]. Fuzzy models describing dynamic processes compute the states x(t), at a time instant t, from the information of the states x and inputs y:…”

Section: Inference Model Processmentioning

confidence: 99%

“…The rule is modeled by t i and describes fuzzy relationship between input and output place propositions of t i . There are several rationales behind which to base a computational paradigm for expert systems on Petri net theory [2,6].…”

Section: Inference Model Processmentioning

confidence: 99%

“…Any input expression value can be described through a combination of membership values in the linguistic fuzzy sets. Note that incorporation of linguistic fuzzy sets provides a tool for natural computing [5,6] as the resulting system is capable of reasoning like human. The idea of modeling gene expression profiles using linguistic fuzzy sets is novel.…”

Section: Implementation Of Breast Cancer Detectionmentioning

confidence: 99%

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Evaluation of breast cancer risk by using formal reasoning algorithm

Hamed

2013

2013 5th International Conference on Computer Science and Information Technology

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we introduce an approach to risk estimation in breast cancer disease based on an intelligent method of Petri net. For the purpose of knowledge representation of risk estimation in breast cancer disease, a specified type of Petri net approach is explicitly introduced in this paper. A woman whose mother, sister or daughter had breast cancer has an increased risk. Having a male relative with breast cancer, can also increase your risk. In the proposed approach, an eight factors profile is first transformed into a mapping form and then the transformed data are mapped into the fuzzy inference system. Since the relations of an eight factors are represented by fuzzy model, the proposed method is robust to noisy and uncertain information. The compatibility of the system to existing models of gender, age, genetic status, menarche age, menopause age, first birth age, alcohol consumption and nutrition habit as factors have been identified in the model of breast cancer has been tested.

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Section: Inference Model Processmentioning

confidence: 99%

Section: Inference Model Processmentioning

confidence: 99%

Section: Implementation Of Breast Cancer Detectionmentioning

confidence: 99%

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Evaluation of breast cancer risk by using formal reasoning algorithm

Hamed

2013

2013 5th International Conference on Computer Science and Information Technology

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“…Furthermore, they can be used for fuzzy knowledge representation, providing a means to deal with uncertain and fuzzy information of expert systems [6]. In recent years, FPNs have received considerable attention from both academics and practitioners and have been widely used in many fields, such as disassembly process planning [7], fault diagnosis [8], production scheduling [9], biological system modeling [10], sequential control [11], as well as other kinds of engineering applications [12]- [15]. Despite its success in various applications, the conventional FPNs have been considerably criticized for a variety of reasons [6], [16], [17], including: the thresholds of places are not considered or assigned with an identical value; the global weights of transitions are neglected in knowledge inference process; and the reachability tree-based reasoning algorithms implemented are laborious and time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Petri nets Using Intuitionistic Fuzzy Sets and Ordered Weighted Averaging Operators

Liu

You

et al. 2016

IEEE Trans. Cybern.

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Fuzzy Petri nets (FPNs) are an important modeling tool for knowledge representation and reasoning, which have been extensively used in a lot of fields. However, the conventional FPN models have been criticized as having many shortcomings in the literature. Many different models have been suggested to enhance the performance of FPNs, but deficiencies still exist in these models. First, various types of uncertain knowledge information provided by domain experts are very hard to be modeled by the existing FPN models. Second, the traditional FPNs determine the results of knowledge reasoning using the min, max, and product operators, which may not work well in many practical applications. In this paper, we propose a new type of FPN model based on intuitionistic fuzzy sets and ordered weighted averaging operators to deal with the problems and improve the effectiveness of the conventional FPNs. Moreover, a max-algebra-based reasoning algorithm is developed in order to implement the intuitionistic fuzzy reasoning formally and automatically. Finally, a case study concerning fault diagnosis of aircraft generator is presented to demonstrate the proposed intuitionistic FPN model. Numerical experiments show that the new FPN model is feasible and quite effective for knowledge representation and reasoning of intuitionistic fuzzy expert systems.

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“…The genomic data manages the accumulation and investigation of information on every one of the genes in a life form. It has been progressively imperative to scientists, helping them to focus on designs from information, which transform into biological facts and knowledge [3]. One such tool utilized for this issue is the fuzzy logic [4], and the Neuro-fuzzy [5], that can fulfill the requirement for a DNA sequencing analysis procedure and give a deliberate and fair-minded approach to prefer this topic.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Method Implemented In Analyzing of Organ System Using Intelligent Tools

Hameed¹,

Hamed²

2016

juhd

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The paper proposes an efficient approach applied in DNA base calling, which concerns efficiency and sensitivity. We utilized the Neuro-Fuzzy model in the analysis issues to determine the confidence value prediction in DNA base calling, that is solved by several attempts applied in the MATLAB tool, the model is implemented for the collected data for each base in the DNA sequencing. The model is designed by using the ANFIS tool, which contains three subsystems a main system. We obtain three features (peakness, height, and spacing) for each base from the three subsystems and in the main system use these three features as the input to predict the confidence value for each base in the DNA. This achieves a high accuracy in the obtained results with high-performance.

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Confidence value prediction of DNA sequencing with Petri net model

Cited by 9 publications

References 23 publications

Evaluation of breast cancer risk by using formal reasoning algorithm

Evaluation of breast cancer risk by using formal reasoning algorithm

Fuzzy Petri nets Using Intuitionistic Fuzzy Sets and Ordered Weighted Averaging Operators

An Optimized Method Implemented In Analyzing of Organ System Using Intelligent Tools

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