Software Reliability Assessment Using Hybrid Neuro-Fuzzy Model

Gandhi, Parul; Khan, Mohammad Zubair; Sharma, Ravi Kumar; Alhazmi, Omar H.; Bhatia, Surbhi; Chakraborty, Chinmay

doi:10.32604/csse.2022.019943

Cited by 10 publications

(4 citation statements)

References 22 publications

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“…Also, Gandhi et al [52] presented a high quality algorithm that can be used to predict the reliability of the program. The proposed algorithm is applied the usage of a hybrid strategy referred to as neuro-fuzzy inference system and it has additionally been utilized to the take a look at data.…”

Section: Previous Studiesmentioning

confidence: 99%

A survey of predicting software reliability using machine learning methods

Khaleel,

Salih

2024

IJ-AI

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<span lang="EN-US">In light of technical and technological progress, software has become an urgent need in every aspect of human life, including the medicine sector and industrial control. Therefore, it is imperative that the software always works flawlessly. The information technology sector has witnessed a rapid expansion in recent years, as software companies can no longer rely only on cost advantages to stay competitive in the market, but programmers must provide reliable and high-quality software, and in order to estimate and predict software reliability using machine learning and deep learning, it was introduced A brief overview of the important scientific contributions to the subject of software reliability, and the researchers' findings of highly efficient methods and techniques for predicting software reliability. </span>

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Section: Previous Studiesmentioning

confidence: 99%

A survey of predicting software reliability using machine learning methods

Khaleel,

Salih

2024

IJ-AI

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“…Currently, with the development of electronic media, especially with the appearance of the Arduino module and thanks to the advantages it presents, the realization of any project has become an easy task (figure 3). In this work, we will use the Arduino module, XBee module and other ways to monitor an ECG signal and its remote emission [25]. The first step of our project is to acquire the ECG signal.…”

Section: Ecg Signal Monitoringmentioning

confidence: 99%

IoT System and Deep Learning Model to Predict Cardiovascular Disease Based on ECG Signal

Sakli,

Baccouch,

Bellali

et al. 2023

Adv. sci. technol. eng. syst. j.

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In this work, our contribution will intervene to reduce the impact of noises on the ECG signals. Various ECG denoising approaches were tested to see how efficient they were in removing dominant noises that add to pure ECG signals. Due to different causes such as interference, muscular noise, body movement related to breathing, and so on, the original signal acquired by the electrodes produces noises. In this article, the electrode signals are monitored using an Internet of Things system that combines an Arduino board and an AD8232 module to generate a one-dimensional signal. These ECG signals are displayed on a computer using the Matlab interface. Following that, an efficient deep learning model was developed to facilitate cardiologists in their diagnosis of ECG signals. These experimental results obtained demonstrate the effectiveness of our proposed model compared to other existing methods in the literature. Finally, the filtered and classified ECG signals are given to the doctor for correct treatment of the patient's condition.

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“…The proposed algorithm is implemented using a hybrid approach named Neuro-Fuzzy Inference System and has also been applied to test data. In this work, a comparison between different techniques of soft computing has been performed [2]. Elghamry, Eldamcesse et al discussed a new algorithm for evaluating the fuzzy reliability of a fuzzy linear consecutive k-out-of-n: F system (Lin/C (k,n: F)) with independent, un-repairable, and non-identical components [13].…”

Section: Related Workmentioning

confidence: 99%

“…The Neuro-Fuzzy approach is the combination of concepts based on fuzzy logic and a neural network. The integration of these two paradigms overcomes the limitations of each technique's isolated concept [2]. Neuro-fuzzy is an intelligent system that combines the human-like reasoning style of fuzzy systems with the learning and connectionist structure of neural networks.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Software Reliability Assessment via Neuro-Fuzzy System

babaie

2022

IJRRS

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Nowadays, the utilization of software engineering in various areas of technology is remarkably increased. As a matter of fact, it is used in many critical applications such as eye surgery, autopilot systems of airplanes, centralized traffic control (CTC), and so on. Therefore, the reliability of software is very important, and it plays an essential role in the lifetime of the software. Software reliability is one of the main characteristics of software quality. Moreover, the rapid assessment of the reliability of the application is essential during the software life cycle. In this paper, I use the neuro-fuzzy methods to assess the software's reliability in order to cope with uncertainties in measuring the actual parameters of the software. By designing neuro-fuzzy inference systems and applying four parameters of the ISO/IEC 9126quality model(i.e., the maturity of software, fault-tolerant, recoverability, and reliability compliance) and finding the parameters of a fuzzy system by exploiting approximation techniques from neural networks, I present an integrated assessment model for evaluation of software reliability. The case study used in this paper to evaluate the proposed method is the software income tax calculator. By applying the input parameters, I observe that the software reliability is 0.65. software reliability in our proposed method is more exact than software reliability in the fuzzy multi-criteria and fuzzy method because The weights of the input parameters have been set by experts and software developers, and simulations are carried out using MATLAB tool (ANFIS). Simulations confirm that the proposed method provides acceptable results.

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Software Reliability Assessment Using Hybrid Neuro-Fuzzy Model

Cited by 10 publications

References 22 publications

A survey of predicting software reliability using machine learning methods

A survey of predicting software reliability using machine learning methods

IoT System and Deep Learning Model to Predict Cardiovascular Disease Based on ECG Signal

A Novel Method for Software Reliability Assessment via Neuro-Fuzzy System

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