scholarly journals An Automated Tool for Inspection of Requirements Engineering Techniques

2019 ◽  
Vol 8 (2S11) ◽  
pp. 800-807
Keyword(s):  
Requirements Engineering ◽  
Process Model ◽  
Research Work ◽  
Software Project ◽  
Clustering Methods ◽  
Client Experiences ◽  
Engineering Technique ◽  
Fuzzy Clustering Method ◽  
Customer Perspective ◽  
Or Organization

From last few decades, researchers and practitioners have well recognized the significance of Requirements Engineering. Requirements Engineering stage is the foundation stone on which the entire building named software can be built. There are several Requirements Engineering (RE) techniques exists but requirements engineer choose a specific technique for a particular software project with their own preferences or organization standards. There is not only little guidance available for analyzing Requirements Engineering techniques but also all the existing researches focus on qualitative measures. There is no consideration of physical measures while analyzing and accepting a technique for a particular project. Nowadays customers satisfaction is also gaining great importance so customer perspective should also be taken into account. We have performed deep literature review and noted that analysis and selection of Requirements Engineering technique should consider all relevant attributes of each techniques and their mapping with project, people or other factors. There is a need to thoroughly comprehend and evaluate all the existing techniques with respect to analyst preferences, client experiences, project attributes, software process model characteristics. To do so, fuzzy clustering method is implemented in MATLAB. The key emphasis of this paper is to study and list all possible Requirements Engineering techniques related to Elicitation, Prioritization, Documentation, Verification and Validation, etc. The research work also analyzes attributes of each RE technique using Fuzzy C mean clustering and K mean clustering methods. The results of clustering provide a set of techniques, from which requirements engineer can select for specific phase of Requirements Engineering. The substantiation of the research work is done with the help of a case study that is having some known problem domain characteristics

scholarly journals The IM Decision Making And Problem-Solving Model: A Unisex Context

2020 ◽  
pp. 76-82
Author(s):  
Ibrahim Suleiman Yahaya ◽  
Maryam M.B Yusuf
Keyword(s):  
Decision Making ◽  
Problem Solving ◽  
Daily Life ◽  
Research Work ◽  
Critical Time ◽  
Decision Makers ◽  
Future Research ◽  
New Development ◽  
Or Organization ◽  
Fast Decision

This paper The search paper aimed at introducing new development in decision-making and problem-solving models which will enable the decision-makers to have more options on the way of handling any give scenarios that might occur in the process of daily life or organizational activities, this will improve fast decision by individual or organization. Decision making is an acceptable part of human daily life. People have to make different important decisions nearly every day, hence the reason that often-making decisions can be a difficult action to take. However, a significant number of observational studies have shown that most individuals are much worse in decision-making in organizations. Thus, people started paying more attention to learning how to make an acceptable decision through the related hypotheses and models that fit their scenarios. Along with the line hundred (100) sample of the design developed model with a Likert-Scale from 1-5 was attached and sent to some prominent leaders who virtually make a decision and solved problems almost every day, for their assessment’s/analysis in order to collect data to determine both input and output of the developed model which some accepted as it was designed while some make changes and other make a recommendation for future research work. The decision-making tools are needed at the critical time of Covid.

Ontology-Based Framework for Quality in Configurable Process Models

2020 ◽  
pp. 464-478
Author(s):  
Loubna El Faquih ◽  
Mounia Fredj
Keyword(s):  
Business Process ◽  
Process Modeling ◽  
Process Model ◽  
Business Processes ◽  
Research Work ◽  
Process Models ◽  
Novel Method ◽  
Ontology Languages ◽  
Variability Management

In recent years, business process modeling has increasingly drawn the attention of enterprises. As a result of the wide use of business processes, redundancy problems have arisen and researchers introduced the variability management, in order to enhance the business process reuse. The most approach used in this context is the Configurable Process Model solution, which consists in representing the variable and the fixed parts together in a unique model. Due to the increasing number of variants, the configurable models become complex and incomprehensible, and their quality is therefore impacted. Most of research work is limited to the syntactic quality of process variants. The approach presented in this paper aims at providing a novel method towards syntactic verification and semantic validation of configurable process models based on ontology languages. We define validation rules for assessing the quality of configurable process models. An example in the e-healthcare domain illustrates the main steps of our approach.

Requirements Engineering Process Improvement and Related Models

2011 ◽  
pp. 18-33
Author(s):  
Badariah Solemon ◽  
Shamsul Sahibuddin ◽  
Abdul Azim Abd Ghani
Keyword(s):  
Requirements Engineering ◽  
Process Improvement ◽  
Process Model ◽  
Good Practice ◽  
Capability Maturity Model ◽  
Maturity Model ◽  
Engineering Process ◽  
Principal Characteristics ◽  
Capability Maturity ◽  
Requirements Engineering Process

Requirements Engineering (RE) is a key discipline in software development, and several standards and models are available to help assess and improve RE processes. However, different standards and models can also help achieve different improvement goals. Thus, organizations are challenged to select these standards and models to best suit their specific context and available resources. This chapter presents a review of selected RE-specific and generic process improvement models that are available in the public domain. The review aims to provide preliminary information that might be needed by organizations in selecting these models. The chapter begins with analyses of how RE maturity is addressed in the Capability Maturity Model Integration (CMMI) for Development. Then, it describes the principal characteristics of, and the assessment and improvement framework applied in four RE-specific process assessment and improvement models: the Requirements Engineering Good Practice Guide (REGPG), the Requirements Engineering Process Maturity(REPM), the Requirements Capability Maturity Model (R-CMM), and the Market-Driven Requirements Engineering Process Model (MDREPM). This chapter also examines the utility and lesson learned of these models.

A Framework for Self-Realizing Process Models for Additive Manufacturing

2011 ◽  
Author(s):  
Sungshik Yim ◽  
David W. Rosen
Keyword(s):  
Neural Network ◽  
Additive Manufacturing ◽  
Process Planning ◽  
Process Model ◽  
Selective Laser Sintering ◽  
Process Models ◽  
Clustering Methods ◽  
In Series ◽  
Design Requirements ◽  
Planning Problems

This research discusses a framework for automating process model realization for additive manufacturing. The models map relationships from design requirements to process variables and can be utilized for future process planning. A repository is employed to collect data and contains previous process plans and corresponding design requirements. The framework organizes data through a statistical clustering method and builds regression models using a multi-layer neural network. Hierarchical and k-means clustering methods are employed in series to manage the data. A two layer neural network and augmented training algorithm are employed to build process models. The framework has been tested with Stereolithography and Selective Laser Sintering process planning problems to demonstrate its usefulness.

Fuzzy C-Means, ANFIS and Genetic Algorithm for Segmenting Astrocytoma –A Type of Brain Tumor

2014 ◽  
Vol 3 (1) ◽  
pp. 16 ◽  
Author(s):  
Minakshi Sharma ◽  
Saourabh Mukherjee
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Brain Tumor ◽  
Fuzzy Inference ◽  
Research Work ◽  
Diagnostic Technique ◽  
Clustering Methods ◽  
Inference System ◽  
Fuzzy C Means ◽  
Soft Computing Techniques

<p>Imaging plays an important role in medical field like medical diagnosis, treatment planning and patient follow up. Image segmentation is the backbone process to accomplish these tasks by dividing an image in to meaningful parts which share similar properties.  Medical Resonance Imaging (MRI) is primary diagnostic technique to do image segmentation. There are several techniques proposed for image segmentation of different parts of body like Region growing, Thresholding, Clustering methods and Soft computing techniques  (Fuzzy Logic, Neural Network, Genetic Algorithm).The proposed research work uses Grey level Co-occurrence Matrix (GLCM) for texture feature extraction, ANFIS(Adaptive Network Fuzzy inference System) plus  Genetic Algorithm for feature selection and FCM(Fuzzy C-Means) for segmentation of  Astrocytoma (Brain Tumor) with all four Grades. The comparative study between FCM, FCM plus K-mean, Genetic Algorithm, ANFIS and proposed technique shows improved Accuracy, Sensitivity and Specificity.</p>

Extension-Based Clustering Method: An Approach to Support Adaptable Design of the Product

2007 ◽  
Author(s):  
Yanwei Zhao ◽  
Huijun Tang ◽  
Nan Su ◽  
Wanliang Wang
Keyword(s):  
Fuzzy Clustering ◽  
Design Method ◽  
Direct Method ◽  
Threshold Value ◽  
Minimal Distance ◽  
Clustering Methods ◽  
Clustering Method ◽  
Adaptable Design ◽  
Fuzzy Clustering Method ◽  
Distance Formula

Design for product adaptability is one of the techniques used to provide customers with products that exactly meet their requirements. Clustering methods have been used extensively in the study of product adaptability design. Of the clustering methods, the fuzzy clustering method is the most widely in the design field. The three main kinds of fuzzy clustering methods are the transitive closure method, the dynamic direct method and the maximum tree method. The dynamic direct clustering method has been found to produce design solutions with the lowest cost. In this paper, a new approach for obtaining adaptable product designs using the clustering method is proposed. The method consists of three steps. Firstly, the extension distance formula is used to determine the distance between two products in a product database. The product design space and the distances between individuals are used as grouping criteria in this step. Secondly, the minimal distance between products is used to obtain the clustering index. Thirdly, the threshold value is used to divide the products in the database into groups. Customer demands and the results obtained from the adaptable function (based on the extension distance formula) are used to evaluate the fitness of the groups and their corresponding products. The product with the largest adaptable function value to demand ratio is selected product. In order to the show the advantage of using the extension-clustering method, both the extension-clustering method and the dynamic direct method are presented and compared. The comparison indicates that the extension-clustering method leads to quicker evaluations of design alternatives and results that more closely match customers’ demands. An example of the adaptable design of circular saws tools is used to demonstrate that with the extension-clustering design method a high variety of intelligent configurations can be obtained with significant rapidity.

scholarly journals Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 158
Author(s):  
Tran Dinh Khang ◽  
Nguyen Duc Vuong ◽  
Manh-Kien Tran ◽  
Michael Fowler
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Methods ◽  
Clustering Method ◽  
Machine Learning Technique ◽  
Practical Applications ◽  
Fuzzy C Means ◽  
Fuzzy Clustering Method ◽  
Learning Technique ◽  
Fuzzy C Means Clustering

Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering technique. Since the advent of the FCM method, many improvements have been made to increase clustering efficiency. These improvements focus on adjusting the membership representation of elements in the clusters, or on fuzzifying and defuzzifying techniques, as well as the distance function between elements. This study proposes a novel fuzzy clustering algorithm using multiple different fuzzification coefficients depending on the characteristics of each data sample. The proposed fuzzy clustering method has similar calculation steps to FCM with some modifications. The formulas are derived to ensure convergence. The main contribution of this approach is the utilization of multiple fuzzification coefficients as opposed to only one coefficient in the original FCM algorithm. The new algorithm is then evaluated with experiments on several common datasets and the results show that the proposed algorithm is more efficient compared to the original FCM as well as other clustering methods.

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