scholarly journals Development of Rule-Based Software Risk Assessment and Management Method with Fuzzy Inference System

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Mustafa Batar ◽  
Kökten Ulaş Birant ◽  
Ali Hakan Işık
Keyword(s):  
Risk Assessment ◽  
Software Development ◽  
Fuzzy Inference ◽  
Development Projects ◽  
Software Projects ◽  
Inference System ◽  
Rule Based ◽  
Management Method ◽  
Software Risk ◽  
Risk Assessment And Management

There is an enormous budget and financial plan in software development projects, and it is required that they take a huge investment to carry on. When looked at, the costs depend on the global substantial information about software development: in 1985, $150 billion; in 2010, $2 trillion; in 2015, $5 trillion; and in 2020, over $7 trillion. Additionally, on the first new days of 2021, a day-by-day Apple Store’s quantity has been approximately $500 million. In spite of the expenditures and the margins that are dramatically expanding and increasing each year, the phase of software development accomplishment is not high enough. In light of the “CHAOS” report arranged in 2015, just 17% of the software projects were finished in an opportune way, in the allotted financial plan, and as per the necessities. However, 53% of the software projects were finished in the long run or potentially over a spending plan as well as without satisfying the prerequisites precisely. In addition, software development projects were not completed and were dropped out as well in the ratio of 30%. Also, the “CHAOS” report published in 2020 has figured out that only 33% of the software projects were completed successfully all over the world. In order to cope with these unsuccessful and failure results, an effective method for software risk assessment and management has to be specified, designated, and applied. In this way, before causing trouble that has the power of preventing successful accomplishment of software development projects, software risks are able to be noticed and distinguished on time. In this study, a new and original rule set, which could be used and carried out effectively in software risk assessment and management, has been designed and developed based on the implementation of fuzzy approached technique integrated with machine learning algorithm—Adaptive Neuro-Fuzzy Inference System (ANFIS). By this approach and technique, machines (computers) are able to create several software risk rules not to be seen, not to be recognized, and not to be told by human beings. In addition, this fuzzy inference approach aims to decrease risks in the software development process in order to increase the success rate of the software projects. Also, the experimental results of this approach show that rule-based software risk assessment and management method has a valid and accurate model with a high accuracy rate and low average testing error.

scholarly journals PROPOSING A NEW METHODOLOGY BASED ON FUZZY LOGIC FOR TUNNELLING RISK ASSESSMENT

2014 ◽  
Vol 20 (1) ◽  
pp. 82-94 ◽  
Author(s):  
Abdolreza Yazdani-Chamzini
Keyword(s):  
Risk Assessment ◽  
Fuzzy Inference ◽  
Assessment Model ◽  
Tunnel Construction ◽  
Construction Operations ◽  
Protective Measures ◽  
Inference System ◽  
Proposed Model ◽  
Mine Development ◽  
Civil Defence

Tunnels are artificial underground spaces that provide a capacity for particular goals such as storage, under-ground transportation, mine development, power and water treatment plants, civil defence. This shows that the tunnel construction is a key activity in developing infrastructure projects. In many situations, tunnelling projects find themselves involved in the situations where unexpected conditions threaten the continuity of the project. Such situations can arise from the prior knowledge limited by the underground unknown conditions. Therefore, a risk analysis that can take into account the uncertainties associated with the underground projects is needed to assess the existing risks and prioritize them for further protective measures and decisions in order to reduce, mitigate and/or even eliminate the risks involved in the project. For this reason, this paper proposes a risk assessment model based on the concepts of fuzzy set theory to evaluate risk events during the tunnel construction operations. To show the effectiveness of the proposed model, the results of the model are compared with those of the conventional risk assessment. The results demonstrate that the fuzzy inference system has a great potential to accurately model such problems.

Methodological provisions for assessing scientific and technical risk and financial losses during development works to create complex technical systems

2021 ◽  
Vol 20 (10) ◽  
pp. 1933-1950
Author(s):  
Nikolai V. FIROV ◽  
Sergei A. SOROKIN
Keyword(s):  
Risk Assessment ◽  
Development Stage ◽  
Financial Resources ◽  
Development Projects ◽  
Technical Risk ◽  
Complex Technical Systems ◽  
Technical Systems ◽  
The Impact ◽  
Risk Assessment And Management ◽  
Financial Losses

Subject. The article addresses scientific and technical risk and financial losses of the customer in the process of research and development works on the creation of complex technical systems. Objectives. The study aims at constructing and analyzing the dependence of scientific and technical risk and financial losses of the customer on the planned volume of development works and the financial resources invested in them. Methods. We apply methods of probability theory and mathematical statistics, system and regression analysis, risk assessment and management. The paper rests on data on completed development projects for complex technical systems creation. Results. We formulated methodological provisions for assessing scientific and technical risk, arising in the process of development works on complex technical systems. The paper presents an algorithm for calculating the expected financial losses from works implementation. The problem of minimizing financial losses associated with scientific and technical risk is formulated and formalized. The feasibility of proposed provisions and recommendations is confirmed by a practical example. Conclusions. To assess risks, it is important to consider the impact of the degree of difference between the main characteristics of developed product and its prototype on the required amount of works at development stage. This enables to build regression dependencies of the volume of works at the development stage on a specified factor, which are later used to assess the scientific and technical risk and associated financial losses.

Rainfall events prediction using rule-based fuzzy inference system

2011 ◽  
Vol 101 (1-2) ◽  
pp. 228-236 ◽  
Author(s):  
Somia A. Asklany ◽  
Khaled Elhelow ◽  
I.K. Youssef ◽  
M. Abd El-wahab
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Rule Based ◽  
Rainfall Events

Fuzzy-Rule Based Adaptive Data Warehouse

2012 ◽  
Vol 3 (1) ◽  
pp. 47-65 ◽  
Author(s):  
Rajdev Tiwari ◽  
Anubhav Tiwari ◽  
Manu Pratap Singh
Keyword(s):  
Decision Making ◽  
Fuzzy Inference ◽  
Knowledge Workers ◽  
Fuzzy Rule ◽  
Data Warehouses ◽  
Large Reservoir ◽  
Inference System ◽  
Rule Based ◽  
Neuro Fuzzy ◽  
New Generation

Data Warehouses (DWs) are aimed to empower the knowledge workers with information and knowledge which helps them in decision making. Technically, the DW is a large reservoir of integrated data that does not provide the intelligence or the knowledge demanded by users. The burden of data analysis and extraction of information and knowledge from integrated data still lies upon the analyst’s shoulder. The overhead of analysts can be taken off by architecting a new generation data warehouses systems those shall be capable of capturing, organizing and representing knowledge along with the data and information in it. This new generation DW may be called as Knowledge Warehouse (KW) shall exhibit decision making capabilities themselves and can also supplement the Decision Support Systems (DSS) in making decisions quickly and effortlessly. This paper proposes and simulates a fuzzy-rule based adaptive knowledge warehouse with capabilities to learn and represent implicit knowledge by means of adaptive neuro fuzzy inference system (ANFIS).

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