A modified ANP and fuzzy inference system based approach for risk assessment of in-house and third party e-procurement systems

2016 ◽  
Vol 9 (2) ◽  
pp. 159-188 ◽  
Author(s):  
M. Ramkumar
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Fuzzy Inference ◽  
New Technology ◽  
Third Party ◽  
Assessment Process ◽  
Inference System ◽  
Content Type ◽  
Technological Risk ◽  
Risk Magnitude

Purpose The increasing complexity and dynamism of new technology implemented or to implement have imposed substantial uncertainties and subjectivities in the risk assessment process. This paper aims to present a risk assessment methodology for e-procurement implementation based on modified analytic network process (ANP) coupled with fuzzy inference systems. Design/methodology/approach ANP is modified in such a way that the experts can provide necessary data precise numerical value, a range of numerical values, a linguistic term or a fuzzy number. The proposed methodology incorporates knowledge and judgements obtained from experts to carry out identification of risk factors and to assess the risk magnitude of the identified risk factors based on factor index, risk likelihood and risk severity. Findings Risk magnitude of third party systems are found to be minor with a belief of 100 per cent, and for in-house systems, the risk is found to be between minor with a belief of 30 per cent and major of 70 per cent. The results indicate that by using the proposed methodology, the technological risk assessment of new technology can be done effectively and efficiently. Research limitations/implications Using the results of this study, the practitioners can better know the pros and cons of implementing both in-house and third party e-procurement systems. Originality/value The modified ANP is used mainly to structure and prioritize the diverse risk factors. Finally, an illustrative example on technological risk assessment of both in-house and third party e-procurement systems is used to demonstrate the applicability of the proposed methodology in real life situations.

scholarly journals IMPROVEMENT OF PROJECT RISK ASSESSMENT METHODS OF IMPLEMENTATION OF AUTOMATED INFORMATION COMPONENTS OF NON-COMMERCIAL ORGANIZATIONAL AND TECHNICAL SYSTEMS

2020 ◽  
Vol 1 ◽  
pp. 48-55
Author(s):  
Аlexander Androshchuk ◽  
Serhii Yevseiev ◽  
Victor Melenchuk ◽  
Olga Lemeshko ◽  
Vladimir Lemeshko
Keyword(s):  
Risk Assessment ◽  
Information Systems ◽  
Fuzzy Inference ◽  
Knowledge Bases ◽  
Assessment Process ◽  
Software Module ◽  
Inference System ◽  
Technical Systems ◽  
Input Variables ◽  
Information Components

The results of a study using the methodological apparatus of the theory of fuzzy logic and automation tools for analyzing input data for risk assessment of projects for the implementation of automated information components of organizational and technical systems are presented. Based on the model of logistics projects for motor transport units, the method for assessing the risks of projects implementing automated information components of non-commercial organizational and technical systems has been improved. To do this, let’s analyze the peculiarities of implementing ERP projects as commercial ones and investigate the specifics of the activities of state institutions, when successful tasks, and not economic indicators, lay the foundation for the assessment. It is considered that it is possible to formulate a system of risk assessment indicators for reducing the effectiveness of projects for implementing automated information systems in non-commercial organizational and technical systems. A meaningful interpretation of the fuzzy approach is carried out regarding the formalization of the risk assessment process for projects of automated information systems of public institutions. A tree of fuzzy inference is constructed based on the results of a study of the description of indicators and expert assessments on the risk assessment of the implementation of the project of such an automated information system. The improved method differs from the known ones by the use of hierarchical fuzzy inference, which makes it possible to quantify, reduce the time to evaluate project risks and improve the quality of decisions. An increase in the number of input variables leads to an increase in complexity (an increase in the number of rules) for constructing a fuzzy inference system. The construction of a hierarchical system of fuzzy inference and knowledge bases can reduce complexity (the number of rules). The development of a software module based on the algorithm of the method as part of corporate automated information systems of non-commercial organizational and technical systems will reduce the time for risk assessment of projects for the implementation of automated information systems.

scholarly journals A comparative risk assessment of dialysis care processes in the home and hospital care contexts

2020 ◽  
Vol 11 (5) ◽  
pp. 985-1002
Author(s):  
Peter Chemweno ◽  
Liliane Pintelon
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Home Care ◽  
Domain Knowledge ◽  
Assessment Process ◽  
Hospital Environment ◽  
Negative Consequences ◽  
Comparative Risk Assessment ◽  
Care Givers ◽  
Comparative Risk

Abstract Dialysis processes within the home care context is associated with risk factors which are not very prominent in the hospital context. This includes risk factors such as unanticipated device malfunction, or erroneous operation of the equipment, which exposes the patient to injury while undergoing dialysis. Importantly, the mentioned risk factors are further attributed to technical aspects such as sub-optimal equipment maintenance or following improper clinical procedures when administering care to the patient. Hence, it is important to follow a methodological approach to identify and assess hazards embedded within the dialysis treatment process, and on this basis, formulate effective strategies to mitigate their negative consequences on patient safety. This paper presents a comparative risk assessment for in-hospital versus in-home dialysis care. For the two cases, the risk assessment considers expertise of care givers involved in administering dialysis. The findings show that performing risk assessment for hospital environment, is more structured owing to expertise of clinicians and care givers responsible for administering dialysis. However, assessing risks for the home-care environment is more challenging owing to absence of domain knowledge, hence a survey approach to structure the risk assessment process is necessary. Moreover, risks in the home care context is influenced by logistical aspects, and lack of domain knowledge for maintaining dialysis equipment. Overall, insights from the comparative studies yields important learning points expected to improve dialysis care as more healthcare providers transfer care to the home environment.

Structure identification and IO space partitioning in a nonlinear fuzzy system for prediction of patient survival after surgery

2017 ◽  
Vol 10 (2) ◽  
pp. 166-182 ◽  
Author(s):  
Shabia Shabir Khan ◽  
S.M.K. Quadri
Keyword(s):  
Pancreatic Cancer ◽  
Fuzzy Inference System ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Patient Survival ◽  
Data Partitioning ◽  
Structure Identification ◽  
Inference System ◽  
Content Type ◽  
Neuro Fuzzy

Purpose As far as the treatment of most complex issues in the design is concerned, approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence, particularly this involves dealing with vagueness, multi-objectivity and good amount of possible solutions. In practical applications, computational techniques have given best results and the research in this field is continuously growing. The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery. The present study involves the construction of such intelligent computational models using different configurations, including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients. Design/methodology/approach On the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools, the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction. The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system (ANFIS) models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data. After evaluating the models over three shuffles of data (training set, test set and full set), the performances were compared in order to find the best design for prediction of patient survival after surgery. The construction and implementation of models have been performed using MATLAB simulator. Findings On applying the hybrid intelligent neuro-fuzzy models with different configurations, the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer. Experimental results and comparison between the constructed models conclude that ANFIS with Fuzzy C-means (FCM) partitioning model provides better accuracy in predicting the class with lowest mean square error (MSE) value. Apart from MSE value, other evaluation measure values for FCM partitioning prove to be better than the rest of the models. Therefore, the results demonstrate that the model can be applied to other biomedicine and engineering fields dealing with different complex issues related to imprecision and uncertainty. Originality/value The originality of paper includes framework showing two-way flow for fuzzy system construction which is further used by the authors in designing the three simulation models with different configurations, including the partitioning methods for prediction of patient survival after surgery. Several experiments were carried out using different shuffles of data to validate the parameters of the model. The performances of the models were compared using various evaluation measures such as MSE.

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.

Risk assessment of mining projects in Ghana

2017 ◽  
Vol 23 (1) ◽  
pp. 22-38 ◽  
Author(s):  
Charles Teye Amoatey ◽  
Samuel Famiyeh ◽  
Peter Andoh
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Academic Research ◽  
Mining Industry ◽  
Commodity Prices ◽  
Mitigation Measures ◽  
Content Type ◽  
Management Culture ◽  
Mining Project ◽  
Mining Projects

Purpose The purpose of this paper is to assess the critical risk factors affecting mining projects in Ghana. Design/methodology/approach A purposive sampling approach was used in selecting the respondents for the study. These were practitioners working on mining projects in Ghana. Findings The study identified 22 risk factors contributing to mining project failure in Ghana. The five most critical mining project risk factors based on both probability of occurrence and impact were unstable commodity prices, inflation/exchange rate, land degradation, high cost of living and government bureaucracy for obtaining licenses. Mitigation measures for addressing the identified risk factors were identified. Research limitations/implications This paper is limited to data collected from practitioners working on mining projects. Due to geographic and logistical constraints, the study did not include the perception of local communities in quantifying the risk factors. Practical implications This paper has documented the critical risk factor affecting the mining industry in Ghana. Though the identified risk types are also prevalent in other sectors of the construction industry, the key findings of this paper emphasize the need for a comprehensive risk management culture in the mining sector. From an academic research perspective, the paper contributes to a conceptual risk assessment framework. Originality/value The information gathered through this research can be utilized in identifying and understanding risks during the early stages of mining project implementation.

Technology intervention for preventing COVID-19 outbreak

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Prateek Pandey ◽  
Ratnesh Litoriya
Keyword(s):  
Fuzzy Inference ◽  
Inference System ◽  
Content Type ◽  
Research Article ◽  
The People ◽  
Entry Points ◽  
The World ◽  
Technology Intervention ◽  
History Of ◽  
Alternative Routes

PurposeThe purpose for writing this article is derived from the misery and chaos prevalent in the world due to the coronavirus pandemic – since late 2019 and still continuing as of December 2020.Design/methodology/approachA blockchain-based solution to verify the country visit trail and disease and treatment history of the passengers who arrive at the immigration counters located at various national borders and entry points is proposed. A fuzzy inference based suspect identifier system is also presented in this article that could be utilized to make further decisions based on the degree of suspicion observed on a particular passenger.FindingsThis paper attempted to put forth a blockchain-based system which consumes the healthcare and visit trail summary of a passenger (appearing for an interview before an immigration officer) and forwards it to a fuzzy inference system to reach to a conclusion that the passenger should be advised to self-quarantine, detained, or should be allowed to enter. Such a system would help to make correct decisions at the immigration counters to check pandemic diseases, like COVID-19, right at the entry points.Research limitations/implicationsThe implications of this work are manifold. First, the proposed framework works independent of the type of pandemic and is a readymade tool to check the spread of disease through infected human carriers. Second, the proposed framework will keep the mortality rates under check, which would give ample time for the authorities to save the lives of the people with co-morbidities and age vulnerabilities (Vichitvanichphong et al., 2018). Third, it is a general phenomenon to restrict the flights from the country where the first few cases of infection are discovered; however, the infected person, at the same time, might travel through alternative routes. The blockchain-enabled proposed framework ensures the detection of such cases at no other cost. Finally, the solution may appear costly in the first place, but it has the potential to hold back the revenue of the countries that would otherwise be spent on reactive measures.Originality/valueAs of now no other study or research article provides the solution to the biggest problem persists in the world in this way. The contribution is original and worth applying.

Failure mode, effects and criticality analysis improvement by using new criticality assessment and prioritization based approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ammar Chakhrit ◽  
Mohammed Chennoufi
Keyword(s):  
Failure Mode ◽  
Conventional Method ◽  
Fuzzy Inference System ◽  
Risk Evaluation ◽  
Failure Modes ◽  
Fuzzy Inference ◽  
Decision Makers ◽  
Inference System ◽  
Content Type ◽  
Criticality Analysis

Purpose This paper aims to enable the analysts of reliability and safety system to assess the criticality and prioritize failure modes perfectly to prefer actions for controlling the risks of undesirable scenarios. Design/methodology/approach To resolve the challenge of uncertainty and ambiguous related to the parameters, frequency, non-detection and severity considered in the traditional approach failure mode effect and criticality analysis (FMECA) for risk evaluation, the authors used fuzzy logic where these parameters are shown as members of a fuzzy set, which fuzzified by using appropriate membership functions. The adaptive neuro-fuzzy inference system process is suggested as a dynamic, intelligently chosen model to ameliorate and validate the results obtained by the fuzzy inference system and effectively predict the criticality evaluation of failure modes. A new hybrid model is proposed that combines the grey relational approach and fuzzy analytic hierarchy process to improve the exploitation of the FMECA conventional method. Findings This research project aims to reflect the real case study of the gas turbine system. Using this analysis allows evaluating the criticality effectively and provides an alternate prioritizing to that obtained by the conventional method. The obtained results show that the integration of two multi-criteria decision methods and incorporating their results enable to instill confidence in decision-makers regarding the criticality prioritizations of failure modes and the shortcoming concerning the lack of established rules of inference system which necessitate a lot of experience and shows the weightage or importance to the three parameters severity, detection and frequency, which are considered to have equal importance in the traditional method. Originality/value This paper is providing encouraging results regarding the risk evaluation and prioritizing failures mode and decision-makers guidance to refine the relevance of decision-making to reduce the probability of occurrence and the severity of the undesirable scenarios with handling different forms of ambiguity, uncertainty and divergent judgments of experts.

scholarly journals Risk-aware decision support with constrained goal models

2018 ◽  
Vol 26 (4) ◽  
pp. 472-490 ◽  
Author(s):  
Nikolaos Argyropoulos ◽  
Konstantinos Angelopoulos ◽  
Haralambos Mouratidis ◽  
Andrew Fish
Keyword(s):  
Risk Assessment ◽  
Automated Reasoning ◽  
Functional System ◽  
Assessment Process ◽  
Security Requirements ◽  
Government Information ◽  
Content Type ◽  
Additional Value ◽  
Goal Models ◽  
Selection Of

Purpose The selection of security configurations for complex information systems is a cumbersome process. Decision-making regarding the choice of security countermeasures has to take into consideration a multitude of, often conflicting, functional and non-functional system goals. Therefore, a structured method to support crucial security decisions during a system’s design that can take account of risk whilst providing feedback on the optimal decisions within specific scenarios would be valuable. Design/methodology/approach Secure Tropos is a well-established security requirements engineering methodology, but it has no concepts of Risk, whilst Constrained Goal Models are an existing method to support relevant automated reasoning tasks. Hence we bridge these methods, by extending Secure Tropos to incorporate the concept of Risk, so that the elicitation and analysis of security requirements can be complimented by a systematic risk assessment process during a system’s design time and supporting the reasoning regarding the selection of optimal security configurations with respect to multiple system objectives and constraints, via constrained goal models. Findings As a means of conceptual evaluation, to give an idea of the applicability of the approach and to check if alterations may be desirable, a case study of its application to an e-government information system is presented. The proposed approach is able to generate security mechanism configurations for multiple optimisation scenarios that are provided, whilst there are limitations in terms of a natural trade-off of information levels of risk assessment that are required to be elicited. Originality/value The proposed approach adds additional value via its flexibility in permitting the consideration of different optimisation scenarios by prioritising different system goals and the automated reasoning support.

Modeling and predict environmental indicators for land leveling using adaptive neuro-fuzzy inference system (ANFIS), and regression

2018 ◽  
Vol 12 (4) ◽  
pp. 484-506 ◽  
Author(s):  
Farhad Mirzaei ◽  
Mahmoud Delavar ◽  
Isham Alzoubi ◽  
Babak Nadjar Arrabi
Keyword(s):  
Energy Consumption ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Environmental Indicators ◽  
Inference System ◽  
Content Type ◽  
Bee Colony ◽  
Fill Volume ◽  
Land Leveling ◽  
Neural Fuzzy

PurposeThe purpose of this paper is to develop three methods including artificial bee colony algorithm (ABC-ANN), regression and adaptive neural fuzzy inference system (ANFIS) to predict the environmental indicators for land leveling and to analysis the sensitivity of these parameters.Design/methodology/approachThis paper develops three methods including artificial bee colony algorithm (ABC-ANN), regression and adaptive neural fuzzy inference system (ANFIS) to predict the environmental indicators for land leveling and to analysis the sensitivity of these parameters. So, several soil properties such as soil, cut/fill volume, soil compressibility factor, specific gravity, moisture content, slope, sand per cent and soil swelling index in energy consumption were investigated. A total of 90 samples were collected from three land areas with the selected grid size of (20 m × 20 m). Acquired data were used to develop accurate models for labor, energy (LE), fuel energy (FE), total machinery cost (TMC) and total machinery energy (TM).FindingsBy applying the three mentioned analyzing methods, the results of regression showed that, only three parameters of sand per cent, slope and soil, cut/fill volume had significant effects on energy consumption. All developed models (Regression, ANFIS and ABC-ANN) had satisfactory performance in predicting aforementioned parameters in various field conditions. The adaptive neural fuzzy inference system (ANFIS) has the most capability in prediction according to least RMSE and the highestR2value of 0.0143, 0.9990 for LE. The ABC-ANN has the most capability in prediction of the environmental and energy parameters with the least RMSE and the highestR2with the related values for TMC, FE and TME (0.0248, 0.9972), (0.0322, 0.9987) and (0.0161, 0.9994), respectively.Originality/valueAs land leveling with machines requires considerable amount of energy, optimizing energy consumption in land leveling operation is of a great importance. So, three approaches comprising: ABC-ANN, ANFIS as powerful and intensive methods and regression as a fast and simplex model have been tested and surveyed to predict the environmental indicators for land leveling and determine the best method. Hitherto, only a limited number of studies associated with energy consumption in land leveling have been done. In mentioned studies, energy was a function of the volume of excavation (cut/fill volume). Therefore, in this research, energy and cost of land leveling are functions of all the properties of the land including slope, coefficient of swelling, density of the soil, soil moisture, special weight and swelling index which will be thoroughly mentioned and discussed. In fact, predicting minimum cost of land leveling for field irrigation according to the field properties is the main goal of this research which is in direct relation with environment and weather pollution.

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