1.Introduction

High risk organizations are increasingly aware of the importance of human and organizational factors and their impact on health and safety on work. The integration of these factors allows companies to achieve safe and successful human performance, by understanding the interactions between humans and other elements of complex systems in order to predict and reduce accidents and incidents.
There are several approaches bringing together the elements that contributed to major accidents in the past, from the implementation of the Seveso I and II Directives to the integration of the Human and Organizational Factors. However, it is difficult for the company to choose the right approach and to know which specific areas of HOFs need to be prioritized. Therefore, the frequently asked questions are : Where are we and where do we want to be ?. These questions are used to define the current state of the company and the desired objective.
The maturity model concept has been developed to answer these questions, by assisting companies in the maturity assessment process and identifying the required areas for improvement. The HOFs maturity model describes the main characteristics allowing a safe human contribution and optimizing the overall performance of the system, it enables the company to determine the maturity in terms of HOFs among a set of suggested levels.
This paper is an extension of work originally presented in “2020 IEEE 6th International Conference on Optimization and Applications” [1], in which a new HOFs maturity model is described. The model is made up of the five crucial factors related to HOFs and proposed five maturity levels that reflect the degree of consideration and integration of these factors through planned procedures.
The difference between the conference paper [1] and this article is the inclusion of fuzzy logic in the weighting calculation phase, by using the Fuzzy AHP method instead of the Analytic Hierarchy Process (AHP) to take into consideration the imprecision and vagueness of human judgements , when assessing the relative importance of the hierarchy elements in order to perform the comparison matrices . Therefore, the proposed methodology consists in combining the FAHP method to determine the weights and the FCEM to assess the HOFs maturity level.
The proposed maturity model is implemented in a Moroccan organization operating in the mining industry to bring out the current situation by measuring its own HOFs maturity level, then define future targets and establish an action plan including the elements to improve.
The next sections are structured as follows: The section “Literature Review” describes the frequently cited models. In section “ HOFs Maturity Model” the elements and levels of the model are presented. While section “Methodology” explains the FAHP and FCEM used. A numeric application is given in “Case Study” section, before the “Conclusion”.

Literature Review

Research indicates a significant growth in the use of maturity models for safety culture assessment and the integration of human and organizational factors, particularly in high-risk areas such as construction and the oil and gas industry. A maturity model describes the key or essential elements that should characterize an organization at a particular maturity level either in relation to safety culture or to HOFs.
The use of maturity models as an assessment tool can be attributed to two main sources, namely, the “Quality Management Maturity Grid (QMMG)” and Westrum’s “Typology of Organisations”. The QMMG was suggested in 1979, it identified the five stages that an organization goes through to achieve the maximum quality level in all aspects of quality management these are: uncertainty, awakening, enlightenment, wisdom and certainty [2].
Typology of organisational cultures was proposed in 1993. Table 1 describes the characteristics of the three organisational types suggested. To allow a meticulous classification, while increasing employees’ accessibility by including familiar terms, the typology was extended to these five levels : Pathological, Reactive, Calculative, Proactive and Generative [3].
Table 1: Westrum’s Typology of Organisations

In [4], the author used this concept of maturity to create a model named “safety culture maturity model (SCCM)” to help oil companies in the United Kingdom to assess culture maturity level and the actions needed to improve it. The five stages of the SCCM are shown in Figure 1.

Figure 1: Safety Culture Maturity Model

Based on the SCCM and a thorough literature review of existing models, the author developed in [5] the “Cultural Maturity Model (CMM)” for companies that have an adequate safety management system and wish to evaluate and improve it. Table 2 summarizes the ten elements of the CMM selected to measure key characteristics related to a positive safety culture, and the five developmental stages suggested.
Table 2: Cultural Maturity Model Elements and Stages

Workforce involvement

In [6, 7], the author proposed a maturity model, based on the extended Westrum’s typology, to describe the evolution of safety culture from the disease stage to a generative end stage. Figure 2 shows the development stages of the model.
From the previous model, a framework was developed to determine the stages of maturity of an organization’s safety culture by conducting in-depth interviews with 26 senior oil industry executives working in various multinational and contracting companies [8,9]. It contains 11 tangible or concrete elements related to the safety management system, and 7 less tangible or abstract elements involving perceptions of the workforce. The eighteen elements are divided as follows:
Tangible or concrete elements: 1. Benchmarking, Trends & Statistics; 2. Audits & Reviews; 3. Incident/Accident Reporting, Investigation & Analysis; 4. Hazard and Unsafe Act reports; 5. Work planning including PTW, Journey Management; 6. Contractor Management; 7. Competency/Training; 8. Work-site Job Safety Techniques; 9. Who Checks Safety on a day to day basis? 10. What is size & status of the HSE Department? 11. What are the rewards of good safety performance?
Less tangible or abstract elements: 1. Who causes accidents in the eyes of management? 2.What happens after an accident? Is the feedback Loop being closed? 3. How do safety meetings feel? 4. Balance between HSE & Profitability? 5. Is management interested in communicating HSE issues with the workforce? 6. Commitment level of the workforce and level of care for colleagues. 7. What is the purpose of procedures?

Figure 2: Safety Culture Model of Hudson

Maturity models have also been used to measure the level of integration of human factors, as they are essential to achieve safe human performance. The “ Human Factors Maturity Model (HFMM)” was designed to help companies wishing to improve their maturity level by determining which items require additional effort in the future [10]. It includes 5 levels from the first Emerging level to a final level called Leading, and 12 key elements of human factors that highlight their relevance to major accidents in the literature. For each item, a card sorting methodology is used to assess the current level of maturity by representatives of organizations, then define the requirements needed to improve maturity to the next level. The elements and levels of the HFMM are shown in Table 3.
Maturity models have been used by several industries in the literature following different measurement methods, which often require precise human judgment to choose one of the maturity levels. And since human judgment is imprecise and vague, the model proposed in this article includes fuzzy logic to remedy this issue [11].
Table 3: Human Factors Maturity Model

HFMM Levels
HFMM Elements
1.        Emerging

2.        Transitional

3.        Planned

4.        Proactive

5.        Leading
1.        Managing Human Failure (including maintenance)

2.        Human Factors in Incident Investigation

3.        Design and Development of Procedures

4.        Training and Competence

5.        Staffing and Workload

6.        Managing Organisational Change

7.        Safety-Critical Communications

8.        Human Factors in Design

9.        Fatigue and Shift work

10.     Safety Culture and Behaviour

11.     Contractor Management

12.     Managing Performance under Pressure

HOFs Maturity Model

A review of the literature was carried out to select the human and organizational factors that affect human performance, in order to reduce accidents and improve the safety level of organizations. The maturity model presented in this article is developed based on the five key factors [1] cited by the Rail & Safety Standards Board (RSSB) in their guide “Understanding human factors” [12] namely:
Design F1: The consideration of HOFs in the design is a crucial step to ensure that the organization does not miss major opportunities for improving human performance at work and to eliminate the risk of accidents, loss of personnel and significant financial costs.
Training F2: Numerous studies demonstrate the effectiveness of training in business growth. The results of training are concrete and measurable both in productivity and in the professional development of the staff. Although a company may have carefully screened its employees, training within the company itself will allow them to have the same vision of things and move in the same direction.
Staffing F3: Recruiting and retaining the right people is critical to the success of every organization. Selecting the wrong skills can lead to organizational weakness.
Culture F4: Culture is essential to allow the organization to differentiate itself. In terms of image on the one hand, it has advantages both internally and externally to consumers. It is indeed a source of cohesion and motivation of employees and it limits conflicts.
Conditions F5: The improvement of working conditions to ensure the well-being of the employee must first affect health and safety. All employees wish to carry out their work in a healthy and pleasant atmosphere, in which they can feel the recognition and consideration of their efforts.
Table 4 lists the sub-factors linked to each of the five factors, which allow to determine the maturity level in terms of HOFs among the five presented in Figure 3. [1]
Table 4: HOFs Maturity Model

Figure 3: Maturity Levels Description

To ensure the understanding and appropriate application of the model, a set of good practices related to each sub-factor have been listed. For the retention sub-factor, as an example, the best practices relevant to employee satisfaction and retention are presented below:
Offer rewards.
Listen to your staff.
Carry out training.
Improve your team culture.
Develop career paths.
Understand why people quit.
Maintain dialogue with staff
Table 5 provides an overview of the questionnaire developed to facilitate data collection during the maturity assessment. The questions are established from the proposed model to bring out the information concerning the sub-factors.

Measurement Methodology

The methodology proposed in this work is based on the use of FAHP method instead of AHP to calculate the factors and sub-factors weightings, that reflect their impacts on human performance and safety. For the HOFs maturity level assessment, the FCEM method is utilized to consider the imprecision and uncertainty of decision-makers’ judgment.
Below are explained the FAHP and FECM methods used for the model implementation.
4.1 Fuzzy AHP
Fuzzy AHP method is an extension of the AHP introduced in 1970 [13], used to solve complex decision-making problems. This method deals with the ambiguity and vagueness of decision-makers that cannot be addressed by precise values by integrating fuzzy logic [11].
Many extensions of AHP have been proposed by several authors such as the geometric mean method [14]. In this paper, the HOFs model utilized the extent analysis approach proposed in [15] given the simplicity and ease of application using triangular fuzzy numbers (TFNs) for the pairwise comparisons.
The steps of the FAHP method are [16, 17]:
Step 1: Construct the AHP hierarchy by identifying factors and sub-factors of the model.
Step 2: Compare by pair the factors and sub-factors to calculate the local weights using the fuzzy scale [18] shown in Table 6 and Figure 4. This scale is used in Chang’s FAHP method described below.

Figure 4: Linguistic Scale for Relative Importance

Assume that X = {x_1,x_2,… ,x_n} is an object set, and U = {u_1,u_2,… ,u_m} is a goal set. For each object, an extent analysis is performed for each goal g_i. Therefore, m values of the extent analysis are obtained for each object, as follows:

where M_(g_i)^j ( j=1, 2, … , m) are TFNs. A TFN is simply denoted (l,m,u).
The value of fuzzy synthetic Si for the i-th object is:

The fuzzy addition operations are performed with m values.

The degree of possibility of S_2=(l_2,m_2,u_2)≥ S_1=(l_1,m_1,u_1) is defined as:

Which can be expressed equivalently as follows:

Table 5: HOFs Questionnaire Overview

Table 6: Linguistic Scale for Relative Importance

where d is the ordinate of the highest intersection point D between μ_(S_1 )and μ_(S_2 )(Figure 5).
Both the values of V(S_2≥ S_1) and V(S_1≥ S_2) are needed to compare S_1 and S_2.

Figure 5: Intersection Between S_1 and S_2

The degree possibility for a convex fuzzy number to be greater than k convex fuzzy numbers S_i (i =1,. . . , k) can be expressed by:
V(S≥ S_1,S_2 ,…,S_k)=V[(S≥ S_1 ),…,(S≥ S_k)]=

for k =1, …, n , k≠i. Then the weight vector W’ is given by:

where A_i (i=1,2,…,n) are n elements.

Finally, the normalized weight vectors are obtained:

where W is a crisp number.
Fuzzy Comprehensive Evaluation Method
The fuzzy comprehensive evaluation method is based on fuzzy logic theory developed in [11]. Unlike the system of classical logic, it aims to deal with uncertainty, subjectivity, and vagueness of human reasoning. The FCEM principles are used with the proposed model to assess the maturity level of HOFs as follows [1, 19, 20, 21, 22]:
Let V={V_1,V_2,…,V_p}, the evaluation set.
Step 1: Construct the evaluation matrix R_ij named the second-class index membership matrix.

where
R_ij is a fuzzy relationship from F_i’s sub-factors (F_ij) to V.
Where: i=1, …, n is the number of factors to be evaluated.
j=1, …, m is the second index of i, and m is the number of sub-factors(F_ij) of a factor (F_i).
R_ij is a fuzzy relationship from F_ij to V.

β =∑_(k=1)^(k=p)▒l_ijk is the experts’ number, k=1, …, p is the evaluation level.

Step 2: Compute the matrix R named the first-class index membership matrix.

W_ij is the weight vector obtained from the FAHP method.

Step 3: Calculate the maturity vector M:

Finally, the maturity level is determined according to the principle of maximum membership degree law as following:
If r_k = Max (■(r_1&⋯&r_p )) , the maturity level is k.

Case study

The HOFs maturity model and the methodologies developed in this paper are implemented in a Moroccan organization operating in the mining industry to define potential future steps essential to improve human performance and safety level. Therefore, a group of decision makers was selected for the acquisition of input data and the application of the FAHP and FCEM methods. The comparisons matrices were done by the representatives of managers, supervisors and operators. Unlike the assessment step, which was carried out separately, first with the Group 1 of managers, next with the Group 2 of supervisors and site operators.

Results

The representatives performed the pairwise comparisons of factors and sub-factors listed in Table 4 using the fuzzy scale illustrated in figure 6 [18], and obtained the results shown in Tables 7, 8, 9, 10, 11, 12.

Table 7: Weights and Comparison Matrix of Factors

Factors and sub-factors weights are computed using Chang’s FAHP method [15] described above. A numerical overview of the steps is given below to calculate weights shown on the right of Table 7.

SF1= (0.10, 0.18, 0.30) ; SF2= (0.08, 0.12, 0.23) ;
SF3= (0.08, 0.14, 0.26) ; SF4= (0.15, 0.25, 0.41) ;
SF5= (0.19, 0.31, 0.50).

V (SF1 ≥ SF2) = 1; V (SF1 ≥ SF3) = 1; V (SF1 ≥SF4) = 0.69;
V (SF1 ≥ SF5) = 0.46.
V (SF2 ≥ SF1) = 0.68; V (SF2 ≥ SF3) = 0.91 ;
V (SF2 ≥ SF4) = 0.38 ; V (SF2 ≥ SF5) = 0.17.
V (SF3 ≥ SF1) = 0.77; V (SF3 ≥ SF2) = 1 ; V (SF3 ≥ SF4) = 0.49 ; V (SF3 ≥ SF5) = 0.28.
V (SF4 ≥ SF1) = 1 ; V (SF4 ≥ SF2) = 1 ; V (SF4 ≥ SF3) = 1 ;
V (SF4 ≥ SF5) =0.77.
V (SF5 ≥ SF1) = 1; V (SF5 ≥ SF2) = 1 ; V (SF5 ≥ SF3) = 1 ;
V (SF5 ≥ SF4) =1.

d’(AF1) = V (SF1 ≥ SF2, SF3, SF4, SF5) = 0.46 ;
d’(AF2) = V (SF2 ≥ SF1, SF3, SF4, SF5) = 0.17 ;
d’(AF3) = V (SF3 ≥ SF1, SF2, SF4, SF5) = 0.28 ;
d’(AF4) = V (SF4 ≥ SF1, SF2, SF3, SF5) =0.77 ;
d’(AF5) = V (SF5 ≥ SF1, SF2, SF3, SF4) =1 .

Then, the weight vector of factors is obtained:
W = (d’(AF1), …, d’(AF5))T= (0.17, 0.06, 0.10, 0.29, 0.37).

Table 8: Weights and Comparison Matrix of F1 Sub-Factors

Table 9: Weights and Comparison Matrix of F2 Sub-Factors

Table 10: Weights and Comparison Matrix of F3 Sub-Factors

Table 11: Weights and Comparison Matrix of F4 Sub-Factors

Table 12: Weights and Comparison Matrix of F5 Sub-Factors

Once the weights were calculated, an evaluation of the sub-factors was conducted by the two groups, separately, using the questionnaire and FCEM. The results obtained are shown in Tables 13, 14.

Table 13: FAHP Weights and Evaluation Results of Group1

Table 14: FAHP Weights and Evaluation Results of Group 2

The sub-factors’ weights and the assessment results are subsequently used to determine the first-index membership matrix of the two groups represented in Tables 15, 16.
Table 15: First-Class Index Membership Matrix of Group 1

Table 16: First-Class Index Membership Matrix of Group 2

Finally, the HOFs maturity vector (HOFM) is determined using Tables 15, 16 and factors’ weights.
HOMF1(Group 1) = (0.02, 0.10, 0.37, 0.38, 0.13)
HOMF2(Group 2) = (0.03, 0.16, 0.37, 0.33, 0.11)

Discussions

According to the maximum membership degree law and maturity vectors (HOMF1, HOMF2) obtained from both groups, the maturity level of the company is “Managed” for Group 1 and “Planned” for Group 2. Therefore, the managers assume that there is a strong consideration of HOFs, and good practices are achieved through planned procedures. While supervisors and operators suppose that the impact of HOFs on safety and human performance is moderately taken into account, and some good practices related to HOFs are fulfilled in a planned manner.
The maturity levels of factors are deducted from the first-class index membership matrices (Tables 15, 16). Then, the results are presented on spider diagrams below. Figure 6 shows the factors’ maturity levels obtained from the assessment carried out with Group 1 of managers, and Figure 7 shows the levels obtained from the evaluation undertaken by Group 2 consisting of supervisors and operators.

Figure 6: Spider Diagram of Group 1

The two diagrams show that no factor is judged at level 5 “Continually Improving”. The results obtained are close for both groups despite the hierarchical diversity. The three factors: “Staffing”, “Training” and “Culture” are at the same maturity level 4 “Managed”. The factor “Conditions” is also at level 3 “Planned” for the two groups. However, the “Design” factor is at level 4 “Managed” for Group 1 and level 3 “Planned” for group 2.

Figure 7: Spider Diagram of Group 2

The purpose of this model is not only to assess maturity in terms of HOF, but also to assist the company in defining future improvement steps to reach the next level of maturity. For this mining organization, it is necessary to provide more effort regarding the “Conditions” factor since it has the highest weighting, and it is evaluated at level 3 for both groups. Then, for the factor “Design” which has such an important impact, it is recommended to take into consideration the opinions of supervisors and operators to improve the current state and satisfy their needs.
After this study, an action plan made up of some good practices essential for improvement was established based on elements weights and the assessment carried out by the groups. Table 17 shows a summary of the proposed action plan.

Conclusion

There is a remarkable growth in the use of maturity models to assess safety culture and HOFs, but also different variations in the elements of these models and measurement methodology. These models can be used for maturity assessment in any industry and as an improvement tool by identifying strengths and weaknesses related to a set of key elements.
This work introduces a maturity model related to HOFs made up of the five factors essential to human performance and safety. The model defines five stages of maturity, from the lower level where the company does not take into consideration the impact of HOFs, to the upper level characterized by continuous enhancement and permanent follow-up of HOFs procedures. The measurement methodology differs here in the first phase, which consists of using Fuzzy AHP method instead of AHP to calculate the weights. And for the second phase, a questionnaire is included to facilitate the collection of data used in the FCEM method.
The HOFs model is applied in a Moroccan mining company to determine the current maturity level and improve it through an appropriate action plan. Teams of managers, supervisors and operators who participated in implementing the model have used it successfully, and it has proven to be a useful tool for evaluation an improvement.
Future work will focus on improving the current HOFs maturity model by testing and adapting it for a wide range of companies, so that it is not limited to a particular area. Also, conducting a sensitivity analysis [23] to test the robustness of the model and better understand relationships between the elements.

Table 17: Improvement Action Plan Related to HOFs

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16. Nour Najeeb Abdalkareem Qubbaj, Anas Abu Taleb, Walid Salameh, "LEACH Based Protocols: A Survey", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1258–1266, 2020. doi: 10.25046/aj0506150
17. Marcel Nicola, Marian Duță, Maria-Cristina Nițu, Ancuța-Mihaela Aciu, Claudiu-Ionel Nicola, "Improved System Based on ANFIS for Determining the Degree of Polymerization", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 664–675, 2020. doi: 10.25046/aj050680
18. Arooba Shahoor, Rida Shaukat, Sumaira Sultan Minhas, Hina Awan, Kashif Saghar, "SharpniZer: A C# Static Code Analysis Tool for Mission Critical Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 561–570, 2020. doi: 10.25046/aj050668
19. Kamal Jaiswal, Serdar Dalkilic, Evangelos Papageorgiou, Balgopal Singh, "Aviation MRO: Impact of Physical Environment Factors on Job Performance in Aircraft Maintenance Organization", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 148–154, 2020. doi: 10.25046/aj050617
20. Saloua Said, Hafida Bouloiz, Maryam Gallab, "Resilience Assessment of System Process Through Fuzzy Logic: Case of COVID-19 Context", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 1247–1260, 2020. doi: 10.25046/aj0505150
21. Yonatan López Santos, Diana Sánchez-Partida, Patricia Cano-Olivos, "Strategic Model to Assess the Sustainability and Competitiveness of Focal Agri-Food Smes and their Supply Chains: A Vision Beyond COVID 19", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 1214–1224, 2020. doi: 10.25046/aj0505147
22. Shimin Vayal Veetil, Varsha Shah, Makarand Lokhande, "Improvised E-Rickshaws for Indian Roads by Effective Battery-Ultracapacitor Hybridization", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 1162–1171, 2020. doi: 10.25046/aj0505141
23. Ines Kechiche, Ines Bousnina, Abdelaziz Samet, "A Review of RPL Objective Function based Enhancement Approaches", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 201–211, 2020. doi: 10.25046/aj050525
24. Ditdit Nugeraha Utama, Sherly Oktafiani, "Generic Decision Support Model for Determining the Best Marketer", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 841–848, 2020. doi: 10.25046/aj050498
25. Adamu Abdullahi Garba, Maheyzah Muhamad Siraj, Siti Hajar Othman, "An Explanatory Review on Cybersecurity Capability Maturity Models", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 762–769, 2020. doi: 10.25046/aj050490
26. Amar Choudhary, Deependra Pandey, Saurabh Bhardwaj, "Overview of Solar Radiation Estimation Techniques with Development of Solar Radiation Model Using Artificial Neural Network", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 589–593, 2020. doi: 10.25046/aj050469
27. Hamidi Meryem, Bouattane Omar, Raihani Abdelhadi, Khalili Tajeddine, "Development of an Adaptive HVAC Fuzzy Logic Controller for Commercial Facilities: A Case Study", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 531–539, 2020. doi: 10.25046/aj050463
28. Siddikov Isamiddin Xakimovich, Umurzakova Dilnoza Maxamadjonovna, "Fuzzy-logical Control Models of Nonlinear Dynamic Objects", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 419–423, 2020. doi: 10.25046/aj050449
29. Trinh Luong Mien, Vo Van An, Bui Thanh Tam, "A Fuzzy-PID Controller Combined with PSO Algorithm for the Resistance Furnace", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 568–575, 2020. doi: 10.25046/aj050371
30. Fadoua Tamtam, Amina Tourabi, "A Framework for Measuring Workforce Agility: Fuzzy Logic Approach Applied in a Moroccan Manufacturing Company", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 411–418, 2020. doi: 10.25046/aj050352
31. Lorenzo Damiani, Roberto Revetria, Emanuele Morra, "Safety in Industry 4.0: The Multi-Purpose Applications of Augmented Reality in Digital Factories", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 248–253, 2020. doi: 10.25046/aj050232
32. Lorenzo Damiani, Roberto Revetria, Emanuele Morra, Pietro Giribone, "A Smart Box for Blood Bags Transport: Simulation Model of the Cooling Autonomy Control System", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 249–255, 2020. doi: 10.25046/aj050131
33. Omar Chamorro-Atalaya, Jaime Yataco-Yataco, Dora Arce-Santillan, "Industrial Network for The Control and Supervision of The Acetic Acid Dispatch Process, and Its Influence on The Reduction of Chemical Contaminants for Operators", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 13–20, 2020. doi: 10.25046/aj050103
34. Sevar Dilkhaz Salahaddin Neamat, "A Comparative Study of Safety Leading and Lagging Indicators Measuring Project Safety Performance", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 306–312, 2019. doi: 10.25046/aj040639
35. Samruan Wiangsamut, Phatthanaphong Chomphuwiset, Suchart Khummanee, "Chatting with Plants (Orchids) in Automated Smart Farming using IoT, Fuzzy Logic and Chatbot", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 163–173, 2019. doi: 10.25046/aj040522
36. Alghamdi Abdullah, Mohammed Thanoon, Anwar Alsulami, "Toward a Smart Campus Using IoT: Framework for Safety and Security System on a University Campus", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 97–103, 2019. doi: 10.25046/aj040512
37. Stephen Craig Stubberud, Kathleen Ann Kramer, Allen Roger Stubberud, "Estimation of Target Maneuvers from Tracked Behavior Using Fuzzy Evidence Accrual", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 468–477, 2019. doi: 10.25046/aj040457
38. Mohamed Hamada, Abdulsalam Latifat Ometere, Odu Nkiruka Bridget, Mohammed Hassan, Saratu Yusuf Ilu, "A Fuzzy-Based Approach and Adaptive Genetic Algorithm in Multi-Criteria Recommender Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 449–457, 2019. doi: 10.25046/aj040454
39. Trinh Luong Mien, "An Adaptive Fuzzy-Sliding Mode Controller for The Bridge Crane", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 3, pp. 164–170, 2019. doi: 10.25046/aj040322
40. Andro Majid, Djoko Budiyanto Setyohadi, Suyoto, "Estimation of Software Development Project Success using Fuzzy Logics", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 280–287, 2019. doi: 10.25046/aj040236
41. Jonathan Lockhart, Carla Purdy, Philip Wilsey, "Critical Embedded Systems Development Using Formal Methods and Statistical Reliability Metrics", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 1, pp. 231–247, 2019. doi: 10.25046/aj040123
42. Nosiri Onyebuchi Chikezie, Onyenwe Ezinne Maureen, Ekwueme Emmanuel Uchenna, "Fuzzy Logic Implementation for Enhanced WCDMA Network Using Selected KPIs", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 1, pp. 114–124, 2019. doi: 10.25046/aj040112
43. Sherif Said, Samer AlKork, Taha Beyrouthy, Murtaza Hassan, OE Abdellatif, M Fayek Abdraboo, "Real Time Eye Tracking and Detection- A Driving Assistance System", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 446–454, 2018. doi: 10.25046/aj030653
44. Wei Long Chen, Fang Lin Chao, "Scientific Principles behind 2017 World Universiade of Safety Lantern and Torch", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 4, pp. 21–25, 2018. doi: 10.25046/aj030403
45. Zeynep Bala Duranay, Hanifi Guldemir, "Fuzzy Logic Based Selective Harmonic Elimination for Single Phase Inverters", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 3, pp. 161–167, 2018. doi: 10.25046/aj030322
46. Kanasottu Anil Naik, Chandra Prakash Gupta, Eugene Fernandez, "Performance improvement of a wind energy system using fuzzy logic based pitch angle control", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 3, pp. 30–37, 2018. doi: 10.25046/aj030304
47. Salma Charmi, Bassem El Badsi, Abderrazak Yangui, "Direct Torque Control Strategy Based on the Emulation of Six-Switch Inverter Operation by a Four-Switch Inverter Using an Adaptive Fuzzy Controller", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 2, pp. 346–356, 2018. doi: 10.25046/aj030237
48. Luca Dariz, Gianpiero Costantino, Massimiliano Ruggeri, Fabio Martinelli, "A Joint Safety and Security Analysis of message protection for CAN bus protocol", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 1, pp. 384–393, 2018. doi: 10.25046/aj030147
49. Gints Poiss, Sandra Vitolina, Janis Marks, "Development of Indicators for Technical Condition Indexing of Power Transformers", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 1, pp. 148–154, 2018. doi: 10.25046/aj030118
50. Ibrahim Alagoz, Thomas Hoiss, Reinhard German, "Improving System Reliability Assessment of Safety-Critical Systems using Machine Learning Optimization Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 1, pp. 49–65, 2018. doi: 10.25046/aj030107
51. Mustafa Oezguel, Florian Deeg, Sebastian M. Sattler, "Mealy-to-Moore Transformation – A state stable design of automata", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 6, pp. 162–174, 2017. doi: 10.25046/aj020621
52. Bülent Sari, Hans-Christian Reuss, "Model-based Development of Safety-critical Functions and ISO 26262 Work Products using modified EAST-ADL", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1252–1259, 2017. doi: 10.25046/aj0203158
53. Ammar Al-Gizi, Sarab Al-Chlaihawi, Aurelian Craciunescu, "Efficiency of Photovoltaic Maximum Power Point Tracking Controller Based on a Fuzzy Logic", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1245–1251, 2017. doi: 10.25046/aj0203157
54. Imad Elzein, Yury Petrenko, "Adapting Model Predictive Control for a PV Station and Evaluating two different MPPT Algorithms P&O and FLC", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 741–748, 2017. doi: 10.25046/aj020394
55. Maulana Erwin Saputra, Safrizal, "Analysis of Learning Development With Sugeno Fuzzy Logic And Clustering", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 5, pp. 26–30, 2017. doi: 10.25046/aj020505
56. Tung T. Nguyen, Issey Takahashi, Takahiro Tanaka, Eijiro Takeuchi, Shinpei Kato, Tatsuya Suzuki, Hitoshi Kanamori, Yoshiki Ninomiya, Hirofumi Aoki, "Development of an automated vehicle stop system for cardiac emergencies", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 669–673, 2017. doi: 10.25046/aj020385
57. Ouafae Kasmi, Amine Baina, Mostafa Bellafkih, "Multi Level Integrity Management in LTE/LTE-A Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 658–668, 2017. doi: 10.25046/aj020384
58. Alvaro Anzueto-Rios, Jose Antonio Moreno-Cadenas, Felipe Gómez-Castañeda, Sergio Garduza-Gonzalez, "Image Segmentation Using Fuzzy Inference System on YCbCr Color Model", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 460–468, 2017. doi: 10.25046/aj020359