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Methodology of automated quality management

  • Shara Toibayeva EMAIL logo and Irbulat Utepbergenov
Published/Copyright: January 17, 2024
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Abstract

This research is devoted to the development of innovative technology for automation of the quality management system (QMS) of enterprises in Kazakhstan and its adaptation to the management system of enterprises. This article deals with quality, as an important strategic tool in business. System effectiveness evaluation of quality management enterprises is of great importance connected with the formation of rational decisions in the management of QMSs, including specificity of quality indicators, multi-level system, the necessity to choose the optimal number of performance indicators, and system status evaluation. The objective and relevance of this research are related to the need to (1) solve the problems of quality management in the digital economy, following from the relevant National programs of the Government of the Republic of Kazakhstan, which are important at this step of in-depth scientific research; (2) guarantee the competitiveness of domestic enterprises with high-quality requirements for products and services; (3) improve the efficiency of automated QMSs; (4) save resources (human and timing) in data processing. The method and model of automated enterprise quality management and intelligent automated system of quality management of enterprise integrated with ruling MICS (Management Information and Control System) subsystems are offered allowing to automate QMS implementation and support processes and increasing the validity, efficiency and effectiveness of management decisions by automating a number of functions of decision makers and personnel.

1 Introduction

Quality is an important strategic tool in business [1]. The goal of business process improvement is the transformation of the enterprise to meet the requirements of modern IT and management ideology in the aspect of the process approach. Problems of evaluating the effectiveness of the company’s QMS are of great importance connected to the formation of rational decisions in the management of quality management systems (QMS), including specificity of quality indicators, multi-level systems, the necessity to choose the optimal number of performance indicators, and system status evaluation [2,3].

Information technology helps to change the relationship between consumption and production, and their interaction requires the exchange of information in order to organize and manage both manufacturers and consumers [4].

A monograph by Kubekov [5,6] has been studied as part of the ontological modeling of knowledge representation and management in the QMS, where the methodology of modeling knowledge components based on ontological engineering is presented, and new definitions are introduced. As to this project, we introduced a methodology for modeling business processes from the detailed engineering of architecture to the marketing of business logic [7,8].

Burkov et al. in Management Theory of Organizational Systems [9] discussed management models of organizational systems, as well as methods of solving management problems. The project [10] provides the basic mechanisms to manage organizational systems, and provides samples for the design of integrated management mechanisms, as well as mathematical models of the theory to manage organizational systems and their applications. We also devote attention to the complexity of computing the solution of problems of optimal management in the models of functioning of active systems, study effective methods of decision-making, and put management problems, using “parallelization” solution algorithms [11,12]. Conceptual and methodological research in management systems are presented in the literature [13].

The main objective for effective management is the stages of formation, implementation, and use of an automated QMS. The management system should provide access to the documentation of the enterprise and qualitative conclusion of the requested information for receiving managerial operations to solve the problems of the enterprise as of a certain moment.

As to the analysis reports of the literature over the past 20 years, we can say that today in the world, especially in Kazakhstan, a scientific direction is developing related to the problems of implementation and automation of QMS, and technology analysis of business processes of various organizations.

2 The main part

It is necessary to solve the problems of introducing and maintaining up-to-date modern management systems in an industrially developed country, where competition, knowledge-intensive, innovative, and technologically sophisticated production are developed.

The Prime Minister of the Republic of Kazakhstan has adopted resolutions No. 28-r dated February 06, 2004, and No. 175-r dated April 27, 2006, in quality management due to the earliest rearrangement of enterprises according to ISO standards, and therefore, in order to achieve its objectives, the country is developing adequate logistics, regulatory and methodological system for the implementation of international standards [14,15].

The regulatory system of the Republic of Kazakhstan [14,16] includes 36 state standards, which are based on the international ISO standards and adopted as state standards of the Republic of Kazakhstan. Every year, the development and implementation of standards are included in the standardization plan of the Republic of Kazakhstan in management.

QMSs are key to maintaining the desired product quality and providing first-class services. QMS automate a wide range of business processes, including product design, standard operating procedures (SOP) development, management analysis, audits, training, claims management, corrective action preventive action (CAPA), etc. QMS users deal with huge amounts of data and various documents in their daily work. Handling such heterogeneous information manually can lead to human error and endanger products and consumers.

The automation of enterprises requires a lot of time and investment. Even if the system is designed and compliant with ISO standards, it will not provide assessment and prompt processing of a large amount of information related to the functioning of the organization. The information required is not communicated to a process on time for all intents and purposes, and as a result, the decisions approved will largely not be fully adequate and are only addressed in an automated system [17].

The critically important fact is that quality management not only requires the use of automation tools but is also as well adapted as possible for their application. The provisions of the ISO 9000 series are based on the modification of the information flows of the enterprise [18], which makes possible the development and application of a running, in a way standard software.

Kazakhstan enterprises certify quality in their organizations as an important business strategy. Moreover, as information technology has developed, a problem arose in the obsolescence of traditional methods of data management on conformity of quality management [19,20].

Today, intelligent methods based on neural network technologies and fuzzy logic are used to solve management problems [21,22,23,24].

Artificial neural networks, based on learning and generalization algorithms, allow in some cases to successfully predict time series and reduce the requirements for mathematical training of subject experts, but neural network models cannot be formally imagined, and it is not possible to provide the results of time-series analysis.

The purpose of the article is the research and development of the methodology of automated management of the enterprise QMS, which allows us to automate the processes of implementation and maintenance of QMS at the enterprises of Kazakhstan and integrate with the existing subsystems of the automated enterprise management system for the implementation of the most important tasks arising from the State Program on digitalization of economic sectors.

3 Discussion

In order to test the efficiency of the proposed methodology, models, and algorithms of automated quality management of the enterprise, a numerical study using the QMS data of the enterprise Innovation & Technologies LLP has been conducted. The data of the enterprise “Innovation & Technologies” LLP are considered received in the course of monitoring of processes by experts (heads of departments). Experts are specialists in the enterprise, and the number of experts is 6.

Intelligent management systems have developed over the past few years [25]. The main direction of development of these systems is the use of fuzzy logic apparatus: fuzzy set, fuzzy modeling, etc.

The fuzzy models of automated management systems are based on fuzzy logic controllers (FLCs) used to develop various automated process control systems (APCS), control systems for complex dynamic systems, etc. The FLC is based on fuzzy logic models: fuzzy link models and inference rules. The following system of linguistic description is popular for the FLC based on a fuzzy production processor: translation into fuzzy values (fuzzy value), fuzzy logical link, composite inference rules, and conversion operators into plain values (defuzzy value). The main step in designing an intelligent fuzzy controller is to create a “knowledge base” using representation methods and knowledge search.

Business processes of an enterprise are divided into several subgroups according to the ST RK ISO 9001 standard: Main processes, management (or managerial), and supporting (or otherwise auxiliary), and the number of processes depends on the specifics of the enterprise, as the standard does not define the exact number of processes but is only recommendatory (Figure 1).

Figure 1 
               Business processes of the enterprise.
Figure 1

Business processes of the enterprise.

The offered fuzzy model of production quality management gives an opportunity to predict indicators of the quality of services provided by the enterprise using “Supporting processes” for further use and introduction into the model.

Input and output variables of the two-level model are presented in Figure 2, where the output of the first level will be one of the inputs for the second level.

Figure 2 
               Two-level QMS assessment model.
Figure 2

Two-level QMS assessment model.

Figure 3 shows the fuzzy inference algorithm using the first level.

Figure 3 
               Fuzzy inference algorithm using the first level.
Figure 3

Fuzzy inference algorithm using the first level.

A Level-I output is an index of the correctness of business processes, the inputs are the variables, i.e. the index of inconsistencies (Inconsistencies) and the index of the correctness of the description of business processes (Figure 3). A sector is divided into three areas for variables, low, average, and high, defining their interval and membership functions.

Similarly, the rest of the enterprise QMS processes are calculated; thus, the possibility of obtaining quantitative estimates of processes in the developed model of intelligent quality management of production and business processes using the Mamdani fuzzy logic apparatus in the enterprise for QMS is shown. Using the program of viewing the surface of the fuzzy model, the adequacy of the constructed model and the influence of input variables on the output variable is proved.

Therefore, we will make a rule database, as shown in Table 1.

Table 1

I Rule database of the evaluation model QMS manual

No. Inconsistencies Correctness of description Process
1 High High Low
2 Average Average Average
3 Low Low High
4 High Low Average
5 High Average Low
6 Low Average High
7 Average Low Average
8 Average High Low

We can use the fuzzy model surface viewer shown in Figure 4 to find out the adequacy of the model, and as input variables affect the output one.

Figure 4 
               Matlab fuzzy model surface view window.
Figure 4

Matlab fuzzy model surface view window.

The test results of the created fuzzy inference model are presented in Table 2. As the systemic error δ (%) is not more than 5% to the original expert data, the developed model is considered adequate [p. 2.8, 118].

Table 2

The test result of the fuzzy output model level 1

No. Inconsistencies Correctness of description Process δ (%)
1 1 1 0.85 4.1
2 5 10.5 0.5 0.0
3 9 20 0.090 0.1
4 2 19 0.40 5.0
5 8 2 0.44 2.9
6 7 7 0.45 1.9
7 4 15 0.35 1.7
8 3 14 0.53 1.4

The rule database (a number of rules T input variable = 3 4 = 81 , after optimization = 30) is shown in Table 3.

Table 3

Level-II rule database of the evaluation model QMS manual

No. Achieving goals Degree of proper functioning of processes Customer satisfaction level Degree of implementation of corrective/preventive actions Performance assessment
1 2 3 4 5 6
1 Bad Low Not satisfactory Low 1
2 Well (medium) Low Not satisfactory Low 1
3 Perfect Low Not satisfactory Low 2
4 Bad Average Average satisfactory Average 2
5 Well (medium) Average Average satisfactory Average 3
6 Perfect Average Average satisfactory Average 4
7 Bad High Satisfactory High 3
8 Well (medium) High Satisfactory High 5
9 Perfect High Satisfactory High 5
10 Bad Average Not satisfactory Low 1
11 Bad High Not satisfactory Low 2
12 Well (medium) Low Average satisfactory Average 2
13 Well (medium) High Average satisfactory Average 4
14 Perfect Low Satisfactory High 3
15 Perfect Average Satisfactory High 5
16 Bad Low Average satisfactory Low 1
17 Bad Low Satisfactory Low 1
18 Well (medium) Average Not satisfactory Average 3
19 Well (medium) Average Satisfactory Average 4
20 Perfect High Not satisfactory High 4
21 Perfect High Average satisfactory High 5
22 Bad Low Not satisfactory Average 1
23 Bad Low Not satisfactory High 1
24 Well (medium) Average Average satisfactory Low 3
25 Well (medium) Average Average satisfactory High 4
26 Perfect High Satisfactory Low 4
27 Perfect High Satisfactory Average 5
28 Bad High Not satisfactory High 2
29 Bad High Average satisfactory High 2
30 Bad High Satisfactory Average 3

We can use the Level-II fuzzy model surface viewer, shown in Figure 5, and determine the adequacy of the model, as input variables of Level II affect the output variable score (Figure 6).

Figure 5 
               Level-II fuzzy model surface viewer window.
Figure 5

Level-II fuzzy model surface viewer window.

Figure 6 
               Level-II fuzzy inference rule viewer window.
Figure 6

Level-II fuzzy inference rule viewer window.

Table 4 shows the model test results showing the positive effects of Level-II input variables on the output variable score.

Table 4

Model testing

No. Achieving goals Degree of proper functioning of processes Customer satisfaction level Degree of implementation of corrective/preventive actions Performance assessment δ, (%)
1 2 3 4 5 6 7
1 1 0 0 1 7.86 1.3
2 62 0 0 1 8.56 0.4
3 94 1 0 1 27.71 4.1
4 2 0.55 0.55 55 35.70 4.1
5 50 0.5 0.5 50 52.41 0.2
6 96 0.55 0.55 55 73.5 3.0
7 5 0.98 0.98 98 56.3 1.4
8 52 0.95 0.9 97 99.7 1.1
9 100 1 1 100 100 0.2
10 7 0.6 0.2 60 52.2 0.4
11 8 0.95 0.2 10 31 0.0
12 55 0.25 0.55 55 38.6 0.5
13 55 0.85 0.55 55 81.2 0.1
14 85 0.25 0.95 95 58.8 0.0
15 85 95 0.65 0.95 98 0.1
16 2 0.3 0.5 20 11.4 2.7
17 2 0.2 0.8 19 14.9 2.8
18 55 0.6 0.2 60 52.4 0.4
19 55 0.6 0.8 60 67.1 0.0
20 90 0.95 0.05 95 81.2 0.1
21 95 0.95 0.51 95 99.1 0.0
22 15 0.15 0.15 50 8.73 0.8
23 15 0.15 0.15 90 8.73 0.8
24 40 0.5 0.5 10 51.7 0.6
25 40 0.5 0.5 90 76.7 0.6
26 97 0.97 0.97 7 81.5 0.0
27 97 0.97 0.97 67 99.6 0.2
28 5 0.97 0.17 97 31.3 0.0
29 5 0.97 0.57 97 32.1 0.9
30 5 0.97 0.97 57 52.4 0.4

Since the relative error does not exceed 5%, we can conclude that the developed rule bases are adequate.

Figure 7 shows the window of the automated quality management software developed by the authors of the enterprise QMS “Progress Analysis.”

Figure 7 
               QMS progress analysis window.
Figure 7

QMS progress analysis window.

Figure 7 shows the window view to fill in the assessments of the company’s QMS indices developed by the intelligent automated QMS of the enterprise.

4 Conclusions and follow-up

Effective data analysis is critical for upgrading and progressive improvement. Quality management technology provides data presentation in one place and facilitates data exchange and analysis. This can help to identify wasteful processes, quality defects, or inefficient equipment in less time.

Companies can improve quality processes in less time and take corrective actions with production management software. Overall results are better products and fewer customer complaints.

Using fuzzy logic theory for the analysis of QMS provides an opportunity to obtain fundamentally new models and methods for analyzing these systems [26].

It is reasonable to use the production form of knowledge stored in the QMS effectiveness assessment, which was confirmed in the development of a model of intelligent quality management of production processes using fuzzy logic apparatus [27].

The follow-up will focus on the development of the implementing models and algorithms for the digital transformation of documentation support for QMS to detect contradictions and inconsistencies in documentation support of QMS of Kazakhstan's Economy. This solution will eliminate the problems of processing high volumes of regulatory documents of the enterprise when accompanied by the automated QMS. The solution should be based on a reality model and be accompanied by the development of a formal language with approaches similar to the creation of well-known declarative programming languages.

Acknowledgement

General scientific research methods are practiced in this research.

  1. Funding information: This project was supported by a grant from the Ministry of Education and Science of the Republic of Kazakhstan (Zhas Galym project No. AR 13268939 Research and development of digital technology to provide consistency in the media of normative documents of the quality management system).

  2. Conflict of interest: Authors state no conflict of interest.

  3. Data availability statement: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Received: 2022-11-23
Revised: 2023-10-17
Accepted: 2023-10-30
Published Online: 2024-01-17

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  100. Special Issue: AESMT-6
  101. Design of a new sorting colors system based on PLC, TIA portal, and factory I/O programs
  102. Forecasting empirical formula for suspended sediment load prediction at upstream of Al-Kufa barrage, Kufa City, Iraq
  103. Optimization and characterization of sustainable geopolymer mortars based on palygorskite clay, water glass, and sodium hydroxide
  104. Sediment transport modelling upstream of Al Kufa Barrage
  105. Study of energy loss, range, and stopping time for proton in germanium and copper materials
  106. Effect of internal and external recycle ratios on the nutrient removal efficiency of anaerobic/anoxic/oxic (VIP) wastewater treatment plant
  107. Enhancing structural behaviour of polypropylene fibre concrete columns longitudinally reinforced with fibreglass bars
  108. Sustainable road paving: Enhancing concrete paver blocks with zeolite-enhanced cement
  109. Evaluation of the operational performance of Karbala waste water treatment plant under variable flow using GPS-X model
  110. Design and simulation of photonic crystal fiber for highly sensitive chemical sensing applications
  111. Optimization and design of a new column sequencing for crude oil distillation at Basrah refinery
  112. Inductive 3D numerical modelling of the tibia bone using MRI to examine von Mises stress and overall deformation
  113. An image encryption method based on modified elliptic curve Diffie-Hellman key exchange protocol and Hill Cipher
  114. Experimental investigation of generating superheated steam using a parabolic dish with a cylindrical cavity receiver: A case study
  115. Effect of surface roughness on the interface behavior of clayey soils
  116. Investigated of the optical properties for SiO2 by using Lorentz model
  117. Measurements of induced vibrations due to steel pipe pile driving in Al-Fao soil: Effect of partial end closure
  118. Experimental and numerical studies of ballistic resistance of hybrid sandwich composite body armor
  119. Evaluation of clay layer presence on shallow foundation settlement in dry sand under an earthquake
  120. Optimal design of mechanical performances of asphalt mixtures comprising nano-clay additives
  121. Advancing seismic performance: Isolators, TMDs, and multi-level strategies in reinforced concrete buildings
  122. Predicted evaporation in Basrah using artificial neural networks
  123. Energy management system for a small town to enhance quality of life
  124. Numerical study on entropy minimization in pipes with helical airfoil and CuO nanoparticle integration
  125. Equations and methodologies of inlet drainage system discharge coefficients: A review
  126. Thermal buckling analysis for hybrid and composite laminated plate by using new displacement function
  127. Investigation into the mechanical and thermal properties of lightweight mortar using commercial beads or recycled expanded polystyrene
  128. Experimental and theoretical analysis of single-jet column and concrete column using double-jet grouting technique applied at Al-Rashdia site
  129. The impact of incorporating waste materials on the mechanical and physical characteristics of tile adhesive materials
  130. Seismic resilience: Innovations in structural engineering for earthquake-prone areas
  131. Automatic human identification using fingerprint images based on Gabor filter and SIFT features fusion
  132. Performance of GRKM-method for solving classes of ordinary and partial differential equations of sixth-orders
  133. Visible light-boosted photodegradation activity of Ag–AgVO3/Zn0.5Mn0.5Fe2O4 supported heterojunctions for effective degradation of organic contaminates
  134. Production of sustainable concrete with treated cement kiln dust and iron slag waste aggregate
  135. Key effects on the structural behavior of fiber-reinforced lightweight concrete-ribbed slabs: A review
  136. A comparative analysis of the energy dissipation efficiency of various piano key weir types
  137. Special Issue: Transport 2022 - Part II
  138. Variability in road surface temperature in urban road network – A case study making use of mobile measurements
  139. Special Issue: BCEE5-2023
  140. Evaluation of reclaimed asphalt mixtures rejuvenated with waste engine oil to resist rutting deformation
  141. Assessment of potential resistance to moisture damage and fatigue cracks of asphalt mixture modified with ground granulated blast furnace slag
  142. Investigating seismic response in adjacent structures: A study on the impact of buildings’ orientation and distance considering soil–structure interaction
  143. Improvement of porosity of mortar using polyethylene glycol pre-polymer-impregnated mortar
  144. Three-dimensional analysis of steel beam-column bolted connections
  145. Assessment of agricultural drought in Iraq employing Landsat and MODIS imagery
  146. Performance evaluation of grouted porous asphalt concrete
  147. Optimization of local modified metakaolin-based geopolymer concrete by Taguchi method
  148. Effect of waste tire products on some characteristics of roller-compacted concrete
  149. Studying the lateral displacement of retaining wall supporting sandy soil under dynamic loads
  150. Seismic performance evaluation of concrete buttress dram (Dynamic linear analysis)
  151. Behavior of soil reinforced with micropiles
  152. Possibility of production high strength lightweight concrete containing organic waste aggregate and recycled steel fibers
  153. An investigation of self-sensing and mechanical properties of smart engineered cementitious composites reinforced with functional materials
  154. Forecasting changes in precipitation and temperatures of a regional watershed in Northern Iraq using LARS-WG model
  155. Experimental investigation of dynamic soil properties for modeling energy-absorbing layers
  156. Numerical investigation of the effect of longitudinal steel reinforcement ratio on the ductility of concrete beams
  157. An experimental study on the tensile properties of reinforced asphalt pavement
  158. Self-sensing behavior of hot asphalt mixture with steel fiber-based additive
  159. Behavior of ultra-high-performance concrete deep beams reinforced by basalt fibers
  160. Optimizing asphalt binder performance with various PET types
  161. Investigation of the hydraulic characteristics and homogeneity of the microstructure of the air voids in the sustainable rigid pavement
  162. Enhanced biogas production from municipal solid waste via digestion with cow manure: A case study
  163. Special Issue: AESMT-7 - Part I
  164. Preparation and investigation of cobalt nanoparticles by laser ablation: Structure, linear, and nonlinear optical properties
  165. Seismic analysis of RC building with plan irregularity in Baghdad/Iraq to obtain the optimal behavior
  166. The effect of urban environment on large-scale path loss model’s main parameters for mmWave 5G mobile network in Iraq
  167. Formatting a questionnaire for the quality control of river bank roads
  168. Vibration suppression of smart composite beam using model predictive controller
  169. Machine learning-based compressive strength estimation in nanomaterial-modified lightweight concrete
  170. In-depth analysis of critical factors affecting Iraqi construction projects performance
  171. Behavior of container berth structure under the influence of environmental and operational loads
  172. Energy absorption and impact response of ballistic resistance laminate
  173. Effect of water-absorbent polymer balls in internal curing on punching shear behavior of bubble slabs
  174. Effect of surface roughness on interface shear strength parameters of sandy soils
  175. Evaluating the interaction for embedded H-steel section in normal concrete under monotonic and repeated loads
  176. Estimation of the settlement of pile head using ANN and multivariate linear regression based on the results of load transfer method
  177. Enhancing communication: Deep learning for Arabic sign language translation
  178. A review of recent studies of both heat pipe and evaporative cooling in passive heat recovery
  179. Effect of nano-silica on the mechanical properties of LWC
  180. An experimental study of some mechanical properties and absorption for polymer-modified cement mortar modified with superplasticizer
  181. Digital beamforming enhancement with LSTM-based deep learning for millimeter wave transmission
  182. Developing an efficient planning process for heritage buildings maintenance in Iraq
  183. Design and optimization of two-stage controller for three-phase multi-converter/multi-machine electric vehicle
  184. Evaluation of microstructure and mechanical properties of Al1050/Al2O3/Gr composite processed by forming operation ECAP
  185. Calculations of mass stopping power and range of protons in organic compounds (CH3OH, CH2O, and CO2) at energy range of 0.01–1,000 MeV
  186. Investigation of in vitro behavior of composite coating hydroxyapatite-nano silver on 316L stainless steel substrate by electrophoretic technic for biomedical tools
  187. A review: Enhancing tribological properties of journal bearings composite materials
  188. Improvements in the randomness and security of digital currency using the photon sponge hash function through Maiorana–McFarland S-box replacement
  189. Design a new scheme for image security using a deep learning technique of hierarchical parameters
  190. Special Issue: ICES 2023
  191. Comparative geotechnical analysis for ultimate bearing capacity of precast concrete piles using cone resistance measurements
  192. Visualizing sustainable rainwater harvesting: A case study of Karbala Province
  193. Geogrid reinforcement for improving bearing capacity and stability of square foundations
  194. Evaluation of the effluent concentrations of Karbala wastewater treatment plant using reliability analysis
  195. Adsorbent made with inexpensive, local resources
  196. Effect of drain pipes on seepage and slope stability through a zoned earth dam
  197. Sediment accumulation in an 8 inch sewer pipe for a sample of various particles obtained from the streets of Karbala city, Iraq
  198. Special Issue: IETAS 2024 - Part I
  199. Analyzing the impact of transfer learning on explanation accuracy in deep learning-based ECG recognition systems
  200. Effect of scale factor on the dynamic response of frame foundations
  201. Improving multi-object detection and tracking with deep learning, DeepSORT, and frame cancellation techniques
  202. The impact of using prestressed CFRP bars on the development of flexural strength
  203. Assessment of surface hardness and impact strength of denture base resins reinforced with silver–titanium dioxide and silver–zirconium dioxide nanoparticles: In vitro study
  204. A data augmentation approach to enhance breast cancer detection using generative adversarial and artificial neural networks
  205. Modification of the 5D Lorenz chaotic map with fuzzy numbers for video encryption in cloud computing
  206. Special Issue: 51st KKBN - Part I
  207. Evaluation of static bending caused damage of glass-fiber composite structure using terahertz inspection
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