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Fulltext An application of fuzzy bipolar weighted correlation coefficient in decision-making problem

Mustafa S, Mahmood S, Salleh Z

PLoS One, 2023;18(12):e0283516.
PMID: 38113247 DOI: 10.1371/journal.pone.0283516

Diagnosing and finding the disease in medical sciences is a complex procedure. The basic steps involved in finding starts with signs, symptoms, and test. This study is based on the diagnosis of a skin disorder. The identification of a disease has been made on the basis of symptoms that sometimes show bipolarity. To address this bipolarity, the bipolar fuzzy sets are used as bipolar fuzzy sets cover the positive as well as negative aspects of a specific symptom. It is combined with the idea of soft sets, which gives more precise results. We have proposed a new technique in which a correlation coefficient is used to measure bipolar fuzzy soft set, which has been applied for diagnosis. The BFSSs deal most effectively with dual and fuzzy information. The correlation coefficient and the weighted correlation coefficient of BFSSs are suggested in this research. Based on said techniques, the decision-making method is suggested under a bipolar fuzzy environment to resolve ambiguous and unclear information. The implementation and effectiveness of the proposed and existing strategy has been checked by numerical computation.

Matched MeSH terms: Fuzzy Logic*
Fulltext Robust fuzzy logic stabilization with disturbance elimination

Danapalasingam KA

ScientificWorldJournal, 2014;2014:171597.
PMID: 25177713 DOI: 10.1155/2014/171597

A robust fuzzy logic controller is proposed for stabilization and disturbance rejection in nonlinear control systems of a particular type. The dynamic feedback controller is designed as a combination of a control law that compensates for nonlinear terms in a control system and a dynamic fuzzy logic controller that addresses unknown model uncertainties and an unmeasured disturbance. Since it is challenging to derive a highly accurate mathematical model, the proposed controller requires only nominal functions of a control system. In this paper, a mathematical derivation is carried out to prove that the controller is able to achieve asymptotic stability by processing state measurements. Robustness here refers to the ability of the controller to asymptotically steer the state vector towards the origin in the presence of model uncertainties and a disturbance input. Simulation results of the robust fuzzy logic controller application in a magnetic levitation system demonstrate the feasibility of the control design.

Matched MeSH terms: Fuzzy Logic*
Fulltext Minimal solution of singular LR fuzzy linear systems

Nikuie M, Ahmad MZ

ScientificWorldJournal, 2014;2014:517218.
PMID: 24737977 DOI: 10.1155/2014/517218

In this paper, the singular LR fuzzy linear system is introduced. Such systems are divided into two parts: singular consistent LR fuzzy linear systems and singular inconsistent LR fuzzy linear systems. The capability of the generalized inverses such as Drazin inverse, pseudoinverse, and {1}-inverse in finding minimal solution of singular consistent LR fuzzy linear systems is investigated.

Matched MeSH terms: Fuzzy Logic*
On Fuzzy Semi-Connectedness

Chaudhuri A, Das P

Sains Malaysiana, 1996;25.

The concepts of fuzzy semi-open and semi-closed sets have been utilised to define four types of semi-separation of fuzzy sets corresponding to the notions of separation, Q-separation, weak separation, strong separation and eight types of semi-connectedness viz SiC, SCi-connectedness for i = 1 ,2 ,3 ,4 corresponding to the notions of O-connectedness, connectedness, Oq-connectedness and ci-connectedness of a fuzzy set. Interrelationship between these notions of semi-connectedness of a fuzzy set and their properties have been discussed.
Konsep set semi-terbuka dan semi-tertutup kabur digunakan untuk mentakrif empat jenis semi-pemisahan bagi set-set kabur sepadan dengan konsep pemisahan, Q-pemisahan, pemisahan lemah, pemisahan kuat dan lapan jenis keberkaitan, iaitu keberkaitan-SiC dan keberkaitan-SCi untuk i=1 ,2 ,3 ,4 sepadan dengan konsep keberkaitan, keberkaitan-Oq dan keberkaitan-Ci bagi set kabur. Hubung kait antara konsep-konsep semi-keberkaitan set kabur ini dan sifat-sifatnya dibincangkan.

Matched MeSH terms: Fuzzy Logic
An improved simplified rules Fuzzy Logic Speed Controller method applied for induction motor drive

Talib MHN, Ibrahim Z, Abd Rahim N, Zulhani R, Nordin N, Farah N, et al.

ISA Trans, 2020 Oct;105:230-239.
PMID: 32475537 DOI: 10.1016/j.isatra.2020.05.040

Fuzzy Logic Speed Controller (FLSC) has been widely used for motor drive due to its robustness and its non-reliance to real plant parameters. However, it is computationally expensive to be implemented in real-time and prone to the fuzzy rules' selection error which results in the failure of the drive's system. This paper proposes an improved simplified rules method for Fuzzy Logic Speed Controller (FLSC) based on the significant crisp output calculations to address these issues. A systematic procedure for the fuzzy rules reduction process is first described. Then, a comprehensive evaluation of the activated crisp output data is presented to determine the fuzzy dominant rules. Based on the proposed method, the number of rules was significantly reduced by 72%. The simplified FLSC rule is tested on the Induction Motor (IM) drives system in which the real-time implementation was carried out in the dSPACE DS1103 controller environment. The simulation and experimental results based on the proposed FLSC have proved the workability of the simplified rules without degrading the motor performance.

Matched MeSH terms: Fuzzy Logic
Fulltext Fuzzy mixed assembly line sequencing and scheduling optimization model using multiobjective dynamic fuzzy GA

Tahriri F, Dawal SZ, Taha Z

ScientificWorldJournal, 2014;2014:505207.
PMID: 24982962 DOI: 10.1155/2014/505207

A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model.

Matched MeSH terms: Fuzzy Logic*
Fulltext A novel approach toward fuzzy generalized bi-ideals in ordered semigroups

Khan FM, Sarmin NH, Khan HU

ScientificWorldJournal, 2014;2014:275947.
PMID: 24883375 DOI: 10.1155/2014/275947

In several advanced fields like control engineering, computer science, fuzzy automata, finite state machine, and error correcting codes, the use of fuzzified algebraic structures especially ordered semigroups plays a central role. In this paper, we introduced a new and advanced generalization of fuzzy generalized bi-ideals of ordered semigroups. These new concepts are supported by suitable examples. These new notions are the generalizations of ordinary fuzzy generalized bi-ideals of ordered semigroups. Several fundamental theorems of ordered semigroups are investigated by the properties of these newly defined fuzzy generalized bi-ideals. Further, using level sets, ordinary fuzzy generalized bi-ideals are linked with these newly defined ideals which is the most significant part of this paper.

Matched MeSH terms: Fuzzy Logic*
Fulltext On the combination of adaptive neuro-fuzzy inference system and deep residual network for improving detection rates on intrusion detection

Liu J, Yinchai W, Siong TC, Li X, Zhao L, Wei F

PLoS One, 2022;17(12):e0278819.
PMID: 36508410 DOI: 10.1371/journal.pone.0278819

Deep Residual Networks (ResNets) are prone to overfitting in problems with uncertainty, such as intrusion detection problems. To alleviate this problem, we proposed a method that combines the Adaptive Neuro-fuzzy Inference System (ANFIS) and the ResNet algorithm. This method can make use of the advantages of both the ANFIS and ResNet, and alleviate the overfitting problem of ResNet. Compared with the original ResNet algorithm, the proposed method provides overlapped intervals of continuous attributes and fuzzy rules to ResNet, improving the fuzziness of ResNet. To evaluate the performance of the proposed method, the proposed method is realized and evaluated on the benchmark NSL-KDD dataset. Also, the performance of the proposed method is compared with the original ResNet algorithm and other deep learning-based and ANFIS-based methods. The experimental results demonstrate that the proposed method is better than that of the original ResNet and other existing methods on various metrics, reaching a 98.88% detection rate and 1.11% false alarm rate on the KDDTrain+ dataset.

Matched MeSH terms: Fuzzy Logic*
Sustainable management of water demand using fuzzy inference system: a case study of Kenyir Lake, Malaysia

Mohd Azlan NNI, Abdul Malek M, Zolkepli M, Mohd Salim J, Ahmed AN

Environ Sci Pollut Res Int, 2021 Apr;28(16):20261-20272.
PMID: 33405154 DOI: 10.1007/s11356-020-11908-4

Sustainable water demand management has become a necessity to the world since the immensely growing population and development have caused water deficit and groundwater depletion. This study aims to overcome water deficit by analyzing water demand at Kenyir Lake, Terengganu, using a fuzzy inference system (FIS). The analysis is widened by comparing FIS with the multiple linear regression (MLR) method. FIS applied as an analysis tool provides good generalization capability for optimum solutions and utilizes human behavior influenced by expert knowledge in water resources management for fuzzy rules specified in the system, whereas MLR can simultaneously adjust and compare several variables as per the needs of the study. The water demand dataset of Kenyir Lake was analyzed using FIS and MLR, resulting in total forecasted water consumptions at Kenyir Lake of 2314.38 m3 and 1358.22 m3, respectively. It is confirmed that both techniques converge close to the actual water consumption of 1249.98 m3. MLR showed the accuracy of the water demand values with smaller forecasted errors to be higher than FIS did. To attain sustainable water demand management, the techniques used can be examined extensively by researchers, educators, and learners by adding more variables, which will provide more anticipated outcomes.

Matched MeSH terms: Fuzzy Logic*
Load-frequency control in an islanded microgrid PV/WT/FC/ESS using an optimal self-tuning fractional-order fuzzy controller

Naderipour A, Abdul-Malek Z, Davoodkhani IF, Kamyab H, Ali RR

Environ Sci Pollut Res Int, 2023 Jun;30(28):71677-71688.
PMID: 34241794 DOI: 10.1007/s11356-021-14799-1

Due to the increased complexity and nonlinear nature of microgrid systems such as photovoltaic, wind-turbine fuel cell, and energy storage systems (PV/WT/FC/ESSs), load-frequency control has been a challenge. This paper employs a self-tuning controller based on the fuzzy logic to overcome parameter uncertainties of classic controllers, such as operation conditions, the change in the operating point of the microgrid, and the uncertainty of microgrid modeling. Furthermore, a combined fuzzy logic and fractional-order controller is used for load-frequency control of the off-grid microgrid with the influence of renewable resources because the latter controller benefits robust performance and enjoys a flexible structure. To reach a better operation for the proposed controller, a novel meta-heuristic whale algorithm has been used to optimally determine the input and output scale coefficients of the fuzzy controller and fractional orders of the fractional-order controller. The suggested approach is applied to a microgrid with a diesel generator, wind turbine, photovoltaic systems, and energy storage devices. The comparison made between the results of the proposed controller and those of the classic PID controller proves the superiority of the optimized fractional-order self-tuning fuzzy controller in terms of operation characteristics, response speed, and the reduction in frequency deviations against load variations.

Matched MeSH terms: Fuzzy Logic*
A Novel Evaluation Framework for Medical LLMs: Combining Fuzzy Logic and MCDM for Medical Relation and Clinical Concept Extraction

Alamoodi AH, Zughoul O, David D, Garfan S, Pamucar D, Albahri OS, et al.

J Med Syst, 2024 Aug 31;48(1):81.
PMID: 39214943 DOI: 10.1007/s10916-024-02090-y

Artificial intelligence (AI) has become a crucial element of modern technology, especially in the healthcare sector, which is apparent given the continuous development of large language models (LLMs), which are utilized in various domains, including medical beings. However, when it comes to using these LLMs for the medical domain, there's a need for an evaluation platform to determine their suitability and drive future development efforts. Towards that end, this study aims to address this concern by developing a comprehensive Multi-Criteria Decision Making (MCDM) approach that is specifically designed to evaluate medical LLMs. The success of AI, particularly LLMs, in the healthcare domain, depends on their efficacy, safety, and ethical compliance. Therefore, it is essential to have a robust evaluation framework for their integration into medical contexts. This study proposes using the Fuzzy-Weighted Zero-InConsistency (FWZIC) method extended to p, q-quasirung orthopair fuzzy set (p, q-QROFS) for weighing evaluation criteria. This extension enables the handling of uncertainties inherent in medical decision-making processes. The approach accommodates the imprecise and multifaceted nature of real-world medical data and criteria by incorporating fuzzy logic principles. The MultiAtributive Ideal-Real Comparative Analysis (MAIRCA) method is employed for the assessment of medical LLMs utilized in the case study of this research. The results of this research revealed that "Medical Relation Extraction" criteria with its sub-levels had more importance with (0.504) than "Clinical Concept Extraction" with (0.495). For the LLMs evaluated, out of 6 alternatives, ( A 4 ) "GatorTron S 10B" had the 1st rank as compared to ( A 1 ) "GatorTron 90B" had the 6th rank. The implications of this study extend beyond academic discourse, directly impacting healthcare practices and patient outcomes. The proposed framework can help healthcare professionals make more informed decisions regarding the adoption and utilization of LLMs in medical settings.

Matched MeSH terms: Fuzzy Logic*
Fulltext Solving fuzzy fractional differential equations using Zadeh's extension principle

Ahmad MZ, Hasan MK, Abbasbandy S

ScientificWorldJournal, 2013;2013:454969.
PMID: 24082853 DOI: 10.1155/2013/454969

We study a fuzzy fractional differential equation (FFDE) and present its solution using Zadeh's extension principle. The proposed study extends the case of fuzzy differential equations of integer order. We also propose a numerical method to approximate the solution of FFDEs. To solve nonlinear problems, the proposed numerical method is then incorporated into an unconstrained optimisation technique. Several numerical examples are provided.

Matched MeSH terms: Fuzzy Logic
Fulltext Fuzzy Lambda-Max criteria weight determination for feature selection in clustering

Nurul Adzlyana Mohd Saadon, Rosma Mohd Dom, Nurazzah Abd Rahman

Pertanika Journal of Science & Technology, 2017;25(103):241-254.
MyJurnal

Clustering refers to reducing selected features involved in determining the clusters. Raw data might come with a lot of features, including unimportant ones. A hybrid similarity measure (discovered in 2014) used in selecting features can be improvised as it might select all the attributes, including insignificant ones. This paper suggests Fuzzy Lambda-Max to be used as a feature selection method since Lambda-Max is normally used in ranking of alternatives. A set of AIDS data is used to measure the performance. Results show that Fuzzy Lambda-Max has the ability to determine criteria weights and ranking the criteria. Hence, feature selection can be done by choosing only the important criteria.

Matched MeSH terms: Fuzzy Logic
Fulltext Identifying the uncertain model parameter of a steam turbine system

Wan Munirah, W.M., Tahir, A., Azmirul, A.

Pertanika Journal of Science & Technology, 2017;25(2):545-560.
MyJurnal

The transformation method (TM) of fuzzy arithmetic is aimed at simulation and analysis of a system. The aim of this paper is to use fuzzy arithmetic based on the TM on a state space of a steam turbine system. The model is then used to identify the degree of influence of each parameter on the system. Simulation and analysis of the system are presented in this paper.

Matched MeSH terms: Fuzzy Logic
Fulltext Based on T-spherical fuzzy environment: A combination of FWZIC and FDOSM for prioritising COVID-19 vaccine dose recipients

Alsalem MA, Alsattar HA, Albahri AS, Mohammed RT, Albahri OS, Zaidan AA, et al.

J Infect Public Health, 2021 Oct;14(10):1513-1559.
PMID: 34538731 DOI: 10.1016/j.jiph.2021.08.026

The problem complexity of multi-criteria decision-making (MCDM) has been raised in the distribution of coronavirus disease 2019 (COVID-19) vaccines, which required solid and robust MCDM methods. Compared with other MCDM methods, the fuzzy-weighted zero-inconsistency (FWZIC) method and fuzzy decision by opinion score method (FDOSM) have demonstrated their solidity in solving different MCDM challenges. However, the fuzzy sets used in these methods have neglected the refusal concept and limited the restrictions on their constants. To end this, considering the advantage of the T-spherical fuzzy sets (T-SFSs) in handling the uncertainty in the data and obtaining information with more degree of freedom, this study has extended FWZIC and FDOSM methods into the T-SFSs environment (called T-SFWZIC and T-SFDOSM) to be used in the distribution of COVID-19 vaccines. The methodology was formulated on the basis of decision matrix adoption and development phases. The first phase described the adopted decision matrix used in the COVID-19 vaccine distribution. The second phase presented the sequential formulation steps of T-SFWZIC used for weighting the distribution criteria followed by T-SFDOSM utilised for prioritising the vaccine recipients. Results revealed the following: (1) T-SFWZIC effectively weighted the vaccine distribution criteria based on several parameters including T = 2, T = 4, T = 6, T = 8, and T = 10. Amongst all parameters, the age criterion received the highest weight, whereas the geographic locations severity criterion has the lowest weight. (2) According to the T parameters, a considerable variance has occurred on the vaccine recipient orders, indicating that the existence of T values affected the vaccine distribution. (3) In the individual context of T-SFDOSM, no unique prioritisation was observed based on the obtained opinions of each expert. (4) The group context of T-SFDOSM used in the prioritisation of vaccine recipients was considered the final distribution result as it unified the differences found in an individual context. The evaluation was performed based on systematic ranking assessment and sensitivity analysis. This evaluation showed that the prioritisation results based on each T parameter were subject to a systematic ranking that is supported by high correlation results over all discussed scenarios of changing criteria weights values.

Matched MeSH terms: Fuzzy Logic
Fulltext Novel dynamic fuzzy Decision-Making framework for COVID-19 vaccine dose recipients

Albahri OS, Zaidan AA, Albahri AS, Alsattar HA, Mohammed R, Aickelin U, et al.

J Adv Res, 2022 Mar;37:147-168.
PMID: 35475277 DOI: 10.1016/j.jare.2021.08.009

INTRODUCTION: The vaccine distribution for the COVID-19 is a multicriteria decision-making (MCDM) problem based on three issues, namely, identification of different distribution criteria, importance criteria and data variation. Thus, the Pythagorean fuzzy decision by opinion score method (PFDOSM) for prioritising vaccine recipients is the correct approach because it utilises the most powerful MCDM ranking method. However, PFDOSM weighs the criteria values of each alternative implicitly, which is limited to explicitly weighting each criterion. In view of solving this theoretical issue, the fuzzy-weighted zero-inconsistency (FWZIC) can be used as a powerful weighting MCDM method to provide explicit weights for a criteria set with zero inconstancy. However, FWZIC is based on the triangular fuzzy number that is limited in solving the vagueness related to the aforementioned theoretical issues.
OBJECTIVES: This research presents a novel homogeneous Pythagorean fuzzy framework for distributing the COVID-19 vaccine dose by integrating a new formulation of the PFWZIC and PFDOSM methods.
METHODS: The methodology is divided into two phases. Firstly, an augmented dataset was generated that included 300 recipients based on five COVID-19 vaccine distribution criteria (i.e., vaccine recipient memberships, chronic disease conditions, age, geographic location severity and disabilities). Then, a decision matrix was constructed on the basis of an intersection of the 'recipients list' and 'COVID-19 distribution criteria'. Then, the MCDM methods were integrated. An extended PFWZIC was developed, followed by the development of PFDOSM.
RESULTS: (1) PFWZIC effectively weighted the vaccine distribution criteria. (2) The PFDOSM-based group prioritisation was considered in the final distribution result. (3) The prioritisation ranks of the vaccine recipients were subject to a systematic ranking that is supported by high correlation results over nine scenarios of the changing criteria weights values.
CONCLUSION: The findings of this study are expected to ensuring equitable protection against COVID-19 and thus help accelerate vaccine progress worldwide.

Matched MeSH terms: Fuzzy Logic
Fulltext Design of agricultural wireless sensor network node optimization method based on improved data fusion algorithm

Ruipeng T, Jianbu Y, Jianrui T, Aridas NK, Talip MSA

PLoS One, 2024;19(11):e0308845.
PMID: 39509355 DOI: 10.1371/journal.pone.0308845

The agricultural WSN (wireless sensor network) has the characteristics of long operation cycle and wide coverage area. In order to cover as much area as possible, farms usually deploy multiple monitoring devices in different locations of the same area. Due to different types of equipment, monitoring data will vary greatly, and too many monitoring nodes also reduce the efficiency of the network. Although there have been some studies on data fusion algorithms, they have problems such as ignoring the dynamic changes of time series, weak anti-interference ability, and poor processing of data fluctuations. So in this study, a data fusion algorithm for optimal node tracking in agricultural wireless sensor networks is designed. By introducing the dynamic bending distance in the dynamic time warping algorithm to replace the absolute distance in the fuzzy association algorithm and combine the sensor's own reliability and association degree as the weighted fusion weight, which improved the fuzzy association algorithm. Finally, another three algorithm were tested for multi-temperature sensor data fusion. Compare with the kalman filter, arithmetic mean and fuzzy association algorithm, the average value of the improved data fusion algorithm is 29.5703, which is close to the average value of the other three algorithms, indicating that the data distribution is more even. Its extremely bad value is 8.9767, which is 10.04%, 1.14% and 9.85% smaller than the other three algorithms, indicating that it is more robust when dealing with outliers. Its variance is 2.6438, which is 2.82%, 0.65% and 0.27% smaller than the other three algorithms, indicating that it is more stable and has less data volatility. The results show that the algorithm proposed in this study has higher fusion accuracy and better robustness, which can obtain the fusion value that truly feedbacks the agricultural environment conditions. It reduces production costs by reducing redundant monitoring devices, the energy consumption and improves the data collection efficiency in wireless sensor networks.

Matched MeSH terms: Fuzzy Logic
Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index

Abba SI, Pham QB, Saini G, Linh NTT, Ahmed AN, Mohajane M, et al.

Environ Sci Pollut Res Int, 2020 Nov;27(33):41524-41539.
PMID: 32686045 DOI: 10.1007/s11356-020-09689-x

In recent decades, various conventional techniques have been formulated around the world to evaluate the overall water quality (WQ) at particular locations. In the present study, back propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and one multilinear regression (MLR) are considered for the prediction of water quality index (WQI) at three stations, namely Nizamuddin, Palla, and Udi (Chambal), across the Yamuna River, India. The nonlinear ensemble technique was proposed using the neural network ensemble (NNE) approach to improve the performance accuracy of the single models. The observed WQ parameters were provided by the Central Pollution Control Board (CPCB) including dissolved oxygen (DO), pH, biological oxygen demand (BOD), ammonia (NH3), temperature (T), and WQI. The performance of the models was evaluated by various statistical indices. The obtained results indicated the feasibility of the developed data intelligence models for predicting the WQI at the three stations with the superior modelling results of the NNE. The results also showed that the minimum values for root mean square (RMS) varied between 0.1213 and 0.4107, 0.003 and 0.0367, and 0.002 and 0.0272 for Nizamuddin, Palla, and Udi (Chambal), respectively. ANFIS-M3, BPNN-M4, and BPNN-M3 improved the performance with regard to an absolute error by 41%, 4%, and 3%, over other models for Nizamuddin, Palla, and Udi (Chambal) stations, respectively. The predictive comparison demonstrated that NNE proved to be effective and can therefore serve as a reliable prediction approach. The inferences of this paper would be of interest to policymakers in terms of WQ for establishing sustainable management strategies of water resources.

Matched MeSH terms: Fuzzy Logic*
Construction 4.0 technology evaluation using fuzzy TOPSIS: comparison between sustainability and resiliency, well-being, productivity, safety, and integrity

Shafei H, Rahman RA, Lee YS

Environ Sci Pollut Res Int, 2024 Feb;31(10):14858-14893.
PMID: 38285259 DOI: 10.1007/s11356-024-31862-9

This study aims to compare the impact of Construction 4.0 technologies on different organizational core values, focusing on sustainability and resiliency, well-being, productivity, safety, and integrity. To achieve that aim, the study objectives are the following: (i) identify the critical Construction 4.0 technologies between core values; (ii) appraise overlapping critical Construction 4.0 technologies between core values; (iii) examine the ranking performance of Construction 4.0 technologies between core values; and (iv) analyze the interrelationships between Construction 4.0 technologies and core values. First, twelve Construction 4.0 technologies were identified from a national strategic plan. Then, the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) that incorporates subjective and objective weights was used to evaluate the impact of the Construction 4.0 technologies on the five core values. Finally, the collected data was analyzed using the following techniques: fuzzy TOPSIS, normalization, overlap analysis, agreement analysis, sensitivity analysis, ranking comparison, and Spearman correlation. The study findings reveal four critical Construction 4.0 technologies that enhance all five core values: building information modeling (BIM), Internet of Things (IoT), big data and predictive analytics, and autonomous construction. Also, there is a high agreement on the Construction 4.0 technologies that enhance well-being and productivity. Lastly, artificial intelligence (AI) has the highest number of very strong relationships among the core values. The originality of this paper lies in its comprehensive comparison of the impact of Construction 4.0 technologies on multiple organizational core values. The study findings provide valuable insights in making strategic decisions in adopting Construction 4.0 technologies.

Matched MeSH terms: Fuzzy Logic*
Fulltext Hamming distance method with subjective and objective weights for personnel selection

Saad R, Ahmad MZ, Abu MS, Jusoh MS

ScientificWorldJournal, 2014;2014:865495.
PMID: 24782670 DOI: 10.1155/2014/865495

Multicriteria decision making (MCDM) is one of the methods that popularly has been used in solving personnel selection problem. Alternatives, criteria, and weights are some of the fundamental aspects in MCDM that need to be defined clearly in order to achieve a good result. Apart from these aspects, fuzzy data has to take into consideration that it may arise from unobtainable and incomplete information. In this paper, we propose a new approach for personnel selection problem. The proposed approach is based on Hamming distance method with subjective and objective weights (HDMSOW's). In case of vagueness situation, fuzzy set theory is then incorporated onto the HDMSOW's. To determine the objective weight for each attribute, the fuzzy Shannon's entropy is considered. While for the subjective weight, it is aggregated into a comparable scale. A numerical example is presented to illustrate the HDMSOW's.

Matched MeSH terms: Fuzzy Logic*

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