Displaying publications 21 - 40 of 137 in total

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  1. 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
  2. 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
  3. 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
  4. 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*
  5. 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*
  6. Fulltext Hybrid PSO-FLC for dynamic global peak extraction of the partially shaded photovoltaic system
    Farh HMH, Eltamaly AM, Othman MF
    PLoS One, 2018;13(11):e0206171.
    PMID: 30388119 DOI: 10.1371/journal.pone.0206171
    Particle Swarm Optimization (PSO) is widely used in maximum power point tracking (MPPT) of photovoltaic (PV) energy systems. Nevertheless, this technique suffers from two main problems in the case of partial shading conditions (PSCs). The first problem is that PSO is a time invariant optimization technique that cannot follow the dynamic global peak (GP) under time variant shading patterns (SPs) and sticks to the first GP that occurs at the beginning. This problem can be solved by dispersing the PSO particles using two new techniques introduced in this paper. The two new proposed PSO re-initialization techniques are to disperse the particles upon the SP changes and the other one is upon a predefined time (PDT). The second problem is regarding the high oscillations around steady state, which can be solved by using fuzzy logic controller (FLC) to fine-tune the output power and voltage from the PV system. The new contribution of this paper is the hybrid PSO-FLC with two PSO particles dispersing techniques that is able to solve the two previous mentioned problems effectively and improve the performance of the PV system in both normal and PSCs. A detailed list of comparisons between hybrid PSO-FLC and original PSO using the two proposed methodologies are achieved. The results prove the superior performance of hybrid PSO-FLC compared to PSO in terms of efficiency, accuracy, oscillations reduction around steady state and soft tuning of the GP tracked.
    Matched MeSH terms: Fuzzy Logic*
  7. A hybrid mathematical programming model for optimal project portfolio selection using fuzzy inference system and analytic hierarchy process
    Tavana M, Khosrojerdi G, Mina H, Rahman A
    Eval Program Plann, 2019 12;77:101703.
    PMID: 31442587 DOI: 10.1016/j.evalprogplan.2019.101703
    The primary goal in project portfolio management is to select and manage the optimal set of projects that contribute the maximum in business value. However, selecting Information Technology (IT) projects is a difficult task due to the complexities and uncertainties inherent in the strategic-operational nature of the process, and the existence of both quantitative and qualitative criteria. We propose a two-stage process to select an optimal project portfolio with the aim of maximizing project benefits and minimizing project risks. We construct a two-stage hybrid mathematical programming model by integrating Fuzzy Analytic Hierarchy Process (FAHP) with Fuzzy Inference System (FIS). This hybrid framework provides the ability to consider both the quantitative and qualitative criteria while considering budget constraints and project risks. We also present a real-world case study in the cybersecurity industry to exhibit the applicability and demonstrate the efficacy of our proposed method.
    Matched MeSH terms: Fuzzy Logic*
  8. Comparative study of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) for modeling of Cu (II) adsorption from aqueous solution using biochar derived from rambutan (Nephelium lappaceum) peel
    Wong YJ, Arumugasamy SK, Chung CH, Selvarajoo A, Sethu V
    Environ Monit Assess, 2020 Jun 17;192(7):439.
    PMID: 32556670 DOI: 10.1007/s10661-020-08268-4
    Presence of copper within water bodies deteriorates human health and degrades natural environment. This heavy metal in water is treated using a promising biochar derived from rambutan (Nephelium lappaceum) peel through slow pyrolysis. This research compares the efficacies of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models and evaluates their capability in estimating the adsorption efficiency of biochar for the removal of Cu (II) ions based on 480 experimental sets obtained in a laboratory batch study. The effects of operational parameters such as contact time, operating temperature, biochar dosage, and initial Cu (II) ion concentration on removing Cu (II) ions were investigated. Eleven different training algorithms in ANN and 8 different membership functions in ANFIS were compared statistically and evaluated in terms of estimation errors, which are root mean squared error (RMSE), mean absolute error (MAE), and accuracy. The effects of number of hidden neuron in ANN model and fuzzy set combination in ANFIS were studied. In this study, ANFIS model with Gaussian membership function and fuzzy set combination of [4 5 2 3] was found to be the best method, with accuracy of 90.24% and 87.06% for training and testing dataset, respectively. Contribution of this study is that ANN, ANFIS, and MLR modeling techniques were used for the first time to study the adsorption of Cu (II) ions from aqueous solutions using rambutan peel biochar.
    Matched MeSH terms: Fuzzy Logic*
  9. Recent Advancements in Fuzzy C-means Based Techniques for Brain MRI Segmentation
    Latif G, Alghazo J, Sibai FN, Iskandar DNFA, Khan AH
    Curr Med Imaging, 2021;17(8):917-930.
    PMID: 33397241 DOI: 10.2174/1573405616666210104111218
    BACKGROUND: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques.

    OBJECTIVE: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers.

    RESULTS: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging.

    CONCLUSION: In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

    Matched MeSH terms: Fuzzy Logic*
  10. Assessing the quality of sardine based on biogenic amines using a fuzzy logic model
    Zare D, Ghazali HM
    Food Chem, 2017 Apr 15;221:936-943.
    PMID: 27979297 DOI: 10.1016/j.foodchem.2016.11.071
    There is an increasing concern about the quality and quality assessment procedures of seafood. In the present study, a model to assess fish quality based on biogenic amine contents using fuzzy logic model (FLM) is proposed. The fish used was sardine (Sardinella sp.) where the production of eight biogenic amines was monitored over fifteen days of storage at 0, 3 and 10°C. Based on the results, histamine, putrescine and cadaverine were selected as input variables and twelve quality grades were considered for quality of fish as output variables for the FLM. Input data were processed by rules established in the model and were then defuzzified according to defined output variables. Finally, the quality of fish was evaluated using the designed model and Pearson correlation between storage times with quality of fish showed r=0.97, 0.95 and 1 for fish stored at 0, 3 and 10°C, respectively.
    Matched MeSH terms: Fuzzy Logic*
  11. A new hyperbox selection rule and a pruning strategy for the enhanced fuzzy min-max neural network
    Mohammed MF, Lim CP
    Neural Netw, 2017 Feb;86:69-79.
    PMID: 27890606 DOI: 10.1016/j.neunet.2016.10.012
    In this paper, we extend our previous work on the Enhanced Fuzzy Min-Max (EFMM) neural network by introducing a new hyperbox selection rule and a pruning strategy to reduce network complexity and improve classification performance. Specifically, a new k-nearest hyperbox expansion rule (for selection of a new winning hyperbox) is first introduced to reduce the network complexity by avoiding the creation of too many small hyperboxes within the vicinity of the winning hyperbox. A pruning strategy is then deployed to further reduce the network complexity in the presence of noisy data. The effectiveness of the proposed network is evaluated using a number of benchmark data sets. The results compare favorably with those from other related models. The findings indicate that the newly introduced hyperbox winner selection rule coupled with the pruning strategy are useful for undertaking pattern classification problems.
    Matched MeSH terms: Fuzzy Logic*
  12. Fulltext Dynamics of fractional order nonlinear system: A realistic perception with neutrosophic fuzzy number and Allee effect
    Alam Khan N, Abdul Razzaq O, Riaz F, Ahmadian A, Senu N
    J Adv Res, 2021 09;32:109-118.
    PMID: 34484830 DOI: 10.1016/j.jare.2020.11.015
    Introduction: The fusion of fractional order differential equations and fuzzy numbers has been widely used in modelling different engineering and applied sciences problems. In addition to these, the Allee effect, which is of high importance in field of biology and ecology, has also shown great contribution among other fields of sciences to study the correlation between density and the mean fitness of the subject.

    Objectives: The present paper is intended to measure uncertain dynamics of an economy by restructuring the Cobb-Douglas paradigm of the renowned Solow-Swan model. The purpose of study is further boosted innovatively by subsuming the perception of logistic growth with Allee effect in the dynamics of physical capital and labor force.

    Methods: Fractional order derivative and neutrosophic fuzzy (NF) theory are applied on the parameters of the Cobb-Douglas equation. Distinctively, cogitating fractional order derivative to study the change at each fractional stage; single-valued triangular neutrosophic fuzzy numbers (SVTNFN) to cope the uncertain situations; logistic growth function with Allee effect to analyze the factors in natural way, are the significant and novel features of this endeavor.

    Results: The incorporation of the aforementioned theories and effects in the Cobb-Douglas equation, resulted in producing maximum sustainable capital investment and maximum capacity of labor force. The solutions in intervals located different possible solutions for different membership degrees, which accumulated the uncertain circumstances of a country.

    Conclusion: Explicitly, these notions add new facts and figures not only in the dynamical study of capital and labor, which has been overlooked in classical models, but also left the door open for discussion and implementation on classical models of different fields.

    Matched MeSH terms: Fuzzy Logic*
  13. 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*
  14. Fulltext Expanded DEMATEL for determining cause and effect group in bidirectional relations
    Falatoonitoosi E, Ahmed S, Sorooshian S
    ScientificWorldJournal, 2014;2014:103846.
    PMID: 24693224 DOI: 10.1155/2014/103846
    Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology has been proposed to solve complex and intertwined problem groups in many situations such as developing the capabilities, complex group decision making, security problems, marketing approaches, global managers, and control systems. DEMATEL is able to realize casual relationships by dividing important issues into cause and effect group as well as making it possible to visualize the casual relationships of subcriteria and systems in the course of casual diagram that it may demonstrate communication network or a little control relationships between individuals. Despite of its ability to visualize cause and effect inside a network, the original DEMATEL has not been able to find the cause and effect group between different networks. Therefore, the aim of this study is proposing the expanded DEMATEL to cover this deficiency by new formulations to determine cause and effect factors between separate networks that have bidirectional direct impact on each other. At the end, the feasibility of new extra formulations is validated by case study in three numerical examples of green supply chain networks for an automotive company.
    Matched MeSH terms: Fuzzy Logic*
  15. Intelligent classification of electrocardiogram (ECG) signal using extended Kalman Filter (EKF) based neuro fuzzy system
    Meau YP, Ibrahim F, Narainasamy SA, Omar R
    Comput Methods Programs Biomed, 2006 May;82(2):157-68.
    PMID: 16638620
    This study presents the development of a hybrid system consisting of an ensemble of Extended Kalman Filter (EKF) based Multi Layer Perceptron Network (MLPN) and a one-pass learning Fuzzy Inference System using Look-up Table Scheme for the recognition of electrocardiogram (ECG) signals. This system can distinguish various types of abnormal ECG signals such as Ventricular Premature Cycle (VPC), T wave inversion (TINV), ST segment depression (STDP), and Supraventricular Tachycardia (SVT) from normal sinus rhythm (NSR) ECG signal.
    Matched MeSH terms: Fuzzy Logic*
  16. Fuzzy time-optimal controller (FTOC) for second order nonlinear systems
    Nagi F, Ahmed SK, Zularnain AT, Nagi J
    ISA Trans, 2011 Jul;50(3):364-75.
    PMID: 21353218 DOI: 10.1016/j.isatra.2011.01.014
    The motivation behind this paper is to seek alternative techniques to achieve a near optimal controller for non-linear systems without solving the analytical problem. In classical optimal control systems, the system states and optimization co-state parameters generate a two-point boundary value problem (TPBVP) using Pontryagin's minimum principle (PMP). The paper contributes a new fuzzy time-optimal controller to the existing fuzzy controllers which has two regular inputs and one bang-bang output. The proposed controller closely approximates the output of the classical time-optimal controller. Further, input membership function are tuned on-line to improve the time-optimal output. The new controller exhibits optimal behaviour for second order non-linear systems. The rules are selected to satisfy the stability and optimality conditions of the new fuzzy time-optimal controller. The paper describes a systematic procedure to design the controller and how to achieve the desired result. To benchmark the new controller performance, a sliding mode controller is used for guidance and comparison purpose. Simulation of three non-linear examples shows promising results. The work described here is expected to incite researcher's interest in fuzzy time-optimal controller design.
    Matched MeSH terms: Fuzzy Logic*
  17. Fulltext An Integrated MCDM Model for Conveyor Equipment Evaluation and Selection in an FMC Based on a Fuzzy AHP and Fuzzy ARAS in the Presence of Vagueness
    Nguyen HT, Dawal SZ, Nukman Y, Rifai AP, Aoyama H
    PLoS One, 2016;11(4):e0153222.
    PMID: 27070543 DOI: 10.1371/journal.pone.0153222
    The conveyor system plays a vital role in improving the performance of flexible manufacturing cells (FMCs). The conveyor selection problem involves the evaluation of a set of potential alternatives based on qualitative and quantitative criteria. This paper presents an integrated multi-criteria decision making (MCDM) model of a fuzzy AHP (analytic hierarchy process) and fuzzy ARAS (additive ratio assessment) for conveyor evaluation and selection. In this model, linguistic terms represented as triangular fuzzy numbers are used to quantify experts' uncertain assessments of alternatives with respect to the criteria. The fuzzy set is then integrated into the AHP to determine the weights of the criteria. Finally, a fuzzy ARAS is used to calculate the weights of the alternatives. To demonstrate the effectiveness of the proposed model, a case study is performed of a practical example, and the results obtained demonstrate practical potential for the implementation of FMCs.
    Matched MeSH terms: Fuzzy Logic*
  18. On fuzzy semantic similarity measure for DNA coding
    Ahmad M, Jung LT, Bhuiyan MA
    Comput Biol Med, 2016 Feb 1;69:144-51.
    PMID: 26773936 DOI: 10.1016/j.compbiomed.2015.12.017
    A coding measure scheme numerically translates the DNA sequence to a time domain signal for protein coding regions identification. A number of coding measure schemes based on numerology, geometry, fixed mapping, statistical characteristics and chemical attributes of nucleotides have been proposed in recent decades. Such coding measure schemes lack the biologically meaningful aspects of nucleotide data and hence do not significantly discriminate coding regions from non-coding regions. This paper presents a novel fuzzy semantic similarity measure (FSSM) coding scheme centering on FSSM codons׳ clustering and genetic code context of nucleotides. Certain natural characteristics of nucleotides i.e. appearance as a unique combination of triplets, preserving special structure and occurrence, and ability to own and share density distributions in codons have been exploited in FSSM. The nucleotides׳ fuzzy behaviors, semantic similarities and defuzzification based on the center of gravity of nucleotides revealed a strong correlation between nucleotides in codons. The proposed FSSM coding scheme attains a significant enhancement in coding regions identification i.e. 36-133% as compared to other existing coding measure schemes tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms.
    Matched MeSH terms: Fuzzy Logic*
  19. Fulltext Enhanced forecasting of rice price and production in Malaysia using novel multivariate fuzzy time series models
    Bilal M, Alrasheedi MA, Aamir M, Abdullah S, Norrulashikin SM, Rezaiy R
    Sci Rep, 2024 Dec 02;14(1):29903.
    PMID: 39622831 DOI: 10.1038/s41598-024-77907-4
    A significant portion of the world's population relies on rice as a primary source of nutrition. In Malaysia, rice production began in the early 1960s, which led to the cultivation of the country's most significant food crop up till the present day. Research on various aspects of the price and production of rice has been done by various methods in the past. In this study, we have adopted novel multivariate fuzzy time series models (MFTS) i.e. fuzzy vector autoregressive models (FVAR) alongside conventional vector autoregressive model (VAR) for assessing rice price and production using a dataset from the Malaysian Agricultural Research and Development Institute (MERDI). The proposed method(s) especially with the usage of Trapezoidal Fuzzy Numbers (TrFNs) have commendable accuracy with great future forecasts over the VAR model. The model selection was made by the least MAPE with the corresponding highest Relative Efficiency as criteria. The study fills the gap in applying advanced fuzzy models for rice forecasting, aiming to improve accuracy using fuzzy vector autoregressive (FVAR) models with Triangular Fuzzy Numbers (TFNs) and Trapezoidal Fuzzy Numbers (TrFNs) over traditional VAR models. The study's findings imply that the enhanced forecasting accuracy of FVAR models with Trapezoidal Fuzzy Numbers (TrFNs) can significantly assist local farmers and stakeholders in making informed decisions about production and pricing. This improved forecasting capability is expected to promote business growth within the Malaysian market and facilitate increased rice exports, ultimately contributing to the country's economic prosperity.
    Matched MeSH terms: Fuzzy Logic*
  20. Fulltext DC-link capacitor voltage regulation with effort-reduction fuzzy logic control for three-level inverter-based shunt active power filter
    Nashiren Farzilah, Mailah, Yap Hoon, Mohd Amran, Mohd Radzi
    Pertanika Journal of Science & Technology, 2017;25(102):11-20.
    MyJurnal
    Shunt active power filter (SAPF) is the most effective solution for current harmonics. In its controller, DC-link capacitor voltage regulation algorithm with either proportional-integral (PI) or fuzzy logic control (FLC) technique has played a significant role in maintaining a constant DC voltage across all the DC-link capacitors. However, PI technique performs poorly with high overshoot and significant time delay under dynamic state conditions, as its parameters are difficult to be tuned without requiring complete knowledge of the designated system. Although FLC technique has been developed to overcome limitations of PI technique, it is mostly developed with high complexity thereby increases computational burden of the designed controller. This paper presents a fuzzy-based DC-link capacitor voltage regulation algorithm with reduced computational efforts to enhance performance of three-phase three-level neutralpoint diode clamped (NPC) inverter-based SAPF in overall DC-link voltage regulation. The proposed method is called effort-reduction FLC technique. The proposed algorithm is developed and evaluated in MATLAB-Simulink. Moreover, conventional algorithm with PI technique is tested for comparison purposes. Simulation results have confirmed improvement achieved by the proposed algorithm in comparison to the conventional algorithm.
    Matched MeSH terms: Fuzzy Logic
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