Displaying publications 61 - 80 of 115 in total

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  1. Groundwater quality assessment for irrigation by adopting new suitability plot and spatial analysis based on fuzzy logic technique
    S C, M V P, S V, M N, K P, Panda B, et al.
    Environ Res, 2022 03;204(Pt A):111729.
    PMID: 34478727 DOI: 10.1016/j.envres.2021.111729
    This study was focused on identifying the region suitable for agriculture-based, using new irrigation groundwater quality plot and its spatio-temporal variation with fuzzy logic technique in a geographic information system (GIS) platform. Six hundred and eighty groundwater samples were collected during pre, southwest, northeast, and post monsoon periods. A new ternary plot was also attempted to determine the irrigation suitability of water by considering four essential parameters such as sodium adsorption ratio (SAR), permeability index (PI), Sodium percentage (Na %), and electrical conductivity (EC). The derived ternary plot was the most beneficial over other available plots, as it incorporated four parameters, and it differs from the US Salinity Laboratory (USSL) plot, such that the groundwater with higher EC could also be used for irrigation purposes, depending on the Na%. The ternary plot revealed that the groundwater predominantly manifested good to moderate category during post, northeast, and southwest monsoons. The assessment with the amount of fertilizer used during the study period showed that the NPK fertilizers were effectively used for irrigation during monsoon periods. Spatial maps on EC, Kelly's ratio, Mg hazard, Na%, PI, potential salinity (PS), SAR, residual sodium carbonate (RSC), and soluble sodium percentage (SSP) were prepared for each season using fuzzy membership values, integrated for each season. A final suitability map derived by an overlay of all the seasonal outputs has identified that the groundwater in the western and the eastern part of the study area are suitable for agriculture. The study recommends cultivation of groundwater-dependent short-term crops, along the western and northern regions of the study area during the pre-monsoon season.
    Matched MeSH terms: Fuzzy Logic
  2. Comparative studies of RSM Box-Behnken and ANN-Anfis fuzzy statistical analysis for seawater biodegradability using TiO2 photocatalyst
    Mohammed N, Palaniandy P, Shaik F, Mewada H, Balakrishnan D
    Chemosphere, 2023 Feb;314:137665.
    PMID: 36581118 DOI: 10.1016/j.chemosphere.2022.137665
    In this approach, a batch reactor was employed to study the degradation of pollutants under natural sunlight using TiO2 as a photocatalyst. The effects of photocatalyst dosage, reaction time and pH were investigated by evaluating the percentage removal efficiencies of total organic carbon (TOC), chemical oxygen demand (COD), biological oxygen demand (BOD) and biodegradability (BOD/COD). Design Expert-Response Surface Methodology Box Behnken Design (BBD) and MATLAB Artificial Neural Network - Adaptive Neuro Fuzzy Inference system (ANN-ANFIS) methods were employed to perform the statistical modelling. The experimental values of maximum percentage removal efficiencies were found to be TOC = 82.4, COD = 85.9, BOD = 30.9% and biodegradability was 0.070. According to RSM-BBD and ANFIS analysis, the maximum percentage removal efficiencies were found to be TOC = 90.3, 82.4; COD = 85.4, 85.9; BOD = 28.9, 30.9% and the biodegradability = 0.074, 0.080 respectively at the pH 7.5, reaction time 300 min and photocatalyst dosage of 4 g L-1. The study reveals both models found to be well predicted as compared with experimental values. The values of R2 for RSM-BBD (0.920) and for ANFIS (0.990) models were almost close to 1. The ANFIS model was found to be marginally better than that of RSM-BBD.
    Matched MeSH terms: Fuzzy Logic
  3. Fulltext Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae
    Chong JWR, Tang DYY, Leong HY, Khoo KS, Show PL, Chew KW
    Bioengineered, 2023 Dec;14(1):2244232.
    PMID: 37578162 DOI: 10.1080/21655979.2023.2244232
    Fucoxanthin is a carotenoid that possesses various beneficial medicinal properties for human well-being. However, the current extraction technologies and quantification techniques are still lacking in terms of cost validation, high energy consumption, long extraction time, and low yield production. To date, artificial intelligence (AI) models can assist and improvise the bottleneck of fucoxanthin extraction and quantification process by establishing new technologies and processes which involve big data, digitalization, and automation for efficiency fucoxanthin production. This review highlights the application of AI models such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS), capable of learning patterns and relationships from large datasets, capturing non-linearity, and predicting optimal conditions that significantly impact the fucoxanthin extraction yield. On top of that, combining metaheuristic algorithm such as genetic algorithm (GA) can further improve the parameter space and discovery of optimal conditions of ANN and ANFIS models, which results in high R2 accuracy ranging from 98.28% to 99.60% after optimization. Besides, AI models such as support vector machine (SVM), convolutional neural networks (CNNs), and ANN have been leveraged for the quantification of fucoxanthin, either computer vision based on color space of images or regression analysis based on statistical data. The findings are reliable when modeling for the concentration of pigments with high R2 accuracy ranging from 66.0% - 99.2%. This review paper has reviewed the feasibility and potential of AI for the extraction and quantification purposes, which can reduce the cost, accelerate the fucoxanthin yields, and development of fucoxanthin-based products.
    Matched MeSH terms: Fuzzy Logic
  4. Fulltext An adaptive handover prediction scheme for seamless mobility based wireless networks
    Sadiq AS, Fisal NB, Ghafoor KZ, Lloret J
    ScientificWorldJournal, 2014;2014:610652.
    PMID: 25574490 DOI: 10.1155/2014/610652
    We propose an adaptive handover prediction (AHP) scheme for seamless mobility based wireless networks. That is, the AHP scheme incorporates fuzzy logic with AP prediction process in order to lend cognitive capability to handover decision making. Selection metrics, including received signal strength, mobile node relative direction towards the access points in the vicinity, and access point load, are collected and considered inputs of the fuzzy decision making system in order to select the best preferable AP around WLANs. The obtained handover decision which is based on the calculated quality cost using fuzzy inference system is also based on adaptable coefficients instead of fixed coefficients. In other words, the mean and the standard deviation of the normalized network prediction metrics of fuzzy inference system, which are collected from available WLANs are obtained adaptively. Accordingly, they are applied as statistical information to adjust or adapt the coefficients of membership functions. In addition, we propose an adjustable weight vector concept for input metrics in order to cope with the continuous, unpredictable variation in their membership degrees. Furthermore, handover decisions are performed in each MN independently after knowing RSS, direction toward APs, and AP load. Finally, performance evaluation of the proposed scheme shows its superiority compared with representatives of the prediction approaches.
    Matched MeSH terms: Fuzzy Logic
  5. Fulltext Tuberculosis disease diagnosis using artificial immune recognition system
    Shamshirband S, Hessam S, Javidnia H, Amiribesheli M, Vahdat S, Petković D, et al.
    Int J Med Sci, 2014;11(5):508-14.
    PMID: 24688316 DOI: 10.7150/ijms.8249
    There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods.
    Matched MeSH terms: Fuzzy Logic
  6. Motor imaginary-based brain-machine interface design using programmable logic controllers for the disabled
    Jeyabalan V, Samraj A, Loo CK
    Comput Methods Biomech Biomed Engin, 2010 Oct;13(5):617-23.
    PMID: 20336561 DOI: 10.1080/10255840903405678
    Aiming at the implementation of brain-machine interfaces (BMI) for the aid of disabled people, this paper presents a system design for real-time communication between the BMI and programmable logic controllers (PLCs) to control an electrical actuator that could be used in devices to help the disabled. Motor imaginary signals extracted from the brain’s motor cortex using an electroencephalogram (EEG) were used as a control signal. The EEG signals were pre-processed by means of adaptive recursive band-pass filtrations (ARBF) and classified using simplified fuzzy adaptive resonance theory mapping (ARTMAP) in which the classified signals are then translated into control signals used for machine control via the PLC. A real-time test system was designed using MATLAB for signal processing, KEP-Ware V4 OLE for process control (OPC), a wireless local area network router, an Omron Sysmac CPM1 PLC and a 5 V/0.3A motor. This paper explains the signal processing techniques, the PLC's hardware configuration, OPC configuration and real-time data exchange between MATLAB and PLC using the MATLAB OPC toolbox. The test results indicate that the function of exchanging real-time data can be attained between the BMI and PLC through OPC server and proves that it is an effective and feasible method to be applied to devices such as wheelchairs or electronic equipment.
    Matched MeSH terms: Fuzzy Logic
  7. Fulltext Discovering the Value Creation System in IoT Ecosystems
    Lopez CA, Castillo LF, Corchado JM
    Sensors (Basel), 2021 Jan 06;21(2).
    PMID: 33418918 DOI: 10.3390/s21020328
    Internet of Things (IoT) should not be seen only as a cost reduction mechanism for manufacturing companies; instead, it should be seen as the basis for transition to a new business model that monetizes the data from an intelligent ecosystem. In this regard, deciphering the operation of the value creation system and finding the balance between the digital strategy and the deployment of technological platforms, are the main motivations behind this research. To achieve the proposed objectives, systems theory has been adopted in the conceptualization stage, later, fuzzy logic has been used to structure a subsystem for the evaluation of input parameters. Subsequently, system dynamics have been used to build a computational representation and later, through dynamic simulation, the model has been adjusted according to iterations and the identified limits of the system. Finally, with the obtained set of results, different value creation and capture behaviors have been identified. The simulation model, based on the conceptualization of the system and the mathematical representation of the value function, allows to establish a frame of reference for the evaluation of the behaviour of IoT ecosystems in the context of the connected home.
    Matched MeSH terms: Fuzzy Logic
  8. Fulltext Issues on trust management in wireless environment
    Sirageldin, Abubakr, Baharum Baharudin, Low, Tang Jung
    Pertanika Journal of Science & Technology, 2013;21(1):183-192.
    MyJurnal
    Developing a trust management scheme in mobile computing environment is increasingly important,
    and the effective trust management model is a challenging task. Business, education, military, and
    entertainment have motivated the growth of ubiquitous and pervasive computing environments, which are always available due to the widespread of portable and embedded devices. Wireless and mobile computing are good example of ubiquitous and pervasive computing environments. Due to the uncertainty and mobility in such environments, the issue of trust has been regarded as an important security problem. Malicious nodes are a major threat to these networks; the trust system can monitor the behaviour of nodes and accordingly rewards well-behaved nodes and punishes misbehaving ones. At present, there are a lot of endeavours on the trust model of the pervasive computing environment. In this paper, a trust management framework for mobile computing is presented. The hybrid framework is based on a fusion of the support vector machine (SVM) and fuzzy logic system. From the results, it can be stated that the framework is effective, dynamic, lightweight, and applicable.
    Matched MeSH terms: Fuzzy Logic
  9. Fulltext Optimized parameters of wastewater treatment system using fuzzy control
    Ilanur Muhaini Mohd Noor, Muhamad Kamal Mohammed Amin
    Journal of Advanced Research in Applied Sciences and Engineering Technology, 2017;5(2):27-35.
    MyJurnal
    This paper aim is to design an education kit for wastewater system that can maintain
    the standard parameters of neutralized wastewater by maintaining the suitable pH
    (Potential Hydronium) level and temperature of the wastewater from industry by using
    fuzzy controller. This study is capable to control the unwanted bacteria by automatic
    regulatory and monitoring the temperature, pH and water level. Fuzzy logic method is
    use to control and monitor pH level as well as the temperature during clarifying process
    because pH control process is a complex physical-chemistry process of strong
    individuality of time-varying and non-linearity properties. Pumps used in the prototype
    need to be controlled precisely to enable either acid or base to be pumped into mix
    tank of the wastewater treatment. The control and monitoring system, which has been
    designed through LabVIEW front panel will ease end user in inspection of the
    parameters involve in wastewater treatment. The entire system output could be
    observed remotely in Data Dashboard application in smartphone or tablet. The GUI
    was designed and interfaced with the prototype constructed to carry out the process
    of controlling and monitoring the required parameters. Few tests were conducted
    repetitively to analyse the performance of the system parameters. It was found that
    the controlled set point fixed within the range of pH 7.6-8.4, temperature 25-29.44
    Celsius and water level of 20cm in this research that was effectively achieved in the
    entire test conducted. In addition, the wastewater system accuracy and performance
    is 96.72% and 90.22% respectively.
    Matched MeSH terms: Fuzzy Logic
  10. Optimized fuzzy inference system to enhance prediction accuracy for influent characteristics of a sewage treatment plant
    Ansari M, Othman F, El-Shafie A
    Sci Total Environ, 2020 Jun 20;722:137878.
    PMID: 32199382 DOI: 10.1016/j.scitotenv.2020.137878
    Sewage treatment plants (STPs) keep sewage contamination within safe levels and minimize the risk of environmental disasters. To achieve optimum operation of an STP, it is necessary for influent parameters to be measured or estimated precisely. In this research, six well-known influent chemical and biological characteristics, i.e., biochemical oxygen demand (BOD), chemical oxygen demand (COD), Ammoniacal Nitrogen (NH3-N), pH, oil and grease (OG) and suspended solids (SS), were modeled and predicted using the Sugeno fuzzy logic model. The membership function range of the fuzzy model was optimized by ANFIS, the integrated Genetic algorithms (GA), and the integrated particle swarm optimization (PSO) algorithms. The results were evaluated by different indices to find the accuracy of each algorithm. To ensure prediction accuracy, outliers in the predicted data were found and replaced with reasonable values. The results showed that both integrated GA-FIS and PSO-FIS algorithms performed at almost the same level and both had fewer errors than ANFIS. As the GA-FIS algorithm predicts BOD with fewer errors than PSO-FIS and the aim of this study is to provide an accurate prediction of missing data, GA-FIS was only used to predict the BOD parameter; the other parameters were predicted by PSO-FIS algorithm. As a result, the model successfully could provide outstanding performance for predicting the BOD, COD, NH3-N, OG, pH and SS with MAE equal to 3.79, 5.14, 0.4, 0.27, 0.02, and 3.16, respectively.
    Matched MeSH terms: Fuzzy Logic
  11. Kernel Bayesian ART and ARTMAP
    Masuyama N, Loo CK, Dawood F
    Neural Netw, 2018 Feb;98:76-86.
    PMID: 29202265 DOI: 10.1016/j.neunet.2017.11.003
    Adaptive Resonance Theory (ART) is one of the successful approaches to resolving "the plasticity-stability dilemma" in neural networks, and its supervised learning model called ARTMAP is a powerful tool for classification. Among several improvements, such as Fuzzy or Gaussian based models, the state of art model is Bayesian based one, while solving the drawbacks of others. However, it is known that the Bayesian approach for the high dimensional and a large number of data requires high computational cost, and the covariance matrix in likelihood becomes unstable. This paper introduces Kernel Bayesian ART (KBA) and ARTMAP (KBAM) by integrating Kernel Bayes' Rule (KBR) and Correntropy Induced Metric (CIM) to Bayesian ART (BA) and ARTMAP (BAM), respectively, while maintaining the properties of BA and BAM. The kernel frameworks in KBA and KBAM are able to avoid the curse of dimensionality. In addition, the covariance-free Bayesian computation by KBR provides the efficient and stable computational capability to KBA and KBAM. Furthermore, Correntropy-based similarity measurement allows improving the noise reduction ability even in the high dimensional space. The simulation experiments show that KBA performs an outstanding self-organizing capability than BA, and KBAM provides the superior classification ability than BAM, respectively.
    Matched MeSH terms: Fuzzy Logic
  12. A biological inspired fuzzy adaptive window median filter (FAWMF) for enhancing DNA signal processing
    Ahmad M, Jung LT, Bhuiyan AA
    Comput Methods Programs Biomed, 2017 Oct;149:11-17.
    PMID: 28802326 DOI: 10.1016/j.cmpb.2017.06.021
    BACKGROUND AND OBJECTIVE: Digital signal processing techniques commonly employ fixed length window filters to process the signal contents. DNA signals differ in characteristics from common digital signals since they carry nucleotides as contents. The nucleotides own genetic code context and fuzzy behaviors due to their special structure and order in DNA strand. Employing conventional fixed length window filters for DNA signal processing produce spectral leakage and hence results in signal noise. A biological context aware adaptive window filter is required to process the DNA signals.

    METHODS: This paper introduces a biological inspired fuzzy adaptive window median filter (FAWMF) which computes the fuzzy membership strength of nucleotides in each slide of window and filters nucleotides based on median filtering with a combination of s-shaped and z-shaped filters. Since coding regions cause 3-base periodicity by an unbalanced nucleotides' distribution producing a relatively high bias for nucleotides' usage, such fundamental characteristic of nucleotides has been exploited in FAWMF to suppress the signal noise.

    RESULTS: Along with adaptive response of FAWMF, a strong correlation between median nucleotides and the Π shaped filter was observed which produced enhanced discrimination between coding and non-coding regions contrary to fixed length conventional window filters. The proposed FAWMF attains a significant enhancement in coding regions identification i.e. 40% to 125% as compared to other conventional window filters tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms.

    CONCLUSION: This study proves that conventional fixed length window filters applied to DNA signals do not achieve significant results since the nucleotides carry genetic code context. The proposed FAWMF algorithm is adaptive and outperforms significantly to process DNA signal contents. The algorithm applied to variety of DNA datasets produced noteworthy discrimination between coding and non-coding regions contrary to fixed window length conventional filters.

    Matched MeSH terms: Fuzzy Logic
  13. Appraisal of adaptive neuro-fuzzy computing technique for estimating anti-obesity properties of a medicinal plant
    Kazemipoor M, Hajifaraji M, Radzi CW, Shamshirband S, Petković D, Mat Kiah ML
    Comput Methods Programs Biomed, 2015 Jan;118(1):69-76.
    PMID: 25453384 DOI: 10.1016/j.cmpb.2014.10.006
    This research examines the precision of an adaptive neuro-fuzzy computing technique in estimating the anti-obesity property of a potent medicinal plant in a clinical dietary intervention. Even though a number of mathematical functions such as SPSS analysis have been proposed for modeling the anti-obesity properties estimation in terms of reduction in body mass index (BMI), body fat percentage, and body weight loss, there are still disadvantages of the models like very demanding in terms of calculation time. Since it is a very crucial problem, in this paper a process was constructed which simulates the anti-obesity activities of caraway (Carum carvi) a traditional medicine on obese women with adaptive neuro-fuzzy inference (ANFIS) method. The ANFIS results are compared with the support vector regression (SVR) results using root-mean-square error (RMSE) and coefficient of determination (R(2)). The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach. The following statistical characteristics are obtained for BMI loss estimation: RMSE=0.032118 and R(2)=0.9964 in ANFIS testing and RMSE=0.47287 and R(2)=0.361 in SVR testing. For fat loss estimation: RMSE=0.23787 and R(2)=0.8599 in ANFIS testing and RMSE=0.32822 and R(2)=0.7814 in SVR testing. For weight loss estimation: RMSE=0.00000035601 and R(2)=1 in ANFIS testing and RMSE=0.17192 and R(2)=0.6607 in SVR testing. Because of that, it can be applied for practical purposes.
    Matched MeSH terms: Fuzzy Logic*
  14. Fulltext Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor
    Rahman HA, Harun SW, Arof H, Irawati N, Musirin I, Ibrahim F, et al.
    J Biomed Opt, 2014 May;19(5):057009.
    PMID: 24839996 DOI: 10.1117/1.JBO.19.5.057009
    An enhanced dental cavity diameter measurement mechanism using an intensity-modulated fiber optic displacement sensor (FODS) scanning and imaging system, fuzzy logic as well as a single-layer perceptron (SLP) neural network, is presented. The SLP network was employed for the classification of the reflected signals, which were obtained from the surfaces of teeth samples and captured using FODS. Two features were used for the classification of the reflected signals with one of them being the output of a fuzzy logic. The test results showed that the combined fuzzy logic and SLP network methodology contributed to a 100% classification accuracy of the network. The high-classification accuracy significantly demonstrates the suitability of the proposed features and classification using SLP networks for classifying the reflected signals from teeth surfaces, enabling the sensor to accurately measure small diameters of tooth cavity of up to 0.6 mm. The method remains simple enough to allow its easy integration in existing dental restoration support systems.
    Matched MeSH terms: Fuzzy Logic*
  15. Design of a fuzzy-based decision support system for coronary heart disease diagnosis
    Lahsasna A, Ainon RN, Zainuddin R, Bulgiba A
    J Med Syst, 2012 Oct;36(5):3293-306.
    PMID: 22252606 DOI: 10.1007/s10916-012-9821-7
    In the present paper, a fuzzy rule-based system (FRBS) is designed to serve as a decision support system for Coronary heart disease (CHD) diagnosis that not only considers the decision accuracy of the rules but also their transparency at the same time. To achieve the two above mentioned objectives, we apply a multi-objective genetic algorithm to optimize both the accuracy and transparency of the FRBS. In addition and to help assess the certainty and the importance of each rule by the physician, an extended format of fuzzy rules that incorporates the degree of decision certainty and importance or support of each rule at the consequent part of the rules is introduced. Furthermore, a new way for employing Ensemble Classifiers Strategy (ECS) method is proposed to enhance the classification ability of the FRBS. The results show that the generated rules are humanly understandable while their accuracy compared favorably with other benchmark classification methods. In addition, the produced FRBS is able to identify the uncertainty cases so that the physician can give a special consideration to deal with them and this will result in a better management of efforts and tasks. Furthermore, employing ECS has specifically improved the ability of FRBS to detect patients with CHD which is desirable feature for any CHD diagnosis system.
    Matched MeSH terms: Fuzzy Logic*
  16. Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata
    Ghanizadeh A, Abarghouei AA, Sinaie S, Saad P, Shamsuddin SM
    Appl Opt, 2011 Jul 1;50(19):3191-200.
    PMID: 21743518 DOI: 10.1364/AO.50.003191
    Iris-based biometric systems identify individuals based on the characteristics of their iris, since they are proven to remain unique for a long time. An iris recognition system includes four phases, the most important of which is preprocessing in which the iris segmentation is performed. The accuracy of an iris biometric system critically depends on the segmentation system. In this paper, an iris segmentation system using edge detection techniques and Hough transforms is presented. The newly proposed edge detection system enhances the performance of the segmentation in a way that it performs much more efficiently than the other conventional iris segmentation methods.
    Matched MeSH terms: Fuzzy Logic*
  17. Computational intelligent gait-phase detection system to identify pathological gait
    Senanayake CM, Senanayake SM
    IEEE Trans Inf Technol Biomed, 2010 Sep;14(5):1173-9.
    PMID: 20801745 DOI: 10.1109/TITB.2010.2058813
    An intelligent gait-phase detection algorithm based on kinematic and kinetic parameters is presented in this paper. The gait parameters do not vary distinctly for each gait phase; therefore, it is complex to differentiate gait phases with respect to a threshold value. To overcome this intricacy, the concept of fuzzy logic was applied to detect gait phases with respect to fuzzy membership values. A real-time data-acquisition system was developed consisting of four force-sensitive resistors and two inertial sensors to obtain foot-pressure patterns and knee flexion/extension angle, respectively. The detected gait phases could be further analyzed to identify abnormality occurrences, and hence, is applicable to determine accurate timing for feedback. The large amount of data required for quality gait analysis necessitates the utilization of information technology to store, manage, and extract required information. Therefore, a software application was developed for real-time acquisition of sensor data, data processing, database management, and a user-friendly graphical-user interface as a tool to simplify the task of clinicians. The experiments carried out to validate the proposed system are presented along with the results analysis for normal and pathological walking patterns.
    Matched MeSH terms: Fuzzy Logic*
  18. A fuzzy logic approach to marine spatial management
    Teh LC, Teh LS
    Environ Manage, 2011 Apr;47(4):536-45.
    PMID: 21359523 DOI: 10.1007/s00267-011-9645-0
    Marine spatial planning tends to prioritise biological conservation targets over socio-economic considerations, which may incur lower user compliance and ultimately compromise management success. We argue for more inclusion of human dimensions in spatial management, so that outcomes not only fulfill biodiversity and conservation objectives, but are also acceptable to resource users. We propose a fuzzy logic framework that will facilitate this task- The protected area suitability index (PASI) combines fishers' spatial preferences with biological criteria to assess site suitability for protection from fishing. We apply the PASI in a spatial evaluation of a small-scale reef fishery in Sabah, Malaysia. While our results pertain to fishers specifically, the PASI can also be customized to include the interests of other stakeholders and resource users, as well as incorporate varying levels of protection.
    Matched MeSH terms: Fuzzy Logic*
  19. An intelligent load shedding scheme using neural networks and neuro-fuzzy
    Haidar AM, Mohamed A, Al-Dabbagh M, Hussain A, Masoum M
    Int J Neural Syst, 2009 Dec;19(6):473-9.
    PMID: 20039470
    Load shedding is some of the essential requirement for maintaining security of modern power systems, particularly in competitive energy markets. This paper proposes an intelligent scheme for fast and accurate load shedding using neural networks for predicting the possible loss of load at the early stage and neuro-fuzzy for determining the amount of load shed in order to avoid a cascading outage. A large scale electrical power system has been considered to validate the performance of the proposed technique in determining the amount of load shed. The proposed techniques can provide tools for improving the reliability and continuity of power supply. This was confirmed by the results obtained in this research of which sample results are given in this paper.
    Matched MeSH terms: Fuzzy Logic*
  20. A proposed hierarchical fuzzy inference system for the diagnosis of arthritic diseases
    Lim CK, Yew KM, Ng KH, Abdullah BJ
    Australas Phys Eng Sci Med, 2002 Sep;25(3):144-50.
    PMID: 12416592 DOI: 10.1007/BF03178776
    Development of computer-based medical inference systems is always confronted with some difficulties. In this paper, difficulties of designing an inference system for the diagnosis of arthritic diseases are described, including variations of disease manifestations under various situations and conditions. Furthermore, the need for a huge knowledge base would result in low efficiency of the inference system. We proposed a hierarchical model of the fuzzy inference system as a possible solution. With such a model, the diagnostic process is divided into two levels. The first level of the diagnosis reduces the scope of diagnosis to be processed by the second level. This will reduce the amount of input and mapping for the whole diagnostic process. Fuzzy relational theory is the core of this system and it is used in both levels to improve the accuracy.
    Matched MeSH terms: Fuzzy Logic*
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