Displaying publications 81 - 100 of 140 in total

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  1. 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
  2. Fulltext The development of Adaptive Neuro-Fuzzy Inference System model to diagnosis diabetes disease data set
    Mamuda M, Sathasivam S
    MATEMATIKA, 2017;33(1):11-19.
    MyJurnal
    Medical diagnosis is the extrapolation of the future course and outcome of a disease and a sign of the likelihood of recovery from that disease. Diagnosis is important because it is used to guide the type and intensity of the medication to be administered to patients. A hybrid intelligent system that combines the fuzzy logic qualitative approach and Adaptive Neural Networks (ANNs) with the capabilities of getting a better performance is required. In this paper, a method for modeling the survival of diabetes patient by utilizing the application of the Adaptive NeuroFuzzy Inference System (ANFIS) is introduced with the aim of turning data into knowledge that can be understood by people. The ANFIS approach implements the hybrid learning algorithm that combines the gradient descent algorithm and a recursive least square error algorithm to update the antecedent and consequent parameters. The combination of fuzzy inference that will represent knowledge in an interpretable manner and the learning ability of neural network that can adjust the membership functions of the parameters and linguistic rules from data will be considered. The proposed framework can be applied to estimate the risk and survival curve between different diagnostic factors and survival time with the explanation capabilities.
    Matched MeSH terms: Logic
  3. 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
  4. Fulltext Developing a digital intervention for cancer survivors: an evidence-, theory- and person-based approach
    Bradbury K, Steele M, Corbett T, Geraghty AWA, Krusche A, Heber E, et al.
    NPJ Digit Med, 2019;2:85.
    PMID: 31508496 DOI: 10.1038/s41746-019-0163-4
    This paper illustrates a rigorous approach to developing digital interventions using an evidence-, theory- and person-based approach. Intervention planning included a rapid scoping review that identified cancer survivors' needs, including barriers and facilitators to intervention success. Review evidence (N = 49 papers) informed the intervention's Guiding Principles, theory-based behavioural analysis and logic model. The intervention was optimised based on feedback on a prototype intervention through interviews (N = 96) with cancer survivors and focus groups with NHS staff and cancer charity workers (N = 31). Interviews with cancer survivors highlighted barriers to engagement, such as concerns about physical activity worsening fatigue. Focus groups highlighted concerns about support appointment length and how to support distressed participants. Feedback informed intervention modifications, to maximise acceptability, feasibility and likelihood of behaviour change. Our systematic method for understanding user views enabled us to anticipate and address important barriers to engagement. This methodology may be useful to others developing digital interventions.
    Matched MeSH terms: Logic
  5. 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
  6. 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
  7. 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
  8. Fulltext Discriminant and convergent validity of measurement tools in postgraduate medical education of a surgical-based discipline: towards assessment program
    Shahid Hassan, Mohamad Najib Mat Pa, Muhamad Saiful Bahri Yusoff
    Education in Medicine Journal, 2012;4(1):22-42.
    MyJurnal
    Background: Summative assessment in postgraduate examination globally employs multiple measures. A standard-setting method decides on pass or fail based on an arbitrarily defined cut-off point on a test score, which is often content expert’s subjective judgment. Contrary to this a standard-setting strategy primarily practices two approaches, a compensatory approach, which decides on overall performance as a sum of all the test scores and a conjunctive approach that requires passing performance for each instrument. However, the challenge using multiple measures is not due to number of measurement tools but due to logic by which the measures are combined to draw inferences on pass or fail in summative assessment. Conjoint University Board of Examination of Masters’ of Otolaryngology and Head-Neck Surgery (ORL-HNS) in Malaysia also uses multiple measures to reach a passing or failing decision in summative assessment. However, the standard setting strategy of assessment is loosely and variably applied to make ultimate decision on pass or fail. To collect the evidences, the summative assessment program of Masters’ of ORL-HNS in School of Medical Sciences at Universiti Sains Malaysia was analyzed for validity to evaluate the appropriateness of decisions in postgraduate medical education in Malaysia. Methodology: A retrospective study was undertaken to evaluate the validity of the conjoint summative assessment results of part II examination of USM candidates during May 2000-May 2011. The Pearson correlation and multiple linear regression tests were used to determine the discriminant and convergent validity of assessment tools. Pearson’s correlation coefficient analyzed the association between assessment tools and the multiple linear regression compared the dominant roles of factor variables in predicting outcomes. Based on outcome of the study, reforms for standard-setting strategy are also recommended towards programming the assessment in a surgical-based discipline. Results: The correlation coefficients of MCQ and essay questions were found not significant (0.16). Long and short cases were shown to have good correlations (0.53). Oral test stood as a component to show fair correlation with written (0.39-0.42) as well as clinical component (0.50-0.66). The predictive values in written tests suggested MCQ predicted by oral (B=0.34, P
    Matched MeSH terms: Logic
  9. Fulltext Logic mining in league of legends
    Liew, Ching Kho, Mohd Shareduwan Mohd Kasihmuddin, Mohd. Asyraf Mansor, Sathasivam, Saratha
    Pertanika Journal of Science & Technology, 2020;28(1):211-225.
    MyJurnal
    Since its debut in 2009, League of Legends (LoL) has been on a rise in becoming an extremely favoured multiplayer online battle arena (MOBA) game. This paper presented a logic mining technique to model the results (Win / Lose) of the LoL games played in 3 regions, namely South Korea, North America and Europe. In this research, a method named k satisfiability based reverse analysis method (kSATRA) was brought forward to obtain the logical relationship among the gameplays and objectives in the game. The logical rule obtained from the LoL games was used to categorize the results of future games. kSATRA made use of the advantages of Hopfield Neural Network and k Satisfiability representation. The data set used in this study included the data of all 10 teams from each region, which composed of all games from Spring Season 2018. The effectiveness of kSATRA in obtaining logical rule in LoL games was tested based on root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and CPU time. Results acquired from the computer simulation showed the robustness of kSATRA in exhibiting the performance of the LoL teams.
    Matched MeSH terms: Logic
  10. Fulltext Semi-analytical modelling and evaluation of uniformly doped silicene nanotransistors for digital logic gates
    Chuan MW, Wong KL, Riyadi MA, Hamzah A, Rusli S, Alias NE, et al.
    PLoS One, 2021;16(6):e0253289.
    PMID: 34125874 DOI: 10.1371/journal.pone.0253289
    Silicene has attracted remarkable attention in the semiconductor research community due to its silicon (Si) nature. It is predicted as one of the most promising candidates for the next generation nanoelectronic devices. In this paper, an efficient non-iterative technique is employed to create the SPICE models for p-type and n-type uniformly doped silicene field-effect transistors (FETs). The current-voltage characteristics show that the proposed silicene FET models exhibit high on-to-off current ratio under ballistic transport. In order to obtain practical digital logic timing diagrams, a parasitic load capacitance, which is dependent on the interconnect length, is attached at the output terminal of the logic circuits. Furthermore, the key circuit performance metrics, including the propagation delay, average power, power-delay product and energy-delay product of the proposed silicene-based logic gates are extracted and benchmarked with published results. The effects of the interconnect length to the propagation delay and average power are also investigated. The results of this work further envisage the uniformly doped silicene as a promising candidate for future nanoelectronic applications.
    Matched MeSH terms: Logic
  11. 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
  12. 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
  13. Fulltext Dynamic electronic institutions in agent oriented cloud robotic systems
    Nagrath V, Morel O, Malik A, Saad N, Meriaudeau F
    Springerplus, 2015;4:103.
    PMID: 25763310 DOI: 10.1186/s40064-015-0810-4
    The dot-com bubble bursted in the year 2000 followed by a swift movement towards resource virtualization and cloud computing business model. Cloud computing emerged not as new form of computing or network technology but a mere remoulding of existing technologies to suit a new business model. Cloud robotics is understood as adaptation of cloud computing ideas for robotic applications. Current efforts in cloud robotics stress upon developing robots that utilize computing and service infrastructure of the cloud, without debating on the underlying business model. HTM5 is an OMG's MDA based Meta-model for agent oriented development of cloud robotic systems. The trade-view of HTM5 promotes peer-to-peer trade amongst software agents. HTM5 agents represent various cloud entities and implement their business logic on cloud interactions. Trade in a peer-to-peer cloud robotic system is based on relationships and contracts amongst several agent subsets. Electronic Institutions are associations of heterogeneous intelligent agents which interact with each other following predefined norms. In Dynamic Electronic Institutions, the process of formation, reformation and dissolution of institutions is automated leading to run time adaptations in groups of agents. DEIs in agent oriented cloud robotic ecosystems bring order and group intellect. This article presents DEI implementations through HTM5 methodology.
    Matched MeSH terms: Logic
  14. 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
  15. Evaluating the transferability of low-cost sensor calibration using ANFIS: a field study in Putrajaya, Malaysia
    Alhasa KM, Nadzir MSM, Ali SHM, Putro WS
    Environ Monit Assess, 2025 Jul 31;197(8):972.
    PMID: 40742620 DOI: 10.1007/s10661-025-14377-9
    This study evaluates the robustness of a previously developed calibration model for low-cost ozone sensors, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). The model was deployed at a different site without retraining. It was tested in Putrajaya, Malaysia, over a 2-month period in 2018 and compared with conventional laboratory-calibrated models for CO and NO₂ sensors. The ANFIS model demonstrated consistently strong performance for the OX-A431 sensor, with R2 values approaching 0.9 and lower RMSE, confirming its transferability. However, deviations of up to 70 ppb were recorded during high ozone episodes. This may be due to the limited input variables used-namely O₃, NO₂, temperature, and relative humidity-while other influential factors such as co-pollutants or atmospheric pressure were not included. In contrast, laboratory-calibrated models for the CO-A4 and NO₂-A43F sensors exhibited poorer performance (R2 = 0.13-0.73), indicating low adaptability under field conditions. These findings underscore the importance of field calibration for low-cost sensors and suggest that incorporating additional environmental and chemical parameters may further improve calibration accuracy. This study contributes to the advancement of generalized machine learning calibration frameworks to enable scalable low-cost sensor networks for ambient air quality monitoring.
    Matched MeSH terms: Fuzzy Logic
  16. Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review
    Ahmadi H, Gholamzadeh M, Shahmoradi L, Nilashi M, Rashvand P
    Comput Methods Programs Biomed, 2018 Jul;161:145-172.
    PMID: 29852957 DOI: 10.1016/j.cmpb.2018.04.013
    BACKGROUND AND OBJECTIVE: Diagnosis as the initial step of medical practice, is one of the most important parts of complicated clinical decision making which is usually accompanied with the degree of ambiguity and uncertainty. Since uncertainty is the inseparable nature of medicine, fuzzy logic methods have been used as one of the best methods to decrease this ambiguity. Recently, several kinds of literature have been published related to fuzzy logic methods in a wide range of medical aspects in terms of diagnosis. However, in this context there are a few review articles that have been published which belong to almost ten years ago. Hence, we conducted a systematic review to determine the contribution of utilizing fuzzy logic methods in disease diagnosis in different medical practices.

    METHODS: Eight scientific databases are selected as an appropriate database and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed as the basis method for conducting this systematic and meta-analysis review. Regarding the main objective of this research, some inclusion and exclusion criteria were considered to limit our investigation. To achieve a structured meta-analysis, all eligible articles were classified based on authors, publication year, journals or conferences, applied fuzzy methods, main objectives of the research, problems and research gaps, tools utilized to model the fuzzy system, medical disciplines, sample sizes, the inputs and outputs of the system, findings, results and finally the impact of applied fuzzy methods to improve diagnosis. Then, we analyzed the results obtained from these classifications to indicate the effect of fuzzy methods in decreasing the complexity of diagnosis.

    RESULTS: Consequently, the result of this study approved the effectiveness of applying different fuzzy methods in diseases diagnosis process, presenting new insights for researchers about what kind of diseases which have been more focused. This will help to determine the diagnostic aspects of medical disciplines that are being neglected.

    CONCLUSIONS: Overall, this systematic review provides an appropriate platform for further research by identifying the research needs in the domain of disease diagnosis.

    Matched MeSH terms: Fuzzy Logic*
  17. Improving the environmental impact of palm kernel shell through maximizing its production of hydrogen and syngas using advanced artificial intelligence
    Rezk H, Nassef AM, Inayat A, Sayed ET, Shahbaz M, Olabi AG
    Sci Total Environ, 2019 Mar 25;658:1150-1160.
    PMID: 30677979 DOI: 10.1016/j.scitotenv.2018.12.284
    Fossil fuel depletion and the environmental concerns have been under discussion for energy production for many years and finding new and renewable energy sources became a must. Biomass is considered as a net zero CO2 energy source. Gasification of biomass for H2 and syngas production is an attractive process. The main target of this research is to improve the production of hydrogen and syngas from palm kernel shell (PKS) steam gasification through defining the optimal operating parameters' using a modern optimization algorithm. To predict the gaseous outputs, two PKS models were built using fuzzy logic based on the experimental data sets. A radial movement optimizer (RMO) was applied to determine the system's optimal operating parameters. During the optimization process, the decision variables were represented by four different operating parameters. These parameters include; temperature, particle size, CaO/biomass ratio and coal bottom ash (CBA) with their operating ranges of (650-750 °C), (0.5-1 mm), (0.5-2) and wt% (0.02-0.10), respectively. The individual and interactive effects of different combinations were investigated on the production of H2 and syngas yield. The optimized results were compared with experimental data and results obtained from Response Surface Methodology (RSM) reported in literature. The obtained optimal values of the operating parameters through RMO were found 722 °C, 0.92 mm, 1.72 and 0.06 wt% for the temperature, particle size, CaO/biomass ratio and coal bottom ash, respectively. The results showed that syngas production was significantly improved as it reached 65.44 vol% which was better than that obtained in earlier studies.
    Matched MeSH terms: Fuzzy Logic*
  18. 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*
  19. A fuzzy integral method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification across multiple subjects
    Cacha LA, Parida S, Dehuri S, Cho SB, Poznanski RR
    J Integr Neurosci, 2016 Dec;15(4):593-606.
    PMID: 28093025 DOI: 10.1142/S0219635216500345
    The huge number of voxels in fMRI over time poses a major challenge to for effective analysis. Fast, accurate, and reliable classifiers are required for estimating the decoding accuracy of brain activities. Although machine-learning classifiers seem promising, individual classifiers have their own limitations. To address this limitation, the present paper proposes a method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification for application across multiple subjects. Similarly, the fuzzy integral (FI) approach has been employed as an efficient tool for combining different classifiers. The FI approach led to the development of a classifiers ensemble technique that performs better than any of the single classifier by reducing the misclassification, the bias, and the variance. The proposed method successfully classified the different cognitive states for multiple subjects with high accuracy of classification. Comparison of the performance improvement, while applying ensemble neural networks method, vs. that of the individual neural network strongly points toward the usefulness of the proposed method.
    Matched MeSH terms: Fuzzy Logic*
  20. 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*
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