Displaying publications 101 - 120 of 140 in total

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  1. 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*
  2. Fulltext A fuzzy controller for lower limb exoskeletons during sit-to-stand and stand-to-sit movement using wearable sensors
    Reza SM, Ahmad N, Choudhury IA, Ghazilla RA
    Sensors (Basel), 2014 Mar 04;14(3):4342-63.
    PMID: 24599193 DOI: 10.3390/s140304342
    Human motion is a daily and rhythmic activity. The exoskeleton concept is a very positive scientific approach for human rehabilitation in case of lower limb impairment. Although the exoskeleton shows potential, it is not yet applied extensively in clinical rehabilitation. In this research, a fuzzy based control algorithm is proposed for lower limb exoskeletons during sit-to-stand and stand-to-sit movements. Surface electromyograms (EMGs) are acquired from the vastus lateralis muscle using a wearable EMG sensor. The resultant acceleration angle along the z-axis is determined from a kinematics sensor. Twenty volunteers were chosen to perform the experiments. The whole experiment was accomplished in two phases. In the first phase, acceleration angles and EMG data were acquired from the volunteers during both sit-to-stand and stand-to-sit motions. During sit-to-stand movements, the average acceleration angle at activation was 11°-48° and the EMG varied from -0.19 mV to +0.19 mV. On the other hand, during stand-to-sit movements, the average acceleration angle was found to be 57.5°-108° at the activation point and the EMG varied from -0.32 mV to +0.32 mV. In the second phase, a fuzzy controller was designed from the experimental data. The controller was tested and validated with both offline and real time data using LabVIEW.
    Matched MeSH terms: Fuzzy Logic*
  3. 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*
  4. 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*
  5. 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*
  6. 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*
  7. 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*
  8. Transforming XML-based electronic patient records for use in medical case based reasoning systems
    Abidi SS, Manickam S
    Stud Health Technol Inform, 2000;77:709-13.
    PMID: 11187645
    Electronic patient records (EPR) can be regarded as an implicit source of clinical behaviour and problem-solving knowledge, systematically compiled by clinicians. We present an approach, together with its computational implementation, to pro-actively transform XML-based EPR into specialised Clinical Cases (CC) in the realm of Medical Case Base Systems. The 'correct' transformation of EPR to CC involves structural, terminological and conceptual standardisation, which is achieved by a confluence of techniques and resources, such as XML, UMLS (meta-thesaurus) and medical knowledge ontologies. We present below the functional architecture of a Medical Case-Base Reasoning Info-Structure (MCRIS) that features two distinct, yet related, functionalities: (1) a generic medical case-based reasoning system for decision-support activities; and (2) an EPR-CC transformation system to transform typical EPR's to CC.
    Matched MeSH terms: Logic
  9. Fulltext Prediction of conductivity by adaptive neuro-fuzzy model
    Akbarzadeh S, Arof AK, Ramesh S, Khanmirzaei MH, Nor RM
    PLoS One, 2014;9(3):e92241.
    PMID: 24658582 DOI: 10.1371/journal.pone.0092241
    Electrochemical impedance spectroscopy (EIS) is a key method for the characterizing the ionic and electronic conductivity of materials. One of the requirements of this technique is a model to forecast conductivity in preliminary experiments. The aim of this paper is to examine the prediction of conductivity by neuro-fuzzy inference with basic experimental factors such as temperature, frequency, thickness of the film and weight percentage of salt. In order to provide the optimal sets of fuzzy logic rule bases, the grid partition fuzzy inference method was applied. The validation of the model was tested by four random data sets. To evaluate the validity of the model, eleven statistical features were examined. Statistical analysis of the results clearly shows that modeling with an adaptive neuro-fuzzy is powerful enough for the prediction of conductivity.
    Matched MeSH terms: Fuzzy Logic
  10. Evaluation of traditional and consolidated rice farms in Guilan Province, Iran, using life cycle assessment and fuzzy modeling
    Khoshnevisan B, Rajaeifar MA, Clark S, Shamahirband S, Anuar NB, Mohd Shuib NL, et al.
    Sci Total Environ, 2014 May 15;481:242-51.
    PMID: 24602908 DOI: 10.1016/j.scitotenv.2014.02.052
    In this study the environmental impact of consolidated rice farms (CF) - farms which have been integrated to increase the mechanization index - and traditional farms (TF) - small farms with lower mechanization index - in Guilan Province, Iran, were evaluated and compared using Life cycle assessment (LCA) methodology and adaptive neuro-fuzzy inference system (ANFIS). Foreground data were collected from farmers using face-to-face questionnaires and background information about production process and inventory data was taken from the EcoInvent®2.0 database. The system boundary was confined to within the farm gate (cradle to farm gate) and two functional units (land and mass based) were chosen. The study also included a comparison of the input-output energy flows of the farms. The results revealed that the average amount of energy consumed by the CFs was 57 GJ compared to 74.2 GJ for the TFs. The energy ratios for CFs and TFs were 1.6 and 0.9, respectively. The LCA results indicated that CFs produced fewer environmental burdens per ton of produced rice. When compared according to the land-based FU the same results were obtained. This indicates that the differences between the two types of farms were not caused by a difference in their production level, but rather by improved management on the CFs. The analysis also showed that electricity accounted for the greatest share of the impact for both types of farms, followed by P-based and N-based chemical fertilizers. These findings suggest that the CFs had superior overall environmental performance compared to the TFs in the study area. The performance metrics of the model based on ANFIS show that it can be used to predict the environmental burdens of rice production with high accuracy and minimal error.
    Matched MeSH terms: Fuzzy Logic
  11. Automated identification of exudates and optic disc based on inverse surface thresholding
    Yazid H, Arof H, Isa HM
    J Med Syst, 2012 Jun;36(3):1997-2004.
    PMID: 21318328 DOI: 10.1007/s10916-011-9659-4
    This paper presents a new approach to detect exudates and optic disc from color fundus images based on inverse surface thresholding. The strategy involves the applications of fuzzy c-means clustering, edge detection, otsu thresholding and inverse surface thresholding. The main advantage of the proposed approach is that it does not depend on manually selected parameters that are normally chosen to suit the tested databases. When applied to two sets of databases the proposed method outperforms a method based on watershed segmentation.
    Matched MeSH terms: Fuzzy Logic
  12. Fulltext Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS
    Zaidan AA, Zaidan BB, Al-Haiqi A, Kiah ML, Hussain M, Abdulnabi M
    J Biomed Inform, 2015 Feb;53:390-404.
    PMID: 25483886 DOI: 10.1016/j.jbi.2014.11.012
    Evaluating and selecting software packages that meet the requirements of an organization are difficult aspects of software engineering process. Selecting the wrong open-source EMR software package can be costly and may adversely affect business processes and functioning of the organization. This study aims to evaluate and select open-source EMR software packages based on multi-criteria decision-making. A hands-on study was performed and a set of open-source EMR software packages were implemented locally on separate virtual machines to examine the systems more closely. Several measures as evaluation basis were specified, and the systems were selected based a set of metric outcomes using Integrated Analytic Hierarchy Process (AHP) and TOPSIS. The experimental results showed that GNUmed and OpenEMR software can provide better basis on ranking score records than other open-source EMR software packages.
    Matched MeSH terms: Fuzzy Logic
  13. Fulltext An application of a hybrid MCDM method for the evaluation of entrepreneurial intensity among the SMEs: a case study
    Rostamzadeh R, Ismail K, Bodaghi Khajeh Noubar H
    ScientificWorldJournal, 2014;2014:703650.
    PMID: 25197707 DOI: 10.1155/2014/703650
    This study presents one of the first attempts to focus on critical success factors influencing the entrepreneurial intensity of Malaysian small and medium sized enterprises (SMEs) as they attempt to expand internationally. The aim of this paper is to evaluate and prioritize the entrepreneurial intensity among the SMEs using multicriteria decision (MCDM) techniques. In this research FAHP is used for finding the weights of criteria and subcriteria. Then for the final ranking of the companies, VIKOR (in Serbian: VlseKriterijumska Optimizacija I Kompromisno Resenje) method was used. Also, as an additional tool, TOPSIS technique, is used to see the differences of two methods applied over the same data. 5 main criteria and 14 subcriteria were developed and implemented in the real-world cases. As the results showed, two ranking methods provided different ranking. Furthermore, the final findings of the research based on VIKOR and TOPSIS indicated that the firms A3 and A4 received the first rank, respectively. In addition, the firm A4 was known as the most entrepreneurial company. This research has been done in the manufacturing sector, but it could be also extended to the service sector for measurement.
    Matched MeSH terms: Fuzzy Logic
  14. A novel approach to detect respiratory phases from pulmonary acoustic signals using normalised power spectral density and fuzzy inference system
    Palaniappan R, Sundaraj K, Sundaraj S, Huliraj N, Revadi SS
    Clin Respir J, 2016 Jul;10(4):486-94.
    PMID: 25515741 DOI: 10.1111/crj.12250
    BACKGROUND: Monitoring respiration is important in several medical applications. One such application is respiratory rate monitoring in patients with sleep apnoea. The respiratory rate in patients with sleep apnoea disorder is irregular compared with the controls. Respiratory phase detection is required for a proper monitoring of respiration in patients with sleep apnoea.

    AIMS: To develop a model to detect the respiratory phases present in the pulmonary acoustic signals and to evaluate the performance of the model in detecting the respiratory phases.

    METHODS: Normalised averaged power spectral density for each frame and change in normalised averaged power spectral density between the adjacent frames were fuzzified and fuzzy rules were formulated. The fuzzy inference system (FIS) was developed with both Mamdani and Sugeno methods. To evaluate the performance of both Mamdani and Sugeno methods, correlation coefficient and root mean square error (RMSE) were calculated.

    RESULTS: In the correlation coefficient analysis in evaluating the fuzzy model using Mamdani and Sugeno method, the strength of the correlation was found to be r = 0.9892 and r = 0.9964, respectively. The RMSE for Mamdani and Sugeno methods are RMSE = 0.0853 and RMSE = 0.0817, respectively.

    CONCLUSION: The correlation coefficient and the RMSE of the proposed fuzzy models in detecting the respiratory phases reveals that Sugeno method performs better compared with the Mamdani method.

    Matched MeSH terms: Fuzzy Logic
  15. Solid waste forecasting using modified ANFIS modeling
    Younes MK, Nopiah ZM, Basri NE, Basri H, Abushammala MF, K N A M
    J Air Waste Manag Assoc, 2015 Oct;65(10):1229-38.
    PMID: 26223583 DOI: 10.1080/10962247.2015.1075919
    Solid waste prediction is crucial for sustainable solid waste management. Usually, accurate waste generation record is challenge in developing countries which complicates the modelling process. Solid waste generation is related to demographic, economic, and social factors. However, these factors are highly varied due to population and economy growths. The objective of this research is to determine the most influencing demographic and economic factors that affect solid waste generation using systematic approach, and then develop a model to forecast solid waste generation using a modified Adaptive Neural Inference System (MANFIS). The model evaluation was performed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the coefficient of determination (R²). The results show that the best input variables are people age groups 0-14, 15-64, and people above 65 years, and the best model structure is 3 triangular fuzzy membership functions and 27 fuzzy rules. The model has been validated using testing data and the resulted training RMSE, MAE and R² were 0.2678, 0.045 and 0.99, respectively, while for testing phase RMSE =3.986, MAE = 0.673 and R² = 0.98.
    Matched MeSH terms: Fuzzy Logic
  16. Fulltext Developing a Shariah-compliant medical services framework in Malaysia: an expert system approach using fuzzy Delphi method and interpretive structural modelling
    Che Jamaludin FI, Abdullah MRTL, Endut MNAA, Saifuddeen SM, Hamimi KA, Harun S
    BMJ Open, 2025 Jan 06;15(1):e082263.
    PMID: 39762112 DOI: 10.1136/bmjopen-2023-082263
    INTRODUCTION: In an increasingly diverse healthcare landscape, addressing the specific needs of Muslim patients has become paramount. The absence of comprehensive frameworks for Shariah-compliant healthcare services often poses challenges for healthcare professionals striving to provide care that aligns with Islamic values.

    OBJECTIVE: The objective of this study was to develop a comprehensive framework for Shariah-compliant healthcare services, ensuring alignment with Islamic practices in healthcare.

    METHODS, SETTING, PARTICIPANTS: This consensus study employed a key input approach using the fuzzy Delphi method (FDM) and interpretive structural modelling. Conducted in Malaysia, the study involved 10 experts from various regions across the country. These experts were selected based on clear criteria that included professionals with experience in Islamic and/or healthcare, while those lacking relevant expertise were excluded.

    RESULTS: The primary outcome was the identification of pertinent elements for the framework, with final elements measured based on expert consensus achieved through FDM. The panel of experts reached consensus on 10 essential elements that form the backbone of the framework for Shariah-compliant healthcare services. These elements include governance, medical ethics, patient care, human resources and professional development, facilities for Islamic worship (ibadah), spiritual care support, end-of-life care, Islamic environment, medicine and drugs, and affordability and accessibility.

    CONCLUSION: Ultimately, the development of this comprehensive framework is a crucial step in addressing the specific needs and concerns of Muslim patients worldwide. By incorporating the input and consensus of experts from various relevant fields, the resulting framework provides healthcare professionals with a solid foundation to deliver healthcare services that align with Islamic values, ultimately promoting the well-being of Muslim patients in Malaysia and tourists globally.

    Matched MeSH terms: Fuzzy Logic
  17. A new brain-computer interface design using fuzzy ARTMAP
    Palaniappan R, Paramesran R, Nishida S, Saiwaki N
    IEEE Trans Neural Syst Rehabil Eng, 2002 Sep;10(3):140-8.
    PMID: 12503778
    This paper proposes a new brain-computer interface (BCI) design using fuzzy ARTMAP (FA) neural network, as well as an application of the design. The objective of this BCI-FA design is to classify the best three of the five available mental tasks for each subject using power spectral density (PSD) values of electroencephalogram (EEG) signals. These PSD values are extracted using the Wiener-Khinchine and autoregressive methods. Ten experiments employing different triplets of mental tasks are studied for each subject. The findings show that the average BCI-FA outputs for four subjects gave less than 6% of error using the best triplets of mental tasks identified from the classification performances of FA. This implies that the BCI-FA can be successfully used with a tri-state switching device. As an application, a proposed tri-state Morse code scheme could be utilized to translate the outputs of this BCI-FA design into English letters. In this scheme, the three BCI-FA outputs correspond to a dot and a dash, which are the two basic Morse code alphabets and a space to denote the end (or beginning) of a dot or a dash. The construction of English letters using this tri-state Morse code scheme is determined only by the sequence of mental tasks and is independent of the time duration of each mental task. This is especially useful for constructing letters that are represented as multiple dots or dashes. This combination of BCI-FA design and the tri-state Morse code scheme could be developed as a communication system for paralyzed patients.
    Matched MeSH terms: Fuzzy Logic*
  18. Fulltext Multiple adaptive neuro-fuzzy inference system with automatic features extraction algorithm for cervical cancer recognition
    Al-batah MS, Isa NA, Klaib MF, Al-Betar MA
    Comput Math Methods Med, 2014;2014:181245.
    PMID: 24707316 DOI: 10.1155/2014/181245
    To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC)) to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE) algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS) is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy.
    Matched MeSH terms: Fuzzy Logic*
  19. A hybrid neural network system for pattern classification tasks with missing features
    Lim CP, Leong JH, Kuan MM
    IEEE Trans Pattern Anal Mach Intell, 2005 Apr;27(4):648-53.
    PMID: 15794170
    A hybrid neural network comprising Fuzzy ARTMAP and Fuzzy C-Means Clustering is proposed for pattern classification with incomplete training and test data. Two benchmark problems and a real medical pattern classification task are employed to evaluate the effectiveness of the hybrid network. The results are analyzed and compared with those from other methods.
    Matched MeSH terms: Fuzzy Logic*
  20. AMI screening using linguistic fuzzy rules
    Ainon RN, Bulgiba AM, Lahsasna A
    J Med Syst, 2012 Apr;36(2):463-73.
    PMID: 20703704 DOI: 10.1007/s10916-010-9491-2
    This paper aims at identifying the factors that would help to diagnose acute myocardial infarction (AMI) using data from an electronic medical record system (EMR) and then generating structure decisions in the form of linguistic fuzzy rules to help predict and understand the outcome of the diagnosis. Since there is a tradeoff in the fuzzy system between the accuracy which measures the capability of the system to predict the diagnosis of AMI and transparency which reflects its ability to describe the symptoms-diagnosis relation in an understandable way, the proposed fuzzy rules are designed in a such a way to find an appropriate balance between these two conflicting modeling objectives using multi-objective genetic algorithms. The main advantage of the generated linguistic fuzzy rules is their ability to describe the relation between the symptoms and the outcome of the diagnosis in an understandable way, close to human thinking and this feature may help doctors to understand the decision process of the fuzzy rules.
    Matched MeSH terms: Fuzzy Logic*
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