Displaying publications 1 - 20 of 126 in total

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  1. Fulltext Word sense disambiguation using hybrid swarm intelligence approach
    Al-Saiagh W, Tiun S, Al-Saffar A, Awang S, Al-Khaleefa AS
    PLoS One, 2018;13(12):e0208695.
    PMID: 30571777 DOI: 10.1371/journal.pone.0208695
    Word sense disambiguation (WSD) is the process of identifying an appropriate sense for an ambiguous word. With the complexity of human languages in which a single word could yield different meanings, WSD has been utilized by several domains of interests such as search engines and machine translations. The literature shows a vast number of techniques used for the process of WSD. Recently, researchers have focused on the use of meta-heuristic approaches to identify the best solutions that reflect the best sense. However, the application of meta-heuristic approaches remains limited and thus requires the efficient exploration and exploitation of the problem space. Hence, the current study aims to propose a hybrid meta-heuristic method that consists of particle swarm optimization (PSO) and simulated annealing to find the global best meaning of a given text. Different semantic measures have been utilized in this model as objective functions for the proposed hybrid PSO. These measures consist of JCN and extended Lesk methods, which are combined effectively in this work. The proposed method is tested using a three-benchmark dataset (SemCor 3.0, SensEval-2, and SensEval-3). Results show that the proposed method has superior performance in comparison with state-of-the-art approaches.
    Matched MeSH terms: Pattern Recognition, Automated
  2. Vocal fold vibratory characteristics of healthy geriatric females--analysis of high-speed digital images
    Ahmad K, Yan Y, Bless D
    J Voice, 2012 Nov;26(6):751-9.
    PMID: 22633334 DOI: 10.1016/j.jvoice.2011.12.002
    A high proportion of the geriatric population suffers from presbylaryngis and presbyphonia; however, our knowledge of vibratory patterns in this population is almost nonexistent. In this study, we investigate the vocal fold vibratory patterns of healthy elderly females to determine which features or combination of them could best describe the geriatric voices.
    Matched MeSH terms: Pattern Recognition, Automated
  3. Fulltext The Role of Serotype Interactions and Seasonality in Dengue Model Selection and Control: Insights from a Pattern Matching Approach
    Ten Bosch QA, Singh BK, Hassan MR, Chadee DD, Michael E
    PLoS Negl Trop Dis, 2016 05;10(5):e0004680.
    PMID: 27159023 DOI: 10.1371/journal.pntd.0004680
    The epidemiology of dengue fever is characterized by highly seasonal, multi-annual fluctuations, and the irregular circulation of its four serotypes. It is believed that this behaviour arises from the interplay between environmental drivers and serotype interactions. The exact mechanism, however, is uncertain. Constraining mathematical models to patterns characteristic to dengue epidemiology offers a means for detecting such mechanisms. Here, we used a pattern-oriented modelling (POM) strategy to fit and assess a range of dengue models, driven by combinations of temporary cross protective-immunity, cross-enhancement, and seasonal forcing, on their ability to capture the main characteristics of dengue dynamics. We show that all proposed models reproduce the observed dengue patterns across some part of the parameter space. Which model best supports the dengue dynamics is determined by the level of seasonal forcing. Further, when tertiary and quaternary infections are allowed, the inclusion of temporary cross-immunity alone is strongly supported, but the addition of cross-enhancement markedly reduces the parameter range at which dengue dynamics are produced, irrespective of the strength of seasonal forcing. The implication of these structural uncertainties on predicted vulnerability to control is also discussed. With ever expanding spread of dengue, greater understanding of dengue dynamics and control efforts (e.g. a near-future vaccine introduction) has become critically important. This study highlights the capacity of multi-level pattern-matching modelling approaches to offer an analytic tool for deeper insights into dengue epidemiology and control.
    Matched MeSH terms: Pattern Recognition, Automated*
  4. Text localisation for Roman words from shop signage
    Nurbaity Sabri, Noor Hazira Yusof, Zaidah Ibrahim, Zolidah Kasiran, Nur Nabilah Abu Mangshor
    Scientific Research Journal, 2017;14(2):49-62.
    MyJurnal
    Text localisation determines the location of the text in an image. This process
    is performed prior to text recognition. Localising text on shop signage is
    a challenging task since the images of the shop signage consist of complex
    background, and the text occurs in various font types, sizes, and colours.
    Two popular texture features that have been applied to localise text in
    scene images are a histogram of oriented gradient (HOG) and speeded up
    robust features (SURF). A comparative study is conducted in this paper
    to determine which is better with support vector machine (SVM) classifier.
    The performance of SVM is influenced by its kernel function and another
    comparative study is conducted to identify the best kernel function. The
    experiments have been conducted using primary data collected by the
    authors. Results indicate that HOG with quadratic kernel function localises
    text for shop signage better than SURF.
    Matched MeSH terms: Pattern Recognition, Automated
  5. Fulltext Surface electromyography signal processing and classification techniques
    Chowdhury RH, Reaz MB, Ali MA, Bakar AA, Chellappan K, Chang TG
    Sensors (Basel), 2013;13(9):12431-66.
    PMID: 24048337 DOI: 10.3390/s130912431
    Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.
    Matched MeSH terms: Pattern Recognition, Automated/methods*
  6. Fulltext Support vector machine based on adaptive acceleration particle swarm optimization
    Abdulameer MH, Sheikh Abdullah SN, Othman ZA
    ScientificWorldJournal, 2014;2014:835607.
    PMID: 24790584 DOI: 10.1155/2014/835607
    Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented.
    Matched MeSH terms: Pattern Recognition, Automated*
  7. Fulltext Sudden event recognition: a survey
    Suriani NS, Hussain A, Zulkifley MA
    Sensors (Basel), 2013 Aug 05;13(8):9966-98.
    PMID: 23921828 DOI: 10.3390/s130809966
    Event recognition is one of the most active research areas in video surveillance fields. Advancement in event recognition systems mainly aims to provide convenience, safety and an efficient lifestyle for humanity. A precise, accurate and robust approach is necessary to enable event recognition systems to respond to sudden changes in various uncontrolled environments, such as the case of an emergency, physical threat and a fire or bomb alert. The performance of sudden event recognition systems depends heavily on the accuracy of low level processing, like detection, recognition, tracking and machine learning algorithms. This survey aims to detect and characterize a sudden event, which is a subset of an abnormal event in several video surveillance applications. This paper discusses the following in detail: (1) the importance of a sudden event over a general anomalous event; (2) frameworks used in sudden event recognition; (3) the requirements and comparative studies of a sudden event recognition system and (4) various decision-making approaches for sudden event recognition. The advantages and drawbacks of using 3D images from multiple cameras for real-time application are also discussed. The paper concludes with suggestions for future research directions in sudden event recognition.
    Matched MeSH terms: Pattern Recognition, Automated/methods*
  8. Fulltext Stochastic optimized relevance feedback particle swarm optimization for content based image retrieval
    Imran M, Hashim R, Noor Elaiza AK, Irtaza A
    ScientificWorldJournal, 2014;2014:752090.
    PMID: 25121136 DOI: 10.1155/2014/752090
    One of the major challenges for the CBIR is to bridge the gap between low level features and high level semantics according to the need of the user. To overcome this gap, relevance feedback (RF) coupled with support vector machine (SVM) has been applied successfully. However, when the feedback sample is small, the performance of the SVM based RF is often poor. To improve the performance of RF, this paper has proposed a new technique, namely, PSO-SVM-RF, which combines SVM based RF with particle swarm optimization (PSO). The aims of this proposed technique are to enhance the performance of SVM based RF and also to minimize the user interaction with the system by minimizing the RF number. The PSO-SVM-RF was tested on the coral photo gallery containing 10908 images. The results obtained from the experiments showed that the proposed PSO-SVM-RF achieved 100% accuracy in 8 feedback iterations for top 10 retrievals and 80% accuracy in 6 iterations for 100 top retrievals. This implies that with PSO-SVM-RF technique high accuracy rate is achieved at a small number of iterations.
    Matched MeSH terms: Pattern Recognition, Automated/methods*
  9. Semi-quantitative and qualitative assessment of breast ultrasound elastography in differentiating between malignant and benign lesions
    Alhabshi SM, Rahmat K, Abdul Halim N, Aziz S, Radhika S, Gan GC, et al.
    Ultrasound Med Biol, 2013 Apr;39(4):568-78.
    PMID: 23384468 DOI: 10.1016/j.ultrasmedbio.2012.10.016
    The purpose of this study was to evaluate the diagnostic value of qualitative and semi-quantitative assessment of ultrasound elastography in differentiating between benign and malignant breast lesions. This prospective study was conducted in two tertiary medical centers. Consecutive B-mode ultrasound and real-time elastographic images were obtained for 67 malignant and 101 benign breast lesions in 168 women. Four experienced radiologists analyzed B-mode ultrasound alone and B-mode ultrasound combined with elastography independently. Conventional ultrasound findings were classified according to the American College of Radiology Breast Imaging Reporting and Data System classification. The elastographic assessment was based on qualitative and semi-quantitative parameters (i.e., strain pattern, width ratio, strain ratio). The sensitivity and specificity of combined elastography and conventional ultrasound were significantly higher than that of conventional ultrasound alone. The sensitivity, specificity, positive predictive value and negative predictive value was 97%, 61.4%, 62.5% and 96.8%, respectively, for conventional ultrasound and 100%, 93%, 99% and 90%, respectively, for combined technique. The semi-quantitative assessment with strain ratio and width ratio in elastography were the most useful parameters in differentiating between benign and malignant breast lesions. Cut-off point values for width ratio of more than 1.1 and strain ratio of more than 5.6 showed a high predictive value of malignancy with specificities of 84% and 76%, respectively (p 
    Matched MeSH terms: Pattern Recognition, Automated/methods*
  10. Remote protein homology detection and fold recognition using two-layer support vector machine classifiers
    Muda HM, Saad P, Othman RM
    Comput Biol Med, 2011 Aug;41(8):687-99.
    PMID: 21704312 DOI: 10.1016/j.compbiomed.2011.06.004
    Remote protein homology detection and fold recognition refer to detection of structural homology in proteins where there are small or no similarities in the sequence. To detect protein structural classes from protein primary sequence information, homology-based methods have been developed, which can be divided to three types: discriminative classifiers, generative models for protein families and pairwise sequence comparisons. Support Vector Machines (SVM) and Neural Networks (NN) are two popular discriminative methods. Recent studies have shown that SVM has fast speed during training, more accurate and efficient compared to NN. We present a comprehensive method based on two-layer classifiers. The 1st layer is used to detect up to superfamily and family in SCOP hierarchy using optimized binary SVM classification rules. It used the kernel function known as the Bio-kernel, which incorporates the biological information in the classification process. The 2nd layer uses discriminative SVM algorithm with string kernel that will detect up to protein fold level in SCOP hierarchy. The results obtained were evaluated using mean ROC and mean MRFP and the significance of the result produced with pairwise t-test was tested. Experimental results show that our approaches significantly improve the performance of remote protein homology detection and fold recognition for all three different version SCOP datasets (1.53, 1.67 and 1.73). We achieved 4.19% improvements in term of mean ROC in SCOP 1.53, 4.75% in SCOP 1.67 and 4.03% in SCOP 1.73 datasets when compared to the result produced by well-known methods. The combination of first layer and second layer of BioSVM-2L performs well in remote homology detection and fold recognition even in three different versions of datasets.
    Matched MeSH terms: Pattern Recognition, Automated/methods*
  11. Fulltext Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models
    AlDahoul N, Md Sabri AQ, Mansoor AM
    Comput Intell Neurosci, 2018;2018:1639561.
    PMID: 29623089 DOI: 10.1155/2018/1639561
    Human detection in videos plays an important role in various real life applications. Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks. Moreover, they are highly susceptible to dynamical events such as illumination changes, camera jitter, and variations in object sizes. On the other hand, the proposed feature learning approaches are cheaper and easier because highly abstract and discriminative features can be produced automatically without the need of expert knowledge. In this paper, we utilize automatic feature learning methods which combine optical flow and three different deep models (i.e., supervised convolutional neural network (S-CNN), pretrained CNN feature extractor, and hierarchical extreme learning machine) for human detection in videos captured using a nonstatic camera on an aerial platform with varying altitudes. The models are trained and tested on the publicly available and highly challenging UCF-ARG aerial dataset. The comparison between these models in terms of training, testing accuracy, and learning speed is analyzed. The performance evaluation considers five human actions (digging, waving, throwing, walking, and running). Experimental results demonstrated that the proposed methods are successful for human detection task. Pretrained CNN produces an average accuracy of 98.09%. S-CNN produces an average accuracy of 95.6% with soft-max and 91.7% with Support Vector Machines (SVM). H-ELM has an average accuracy of 95.9%. Using a normal Central Processing Unit (CPU), H-ELM's training time takes 445 seconds. Learning in S-CNN takes 770 seconds with a high performance Graphical Processing Unit (GPU).
    Matched MeSH terms: Pattern Recognition, Automated/methods*
  12. Random walker with improved weighting function for interactive medical image segmentation
    Yin LK, Rajeswari M
    Biomed Mater Eng, 2014;24(6):3333-41.
    PMID: 25227043 DOI: 10.3233/BME-141156
    To segment an image using the random walks algorithm; users are often required to initialize the approximate locations of the objects and background in the image. Due to its segmenting model that is mainly reflected by the relationship among the neighborhood pixels and its boundary conditions, random walks algorithm has made itself sensitive to the inputs of the seeds. Instead of considering the relationship between the neighborhood pixels solely, an attempt has been made to modify the weighting function that accounts for the intensity changes between the neighborhood nodes. Local affiliation within the defined neighborhood region of the two nodes is taken into consideration by incorporating an extra penalty term into the weighting function. Besides that, to better segment images, particularly medical images with texture features, GLCM variance is incorporated into the weighting function through kernel density estimation (KDE). The probability density of each pixel belonging to the initialized seeds is estimated and integrated into the weighting function. To test the performance of the proposed weighting model, several medical images that mainly made up of 174-brain tumor images are experimented. These experiments establish that the proposed method produces better segmentation results than the original random walks.
    Matched MeSH terms: Pattern Recognition, Automated/methods*
  13. Random multispace quantization as an analytic mechanism for BioHashing of biometric and random identity inputs
    Teoh AB, Goh A, Ngo DC
    IEEE Trans Pattern Anal Mach Intell, 2006 Dec;28(12):1892-901.
    PMID: 17108365
    Biometric analysis for identity verification is becoming a widespread reality. Such implementations necessitate large-scale capture and storage of biometric data, which raises serious issues in terms of data privacy and (if such data is compromised) identity theft. These problems stem from the essential permanence of biometric data, which (unlike secret passwords or physical tokens) cannot be refreshed or reissued if compromised. Our previously presented biometric-hash framework prescribes the integration of external (password or token-derived) randomness with user-specific biometrics, resulting in bitstring outputs with security characteristics (i.e., noninvertibility) comparable to cryptographic ciphers or hashes. The resultant BioHashes are hence cancellable, i.e., straightforwardly revoked and reissued (via refreshed password or reissued token) if compromised. BioHashing furthermore enhances recognition effectiveness, which is explained in this paper as arising from the Random Multispace Quantization (RMQ) of biometric and external random inputs.
    Matched MeSH terms: Pattern Recognition, Automated/methods*
  14. RAIRS2 a new expert system for diagnosing tuberculosis with real-world tournament selection mechanism inside artificial immune recognition system
    Saybani MR, Shamshirband S, Golzari S, Wah TY, Saeed A, Mat Kiah ML, et al.
    Med Biol Eng Comput, 2016 Mar;54(2-3):385-99.
    PMID: 26081904 DOI: 10.1007/s11517-015-1323-6
    Tuberculosis is a major global health problem that has been ranked as the second leading cause of death from an infectious disease worldwide, after the human immunodeficiency virus. Diagnosis based on cultured specimens is the reference standard; however, results take weeks to obtain. Slow and insensitive diagnostic methods hampered the global control of tuberculosis, and scientists are looking for early detection strategies, which remain the foundation of tuberculosis control. Consequently, there is a need to develop an expert system that helps medical professionals to accurately diagnose the disease. The objective of this study is to diagnose tuberculosis using a machine learning method. Artificial immune recognition system (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. In order to increase the classification accuracy, this study introduces a new hybrid system that incorporates real tournament selection mechanism into the AIRS. This mechanism is used to control the population size of the model and to overcome the existing selection pressure. Patient epacris reports obtained from the Pasteur laboratory in northern Iran were used as the benchmark data set. The sample consisted of 175 records, from which 114 (65 %) were positive for TB, and the remaining 61 (35 %) were negative. The classification performance was measured through tenfold cross-validation, root-mean-square error, sensitivity, and specificity. With an accuracy of 100 %, RMSE of 0, sensitivity of 100 %, and specificity of 100 %, the proposed method was able to successfully classify tuberculosis cases. In addition, the proposed method is comparable with top classifiers used in this research.
    Matched MeSH terms: Pattern Recognition, Automated*
  15. Quantitative assessment of breast density from digitized mammograms into Tabar's patterns
    Jamal N, Ng KH, Looi LM, McLean D, Zulfiqar A, Tan SP, et al.
    Phys Med Biol, 2006 Nov 21;51(22):5843-57.
    PMID: 17068368
    We describe a semi-automated technique for the quantitative assessment of breast density from digitized mammograms in comparison with patterns suggested by Tabar. It was developed using the MATLAB-based graphical user interface applications. It is based on an interactive thresholding method, after a short automated method that shows the fibroglandular tissue area, breast area and breast density each time new thresholds are placed on the image. The breast density is taken as a percentage of the fibroglandular tissue to the breast tissue areas. It was tested in four different ways, namely by examining: (i) correlation of the quantitative assessment results with subjective classification, (ii) classification performance using the quantitative assessment technique, (iii) interobserver agreement and (iv) intraobserver agreement. The results of the quantitative assessment correlated well (r2 = 0.92) with the subjective Tabar patterns classified by the radiologist (correctly classified 83% of digitized mammograms). The average kappa coefficient for the agreement between the readers was 0.63. This indicated moderate agreement between the three observers in classifying breast density using the quantitative assessment technique. The kappa coefficient of 0.75 for intraobserver agreement reflected good agreement between two sets of readings. The technique may be useful as a supplement to the radiologist's assessment in classifying mammograms into Tabar's pattern associated with breast cancer risk.
    Matched MeSH terms: Pattern Recognition, Automated/methods
  16. Quantification of diffusion tensor imaging in normal white matter maturation of early childhood using an automated processing pipeline
    Loh KB, Ramli N, Tan LK, Roziah M, Rahmat K, Ariffin H
    Eur Radiol, 2012 Jul;22(7):1413-26.
    PMID: 22434420 DOI: 10.1007/s00330-012-2396-3
    OBJECTIVES: The degree and status of white matter myelination can be sensitively monitored using diffusion tensor imaging (DTI). This study looks at the measurement of fractional anistropy (FA) and mean diffusivity (MD) using an automated ROI with an existing DTI atlas.

    METHODS: Anatomical MRI and structural DTI were performed cross-sectionally on 26 normal children (newborn to 48 months old), using 1.5-T MRI. The automated processing pipeline was implemented to convert diffusion-weighted images into the NIfTI format. DTI-TK software was used to register the processed images to the ICBM DTI-81 atlas, while AFNI software was used for automated atlas-based volumes of interest (VOIs) and statistical value extraction.

    RESULTS: DTI exhibited consistent grey-white matter contrast. Triphasic temporal variation of the FA and MD values was noted, with FA increasing and MD decreasing rapidly early in the first 12 months. The second phase lasted 12-24 months during which the rate of FA and MD changes was reduced. After 24 months, the FA and MD values plateaued.

    CONCLUSION: DTI is a superior technique to conventional MR imaging in depicting WM maturation. The use of the automated processing pipeline provides a reliable environment for quantitative analysis of high-throughput DTI data.

    KEY POINTS: Diffusion tensor imaging outperforms conventional MRI in depicting white matter maturation. • DTI will become an important clinical tool for diagnosing paediatric neurological diseases. • DTI appears especially helpful for developmental abnormalities, tumours and white matter disease. • An automated processing pipeline assists quantitative analysis of high throughput DTI data.

    Matched MeSH terms: Pattern Recognition, Automated/methods
  17. Fulltext QuaBingo: A Prediction System for Protein Quaternary Structure Attributes Using Block Composition
    Tung CH, Chen CW, Guo RC, Ng HF, Chu YW
    Biomed Res Int, 2016;2016:9480276.
    PMID: 27610389 DOI: 10.1155/2016/9480276
    Background. Quaternary structures of proteins are closely relevant to gene regulation, signal transduction, and many other biological functions of proteins. In the current study, a new method based on protein-conserved motif composition in block format for feature extraction is proposed, which is termed block composition. Results. The protein quaternary assembly states prediction system which combines blocks with functional domain composition, called QuaBingo, is constructed by three layers of classifiers that can categorize quaternary structural attributes of monomer, homooligomer, and heterooligomer. The building of the first layer classifier uses support vector machines (SVM) based on blocks and functional domains of proteins, and the second layer SVM was utilized to process the outputs of the first layer. Finally, the result is determined by the Random Forest of the third layer. We compared the effectiveness of the combination of block composition, functional domain composition, and pseudoamino acid composition of the model. In the 11 kinds of functional protein families, QuaBingo is 23% of Matthews Correlation Coefficient (MCC) higher than the existing prediction system. The results also revealed the biological characterization of the top five block compositions. Conclusions. QuaBingo provides better predictive ability for predicting the quaternary structural attributes of proteins.
    Matched MeSH terms: Pattern Recognition, Automated/methods
  18. Profiling the features of pre-segmented healthy liver CT scans: towards fast detection of liver lesions in emergency scenario
    Pasha MF, Hong KS, Rajeswari M
    Annu Int Conf IEEE Eng Med Biol Soc, 2011;2011:5169-73.
    PMID: 22255503 DOI: 10.1109/IEMBS.2011.6091280
    Automating the detection of lesions in liver CT scans requires a high performance and robust solution. With CT-scan start to become the norm in emergency department, the need for a fast and efficient liver lesions detection method is arising. In this paper, we propose a fast and evolvable method to profile the features of pre-segmented healthy liver and use it to detect the presence of liver lesions in emergency scenario. Our preliminary experiment with the MICCAI 2007 grand challenge datasets shows promising results of a fast training time, ability to evolve the produced healthy liver profiles, and accurate detection of the liver lesions. Lastly, the future work directions are also presented.
    Matched MeSH terms: Pattern Recognition, Automated/methods*
  19. Fulltext Primary stability recognition of the newly designed cementless femoral stem using digital signal processing
    Baharuddin MY, Salleh ShH, Hamedi M, Zulkifly AH, Lee MH, Mohd Noor A, et al.
    Biomed Res Int, 2014;2014:478248.
    PMID: 24800230 DOI: 10.1155/2014/478248
    Stress shielding and micromotion are two major issues which determine the success of newly designed cementless femoral stems. The correlation of experimental validation with finite element analysis (FEA) is commonly used to evaluate the stress distribution and fixation stability of the stem within the femoral canal. This paper focused on the applications of feature extraction and pattern recognition using support vector machine (SVM) to determine the primary stability of the implant. We measured strain with triaxial rosette at the metaphyseal region and micromotion with linear variable direct transducer proximally and distally using composite femora. The root mean squares technique is used to feed the classifier which provides maximum likelihood estimation of amplitude, and radial basis function is used as the kernel parameter which mapped the datasets into separable hyperplanes. The results showed 100% pattern recognition accuracy using SVM for both strain and micromotion. This indicates that DSP could be applied in determining the femoral stem primary stability with high pattern recognition accuracy in biomechanical testing.
    Matched MeSH terms: Pattern Recognition, Automated/methods*
  20. Optimization of MFCC parameters using Particle Swarm Optimization for diagnosis of infant hypothyroidism using Multi- Layer Perceptron
    Zabidi A, Lee YK, Mansor W, Yassin IM, Sahak R
    Annu Int Conf IEEE Eng Med Biol Soc, 2010;2010:1417-20.
    PMID: 21096346 DOI: 10.1109/IEMBS.2010.5626712
    This paper presents a new application of the Particle Swarm Optimization (PSO) algorithm to optimize Mel Frequency Cepstrum Coefficients (MFCC) parameters, in order to extract an optimal feature set for diagnosis of hypothyroidism in infants using Multi-Layer Perceptrons (MLP) neural network. MFCC features is influenced by the number of filter banks (f(b)) and the number of coefficients (n(c)) used. These parameters are critical in representation of the features as they affect the resolution and dimensionality of the features. In this paper, the PSO algorithm was used to optimize the values of f(b) and n(c). The MFCC features based on the PSO optimization were extracted from healthy and unhealthy infant cry signals and used to train MLP in the classification of hypothyroid infant cries. The results indicate that the PSO algorithm could determine the optimum combination of f(b) and n(c) that produce the best classification accuracy of the MLP.
    Matched MeSH terms: Pattern Recognition, Automated/methods*
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