MyMedR

A new hyperbox selection rule and a pruning strategy for the enhanced fuzzy min-max neural network

Mohammed MF, Lim CP

Neural Netw, 2017 Feb;86:69-79.

PMID: 27890606 DOI: 10.1016/j.neunet.2016.10.012

In this paper, we extend our previous work on the Enhanced Fuzzy Min-Max (EFMM) neural network by introducing a new hyperbox selection rule and a pruning strategy to reduce network complexity and improve classification performance. Specifically, a new k-nearest hyperbox expansion rule (for selection of a new winning hyperbox) is first introduced to reduce the network complexity by avoiding the creation of too many small hyperboxes within the vicinity of the winning hyperbox. A pruning strategy is then deployed to further reduce the network complexity in the presence of noisy data. The effectiveness of the proposed network is evaluated using a number of benchmark data sets. The results compare favorably with those from other related models. The findings indicate that the newly introduced hyperbox winner selection rule coupled with the pruning strategy are useful for undertaking pattern classification problems.

Improved GART neural network model for pattern classification and rule extraction with application to power systems

Yap KS, Lim CP, Au MT

IEEE Trans Neural Netw, 2011 Dec;22(12):2310-23.

PMID: 22067292 DOI: 10.1109/TNN.2011.2173502

Generalized adaptive resonance theory (GART) is a neural network model that is capable of online learning and is effective in tackling pattern classification tasks. In this paper, we propose an improved GART model (IGART), and demonstrate its applicability to power systems. IGART enhances the dynamics of GART in several aspects, which include the use of the Laplacian likelihood function, a new vigilance function, a new match-tracking mechanism, an ordering algorithm for determining the sequence of training data, and a rule extraction capability to elicit if-then rules from the network. To assess the effectiveness of IGART and to compare its performances with those from other methods, three datasets that are related to power systems are employed. The experimental results demonstrate the usefulness of IGART with the rule extraction capability in undertaking classification problems in power systems engineering.

A hybrid ART-GRNN online learning neural network with a epsilon -insensitive loss function

Yap KS, Lim CP, Abidin IZ

IEEE Trans Neural Netw, 2008 Sep;19(9):1641-6.

PMID: 18779094 DOI: 10.1109/TNN.2008.2000992

In this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized regression neural network (GRNN). It is an enhanced version of the GRNN, which preserves the online learning properties of adaptive resonance theory (ART). A series of empirical studies to assess the effectiveness of GART in classification, regression, and time series prediction tasks is conducted. The results demonstrate that GART is able to produce good performances as compared with those of other methods, including the online sequential extreme learning machine (OSELM) and sequential learning radial basis function (RBF) neural network models.

Predicting drug dissolution profiles with an ensemble of boosted neural networks: a time series approach

Goh WY, Lim CP, Peh KK

IEEE Trans Neural Netw, 2003;14(2):459-63.

PMID: 18238031 DOI: 10.1109/TNN.2003.809420

Applicability of an ensemble of Elman networks with boosting to drug dissolution profile predictions is investigated. Modifications of AdaBoost that enables its use in regression tasks are explained. Two real data sets comprising in vitro dissolution profiles of matrix-controlled-release theophylline pellets are employed to assess the effectiveness of the proposed system. Statistical evaluation and comparison of the results are performed. This work positively demonstrates the potentials of the proposed system for predicting desired drug dissolution characteristics in pharmaceutical product formulation tasks.

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.

Prediction of drug release profiles using an intelligent learning system: an experimental study in transdermal iontophoresis

Lim CP, Quek SS, Peh KK

J Pharm Biomed Anal, 2003 Feb 05;31(1):159-68.

PMID: 12560060

This paper investigates the use of a neural-network-based intelligent learning system for the prediction of drug release profiles. An experimental study in transdermal iontophoresis (TI) is employed to evaluate the applicability of a particular neural network (NN) model, i.e. the Gaussian mixture model (GMM), in modeling and predicting drug release profiles. A number of tests are systematically designed using the face-centered central composite design (CCD) approach to examine the effects of various process variables simultaneously during the iontophoresis process. The GMM is then applied to model and predict the drug release profiles based on the data samples collected from the experiments. The GMM results are compared with those from multiple regression models. In addition, the bootstrap method is used to assess the reliability of the network predictions by estimating confidence intervals associated with the results. The results demonstrate that the combination of the face-centered CCD and GMM can be employed as a useful intelligent tool for the prediction of time-series profiles in pharmaceutical and biomedical experiments.

Application of autonomous neural network systems to medical pattern classification tasks

Lim CP, Harrison RF, Kennedy RL

Artif Intell Med, 1997 Nov;11(3):215-39.

PMID: 9413607

This paper presents a study of the application of autonomously learning multiple neural network systems to medical pattern classification tasks. In our earlier work, a hybrid neural network architecture has been developed for on-line learning and probability estimation tasks. The network has been shown to be capable of asymptotically achieving the Bayes optimal classification rates, on-line, in a number of benchmark classification experiments. In the context of pattern classification, however, the concept of multiple classifier systems has been proposed to improve the performance of a single classifier. Thus, three decision combination algorithms have been implemented to produce a multiple neural network classifier system. Here the applicability of the system is assessed using patient records in two medical domains. The first task is the prognosis of patients admitted to coronary care units; whereas the second is the prediction of survival in trauma patients. The results are compared with those from logistic regression models, and implications of the system as a useful clinical diagnostic tool are discussed.

Case-control comparison brain lesion segmentation for early infarct detection

Ting FF, Sim KS, Lim CP

Comput Med Imaging Graph, 2018 11;69:82-95.

PMID: 30219737 DOI: 10.1016/j.compmedimag.2018.08.011

Computed Tomography (CT) images are widely used for the identification of abnormal brain tissues following infarct and hemorrhage of a stroke. The treatment of this medical condition mainly depends on doctors' experience. While manual lesion delineation by medical doctors is currently considered as the standard approach, it is time-consuming and dependent on each doctor's expertise and experience. In this study, a case-control comparison brain lesion segmentation (CCBLS) method is proposed to segment the region pertaining to brain injury by comparing the voxel intensity of CT images between control subjects and stroke patients. The method is able to segment the brain lesion from the stacked CT images automatically without prior knowledge of the location or the presence of the lesion. The aim is to reduce medical doctors' burden and assist them in making an accurate diagnosis. A case study with 300 sets of CT images from control subjects and stroke patients is conducted. Comparing with other existing methods, the outcome ascertains the effectiveness of the proposed method in detecting brain infarct of stroke patients.

A model-based bone milling state identification method via force sensing for a robotic surgical system

Al-Abdullah KI, Lim CP, Najdovski Z, Yassin W

Int J Med Robot, 2019 Jun;15(3):e1989.

PMID: 30721570 DOI: 10.1002/rcs.1989

BACKGROUND: This paper presents a model-based bone milling state identification method that provides intraoperative bone quality information during robotic bone milling. The method helps surgeons identify bone layer transitions during bone milling.

METHODS: On the basis of a series of bone milling experiments with commercial artificial bones, an artificial neural network force model is developed to estimate the milling force of different bone densities as a function of the milling feed rate and spindle speed. The model estimations are used to identify the bone density at the cutting zone by comparing the actual milling force with the estimated one.

RESULTS: The verification experiments indicate the ability of the proposed method to distinguish between one cortical and two cancellous bone densities.

CONCLUSIONS: The significance of the proposed method is that it can be used to discriminate a set of different bone density layers for a range of the milling feed rate and spindle speed.

Use of artificial neural networks to predict drug dissolution profiles and evaluation of network performance using similarity factor

Peh KK, Lim CP, Quek SS, Khoh KH

Pharm Res, 2000 Nov;17(11):1384-8.

PMID: 11205731

PURPOSE: To use artificial neural networks for predicting dissolution profiles of matrix-controlled release theophylline pellet preparation, and to evaluate the network performance by comparing the predicted dissolution profiles with those obtained from physical experiments using similarity factor.

METHODS: The Multi-Layered Perceptron (MLP) neural network was used to predict the dissolution profiles of theophylline pellets containing different ratios of microcrystalline cellulose (MCC) and glyceryl monostearate (GMS). The concepts of leave-one-out as well as a time-point by time-point estimation basis were used to predict the rate of drug release for each matrix ratio. All the data were used for training, except for one set which was selected to compare with the predicted output. The closeness between the predicted and the reference dissolution profiles was investigated using similarity factor (f2).

RESULTS: The f2 values were all above 60, indicating that the predicted dissolution profiles were closely similar to the dissolution profiles obtained from physical experiments.

CONCLUSION: The MLP network could be used as a model for predicting the dissolution profiles of matrix-controlled release theophylline pellet preparation in product development.

Fulltext An intelligent payment card fraud detection system

Seera M, Lim CP, Kumar A, Dhamotharan L, Tan KH

Ann Oper Res, 2021 Jun 08.

PMID: 34121790 DOI: 10.1007/s10479-021-04149-2

Payment cards offer a simple and convenient method for making purchases. Owing to the increase in the usage of payment cards, especially in online purchases, fraud cases are on the rise. The rise creates financial risk and uncertainty, as in the commercial sector, it incurs billions of losses each year. However, real transaction records that can facilitate the development of effective predictive models for fraud detection are difficult to obtain, mainly because of issues related to confidentially of customer information. In this paper, we apply a total of 13 statistical and machine learning models for payment card fraud detection using both publicly available and real transaction records. The results from both original features and aggregated features are analyzed and compared. A statistical hypothesis test is conducted to evaluate whether the aggregated features identified by a genetic algorithm can offer a better discriminative power, as compared with the original features, in fraud detection. The outcomes positively ascertain the effectiveness of using aggregated features for undertaking real-world payment card fraud detection problems.

Fulltext Anti-hyperglycaemic activity of swietenia macrophylla king (meliaceae) seed extracts in normoglycaemic rats undergoing glucose tolerance tests

Hashim MA, Yam MF, Hor SY, Lim CP, Asmawi MZ, Sadikun A

Chin Med, 2013;8(1):11.

PMID: 23684219 DOI: 10.1186/1749-8546-8-11

Swietenia macrophylla King (Meliaceae) is used to treat diabetes mellitus in Malaysia. This study aims to evaluate the anti-hyperglycaemic potential of petroleum ether (PE), chloroform (CE) and methanol (ME) extracts of S. macrophylla seeds, in normoglycaemic and streptozotocin (STZ)-induced diabetic rats.

Evaluation of anti-cancer activity of Acanthester planci extracts obtained by different methods of extraction

Mutee AF, Salhimi SM, Ghazali FC, Aisha AF, Lim CP, Ibrahim K, et al.

Pak J Pharm Sci, 2012 Oct;25(4):697-703.

PMID: 23009983

Acanthaster planci, the crown-of-thorns starfish, naturally endowed with the numerous toxic spines around the dorsal area of its body. Scientific investigations demonstrated several toxico-pharmacological efficacies of A. planci such as, myonecrotic activity, hemorrhagic activity, hemolytic activity, mouse lethality, phospholipase A2 (PLA2) activity, capillary permeability-increasing activity, edema-forming activity, anticoagulant activity and histamine-releasing activity from mast cells. The present study was performed to evaluate the cytotoxic activity of A. planci extracts obtained by different methods of extraction on MCF-7 and HCT-116, human breast and colon cancer cell lines, respectively. Results of the cell proliferation assay showed that PBS extract exhibited very potent cytotoxic activity against both MCF-7 and HCT-116 cell lines with IC(50) of 13.48 μg/mL and 28.78 μg/mL, respectively, while the extracts prepared by Bligh and Dyer method showed moderate cytotoxicity effect against MCF-7 and HCT-116 cell lines, for chloroform extract, IC(50) = 121.37 μg/mL (MCF-7) and 77.65 μg/mL (HCT-116), and for methanol extract, IC(50) = 46.11 μg/mL (MCF-7) and 59.29 μg/mL (HCT-116). However, the extracts prepared by sequential extraction procedure from dried starfish found to be ineffective. This study paves the way for further investigation on the peptide composition in the PBS extract of the starfish to discover potential chemotherapeutic agents.

Acute and subchronic oral toxicity of Coriolus versicolor standardized water extract in Sprague-Dawley rats

Hor SY, Ahmad M, Farsi E, Lim CP, Asmawi MZ, Yam MF

J Ethnopharmacol, 2011 Oct 11;137(3):1067-76.

PMID: 21767625 DOI: 10.1016/j.jep.2011.07.007

Coriolus versicolor, which is known as Yun Zhi, is one of the commonly used Chinese medicinal herbs. Recent studies have demonstrated its antitumor activities on cancer cells which led to its widespread use in cancer patient. However, little toxicological information is available regarding its safety. The present study evaluated the potential toxicity of Coriolus versicolor standardized water extract after acute and subchronic administration in rats.

Toxicity evaluation of a standardised 50% ethanol extract of Orthosiphon stamineus

Mohamed EA, Lim CP, Ebrika OS, Asmawi MZ, Sadikun A, Yam MF

J Ethnopharmacol, 2011 Jan 27;133(2):358-63.

PMID: 20937371 DOI: 10.1016/j.jep.2010.10.008

The present investigation was carried out to evaluate the safety of standardised 50% ethanol extract of Orthosiphon stamineus plant by determining its potential toxicity after acute and subchronic administration in rats.

Fulltext Credit card fraud detection using a hierarchical behavior-knowledge space model

Nandi AK, Randhawa KK, Chua HS, Seera M, Lim CP

PLoS One, 2022 01 20;17(1):e0260579.

PMID: 35051184 DOI: 10.1371/journal.pone.0260579

With the advancement in machine learning, researchers continue to devise and implement effective intelligent methods for fraud detection in the financial sector. Indeed, credit card fraud leads to billions of dollars in losses for merchants every year. In this paper, a multi-classifier framework is designed to address the challenges of credit card fraud detections. An ensemble model with multiple machine learning classification algorithms is designed, in which the Behavior-Knowledge Space (BKS) is leveraged to combine the predictions from multiple classifiers. To ascertain the effectiveness of the developed ensemble model, publicly available data sets as well as real financial records are employed for performance evaluations. Through statistical tests, the results positively indicate the effectiveness of the developed model as compared with the commonly used majority voting method for combination of predictions from multiple classifiers in tackling noisy data classification as well as credit card fraud detection problems.

A Comparison of Once- and Thrice-Weekly Erythropoietin Dosing for the Treatment of Anaemia of Prematurity

Lim CP, Md-Redzuan A, Lai YK, Borhanuddin B, Cheah FC

Ann Acad Med Singap, 2017 10;46(10):395-398.

PMID: 29177368

Fulltext Angiotensin receptor-neprilysin inhibitor improves New York Heart Association class and N-terminal-pro B-type natriuretic peptide levels: initial experience in a Singapore single-centre cohort

Koh N, Goh VJ, Teoh CK, Hon JS, Teo LLY, Lim CP, et al.

Singapore Med J, 2021 Jul;62(7):359-361.

PMID: 34409482 DOI: 10.11622/smedj.2021092

Corrigendum to 'Single domain antibodies derived from ancient animals as broadly neutralizing agents for SARS-CoV-2 and other coronaviruses': Biomedical Engineering Advances 4 (2022) 100054

Lim HT, Kok BH, Lim CP, Majeed ABA, Leow CY, Leow CH

Biomed Eng Adv, 2023 Jun;5:100086.

PMID: 37091500 DOI: 10.1016/j.bea.2023.100086

[This corrects the article DOI: 10.1016/j.bea.2022.100054.].

Fulltext Single domain antibodies derived from ancient animals as broadly neutralizing agents for SARS-CoV-2 and other coronaviruses

Lim HT, Kok BH, Lim CP, Abdul Majeed AB, Leow CY, Leow CH

Biomed Eng Adv, 2022 Dec;4:100054.

PMID: 36158162 DOI: 10.1016/j.bea.2022.100054

With severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as an emergent human virus since December 2019, the world population is susceptible to coronavirus disease 2019 (COVID-19). SARS-CoV-2 has higher transmissibility than the previous coronaviruses, associated by the ribonucleic acid (RNA) virus nature with high mutation rate, caused SARS-CoV-2 variants to arise while circulating worldwide. Neutralizing antibodies are identified as immediate and direct-acting therapeutic against COVID-19. Single-domain antibodies (sdAbs), as small biomolecules with non-complex structure and intrinsic stability, can acquire antigen-binding capabilities comparable to conventional antibodies, which serve as an attractive neutralizing solution. SARS-CoV-2 spike protein attaches to human angiotensin-converting enzyme 2 (ACE2) receptor on lung epithelial cells to initiate viral infection, serves as potential therapeutic target. sdAbs have shown broad neutralization towards SARS-CoV-2 with various mutations, effectively stop and prevent infection while efficiently block mutational escape. In addition, sdAbs can be developed into multivalent antibodies or inhaled biotherapeutics against COVID-19.

Displaying publications 1 - 20 of 29 in total