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Fulltext TranCEP: Predicting the substrate class of transmembrane transport proteins using compositional, evolutionary, and positional information

Alballa M, Aplop F, Butler G

PLoS One, 2020;15(1):e0227683.
PMID: 31935244 DOI: 10.1371/journal.pone.0227683

Transporters mediate the movement of compounds across the membranes that separate the cell from its environment and across the inner membranes surrounding cellular compartments. It is estimated that one third of a proteome consists of membrane proteins, and many of these are transport proteins. Given the increase in the number of genomes being sequenced, there is a need for computational tools that predict the substrates that are transported by the transmembrane transport proteins. In this paper, we present TranCEP, a predictor of the type of substrate transported by a transmembrane transport protein. TranCEP combines the traditional use of the amino acid composition of the protein, with evolutionary information captured in a multiple sequence alignment (MSA), and restriction to important positions of the alignment that play a role in determining the specificity of the protein. Our experimental results show that TranCEP significantly outperforms the state-of-the-art predictors. The results quantify the contribution made by each type of information used.

Matched MeSH terms: Algorithms
Fulltext Tracking-learning-detection using extreme learning machine with Haar-like features

Melisa Anak Adeh, Mohd Ibrahim Shapiai, Ayman Maliha, Muhammad Hafiz Md Zaini

Journal of Advanced Research in Applied Sciences and Engineering Technology, 2016;5(2):1-11.
MyJurnal

Nowadays, the applications of tracking moving object are commonly used in various
areas especially in computer vision applications. There are many tracking algorithms
have been introduced and they are divided into three groups which are generative
trackers, discriminative trackers and hybrid trackers. One of the methods is TrackingLearning-Detection
(TLD) framework which is an example of the hybrid trackers where
combination between the generative trackers and the discriminative trackers occur. In
TLD, the detector consists of three stages which are patch variance, ensemble classifier
and KNearest Neighbor classifier. In the second stage, the ensemble classifier depends
on simple pixel comparison hence, it is likely fail to offer a better generalization of the
appearances of the target object in the detection process. In this paper, OnlineSequential
Extreme Learning Machine (OS-ELM) was used to replace the ensemble
classifier in the TLD framework. Besides that, different types of Haar-like features were
used for the feature extraction process instead of using raw pixel value as the features.
The objectives of this study are to improve the classifier in the second stage of detector
in TLD framework by using Haar-like features as an input to the classifier and to get a
more generalized detector in TLD framework by using OS-ELM based detector. The
results showed that the proposed method performs better in Pedestrian 1 in terms of
F-measure and also offers good performance in terms of Precision in four out of six
videos.

Matched MeSH terms: Algorithms
Tracking of EEG activity using motion estimation to understand brain wiring

Nisar H, Malik AS, Ullah R, Shim SO, Bawakid A, Khan MB, et al.

Adv Exp Med Biol, 2015;823:159-74.
PMID: 25381107 DOI: 10.1007/978-3-319-10984-8_9

The fundamental step in brain research deals with recording electroencephalogram (EEG) signals and then investigating the recorded signals quantitatively. Topographic EEG (visual spatial representation of EEG signal) is commonly referred to as brain topomaps or brain EEG maps. In this chapter, full search full search block motion estimation algorithm has been employed to track the brain activity in brain topomaps to understand the mechanism of brain wiring. The behavior of EEG topomaps is examined throughout a particular brain activation with respect to time. Motion vectors are used to track the brain activation over the scalp during the activation period. Using motion estimation it is possible to track the path from the starting point of activation to the final point of activation. Thus it is possible to track the path of a signal across various lobes.

Matched MeSH terms: Algorithms*
TraPy-MAC: Traffic Priority Aware Medium Access Control Protocol for Wireless Body Area Network

Ullah F, Abdullah AH, Kaiwartya O, Cao Y

J Med Syst, 2017 Jun;41(6):93.
PMID: 28466452 DOI: 10.1007/s10916-017-0739-y

Recently, Wireless Body Area Network (WBAN) has witnessed significant attentions in research and product development due to the growing number of sensor-based applications in healthcare domain. Design of efficient and effective Medium Access Control (MAC) protocol is one of the fundamental research themes in WBAN. Static on-demand slot allocation to patient data is the main approach adopted in the design of MAC protocol in literature, without considering the type of patient data specifically the level of severity on patient data. This leads to the degradation of the performance of MAC protocols considering effectiveness and traffic adjustability in realistic medical environments. In this context, this paper proposes a Traffic Priority-Aware MAC (TraPy-MAC) protocol for WBAN. It classifies patient data into emergency and non-emergency categories based on the severity of patient data. The threshold value aided classification considers a number of parameters including type of sensor, body placement location, and data transmission time for allocating dedicated slots patient data. Emergency data are not required to carry out contention and slots are allocated by giving the due importance to threshold value of vital sign data. The contention for slots is made efficient in case of non-emergency data considering threshold value in slot allocation. Moreover, the slot allocation to emergency and non-emergency data are performed parallel resulting in performance gain in channel assignment. Two algorithms namely, Detection of Severity on Vital Sign data (DSVS), and ETS Slots allocation based on the Severity on Vital Sign (ETS-SVS) are developed for calculating threshold value and resolving the conflicts of channel assignment, respectively. Simulations are performed in ns2 and results are compared with the state-of-the-art MAC techniques. Analysis of results attests the benefit of TraPy-MAC in comparison with the state-of-the-art MAC in channel assignment in realistic medical environments.

Matched MeSH terms: Algorithms
Fulltext Towards dynamic remote data auditing in computational clouds

Sookhak M, Akhunzada A, Gani A, Khurram Khan M, Anuar NB

ScientificWorldJournal, 2014;2014:269357.
PMID: 25121114 DOI: 10.1155/2014/269357

Cloud computing is a significant shift of computational paradigm where computing as a utility and storing data remotely have a great potential. Enterprise and businesses are now more interested in outsourcing their data to the cloud to lessen the burden of local data storage and maintenance. However, the outsourced data and the computation outcomes are not continuously trustworthy due to the lack of control and physical possession of the data owners. To better streamline this issue, researchers have now focused on designing remote data auditing (RDA) techniques. The majority of these techniques, however, are only applicable for static archive data and are not subject to audit the dynamically updated outsourced data. We propose an effectual RDA technique based on algebraic signature properties for cloud storage system and also present a new data structure capable of efficiently supporting dynamic data operations like append, insert, modify, and delete. Moreover, this data structure empowers our method to be applicable for large-scale data with minimum computation cost. The comparative analysis with the state-of-the-art RDA schemes shows that the proposed scheme is secure and highly efficient in terms of the computation and communication overhead on the auditor and server.

Matched MeSH terms: Algorithms*
Fulltext Totally opportunistic routing algorithm (TORA) for underwater wireless sensor network

Rahman Z, Hashim F, Rasid MFA, Othman M

PLoS One, 2018;13(6):e0197087.
PMID: 29874237 DOI: 10.1371/journal.pone.0197087

Underwater Wireless Sensor Network (UWSN) has emerged as promising networking techniques to monitor and explore oceans. Research on acoustic communication has been conducted for decades, but had focused mostly on issues related to physical layer such as high latency, low bandwidth, and high bit error. However, data gathering process is still severely limited in UWSN due to channel impairment. One way to improve data collection in UWSN is the design of routing protocol. Opportunistic Routing (OR) is an emerging technique that has the ability to improve the performance of wireless network, notably acoustic network. In this paper, we propose an anycast, geographical and totally opportunistic routing algorithm for UWSN, called TORA. Our proposed scheme is designed to avoid horizontal transmission, reduce end to end delay, overcome the problem of void nodes and maximize throughput and energy efficiency. We use TOA (Time of Arrival) and range based equation to localize nodes recursively within a network. Once nodes are localized, their location coordinates and residual energy are used as a matrix to select the best available forwarder. All data packets may or may not be acknowledged based on the status of sender and receiver. Thus, the number of acknowledgments for a particular data packet may vary from zero to 2-hop. Extensive simulations were performed to evaluate the performance of the proposed scheme for high network traffic load under very sparse and very dense network scenarios. Simulation results show that TORA significantly improves the network performance when compared to some relevant existing routing protocols, such as VBF, HHVBF, VAPR, and H2DAB, for energy consumption, packet delivery ratio, average end-to-end delay, average hop-count and propagation deviation factor. TORA reduces energy consumption by an average of 35% of VBF, 40% of HH-VBF, 15% of VAPR, and 29% of H2DAB, whereas the packet delivery ratio has been improved by an average of 43% of VBF, 26% of HH-VBF, 15% of VAPR, and 25% of H2DAB. Moreover, the average end-to-end delay has been reduced by 70% of VBF, 69% of HH-VBF, 46% of VAPR, and 73% of H2DAB. Furthermore, average hope-count has been improved by 57%, 53%, 16% and 31% as compared to VBF, HHVBF, VAPR, and H2DAB, respectively. Also, propagation delay has been reduced by 34%, 30%, 15% and 23% as compared to VBF, HHVBF, VAPR, and H2DAB, respectively.

Matched MeSH terms: Algorithms*
Topologically significant directed random walk with applied walker network in cancer environment

Seah CS, Kasim S, Saedudin RR, Md Fudzee MF, Mohamad MS, Hassan R, et al.

Pak J Pharm Sci, 2019 May;32(3 Special):1395-1408.
PMID: 31551221

Numerous cancer studies have combined different datasets for the prognosis of patients. This study incorporated four networks for significant directed random walk (sDRW) to predict cancerous genes and risk pathways. The study investigated the feasibility of cancer prediction via different networks. In this study, multiple micro array data were analysed and used in the experiment. Six gene expression datasets were applied in four networks to study the effectiveness of the networks in sDRW in terms of cancer prediction. The experimental results showed that one of the proposed networks is outstanding compared to other networks. The network is then proposed to be implemented in sDRW as a walker network. This study provides a foundation for further studies and research on other networks. We hope these finding will improve the prognostic methods of cancer patients.

Matched MeSH terms: Algorithms
Time-domain reconstruction using sensitivity coefficients for limited view ultrawide band tomography

Abdullah MZ, Yin W, Bilal M, Armitage DW, Mackin R, Peyton AJ

Rev Sci Instrum, 2007 Aug;78(8):084703.
PMID: 17764343

This article addresses time-domain ultrawide band (UWB) electromagnetic tomography for reconstructing the unknown spatial characteristic of an object from observations of the arrivals of short electromagnetic (EM) pulses. Here, the determination of the first peak arrival of the EM traces constitutes the forward problem, and the inverse problem aims to reconstruct the EM property distribution of the media. In this article, the finite-difference time-domain method implementing a perfectly matched layer is used to solve the forward problem from which the system sensitivity maps are determined. Image reconstruction is based on the combination of a linearized update and regularized Landweber minimization algorithm. Experimental data from a laboratory UWB system using targets of different contrasts, sizes, and shapes in an aqueous media are presented. The results show that this technique can accurately detect and locate unknown targets in spite of the presence of significant levels of noise in the data.

Matched MeSH terms: Algorithms*
Thought-Actuated Wheelchair Navigation with Communication Assistance Using Statistical Cross-Correlation-Based Features and Extreme Learning Machine

Nataraj SK, Paulraj MP, Yaacob SB, Adom AHB

J Med Signals Sens, 2020 11 11;10(4):228-238.
PMID: 33575195 DOI: 10.4103/jmss.JMSS_52_19

Background: A simple data collection approach based on electroencephalogram (EEG) measurements has been proposed in this study to implement a brain-computer interface, i.e., thought-controlled wheelchair navigation system with communication assistance.
Method: The EEG signals are recorded for seven simple tasks using the designed data acquisition procedure. These seven tasks are conceivably used to control wheelchair movement and interact with others using any odd-ball paradigm. The proposed system records EEG signals from 10 individuals at eight-channel locations, during which the individual executes seven different mental tasks. The acquired brainwave patterns have been processed to eliminate noise, including artifacts and powerline noise, and are then partitioned into six different frequency bands. The proposed cross-correlation procedure then employs the segmented frequency bands from each channel to extract features. The cross-correlation procedure was used to obtain the coefficients in the frequency domain from consecutive frame samples. Then, the statistical measures ("minimum," "mean," "maximum," and "standard deviation") were derived from the cross-correlated signals. Finally, the extracted feature sets were validated through online sequential-extreme learning machine algorithm.
Results and Conclusion: The results of the classification networks were compared with each set of features, and the results indicated that μ (r) feature set based on cross-correlation signals had the best performance with a recognition rate of 91.93%.

Matched MeSH terms: Algorithms
Thermodynamic heuristics with case-based reasoning: combined insights for RNA pseudoknot secondary structure

Al-Khatib RM, Rashid NA, Abdullah R

J Biomol Struct Dyn, 2011 Aug;29(1):1-26.
PMID: 21696223

The secondary structure of RNA pseudoknots has been extensively inferred and scrutinized by computational approaches. Experimental methods for determining RNA structure are time consuming and tedious; therefore, predictive computational approaches are required. Predicting the most accurate and energy-stable pseudoknot RNA secondary structure has been proven to be an NP-hard problem. In this paper, a new RNA folding approach, termed MSeeker, is presented; it includes KnotSeeker (a heuristic method) and Mfold (a thermodynamic algorithm). The global optimization of this thermodynamic heuristic approach was further enhanced by using a case-based reasoning technique as a local optimization method. MSeeker is a proposed algorithm for predicting RNA pseudoknot structure from individual sequences, especially long ones. This research demonstrates that MSeeker improves the sensitivity and specificity of existing RNA pseudoknot structure predictions. The performance and structural results from this proposed method were evaluated against seven other state-of-the-art pseudoknot prediction methods. The MSeeker method had better sensitivity than the DotKnot, FlexStem, HotKnots, pknotsRG, ILM, NUPACK and pknotsRE methods, with 79% of the predicted pseudoknot base-pairs being correct.

Matched MeSH terms: Algorithms
Fulltext Theoretical development and critical analysis of burst frequency equations for passive valves on centrifugal microfluidic platforms

Thio TH, Soroori S, Ibrahim F, Al-Faqheri W, Soin N, Kulinsky L, et al.

Med Biol Eng Comput, 2013 May;51(5):525-35.
PMID: 23292292 DOI: 10.1007/s11517-012-1020-7

This paper presents a theoretical development and critical analysis of the burst frequency equations for capillary valves on a microfluidic compact disc (CD) platform. This analysis includes background on passive capillary valves and the governing models/equations that have been developed to date. The implicit assumptions and limitations of these models are discussed. The fluid meniscus dynamics before bursting is broken up into a multi-stage model and a more accurate version of the burst frequency equation for the capillary valves is proposed. The modified equations are used to evaluate the effects of various CD design parameters such as the hydraulic diameter, the height to width aspect ratio, and the opening wedge angle of the channel on the burst pressure.

Matched MeSH terms: Algorithms
Fulltext Theoretical analysis of numerical integration rule for nonlinear goursat problem

Deraman, R.F., Mahayadin, M., Mohd Ruslan, S.Z., Othman, N.I., Nasir, M.A.S.

Journal of Advanced Research in Applied Sciences and Engineering Technology, 2016;5(1):1-6.
MyJurnal

Many nonlinear problems that arise in various science and engineering fields can be
modelled by the Goursat partial differential equations. Modelling these non-linear
problems using the Goursat partial differential equations has not received much
attention especially the theoretical aspect . The proposed scheme of solution is
supported by examining a nonlinear Goursat problem. The verification of the
theoretical results from several series of numerical experiments are discussed. Results
obtained from Taylor series expansion show that the proposed new scheme is
consistent. By using the von Neumann analysis and essence of stability, the proposed
new scheme is found to be unconditionally stable. In addition, the trend of the
numerical results shows that the new scheme is also convergent.

Matched MeSH terms: Algorithms
The transcript level of interleukin-6 in the cartilage of idiopathic osteoarthritis of knee

Moktar NM, Yusof HM, Yahaya NH, Muhamad R, Das S

Clin Ter, 2010;161(1):25-8.
PMID: 20393674

AIMS: The mRNA level for interleukin-6 (IL-6) is an important marker of osteoarthritis (OA). The present study aimed to investigate the level of IL-6 mRNA in the cartilage of OA knee while comparing it to the normal cartilage obtained from the same patient.
MATERIALS AND METHODS: A total of 21 patients who underwent total knee replacement were recruited for this study. Sectioning of the destructive cartilage was performed in the medial part of the proximal tibiofemoral cartilage. The unaffected lateral part of the knee in the same patient, served as a control. The mRNA level for IL-6 was assessed using LightCycler 2.0 quantitative real-time polymerase chain reaction (qRT-PCR). actin mRNA was used as an endogenous control.
RESULTS: Twelve out of 21 patients (57.1%) exhibited up regulation of IL-6 mRNA in the OA cartilage as compared to the normal cartilage. The rest of the patients (42.9%) showed down regulation of IL-6 mRNA. The statistical analysis showed there was insignificant level of IL-6 mRNA in the OA (1.91 +/- 0.45) as compared to the normal cartilage (1.13 +/- 0.44) (p > 0.05). The inter-individual variation in the level of IL-6 mRNA in the cartilage of idiopathic knee was in accordance with previous findings.
CONCLUSIONS: These observations suggest IL-6 could also act as a catabolic agent in some patients or its expression might be influenced by other cytokines.
Study site: Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM), Kuala Lumpur, Malaysia

Matched MeSH terms: Algorithms
The role of neuron-glia interactions in the emergence of ultra-slow oscillations

Chan SC, Mok SY, Ng DW, Goh SY

Biol Cybern, 2017 Dec;111(5-6):459-472.
PMID: 29128889 DOI: 10.1007/s00422-017-0740-z

Ultra-slow cortical oscillatory activity of 1-100 mHz has been recorded in human by electroencephalography and in dissociated cultures of cortical rat neurons, but the underlying mechanisms remain to be elucidated. This study presents a computational model of ultra-slow oscillatory activity based on the interaction between neurons and astrocytes. We predict that the frequency of these oscillations closely depends on activation of astrocytes in the network, which is reflected by oscillations of their intracellular calcium concentrations with periods between tens of seconds and minutes. An increase of intracellular calcium in astrocytes triggers the release of adenosine triphosphate from these cells which may alter transmission at nearby synapses by increasing or decreasing neurotransmitter release. These results provide theoretical support for the emerging awareness of astrocytes as active players in the regulation of neural activity and identify neuron-astrocyte interactions as a potential primary mechanism for the emergence of ultra-slow cortical oscillations.

Matched MeSH terms: Algorithms
Fulltext The relationship between perceptions and self-paid hepatitis B vaccination: A structural equation modeling approach

Rajamoorthy Y, Radam A, Taib NM, Rahim KA, Wagner AL, Mudatsir M, et al.

PLoS One, 2018;13(12):e0208402.
PMID: 30521602 DOI: 10.1371/journal.pone.0208402

BACKGROUND: Malaysia has a comprehensive, publicly-funded immunization program for hepatitis B (HepB) among infants, but adults must pay for the vaccine. The number of HepB carriers among adults is expected to increase in the future; therefore, we examined the impact of five constructs (cues to action, perceived barriers, perceived benefit, perceived severity, and perceived susceptibility) on adults' willingness to pay (WTP) for HepB vaccine; secondarily, we examined the association between perceived barriers and perceived benefits.
METHODS: Adults were selected through a stratified, two-stage cluster community sample in Selangor, Malaysia. The reliability, convergent validity, and discriminant validity of the measurement model were assessed before implementing a partial least squares structural equation model (PLS-SEM) to evaluate the significance of the structural paths.
RESULTS: A total of 728 participants were enrolled. The five constructs all showed adequate internal reliability, convergent validity, and discriminant validity. There was a significant, positive relationship to WTP from constructs (perceived barriers [Path coefficient (β) = 0.082, P = 0.036], perceived susceptibility [β = 0.214, P<0.001], and cues to action [β = 0.166, P<0.001]), and the model all together accounted for 8.8% of the variation in WTP. There was a significant, negative relationship between perceived barriers and perceived benefit [β = -0.261, P<0.001], which accounted for 6.8% of variation in perceived benefit.
CONCLUSIONS: Policy and programs should be targeted that can modify individuals' thoughts about disease risk, their obstacles in obtaining the preventive action, and their readiness to obtain a vaccine. Such programs include educational materials about disease risk and clinic visits that can pair HepB screening and vaccination.

Matched MeSH terms: Algorithms
Fulltext The recognition of multi-class protein folds by adding average chemical shifts of secondary structure elements

Feng Z, Hu X, Jiang Z, Song H, Ashraf MA

Saudi J Biol Sci, 2016 Mar;23(2):189-97.
PMID: 26980999 DOI: 10.1016/j.sjbs.2015.10.008

The recognition of protein folds is an important step in the prediction of protein structure and function. Recently, an increasing number of researchers have sought to improve the methods for protein fold recognition. Following the construction of a dataset consisting of 27 protein fold classes by Ding and Dubchak in 2001, prediction algorithms, parameters and the construction of new datasets have improved for the prediction of protein folds. In this study, we reorganized a dataset consisting of 76-fold classes constructed by Liu et al. and used the values of the increment of diversity, average chemical shifts of secondary structure elements and secondary structure motifs as feature parameters in the recognition of multi-class protein folds. With the combined feature vector as the input parameter for the Random Forests algorithm and ensemble classification strategy, we propose a novel method to identify the 76 protein fold classes. The overall accuracy of the test dataset using an independent test was 66.69%; when the training and test sets were combined, with 5-fold cross-validation, the overall accuracy was 73.43%. This method was further used to predict the test dataset and the corresponding structural classification of the first 27-protein fold class dataset, resulting in overall accuracies of 79.66% and 93.40%, respectively. Moreover, when the training set and test sets were combined, the accuracy using 5-fold cross-validation was 81.21%. Additionally, this approach resulted in improved prediction results using the 27-protein fold class dataset constructed by Ding and Dubchak.

Matched MeSH terms: Algorithms
The recognition of anti-nuclear antibody's dense fine speckled pattern and the detection of anti-DFS70 antibodies in the laboratory practice: Its prevalence and clinical significance

Wahab AA, Jauhary EJ, Ding CH

Malays J Pathol, 2023 Aug;45(2):157-173.
PMID: 37658526

Anti-nuclear antibody test (ANA) is the test commonly requested for the working diagnosis of systemic autoimmune rheumatic diseases (SARDs) particularly in ANA-associated rheumatic diseases (AARDs) such as SLE, systemic sclerosis, Sjogren syndrome, mixed connective tissue diseases, and dermatomyositis. Dense fine speckled (DFS) pattern is an ANA fluorescence pattern that is commonly encountered in laboratories. This pattern is largely detected among the healthy population and in non-SARDs patients. Although this pattern is still can be observed among SARDs patients, the low prevalence of monospecific or isolated anti-DFS70 antibodies makes it useful for ruling out AARDs diagnosis. Thus, the inclusion of anti-DFS70 antibodies is perhaps logical for the exclusion of SARDs/AARDs. This review provides evidence of the prevalence of anti-DFS70 antibodies in different populations including healthy individuals, patients with SARDs and non- SARDs. The algorithm that includes the detection of anti-DFS70 antibodies during ANA screening is also suggested.

Matched MeSH terms: Algorithms
The potential role of artificial intelligence-assisted chest X-ray imaging in detecting early-stage lung cancer in the community-a proposed algorithm for lung cancer screening in Malaysia

Sachithanandan A, Lockman H, Azman RR, Tho LM, Ban EZ, Ramon V

Med J Malaysia, 2024 Jan;79(1):9-14.
PMID: 38287751

INTRODUCTION: The poor prognosis of lung cancer has been largely attributed to the fact that most patients present with advanced stage disease. Although low dose computed tomography (LDCT) is presently considered the optimal imaging modality for lung cancer screening, its use has been hampered by cost and accessibility. One possible approach to facilitate lung cancer screening is to implement a risk-stratification step with chest radiography, given its ease of access and affordability. Furthermore, implementation of artificial-intelligence (AI) in chest radiography is expected to improve the detection of indeterminate pulmonary nodules, which may represent early lung cancer.
MATERIALS AND METHODS: This consensus statement was formulated by a panel of five experts of primary care and specialist doctors. A lung cancer screening algorithm was proposed for implementation locally.
RESULTS: In an earlier pilot project collaboration, AI-assisted chest radiography had been incorporated into lung cancer screening in the community. Preliminary experience in the pilot project suggests that the system is easy to use, affordable and scalable. Drawing from experience with the pilot project, a standardised lung cancer screening algorithm using AI in Malaysia was proposed. Requirements for such a screening programme, expected outcomes and limitations of AI-assisted chest radiography were also discussed.
CONCLUSION: The combined strategy of AI-assisted chest radiography and complementary LDCT imaging has great potential in detecting early-stage lung cancer in a timely manner, and irrespective of risk status. The proposed screening algorithm provides a guide for clinicians in Malaysia to participate in screening efforts.

Matched MeSH terms: Algorithms
Fulltext The performance of robust modification of breusch-godfrey test in the presence of outliers

Lim, H. A., Midi, H.

Pertanika Journal of Science & Technology, 2014;22(1):81-93.
MyJurnal

Autocorrelation problem causes unduly effects on the variance of Ordinary Least Squares (OLS) estimates. Hence, it is very essential to detect the autocorrelation problem so that appropriate remedial measures can be taken. The Breusch-Godfrey (BG) test is the most popular and commonly used test for the detection of autocorrelation. Since this test is based on the OLS estimates, which are not robust, it is easily affected by outliers. In this paper, we propose a robust Breusch-Godfrey (MBG) test which is not easily affected by outliers. The results of the study indicate that the MBG test is more powerful than the BG test in the detection of autocorrelation problem.

Matched MeSH terms: Algorithms
Fulltext The performance of expectation maximization (EM) Algorithm in Gaussian Mixed Models (GMM)

Mohd. Izhan Mohd. Yusoff, Mohd. Rizam Abu Bakar, Abu Hassan Shaari Mohd. Nor

Pertanika Journal of Science & Technology, 2009;17(2):-.
MyJurnal

Expectation Maximization (EM) algorithm has experienced a significant increase in terms of usage in many fields of study. In this paper, the performance of the said algorithm in finding the Maximum Likelihood for the Gaussian Mixed Models (GMM), a probabilistic model normally used in fraud detection and recognizing a person’s voice in speech recognition field, is shown and discussed. At the end of the paper, some suggestions for future research works will also be given.

Matched MeSH terms: Algorithms

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