Displaying publications 1 - 20 of 1720 in total

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  1. Rezaiy R, Shabri A
    Water Sci Technol, 2024 Feb;89(3):745-770.
    PMID: 38358500 DOI: 10.2166/wst.2024.028
    This study introduces ensemble empirical mode decomposition (EEMD) coupled with the autoregressive integrated moving average (ARIMA) model for drought prediction. In the realm of drought forecasting, we assess the EEMD-ARIMA model against the traditional ARIMA approach, using monthly precipitation data from January 1970 to December 2019 in Herat province, Afghanistan. Our evaluation spans various timescales of standardized precipitation index (SPI) 3, SPI 6, SPI 9, and SPI 12. Statistical indicators like root-mean-square error, mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 are employed. To comprehend data features thoroughly, each SPI series initially computed from the original monthly precipitation time series. Subsequently, each SPI undergoes decomposition using EEMD, resulting in intrinsic mode functions (IMFs) and one residual series. The next step involves forecasting each IMF component and residual using the corresponding ARIMA model. To create an ensemble forecast for the initial SPI series, the predicted outcomes of the modeled IMFs and residual series are finally added. Results indicate that EEMD-ARIMA significantly enhances drought forecasting accuracy compared to conventional ARIMA model.
    Matched MeSH terms: Time Factors
  2. Chughtai JU, Haq IU, Islam SU, Gani A
    Sensors (Basel), 2022 Dec 12;22(24).
    PMID: 36560104 DOI: 10.3390/s22249735
    Travel time prediction is essential to intelligent transportation systems directly affecting smart cities and autonomous vehicles. Accurately predicting traffic based on heterogeneous factors is highly beneficial but remains a challenging problem. The literature shows significant performance improvements when traditional machine learning and deep learning models are combined using an ensemble learning approach. This research mainly contributes by proposing an ensemble learning model based on hybridized feature spaces obtained from a bidirectional long short-term memory module and a bidirectional gated recurrent unit, followed by support vector regression to produce the final travel time prediction. The proposed approach consists of three stages-initially, six state-of-the-art deep learning models are applied to traffic data obtained from sensors. Then the feature spaces and decision scores (outputs) of the model with the highest performance are fused to obtain hybridized deep feature spaces. Finally, a support vector regressor is applied to the hybridized feature spaces to get the final travel time prediction. The performance of our proposed heterogeneous ensemble using test data showed significant improvements compared to the baseline techniques in terms of the root mean square error (53.87±3.50), mean absolute error (12.22±1.35) and the coefficient of determination (0.99784±0.00019). The results demonstrated that the hybridized deep feature space concept could produce more stable and superior results than the other baseline techniques.
    Matched MeSH terms: Time Factors
  3. LLEWELLYN-JONES D
    Med J Malaya, 1958 Sep;13(1):43-8.
    PMID: 13589368
    Matched MeSH terms: Time Factors*
  4. Nathan, Nivethiny
    MyJurnal
    The timing of ABG procedure in a cleft patient is crucial to provide room and bony
    support for the eruption of canine. However, there seems to be a delay in execution of this
    procedure in certain centres. Material and Methods: Sample consists of records of cleft patients
    treated from 2000-2016. The date and age for commencement of active orthodontic treatment,
    date referred for ABG and date ABG done were retrieved. The centres that conducted these
    surgeries identified. (Copied from article).
    Matched MeSH terms: Time Factors*
  5. Ahmad BA, Khairatul K, Farnaza A
    Malays Fam Physician, 2017;12(1):14-21.
    PMID: 28503269 MyJurnal
    Waiting is a common phenomenon in the doctor's waiting room. The purpose of this audit is to assess patient waiting time and doctor consultation time in a primary healthcare clinic and to formulate strategies for improvement. This audit was conducted at a primary care clinic for 4 weeks using the universal sampling method. All patients who attended the clinic during this period was included in the study except for those who required more time to be seen such as those who were critically ill, aggressive or those who came for repeat medication or procedures only without needing to see the doctor. The time of arrival was captured using the queue management system (QMS) and then the patient was given a timing chit which had to be manually filled by the staff at every station. The waiting time for registration, pre-consultation, consultation, appointment, payment and pharmacy were recorded as well as consultation time. The data were entered into the statistical software SPSS version 17 for analysis. version 17. Results showed that more than half of the patients were registered within 15 minutes (53%) and the average total waiting time from registration to seeing a doctor was 41 minutes. Ninety-nine percentage of patients waited less than 30 minutes to get their medication. The average consultation time was 18.21 minutes. The problems identified in this audit were addressed and strategies formulated to improve the waiting and consultation time were carried out including increasing the number of staff at the registration counter, enforcing the staggered appointment system for follow-up patients and improving the queuing system for walk-in patients.
    Matched MeSH terms: Time Factors*
  6. Asiaweek, 1995 Sep 29.
    PMID: 12290445
    Matched MeSH terms: Time Factors*
  7. Abdul Razak F, Jensen HJ
    PLoS One, 2014;9(6):e99462.
    PMID: 24955766 DOI: 10.1371/journal.pone.0099462
    'Causal' direction is of great importance when dealing with complex systems. Often big volumes of data in the form of time series are available and it is important to develop methods that can inform about possible causal connections between the different observables. Here we investigate the ability of the Transfer Entropy measure to identify causal relations embedded in emergent coherent correlations. We do this by firstly applying Transfer Entropy to an amended Ising model. In addition we use a simple Random Transition model to test the reliability of Transfer Entropy as a measure of 'causal' direction in the presence of stochastic fluctuations. In particular we systematically study the effect of the finite size of data sets.
    Matched MeSH terms: Time Factors
  8. Lan BL, Borondo F
    Phys Rev E Stat Nonlin Soft Matter Phys, 2011 Mar;83(3 Pt 2):036201.
    PMID: 21517569
    Newtonian and special-relativistic predictions, based on the same model parameters and initial conditions for the trajectory of a low-speed scattering system are compared. When the scattering is chaotic, the two predictions for the trajectory can rapidly diverge completely, not only quantitatively but also qualitatively, due to an exponentially growing separation taking place in the scattering region. In contrast, when the scattering is nonchaotic, the breakdown of agreement between predictions takes a very long time, since the difference between the predictions grows only linearly. More importantly, in the case of low-speed chaotic scattering, the rapid loss of agreement between the Newtonian and special-relativistic trajectory predictions implies that special-relativistic mechanics must be used, instead of the standard practice of using Newtonian mechanics, to correctly describe the scattering dynamics.
    Matched MeSH terms: Time Factors
  9. Saif AF, Prabuwono AS, Mahayuddin ZR
    PLoS One, 2015;10(6):e0126212.
    PMID: 26030818 DOI: 10.1371/journal.pone.0126212
    Fast and computationally less complex feature extraction for moving object detection using aerial images from unmanned aerial vehicles (UAVs) remains as an elusive goal in the field of computer vision research. The types of features used in current studies concerning moving object detection are typically chosen based on improving detection rate rather than on providing fast and computationally less complex feature extraction methods. Because moving object detection using aerial images from UAVs involves motion as seen from a certain altitude, effective and fast feature extraction is a vital issue for optimum detection performance. This research proposes a two-layer bucket approach based on a new feature extraction algorithm referred to as the moment-based feature extraction algorithm (MFEA). Because a moment represents the coherent intensity of pixels and motion estimation is a motion pixel intensity measurement, this research used this relation to develop the proposed algorithm. The experimental results reveal the successful performance of the proposed MFEA algorithm and the proposed methodology.
    Matched MeSH terms: Time Factors
  10. George PP
    Med J Malaysia, 2002 Mar;57(1):128-31; quiz 132.
    PMID: 14569733
    Schizophrenia is a common and devastating illness. Patients with schizophrenia may develop many disabilities both due to the disease process as well as due to side effects of the medication used. There are many advances in the treatment of schizophrenia, which can effectively reduce many of these disabilities. Treatment of schizophrenia is a primary health care responsibility and thus all health care personnel need to equip themselves with the latest knowledge on management issues. This article outlines the current management issues in schizophrenia.
    Matched MeSH terms: Time Factors
  11. Ng TS
    Med J Malaysia, 1987 Jun;42(2):86-9.
    PMID: 3503195
    Temporary cardiac pacing is an established therapeutic modality in the treatment of heart block and bradyarrythmias due to various causes like drugs and metabolic causes and prior to permanent pacing. Temporary pacing using fluoroscopy is best but the image intensifier is not available to all medical units. The method of temporary pacing at the bedside is described in detail.
    Matched MeSH terms: Time Factors
  12. Tan SY, Arshad H, Abdullah A
    PLoS One, 2019;14(1):e0207191.
    PMID: 30605474 DOI: 10.1371/journal.pone.0207191
    Mobile Augmented Reality (MAR) requires a descriptor that is robust to changes in viewing conditions in real time application. Many different descriptors had been proposed in the literature for example floating-point descriptors (SIFT and SURF) and binary descriptors (BRIEF, ORB, BRISK and FREAK). According to literature, floating-point descriptors are not suitable for real-time application because its operating speed does not satisfy real-time constraints. Binary descriptors have been developed with compact sizes and lower computation requirements. However, it is unclear which binary descriptors are more appropriate for MAR. Hence, a distinctive and efficient accuracy measurement of four state-of-the-art binary descriptors, namely, BRIEF, ORB, BRISK and FREAK were performed using the Mikolajczyk dataset and ALOI dataset to identify the most appropriate descriptor for MAR in terms of computation time and robustness to brightness, scale and rotation changes. The obtained results showed that FREAK is the most appropriate descriptor for MAR application as it able to produce an application that are efficient (shortest computation time) and robust towards scale, rotation and brightness changes.
    Matched MeSH terms: Time Factors
  13. Hosen MA, Jumaat MZ, Alengaram UJ, Islam ABMS, Bin Hashim H
    Polymers (Basel), 2016 Mar 03;8(3).
    PMID: 30979167 DOI: 10.3390/polym8030067
    Existing structural components require strengthening after a certain period of time due to increases in service loads, errors in design, mechanical damage, and the need to extend the service period. Externally-bonded reinforcement (EBR) and near-surface mounted (NSM) reinforcement are two preferred strengthening approach. This paper presents a NSM technique incorporating NSM composites, namely steel and carbon fiber-reinforced polymer (CFRP) bars, as reinforcement. Experimental and analytical studies carried out to explore the performance of reinforced concrete (RC) members strengthened with the NSM composites. Analytical models were developed in predicting the maximum crack spacing and width, concrete cover separation failure loads, and deflection. A four-point bending test was applied on beams strengthened with different types and ratios of NSM reinforcement. The failure characteristics, yield, and ultimate capacities, deflection, strain, and cracking behavior of the beams were evaluated based on the experimental output. The test results indicate an increase in the cracking load of 69% and an increase in the ultimate load of 92% compared with the control beam. The predicted result from the analytical model shows good agreement with the experimental result, which ensures the competent implementation of the present NSM-steel and CFRP technique.
    Matched MeSH terms: Time Factors
  14. Nurul Husna Hassan, Roslizawati Ab Lah
    MyJurnal
    Black snail, Faunus ater is an abundant species in Malaysia yet not many research have focused on its physiological and biological activities.  This research aimed to assess the condition index (CI) and reproductive status based on the gonadosomatic index (GSI) for short-term duration. Samples were collected monthly from Merchang Lagoon, from November 2018 to January 2019. Four different types of condition indices equation were applied in this study and the results revealed that there were significant differences between four equations for measuring the CI (P=0.000). However, the result for the GSI shows no significant difference between three month of sampling (P>0.05). CI based on fresh weight measurement (Fww/Tww x 100) and dry weight measurement (Fdw/Fww x 100) reached its peak when GSI decreased. The rest of the trend for CI fluctuated and CI was not affected by GSI. Overall, this study concluded that, there were trends observed in CI and GSI for the black snails. However, it is suggested that longer term observation in future research is needed have a better understanding on the black snails.
    Matched MeSH terms: Time Factors
  15. Sayang Mohd Deni, Suhaila Jamaluddin, Wan Zawiah Wan Zin, Abdul Aziz Jemain
    This study attempts to trace changes in the wet spells over Peninsular Malaysia based on the daily rainfall data from 32 selected rainfall stations which include four sub-regions; northwest, west, south and east, for the period of 1975 to 2004. Six wet spells indices comprising of the main characteristics (maximum, mean, standard deviation), the persistency of two consecutive wet days and the frequency of the short and long duration of wet spells will be used to identify whether or not these indices increase or decrease over Peninsular Malaysia during the monsoon seasons. The study indicates that the eastern areas of the peninsula could be considered as the wettest areas since almost all the indices of wet spells over these areas are higher than over the other regions during the northeast monsoon (NE). The Mann-Kendall (MK) trend test revealed that almost all of the stations located in the eastern areas of the peninsula exhibited a positive trend in the mean, variability and persistency of wet spells indices during the NE monsoon, while a negative trend was observed during the southwest monsoon (SW) in these areas. Moreover, these indices showed a positive trend, and at the same time a decreasing trend was observed in the frequency of the long wet spells in most stations located over the west coast of Peninsular Malaysia during the SW monsoon for the period of 1975 to 2004.
    Matched MeSH terms: Time Factors
  16. Hussain S, Mustafa MW, Al-Shqeerat KHA, Saeed F, Al-Rimy BAS
    Sensors (Basel), 2021 Dec 17;21(24).
    PMID: 34960516 DOI: 10.3390/s21248423
    This study presents a novel feature-engineered-natural gradient descent ensemble-boosting (NGBoost) machine-learning framework for detecting fraud in power consumption data. The proposed framework was sequentially executed in three stages: data pre-processing, feature engineering, and model evaluation. It utilized the random forest algorithm-based imputation technique initially to impute the missing data entries in the acquired smart meter dataset. In the second phase, the majority weighted minority oversampling technique (MWMOTE) algorithm was used to avoid an unequal distribution of data samples among different classes. The time-series feature-extraction library and whale optimization algorithm were utilized to extract and select the most relevant features from the kWh reading of consumers. Once the most relevant features were acquired, the model training and testing process was initiated by using the NGBoost algorithm to classify the consumers into two distinct categories ("Healthy" and "Theft"). Finally, each input feature's impact (positive or negative) in predicting the target variable was recognized with the tree SHAP additive-explanations algorithm. The proposed framework achieved an accuracy of 93%, recall of 91%, and precision of 95%, which was greater than all the competing models, and thus validated its efficacy and significance in the studied field of research.
    Matched MeSH terms: Time Factors
  17. Zaini N, Ean LW, Ahmed AN, Malek MA
    Environ Sci Pollut Res Int, 2022 Jan;29(4):4958-4990.
    PMID: 34807385 DOI: 10.1007/s11356-021-17442-1
    Rapid progress of industrial development, urbanization and traffic has caused air quality reduction that negatively affects human health and environmental sustainability, especially among developed countries. Numerous studies on the development of air quality forecasting model using machine learning have been conducted to control air pollution. As such, there are significant numbers of reviews on the application of machine learning in air quality forecasting. Shallow architectures of machine learning exhibit several limitations and yield lower forecasting accuracy than deep learning architecture. Deep learning is a new technology in computational intelligence; thus, its application in air quality forecasting is still limited. This study aims to investigate the deep learning applications in time series air quality forecasting. Owing to this, literature search is conducted thoroughly from all scientific databases to avoid unnecessary clutter. This study summarizes and discusses different types of deep learning algorithms applied in air quality forecasting, including the theoretical backgrounds, hyperparameters, applications and limitations. Hybrid deep learning with data decomposition, optimization algorithm and spatiotemporal models are also presented to highlight those techniques' effectiveness in tackling the drawbacks of individual deep learning models. It is clearly stated that hybrid deep learning was able to forecast future air quality with higher accuracy than individual models. At the end of the study, some possible research directions are suggested for future model development. The main objective of this review study is to provide a comprehensive literature summary of deep learning applications in time series air quality forecasting that may benefit interested researchers for subsequent research.
    Matched MeSH terms: Time Factors
  18. Vivekanandhan G, Abdolmohammadi HR, Natiq H, Rajagopal K, Jafari S, Namazi H
    Math Biosci Eng, 2023 Jan;20(3):4760-4781.
    PMID: 36896521 DOI: 10.3934/mbe.2023220
    Human evolution is carried out by two genetic systems based on DNA and another based on the transmission of information through the functions of the nervous system. In computational neuroscience, mathematical neural models are used to describe the biological function of the brain. Discrete-time neural models have received particular attention due to their simple analysis and low computational costs. From the concept of neuroscience, discrete fractional order neuron models incorporate the memory in a dynamic model. This paper introduces the fractional order discrete Rulkov neuron map. The presented model is analyzed dynamically and also in terms of synchronization ability. First, the Rulkov neuron map is examined in terms of phase plane, bifurcation diagram, and Lyapunov exponent. The biological behaviors of the Rulkov neuron map, such as silence, bursting, and chaotic firing, also exist in its discrete fractional-order version. The bifurcation diagrams of the proposed model are investigated under the effect of the neuron model's parameters and the fractional order. The stability regions of the system are theoretically and numerically obtained, and it is shown that increasing the order of the fractional order decreases the stable areas. Finally, the synchronization behavior of two fractional-order models is investigated. The results represent that the fractional-order systems cannot reach complete synchronization.
    Matched MeSH terms: Time Factors
  19. Zhou S, Hudin NS
    PLoS One, 2024;19(4):e0299087.
    PMID: 38635519 DOI: 10.1371/journal.pone.0299087
    In recent years, the global e-commerce landscape has witnessed rapid growth, with sales reaching a new peak in the past year and expected to rise further in the coming years. Amid this e-commerce boom, accurately predicting user purchase behavior has become crucial for commercial success. We introduce a novel framework integrating three innovative approaches to enhance the prediction model's effectiveness. First, we integrate an event-based timestamp encoding within a time-series attention model, effectively capturing the dynamic and temporal aspects of user behavior. This aspect is often neglected in traditional user purchase prediction methods, leading to suboptimal accuracy. Second, we incorporate Graph Neural Networks (GNNs) to analyze user behavior. By modeling users and their actions as nodes and edges within a graph structure, we capture complex relationships and patterns in user behavior more effectively than current models, offering a nuanced and comprehensive analysis. Lastly, our framework transcends traditional learning strategies by implementing advanced meta-learning techniques. This enables the model to autonomously adjust learning parameters, including the learning rate, in response to new and evolving data environments, thereby significantly enhancing its adaptability and learning efficiency. Through extensive experiments on diverse real-world e-commerce datasets, our model demonstrates superior performance, particularly in accuracy and adaptability in large-scale data scenarios. This study not only overcomes the existing challenges in analyzing e-commerce user behavior but also sets a foundation for future exploration in this dynamic field. We believe our contributions provide significant insights and tools for e-commerce platforms to better understand and cater to their users, ultimately driving sales and improving user experiences.
    Matched MeSH terms: Time Factors
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