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Fulltext A Study on Regional GDP Forecasting Analysis Based on Radial Basis Function Neural Network with Genetic Algorithm (RBFNN-GA) for Shandong Economy

Zhang Q, Abdullah AR, Chong CW, Ali MH

Comput Intell Neurosci, 2022;2022:8235308.
PMID: 35126503 DOI: 10.1155/2022/8235308

Gross domestic product (GDP) is an important indicator for determining a country's or region's economic status and development level, and it is closely linked to inflation, unemployment, and economic growth rates. These basic indicators can comprehensively and effectively reflect a country's or region's future economic development. The center of radial basis function neural network and smoothing factor to take a uniform distribution of the random radial basis function artificial neural network will be the focus of this study. This stochastic learning method is a useful addition to the existing methods for determining the center and smoothing factors of radial basis function neural networks, and it can also help the network more efficiently train. GDP forecasting is aided by the genetic algorithm radial basis neural network, which allows the government to make timely and effective macrocontrol plans based on the forecast trend of GDP in the region. This study uses the genetic algorithm radial basis, neural network model, to make judgments on the relationships contained in this sequence and compare and analyze the prediction effect and generalization ability of the model to verify the applicability of the genetic algorithm radial basis, neural network model, based on the modeling of historical data, which may contain linear and nonlinear relationships by itself, so this study uses the genetic algorithm radial basis, neural network model, to make, compare, and analyze judgments on the relationships contained in this sequence.

Matched MeSH terms: Forecasting
Fulltext PM2.5 forecasting for an urban area based on deep learning and decomposition method

Zaini N, Ean LW, Ahmed AN, Abdul Malek M, Chow MF

Sci Rep, 2022 Oct 20;12(1):17565.
PMID: 36266317 DOI: 10.1038/s41598-022-21769-1

Rapid growth in industrialization and urbanization have resulted in high concentration of air pollutants in the environment and thus causing severe air pollution. Excessive emission of particulate matter to ambient air has negatively impacted the health and well-being of human society. Therefore, accurate forecasting of air pollutant concentration is crucial to mitigate the associated health risk. This study aims to predict the hourly PM2.5 concentration for an urban area in Malaysia using a hybrid deep learning model. Ensemble empirical mode decomposition (EEMD) was employed to decompose the original sequence data of particulate matter into several subseries. Long short-term memory (LSTM) was used to individually forecast the decomposed subseries considering the influence of air pollutant parameters for 1-h ahead forecasting. Then, the outputs of each forecast were aggregated to obtain the final forecasting of PM2.5 concentration. This study utilized two air quality datasets from two monitoring stations to validate the performance of proposed hybrid EEMD-LSTM model based on various data distributions. The spatial and temporal correlation for the proposed dataset were analysed to determine the significant input parameters for the forecasting model. The LSTM architecture consists of two LSTM layers and the data decomposition method is added in the data pre-processing stage to improve the forecasting accuracy. Finally, a comparison analysis was conducted to compare the performance of the proposed model with other deep learning models. The results illustrated that EEMD-LSTM yielded the highest accuracy results among other deep learning models, and the hybrid forecasting model was proved to have superior performance as compared to individual models.

Matched MeSH terms: Forecasting
A systematic literature review of deep learning neural network for time series air quality forecasting

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: Forecasting
The future of medical education in Malaysia

Zaini A

Med J Malaysia, 2002 Dec;57 Suppl E:5-7.
PMID: 12733184
Matched MeSH terms: Forecasting
Landfill area estimation based on integrated waste disposal options and solid waste forecasting using modified ANFIS model

Younes MK, Nopiah ZM, Basri NE, Basri H, Abushammala MF, Younes MY

Waste Manag, 2016 Sep;55:3-11.
PMID: 26522806 DOI: 10.1016/j.wasman.2015.10.020

Solid waste prediction is crucial for sustainable solid waste management. The collection of accurate waste data records is challenging in developing countries. Solid waste generation is usually correlated with economic, demographic and social factors. However, these factors are not constant due to population and economic growth. The objective of this research is to minimize the land requirements for solid waste disposal for implementation of the Malaysian vision of waste disposal options. This goal has been previously achieved by integrating the solid waste forecasting model, waste composition and the Malaysian vision. The modified adaptive neural fuzzy inference system (MANFIS) was employed to develop a solid waste prediction model and search for the optimum input factors. The performance of the model was evaluated using the root mean square error (RMSE) and the coefficient of determination (R(2)). The model validation results are as follows: RMSE for training=0.2678, RMSE for testing=3.9860 and R(2)=0.99. Implementation of the Malaysian vision for waste disposal options can minimize the land requirements for waste disposal by up to 43%.

Matched MeSH terms: Forecasting
Prediction of municipal solid waste generation using nonlinear autoregressive network

Younes MK, Nopiah ZM, Basri NE, Basri H, Abushammala MF, Maulud KN

Environ Monit Assess, 2015 Dec;187(12):753.
PMID: 26573690 DOI: 10.1007/s10661-015-4977-5

Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generation rate is increasing rapidly, due to population growth and new consumption trends that characterize society. This paper proposes an artificial neural network (ANN) approach using feedforward nonlinear autoregressive network with exogenous inputs (NARX) to predict annual solid waste generation in relation to demographic and economic variables like population number, gross domestic product, electricity demand per capita and employment and unemployment numbers. In addition, variable selection procedures are also developed to select a significant explanatory variable. The model evaluation was performed using coefficient of determination (R(2)) and mean square error (MSE). The optimum model that produced the lowest testing MSE (2.46) and the highest R(2) (0.97) had three inputs (gross domestic product, population and employment), eight neurons and one lag in the hidden layer, and used Fletcher-Powell's conjugate gradient as the training algorithm.

Matched MeSH terms: Forecasting
Solid waste forecasting using modified ANFIS modeling

Younes MK, Nopiah ZM, Basri NE, Basri H, Abushammala MF, K N A M

J Air Waste Manag Assoc, 2015 Oct;65(10):1229-38.
PMID: 26223583 DOI: 10.1080/10962247.2015.1075919

Solid waste prediction is crucial for sustainable solid waste management. Usually, accurate waste generation record is challenge in developing countries which complicates the modelling process. Solid waste generation is related to demographic, economic, and social factors. However, these factors are highly varied due to population and economy growths. The objective of this research is to determine the most influencing demographic and economic factors that affect solid waste generation using systematic approach, and then develop a model to forecast solid waste generation using a modified Adaptive Neural Inference System (MANFIS). The model evaluation was performed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the coefficient of determination (R²). The results show that the best input variables are people age groups 0-14, 15-64, and people above 65 years, and the best model structure is 3 triangular fuzzy membership functions and 27 fuzzy rules. The model has been validated using testing data and the resulted training RMSE, MAE and R² were 0.2678, 0.045 and 0.99, respectively, while for testing phase RMSE =3.986, MAE = 0.673 and R² = 0.98.

Matched MeSH terms: Forecasting
Wavelet-based time series model to improve the forecast accuracy of PM10 concentrations in Peninsular Malaysia

Yong NK, Awang N

Environ Monit Assess, 2019 Jan 11;191(2):64.
PMID: 30635772 DOI: 10.1007/s10661-019-7209-6

This study presents the use of a wavelet-based time series model to forecast the daily average particulate matter with an aerodynamic diameter of less than 10 μm (PM10) in Peninsular Malaysia. The highlight of this study is the use of a discrete wavelet transform (DWT) in order to improve the forecast accuracy. The DWT was applied to convert the highly variable PM10 series into more stable approximations and details sub-series, and the ARIMA-GARCH time series models were developed for each sub-series. Two different forecast periods, one was during normal days, while the other was during haze episodes, were designed to justify the usefulness of DWT. The models' performance was evaluated by four indices, namely root mean square error, mean absolute percentage error, probability of detection and false alarm rate. The results showed that the model incorporated with DWT yielded more accurate forecasts than the conventional method without DWT for both the forecast periods, and the improvement was more prominent for the period during the haze episodes.

Matched MeSH terms: Forecasting/methods*
Hip fractures in Singapore: ethnic differences and temporal trends in the new millennium

Yong EL, Ganesan G, Kramer MS, Logan S, Lau TC, Cauley JA, et al.

Osteoporos Int, 2019 Apr;30(4):879-886.
PMID: 30671610 DOI: 10.1007/s00198-019-04839-5

Despite an increase in absolute numbers, the age-standardized incidence of hip fractures in Singapore declined in the period 2000 to 2017. Among the three major ethnic groups, Chinese women had the highest fracture rates but were the only group to show a temporal decline.
INTRODUCTION: A study published in 2001 predicted a 30-50% increase in Singapore hip fracture incidence rates over the ensuing 30 years. To test that prediction, we examined the incidence of hip fracture in Singapore from 2000 to 2017.
METHODS: We carried out a population-based study of hip fractures among Singapore residents aged ≥ 50 years. National medical insurance claims data were used to identify admissions with a primary discharge diagnosis of hip fracture. Age-adjusted rates, based on the age distribution of the Singapore population of 2000, were analyzed separately by sex and ethnicity (Chinese, Malay, or Indian).
RESULTS: Over the 18-year study period, 36,082 first hip fractures were recorded. Total hip fracture admissions increased from 1487 to 2729 fractures/year in the years 2000 to 2017. Despite this absolute increase, age-adjusted fracture rates declined, with an average annual change of - 4.3 (95% CI - 5.0, - 3.5) and - 1.1 (95% CI - 1.7, - 0.5) fractures/100,000/year for women and men respectively. Chinese women had 1.4- and 1.9-fold higher age-adjusted rates than Malay and Indian women: 264 (95% CI 260, 267) versus 185 (95% CI 176, 193) and 141 (95% CI 132, 150) fractures/100,000/year, respectively. Despite their higher fracture rates, Chinese women were the only ethnic group exhibiting a decline, most evident in those ≥ 85 years, in age-adjusted fracture rate of - 5.3 (95% CI - 6.0, - 4.5) fractures/100,000/year.
CONCLUSION: Although the absolute number of fractures increased, steep drops in elderly Chinese women drove a reduction in overall age-adjusted hip fracture rates. Increases in the older population will lead to a rise in total number of hip fractures, requiring budgetary planning and new preventive strategies.

Matched MeSH terms: Forecasting
Fulltext Therapeutic radionuclides in nuclear medicine: current and future prospects

Yeong CH, Cheng MH, Ng KH

J Zhejiang Univ Sci B, 2014 Oct;15(10):845-63.
PMID: 25294374 DOI: 10.1631/jzus.B1400131

The potential use of radionuclides in therapy has been recognized for many decades. A number of radionuclides, such as iodine-131 ((131)I), phosphorous-32 ((32)P), strontium-90 ((90)Sr), and yttrium-90 ((90)Y), have been used successfully for the treatment of many benign and malignant disorders. Recently, the rapid growth of this branch of nuclear medicine has been stimulated by the introduction of a number of new radionuclides and radiopharmaceuticals for the treatment of metastatic bone pain and neuroendocrine and other malignant or non-malignant tumours. Today, the field of radionuclide therapy is enjoying an exciting phase and is poised for greater growth and development in the coming years. For example, in Asia, the high prevalence of thyroid and liver diseases has prompted many novel developments and clinical trials using targeted radionuclide therapy. This paper reviews the characteristics and clinical applications of the commonly available therapeutic radionuclides, as well as the problems and issues involved in translating novel radionuclides into clinical therapies.

Matched MeSH terms: Forecasting
Fulltext Medical manpower needs by the year 2000

Yeoh PH

Med J Malaysia, 1988 Sep;43(3):195-9.
PMID: 3241576
Matched MeSH terms: Forecasting
Fulltext Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh

Yaseen ZM, Ali M, Sharafati A, Al-Ansari N, Shahid S

Sci Rep, 2021 Feb 09;11(1):3435.
PMID: 33564055 DOI: 10.1038/s41598-021-82977-9

A noticeable increase in drought frequency and severity has been observed across the globe due to climate change, which attracted scientists in development of drought prediction models for mitigation of impacts. Droughts are usually monitored using drought indices (DIs), most of which are probabilistic and therefore, highly stochastic and non-linear. The current research investigated the capability of different versions of relatively well-explored machine learning (ML) models including random forest (RF), minimum probability machine regression (MPMR), M5 Tree (M5tree), extreme learning machine (ELM) and online sequential-ELM (OSELM) in predicting the most widely used DI known as standardized precipitation index (SPI) at multiple month horizons (i.e., 1, 3, 6 and 12). Models were developed using monthly rainfall data for the period of 1949-2013 at four meteorological stations namely, Barisal, Bogra, Faridpur and Mymensingh, each representing a geographical region of Bangladesh which frequently experiences droughts. The model inputs were decided based on correlation statistics and the prediction capability was evaluated using several statistical metrics including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), Willmott's Index of agreement (WI), Nash Sutcliffe efficiency (NSE), and Legates and McCabe Index (LM). The results revealed that the proposed models are reliable and robust in predicting droughts in the region. Comparison of the models revealed ELM as the best model in forecasting droughts with minimal RMSE in the range of 0.07-0.85, 0.08-0.76, 0.062-0.80 and 0.042-0.605 for Barisal, Bogra, Faridpur and Mymensingh, respectively for all the SPI scales except one-month SPI for which the RF showed the best performance with minimal RMSE of 0.57, 0.45, 0.59 and 0.42, respectively.

Matched MeSH terms: Forecasting
Predictors of acute neurological complication following tetralogy of Fallot operation in Serdang Hospital, Malaysia

Yap MT, Yubbu P, Yong SW, Hing WV, Ong YS, Devaraj NK, et al.

Med J Malaysia, 2020 09;75(5):494-501.
PMID: 32918416

BACKGROUND: The long waiting time for Tetralogy of Fallot (TOF) operation may potentially increase the risk of hypoxic insult. Therefore, the objective of this study is to determine the frequency of acute neurological complications following primary TOF repair and to identify the peri-operative risk factors and predictors for the neurological sequelae.
METHODS: A retrospective review of the medical and surgical notes of 68 patients who underwent TOF repair in Hospital Serdang, from January 2013 to December 2017 was done. Univariate and multivariate analyses of demographics and perioperative clinical data were performed to determine the risk for the development of acute neurological complications (ANC) among these patients.
RESULTS: ANC was reported in 13 cases (19.1%) with delirium being the most common manifestation (10/68, 14.7%), followed by seizures in 4 (5.9%) and abnormal movements in two patients (2.9%). Univariate analyses showed that the presence of right ventricular (RV) dysfunction, prolonged duration of inotropic support (≥7 days), prolonged duration of mechanical ventilation (≥7 days), longer length of ICU stays (≥7 days), and longer length of hospital stay (≥14 days), were significantly associated with the presence of ANCs (p<0.05). However, multivariate analyses did not show any significant association between these variables and the development of ANC (p>0.05). The predictors for the development of postoperative delirium were pre-operative oxygen saturation less than 75% (Odds Ratio, OR=16.90, 95% Confidence Interval, 95%CI:1.36, 209.71) and duration of ventilation of more than 7 days (OR=13.20, 95%CI: 1.20, 144.98).
CONCLUSION: ANC following TOF repair were significantly higher in patients with RV dysfunction, in those who required a longer duration of inotropic support, mechanical ventilation, ICU and hospital stay. Low pre-operative oxygen saturation and prolonged mechanical ventilation requirement were predictors for delirium which was the commonest neurological complications observed in this study. Hence, routine screening for delirium using an objective assessment tool should be performed on these high-risk patients to enable accurate diagnosis and early intervention to improve the overall outcome of TOF surgery in this country.

Matched MeSH terms: Forecasting
Dengue vector control: present status and future prospects

Yap HH, Chong NL, Foo AE, Lee CY

Gaoxiong Yi Xue Ke Xue Za Zhi, 1994 Dec;10 Suppl:S102-8.
PMID: 7844836

Dengue Fever (DF) and Dengue Haemorrhagic Fever (DHF) have been the most common urban diseases in Southeast Asia since the 1950s. More recently, the diseases have spread to Central and South America and are now considered as worldwide diseases. Both Aedes aegypti and Aedes albopictus are involved in the transmission of DF/DHF in Southeast Asian region. The paper discusses the present status and future prospects of Aedes control with reference to the Malaysian experience. Vector control approaches which include source reduction and environmental management, larviciding with the use of chemicals (synthetic insecticides and insect growth regulators and microbial insecticide), and adulticiding which include personal protection measures (household insecticide products and repellents) for long-term control and space spray (both thermal fogging and ultra low volume sprays) as short-term epidemic measures are discussed. The potential incorporation of IGRs and Bacillus thuringiensis-14 (Bti) as larvicides in addition to insecticides (temephos) is discussed. The advantages of using water-based spray over the oil-based (diesel) spray and the use of spray formulation which provide both larvicidal and adulticidal effects that would consequently have greater impact on the overall vector and disease control in DF/DHF are highlighted.

Matched MeSH terms: Forecasting
Adult palliative care 2004-2030 population study: estimates and projections in Malaysia

Yang SL, Woon YL, Teoh CCO, Leong CT, Lim RBL

BMJ Support Palliat Care, 2020 Aug 21.
PMID: 32826260 DOI: 10.1136/bmjspcare-2020-002283

OBJECTIVES: To estimate past trends and future projection of adult palliative care needs in Malaysia.
METHODS: This is a population-based secondary data analysis using the national mortality registry from 2004 to 2014. Past trend estimation was conducted using Murtagh's minimum and maximum methods and Gómez-Batiste's method. The estimated palliative care needs were stratified by age groups, gender and administrative states in Malaysia. With this, the projection of palliative care needs up to 2030 was conducted under the assumption that annual change remains constant.
RESULTS: The palliative care needs in Malaysia followed an apparent upward trend over the years regardless of the estimation methods. Murtagh's minimum estimation method showed that palliative care needs grew 40% from 71 675 cases in 2004 to 100 034 cases in 2014. The proportion of palliative care needs in relation to deaths hovered at 71% in the observed years. In 2030, Malaysia should anticipate the population needs to be at least 239 713 cases (240% growth from 2014), with the highest needs among age group ≥80-year-old in both genders. Sarawak, Perak, Johor, Selangor and Kedah will become the top five Malaysian states with the highest number of needs in 2030.
CONCLUSION: The need for palliative care in Malaysia will continue to rise and surpass its service provision. This trend demands a stepped-up provision from the national health system with advanced integration of palliative care services to narrow the gap between needs and supply.

Matched MeSH terms: Forecasting
Fulltext Common institutional investors and the quality of management earnings forecasts-Empirical and machine learning evidences

Yang S, Li X, Jiang Z, Xiao M

PLoS One, 2023;18(10):e0290126.
PMID: 37844110 DOI: 10.1371/journal.pone.0290126

Based on the data of the Chinese A-share listed firms in China Shanghai and Shenzhen Stock Exchange from 2014 to 2021, this article explores the relationship between common institutional investors and the quality of management earnings forecasts. The study used the multiple linear regression model and empirically found that common institutional investors positively impact the precision of earnings forecasts. This article also uses graph neural networks to predict the precision of earnings forecasts. Our findings have shown that common institutional investors form external supervision over restricting management to release a wide width of earnings forecasts, which helps to improve the risk warning function of earnings forecasts and promote the sustainable development of information disclosure from management in the Chinese capital market. One of the marginal contributions of this paper is that it enriches the literature related to the economic consequences of common institutional shareholding. Then, the neural network method used to predict the quality of management forecasts enhances the research method of institutional investors and the behavior of management earnings forecasts. Thirdly, this paper calls for strengthening information sharing and circulation among institutional investors to reduce information asymmetry between investors and management.

Matched MeSH terms: Forecasting
Recent trends for practical rehabilitation robotics, current challenges and the future

Yakub F, Md Khudzari AZ, Mori Y

Int J Rehabil Res, 2014 Mar;37(1):9-21.
PMID: 24126254 DOI: 10.1097/MRR.0000000000000035

This paper presents and studies various selected literature primarily from conference proceedings, journals and clinical tests of the robotic, mechatronics, neurology and biomedical engineering of rehabilitation robotic systems. The present paper focuses of three main categories: types of rehabilitation robots, key technologies with current issues and future challenges. Literature on fundamental research with some examples from commercialized robots and new robot development projects related to rehabilitation are introduced. Most of the commercialized robots presented in this paper are well known especially to robotics engineers and scholars in the robotic field, but are less known to humanities scholars. The field of rehabilitation robot research is expanding; in light of this, some of the current issues and future challenges in rehabilitation robot engineering are recalled, examined and clarified with future directions. This paper is concluded with some recommendations with respect to rehabilitation robots.

Matched MeSH terms: Forecasting
Nanospray Drying Technology: Existing Limitations and Future Challenges

Wui WT

Recent Pat Drug Deliv Formul, 2015;9(3):185-6.
PMID: 25966873
Matched MeSH terms: Forecasting
Poverty and corruption compromise tropical forest reserves

Wright SJ, Sanchez-Azofeifa GA, Portillo-Quintero C, Davies D

Ecol Appl, 2007 Jul;17(5):1259-66.
PMID: 17708206

We used the global fire detection record provided by the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) to determine the number of fires detected inside 823 tropical and subtropical moist forest reserves and for contiguous buffer areas 5, 10, and 15 km wide. The ratio of fire detection densities (detections per square kilometer) inside reserves to their contiguous buffer areas provided an index of reserve effectiveness. Fire detection density was significantly lower inside reserves than in paired, contiguous buffer areas but varied by five orders of magnitude among reserves. The buffer: reserve detection ratio varied by up to four orders of magnitude among reserves within a single country, and median values varied by three orders of magnitude among countries. Reserves tended to be least effective at reducing fire frequency in many poorer countries and in countries beset by corruption. Countries with the most successful reserves include Costa Rica, Jamaica, Malaysia, and Taiwan and the Indonesian island of Java. Countries with the most problematic reserves include Cambodia, Guatemala, Paraguay, and Sierra Leone and the Indonesian portion of Borneo. We provide fire detection density for 3964 tropical and subtropical reserves and their buffer areas in the hope that these data will expedite further analyses that might lead to improved management of tropical reserves.

Matched MeSH terms: Forecasting/methods*
Fulltext The future of the GP

Wilkinson IE

Br J Gen Pract, 1992 Feb;42(355):84.
PMID: 1493024
Matched MeSH terms: Forecasting

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