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Fulltext Workshops of the Sixth International Brain-Computer Interface Meeting: brain-computer interfaces past, present, and future

Huggins JE, Guger C, Ziat M, Zander TO, Taylor D, Tangermann M, et al.

Brain Comput Interfaces (Abingdon), 2017;4(1-2):3-36.
PMID: 29152523 DOI: 10.1080/2326263X.2016.1275488

The Sixth International Brain-Computer Interface (BCI) Meeting was held 30 May-3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain-machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the general public. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and highlighting important issues and calls for action to support future research and development.

Matched MeSH terms: Forecasting
Fulltext Review on human toxoplasmosis in Malaysia: the past, present and prospective future

Nissapatorn V, Abdullah KA

Southeast Asian J. Trop. Med. Public Health, 2004 Mar;35(1):24-30.
PMID: 15272740

We reviewed various studies regarding human toxoplasmosis in Malaysia. They showed a varying prevalence of specific Toxoplasma antibodies among the Malaysian population. The Malays have shown the highest seroprevalence of toxoplasmosis, by most studies, when compared to other races. Demographic profiles have shown that Toxoplasma seropositivity is higher in males than females, lower in people with higher incomes, higher in the unemployed and tends to increase with age. In general, the route of transmission, such as contact with a cat, consumption of undercooked meat and blood transfusion were shown to have no significant association with Toxoplasma seropositivity (p > 0.05). The immune status (CD4 cell count < 200 cell/mm3) was strongly associated with toxoplasmic encephalitis (p < 0.05).

Matched MeSH terms: Forecasting
Fulltext A machine learning approach of predicting high potential archers by means of physical fitness indicators

Muazu Musa R, P P Abdul Majeed A, Taha Z, Chang SW, Ab Nasir AF, Abdullah MR

PLoS One, 2019;14(1):e0209638.
PMID: 30605456 DOI: 10.1371/journal.pone.0209638

k-nearest neighbour (k-NN) has been shown to be an effective learning algorithm for classification and prediction. However, the application of k-NN for prediction and classification in specific sport is still in its infancy. The present study classified and predicted high and low potential archers from a set of physical fitness variables trained on a variation of k-NN algorithms and logistic regression. 50 youth archers with the mean age and standard deviation of (17.0 ± 0.56) years drawn from various archery programmes completed a one end archery shooting score test. Standard fitness measurements of the handgrip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were conducted. Multiple linear regression was utilised to ascertain the significant variables that affect the shooting score. It was demonstrated from the analysis that core muscle strength and vertical jump were statistically significant. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the significant variables identified. k-NN model variations, i.e., fine, medium, coarse, cosine, cubic and weighted functions as well as logistic regression, were trained based on the significant performance variables. The HACA clustered the archers into high potential archers (HPA) and low potential archers (LPA). The weighted k-NN outperformed all the tested models at itdemonstrated reasonably good classification on the evaluated indicators with an accuracy of 82.5 ± 4.75% for the prediction of the HPA and the LPA. Moreover, the performance of the classifiers was further investigated against fresh data, which also indicates the efficacy of the weighted k-NN model. These findings could be valuable to coaches and sports managers to recognise high potential archers from a combination of the selected few physical fitness performance indicators identified which would subsequently save cost, time and energy for a talent identification programme.

Matched MeSH terms: Forecasting/methods*
Fulltext Developing forecasting model for future pandemic applications based on COVID-19 data 2020-2022

Wan Mohamad Nawi WIA, K Abdul Hamid AA, Lola MS, Zakaria S, Aruchunan E, Gobithaasan RU, et al.

PLoS One, 2023;18(5):e0285407.
PMID: 37172040 DOI: 10.1371/journal.pone.0285407

Improving forecasting particularly time series forecasting accuracy, efficiency and precisely become crucial for the authorities to forecast, monitor, and prevent the COVID-19 cases so that its spread can be controlled more effectively. However, the results obtained from prediction models are inaccurate, imprecise as well as inefficient due to linear and non-linear patterns exist in the data set, respectively. Therefore, to produce more accurate and efficient COVID-19 prediction value that is closer to the true COVID-19 value, a hybrid approach has been implemented. Thus, aims of this study is (1) to propose a hybrid ARIMA-SVM model to produce better forecasting results. (2) to investigate in terms of the performance of the proposed models and percentage improvement against ARIMA and SVM models. statistical measurements such as MSE, RMSE, MAE, and MAPE then conducted to verify that the proposed models are better than ARIMA and SVM models. Empirical results with three real datasets of well-known cases of COVID-19 in Malaysia show that, compared to the ARIMA and SVM models, the proposed model generates the smallest MSE, RMSE, MAE and MAPE values for the training and testing datasets, means that the predicted value from the proposed model is closer to the actual value. These results prove that the proposed model can generate estimated values more accurately and efficiently. As compared to ARIMA and SVM, our proposed models perform much better in terms of error reduction percentages for all datasets. This is demonstrated by the maximum scores of 73.12%, 74.6%, 90.38%, and 68.99% in the MAE, MAPE, MSE, and RMSE, respectively. Therefore, the proposed model can be the best and effective way to improve prediction performance with a higher level of accuracy and efficiency in predicting cases of COVID-19.

Matched MeSH terms: Forecasting
Fulltext Housing improvement using quality function development in Melaka resident area: a case study

Salim, M. A., Wan Mohamad, W. M. F., Maksom, Z., Kamat, S. R., Sukarma, L., Putra, A., et al.

Journal of Advanced Review on Scientific Research, 2014;4(1):7-19.
MyJurnal

This paper presents the housing improvement proposition in the Melaka resident area.Quality Function Deployment is used as a method to analyze customer behavior regarding customer requirement, satisfaction and comparison among the developers. By using this method, the main requirement by the buyer for their bungalow is their need of sufficient space to place their appliances in the house. At the end of the study, the details of buyer requirements are plotted into House of Quality, where it is believed to improve the quality of future bungalow house development in Melaka.

Matched MeSH terms: Forecasting
Is monetary variable a determinant in the ringgit-dollar exchange rates model?: a cointegration approach

Gharleghi B, Abu Hassan Shaari Md Nor

Sains Malaysiana, 2012;41:1163-1169.

The main aim of this paper was to validate the relative price monetary model (RPMM) of exchange rate determination for the Malaysian exchange rate (RM/USD) using monthly data set from 1986-2010. The Johansen multivariate cointegration test and vector error correction model were employed. Because the time period under consideration includes the South
East Asian financial crisis, the analysis is done using two time periods; the full time period as well as the period after the crisis. Two interesting results were observed from this empirical exercise. First, there is a long-run relationship between exchange rate and the selected macro variables only for the period after the crisis. Second, the forecasting performance of monetary approach based on the error correction model outperformed the Random Walk model.

Matched MeSH terms: Forecasting
Asymmetry dynamic volatility forecast evaluations using interday and intraday data

Chin WC, Chin WC, Zaidi Isa, Abu Hassan Shaari Mohd Nor

Sains Malaysiana, 2012;41:1287-1299.

The accuracy of financial time series forecasts often rely on the model precision and the availability of actual observations for forecast evaluations. This study aimed to tackle these issues in order to obtain a suitable asymmetric time-varying volatility model that outperformed in the forecast evaluations based on interday and intraday data. The model precision was examined based on the most appropriate time-varying volatility representation under the autoregressive conditional heteroscedascity framework. For forecast precision, the evaluations were conducted under three loss functions using the volatility proxies and realized volatility. The empirical studies were implemented on two major financial markets and the estimated results are applied in quantifying their market risks. Empirical results indicated that Zakoian model provided the best in-sample forecasts whereas DGE on the other hand indicated better out-of-sample forecasts. For the type of volatility proxy selection, the implementation of intraday data in the latent volatility indicated significant improvement in all the time horizon forecasts.

Matched MeSH terms: Forecasting
Regression and multivariate models for predicting particulate matter concentration level

Nazif A, Mohammed NI, Malakahmad A, Abualqumboz MS

Environ Sci Pollut Res Int, 2018 Jan;25(1):283-289.
PMID: 29032528 DOI: 10.1007/s11356-017-0407-2

The devastating health effects of particulate matter (PM10) exposure by susceptible populace has made it necessary to evaluate PM10 pollution. Meteorological parameters and seasonal variation increases PM10 concentration levels, especially in areas that have multiple anthropogenic activities. Hence, stepwise regression (SR), multiple linear regression (MLR) and principal component regression (PCR) analyses were used to analyse daily average PM10 concentration levels. The analyses were carried out using daily average PM10 concentration, temperature, humidity, wind speed and wind direction data from 2006 to 2010. The data was from an industrial air quality monitoring station in Malaysia. The SR analysis established that meteorological parameters had less influence on PM10 concentration levels having coefficient of determination (R 2) result from 23 to 29% based on seasoned and unseasoned analysis. While, the result of the prediction analysis showed that PCR models had a better R 2 result than MLR methods. The results for the analyses based on both seasoned and unseasoned data established that MLR models had R 2 result from 0.50 to 0.60. While, PCR models had R 2 result from 0.66 to 0.89. In addition, the validation analysis using 2016 data also recognised that the PCR model outperformed the MLR model, with the PCR model for the seasoned analysis having the best result. These analyses will aid in achieving sustainable air quality management strategies.

Matched MeSH terms: Forecasting
Fulltext Trends in Prevalence of Tobacco Use by Sex and Socioeconomic Status in 22 Sub-Saharan African Countries, 2003-2019

Sreeramareddy CT, Acharya K

JAMA Netw Open, 2021 12 01;4(12):e2137820.
PMID: 34878548 DOI: 10.1001/jamanetworkopen.2021.37820

Importance: Tobacco companies have shifted their marketing and production to sub-Saharan African countries, which are in an early stage of the tobacco epidemic.
Objective: To estimate changes in the prevalence of current tobacco use and socioeconomic inequalities among male and female participants from 22 sub-Saharan African countries from 2003 to 2019.
Design, Setting, and Participants: Secondary data analyses were conducted of sequential Demographic and Health Surveys in 22 sub-Saharan African countries including male and female participants aged 15 to 49 years. The baseline surveys (2003-2011) and the most recent surveys (2011-2019) were pooled.
Exposures: Household wealth index and highest educational level were the markers of inequality.
Main Outcomes and Measures: Sex-specific absolute and relative changes in age-standardized prevalence of current tobacco use in each country and absolute and relative measures of inequality using pooled data.
Results: The survey samples included 428 197 individuals (303 232 female participants [70.8%]; mean [SD] age, 28.6 [9.8] years) in the baseline surveys and 493 032 participants (348 490 female participants [70.7%]; mean [SD] age, 28.5 [9.4] years) in the most recent surveys. Both sexes were educated up to primary (35.7%) or secondary school (40.0%). The prevalence of current tobacco use among male participants ranged from 6.1% (95% CI, 5.2%-6.9%) in Ghana to 38.3% (95% CI, 35.8%-40.8%) in Lesotho in the baseline surveys and from 4.5% (95% CI, 3.7%-5.3%) in Ghana to 46.0% (95% CI, 43.2%-48.9%) in Lesotho during the most recent surveys. The decrease in prevalence ranged from 1.5% (Ghana) to 9.6% (Sierra Leone). The World Health Organization target of a 30% decrease in smoking was achieved among male participants in 8 countries: Rwanda, Nigeria, Ethiopia, Benin, Liberia, Tanzania, Burundi, and Cameroon. For female participants, the number of countries having a prevalence of smoking less than 1% increased from 9 in baseline surveys to 16 in the most recent surveys. The World Health Organization target of a 30% decrease in smoking was achieved among female participants in 15 countries: Cameroon, Namibia, Mozambique, Mali, Liberia, Nigeria, Burundi, Tanzania, Malawi, Kenya, Rwanda, Zimbabwe, Ethiopia, Burkina Faso, and Zambia. For both sexes, the prevalence of tobacco use and the decrease in prevalence of tobacco use were higher among less-educated individuals and individuals with low income. In both groups, the magnitude of inequalities consistently decreased, and its direction remained the same. Absolute inequalities were 3-fold higher among male participants, while relative inequalities were nearly 2-fold higher among female participants.
Conclusions and Relevance: Contrary to a projected increase, tobacco use decreased in most sub-Saharan African countries. Persisting socioeconomic inequalities warrant the stricter implementation of tobacco control measures to reach less-educated individuals and individuals with low income.

Matched MeSH terms: Forecasting
Trends in sustainable landfilling in Malaysia, a developing country

Fauziah SH, Agamuthu P

Waste Manag Res, 2012 Jul;30(7):656-63.
PMID: 22455994 DOI: 10.1177/0734242X12437564

In Malaysia, landfills are being filled up rapidly due to the current daily generation of approximately 30,000 tonnes of municipal solid waste. This situation creates the crucial need for improved landfilling practices, as sustainable landfilling technology is yet to be achieved here. The objective of this paper is to identify and evaluate the development and trends in landfilling practices in Malaysia. In 1970, the disposal sites in Malaysia were small and prevailing waste disposal practices was mere open-dumping. This network of relatively small dumps, typically located close to population centres, was considered acceptable for a relatively low population of 10 million in Malaysia. In the 1980s, a national programme was developed to manage municipal and industrial wastes more systematically and to reduce adverse environmental impacts. The early 1990s saw the privatization of waste management in many parts of Malaysia, and the establishment of the first sanitary landfills for MSW and an engineered landfill (called 'secure landfill' in Malaysia) for hazardous waste. A public uproar in 2007 due to contamination of a drinking water source from improper landfilling practices led to some significant changes in the government's policy regarding the country's waste management strategy. Parliament passed the Solid Waste and Public Cleansing Management (SWPCM) Act 2007 in August 2007. Even though the Act is yet to be implemented, the government has taken big steps to improve waste management system further. The future of the waste management in Malaysia seems somewhat brighter with a clear waste management policy in place. There is now a foundation upon which to build a sound and sustainble waste management and disposal system in Malaysia.

Matched MeSH terms: Forecasting
Fulltext Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting

Tariq MU, Ismail SB, Babar M, Ahmad A

PLoS One, 2023;18(7):e0287755.
PMID: 37471397 DOI: 10.1371/journal.pone.0287755

The pandemic has significantly affected many countries including the USA, UK, Asia, the Middle East and Africa region, and many other countries. Similarly, it has substantially affected Malaysia, making it crucial to develop efficient and precise forecasting tools for guiding public health policies and approaches. Our study is based on advanced deep-learning models to predict the SARS-CoV-2 cases. We evaluate the performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer Perceptron, Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN). We trained these models and assessed them using a detailed dataset of confirmed cases, demographic data, and pertinent socio-economic factors. Our research aims to determine the most reliable and accurate model for forecasting SARS-CoV-2 cases in the region. We were able to test and optimize deep learning models to predict cases, with each model displaying diverse levels of accuracy and precision. A comprehensive evaluation of the models' performance discloses the most appropriate architecture for Malaysia's specific situation. This study supports ongoing efforts to combat the pandemic by offering valuable insights into the application of sophisticated deep-learning models for precise and timely SARS-CoV-2 case predictions. The findings hold considerable implications for public health decision-making, empowering authorities to create targeted and data-driven interventions to limit the virus's spread and minimize its effects on Malaysia's population.

Matched MeSH terms: Forecasting
Fulltext Forecasting the effects of vaccination on the COVID-19 pandemic in Malaysia using SEIRV compartmental models

Lim MC, Singh S, Lai CH, Gill BS, Kamarudin MK, Md Zamri ASS, et al.

Epidemiol Health, 2023;45:e2023093.
PMID: 37905314 DOI: 10.4178/epih.e2023093

OBJECTIVES: This study aimed to develop susceptible-exposed-infectious-recovered-vaccinated (SEIRV) models to examine the effects of vaccination on coronavirus disease 2019 (COVID-19) case trends in Malaysia during Phase 3 of the National COVID-19 Immunization Program amidst the Delta outbreak.
METHODS: SEIRV models were developed and validated using COVID-19 case and vaccination data from the Ministry of Health, Malaysia, from June 21, 2021 to July 21, 2021 to generate forecasts of COVID-19 cases from July 22, 2021 to December 31, 2021. Three scenarios were examined to measure the effects of vaccination on COVID-19 case trends. Scenarios 1 and 2 represented the trends taking into account the earliest and latest possible times of achieving full vaccination for 80% of the adult population by October 31, 2021 and December 31, 2021, respectively. Scenario 3 described a scenario without vaccination for comparison.
RESULTS: In scenario 1, forecasted cases peaked on August 28, 2021, which was close to the peak of observed cases on August 26, 2021. The observed peak was 20.27% higher than in scenario 1 and 10.37% lower than in scenario 2. The cumulative observed cases from July 22, 2021 to December 31, 2021 were 13.29% higher than in scenario 1 and 55.19% lower than in scenario 2. The daily COVID-19 case trends closely mirrored the forecast of COVID-19 cases in scenario 1 (best-case scenario).
CONCLUSIONS: Our study demonstrated that COVID-19 vaccination reduced COVID-19 case trends during the Delta outbreak. The compartmental models developed assisted in the management and control of the COVID-19 pandemic in Malaysia.

Matched MeSH terms: Forecasting
Time series analysis of surface ozone monitoring records in Kemaman, Malaysia

Marzuki Ismail, Mohd Zamri Ibrahim, Tg. Azmina Ibrahim, Ahmad Makmon Abdullah

Sains Malaysiana, 2011;40:1179-1186.

Time series analysis and forecasting has become a major tool in many applications in air pollution and environmental management fields. Among the most effective approaches for analyzing time series data is the model introduced by Box and Jenkins, ARIMA (Autoregressive Integrated Moving Average). In this study we used Box-Jenkins methodology to build ARIMA model for monthly ozone data taken from an Automatic Air Quality Monitoring System in Kemaman station for the period from 1996 to 2007 with a total of 144 readings. Parametric seasonally adjusted ARIMA (0,1,1) (1,1,2)12 model was successfully applied to predict the long-term trend of ozone concentration. The detection of a steady statistical significant upward trend for ozone concentration in Kemaman is quite alarming. This is likely due to sources of ozone precursors related to industrial activities from nearby areas and the increase in road traffic volume.

Matched MeSH terms: Forecasting
Groundwater level forecasting with machine learning models: A review

Boo KBW, El-Shafie A, Othman F, Khan MMH, Birima AH, Ahmed AN

Water Res, 2024 Mar 15;252:121249.
PMID: 38330715 DOI: 10.1016/j.watres.2024.121249

Groundwater, the world's most abundant source of freshwater, is rapidly depleting in many regions due to a variety of factors. Accurate forecasting of groundwater level (GWL) is essential for effective management of this vital resource, but it remains a complex and challenging task. In recent years, there has been a notable increase in the use of machine learning (ML) techniques to model GWL, with many studies reporting exceptional results. In this paper, we present a comprehensive review of 142 relevant articles indexed by the Web of Science from 2017 to 2023, focusing on key ML models, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), evolutionary computing (EC), deep learning (DL), ensemble learning (EN), and hybrid-modeling (HM). We also discussed key modeling concepts such as dataset size, data splitting, input variable selection, forecasting time-step, performance metrics (PM), study zones, and aquifers, highlighting best practices for optimal GWL forecasting with ML. This review provides valuable insights and recommendations for researchers and water management agencies working in the field of groundwater management and hydrology.

Matched MeSH terms: Forecasting
Fulltext Drought characterisation in Peninsular Malaysia using DrinC software

Md. Munir Hayet Khan, Nur Shazwani Muhammad, Ahmed El-Shafie

Pertanika Journal of Science & Technology, 2017;25(105):81-90.
MyJurnal

Prolonged drought conditions have adverse environmental and socio-economic impacts due to unmet water demands. Defining drought is difficult because of its onset and ending time. Therefore, characterisation of drought is essential for drought management operations. Thus, drought indices come in handy and are a practical approach to assimilate large amounts of data into quantitative information which can then be applied for drought forecasting, declaring drought levels, contingency planning and impact assessments. This study analyses drought events using indices, namely SPI and Deciles Index, computed with DrinC software program but are not popular in Malaysia. It is observed that both indices are identical and suitable for drought occurrences.

Matched MeSH terms: Forecasting
Transformation of nitrogen dioxide into ozone and prediction of ozone concentrations using multiple linear regression techniques

Ghazali NA, Ramli NA, Yahaya AS, Yusof NF, Sansuddin N, Al Madhoun WA

Environ Monit Assess, 2010 Jun;165(1-4):475-89.
PMID: 19440846 DOI: 10.1007/s10661-009-0960-3

Analysis and forecasting of air quality parameters are important topics of atmospheric and environmental research today due to the health impact caused by air pollution. This study examines transformation of nitrogen dioxide (NO(2)) into ozone (O(3)) at urban environment using time series plot. Data on the concentration of environmental pollutants and meteorological variables were employed to predict the concentration of O(3) in the atmosphere. Possibility of employing multiple linear regression models as a tool for prediction of O(3) concentration was tested. Results indicated that the presence of NO(2) and sunshine influence the concentration of O(3) in Malaysia. The influence of the previous hour ozone on the next hour concentrations was also demonstrated.

Matched MeSH terms: Forecasting
Fulltext Improving forecasting accuracy for stock market data using EMD-HW bagging

Awajan AM, Ismail MT, Al Wadi S

PLoS One, 2018;13(7):e0199582.
PMID: 30016323 DOI: 10.1371/journal.pone.0199582

Many researchers documented that the stock market data are nonstationary and nonlinear time series data. In this study, we use EMD-HW bagging method for nonstationary and nonlinear time series forecasting. The EMD-HW bagging method is based on the empirical mode decomposition (EMD), the moving block bootstrap and the Holt-Winter. The stock market time series of six countries are used to compare EMD-HW bagging method. This comparison is based on five forecasting error measurements. The comparison shows that the forecasting results of EMD-HW bagging are more accurate than the forecasting results of the fourteen selected methods.

Matched MeSH terms: Forecasting*
Implications of climate change damage for agriculture: sectoral evidence from Pakistan

Ahmed A, Devadason ES, Al-Amin AQ

Environ Sci Pollut Res Int, 2016 Oct;23(20):20688-20699.
PMID: 27473615

This paper gives a projection of the possible damage of climate change on the agriculture sector of Pakistan for the period 2012-2037, based on a dynamic approach, using an environment-related applied computable general equilibrium model (CGE). Climate damage projections depict an upward trend for the period of review and are found to be higher than the global average. Further, the damage to the agricultural sector exceeds that for the overall economy. By sector, climatic damage disproportionately affects the major and minor crops, livestock and fisheries. The largest losses following climate change, relative to the other agricultural sectors, are expected for livestock. The reason for this is the orthodox system of production for livestock, with a low adaptability to negative shocks of climate change. Overall, the findings reveal the high exposure of the agriculture sector to climate damage. In this regard, policymakers in Pakistan should take seriously the effects of climate change on agriculture and consider suitable technology to mitigate those damages.

Matched MeSH terms: Forecasting
Fulltext Forecasting stock prices changes using long-short term memory neural network with symbolic genetic programming

Li Q, Kamaruddin N, Yuhaniz SS, Al-Jaifi HAA

Sci Rep, 2024 Jan 03;14(1):422.
PMID: 38172568 DOI: 10.1038/s41598-023-50783-0

This study introduces an augmented Long-Short Term Memory (LSTM) neural network architecture, integrating Symbolic Genetic Programming (SGP), with the objective of forecasting cross-sectional price returns across a comprehensive dataset comprising 4500 listed stocks in the Chinese market over the period from 2014 to 2022. Using the S&P Alpha Pool Dataset for China as basic input, this architecture incorporates data augmentation and feature extraction techniques. The result of this study demonstrates significant improvements in Rank Information coefficient (Rank IC) and IC information ratio (ICIR) by 1128% and 5360% respectively when it is applied to fundamental indicators. For technical indicators, the hybrid model achieves a 206% increase in Rank IC and an impressive surge of 2752% in ICIR. Furthermore, the proposed hybrid SGP-LSTM model outperforms major Chinese stock indexes, generating average annualized excess returns of 31.00%, 24.48%, and 16.38% compared to the CSI 300 index, CSI 500 index, and the average portfolio, respectively. These findings highlight the effectiveness of SGP-LSTM model in improving the accuracy of cross-sectional stock return predictions and provide valuable insights for fund managers, traders, and financial analysts.

Matched MeSH terms: Forecasting
Fulltext Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry-A Systematic Review

Ahmed N, Abbasi MS, Zuberi F, Qamar W, Halim MSB, Maqsood A, et al.

Biomed Res Int, 2021;2021:9751564.
PMID: 34258283 DOI: 10.1155/2021/9751564

Objective: The objective of this systematic review was to investigate the quality and outcome of studies into artificial intelligence techniques, analysis, and effect in dentistry.
Materials and Methods: Using the MeSH keywords: artificial intelligence (AI), dentistry, AI in dentistry, neural networks and dentistry, machine learning, AI dental imaging, and AI treatment recommendations and dentistry. Two investigators performed an electronic search in 5 databases: PubMed/MEDLINE (National Library of Medicine), Scopus (Elsevier), ScienceDirect databases (Elsevier), Web of Science (Clarivate Analytics), and the Cochrane Collaboration (Wiley). The English language articles reporting on AI in different dental specialties were screened for eligibility. Thirty-two full-text articles were selected and systematically analyzed according to a predefined inclusion criterion. These articles were analyzed as per a specific research question, and the relevant data based on article general characteristics, study and control groups, assessment methods, outcomes, and quality assessment were extracted.
Results: The initial search identified 175 articles related to AI in dentistry based on the title and abstracts. The full text of 38 articles was assessed for eligibility to exclude studies not fulfilling the inclusion criteria. Six articles not related to AI in dentistry were excluded. Thirty-two articles were included in the systematic review. It was revealed that AI provides accurate patient management, dental diagnosis, prediction, and decision making. Artificial intelligence appeared as a reliable modality to enhance future implications in the various fields of dentistry, i.e., diagnostic dentistry, patient management, head and neck cancer, restorative dentistry, prosthetic dental sciences, orthodontics, radiology, and periodontics.
Conclusion: The included studies describe that AI is a reliable tool to make dental care smooth, better, time-saving, and economical for practitioners. AI benefits them in fulfilling patient demand and expectations. The dentists can use AI to ensure quality treatment, better oral health care outcome, and achieve precision. AI can help to predict failures in clinical scenarios and depict reliable solutions. However, AI is increasing the scope of state-of-the-art models in dentistry but is still under development. Further studies are required to assess the clinical performance of AI techniques in dentistry.

Matched MeSH terms: Forecasting

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