8,670 research outputs found
Multivariate time series analysis for short-term forecasting of ground level ozone (O3) in Malaysia
The declining of air quality mostly affects the elderly, children, people with asthma,
as well as a restriction on outdoor activities. Therefore, there is an importance to
provide a statistical modelling to forecast the future values of surface layer ozone (O3)
concentration. The objectives of this study are to obtain the best multivariate time
series (MTS) model and develop an online air quality forecasting system for O3
concentration in Malaysia. The implementations of MTS model improve the recent
statistical model on air quality for short-term prediction. Ten air quality monitoring
stations situated at four (4) different types of location were selected in this study. The
first type is industrial represent by Pasir Gudang, Perai, and Nilai, second type is urban
represent by Kuala Terengganu, Kota Bharu, and Alor Setar. The third is suburban
located in Banting, Kangar, and Tanjung Malim, also the only background station at
Jerantut. The hourly record data from 2010 to 2017 were used to assess the
characteristics and behaviour of O3 concentration. Meanwhile, the monthly record data
of O3, particulate matter (PM10), nitrogen dioxide (NO2), sulphur dioxide (SO2),
carbon monoxide (CO), temperature (T), wind speed (WS), and relative humidity (RH)
were used to examine the best MTS models. Three methods of MTS namely vector
autoregressive (VAR), vector moving average (VMA), and vector autoregressive
moving average (VARMA), has been applied in this study. Based on the performance
error, the most appropriate MTS model located in Pasir Gudang, Kota Bharu and
Kangar is VAR(1), Kuala Terengganu and Alor Setar for VAR(2), Perai and Nilai for
VAR(3), Tanjung Malim for VAR(4) and Banting for VAR(5). Only Jerantut obtained
the VMA(2) as the best model. The lowest root mean square error (RMSE) and
normalized absolute error is 0.0053 and <0.0001 which is for MTS model in Perai and
Kuala Terengganu, respectively. Meanwhile, for mean absolute error (MAE), the
lowest is in Banting and Jerantut at 0.0013. The online air quality forecasting system
for O3 was successfully developed based on the best MTS models to represent each
monitoring station
Causative factors of construction and demolition waste generation in Iraq Construction Industry
The construction industry has hurt the environment from the waste generated during
construction activities. Thus, it calls for serious measures to determine the causative
factors of construction waste generated. There are limited studies on factors causing
construction, and demolition (C&D) waste generation, and these limited studies only
focused on the quantification of construction waste. This study took the opportunity to
identify the causative factors for the C&D waste generation and also to determine the
risk level of each causal factor, and the most important minimization methods to
avoiding generating waste. This study was carried out based on the quantitative
approach. A total of 39 factors that causes construction waste generation that has been
identified from the literature review were considered which were then clustered into 4
groups. Improved questionnaire surveys by 38 construction experts (consultants,
contractors and clients) during the pilot study. The actual survey was conducted with
a total of 380 questionnaires, received with a response rate of 83.3%. Data analysis
was performed using SPSS software. Ranking analysis using the mean score approach
found the five most significant causative factors which are poor site management, poor
planning, lack of experience, rework and poor controlling. The result also indicated
that the majority of the identified factors having a high-risk level, in addition, the better
minimization method is environmental awareness. A structural model was developed
based on the 4 groups of causative factors using the Partial Least Squared-Structural
Equation Modelling (PLS-SEM) technique. It was found that the model fits due to the
goodness of fit (GOF ≥ 0.36= 0.658, substantial). Based on the outcome of this study,
39 factors were relevant to the generation of construction and demolition waste in Iraq.
These groups of factors should be avoided during construction works to reduce the
waste generated. The findings of this study are helpful to authorities and stakeholders
in formulating laws and regulations. Furthermore, it provides opportunities for future
researchers to conduct additional research’s on the factors that contribute to
construction waste generation
A Study in Image Watermarking Schemes using Neural Networks
The digital watermarking technique, an effective way to protect image, has become the research focus on neural network. The purpose of this paper is to provide a brief study on broad theories and discuss the different types of neural networks for image watermarking. Most of the research interest image watermarking based on neural network in discrete wavelet transform or discrete cosine transform. Generally image watermarking based on neural network to solve the problem on to reduce the error, improve the rate of the learning, achieves goods imperceptibility and robustness. It will be useful for researches to implement effective image watermarking by using neural network
An innovative metaheuristic strategy for solar energy management through a neural networks framework
Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development
Development of soft computing and applications in agricultural and biological engineering
Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
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