Spatial and temporal torrential rainfall guided cluster pattern based on dimension reduction methods

This thesis identifies the spatial and temporal cluster patterns for torrential rainfall data in Peninsular Malaysia. Two dimension reduction methods are used to improve the cluster patterns of the torrential rainfall data. Firstly, a robust dimension reduction method in Principal Component Analysis (PCA) is used to rectify the issue of unbalanced clusters in rainfall patterns due to the skewed nature of rainfall data. A robust measure in PCA using Tukey’s biweight correlation to downweigh observations is introduced and the optimum breakdown point to extract the number of components in PCA using this approach is proposed. The simulated data indicates a breakdown optimum point of at 70% cumulative percentage of variance to give a good balance in extracting the number of components to avoid variations of low frequency or insignificant spatial scale in the clusters. The results show a significance improvement with the robust PCA than the PCA based Pearson correlation in terms of the average number of clusters obtained and its cluster quality. Secondly, based on the decomposing properties in Singular Spectrum Analysis (SSA), a two-way approach to identify the range of local time scale for a cluster of torrential rainfall pattern by discriminating the noise in a time series trend is introduced. Firstly, appropriate window length for the trajectory matrix and adjustments on the coinciding singular values obtained from the decomposed time series matrix based on a restricted singular value decomposition (SVD) using iterative oblique SSA (Iterative O-SSA) is proposed. In addition, a guided clustering method called Robust Sparse k-means (RSk-means) to discriminate the eigenvectors from this iterative procedure is suggested to identify the trend and noise components more objectively. The modified SSA indicates strongest separability between the reconstructed components based on a simulated skewed and short time series rainfall data to effectively identify the local time scale

A new hyhbrid coefficient of conjugate gradient method

Hybridization is one of the popular approaches in modifying the conjugate gradient method. In this paper, a new hybrid conjugate gradient is suggested and analyzed in which the parameter is evaluated as a convex combination of  while using exact line search. The proposed method is shown to possess both sufficient descent and global convergence properties. Numerical performances show that the proposed method is promising and has overpowered other hybrid conjugate gradient methods in its number of iterations and central processing unit per time.

A comparison on classical-hybrid conjugate gradient method under exact line search

One of the popular approaches in modifying the Conjugate Gradient (CG) Method is hybridization. In this paper, a new hybrid CG is introduced and its performance is compared to the classical CG method which are Rivaie-Mustafa-Ismail-Leong (RMIL) and Syarafina-Mustafa-Rivaie (SMR) methods. The proposed hybrid CG is evaluated as a convex combination of RMIL and SMR method. Their performance are analyzed under the exact line search. The comparison performance showed that the hybrid CG is promising and has outperformed the classical CG of RMIL and SMR in terms of the number of iterations and central processing unit per time

An efficient method to improve the clustering performance using hybrid robust principal component analysis-spectral biclustering in rainfall patterns identification

In this study, hybrid RPCA-spectral biclustering model is proposed in identifying the Peninsular Malaysia rainfall pattern. This model is a combination between Robust Principal Component Analysis (RPCA) and biclustering in order to overcome the skewness problem that existed in the Peninsular Malaysia rainfall data. The ability of Robust PCA is more resilient to outlier given that it assesses every observation and downweights the ones which deviate from the data center compared to classical PCA. Meanwhile, two way-clustering able to simultaneously cluster along two variables and exhibit a high correlation compared to one-way cluster analysis. The experimental results showed that the best cumulative percentage of variation in between 65%-70% for both Robust and classical PCA. Meanwhile, the number of clusters has improved from six disjointed cluster in Robust PCA-kMeans to eight disjointed cluster for the proposed model. Further analysis shows that the proposed model has smaller variation with the values of 0.0034 compared to 0.030 in Robust PCAkMeans model. Evident from this analysis, it is proven that the proposed RPCA-spectral biclustering model is predominantly acclimatized to the identifying rainfall patterns in Peninsular Malaysia due to the small variation of the clustering result

Identification of rainfall patterns on hydrological simulation using robust principal component analysis

A robust dimension reduction method in Principal Component Analysis (PCA) was used to rectify the issue of unbalanced clusters in rainfall patterns due to the skewed nature of rainfall data. A robust measure in PCA using Tukey’s biweight correlation to downweigh observations was introduced and the optimum breakdown point to extract the number of components in PCA using this approach is proposed. A set of simulated data matrix that mimicked the real data set was used to determine an appropriate breakdown point for robust PCA and compare the performance of the both approaches. The simulated data indicated a breakdown point of 70% cumulative percentage of variance gave a good balance in extracting the number of components. The results showed a more significant and substantial improvement with the robust PCA than the PCA based Pearson correlation in terms of the average number of clusters obtained and its cluster quality

Internet of things (IoT); security requirements, attacks and counter measures

Internet of Things (IoT) is a network of connected and communicating nodes. Recent developments in IoT have led to advancements like smart home, industrial IoT and smart healthcare etc. This smart life did bring security challenges along with numerous benefits. Monitoring and control in IoT is done using smart phone and web browsers easily. There are different attacks being launched on IoT layers on daily basis and to ensure system security there are seven basic security requirements which must be met. Here we have used these requirements for classification and subdivided them on the basis of attacks, followed by degree of their severity, affected system components and respective countermeasures. This work will not only give guidelines regarding detection and removal of attacks but will also highlight the impact of these attacks on system, which will be a decision point to safeguard system from high impact attacks on priority basis

Análisis estadístico multivariado de los aceites esenciales de especies de Beilschmiedia de Malasia peninsular

Identification of the chemical composition of essential oils is very important for ensuring the quality of finished herbal products. The objective of the study was to analyze the chemical components present in the essential oils of five Beilschmiedia species (i.e. B. kunstleri, B. maingayi, B. penangiana, B. madang, and B. glabra) by multivariate data analysis using principal component analysis (PCA) and hierarchical clustering analysis (HCA) methods. The essential oils were obtained by hydrodistillation and fully characterized by gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS). A total of 108 chemical components were successfully identified from the essential oils of five Beilschmiedia species. The essential oils were characterized by high proportions of β-caryophyllene (B. kunstleri), δ-cadinene (B. penangiana and B. madang), and β-eudesmol (B. maingayi and B. glabra). Principal component analysis (PCA) and hierarchical cluster analysis (HCA) revealed that chemical similarity was highest for all samples, except for B. madang. The multivariate data analysis may be used for the identification and characterization of essential oils from different Beilschmiedia species that are to be used as raw materials of traditional herbal products.La identificación de la composición química de los aceites esenciales es muy importante para garantizar la calidad de los productos herbales terminados. El objetivo del estudio fue analizar los componentes químicos presentes en los aceites esenciales de cinco especies de Beilschmiedia (B. kunstleri, B. maingayi, B. penangiana, B. madang y B. glabra) mediante análisis de datos multivariados utilizando los métodos de análisis de componente principal (PCA) y análisis de agrupamiento jerárquico (HCA). Los aceites esenciales se obtuvieron por hidrodestilación y se caracterizaron completamente por cromatografía de gases (GC) y cromatografía de gases-espectrometría de masas (GC-MS). Se identificaron con éxito un total de 108 componentes químicos a partir de los aceites esenciales de las cinco especies de Beilschmiedia. Los aceites esenciales se caracterizaron por altas proporciones de β-cariofileno (B. kunstleri), δ-cadineno (B. penangiana y B. madang) y β-eudesmol (B. maingayi y B. glabra). El análisis de componentes principales (PCA) y el análisis de conglomerados jerárquicos (HCA) revelaron que la similitud química fue más alta para todas las muestras, excepto para B. madang. El análisis de datos multivariados puede usarse para la identificación y caracterización de aceites esenciales de diferentes especies de Beilschmiedia que se utilizan como materias primas de productos herbales tradicionales

Performance comparison of group chain sampling plan and modified group chain sampling plan based on mean product lifetime for rayleigh distribution

The performance of a sampling plan from group sampling family is measured by its minimum number of groups and probability of lot acceptance. Basically, once the minimum number of groups is determined, the corresponding probability of acceptance can be obtained for various sets of design parameters. This article compares the performance of two acceptance sampling plans namely group chain sampling plan (GChSP) and modified group chain sampling plan (MGChSP-1) based on the mean product lifetime for Rayleigh distribution. GChSP and MGShSP were developed based on the operating procedure in both chain sampling plan (1955) and group sampling plan (2009). The findings proved that the MGChSP performed better than the GChSP

Development and validation of early childhood care and education pre-service lecturer instrument

This paper presents to develop and validate the Early Childhood Care and Pre-Service Lecturer Instrument constructed to determine their level of competencies toward the quality of early childhood carers-educators’ professionalism in Malaysia. Components which affect the early childhood quality were characterized through inclusive literature reviews alongside interviews conducted with experts and experienced lecturers. In this study, two experts were elected to review this instrument so as to enhance its validity while 70 more lecturers in Malaysia were involved. There are four scales in principal component analysis pertaining the quality of early childhood professionalism, namely: (1) disposition, (2) knowledge, (3) skills, and (4) practices. The component loading range or respective instrument item were between 0.56 and 0.79, while the range for respective scales the alpha reliability coefficient were between 0.90 and 0.94. Concisely, the findings from this study corroborated the weight and consistency of the ECCE Pre-Service Lecturer Instrument

A comparative study of different imputation methods for daily rainfall data in east-coast Peninsular Malaysia

Rainfall data are the most significant values in hydrology and climatology modelling. However, the datasets are prone to missing values due to various issues. This study aspires to impute the rainfall missing values by using various imputation method such as Replace by Mean, Nearest Neighbor, Random Forest, Non-linear Interactive Partial Least-Square (NIPALS) and Markov Chain Monte Carlo (MCMC). Daily rainfall datasets from 48 rainfall stations across east-coast Peninsular Malaysia were used in this study. The dataset were then fed into Multiple Linear Regression (MLR) model. The performance of abovementioned methods were evaluated using Root Mean Square Method (RMSE), Mean Absolute Error (MAE) and Nash-Sutcliffe Efficiency Coefficient (CE). The experimental results showed that RF coupled with MLR (RF-MLR) approach was attained as more fitting for satisfying the missing data in east-coast Peninsular Malaysia