Manufacturing Of Robust Natural Fiber Preforms Utilizing Bacterial Cellulose as Binder

We present a novel method of manufacturing rigid and robust short natural fiber preforms using a papermaking process. Bacterial cellulose acts simultaneously as the binder for the loose fibers and provides rigidity to the fiber preforms. These preforms can be infused with a resin to produce truly green hierarchical composites

Embedded Scale United Moment Invariant for Identification of Handwriting Individuality

Past few years, a lot of research on moment functions have been explored in pattern recognition. Several new techniques have been investigated to improve conventional regular moment by proposing the scaling factor of geometrical function. In this paper, integrated scaling formulations of Aspect Invariant Moment and Higher Order Scaling Invariant with United Moment Invariant are presented in Writer Identification to seek the invarianceness of authorship or individuality of handwriting perseverance. Mathematical proving and results of computer simulations are included to verify the validity of the proposed technique in identifying eccentricity of the author in Writer Identification

DISCRETIZATION OF INTEGRATED MOMENT INVARIANTS FOR WRITER IDENTIFICATION

Conservative regular moments have been proven to exhibit some shortcomings in the original formulations of moment functions in terms of scaling factor. Hence, an incorporated scaling factor of geometric functions into United Moment Invariant function is proposed for mining the feature of unconstrained words. Subsequently, the discrete proposed features undertake discretization procedure prior to classification for better feature representation and splendid classification accuracy. Collectively, discrete values are finite intervals in a continuous spectrum of values and well known to play important roles in data mining and knowledge discovery. Many induction algorithms found in the literature requires that training data contains only discrete features and some works better on discretized data; in particular rule based approaches like rough sets. Hence, in this study, an integrated scaling formulation of Aspect Scaling Invariant is presented in Writer Identification to hunt for the individuality perseverance. Successive exploration is executed to investigate for the suitability of discretization techniques in probing the issues of writer authorship. Mathematical proving and results of computer simulations are embraced to attest the feasibility of the proposed technique in Writer Identification. The results disclose that the proposed discretized invariants reveal 99% accuracy of classification by using 3520 training data and 880 testing data

Embedded Scale United Moment Invariant for Identification of Handwriting Individuality

Past few years, a lot of research on moment functions have been explored in pattern recognition. Several new techniques have been investigated to improve conventional regular moment by proposing the scaling factor of geometrical function. In this paper, integrated scaling formulations of Aspect Invariant Moment and Higher Order Scaling Invariant with United Moment Invariant are presented in Writer Identification to seek the invarianceness of authorship or individuality of handwriting perseverance. Mathematical proving and results of computer simulations are included to verify the validity of the proposed technique in identifying eccentricity of the author in Writer Identification

Computerization family counseling: Is it practical to building the welfare and happiness of Muslim families in Malaysia

Family consultation services provided by Malaysian Islamic Religious Offices have emerged as a highly beneficial and practical approach to addressing family difficulties, enhancing well-being, and achieving positive outcomes for Muslim marriages in Malaysia. These services play a vital role in nurturing healthy family relationships and ensuring the overall welfare of Muslim families within the context of religious beliefs. This research seeks to assess the practicality of these services in the current context and propose solutions to enhance overall service delivery. While family consultation holds significant potential, it is essential to address its practical challenge with a focus on its service accessibility. This research attempts to illuminate the usefulness of family consultation services provided by Malaysian Islamic Religious Offices, contributing to the ongoing discourse on its effectiveness for Muslims in the present time. The findings of this study can assist in enhancing the practicality and maximizing the benefits of family consultation services provided by the authority. By gaining insights into the strengths, limitations, and benefits and fostering positive impact, counselors can devise strategies to better meet the needs of Muslim families seeking support. Family consultation services also can continue to contribute to the overall well-being and happiness of Muslim families in Malaysia

Selection of classification models from repository of model for water quality dataset

This paper proposes a new technique, Model Selection Technique (MST) for selection andranking of models from the repository of models by combining three performance measures(Acc, TPR and TNR). This technique provides weightage to each performance measure to findthe most suitable model from the repository of models. A number of classification modelshave been generated to classify water quality using the most significant features andclassifiers such as J48, JRip and BayesNet. To validate this technique proposed, the waterquality dataset of Kinta River was used in this research. The results demonstrate that theFunction classifier is the optimal model with the most outstanding accuracy of 97.02%, TPR =0.96 and TNR = 0.98. In conclusion, MST is able to find the most relevant model from therepository of models by using weights in classifying the water quality dataset.Keywords: selection of models; water quality; classification model; models repository

Bio-Inspired Generalized Global Shape Approach for Writer Identification

Abstract—Writer identification is one of the areas in pattern recognition that attract many researchers to work in, particularly in forensic and biometric application, where the writing style can be used as biometric features for authenticating an identity. The challenging task in writer identification is the extraction of unique features, in which the individualistic of such handwriting styles can be adopted into bio-inspired generalized global shape for writer identification. In this paper, the feasibility of generalized global shape concept of complimentary binding in Artificial Immune System (AIS) for writer identification is explored. An experiment based on the proposed framework has been conducted to proof the validity and feasibility of the proposed approach for off-line writer identification

Study of heterosis in Bangladeshi chilli (Capsicum annuum L.) landraces

Chilli is an important cash crop in Bangladesh but average yield is very low (0.89 t ha-1) and genetic potentiality of Bangladeshi chilli landraces for hybrid variety development has not been evaluated. The objective of this study was to find out heterotic behavior following Gardner and Eberhart model (1966) II. Six different homozygous divergent parents CCA 2, CCA 5, BARI Morich 1, CCA 11, CCA 15 and CCA 19 were used to estimate heterosis. A significant amount of heterosis was present in yield and yield contributing traits. Estimate of variety heterosis for yield per plant was significantly positive in CCA 5 and BARI Morich 1. In BARI Morich 1, the significant and positive variety heterosis for yield per plant was associated with significant and positive estimates of heterosis for number of fruits per plant and number of seeds per fruit, suggesting that these yield traits contributed to the final heterosis manifested through yield. Indigenous×exotic crosses showed significant amount of heterosis. It is possible to emphasize indigenous×exotic crosses for good fruit yield, particularly to be used as commercial hybrids. Hybrids of BARI Morich 1×CCA 19 and CCA 5×BARI Morich 1 showed better performance

Recognition of Contour Invariants with NeuroFuzzy Classifier

In this study, we explore contour invariants for handwritten digits recognitions with neuro-fuzzy classifier. We use fuzzy triangular function in backpropagation network to initialize the weights. The results reveal that fuzzy triangular membership function manages to decrease the network convergence rate with proper parameter setting. In this study, unthinned images are appropriate for training and classification purpose as it preserves the images significant features. From our experiments, the results show that contour invariants exhibits highest rate of classification compares to geometric and zernike invariants