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Abstract

Details

Machine Learning and Artificial Intelligence in Marketing and Sales
Type: Book
ISBN: 978-1-80043-881-1

Book part
Publication date: 23 June 2016

Yulia Kotlyarova, Marcia M. A. Schafgans and Victoria Zinde-Walsh

For kernel-based estimators, smoothness conditions ensure that the asymptotic rate at which the bias goes to zero is determined by the kernel order. In a finite sample…

Abstract

For kernel-based estimators, smoothness conditions ensure that the asymptotic rate at which the bias goes to zero is determined by the kernel order. In a finite sample, the leading term in the expansion of the bias may provide a poor approximation. We explore the relation between smoothness and bias and provide estimators for the degree of the smoothness and the bias. We demonstrate the existence of a linear combination of estimators whose trace of the asymptotic mean-squared error is reduced relative to the individual estimator at the optimal bandwidth. We examine the finite-sample performance of a combined estimator that minimizes the trace of the MSE of a linear combination of individual kernel estimators for a multimodal density. The combined estimator provides a robust alternative to individual estimators that protects against uncertainty about the degree of smoothness.

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Essays in Honor of Aman Ullah
Type: Book
ISBN: 978-1-78560-786-8

Keywords

Article
Publication date: 3 December 2021

Mohd Aaqib Sheikh, Charanjiv Singh Saini and Harish Kumar Sharma

The study was aimed to explore the potential impact of microwave heating (450 W for 2, 4, 6 and 8 min) on antioxidant activity, anti-nutritional factors, volatile and…

Abstract

Purpose

The study was aimed to explore the potential impact of microwave heating (450 W for 2, 4, 6 and 8 min) on antioxidant activity, anti-nutritional factors, volatile and phenolic compounds of the plum kernels.

Design/methodology/approach

Plum kernels are rich in proteins, lipids and bioactive compounds that are mostly underused and undervalued.

Findings

The results showed that microwave heating at 450 W for 6 min significantly (p < 0.05) increased the antioxidant activity, total phenolic and flavonoid content, while the longer treatment time (450 W for 8 min) adversely affected the phenolic compounds. Most importantly, the anti-nutritional factors like amygdalin, hydrocyanic acid, phytic acid and tannin content were reduced up to 87.1, 84.7, 20.9 and 46.2%, respectively at 450 W for 6 min treatment conditions, which was confirmed from the larger shifts observed in FT-IR spectra near 1,157 cm−1. Microwave heating at 450 W for 6 min also proved beneficial in improving the bioavailability of volatile and phenolic compounds including chlorogenic acid, gallic acid, syringic acid, (+)-catechin, caffeic acid, ß-carotene, trans-ferulic acid, rutin trihydrate, 3,4-dihydroxybenzoic acid, tannic acid and quercetin by liberating them from the plant matrix.

Originality/value

The results thus indicate that controlled microwave heating could be an effective approach for the reduction of anti-nutritional factors besides leading to an overall improvement in antioxidant potential and volatile and phenolic compounds. This novel technological approach can proliferate the use of plum kernels in different diversified food formulations.

Details

British Food Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0007-070X

Keywords

Article
Publication date: 9 November 2015

Babatunde Sunday Ogunsina, Motunrayo Funke Olumakaiye, Chiemela Eyinnaya Chinma and Opeyemi Paul Akomolafe

This paper aims to investigate the effect of hydrothermal treatment by conventional, microwave and pressure cooking on the cooking properties, proximate composition and…

Abstract

Purpose

This paper aims to investigate the effect of hydrothermal treatment by conventional, microwave and pressure cooking on the cooking properties, proximate composition and organoleptic characteristics of dehulled Moringa oleifera seeds.

Design/methodology/approach

Samples of Moringa oleifera seeds were subjected to cooking for the minimum time by each of the methods under study. Cooking properties, proximate composition and organoleptic characteristics were determined following standard analytical procedures.

Findings

The results showed that the average cooking time were 25, 30 and 40 mins for conventionally, pressure- and microwave-cooked moringa kernels, respectively. There was no significant difference in cooking weight, moisture absorbed and water uptake ratio of conventionally and pressure-cooked samples. The protein content of moringa kernel reduced from 41.9 for raw kernels to 40.2, 41.2 and 36.9 per cent for conventionally, pressure- and microwave-cooked samples, respectively. Pressure and microwave cooking indicated 40.1 and 39.3 g/100g of crude fat, whereas raw kernels indicated 37.1 g/100g. Conventionally and pressure-cooked kernels had lower fibre content than the raw kernels, but there was no significant difference in the ash contents of the samples. Cooking influenced the proximate composition and colour of moringa kernels. Microwave cooking indicated higher values of cookability than other cooking methods considered in this study, but no significant difference was observed in the organoleptic characteristics of moringa kernels due to the cooking methods.

Practical implications

Given the high protein and vital nutrients content which are seldom found in daily diets, moringa kernels may be considered by processors of edible nuts and kernels for food-based applications such as cooked, roasted, mixed or spiced kernels.

Originality/value

This work is perhaps the first to document moringa seeds processing by hydrothermal treatment.

Details

Nutrition & Food Science, vol. 45 no. 6
Type: Research Article
ISSN: 0034-6659

Keywords

Article
Publication date: 6 March 2017

Ayodeji Bolaji Ilori, Akinloye Lawal and Olayemi Oladehinde Simeon-Oke

This study aims to examine the innovations available in the small-scale palm kernel processing firms in southwestern Nigeria. The sample population of 265 respondents was…

1102

Abstract

Purpose

This study aims to examine the innovations available in the small-scale palm kernel processing firms in southwestern Nigeria. The sample population of 265 respondents was obtained through respondent-driven sampling tools. The research tools used were questionnaire, personal observations, interviews and secondary data collection approach. The questionnaire was administered to palm kernel processors and elicited information on innovations available in the firms. Both descriptive and inferential statistical techniques were used for data analysis.

Design/methodology/approach

The study area consisted of Oyo, Ogun, Osun and Ondo states in the southwestern Nigeria, because of the abundant supply of palm kernel as well as the presence of small and medium palm kernel oil (PKO) processing firms. The study population consists of all small palm kernel processing enterprises in these states. A total of 265 firms were purposively selected for the study. The sampling procedure involved the initial purposive selection of a palm kernel processing firm in a location, from where other firms within the locality were then identified. The primary data were collected through the use of questionnaire, interview and personal observation.

Findings

The results of the study showed that only process, organisational and market innovations were recorded by the palm kernel processing firms. Apart from the sieving operation where majority of the firms (91.30 per cent) used manual method, other unit operations were done mechanically. There was evidence of one or two innovation(s) available in the unit operations of these firms. Also, improvements were carried out occasionally in the process and marketing operations; the major sources of ideas for innovation were from related firms and customers. The organisations where majority of these firms had linkage with were Nigeria Palm Kernel Processing Association, government agencies and customer/suppliers.

Research limitations/implications

The findings are limited to the south-western part of Nigeria, there is need to extend the study to other states in the southern part where palm produce is the major cash crop. This will assist in making better generalisation on the innovation and innovation capability of the processors in Nigeria.

Practical implications

The study showed that the palm kernel processing firms experienced low innovation capability which could be due to their weak interactions with the knowledge institutions. Hence, there is need for these palm kernel processing firms to establish strong linkage with the knowledge institutions where their innovation capability can be enhanced.

Social implications

The findings in this paper can serve as an input to the design of policies that can enhance the innovation capability of the various actors in the value chain. This will assist in preventing wastages, increasing the quantity and quality of products and creating job opportunities. This is because the quality of PKO depends on the processing method; hence, better process innovation will improve the characteristics of the oil and widen its application.

Originality/value

Much has been written about palm kernel processing in medium and large enterprises, but information is still scanty on the small-scale processing enterprises. This paper contributed to knowledge by examining the innovations existing in the palm kernel processing enterprises in the south-western part of Nigeria and the innovation capability possessed by these enterprises.

Details

International Journal of Innovation Science, vol. 9 no. 1
Type: Research Article
ISSN: 1757-2223

Keywords

Article
Publication date: 1 February 1979

JOSEF KITTLER

In the paper the problem of designing a pattern recognition system for processing incomplete pattern vectors is considered. An efficient method of integrating the small…

Abstract

In the paper the problem of designing a pattern recognition system for processing incomplete pattern vectors is considered. An efficient method of integrating the small core probability density function (p.d.f.) estimator employing Gaussian kernels with general parameter matrices has been proposed. As a result these general kernels satisfy the basic requirements of integrability and, therefore, they can be used in p.d.f. estimators for classification systems processing incomplete pattern vectors. In comparison with the Gaussian kernel having a diagonal parameter matrix, the general kernel is better suited for reconstructing multivariate p.d.f. of pattern vectors with correlated components. Also determination of the optimal parameters of the general kernel is much simpler for the use of the computationally demanding maximum likelihood parameter estimation method can be obviated.

Details

Kybernetes, vol. 8 no. 2
Type: Research Article
ISSN: 0368-492X

Article
Publication date: 1 February 2016

Manoj Manuja and Deepak Garg

Syntax-based text classification (TC) mechanisms have been overtly replaced by semantic-based systems in recent years. Semantic-based TC systems are particularly useful in…

Abstract

Purpose

Syntax-based text classification (TC) mechanisms have been overtly replaced by semantic-based systems in recent years. Semantic-based TC systems are particularly useful in those scenarios where similarity among documents is computed considering semantic relationships among their terms. Kernel functions have received major attention because of the unprecedented popularity of SVMs in the field of TC. Most of the kernel functions exploit syntactic structures of the text, but quite a few also use a priori semantic information for knowledge extraction. The purpose of this paper is to investigate semantic kernel functions in the context of TC.

Design/methodology/approach

This work presents performance and accuracy analysis of seven semantic kernel functions (Semantic Smoothing Kernel, Latent Semantic Kernel, Semantic WordNet-based Kernel, Semantic Smoothing Kernel having Implicit Superconcept Expansions, Compactness-based Disambiguation Kernel Function, Omiotis-based S-VSM semantic kernel function and Top-k S-VSM semantic kernel) being implemented with SVM as kernel method. All seven semantic kernels are implemented in SVM-Light tool.

Findings

Performance and accuracy parameters of seven semantic kernel functions have been evaluated and compared. The experimental results show that Top-k S-VSM semantic kernel has the highest performance and accuracy among all the evaluated kernel functions which make it a preferred building block for kernel methods for TC and retrieval.

Research limitations/implications

A combination of semantic kernel function with syntactic kernel function needs to be investigated as there is a scope of further improvement in terms of accuracy and performance in all the seven semantic kernel functions.

Practical implications

This research provides an insight into TC using a priori semantic knowledge. Three commonly used data sets are being exploited. It will be quite interesting to explore these kernel functions on live web data which may test their actual utility in real business scenarios.

Originality/value

Comparison of performance and accuracy parameters is the novel point of this research paper. To the best of the authors’ knowledge, this type of comparison has not been done previously.

Details

Program, vol. 50 no. 1
Type: Research Article
ISSN: 0033-0337

Keywords

Article
Publication date: 26 July 2013

Mathias Vermeulen, Tom Claessens, Benjamin Van Der Smissen, Cedric S. Van Holsbeke, Jan W. De Backer, Peter Van Ransbeeck and Pascal Verdonck

The purpose of this paper is to use rapid prototyping technology, in this case fused deposition modeling (FDM), to manufacture 2D and 3D particle image velocimetry (PIV…

Abstract

Purpose

The purpose of this paper is to use rapid prototyping technology, in this case fused deposition modeling (FDM), to manufacture 2D and 3D particle image velocimetry (PIV) compatible patient‐specific airway models.

Design/methodology/approach

This research has been performed through a case study where patient‐specific airway geometry was used to manufacture a PIV compatible model. The sacrificial kernel of the airways was printed in waterworks™ which is a support material used by Stratasys Maxum FDM devices. Transparent silicone with known refractive index was vacuum casted around the kernel and after curing out, the kernel was removed by washing out in sodium hydroxide.

Findings

The resulting PIV model was tested in an experimental PIV setup to check the PIV compatibility. The results showed that the model performs quite well when the refractive index (RI) of the silicone and the fluid are matched.

Research limitations/implications

Drawbacks such as the surface roughness, due to the size of the printing layers, and the yellowing of the silicone, due to the wash out of the kernel, need to be overcome.

Originality/value

The paper presents the manufacturing process for making complex thick walled patient‐specific PIV models starting from a strong workable sacrificial kernel. This removable kernel is obtained by switching the building and the support materials of the FDM machine. In this way, the kernel was printed in support material while the building material was used to support the kernel during printing. The model was tested in a PIV setup and the results show that the airway model is suitable for performing particle image velocimetry.

Article
Publication date: 4 January 2016

Nianyun Liu, Jingsong Li, Quan Liu, Hang Su and Wei Wu

Higher order statistics (HOS)-based blind source separation (BSS) technique has been applied to separate data to obtain a better performance than second order…

Abstract

Purpose

Higher order statistics (HOS)-based blind source separation (BSS) technique has been applied to separate data to obtain a better performance than second order statistics-based method. The cost function constructed from the HOS-based separation criterion is a complicated nonlinear function that is difficult to optimize. The purpose of this paper is to effectively solve this nonlinear optimization problem to obtain an estimation of the source signals with a higher accuracy than classic BSS methods.

Design/methodology/approach

In this paper, a new technique based on HOS in kernel space is proposed. The proposed approach first maps the mixture data into a high-dimensional kernel space through a nonlinear mapping and then constructs a cost function based on a higher order separation criterion in the kernel space. The cost function is constructed by using the kernel function which is defined as inner products between the images of all pairs of data in the kernel space. The estimations of the source signals is obtained through the minimizing the cost function.

Findings

The results of a number of experiments on generic synthetic and real data show that HOS separation criterion in kernel space exhibits good performance for different kinds of distributions. The proposed method provided higher signal-to-interference ratio and less sensitive to the source distribution compared to FastICA and JADE algorithms.

Originality/value

The proposed method combines the advantage of kernel method and the HOS properties to achieve a better performance than using a single one. It does not require to compute the coordinates of the data in the kernel space explicitly, but computes the kernel function which is simple to optimize. The use of nonlinear function space allows the algorithm more accurate and more robust to different kinds of distributions.

Details

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 35 no. 1
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 14 June 2019

Pingping Xiong, Zhiqing He, Shiting Chen and Mao Peng

In recent years, domestic smog has become increasingly frequent and the adverse effects of smog have increasingly become the focus of public attention. It is a way to…

Abstract

Purpose

In recent years, domestic smog has become increasingly frequent and the adverse effects of smog have increasingly become the focus of public attention. It is a way to analyze such problems and provide solutions by mathematical methods.

Design/methodology/approach

This paper establishes a new gray model (GM) (1,N) prediction model based on the new kernel and degree of grayness sequences under the case that the interval gray number distribution information is known. First, the new kernel and degree of grayness sequences of the interval gray number sequence are calculated using the reconstruction definition of the kernel and degree of grayness. Then, the GM(1,N) model is formed based on the above new sequences to simulate and predict the kernel and degree of the grayness of the interval gray number sequence. Finally, the upper and lower bounds of the interval gray number are deduced based on the calculation formulas of the kernel and degree of grayness.

Findings

To verify further the practical significance of the model proposed in this paper, the authors apply the model to the simulation and prediction of smog. Compared with the traditional GM(1,N) model, the new GM(1,N) prediction model established in this paper has better prediction effect and accuracy.

Originality/value

This paper improves the traditional GM(1,N) prediction model and establishes a new GM(1,N) prediction model in the case of the known distribution information of the interval gray number of the smog pollutants concentrations data.

Details

Kybernetes, vol. 49 no. 3
Type: Research Article
ISSN: 0368-492X

Keywords

1 – 10 of over 4000