The effect of inventory management on profitability of cement manufacturing companies in Kenya: A case study of listed cement manufacturing companies in Kenya. International Journal of Management and Commerce Innovations3 2 International Business Management5 5 Journal of Small Business and Enterprise Development20 3 Arabian Journal of Business and Management Review1 12 Cash management and corporate profitability: A study of selected listed manufacturing firms in Nigeria.
International Journal of Technology Marketin8 1 Foundations of descriptive and inferential statistics version 3. Karlshochschule International University: Karlsruhe. DOI: Management of corporate liquidity and profitability: An empirical study. International Journal of Marketing rationale essay Technology1 6 Impact of working capital management master thesis on steganography on profitability: A case of the Pakistan Cement Industry.
All rights reserved. Research Model adapted from Nzitunga, Reliability statistics for the scale used in this study. Abdi, H. Abbasali, P. Abuzayed, B. Ahmet, G. Al-Debi'e, M. Ali, A. Al-Mwalla, M. Agyei, S. Akinyomi, O. Akoto, R. Amalendu, B. Amoako, K. Anichebe, N. Arshad, Z. Aruldoss, S. Barine, M. Bartlett, C. Bhattacherjee, A. Bobitan, N. Cooper, D. Dong, H. Eljelly, A, Eneje, B. Enow, S. Ghaziani, B. Hair, J. Jauch, H. For one thing, the current assets of a typical manufacturing firm accounts for over half of its total assets Abdul and Mohamed, One reason why managers spend considerable time on day-to-day management of working capital is that current assets are short-lived investments that are continually being converted into other asset types Rao, Liquidity for the on-going firm is not reliant on master thesis on working capital management liquidation value of its assets, but rather on the operating cash flows generated by those assets Soenen, Working Capital Management is therefore a sensitive area in the field of financial management Joshi, It involves the decision of the amount and composition of current assets and the financing of these assets.
Efficient working capital management involves planning and control of current assets and current liabilities in a manner to strike a balance between liquidity and profitability. Harris pointed out that working capital management is a simple and straightforward concept of ensuring the ability of the firm to fund the difference between the short term assets and short term liabilities.
The ultimate objective of any firm is to maximize shareholders wealth and maximizing shareholders wealth can be achieved by a firm maximizing its profit. A firm that wishes to maximize profit must strike a balance between current assets and current liabilities and hence keeping abreast of the liquidity and profitability trade-off. Preserving liquidity and profitability of the firm is an important objective as increasing profit at the expense of liquidity can bring serious problems to the firm and vice-versa.
There are chances of imbalance of current assets and current liability during the life cycle of a firm and profitability will be affected if this occurs. Numerous studies on the drivers and financial impact of working capital management for different manufacturing firms for different countries of the world have been published in recent times. However, inter-country studies of world leading firms in a given industry are spare.
Attempting to. Working capital management is a managerial accounting strategy focusing on maintaining efficient levels of both components of working capital, current assets and current liabilities in respect to each other.
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Order ID or. Palmer S. Street W. Business and Economics Journal Gomes D. Retrieved from Masther thesis. Jose M. Lancaster C. Corporate Return and Cash Conversion Cycle. Journal of Economics and Finance 20 1 Karadagli E. Kieschnick R. Laplante M. Review of Finance 17 5 Kim J.
An integrated evaluat ion of investment in inventory a nd credit: a cash flow approach. Journal of Business Finance and Accounting 17 3 Contemporary Economics 8 4 Kwenda F. Journal of Economics and Behavioral Studies 6 7 Lazaridis I. Mathuva D. Research Journal of Business Management. Mohamad N. International Hournal of Business and Management 5 11 Adjusted R Square R2 signifies that 14 per cent of the variations in the ROCE are explained by the independent variable Standard Error of regression coefficients being not very high, reveals that there survives actually line of estimates among the variables.
VIF was less than 2, which indicates there was no multicollinearity problem. Also Durbin-Watson statistics indicates that residuals were not serially correlated.
F statistics with profitability indicates that network on chip master thesis regression model is perfectly fitted. An insignificant variability in profitability could be the result of the combined upshot accepted in the study and numerous other working capital management connected unexplained variables.
Table 4. The underlying principle of this study is to investigate the working capital management efficiency and profitability relationship. A descriptive statistics discloses that liquidity and solvency position was very pleasing and sensibly competent working capital management was found however liquidity position has no shock on profitability. This study moreover shows there was no significant relationship between working capital management and profitability.
The study additionally demonstrates a small connection between WCM in terms of working capital cycle and profitability and working capital cycle has no significant shock on profitability. Multiple regression homework creator corroborate a lower degree of relationship between the working capital management and profitability.
As a result, company manger should anxiety on working capital management, mainly unexplained variables in justification of making shareholder wealth. But this study was not free from a few limitations, that is, special ratios used in this study were taken from CMIE data base, only eight working capital management indicators were considered and all pharmaceutical companies were not considered.
This may be our future study. Use of this Web site signifies your agreement to the terms and conditions. Special Issues. SciencePG Frontiers. Contact Us. Volume 1, Issue 1, JunePages: Abstract Keywords 1. Introduction 2. Literature Review 3. Methodology of the Study 4. Empirical Results and Analysis 4. Descriptive Statistics 4. Correlation Statistics 4. Multiple Regression Statistics 5. Conclusions References. Introduction Working capital management efficiency can have a noteworthy shock on both the liquidity and profitability of a company Shin and Soenen, Descriptive Statistics For determining working capital position in terms of level, proper level of working capital is essential with which contrast can be prepared.
Descriptive Statistics on Current Ratios Current ratio is an assessment of overall liquidity and is basically used to make the interpretation of liquidity of firm in the short-run. Descriptive Statistics of Quick Ratios Quick ratio is more specific test of liquidity than current ratio. Descriptive Statistics of Debt-Equity Ratios Short-term debt-equity ratio is an indicator of working capital and liquidity position and also important for reliability of financial position as well as financial policies in a short period of the firm.
Quayyum, S. International Journal of Business and Management, 7 1pp. Ramey, V. The source of fluctuations in money: Evidence from trade credit. Journal of Monetary Economics, Reilly G. Journal of Cost Management, 14 6pp. Richards, V. Financial Management, 9, pp. Sack, K. Retailing: General Industry Survey. Samiloglu, F. The international Journal of Applied Economics and Finance, 2 1pp.
Sartoris, W. Journal of Finance, 38, pp. Schwartz, R. An Economic Model of Trade Credit. Journal of Financial and Quantitative Analysis, 9, pp. Shah, S. European Journal of Scientific Research, 15 3pp. Sharma, A. Global Business Review, 12 1pp.
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Carousel Previous Carousel Next. Craft, Inc. Emotional Intelligence 2. Jump to Page. Search inside document. The stakeholders and management staff 1. Days sales outstanding is calculated as: 2 Inventory Turnover in Days ITID : A ratio showing how many times a company's inventory is sold and replaced over a period. Master thesis on internet banking as: 4 Cash Conversion Cycle CCC : A metric that expresses the length of time, in days, that it takes for a company to convert resource inputs into cash flows.
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Noman Khosa. Manoj Kanwar Rathore. The effect of working capital on profitability in computer and electrical equipment industry. Virkkala, Ville. Laskentatoimen laitos Department of Accounting. This master's thesis studies the impact of working capital on corporate profitability and shareholder value in 1, publicly listed US computer and electrical equipment companies in ?Avoid Foreign Sites.
Why Master thesis on branding Us? Ask a Question. Installment Plan. Writing Features. Place an Order. Never Resold. Payment Options. OpenCV 3. Python v3. Optional Matplotlib 2. But it's completely optional. Note: If you don't want to install matplotlib, then replace matplotlib code with OpenCV code as shown below:. It takes two arguments; the first one is the image you want to post and the second is the colormap gray, RGB in which the image is in.
It also takes two arguments: the first one is the name of the window that will pop-up to show the picture and the second one is the image you want to display. The function delays for x milliseconds any keyboard event. If 0 is pressed, it waits indefinitely how to write an economic essay a keystroke, if any other key is pressed the program continues.
Let's import the necessary libraries first. Master thesis on branding, the names of these libraries are self-descriptive so you can put 2 and 2 together. To show the colored image using matplotlib we have to convert it to RGB space. The following is a helper function to do exactly that:. It takes as input an image to transform, and a color space code like cv2.
Let's start with a simple task, load our input image, convert it to grayscale mode and then display it. From homework step is necessary because many operations in OpenCV are done in grayscale for performance reasons.
OpenCV provides us with a class cv2. Well, again OpenCV's CascadedClassifier has made it simple for us as it comes with the function detectMultiScalewhich detects exactly that. Image: The first input is the grayscale image. So make sure the image is in grayscale.
There are other parameters as well, and you can review the full details of these functions here. These parameters need to be tuned according to your own data. Now that we know a straightforward way to detect faces, we could now find the face in our test image.
The following code will try to detect a face from the image and, if detected, it will print the number of faces that it has found, which in our case should be 1. Only 1 since no other spiritual being is out there. Next, let's loop over the list of faces rectangles it returned and drew those rectangles using yet another built-in OpenCV rectangle function on our original colored image to see if it found the right faces:.
Unfortunately, this code is more scattered than my attention span during high school. Notice that the face detection relies on an external model already trained for. Next, the focus is on how to efficiently extract features from the face patches and make the in-class i. To do so, a Siamese Network is being trained, based on pairs of faces, to perform well on the verification task. After a good Siamese Network has been trained, it can support the last step - the classification - consisting in assigning an identity to an input face image probe by leading some similarity computation between this input picture and identified face images contained in a gallery.
Following that, the final predicted identity is the one the input face was the closest during the comparison process. Besides those 4 main steps, to support the learning process based on few data, 2 extra steps are being defined.
The original LPB operator has been extended to consider different neighborhood sizes Ojala et al. For example, the operator LBP 4, 1 uses only 4 neigh- bors while LBP 16, 2 considers the 16 neighbors on a circle of radius 2.
In general, the operator LBP P, R refers to a neighborhood size of P of equally spaced pixels on a circle of radius R that form a circularly symmetric neighbor set. LBP P, R produces 2p different out- put values, corresponding to the 2P different binary patterns that can be formed by the P pixels in the neighbor set.
It has been shown that certain bins contain more information than others . Therefore, it is possible to use only a subset of the 2P local binary patterns to describe the textured images. Ojala et al. For example, and contain 0 transition while and contain 2 transi- tions and so on. Thus a pattern is uniform if the number of bitwise transitions is. For the neighborhood the numbers are andrespectively.
Since the LBP descriptor of most important information in a facial image such as edges, lines, spots etc see Figure 4. Therefore, the uniform pattern is preferred for face recognition. The pixel values are bilinearly interpolated whenever the sampling point is not in the center of a pixel.
In this section, the model used in this thesis is described. The model is based on three main steps. The result of this step is then used to filter the original image and then thresholding it, yielding a binary skin map which shows us the network on chip master thesis regions in the original image.
These skin regions will be later sent to Face Detection process. The skin detection is performed using a Gaussian Mixture Model.
To estimate the parameters of the Gaussian mixture the EM algorithm is employed when the parameters are evaluated by an E-step expectation and M-step maximizationrespectively.
The algorithm begins by making some initial guess for the parameters of the Gaussian mixture model. The initial value here is obtained using k-means clustering.
The samples are initially labelled using the k-means clustering where k is the number of components in the mixture model. We can then evaluate the new parameters using the following equation:. Before we find the parameters we need choose a certain number of mixture compo- nents k which gives us the best performance in our application. A recent work , shows that a single Gaussian distribution is not sufficient to model human skin color and nor effective in a general application.
Thus, k must be equal to or greater than 2. As said before, the number of components used by different researchers varies significantly. Furthermore, the size of skin samples seems to be a crucial factor in this process. This sample is initially labelled using the k-means clustering and then the final parameter values are obtained by using EM algorithm. Finally we will sum up the discussion with finding a reasonable threshold and skin color space in this work.
Since we focus on the face detection procedure, we homework planner template for second graders to have a threshold that gives us a region where we are not loosing information and also raises the speed up of the system.
The algorithm of the method is given in Algorithm 1. First of all we consider the weak classifiers and the way of building them. The output is a binary number 0 or 1 and depends on whether the feature value is less than the given threshold.
Both methods rely on estimating two probability distributions when applied for feature values to the positive samples face data and the negative samples non-face datarespectively.
The threshold can then be determined either by taking the average of their means or by finding the cross-over point. The cross-over point corresponds to. In this work, the cross-over point is used. The Figure 5. Figure 5. An example of how the distribution of feature values for a specific feature may look like over the set of all training samples.
As mentioned before a set of the features defines a set of weak classifiers. Now the set of weak classifiers a binary set is ready and it is time to employ AdaBoost to find a forming of a strong classifier through weak ones.
The idea of combining weak classifiers to strong ones is master thesis on working capital management logical step that we as humans use during the decision making process. For example, to determine that someone is who they say they are we may ask them a series of questions, each one possibly no stronger than the prior, but when the person has answered all the questions we make a stronger decision about the validity of the persons identity .
The main factor in AdaBoost is the application of weight distributions. The process is started by an initial weight distribution, at each iteration after calling the weak classifiers the weight distribution will be updated if the classification is correct. Therefore, weak classifiers that manage to classify difficult sample images are given higher weighting in the final strong classifier. The algorithm of AdaBoost and how to define a strong classifier from weak ones is given below.
Input: Example images x1. For each feature j train a classifier hj which is restricted to using a single feature.
Then, choose the classifier ht as the hj that gives the lowest error. Set et to that ej. The elegant key behind the cascade is based on a tree structure of classifiers. In the manner that in the early stages of the tree, classifiers are largely naive. As a positive sample progress through the cascade, assuming that the sample is indeed positively classi- fied, then the process of classification will become finer, and the number of features that are evaluated will increase. Indeed, in detection task in a large image a large majority of the sub-windows observed by the scanning classifier will be rejected and just a small regional area s in the image might be the target s.
Therefore, the generality of the first num- ber of stages must be sufficiently high to reduce the number of these false positive sub-windows from processing into the higher stages of the cascade. The aim is to provide inference with the lowest possible false positives, and highest possible de- tection rate. The Figur 5. Based on this fact and deeper reasoning for the motivation of the cascade, they provide a general algorithm for the training process that the cascade must undertake in order to build its stages.
The algorithm is shown in algorithm of Table 2. The minimum acceptable detection rate d and the maximum false positive rate f are required in the algorithm. Input: Allowed false positive rates fdetection rates d and the target false pos- itive rate Ftarget.
A set P of positive faces and of N negative examples non-faces. The LBP operator encodes only the occurrences of the micro-patterns without any indication about their locations. For efficient face representation, the spatial information must be returned. For this purpose, the face image is divided into several regions or blocks from which the local binary pattern histograms are computed and concatenated into a single histogram see Figure 5.
In such a representation, the texture of facial regions is encoded by the LBP while the shape of the face is recovered by the concatenation of different local histograms. To compare a target face histogram S to a model histogram M one can use the nearest neighbor classification scheme. There are other parameters that should be chosen to optimize the performance of the proposed representation.
The first one is choosing the LBP operator. Choosing an operator that produces a large amount of different labels makes the histogram long and thus calculating the distances gets slow. Using a small number of labels makes the feature vector shorter, but also means losing more information. As a result, ideally, the aim is to find the LBP operator which gives a short feature his- togram and captures all the information needed to discriminate between the faces.
Another parameter is the division of the images master thesis on advertising regions. A large number of small regions produces long feature vectors causing high memory consumption and slow classification, whereas using large regions causes more spatial information to be lost. The experimental research by A. Hadid is done with different LBP operators and window sizes. As expected, a larger window size causes a decreased recognition rate because of the loss of spatial information.
Furtheremore, according to psychological findings some facial features such as eyes play more important roles in face recognition than other features such as nose. Hence, when a facial image is divided into several regions we can expect that some of the regions carry more useful information that others in term of distinguish- ing between faces. Therefore, using different weights for the regions may improve the recognition rate.
Choosing weights should be depended on the importance of the corresponding regions in recognition. For instance, since the eye regions are important for recognition, a high weight can be attributed to the corresponding re- gions. In this work the recognition method is based on non-weighted Master thesis on steganography approach. The mean recognition rate for three LBP operators as a function of the window. The five smallest windows were not tested using the LBPu2 16, 2 operator because of the high dimension of the feature vector that would have been produced.
For fair perfor- mance evaluation of different master thesis on steganography color modelling methods, identical testing condi- tions are preferred.
Unfortunately, many skin detection methods provide results on their own, publicly unavailable, databases. In this work, for evaluating the Gaussian mixture model we try to make an identical testing condition. The system needs initial skin samples.
This step is done using manually segmentation of our current face image database. In fact, we do know that all the points in test database lie in the skin color region. We then challenge the Gaussian mixture model to determine the probability density of test database to lie into the skin region. The skin region in the color space obviously has been defined by estimating the parameters of the Gaussian mixture, using EM al- gorithm described before.
Finally, by thresholding the probability density functions we will obtain a binary image containing 0 and 1. The corresponding threshold will be discussed and will be obtained experimentally. As mentioned before, the number of components must be greater than.
It is obvious that a high number of components will yield to a time-consuming process or equal. Since we were trying to reduce the process time to approach a fast method, four different numbers of components have been chosen here.
We studied the behavior of the system when the numbers of components are 2, 3, 4 and finally 5. Each experiment consists of the evalua- tion of each single number of components on each skin sample databases with two different space colors. Indeed, to have a fair performance evaluation the code ran five custom essays for sale for each evaluation and the average result has been considered.
The result of evaluation. Sample A and Sample B are the skin samples of size x and x respectively. In the case of choosing a color space concerning the Figure 6. But, as discussed in Chapter 2, color transformation would be required. In order to avoid such a huge amount of heavy calculations, and since most cameras provide RGB images directly and with respect to our result a choice of 2 for number of components in Gaussian mixture model on RGB format is the best alternative in this thesis.
The result shows a reliability of Furthermore, since skin color detection works as a pre-process step for our face detection, we do not want to loose any information of skin area in an image, when we minimize the input image and prepare a new input image consisting of skin areas for the face detection step.
In order to have better performance and faster method, we can speed up the system by using the result of skin detection step. The Figure 6. After implementing Gaussian Mixture with two components the binary image top right appears. The skin region will then be indicated from binary image. A rectangular cut master thesis on branding the skin region shown in bottom left.
As database face and non-faces are used to make the features. After running the AdaBoost algorithm a strong classifier has been obtained. The question here is how we can be sure that the obtained strong classifier is working well. For this issue we test it on the portion of test data. Once again the threshold is taken by using cross-over point.
By challenging the algorithm we find the values for both. For simplifying the definition of true- positive and true-negative the table below shows the relation between these two. The number of true-positive and false-positive will depend on the threshold applied to the final strong classifier. From this curve you can ascertain what loss in classifier specificity false-positive rate you will have to endure for a required accuracy true-positive rate. In the Figure 6. The next step is implementing the casecade algorithm to obtain cascade, or in other words, the best features to have faster classification.
For this issue, a Matlab function is employed to visualize the feature.