An improved functional link neural network for data classification

The goal of classification is to assign the pre-specified group or class to an instance based on the observed features related to that instance. The implementation of several classification models is challenging as some only work well when the underlying assumptions are satisfied. In order to generate the complex mapping between input and output space to build the arbitrary complex non-linear decision boundaries, neural networks has become prominent tool with wide range of applications. The recent techniques such as Multilayer Perceptron (MLP), standard Functional Link Neural Network (FLNN) and Chebyshev Functional Link Neural Network (CFLNN) outperformed their existing regression, multiple regression, quadratic regression, stepwise polynomials, K-nearest neighbor (K-NN), Naïve Bayesian classifier and logistic regression. This research work explores the insufficiencies of well- known CFLNN model where CFLNN utilizes functional expansion with large number of degree and coefficient value for inputs enhancement which increase computational complexity of the network. Accordingly, two alternative models namely; Genocchi Functional Link Neural Network (GFLNN) and Chebyshev Wavelets Functional Link Neural Network (CWFLNN) are proposed. The novelty of these approaches is that, GFLNN presents the functional expansions with less degree and small coefficient values to make less computational inputs for training to overcome the drawbacks of CFLNN. Whereas, CWFLNN is capable to generate more number of small coefficient value based basis functions with same degree of polynomials as compared to other polynomials and it has orthonormality condition therefore it has more accurate constant of functional expansion and can approximate the functions within the interval. These properties of CWFLNN are used to overcome the deficiencies of GFLNN. The significance of proposed models is verified by using statistical tests such as Freidman test based on accuracy ranking and pairwise comparison test. Moreover, MLP, standard FLNN and CFLNN are used for comparison. For experiments, benched marked data sets from UCI repository, SVMLIB data set and KEEL data sets are utilized. The CWFLNN reveals significant improvement (due to its generating more numbers of basis function property) in terms of classification accuracy and reduces the computational work

Hybrid Image Steganography Method with Random Embedding of Encrypted Message

The main challenge for embedding encrypted message in an input image is to get better the security of the confidential information through hybrid-based image steganography method. Moreover, earlier LSB based solutions existed in which either secret information embedded without encryption or embedded un-randomly in an image and existing MSB based information concealing solutions minimizes information capacity and image quality too. Most of existing steganographic systems either based on  LSB or  MSB but only some hybrid solutions are available in which either the confidential message is not encoded before embedding it into the image and the embedding system is also not random based.  The existing well known hybrid based image steganography techniques are not only deficient in performance but also deficient in embedding of encoded data in an image. To overcome these issues, a Hybrid-LSB-MSB based image steganography and multi-operation data encryption method is proposed in this article. Proposed method is not only randomly embeds the confidential information in a cover image but also provided the facility to encode the confidential information before substituting. The Hybrid-LSB-MSB based proposed image steganography method is compared with earlier Hybrid based image steganography method by using Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR) values including payload capacity. Higher PSNR and Lower MSE values signify effective steganography quality. The experimental results show that proposed method retains higher PSNR and lesser MSE values as contrasted to the existing methods thereby effective in steganographic properties.   &nbsp

Design, effectiveness and role of visual merchandising in creating customer appeal

This study aims to find out how and to what extent outlets incorporate visual merchandising, that appeals to the customers and lead to a potential purchase. The survey method was followed to conduct the study and data were collected through sampling techniques from identified respondents, who were selected through convenient and judgment methods. The major findings in the light of the objectives of this project were that most of the stores need to have attractive window displays, proper stores layout, appealing visual merchandising themes to attract present and potential customers into the store. It is also understood that the most important aspect of visual merchandising is to have proper lighting and attractive display themes. The output of the study unfolds that the most of the merchandiser’s main focus is to display the newest trend and best moving items into the display windows and visual merchandising was found to be very helpful for converting potential customers into real customers.Visual merchandising, in-store display, visual sensor appeal, silent communication tool, store layout

A Framework for Generic and Energy Efficient Context Recognition for Personal Mobile Devices

The advancements in the field of mobile computing over the last decade have enabled the scientific community to expedite the theoretical and experimental work to achieve the vision of ubiquitous computing. As ubiquitous computing aims to provide seamless and distraction free task support to its users, one of the essential pieces of information required by the ubiquitous computing systems to do so is the context of its users. Context of a user can be defined as the information that describes the task the user is performing and the environment in which the user is currently present. Among various platforms that are commonly used to determine user's context, the personal mobile devices like smart phones stand out as one of the most widely used and widely evaluated ones. However, despite numerous advantages that are provided by modern day personal mobile devices, such as high computational and communication capabilities, variety of on-board sensors to capture raw data related to user's motion and environment, high resolution displays to enable interaction with other services and systems, these devices suffer from limited battery resources. In contrast to the advancements in other domains, the advancements in the battery domain have not been up to the mark. Consequently, the context recognition applications developed for these devices suffer from the trade-off between achieving accuracy and longevity of other device's basic operations. As a result, most of the existing context recognition applications for these devices are fine tuned for specific context types and thereby lacks generality. The situation gets worse when a number of context recognition applications are executed simultaneously, thus competing for limited resources and consuming the device's battery additively. To address the aforementioned issues, this thesis provides a generic and energy efficient context recognition framework for personal mobile devices. The main contribution consists of a generic framework to support development of context recognition applications supported by algorithms to achieve their energy efficient execution. The proposed framework consists of two systems namely the component system and the activation system. The component system allows developers to create context recognition applications using a component abstraction. This enables runtime analysis of applications' structures to adopt our novel energy efficiency mechanism. The activation system uses a state machine abstraction to allow context dependent activation of context recognition configurations pertaining to relevant user's tasks such that only needed configurations are executed to determine only the relevant context characteristics, thereby enabling energy efficiency. The activation system also provides generic applicability of four different energy efficiency techniques, already used in different existing systems but mostly for specific context characteristics. To aid rapid prototyping, both systems are equipped with off-line development tools. The tools include graphical editors and a component tool-kit. The graphical editors allow developers to create component configurations used by the component system and state machines used by the activation system. These editors enable developers to create component configurations and state machines by simply dragging, dropping and connecting different models used in our component and state machine abstractions. These tools also provide validation and code generation utilities. In addition to the graphical editor, the framework provides a component tool-kit which consists of a number of already implemented sensing, preprocessing and classification components which can be re-used in new applications. In order to provide the energy efficient execution of context recognition applications, the thesis introduces a novel energy efficiency technique called configuration folding. Configuration folding analyses structures of simultaneously executing context recognition applications to identify redundant functionalities between them and as an output produces a single redundancy free context recognition configuration which holds the structural integrity of all applications. Consequently, the overall energy expenditure is reduced compared to the original expenditure when redundant functionalities are not removed. The experimental evaluation of configuration folding on test applications shows energy savings between 13 and 48 %. The thesis also investigates optimization possibilities in configuration folding in case the redundant functionalities between the applications differ in parametrization. Towards this end, the thesis identifies commonly used parameters in context recognition applications and defines relations between them. Finally, an extended version of configuration folding is introduced to handle the differences in parametrization. The evaluation of the extended version of configuration folding on test scenarios shows energy saving of up to 45%. The contributions in this thesis have been evaluated extensively. The framework has been used in number of European Commission (EC) projects and in student projects and theses at the University of Duisburg-Essen, Germany. Using the component system and the activation system, a number of applications have been developed in those projects. Some of these applications include crowd density estimation in buses, bus ride detections, navigation application for buses in Madrid, user movement detection, user localization, fall detection application etc. Moreover, the component system, the activation system and the configuration folding technique have been published in different prestigious conferences and workshops

Numerical solution of fractional diffusion wave equation and fractional klein–gordon equation via two-dimensional genocchi polynomials with a ritz–galerkin method

In this paper, two-dimensional Genocchi polynomials and the Ritz–Galerkin method were developed to investigate the Fractional Diffusion Wave Equation (FDWE) and the Fractional Klein–Gordon Equation (FKGE). A satisfier function that satisfies all the initial and boundary conditions was used. A linear system of algebraic equations was obtained for the considered equation with the help of two-dimensional Genocchi polynomials along with the Ritz–Galerkin method. The FDWE and FKGE, including the nonlinear case, were reduced to solve the linear system of the algebraic equation. Hence, the proposed method was able to greatly reduce the complexity of the problems and provide an accurate solution. The effectiveness of the proposed technique is demonstrated through several examples

Firm and country specific determinants of corporate cash holdings : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University

After the 2008 Global Financial Crisis (GFC), firm cash holdings came to the attention of scholars. During the crisis, many non-financial firms faced severe liquidity shortage due to poor cash inflows and higher interest rates for external financing. This led to numerous cases of bankruptcy. Firms hold cash for two reasons. The first reason is to transaction costs associated with liquidating assets to cover daily operational costs. The second reason firms hold cash is to create a buffer to meet their obligations. Cash holding strategies determine a firm’s future growth and survival. However, if a firm holds too much cash, it can increase their opportunity costs. Holding large amounts of cash can also be costly as well, due to low or zero returns on idle assets and potential agency problems associated with free cash flows. Conversely, holding too little cash leads to low levels of investment. It can also lead to liquidity shortages, meaning that a firm is unable to meet its financial obligations. While previous studies (Baumol, 1952; Miller & Orr, 1966; Tobin, 1956) provide different cash holding strategies, these are theoretical in nature and may not have been tested empirically. In this study, we investigate the effect of firm-specific factors (working capital and the cash conversion cycle) and macroeconomic factors (the interest rate, the inflation rate, the foreign exchange rate, and economic policy uncertainty) on non-financial firms’ cash holdings. This is the first study that includes three types of factors: firm-specific variables, macroeconomic variables and macroeconomic uncertainty (economic policy uncertainty) in developed and developing economies. We examine firms in both developed and developing countries. Businesses obtain bank financing for investments purposes when they have insufficient internal funds. Firms typically mortgage their fixed assets to borrow money (Kiyotaki & Moore, 2002). Cash holdings are critical when firm cash inflows are insufficient to meet their capital demands. We choose two developed (the US and Japan) and two developing (China and India) countries. Our data is panel and we use firm-year observation for analyses. We divide our dataset into two configurations. First, we divide dataset according to time periods i.e., pre, post and during 2008 GFC for each country. Secondly, we divide our dataset into two groups (i) financially constrained and (ii) financially unconstrained. Financially constrained firms have easier access to financial markets which is crucial when faced with liquidity shortages. This is not the case for financially constrained firms. We apply the system generalized method of moments (SGMM) to overcome the problem of endogeneity and produce unbiased results. We find that financially unconstrained firms have higher cash inflows and assets (current and non-current) than financially constrained firms in developed and developing countries for the period of 2003 - 2015. We also find divergent results for developed and developing countries. The findings reveal that all independent variables (working capital, the cash conversion cycle, the interest rate, the inflation rate, the foreign exchange rate, and economic policy uncertainty) have a significant effect on cash holdings, for both financially constrained and unconstrained firms. The direction (positive/negative) of the relationship varies according to the country: that is, developed or developing. Our findings indicate that changes in the cash holdings of non-financial firms also depend on macroeconomic variables. Financially constrained firms have a propensity to increase cash to avoid liquidity shortages. Our results indicate that cash holdings are important for all non-financial firms in developed and developing economies. Cash holdings provide a financial buffer in times of crisis and enable firms to invest in positive NPV projects. This research thus endorses aspects of precautionary motive theory and transaction motive theory

Effect of Feed Cycling on Specific Growth Rate, Condition Factor, Body Composition And Rna/Dna Ratio Of Cirrhinus mrigala.

A randomly selected 60 samples of Cirrhinus mrigala, fingerling sized, were collected from Qadria fish hatchery and farm, Matital road, Multan. Fish were divided into three groups namely, control, 5 and 10 days cyclic feeding group. Specific growth rate (% g day-1), condition factor (K), body composition andRNA/DNA ratio of individual specimen and of each group were calculated. It was found that there was highly significant effect of feed cycling on specific growth rate (P0.05) suggesting a compensatory growth which was independent of length of starvation. Feed cycling had marked effect (

Avoiding contingent incidents by quadrotors due to one or two propellers failure

With the increasing impact of drones in our daily lives, safety issues have become a primary concern. In this study, a novel supervisor-based active fault-tolerant (FT) control system is presented for a rotary-wing quadrotor to maintain its pose in 3D space upon losing one or two propellers. Our approach allows the quadrotor to make controlled movements about a primary axis attached to the body-fixed frame. A multi-loop cascaded control architecture is designed to ensure robustness, stability, reference tracking, and safe landing. The altitude control is performed using a proportional-integral-derivative (PID) controller, whereas linear-quadratic-integral (LQI) and model-predictive-control (MPC) have been investigated for reduced attitude control and their performance is compared based on absolute and mean-squared error. The simulation results affirm that the quadrotor remains in a stable region, successfully performs the reference tracking, and ensures a safe landing while counteracting the effects of propeller(s) failures