http://dspace.jgu.edu.in:8080/jspui/bitstream/10739/2740/1/A%20meta%20analysis%20of%20antecedents%20and%20consequences%20of%20trust%20in%20mobile.pdf

A meta-analysis of antecedents and consequences of trust in mobile commerce

Online service quality and perceived value in mobile government success: An empirical study of mobile police in China

Fractures of bone are a common affliction in orthopedic wards at any given time. Trained radiologists generally identify abnormal pathologies including fractures with a relatively high level of accuracy. However studies examining reader accuracy have shown that in some cases the miss rate can be as high when reading x-rays containing multiple abnormalities. Accurate diagnosis of fractures is vital to the effective management of patient injuries. As a result, detection of long-bone fractures is an important orthopedics and radiologic problem, and it is proposed that a novel computer aided detection system could help lowering the miss rate. This paper examines the development of such a system, for the detection of long-bone fractures. This project fully employed MATLAB 7.8.0 (.r2009a) as the programming tool for loading image, image processing and user interface development. Results obtained demonstrate the performance of the femur's long bone fracture detection system with some limitations.

/pdf/gray-level-co-occurrence-matrix-bone-fracture-detection-28nkakqwyn.pdf

Gray-level co-occurrence matrix bone fracture detection

Problem statement: Currently doctors in orthopedic wards inspect the bone x-ray images according to their experience and knowledge in bone fracture analysis. Manual examination of x-rays has multitude drawbacks. The process is time-consuming and subjective. Approach: Since detection of fractures is an important orthopedics and radiologic problem and therefore a Computer Aided Detection(CAD) system should be developed to improve the scenario. In this study, a fracture detection CAD based on GLCM recognition could improve the current manual inspection of x-ray images system. The GLCM for fracture and non-fracture bone is computed and analysis is made. Features of Homogeneity, contrast, energy, correlation are calculated to classify the fractured bone. Results: 30 images of femur fractures have been tested, the result shows that the CAD system can differentiate the x-ray bone into fractured and nonfractured femur. The accuracy obtained from the system is 86.67. Conclusion: The CAD system is proved to be effective in classifying the digital radiograph of bone fracture. However the accuracy rate is not perfect, the performance of this system can be further improved using multiple features of GLCM and future works can be done on classifying the bone into different degree of fracture specifically.

Gray-Level Co-occurrence Matrix Bone Fracture Detection

Due to the plate formats and multiform outdoor illumination conditions during the image acquisition phase, it is challenging to find effective license plate detection (LPD) method. This paper aims to develop a new detection method for identifying vehicle license plates under low quality images using image processing techniques. In this research, a robust method using a large number of AdaBoost cascades with three levels pre-processing local binary patterns classifiers (3L-LBPs) are used to detect license plates (LPs) regions. The method achieves a very high accuracy for detecting LP number from one vehicle image. The proposed method was tested and trained with the images from 630 and 400 vehicles, respectively. The images involve many difficult conditions, such as low/high contrast, dusk, dirt, fogy, and distortion problems. The experimental results demonstrate very satisfactory performance for LP detection in term of speed and accuracy, and were better than the most of the existing methods. The processing time for the whole testing LPD system was about 1.63 seconds to 2 seconds. The overall probability detection, precision, and f-measurement are 98.56%, 95.9% and 97.19%, respectively; with false positive rate 5.6%.

Ensemble of adaboost cascades of 3L-LBPs classifiers for license plates detection with low quality images

the artificial neural network training algorithm is implemented in MATLAB language. This implementation is focused on the network parameters in order to get the optimal architecture of the network that means (the optimal neural network is the network that can reach the goals in minimum number of training iterations and minimum time of training). Many examples were tested and it was shown that using one hidden layer with number of neuron equal to the square of the number of inputs will lead to optimal neural network by mean of reducing the number of training stages (number of training iterations) and thus the processing time needed to train the network.

/pdf/performance-evaluation-of-artificial-neural-networks-for-5cidpkau6x.pdf

Performance evaluation of artificial neural networks for spatial data analysis

Problem statement: practical approach of detecting edge map was proposed. Approach: The methodology of this approach was presented and tested in order to select the best value of the operator, used to smooth and get the gradient, and the threshold value used to convert the gray gradient to binary edge map, so a practical value of the threshold and edge operator coefficient was investigated, these values used to calculate the gradient in order to get a better edge-map. Results: While increasing the value of C (constant C the first parameter in our practical approach of detecting the edge), we narrow the range of t and at the same time the value of the suitable t will be increased toward 1. Conclusion: This approach can be used to get the best edge map and to get a clear edge map, which can be used later in image segmentation and object extraction.

/pdf/a-practical-approach-of-selecting-the-edge-detector-pse3t3w7j8.pdf

A Practical Approach of Selecting the Edge Detector Parameters to Achieve a Good Edge Map of the Gray Image

The main purpose of this study is to introduce and examine the factors affecting the intention to adopt and use of smart government services among Jordanian citizens. This study proposed a theoretical model based on diffusion of innovation theory (DOI) and integrated external constructs perceived risk, trust, and quality. Furthermore, this study is intended to investigate the moderating effect of gender and experience. Data were collected from undergraduate university students by using a paper-based questionnaire and analysed by using a WarpPLS 5.0 software. The results show that relative advantage, trialability and observability are important factors in explaining the individual's intention. Meanwhile, compatibility and complexity do not exert any significant influence. Moreover, experience plays an important role in moderating some of the hypothesised relationships. Meanwhile, gender does not have any moderation influence. This study's model explained approximately 40% of the variance in intention to adopt and use s-government services. Implications, limitations and suggestions for future studies are discussed.

Exploring perceived risk, perceived trust, perceived quality and the innovative characteristics in the adoption of smart government services in Jordan

Problem statement: A new color model for digital image was discussed; this model can be used to separate low and high frequencies in the image without loosing any information from the image. Approach: A comparative study between different color models (RGB, HSI) applied to a very large microscopic image analysis and the proposed model was presented. Such analysis of different color models is needed in order to carry out a successful detection and therefore a classification of different Regions of Interest (ROIs) within the image. Results: This, in turn, allows both distinguishing possible ROIs and retrieving their proper color for further ROI analysis. This analysis was not commonly done in many biomedical applications that deal with color images. Other important aspects were the computational cost of the different processing algorithms according to the color model. The proposed model took these aspects into consideration and the experimental results showed the advantages of proposed model compared with HSI model by decreasing the computational time for various image-processing operations. Conclusion: The proposed model can be used in different application such as separating low and high frequencies from the image.

/pdf/color-image-reconstruction-using-a-new-r-g-i-model-k758nofksg.pdf

Color Image Reconstruction Using A New R'G'I Model

This paper discusses a new color model for digital image, which can be used to separate low, and high frequencies in the image without loosing any information from the image (12). The proposed model was used as an initial step to image segmentation and the practical results shows the efficiency of using this method to get the actual objects of the color image. The proposed model can be used in different application such as separating low and high frequencies from the image, which are proposed in (10), and it also can be used to segment different types of color images. Index term: Digital image processing (DIP), RGB, HSI, Direct transform, inverse transform, color model, object, segmentation.

/pdf/reconstructed-color-image-segmentation-2o7vnonl42.pdf

Akram A. Moustafa

Papers

Performance evaluation of artificial neural networks for spatial data analysis

A Practical Approach of Selecting the Edge Detector Parameters to Achieve a Good Edge Map of the Gray Image

Exploring perceived risk, perceived trust, perceived quality and the innovative characteristics in the adoption of smart government services in Jordan

Color Image Reconstruction Using A New R'G'I Model

Reconstructed Color Image Segmentation