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This study investigates the impact of extensive reading (ER) program, which integrates extensive reading with in-class follow-up writing activities, on Turkish EFL learners' L2 reading/writing and foreign language self-concept (FLSC). In this research, conducted in a Turkish university, experimental design was used and the experimental group was exposed to ER for six weeks different from the control group. Data was collected by means of L2 reading and writing tests and Foreign Language Self-concept Scale (FLSCS) (Er, 2007) given at the beginning and at the end of the intervention, and by means of focus group interviews carried out with 10 randomly chosen experimental group students. Analyses of the data showed a significant difference between control and experimental groups regarding their L2 reading and writing performance. The difference between the groups' FLSC was found to be insignificant, though within group analyses indicated that ER affected the students' FLSC positively. Qualitative data, interviews, supported the quantitative data and revealed that the project also improved students' motivation and self-confidence as well as various aspects of L2 language ability and fostered their positive self images as EFL learners. The insights gained from the study provide important implications for English language teaching programmes in EFL context.
Images which are captured in outdoor scenes often degrade in quality due to atmospheric conditions induced by suspending particles like mist, haze, fog, etc. which scatter and absorb light before it reaches to the camera. The weather-degraded images directly affect the efficiency of computerized surveillance and monitoring system. In this paper, we propose an efficient haze removal technique which integrates dark channel prior with an ant colony optimization algorithm. Firstly, we use the dark channel prior method to compute dark channel in the hazy image and secondly, ant colony optimization algorithm is applied to make restoration factor for hazy images, adaptive between 0 to 1. The restoration factor in all the previous work is fixed value i.e. 0.1. Experimental-results show that proposed algorithm is effective, robust and is yielding high-contrast images.
In nature, the organisms have a limited lifespan and they grow older with time. Aging is an essential process which leads to the maintenance of species diversity in environment. Every group of species is lead by a leader. As the lifespan of every organism is limited, at a certain point of its life time, the organism deteriorates and become inefficient to lead its group. In this situation, a new leader is found who can efficiently lead its group. The lifespan of the leader and its leading power is checked, if it is not efficient enough, a new challenger is found to lead the group. This aging mechanism is applied to the stochastic process of Particle Swarm Optimization(PSO), in order to remove the limitations that existed in PSO such as: it gets stuck in local optima and the algorithm converges pre-maturely. When aging leader algorithm is applied to PSO, these limitations are removed in an efficient manner. This paper presents some issues that occur while designing and implementing a variant of PSO (Particle Swarm Optimization) i.e. ALC-PSO (PSO with Aging Leader and Challengers) which can highly improve the performance of PSO by applying the process of aging to the members of the swarm , bringing its members to the best position.
Test functions play an important role in validating and comparing the performance of optimization algorithms. The test functions should have some diverse properties, which can be useful in testing of any new algorithm. The efficiency, reliability and validation of optimization algorithms can be done by using a set of standard benchmarks or test functions. For any new optimization, it is necessary to validate its performance and compare it with other existing algorithms using a good set of test functions. Optimization problems are widely used in various fields of science and technology. Sometimes such problems can be very complex. Particle Swarm Optimization is a stochastic algorithm used for solving such optimization problems. This paper transplants some of the test functions which can be used to test the performance of Particle Swarm Optimization (PSO) algorithm, in order to improve its performance and have better results. Different test functions can be used for different types of problems. These test functions have a specific range and values, which can be applied in different situations. These functions, when applied to the PSO algorithm, can give the better comparison of results. The test functions that have been the most commonly adopted to assess performance of PSO-based algorithms and details of each of them are provided, such as the search range, the position of their known optima, and other relevant properties.
In Digital image processing; many researches have been done on image denoising so far. Nowadays, the noise detection from an image is the most challenging task. Though, the various algorithms introduced for the detection of noise type from a noisy image, but these algorithms work only for detection of single type of noise. To overcome the limitation of the previous built algorithms, we investigate the data mining technique called Support Vector Machine. The SVM is a powerful supervised learning method which is to be used for the detection of mixed noise models. Broadly, this technique detects the different types of noise from a mixed noise image; noise can be either single or mixed type of noise. The different parameters have combined to describe the properties of these different noise models so as to perform the detection. The detecting algorithm has been achieved by applying the SVM on the training dataset of different medical images and further extensive tests are performed on the test dataset for detection of each noise type model. This detection technique clearly outperforms various techniques with the high accuracy of results for different proposed noise models.
A lot of time of the users is consumed in searching appropriate papers related to the desired topic. It takes time to look through the paper also. In this paper, a hybrid method is introduced to classify research papers. This algorithm is designed to classify all research papers at the time of uploading in the repository. Hence it becomes easy to explore appropriate paper on a specific topic in minimum time. A data set has generated with research papers on different topics like natural language processing, machine learning, etc. The proposed algorithm passes the most frequent items fetched from the training data set to k-nearest neighbor method instead of the whole data set, to make clusters. The performance of the proposed method is compared with traditional KNN method which results the accuracy, improved by the factor of 7.46%.
This paper presents a survey on Radio over fiber for Wireless Broadband Access technology like WiMAX (Fixed and Mobile). Radio over Fiber has achieved an effective delivery of wireless and baseband signal, and has also reduces the power consumption. Whereas recent fast growing broadband access technology through wireless is WiMAX (Worldwide Interoperability for Microwave Access). In this survey, the Radio over fiber implements into wireless broadband access technologies like WiMAX. And also we will be carried out different keying and technical concept from the different methodology.
The text localization and recognition in real time scene text images is still a big issue in current application. Mobile application and digitization in real world gives a vital and broad impact on real time scene text images. However, the efficiency of recognition rate depends upon the text localization, i.e., higher the purity of text background segmentation and decomposition, higher the rate of accuracy for the image recognition. In this paper, we present a new scene text detection algorithm based on Stroke detection and Hog Transform method. The method introduces an approach for character detection and recognition which combines the advantages of Hog Transform and Connected Component methods. Characters are detected and recognized on the basis of image regions which contain strokes of specific orientations in a specific relative position, where the strokes are efficiently detected by convolving the image gradient field with a set of oriented bar filters. The method was evaluated on a standard dataset consisting mostly real time images where it achieves state-of-the-art results in both text localization and recognition. The results clearly depict the higher bit of accuracy in terms of localization and recognition for a collected dataset.
Video generation is the strategy of making videos by discovering moving pictures (videography) and making blends in live creation. During this aspect of videos creation, sequence of images gets contorted in the image acquisition and transmission phase. This paper presents a non-linear patch model for enhancing the degraded quality of video sequences. The proposed method navigates the frames of videos by selecting a patch of considerable width and with the assistance of this patch, a regression model is applied in order to have a robust effect while performing de-noising, de-blurring or super-resolution. The proposed work implements on three models i.e. search model, regression model and non-linear patch model. Various filters have been propounded e.g. kernel filters, total variation filters, adaptive median filter etc. Regression model is applied to every frame with predefined number of iterations with estimated number of frames. The proposed method inculpates two kinds of noise i.e. Gaussian noise and impulse noise. Various performance comparison metrics have been evaluated to check the coherence and productivity of imaging system like Peak signal to noise ratio (PSNR), Mean squared error (MSE), Root mean squared error (RMSE), Standard deviation (SD), Linear correlation and structural entropy.