The data hiding is known as a procedure which is used to achieve composite signals by embedding message signals into the host picture. Data hiding approach represents a class of processes which are implemented for embedding data into different formats. In this approach, secret message occurs along with the secret information. The steganography is defined as a process through which information produced from one source can be concealed into other sources. This thesis work presents a novel methodology for image steganography. The tested result demonstrated that the proposed technique shows its supremacy in terms of PSNR and MSE. The PSNR value of proposed approach is increased up to 15 percent in comparison with earlier approach. In comparison with earlier approach, the MSE value of proposed algorithm is decreased up to 10 percent.
Technology is advancing and can be used to tackle the problem of image classification. This review researches ecological change over a 30-year time frame and endeavors to pick up a superior comprehension of human effects on a dry domain and their outcomes for territorial advancement. Multi fleeting remotely detected symbolism was obtained and incorporated to set up the reason for change recognition and process examination. Arrive cover changes were explored in two classifications, to be specific all out change utilizing picture grouping and quantitative change utilizing a vegetation list. The outcomes demonstrate that human-incited arrive cover changes have been minor in this remote region. In any case, the pace of development of human-instigated change has been quickening since the mid-1990s. The proposed literature provides mechanism to tackle issue of remote sensing and provide the information about change that is experimentally validated. Image processing techniques are used for the purpose of classification. This literature is organised as 1) Pre-processing: used to eliminate distortion present within the image 2) Segmentation -is performed to extract required information in the form of black and white region 3) Clustering-provide information by reducing the image on distinct levels of pixels extraction 4) Classification-fuzzy neural systemis used to classify extracted data into classes specified. Obtained result is compared against MSVM(Multi class support vector machine) that shows significant improvement.
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 prematurely. 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.
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