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The timely diagnosis of skin lesion diseases is highly difficult for people living in rural or far flung areas due to dearth of qualified dermatologists. However, the dermatologists can diagnose skin lesion diseases by carefully examining the high-quality images at their clinics or from a distance area. Further, the computerized automatic diagnostic system may assist primary health professionals for quick and accurate diagnosis of these skin diseases. Thus, there is a need for medical image processing and analysis of skin lesion images to enhance their visibility properties. An efficient and effective skin lesion detection and identification software tool will provide a better classification system and may enhance the automation of skin lesion diagnosis. In this work, detection of skin lesions from human skin images is conducted by utilizing three image processing segmentation methodologies namely—Edge Detection using Ant Colony Optimization, Color Space-based Thresholding, Genetic Algorithm-based Segmentation and FCM-Based Image Segmentation. In order to quantitatively collate the working of three techniques, the entropy values of skin lesion images are considered. Application of FCM-based Segmentation yields in far better attribute of skin lesion images as compared to Genetic Algorithm-based Segmentation, Edge Detection using Ant Colony Optimization and Color Space-based Thresholding. [ABSTRACT FROM AUTHOR]

The remote sensed images carry large amount of crucial information. Image processing, a field of signal processing, helps in analysis of remote sensed data. One of the major processing areas is image segmentation with edge detection, which helps in segmenting an image into various sub regions. These regions identified from images, captured over long span of time can help in identification of change detection. This paper presents an application of nature-inspired algorithms viz.: Ant Colony Algorithm, Particle Swarm Optimization and Genetic Algorithm to optimize edge detection procedure. These methods have been implemented on a set of 15 satellite images and further enhancement is done by application of adaptive thresholding using Python. For qualitative analysis, entropy of each output image is computed. The comparison of computer results revealed that particle swarm optimization outperforms conventional methods, i.e., Sobel, Canny and Prewitt as well as ACO and GA. The PSO-based method is able to find more edges and presents far superior quality output images for further analysis with respect to change detection. [ABSTRACT FROM AUTHOR]

Meta-heuristic Artificial Bee Colony Algorithm finds its applications in the optimization of numerical problems. The intelligent searching behaviour of honey bees forms the base of this algorithm. The Artificial Bee Colony Algorithm is responsible for performing a global search along with a local search. One of the major usage areas of Artificial Bee Colony Algorithm is software testing, such as in structural testing and test suite optimization. The implementation of Artificial Bee Colony Algorithm in the field of data flow testing is still unexplored. In data flow testing, the definition-use paths which are not definition-clear paths are the potential trouble spots. The main aim of this paper is to present a simple and novel algorithm by making use of artificial bee colony algorithm in the field of data flow testing to find out and prioritize the definition-use paths which are not definition-clear paths. [ABSTRACT FROM AUTHOR]

The remote sensed satellite images are big repository of information and provide the coverage of large areas. However, these images may not be able to describe the finer details of area being covered. Satellite Image optimization is the process of augmenting the components of an image for better and effective interpretations from satellite images. In order to obtain better visibility properties to fetch more information, various artificial intelligence techniques can be considered for the optimization process. Finding out the best technique for optimization is a challenging and time-consuming task [U1]. In this paper, applications of swarm-based artificial intelligence techniques such as ant colony optimization, particle swarm optimization, bat algorithm, artificial bee colony algorithm etc. are being analysed to process the remote sensed data. The detailed comparison with respect to classifier, utility, images considered, and observation are discussed. The comprehensive analysis revealed that particle swarm optimization is the most widely used technique. Further, various application areas such as land-use land-cover are discussed with possibilities of future research [U2]. [ABSTRACT FROM AUTHOR]