As open source software projects gain acceptance in mission critical domains of a technology-enabled society, they must adapt their lightweight processes and tools to address the quality needs of these domains. One such domain is technology-supported surgical interventions. This paper presents an in-development toolset for validating the architecture of the open source Image-guided Surgical Toolkit (IGSTK). IGSTK components must conform to a specific architecture pattern based on state machines. Applications built on top of the IGSTK framework process and route instructions under specific constraints imposed by the architecture. This toolset focuses on validating that component state machines in IGSTK are designed and implemented in accordance with the architecture’s constraints. These tools employ open source components as well, and apply theoretical concepts from computer science to a practical problem of guaranteeing safety in a mission critical domain.

Plant disease identification is an important application for plant protection in agriculture production. The early detection of crop disease helps to reduce the effect of disease in cultivation. The detection of disease should be done precisely. Hence the hyperspectral sensors are extensively used in plant disease detection. Artificial intelligence and machine learning-based techniques have been presented in many works for plant disease detection. Deep learning is the latest method used in image processing and pattern recognition with improved accuracy. For plant disease detection, accurate classification of disease can be obtained with the utilization of deep learning techniques. In this paper, adaptive extreme learning machine (AELT) is presented for classifying the disease. Before the classification process, the segmentation and feature extraction process is performed to improve the disease detection accuracy. Multilevel thresholding-based K-means clustering with probability-induced butterfly optimization algorithm is presented for segmentation. The entropy-based features are extracted from plant images. The features are applied to the AELT classifier. The results are evaluated with the standard dataset and compared with the state of art techniques.

Images transfer more information about expressing an occasion than words. The skillful knowledge should exist to perceive the change in objects present in those images. Also, there is no sign existing to crisscross the objects are liable or not. The internet images should be legitimate to approve its truthiness. The cloned image forgery can be performed by many sophisticated cameras and by editing software. Therefore, a proposal should present to detect the change in counterfeit or forgery-based images to warrant the objects existing in the image are true. The input images are pre-processed to generate the Gray world and illuminant maps. The faces in the input images are segmented using both automatic and semi-automatic methods. The texture from the faces is bred using the values from pixels of locality. Canny detector is applied to distinguish the edge in the face. The features acquired from the faces are compared with each other to sense the counterfeit face that is spliced in the original image to make them as the composite image. The features are then trained to catalog them as forgery or no forgery. Existing methodologies has the capability to identify forgery in image with the extreme of two faces. The proposed method has the prospect to spot every faces present in the image. The cloned counterfeit faces are removed from the image by spatial contextual correlation strategy of image completion. Experimental results show that the proposed methodology achieves well than the other approaches present in the literature.

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