Improved multiple secret sharing using generalized chaotic image scrambling

Multimedia Tools and Applications - This paper presents a new technique on (n,?n)-Multiple Secret Sharing (MSS) of color images. In this task, n shared images are generated from n secret...     

Modeling in the design of a KBS validation system

We describe and illustrate the modeling issues in the design of a system for validation of knowledge based systems (KBSs). the domain of such a validation system is “KBSs and their validation problems.” the basic idea in our solution is the following. Since different KBSs may use different knowledge representation languages, we first represent the target KBS (i.e., the KBS to be validated) in a general formal model of KBS, and then validate it in this form. the advantage of this strategy is that validation problem solving needs only to refer to the common language of the general formal model. We present a set of possible conceptual abstraction levels in such a model, and argue that each level is associated with a related view on validation problems. Since high level characterizations are difficult to abstract from current knowledge representation languages, we consider the formal aspects of modeling mainly at the “lowest” level, the so-called inference primitive level. We illustrate the approach by formalizing a solution for selected modeling issues at this level.     

Random Forest classification model of basal stem rot disease caused by Ganoderma boninense in oil palm plantations

Ganoderma boninense is a fungus that causes basal stem rot (BSR) disease in oil palm plantations. BSR is a major disease in oil palm plantations in both Indonesia and Malaysia. There is no effective treatment for curing BSR; current treatments only prolong the life of oil palms. One strategy to control BSR is early detection of G. boninense infection. Based on the infection symptoms, many researchers have applied remote-sensing techniques for early detection and mapping of BSR disease in oil palms. The main objectives of this article were to evaluate the potential of machine-learning models for predicting BSR disease in oil palm plantations and to produce maps of the distribution of BSR disease. QuickBird imagery archived on 4 August 2008 was applied in three classifier models: Support Vector Machine, Random Forest (RF), and classification and regression tree models The RF model was best at predicting, classifying, and mapping oil palm BSR in terms of overall accuracy (OA), producer accuracy, user accuracy, and kappa value. Using 75% of the data for training and 25% for testing, the RF classifier model achieved 91% OA. In addition, this model separated the healthy and unhealthy oil palms in the study sites into 37,617 (75%) and 12,320 (25%) individuals, respectively.