We propose and experimentally evaluate a software solution for automatic detection and classification of plant leaf diseases. The proposed solution is an improvement to the solution proposed in [1] as it provides faster and more accurate solution. The developed processing scheme consists of four main phases as in [1]. The following two steps are added successively after the segmentation phase. In the first step we identify the mostlygreen colored pixels. Next, these pixels are masked based on specific threshold values that are computed using Otsu's method, then those mostly green pixels are masked. The other additional step is that the pixels with zeros red, green and blue values and the pixels on the boundaries of the infected cluster (object) were completely removed. The experimental results demonstrate that the proposed technique is a robust technique for the detection of plant leaves diseases. The developed algorithm"s efficiency can successfully detect and classify the examined diseases with a precision between 83% and 94%, and can achieve 20% speedup over the approach proposed in [1].

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Flower classification is a challenging task due to the wide range of flower species, which have a similar shape, appearance or surrounding objects such as leaves and grass. In this study, the authors propose a novel two-step deep learning classifier to distinguish flowers of a wide range of species. First, the flower region is automatically segmented to allow localisation of the minimum bounding box around it. The proposed flower segmentation approach is modelled as a binary classifier in a fully convolutional network framework. Second, they build a robust convolutional neural network classifier to distinguish the different flower types. They propose novel steps during the training stage to ensure robust, accurate and realtime classification. They evaluate their method on three well known flower datasets. Their classification results exceed 97% on all datasets, which are better than the state-of-the-art in this domain.

We presented a fully automated CAD system that seamlessly integrates within the clinical work flow of the radiologist. Our clinically motivated features resulted in a great performance of both the supervised and unsupervised learners that we utilize to validate our CAD system. Our CAD system results are promising to serve in clinical applications after extensive validation.

Digital pathology has been clinically approved for over a decade to replace traditional methods of diagnosis. Many challenges appear when digitising the whole slide scan into high resolution images including memory and time management. Whole slide images require huge memory space if the tissue is not pre-localised for the scanner. The authors propose a set of clinically motivated features representing colour, intensity, texture and location to segment and localise the tissue from the whole slide image. This step saves both the scanning time and the required memory space. On average, it reduces scanning time up to 40% depending on the tissue type. The authors propose, using unsupervised learning, to segment and localise tissue by clustering. Unlike supervised methods, this method does not require the ground truth which is time consuming for domain experts. The authors proposed method achieves an average of 96% localisation accuracy on a large dataset. Moreover, the authors outperform the previously proposed supervised learning results on the same data.

Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in: Abstract Scrambling is a process that has proved to be very effective in increasing the quality of data hiding, watermarking, and encryption applications. Cellular automata are used in diverse and numerous applications because of their ability to obtain complex global behavior from simple and localized rules. In this paper we apply cellular automata in the field of audio scrambling because of the potential it holds in achieving a high scrambling degree. We also analyze the effect of using different cellular automata types on audio scrambling and we test different cellular automata rules with different Lambda values. The relation between the robustness and the scrambling degree is also studied. Experimental results show that the proposed technique is robust to data loss attack and can be applied to different applications based on the scrambling degree required.

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