Images are very important forms of data that are widely used nowadays. Every day, millions of grey and color images are transferred via the web. Protecting these images from unauthorized persons is an important issue. Image encryption is one of the image-securing techniques. One advantage of using encryption is that the plain image is converted to an unrecognized one. Also, the plain image is restored without any loss of information. In this paper, a color image encryption technique using blocks scrambling and chaos is presented. First, the plain image is decomposed into three channels: R, G, and B. Then, each channel is divided into sub-images and blocks. Our encryption algorithm depends on two main steps: scrambling and diffusion. First, the pixels’ arrangement in sub-images and blocks is changed, and then scrambling between sub-images is done to get the scrambled image. Then, in diffusion, the scrambled image diffuses using the logistic map to get the encrypted image. The experimental results show that our proposed algorithm has good performance in encrypting color images.

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The single watermarking algorithm for medical images faced several inherent problems like lack of security. In
this paper, a new geometrically invariant multiple zero-watermarking method is proposed to secure medical
images. We proposed a novel set of multi-channels shifted Gegenbauer moments of fractional orders (FrMGMs).
These moments are used to extract the geometrically invariant features from the color medical images. Then we
construct a featured image of the original medical image using the magnitude of the selected precise FrMGMs
moments. Finally, we applied a scrambling method to scramble the watermark image and then the exclusive OR
operation to the images’ feature and scrambled watermark to construct a zero-watermark. Experimental results
proved that the proposed approach effectively provided better robustness to various standard attacks and outperformed the existing single-, dual-, & triple-zero-watermarking algorithms for medical images.

The problem of finding similarity between natural language sentences is crucial for many applications in Natural Language Processing (NLP). Moreover, accurate calculation of similarity between sentences is highly needed. Many approaches depends on word-to-word similarity to measure sentences similarity. This paper proposes a new approach to improve accuracy of sentences similarity calculation. The proposed approach combines different similarity measures in calculation of sentences similarity. In addition to traditional word-to-word similarity measure the proposed approach exploits sentences semantic structure. Discourse representation structure (DRS) which is a semantic representation for natural sentences is generated and used to calculated structure similarity. Furthermore, word order similarity is measured to consider order of words in sentences. Experiments show that exploiting structural information achieves good results. Moreover, the proposed method outperforms the current approaches on Pilot standard benchmark dataset achieving 0.8813 peasron correlation with human similarity.
 

The problem of measuring similarity between sentences is crucial for many applications in Natural Language Processing (NLP). Most of the proposed approaches depend on similarity of words in sentences. This research considers semantic relations between words in calculating sentence similarity. This paper uses Discourse Representation Structure (DRS) of natural language sentences to measure similarity. DRS captures the structure and semantic information of sentences. Moreover, the estimation of similarity between sentences depends on semantic coverage of relations of the �first sentence in the other sentence. Experiments show that exploiting structural information achieves better results than traditional word-to- word approaches. Moreover, the proposed method outperforms similar approaches on a standard benchmark dataset.

Automated classification of malignant and benign breast cancer lesions using neural networks on digitized mammograms

ASGOP: An aggregated similarity-based greedy-oriented approach for relational DDBSs design

In this paper, a proposed algorithm that dynamically changes the neural network structure is presented. The structure is changed based on some features in the cascade correlation algorithm. Cascade correlation is an important algorithm that is used to solve the actual problem by artificial neural networks as a new architecture and supervised learning algorithm. This process optimizes the architectures of the network which intends to accelerate the learning process and produce better performance in generalization. Many researchers have to date proposed several growing algorithms to optimize the feedforward neural network architectures. The proposed algorithm has been tested on various medical data sets. The results prove that the proposed algorithm is a better method to evaluate the accuracy and flexibility resulting from it. View Full-Text