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

Zero-watermarking methods provide promising solutions and impressive performance
for copyright protection of images without changing the original images. In this paper, a novel
zero-watermarking method for color images is envisioned. Our envisioned approach is based on
multi-channel orthogonal Legendre Fourier moments of fractional orders, referred to as MFrLFMs. In this
method, a highly precise Gaussian integration method is utilized to calculate MFrLFMs. Then, based
on the selected accurate MFrLFMs moments, a zero-watermark is constructed. Due to their accuracy,
geometric invariances, and numerical stability, the proposed MFrLFMs-based zero-watermarking method
shows excellent resistance against various attacks. Performed experiments using the proposed watermarking
method show the outperformance over existing watermarking algorithms.

The TCP SYN flooding (half-open connection) attack is a type of DDoS attack, which denies
the services by consuming the server resources. This attack prevents legitimate users
from using their desired service. The SYN flooding attack exploits the normal TCP three-way
handshake by sending stream of SYN packets to the server with spoofed IP addresses. The
detection of this attack is hard since the internet routing infrastructure cannot differenti-
ate between legitimate and spoofed SYN packets. In this paper we present a new detection
method for the SYN flooding attack based on Multifractal Detrended Fluctuation Analysis
(MFDFA) in addition to an adaptive threshold, thus we can detect the abnormal behavior in
the TCP protocol time series.