Purification,structural data and biological properties of polysaccharide from Prunus amygdalus gum

This work demonstrates the efficiency of almond gum polysaccharides (AGPs) as bioactive compounds. AGPs were first extracted using H₂O₂, in the presence of NaOH, at different times and temperatures. The optimal extraction conditions were 4% H₂O₂ and 2 N NaOH, for 7 h at 50 °C, leading to an extraction yield of 58.2% (w/w). After a purification step, the retained AGPs were characterised using high‐performance liquid chromatography showing a molecular weight of 99.3 kDa. The monosaccharide composition of AGPs were assessed using gas chromatography–mass spectrometry. AGPs were found to be a complex heteropolysaccharide with a repeating unit mainly composed of galactose, arabinose, xylose, mannose, rhamnose, and glucuronic acid with the respective ratios: 45:26:7:10:1:11. The acidic nature of the polysaccharide is due to the presence of glucuronic acid. Total antioxidant activity, free radical‐scavenging activity and reducing power assay of AGPs were investigated. The obtained results showed high antioxidant activities of AGPs. Furthermore, beyond 60 mg mL^?1, AGPs exhibited bacterial growth inhibition for five pathogenic strains: Escherichia coli, Staphylococcus aureus, Enterococcus feacalis, Pseudomonas aeruginosa and Salmonella typhimurium.     

Visual tracking using convolutional features with sparse coding

Artificial Intelligence Review - Visual object tracking has become one of the most active research topics in computer vision, and it has been applied in several commercial...     

The rare caribbean spongeLeucosolenia canariensis: Phospholipid fatty acids and sterols

The phospholipid fatty acid composition of the Calcarean spongeLeucosolenia canariensis was studied, and no Δ^5,9 fatty acids were detected. These results are in contrast to the phospholipids from sponges belonging to the class Demospongiae where Δ5,9 fatty acids are predominant. Odd branched-chain fatty acids between 17 and 19 carbons accounted for 26% of the total fatty acids ofL. canariensis, while straight-chain fatty acids between 16 and 22 carbons accounted for 61% of the total fatty acid composition. The sterol composition ofL. canariensis is also reported, and only Δ^5,7,22 sterols were observed.     

Speech enhancement with an adaptive Wiener filter

This paper proposes an adaptive Wiener filtering method for speech enhancement. This method depends on the adaptation of the filter transfer function from sample to sample based on the speech signal statistics; the local mean and the local variance. It is implemented in the time domain rather than in the frequency domain to accommodate for the time-varying nature of the speech signals. The proposed method is compared to the traditional frequency-domain Wiener filtering, spectral subtraction and wavelet denoising methods using different speech quality metrics. The simulation results reveal the superiority of the proposed Wiener filtering method in the case of Additive White Gaussian Noise (AWGN) as well as colored noise.     

An efficient singular value decomposition algorithm for digital audio watermarking

The singular value decomposition (SVD) mathematical technique is utilized, in this paper, for audio watermarking in time and transform domains. Firstly, the audio signal in time or an appropriate transform domain is transformed to a 2-D format. The SVD algorithm is applied on this 2-D matrix, and an image watermark is added to the matrix of singular values (SVs) with a small weight, to guarantee the possible extraction of the watermark without introducing harmful distortions to the audio signal. The transformation of the audio signal between the 1-D and 2-D formats is performed in the well-known lexicographic ordering method used in image processing. A comparison study is presented in the paper between the time and transform domains as possible hosting media for watermark embedding. Experimental results are in favor of watermark embedding in the time domain if the distortion level in the audio signal is to be kept as low as possible with a high detection probability. The proposed algorithm is utilized also for embedding chaotic encrypted watermarks to increase the level of security. Experimental results show that watermarks embedded with the proposed algorithm can survive several attacks. A segment-by-segment implementation of the proposed SVD audio watermarking algorithm is also presented to enhance the detectability of the watermark in the presence of severe attacks.     

Detection of Abnormal Activities from Various Signals Based on Statistical Analysis

We perceive big data with massive datasets of complex and variegated structures in the modern era. Such attributes formulate hindrances while analyzing and storing the data to generate apt aftermaths. Privacy and security are the colossal perturb in the domain space of extensive data analysis. In this paper, our foremost priority is the computing technologies that focus on big data, IoT (Internet of Things), Cloud Computing, Blockchain, and fog computing. Among these, Cloud Computing follows the role of providing on-demand services to their customers by optimizing the cost factor. AWS, Azure, Google Cloud are the major cloud providers today. Fog computing offers new insights into the extension of cloud computing systems by procuring services to the edges of the network. In collaboration with multiple technologies, the Internet of Things takes this into effect, which solves the labyrinth of dealing with advanced services considering its significance in varied application domains. The Blockchain is a dataset that entertains many applications ranging from the fields of crypto-currency to smart contracts. The prospect of this research paper is to present the critical analysis and review it under the umbrella of existing extensive data systems. In this paper, we attend to critics' reviews and address the existing threats to the security of extensive data systems. Moreover, we scrutinize the security attacks on computing systems based upon Cloud, Blockchain, IoT, and fog. This paper lucidly illustrates the different threat behaviour and their impacts on complementary computational technologies. The authors have mooted a precise analysis of cloud-based technologies and discussed their defense mechanism and the security issues of mobile healthcare.

     

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy. Deep learning provides a high performance for several medical image analysis applications. This paper proposes a deep learning model for the medical image fusion process. This model depends on Convolutional Neural Network (CNN). The basic idea of the proposed model is to extract features from both CT and MR images. Then, an additional process is executed on the extracted features. After that, the fused feature map is reconstructed to obtain the resulting fused image. Finally, the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching (HM), Histogram Equalization (HE), fuzzy technique, fuzzy type Π, and Contrast Limited Histogram Equalization (CLAHE). The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement quality. Different realistic datasets of different modalities and diseases are tested and implemented. Also, real datasets are tested in the simulation analysis.     

Applying Dynamic Training‐Subset Selection Methods Using Genetic Programming for Forecasting Implied Volatility

Volatility is a key variable in option pricing, trading, and hedging strategies. The purpose of this article is to improve the accuracy of forecasting implied volatility using an extension of genetic programming (GP) by means of dynamic training‐subset selection methods. These methods manipulate the training data in order to improve the out‐of‐sample patterns fitting. When applied with the static subset selection method using a single training data sample, GP could generate forecasting models, which are not adapted to some out‐of‐sample fitness cases. In order to improve the predictive accuracy of generated GP patterns, dynamic subset selection methods are introduced to the GP algorithm allowing a regular change of the training sample during evolution. Four dynamic training‐subset selection methods are proposed based on random, sequential, or adaptive subset selection. The latest approach uses an adaptive subset weight measuring the sample difficulty according to the fitness cases' errors. Using real data from S&P500 index options, these techniques are compared with the static subset selection method. Based on mean squared error total and percentage of non‐fitted observations, results show that the dynamic approach improves the forecasting performance of the generated GP models, especially those obtained from the adaptive‐random training‐subset selection method applied to the whole set of training samples.