A switched chaotic encryption scheme using multi-mode generalized modified transition map

Multimedia Tools and Applications - This paper presents a multi-mode generalized modified transition chaotic map and a switched chaotic encryption scheme based on it. Eight different modes of...     

Extending Gardiner’s code for Hieroglyphic recognition and English mapping

Multimedia Tools and Applications - “Knowledge is power”… Writing is the main way to preserve the humanity knowledge across the ages. Therefore, an automatic and accurate mapping...     

Predicting the COVID-19 infection with fourteen clinical features using machine learning classification algorithms

Multimedia Tools and Applications - While the RT-PCR is the silver bullet test for confirming the COVID-19 infection, it is limited by the lack of reagents, time-consuming, and the need for...     

Improving the performance of LSSVM model in predicting the safety factor for circular failure slope through optimization algorithms

Circular failure can be seen in weak rocks, the slope of soil, mine dump, and highly jointed rock mass. The challenging issue is to accurately predict the safety factor (SF) and the behavior of slopes. The aim of this study is to offer advanced and accurate models to predict the SF of slopes through machine learning methods improved by optimization algorithms. To this view, three different methods, i.e., trial and error (TE) method, gravitational search algorithm (GSA), and whale optimization algorithm (WOA) were used to investigate the proper control parameters of least squares support vector machine (LSSVM) method. In the constructed LSSVM-TE, LSSVM-GSA and LSSVM-WOA methods, six effective parameters on the SF, such as pore pressure ratio and angle of internal friction, were used as the input parameters. The results of the error criteria indicated that both GSA and WOA can improve the performance prediction of the LSSVM method in predicting the SF. However, the LSSVM-WOA method, with root mean square error of 0.141, performed better than the LSSVM-GSA with root mean square error of 0.170.

     

Comparison of dragonfly algorithm and Harris hawks optimization evolutionary data mining techniques for the assessment of bearing capacity of footings over two-layer foundation soils

Engineering with Computers - By assist of novel evolutionary science, the classification accuracy of neural computing is improved in analyzing the bearing capacity of footings over two-layer...     

Feature selection techniques in the context of big data: taxonomy and analysis

Applied Intelligence - Recent advancements in Information Technology (IT) have engendered the rapid production of big data, as enormous volumes of data with high dimensional features grow...     

A smart intuitionistic fuzzy-based framework for round-robin short-term scheduler

The Journal of Supercomputing - A smart intuitionistic fuzzy-based framework is designed to facilitate adaptability by providing continuous changes in the size of time slice to scheduler at run...     

Smart-Fragile Authentication Scheme for Robust Detecting of Tampering Attacks on English Text

Content authentication, integrity verification, and tampering detection of digital content exchanged via the internet have been used to address a major concern in information and communication technology. In this paper, a text zero-watermarking approach known as Smart-Fragile Approach based on Soft Computing and Digital Watermarking (SFASCDW) is proposed for content authentication and tampering detection of English text. A first-level order of alphanumeric mechanism, based on hidden Markov model, is integrated with digital zero-watermarking techniques to improve the watermark robustness of the proposed approach. The researcher uses the first-level order and alphanumeric mechanism of Markov model as a soft computing technique to analyze English text. Moreover, he extracts the features of the interrelationship among the contexts of the text, utilizes the extracted features as watermark information, and validates it later with the studied English text to detect any tampering. SFASCDW has been implemented using PHP with VS code IDE. The robustness, effectiveness, and applicability of SFASCDW are proved with experiments involving four datasets of various lengths in random locations using the three common attacks, namely insertion, reorder, and deletion. The SFASCDW was found to be effective and could be applicable in detecting any possible tampering.     

Tunable microwave absorption performance of carbon fiber-reinforced reaction bonded silicon nitride composites

The hybrid network of Si₃N₄ whiskers and conducting carbon fiber has great potential for microwave absoprtion applications. The high electrical conductivity of the carbon fiber helps to transform the microwave transparent Si₃N₄ into microwave absorbing materials. Herein, the microwave absorption performance of 5–20 vol % of carbon fiber reinforced reaction bonded Si₃N₄ (C_f-RBSN) composites have been discussed in detail. The C_f reinforcement tuned the X-band dielectric properties of the RBSN composites. The 5 vol % C_f-RBSN composite exhibit a minimum reflection loss (RL_min) of ?36.16 dB (99.998% microwave absorption) at 11.89 GHz and a high specific reflection loss of 920 dB. g^?1 for 5.9 mm thickness, while 20 vol % C_f-RBSN composites resulted in RL_min of ?22.86 dB at 11.56 GHz with a low thickness of 1.5 mm. Thus, the superior microwave absorption performance of the prepared lightweight composites results from the multiple interfacial polarization, dipole polarization, and conduction loss due to the 3D network of interconnected Si₃N₄ whiskers and C_f.     

Battery Recovery-Aware Optimization for Embedded System Communications

Wireless Personal Communications - In this paper, we consider a point-to-point wireless communications for embedded battery powered systems. We aim to provide an optimal use of all the usable...