Survey of Deep-Learning Techniques in Big-Data Analytics

Big-data research studies relying upon Deep-learning methods are revitalized the decision-making mechanism in the business sectors and the enterprise domains. The firms’ operational parameters also have the dependency of the Big-data analytics phase, their way of managing the data, and to evolve the outcomes of Big-data implementation by using the Deep-learning algorithms. Deep-learning approaches enhancements in Big-data applications facilitate the decision-making process such as the information-processing to the employees, analytical potentials augmentation, and in the transition of more innovative work. In this DL-approach, the robust-patterns of the data-predictions resulted from the unstructured information by conceptualizing the Decision-making methods. Hence this paper reviewed the impact of the Deep-learning process utilizing the Big-data in the enterprise and Business sectors. Also this study provides a comprehensive survey of all the Deep-learning techniques illustrating the efficiency of Big-Data processing and their impacts of operational parameters. Further it concentrating the data-dimensionality factors and the Big-data complications rectifying by utilizing the DL-algorithms, usage of Machine-learning or deep-learning process for the decision-making mechanism in the Enterprise sectors and business sectors. This research discussed the predictions of the Big-data analytics resulting to the decision parameters within the organisations, and in the management of larger scale of datasets in Big-data analytics processing by utilizing the Deep-learning implementations. The comparative analysis of the reviewed studies has also been described by comparing existing approaches of Deep-learning methodologies in employing Big-data analytics.

     

Generative Adversarial Networks Based Neural Session Key Exchange Protocol for Secured Transmission of Information

Wireless Personal Communications - Generative Adversarial Networks (GAN)-based neural session key exchange protocol is presented in this paper for secured transmission of information. An intruder...     

Depicting the role of gas-solid interactions on the hydrodynamics of converging pneumatic riser

The understanding of the flow characteristics and effect of gas-solid interactions in pneumatic risers is fundamental to investigate to ensure effective design cost-effective operation. Thus, to understand the effect of gas-solid interactions on the hydrodynamics of newly proposed conversing risers, this study mainly focused on predicting pressure drop in the dilute phase pneumatic conveying system. The experiments were conducted in a converging riser having a convergence angle of 0.2693°. Various solid particles such as sago, black mustard, and alumina have been considered to study the effect of particle sizes and density on the pressure drop. The experimental outcomes indicate that the total pressure drop increases with an increase in the solid density and gas mass flow rate. Moreover, smaller particle sizes are also increased the pressure drop. An empirical correlation is developed for the prediction of total pressure drop ΔP_T in converging pneumatic riser via dimensional analysis. All dependent variables such as particle and air density, drag force, acceleration due to gravity, the mass flow rate of air and particle, the diameter of particle and converging riser, the height of converging riser were considered to develop the empirical correlation. The established relationship is tested, and experimental data have been fitted for its validation. The estimated relative error of less than 0.05 proved the significance of the developed correlation. Hence, it can be stated that the established relationship is useful in studying the effects of various parameters on the pressure drop across the length of the conversing riser.     

CCH: a clique based asymmetric rendezvous scheme for cognitive radio ad-hoc networks

Wireless Networks - Rendezvous (RDV) in cognitive radio ad-hoc networks (CRAHNs) is one of the key ways for a pair of unknown cognitive radio users to initiate communications. CRAHN is a...     

Correction to: Comparative analysis of limit equilibrium and numerical methods for prediction of a landslide

Bulletin of Engineering Geology and the Environment - Landslides that occur due to the rapid motion of a rock-mass are a primary risk in mountainous terrains and are a danger to human life and...     

Multicriteria decision-making using Archimedean aggregation operators in Pythagorean hesitant fuzzy environment

In multicriteria decision-making (MCDM), the existing aggregation operators are mostly based on algebraic t-conorm and t-norm. But, Archimedean t-conorms and t-norms are the generalized forms of t-conorms and t-norms which include algebraic, Einstein, Hamacher, Frank, and other types of t-conorms and t-norms. From that view point, in this paper the concepts of Archimedean t-conorm and t-norm are introduced to aggregate Pythagorean hesitant fuzzy information. Some new operational laws for Pythagorean hesitant fuzzy numbers based on Archimedean t-conorm and t-norm have been proposed. Using those operational laws, Archimedean t-conorm and t-norm-based Pythagorean hesitant fuzzy weighted averaging operator and weighted geometric operator are developed. Some of their desirable properties have also been investigated. Afterwards, these operators are applied to solve MCDM problems in Pythagorean hesitant fuzzy environment. The developed Archimedean aggregation operators are also applicable in Pythagorean fuzzy contexts also. To demonstrate the validity, practicality, and effectiveness of the proposed method, a practical problem is considered, solved, and compared with other existing method.