Data mining-based recommendation system using social networks-an analytical study.

In the current age, social media is commonly used and shares enormous data. However, a huge amount of data makes it difficult to deal with. It requires a lot of storage and processing time. The content produced by social media needs to be stored efficiently by using data mining methods for providing suitable recommendations. The goal of the study is to perform a systematic literature review (SLR) which finds, analyzes, and evaluates studies that relate to data mining-based recommendation systems using social networks (DRSN) from 2011 to 2021 and open up a path for scientific investigations to enhance the development of recommendation systems in a social network. The SLR follows Kitchenhem's methodology for planning, guiding, and reporting the review. A systematic study selection procedure results in 42 studies that are analyzed in this article. The selected articles are examined on the base of four research questions. The research questions focus on publication venues, and chronological, and geographical distribution in DRSN. It also deals with approaches used to formulate DRSN, along with the dataset, size of the dataset, and evaluation metrics that validate the result of the selected study. Lastly, the limitations of the 42 studies are discussed. As a result, most articles published in 2018 acquired 21% of 42 articles, Whereas, China contributes 40% in this domain by comparing to other countries. Furthermore, 61% of articles are published in IEEE. Moreover, approximately 21% (nine out of 42 studies) use collaborative filtering for providing recommendations. Furthermore, the Twitter data set is common in that 19% of all other data sets are used, and precision and recall both cover 28% of selected articles for providing recommendations in social networks. The limitations show a need for a hybrid model that concatenates different algorithms and methods for providing recommendations. The study concludes that hybrid models may help to provide suitable recommendations on social media using data mining rules.

Magnetic Dipole and Thermophoretic Particle Deposition Impact on Bioconvective Oldroyd-B Fluid Flow over a Stretching Surface with Cattaneo-Christov Heat Flux.

This study emphasizes the performance of two-dimensional electrically non-conducting Oldroyd-B fluid flowing across a stretching sheet with thermophoretic particle deposition. The heat and mass transfer mechanisms are elaborated in the presence of a magnetic dipole, which acts as an external magnetic field. The fluid possesses magnetic characteristics due to the presence of ferrite particles. The gyrotactic microorganisms are considered to keep the suspended ferromagnetic particles stable. Cattaneo-Christov heat flux is cogitated instead of the conventional Fourier law. Further, to strengthen the heat transfer and mass transfer processes, thermal stratification and chemical reaction are employed. Appropriate similarity transformations are applied to convert highly nonlinear coupled partial differential equations into non-linear ordinary differential equations (ODEs). To numerically solve these ODEs, an excellent MATLAB bvp4c approach is used. The physical behavior of important parameters and their graphical representations are thoroughly examined. The tables are presented to address the thermophoretic particle velocity deposition, rate of heat flux, and motile microorganisms' density number. The results show that the rate of heat transfer decreases as the value of the thermal relaxation time parameter surges. Furthermore, when the thermophoretic coefficient increases, the velocity of thermophoretic deposition decreases.

Morphological, Physical, and Mechanical Properties of Sugar-Palm (Arenga pinnata (Wurmb) Merr.)-Reinforced Silicone Rubber Biocomposites.

The development of environmentally benign silicone composites from sugar palm fibre and silicone rubber was carried out in this study. The mechanical, physical, and morphological properties of the composites with sugar palm (SP) filler contents ranging from 0% to 16% by weight (wt%) were investigated. Based on the uniaxial tensile tests, it was found that the increment in filler content led to higher stiffness. Via dynamic mechanical analysis (DMA), the viscoelastic properties of the silicone biocomposite showed that the storage modulus and loss modulus increased with the increment in filler content. The physical properties also revealed that the density and moisture absorption rate increased as the filler content increased. Inversely, the swelling effect of the highest filler content (16 wt%) revealed that its swelling ratio possessed the lowest rate as compared to the lower filler addition and pure silicone rubber. The morphological analysis via scanning electron microscopy (SEM) showed that the sugar palm filler was evenly dispersed and no agglomeration could be seen. Thus, it can be concluded that the addition of sugar palm filler enhanced the stiffness property of silicone rubber. These new findings could contribute positively to the employment of natural fibres as reinforcements for greener biocomposite materials.