Dr. M. Prakash
@vmkvasc.edu.in
Professor & Head, Department of Computer Science
Vinayaka Mission's Kirupananda Variyar arts and Science College, Vinayaka Mission's Research Foundation Deemed University
Scopus Publications
Scopus Publications
M. Sivaram, M. Kaliappan, S. Jeya Shobana, M. Viju Prakash, V. Porkodi, K. Vijayalakshmi, S. Vimal, and A. Suresh
Springer Science and Business Media LLC
Rakesh Kancharla, Venkata Rao Maddumala, T. V. N. Prasanna, Lokaiah Pullagura, Ratna Raju Mukiri, and M. Viju Prakash
Hindawi Limited
Present technology has been evaluated greatly over the past decades, where new particles are being designed and fabricated to fulfill specific needs. The field of nano- and micromaterials has prospered in many disciplines. It has been recently used in reinforced concrete in the production of high-strength, high-performance concrete. Microsilica (MS) and nanosilica (NS) particles have proven to be highly profitable to the concrete mix. Concrete has become denser with considerable improvement in their mechanical characteristics, particularly compressive strength. This proposed method includes a comparative study of the flexural bending behavior of conventional reinforced concrete (without MS or NS) slabs with other slabs. Each has various mixes of MS and NS particles incorporated into the concrete mix. The material content utilized in the slabs is kept constant by replacing a portion of the cement with an equivalent amount of either NS or MS particles or both. MS particles are altered from 0, 5, and 10% while NS particles are altered from 0, 0.5, and 1.0%. It cracks the widths and has higher final load-bearing capacity.
Ch. Nanda Krishna, Madhavi Katamaneni, Kalyan Chakravarti Yelavarti, B. Sobhan Babu, B. Ravi Kumar, and M. Viju Prakash
Hindawi Limited
Wood is a wide flexible material appreciated extremely for its cost-effectiveness, great quantity, and biocompatibility. In addition, naturally existing materials possess prominent biomedical applications, and they can withstand efficiently when compared to other materials like glass, steel, and plastics. The present study revealed the prepared chitosan, silver nanoparticles incorporated with Borassus flabellifer trichome, and fabrication of Prosopis juliflora wood-based biomaterial. A characterization study was done by UV-visible spectroscopic analysis, FTIR analysis, and SEM analysis expressing and confirming a significant characteristic and morphological property of the prepared biomaterial.
Samuel Raj Samuel Raj, M. V. Prakash, T. Prince, K. Shankar, V. Varadarajan and Fredi Nonyelu
Web applications are utilized on an extensive scale across the globe and it handles sensitive individual information of users. Structured Query Language (SQL) Data Inference (DI) and injection are procedures that abuse a security defenselessness occurring in the database layer of an application. This research article focuses on website page database security with the help of optimization and encryption methods for Web of Things Environments. Initially, the selected queries in webpage application are injected as per Discrete Bee Colony Optimization (DBCO) procedure. After the Proxy filtering, the injection prevention model is utilized, the injected data with various queries of different special characters are utilized. At long last, the attack gets detected depending on the user query with the assistance of query tree mechanism. Besides, an effective Fully Homomorphic Encryption (FHE) encryption is proposed in the study. From the implementation results, it is to be noted that the proposed method achieved 93.56% security level for the prevented webpage implication-based databases. The effect on the businesses must be comprehended to decrease the risk involved in SQL and DI injection assaults.
M. Viju Prakash, V. Porkodi, S. Rajanarayanan, Mujeebudheen Khan, Banar Fareed Ibrahim, and M. Sivaram
IEEE
This Fog Computing is an extension of Cloud Computing technology to the network edge, which enables a newer breed of services and applications. Resource sharing and resource discovery is considered to be critical for the performance of fog computing applications. In general, the methods to reduce are consumption of energy in heterogonous network is very minimal. Considering the higher consumption of energy in heterogeneous networks as a problem statement, this work proposes a machine learning model to reduce the consumption of energy demand in fog computing Internet of Things (IoT) services. Considering the problem, the machine learning model adopts network density, latency and mobility as its energy constraints and designs an objective function to support the lower energy consumption in the network. The simulation of the proposed method is carried out between the proposed and existing methods in terms of various performance metrics. The result shows that the proposed machine learning method in Fog IoT environment is efficient in conserving the energy than the other methods.