PSG College of Technology

About: PSG College of Technology is a based out in . It is known for research contribution in the topics: Machining & Thin film. The organization has 3174 authors who have published 3575 publications receiving 40690 citations.

An Automated Assessment of Students' Learning in e-Learning Using Concept Map and Ontology Mapping

Abstract: E-learning plays vital role in education and its importance is constantly increasing. The key challenge in the teaching learning process of e-learning is assessing the students' learning. Learning means the acquisition of knowledge or skills through experience, practice, or study, or by being taught. Assessing the students learning on the topics being taught is very important in e-learning environment. Based on the student's learning, the system can change / update the pedagogy, recommendations can be made for further study, and the students' performance can be evaluated. Many e-learning systems assess student learning by conducting tests, quizzes or assignments. In this paper, we propose concept map based on assessment from students' learning using ontology mapping. Concept map is an effective tool for determining what a student knows in the topic covered. The concept map created by the student is converted into ontology and is then mapped with the reference ontology created by the expert. The experimental evaluation is presented and discussed.

Automata Based Hybrid PSO–GWO Algorithm for Secured Energy Efficient Optimal Routing in Wireless Sensor Network

Abstract: The main objective in wireless sensor networks is to exploit efficiently the sensor nodes and to prolong the lifetime of the network. The discussion of energy is a significant concern to extend the lifetime of the network. Moreover, a nature inspired hybrid optimization approach called hybrid Particle Swarm Optimization–Grey Wolf Optimizer (PSO–GWO) is used in this work to efficiently utilize the energy and to transmit the data securely in an augmented path. A Learning Dynamic Deterministic Finite Automata (LD2FA) has been innovated and initiated to learn the dynamic role of the environment. LD2FA is mainly used to provide the learned and accepted string to hybrid PSO–GGWO so that the routes are optimized. Hybrid PSO–GWO is used to choose the optimal next node for each path to obtain the optimal route. The simulation results are obtained in MATLAB for 100–700 sensor nodes in a region of 500 × 500 m2 which demonstrate that the proposed LD2FA based Hybrid PSO–GWO algorithm obtains better results when compared with existing algorithms. It is observed that LD2FA based Hybrid PSO–GWO has an increase of 18% and 48% betterment in lifetime of the network than PSO and GLBCA, nearly 57% and 75% increase in network lifetime when compared with GA and LDC respectively. It also shows an improvement of 24% increase compared to cluster-based IDS, nearly a rise of 90% throughput when compared with lightweight IDS. The consumption of energy is reduced by 13% and 15% than PSO and GA and an increase of 15% utilization of energy than LDC. Therefore, LD2FA based Hybrid PSO–GWO is been considered to efficiently utilize energy in an optimal route.

Soft g* closed and soft g*open sets in soft topological spaces

Abstract: In this paper, we introduce the concept of soft g*closed sets and soft g* open sets in soft topological space which are defined over an initial universe set with a fixed set of parameters together with its corresponding soft g*closure and soft g*interior operators. Also, we introduce the concept of soft and regular spaces and investigate the relationship between them.

Sustainable Biopolymer Fibers—Production, Properties and Applications

Abstract: The ultimatum for renewable raw materials is growing steadily as the drive for a green economy and a sustainable future accelerates. Escalating environmental problems and changing attitudes of consumers have made petroleum-based manufactured products more expensive and less desirable in the present world. Biopolymers, which are biological or biologically derived polymers, are a petroleum-free source of fibers for the textile industry and have a significant positive impact by reducing the dependence on fossil fuels as well as the carbon foot print and may even offer cost and durability benefits compared with synthetic textiles. This chapter deals with the less investigated and emerging biopolymer fibers, which will have huge impact on sustainable luxury fashion in the future. Bio-fibers from animal protein (spider silk, hag fish slime), regenerated cellulose (seaweed), and regenerated protein (milk fiber) as well as biopolymers synthesized from bio-derived monomers (PLA, PTT) are discussed in depth. The raw materials for production/extraction of fibers and their properties, applications, and ecological impacts are discussed.