Online Campus Recruitment System-A Machine Learning Model

As the job market for college students heats up, firms are paying more attention to campus recruiting as the major way of employing college graduates. This research looks at the indicators and reasons for possible hazards for companies when recruiting on college campuses. Several measures are advised in the interim, which may assist organizations to decrease the hazards connected with campus recruiting and boost its success rate. Training and placement cell operations are expedited, and students are put in the most coordinated scenario feasible, all owing to the campus recruiting system. This promotes the aggregation of student knowledge to boost the selection rate and simplifies the process of automatically creating management data. The major purpose of online training and placement is to automate the placement cell. CV validation, advertising job vacancies to a student community, maintaining contact with companies to invite students to internships and other events, monitoring the selection process, and engaging with a broad variety of users

NEW FIXED POINT RESULTS FOR T-CONTRACTIVE MAPPING WITH c-DISTANCE IN CONE METRIC SPACES

In this article, we generalize and improve the results of Fadail et al.[Z. M. Fadail and S. M. Abusalim, Int. Jour. of Math. Anal., Vol. 11, No. 8(2017), pp. 397-405.] and Dubey et al.[AnilKumar Dubey and Urmila Mishra, Non. Func. Anal. Appl., Vol. 22, No. 2(2017), pp 275-286.] under the concept of a c-distance in cone metric spaces. We prove the existence and uniqueness of the fixed point for T -contractive type mapping under the concept of c-distance in cone metric spaces

An optimized cost-based data allocation model for heterogeneous distributed computing systems

Continuous attempts have been made to improve the flexibility and effectiveness of distributed computing systems. Extensive effort in the fields of connectivity technologies, network programs, high processing components, and storage helps to improvise results. However, concerns such as slowness in response, long execution time, and long completion time have been identified as stumbling blocks that hinder performance and require additional attention. These defects increased the total system cost and made the data allocation procedure for a geographically dispersed setup difficult. The load-based architectural model has been strengthened to improve data allocation performance. To do this, an abstract job model is employed, and a data query file containing input data is processed on a directed acyclic graph. The jobs are executed on the processing engine with the lowest execution cost, and the system's total cost is calculated. The total cost is computed by summing the costs of communication, computation, and network. The total cost of the system will be reduced using a Swarm intelligence algorithm. In heterogeneous distributed computing systems, the suggested approach attempts to reduce the system's total cost and improve data distribution. According to simulation results, the technique efficiently lowers total system cost and optimizes partitioned data allocation