Job and Career Recommendation System using Dynamically Stabilized Recurrent Neural Network Optimized with Secretary Bird Optimization Algorithm

Authors- Minimol V, Assistant Professor Monika Verma, Associate Professor Pawan Kumar Patnaik

Abstract-The pressure to find employment for college graduates is growing as a result of the ongoing expansion of college and university enrolment scales. This emphasizes the inadequacies in college students’ employability, which can be strengthened and improved by political also ideological education’s function. In this manuscript, Job and Career Recommendation system using Dynamically Stabilized Recurrent Neural Network Optimized with Secretary Bird Optimization Algorithm (JCRS-DSRNN-SBOA) is projected. The input information is collected via Real time information set. The pre-processing segment then receives the information. In pre- processing segment, it removes unwanted information also replaces the missing values using Strong Tracking Variational Bayesian Adaptive Kalman Filter (STVBAKF). A preprocessed data is given to Synchro-Transient-Extracting Transform (STET) in order to extract the attributes of enterprise description and pupil conduct. Then extracted attributes are fed into Dynamically Stabilized Recurrent Neural Network (DSRNN) for job and career recommendation. Generally speaking, DSRNN does not disclose the application of optimization techniques to ascertain the ideal parameters for ensuring an accurate career and job recommendation system. Hence, Secretary Bird Optimization Algorithm (SBOA) is suggested here to optimize DSRNN, which precisely construct the job and career recommendation system. The proposed JCRS-DSRNN-SBOA method is implemented and the performance measures like Accuracy, Precision and Root Mean Square Error (RMSE) are evaluated. Proposed JCRS-DSRNN-SBOA method attains 21.19%, 23.82% and 21.98% higher accuracy, 23.54%, 22.65% and 23.18% higher precision are analyzed with existing techniques like Employment Management for College Students based on Deep Learning and Big Data (EMCS-DL-BD), C3-IoC: A career guidance system for assessing student skills using machine learning and network visualization (CGS-ASS-ML), and Enhanced Deep Semantic Structure Modelling method for job recommendation (EDSSM-JR) respectively.

DOI: /10.61463/ijset.vol.12.issue5.254