Volume no :
10 |
Issue no :
01
Article Type :
Google Scholar
Author :
Dr. K. Balaji Shanmugam , Aparna R, Gomathipriya R, Harini N S, Harini K
Published Date :
20 - March - 2026
Publisher :
Journal of Artificial Intelligence and Cyber Security (JAICS)
Page No: 1 - 8
Abstract : Smart Career Guidance System using Artificial Intelligence is an intelligent decision-support platform designed to assist final year students in selecting suitable career paths based on their skills, interests, and academic performance. The system integrates student assessment data such as aptitude tests, personality analysis, and skill evaluations with AI-based recommendation techniques to analyze individual strengths and career preferences. A smart career analysis engine processes the collected data to generate personalized career suggestions, skill improvement recommendations, and industry-relevant career pathways. The platform also provides detailed information about required qualifications, job roles, and future opportunities for each recommended career option. In addition, the system presents analytical dashboards and generates automated career guidance reports to help students understand their professional potential. By combining intelligent analytics, assessment-based evaluation, and interactive visualization, the platform provides a scalable and efficient solution for career planning. The proposed system enhances decision-making accuracy and supports students in making informed career choices through data-driven guidance and personalized recommendations.
Reference:

1.
Deepa, R., Karthick, R., Velusamy, J., & Senthilkumar, R. (2025). Performance analysis of multiple-input multiple-output orthogonal frequency division multiplexing system using arithmetic optimization algorithm. Computer Standards & Interfaces, 92, 103934.
2.
Senthilkumar,Dr.P.Venkatakrishnan,Dr.N.Balaji, Intelligent based novel embedded system based IoT Enabled air pollution monitoring system, ELSEVIER Microprocessors and Microsystems Vol.77, June 2020
3.
M. Muthalakshmi, N.Mythili, Gurkirpal Singh, R.Senthilkumar (2025). Innovative Approaches for Evaluating Sugarcane Quality: Utilizing Near-Infrared Spectroscopy to Forecast Brix, Pol, and Fiber Content in Commercial Agricultural Domains. Journal of Food Processing, Wiley, https://doi.org/10.1111/jfpe.70233
4.
Senthilkumar Ramachandraarjunan, Venkatakrishnan Perumalsamy & Balaji Narayanan 2022, ‘IoT based artificial intelligence indoor air quality monitoring system using enabled RNN algorithm techniques’, in Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2853-2868
5.
N. Nagarani, M. Muthalakshmi , E. S. Vinothkumar and R. Senthilkumar (2026) ‘Optimized Contrastive Multi-Level Graph Neural Networks-Based Pigment Epithelial Detachment Detection in OCT images’ International Journal of Information Technology & Decision Making 2026 World Scientific DOI: 10.1142/S0219622026500343
6.
Sanitha P C; Syed Nageena Parveen; Shaik Thaherbasha; M. Shanmugapriya; T. Kalaivani; R. Senthilkumar, Transparent Nutrition: An Explainable AI-based Diet Tracking System for Preventing Nutrition-Related Disorders. 2025 3rd International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) DOI:10.1109/ICoICI65217.2025.11252549
7.
T. Jayasri; M.R. Archana Jenis; P.B. Aswathy; S. Manoranjitham; Christo George; R. Senthilkumar Identity-First Defense in Zero Trust Security Architecture to Protect Cyberspace 3rd International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) DOI:10.1109/ICoICI65217.2025.11254505
8.
J. Uthayakumar; Swapna; A. Ravikumar; S. Sreeraj; R. Senthilkumar; Babu Pandipati AI-Driven Water Resource Management Systems 2025 2nd International Conference on Computing and Data Science (ICCDS) DOI: 10.1109/ICCDS64403 .2025.11209318
9.
R.Swathiramya; V.V.Karthikeyan; P.Sumathi; Sruthy K V; Afreen Hussain; R.Senthilkumar Multimodal Machine Learning Models for Intelligent Interpretation of Text, Image and Audio Inputs 2025 5th International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT) DOI:10.1109/ICERECT65215.2025.11377322

10. Srinju.M; Dr.V.Dhanasekaran; S. Guruprasath; Dr.K.Edison Prabhu; K.J Godlin Debby; Dr.R.Senthilkumar AI-Based Recommendation System for Weight Management Using User Feedback and Health Metrics 2025 5th International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT) DOI: 10.1109/ICERECT65215.2025.11379842
11. Hau, E. (2013). Wind Turbines: Fundamentals, Technologies, Application, Economics (3rd ed.). Springer.
12.
Ackermann, T. (2012). Wind Power in Power Systems (2nd ed.). Wiley.
13.
Ahmed, S., Khalid, M., & Akram, U. (2021). Renewable Energy Feasibility Analysis Using Data Analytics Techniques. Energy Reports.
14.
Li, H., & Chen, Z. (2008). Overview of Different Wind Generator Systems and Their Comparisons. IET Renewable Power Generation.
15.
World Energy Council. (2022). Renewable Energy Policies and Market Trends.
16.
International Renewable Energy Agency (IRENA). (2023). Renewable Power Generation Costs Report.
17.
Singh, S., & Bhardwaj, A. (2022). Wind Energy Potential Assessment Using Meteorological Data. Renewable Energy Journal.
18.
Zhao, X., & Ren, L. (2021). Machine Learning Applications in Wind Energy Prediction. Applied Energy.
19.
Bhattacharya, S., & Mukherjee, S. (2020). Wind Energy Resource Assessment and Feasibility Study for Renewable Energy Systems. Energy Procedia.
20.
Kumar, A., & Saini, R. P. (2019). Wind Energy Resource Assessment for Sustainable Energy Planning. Renewable Energy.