Alumni Connect: A Smart Bridge Between Students, Alumni and Institute

Abstract

In recent years, maintaining effective communication and collaboration between educational institutions and their alumni has become increasingly important for academic growth and career development. Traditional alumni management systems often fail to provide personalized interactions, intelligent networking, and meaningful engagement opportunities for students and alumni. To address these challenges, this project proposes a Machine Learning–based Alumni Connect Portal that serves as a smart digital platform for connecting students, alumni, and institutions. The Alumni Connect Portal is designed to facilitate mentorship, career guidance, job and internship referrals, event participation, and professional networking. The system utilizes Machine Learning algorithms to analyze user profiles, including academic background, skills, interests, and professional experience, in order to generate accurate and personalized recommendations. These recommendations help students identify suitable alumni mentors and relevant career opportunities, while enabling alumni to engage more effectively with students who align with their expertise. The platform is implemented as a web-based application with dedicated modules for students, alumni, and administrators. The administrator module ensures secure user management, data verification, and system monitoring. The Machine Learning model improves recommendation accuracy over time by learning from user interactions and feedback, thereby enhancing overall system efficiency and user satisfaction. By automating the alumni–student matching process and enabling data-driven decision-making, the proposed system reduces manual effort, increases engagement, and strengthens institutional alumni relations. The Alumni Connect Portal demonstrates how Machine Learning can be effectively applied to educational networking systems to bridge the gap between academia and industry, support career development, and promote long-term professional collaboration.

Introduction

1. Depression Detection using Reddit Data (NLP Study)

Depression detection is explored as a large-scale NLP problem using Reddit data, where traditional diagnosis methods are limited by stigma and underreporting. The study compares a TF-IDF + Logistic Regression model with a CNN-based deep learning model. While both achieve similar accuracy (~93%), Logistic Regression offers better precision and interpretability, whereas CNN improves recall, making it better at identifying at-risk users. The work highlights trade-offs between false positives and false negatives and emphasizes the need for ethical, fair, and interpretable mental health AI systems.

2. DevOps-Based Cloud-Native GoLang System

This project focuses on building a fully automated DevOps pipeline for a GoLang application using Docker, Kubernetes, CI/CD tools, monitoring systems (Prometheus/Grafana), and security practices. The system emphasizes scalability, reliability, and automation across the code-to-production lifecycle. Key components include containerization, orchestration, continuous deployment, real-time monitoring, and load testing. Results show improved scalability and efficiency compared to traditional deployment methods, with future enhancements proposed for security and multi-cloud support.

3. Hate Speech Detection and Masking System

This work addresses the rise of hate speech on social media by developing an automated ML-based system integrated as a browser extension. It uses NLP techniques (like TF-IDF and classifiers such as Logistic Regression, Naive Bayes, and deep learning models) to detect and mask offensive content in real time. The system improves over manual moderation by enabling instant filtering across platforms like Instagram, Reddit, WhatsApp, and YouTube. The study highlights limitations of earlier keyword-based and ML approaches and emphasizes real-time, contextual detection.

4. Reinforcement Learning for Robot Locomotion

This research focuses on improving robot movement using Deep Reinforcement Learning (PPO, SAC, TD3) instead of traditional control methods like ZMP and PID. RL enables robots to learn adaptive, energy-efficient locomotion in complex environments such as rough terrain and dynamic obstacles. Key advancements include sim-to-real transfer, domain randomization, hierarchical RL, and meta-learning. Despite strong results, challenges remain in sample efficiency, reward design, and real-world deployment. Applications include rescue robots, delivery systems, and assistive robotics.

5. Energy-Efficient Medical IoT Monitoring System

This project develops a low-power IoT healthcare monitoring system using biomedical sensors, microcontrollers, and cloud connectivity. It analyzes power consumption across different modes (sensing, processing, transmission, sleep) and applies duty cycling and adaptive transmission to extend battery life. Data is collected via devices like PowerLab and displayed in real time. Results show improved energy efficiency without affecting accuracy. The system supports real-time patient monitoring with secure data handling and is evaluated under different operating scenarios.

6. ML-Based Alumni Connect Portal

This project builds an intelligent alumni–student networking platform using machine learning to improve mentorship and career guidance. Unlike traditional static systems, it uses recommendation algorithms to match students with relevant alumni based on skills, interests, and goals. The system supports role-based access for students, alumni, and admins, and includes mentorship, job postings, analytics, and verification features. The goal is to improve engagement, employability, and structured networking through data-driven recommendations.

Conclusion

This research presents the design and implementation of a Machine Learning–based Alumni Connect Portal aimed at improving interaction between students, alumni, and educational institutions through intelligent and data-driven networking. Unlike traditional alumni systems that act as static directories with limited engagement, the proposed system provides a more interactive and personalized platform. The portal integrates a hybrid recommendation model that combines profile similarity and user interaction data to generate personalized mentorship suggestions, job opportunities, and event notifications. The system is designed using a modular architecture consisting of student, alumni, and admin modules, ensuring secure access, smooth workflows, and efficient system management. The use of Machine Learning enables the system to continuously improve recommendation accuracy based on user activity. The results show that the system enhances user engagement, simplifies the process of finding suitable mentors, and promotes meaningful alumni–student interactions. It also helps administrators by providing analytics and automation for better decision-making. However, the system has some limitations such as the cold-start problem for new users, dependency on data quality, and the need for periodic model updates. In conclusion, the proposed Alumni Connect Portal offers a scalable and intelligent solution for alumni engagement. It supports personalized mentorship, structured networking, and improved career development for students, while strengthening the connection between academia and industry.

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Copyright

Copyright © 2026 Dhiraj Dhone, Akash Marodkar, Dhanshri Chavan, Surabhi Khedkar, Kartik Shende, Prof. Deepak Dhanwani. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.