This Idea Hub is a unified web-based platform de- signed to simplify academic project management and enhance collaboration among students, mentors, and administrators. Traditional methods such as emails, messaging applications, and manual documentation often lead to inefficiencies, poor organization, and lack of transparency in communication. To overcome these challenges, Idea Hub integrates essential features like idea submission, team formation, mentor supervision, and progress tracking into a single, centralized system. The platform uses role-based access control to ensure structured communication and accountability among all users. It is built on a three-tier architecture, which supports scalability, modularity, and efficient system design. The application is developed using Flask as the backend framework, SQLite for data storage, and HTML, CSS, and JavaScript for a responsive user interface. Overall, Idea Hub provides a secure and organized environment, reduces unstructured communication, and offers flexibility for future enhancements such as real-time communication and analytics.

Keywords : Idea Hub, Academic Collaboration, Project Management System, Web Application, Innovation Platform

Authors : V. Neelima

Title : IDEA HUB : A Collaborative Platform For College Project Innovation

Volume/Issue : 2026;8(3 (May - June))

Page No : 15 - 22

This is an Internet of Things (IoT) based smart helmet system that is designed to improve the safety of two-wheeler riders with collision detection and an emergency alert system. The device uses ESP32 as the main controlling unit and an MPU6050 sensor module that has a three-axis accelerometer and gyroscope. The system continuously monitors motion and acceleration. If a sudden increase in acceleration beyond predefined threshold values is detected, it is considered as an accident. When an accident is detected, the system fetches the rider’s location using the NEO-6M GPS module and then sends the crash data to the backend server via an HTTP POST request over Wi-Fi. The request contains the crash data such as an alert message and GPS coordinates (Latitude and Longitude). The backend server has an API endpoint (/api/crash) that receives the POST requests from the ESP32. When a POST request is made, the backend receives the data and processes it. After receiving the crash information, the backend uses a cloud-based SMS services to send an SMS to pre-defined emergency contacts. The SMS consists of a short alert message paired with the coordinates and a Google Maps link generated from the coordinates received from the GPS module. This helps in locating the rider in case of an emergency. The proposed system is a prime example of the combined working of IoT-driven sensors, embedded processing and wireless communication, and thereby reducing the time for emergency response time and improving the overall road safety of the motorcycle/scooter operators.

Keywords : IoT, Smart Helmet, ESP32 Microcontroller, MPU6050 Sensor, SMS Notification, Road Safety.

Authors : Nagirimadugu Kushal Reddy

Title : IoT based Smart Helmet for Two Wheeler Accident Detection and Emergency Alert System Using ESP32 and Cloud SMS Integration

Volume/Issue : 2026;8(3 (May - June))

Page No : 8 - 14

The data-driven approach towards developing a crop yield prediction system intends to predict the yields of crops in agriculture based on real-time data from farms with the aid of computational techniques. The factors considered by this system include soil characteristics, weather, rainfall, temperature, fertilizer usage, and irrigation that greatly affect the crop yield. In terms of data analysis, the advanced models employed are the Gaussian Process Regression (GPR) for probabilistic prediction and the Autoregressive Integrated Moving Average (ARIMA) for time-series forecasting. The models learn from previous data in agriculture to accurately predict yields. The proposed solution is provided as a web-based application, developed with Flask, with a frontend interface designed through HTML, CSS, and JavaScript. Users can input agricultural data and receive live yield predictions, thus improving decision-making, resource allocation, and agricultural production efficiency.

Keywords : Crop Yield Prediction, Gaussian Process Regression, ARIMA, Time-series forecasting, Flask

Authors :

Title : Data Driven Crop Yield Prediction Using Probabilistic Regression and Time Series Forecasting Techniques

Volume/Issue : 2026;8(3 (May - June))

Page No : 1 - 7