Tech.Future Hackathon—an initiative of FITT, IIT Delhi is designed to propel visionary entrepreneurs from
the Research & Development phase to the creation of workable prototypes and Minimum Viable Products (MVPs)
tailored for Industry 5.0 use cases. Our nationwide hackathon is a dynamic platform that empowers innovators
to tackle some of the most pressing challenges, fostering collaborative problem-solving within the vibrant
environment of FITT, IIT Delhi.
Tech.Future is dedicated to nurturing out-of-the-box thinking among emerging techpreneurs. We invite you to
harness the power of cutting-edge technologies such as IoT, AI/ML, Deep Learning, AR/VR, Computer Vision,
and Robotics across diverse domains including Transportation, Mobility, Clean-tech, Renewable Energy,
Environment, Smart Infrastructure, and Sustainable Living. The Tech.Future Hackathon is not just an event;
it's a launchpad for your ideas and the ideal starting point for your startup journey.
Join us in shaping the future of technology and making a lasting impact. Let's innovate, collaborate, and
transform ideas into reality at Tech.Future Deep Tech Hackathon!
Anomaly Detection in Network Security know
Anomaly Detection in Network Security
Develop an advanced system for detecting anomalies in network traffic data, effectively identifying
patterns or behaviors that may signal potential security threats or attacks.
- Data Collection:
Collect comprehensive network traffic data, including packet details, communication patterns, and user
Utilize sources such as firewalls, intrusion detection systems, and network logs.
- Feature Engineering:
Extract relevant features from the collected data, considering factors like packet size, frequency of
communication, and protocol deviations. Transform raw data into a format suitable for anomaly detection
- Machine Learning Models:
Implement machine learning models, such as clustering algorithms or neural networks, to learn normal
patterns from historical data. Train the model to recognize deviations from these patterns as potential
- Real-time Monitoring:
Ensure the system can monitor network traffic in real-time, promptly identifying anomalies as they
Implement mechanisms for continuous learning to adapt to evolving network behaviors.
- Alerting Mechanism:
Integrate an alerting system to notify administrators or security teams when potential anomalies are
Include severity levels to prioritize and respond to different types of threats.
Predictive Maintenance and Automatic Maintenance for Industrial Equipment know more...
Predictive Maintenance and Automatic Maintenance for
Create a model for predictive maintenance leveraging sensor data to forecast potential failures in
equipment. This proactive approach allows for timely maintenance, minimizing downtime and optimizing
- Sensor Data Acquisition:
Gather sensor data from industrial equipment, including parameters like temperature, vibration, and
Ensure a reliable and continuous stream of data to facilitate accurate predictions.
- Data Preprocessing:
Clean and preprocess the sensor data, handling missing values and outliers. Normalize or standardize
ensure consistency and improve model performance.
- Machine Learning Model Selection:
Choose suitable machine learning models, such as regression or time-series analysis, to predict
Train the model using historical data, linking sensor readings to past incidents of equipment breakdown.
- Threshold Determination:
Establish threshold values for predicted failure probabilities, indicating when maintenance actions
taken. Fine-tune these thresholds based on the specific requirements of the industrial setting.
- Integration with Maintenance Systems:
Integrate the predictive maintenance system with existing maintenance workflows, enabling seamless
and coordination between the predictive model and maintenance teams.
Mental Health Support and disease Prevention Engagement know more...
Mental Health Support and disease Prevention
Engineer a solution using natural language processing to offer empathetic mental health support. This
should accurately recognize and respond to users' emotional states, providing a compassionate and
- Emotion Recognition:
Implement natural language processing techniques to analyze text or speech data and accurately recognize
emotional states. Consider sentiment analysis, tone detection, and contextual understanding.
- Empathetic Response Generation:
Develop a response generation system that takes into account the recognized emotions. Use pre-defined
machine learning models to generate empathetic and contextually appropriate responses.
- Continuous Learning:
Implement mechanisms for continuous learning, allowing the system to adapt to users' changing emotional
over time. Regularly update the model based on user interactions and feedback.
- Privacy and Security Measures:
Prioritize user privacy by incorporating robust security measures. Ensure that sensitive information is
appropriately and that the system adheres to relevant privacy regulations.
- User Engagement Features:
Enhance user engagement by incorporating features like personalized recommendations, goal tracking, and
assessments. Foster a supportive and interactive environment for users seeking mental health support.
Carbon Footprint Tracking for Businesses know
Carbon Footprint Tracking for Businesses
Design a comprehensive tool to aid businesses in monitoring and reducing their carbon footprint. This
should analyze diverse data sources such as energy consumption, transportation, and supply chains,
actionable insights for the implementation of sustainability initiatives.
- Data Integration:
Aggregate data from various sources, including energy bills, transportation logs, and supply chain
records. Integrate this diverse dataset into a unified platform for comprehensive analysis.
- Data Analysis and Visualization:
Utilize data analysis techniques to identify patterns, trends, and areas of high carbon emissions.
Develop interactive visualizations to help businesses understand their carbon footprint and identify key
areas for improvement.
- Benchmarking and Comparison:
Implement benchmarking features to compare a business's carbon footprint against industry standards or
similar organizations. Provide insights into how well the business is performing in terms of
- Actionable Recommendations:
Generate actionable recommendations based on the analysis to guide businesses in reducing their carbon
footprint. Offer insights into areas where efficiency improvements or changes in processes can lead to
significant environmental impact.
- Monitoring and Reporting:
Establish a continuous monitoring system to track ongoing carbon footprint metrics. Generate regular
reports that showcase progress, highlight achievements, and provide guidance for further improvement.
Why should you Apply?
All Shortlisted Teams will receive the following benefits
Swags and Goodies
Real World Solutions
Top 5 Selected Teams will receive the following benefits