AI based project management tool

Basic knowledge required:
Whatsapp
Macro programming in MS Excel (or) Python / LLM programming Chat Gpt for code references

Description of the Statement:
Using whatsapp / Email information, the AI tool reads information and updates project information in project management system thereby eliminating a manual intervention for updation.

• Step 1 : Read watsapp messages
• Step 2 : Generate phrases / word data base (for bucketing under various head like “TV Power”, “Network” etc.,
• Step 3 : Generate bubble chart based on the word cloud (count based) where the size of the bubble is the count size.
• Step 4 : Provide as future scope the linkage to project software wherein similar instances can be interpreted for completion percentage updation

Outcome for validation: Bubble chart of word cloud like below example Advanced Visualisations for Text Data Analysis | by Petr Korab | Towards Data Science

E Waste recycling system of used Laptops and electronics system

Basic knowledge:

• E waste management knowledge
• Review of literature for ideation
• MS power point for model creation

Description of the Statement: Design & Modelling of E-waste handling system right from collection, segregation, reclamation and packaging for sale of precious metals and other materials.
Outcome for validation: Business case presentation from

1. Problem Statement
2. Review of literature
3. Ideation for lean system design
4. Automation ideas
5. Overall work flow
6. Budget (Capex)for a typical system

AI based Electrical energy consumption forecasting

Basic knowledge:

• MS Excel,
• Chat GPT for search
• Macro programming in MS Excel,
• What if Analysis
• Power purchase / power agreement
• Electricity board rules and regulations pertaining to industrial power

Description of the Statement: Industries use multiple sources of energy like Wind, Solar, Electricity board, Power purchase, Thermal, Geo thermal, Generator based ( renewable fuel like Hydrogen) etc., Multiple benefits are available for these energy sources. However decision making on which source to use based on historical consumption data or present consumption trend is difficult and needs to be addressed. This will help with optimizing electricity payments and results in savings for organizations. Simple to use user interface for input data, Data table for benefits, AI based decision making using Operation research or what if analysis etc., would be the key USP for the project outcome.
Outcome for validation: MS Excel work book with the simulation tool defined

Smart perishable monitoring system for large scale supermarkets.

Basic knowledge:

• MS Powerpoint
• MS Excel
• Macro programming
• Chat GPT for search

Description of the Statement: Existing supermarket invoicing system lacks provision of expiry date for the perishable items, leading to deadstock build up and loss of revenue. Using smart perishable monitoring system, the expiration date to be tracked in addition to RFID sticker based bin location will help with optimising the loss component resulting in better management and profitability.
Outcome for validation: Business case presentation from

1. Problem Statement
2. Review of literature
3. Idea cloud
4. Overall work flow model
5. MIS graph in MS excel for the perishable grouping
6. Alert message model (different types)

Real-time Health Monitoring and Diagnostics in Electric Vehicle.

Develop a real-time, in-vehicle electronic health monitoring and diagnostics system capable of identifying, recording, and communicating electronic faults within an electric vehicle's system. The solution should address diagnostic needs for detecting common electrical and electronic failures—such as sensor malfunctions, circuit abnormalities, and signal inconsistencies—in real time.

Basic knowledge:

1. Automotive Electronics – Familiarity with automotive sensors, actuators, ECUs, and the communication protocol.
2. Microcontrollers and Programming – Knowledge of microcontroller basics, especially Arduino or similar platforms, and programming in C or Python.
3. Signal Processing and Diagnostics – Basic understanding of signal processing, and troubleshooting techniques in electric vehicles.
4. Wireless Communication (Optional) – Familiarity with Bluetooth or Wi-Fi modules for wireless data transfer.
5. Data Logging and Analysis – Understanding of data logging, storage formats, and basic analytical methods.

Description of the Statement:

Vehicles today are equipped with multiple sensors and controllers to ensure efficient operation and safety. However, electronic components can fail due to various factors like temperature fluctuations, physical wear, and electrical surges. Traditional diagnostic tools require manual intervention and are limited in real-time monitoring capabilities. In this hackathon, students are tasked with creating a real-time health monitoring and diagnostics system for an electric vehicle’s electronic subsystems. The solution should detect and log issues in real-time, alerting users to specific failures (e.g., sensor disconnects, signal noise, or ECU communication errors) through an interface. The system could use a microcontroller (such as an Arduino or Raspberry Pi) to continuously monitor the status of selected sensors and actuators, flagging abnormal readings or losses in connectivity. An optional goal is to implement wireless data transfer to a mobile device or server for further analysis.

Outcome for validation: Business case presentation from

1. Real-time Monitoring – The system must be able to display real-time data from simulated sensors and actuators, showing clear, understandable diagnostic information.
2. Fault Detection – The solution should autonomously detect faults and abnormalities in a given set of conditions, triggering alerts based on user-defined thresholds or preset values.
3. User Interface (Optional) – A basic interface displaying live data, fault logs, and summaries is desirable. This could be a simple LCD display, mobile app interface, or web dashboard.
4. Data Logging and Report Generation – It should log fault events with timestamps, storing them locally or sending them to an external device for analysis. A sample report with key diagnostics should be provided as a final output.
5. Proof of Concept (POC) – Participants should demonstrate the system's functionality with at least two example sensors (e.g., voltage, current, temperature, etc.) and one type of communication failure to simulate a real-world scenario like a wireless network or any other communication protocol

Utilizing AI for chronic disease analysis, timely patient communication, effective monitoring, detailed analysis, and strategies to improve and control health outcomes.

Problem Statement:

The increasing prevalence of chronic diseases, such as diabetes, hypertension, and cardiovascular conditions, presents a significant challenge for healthcare systems. Patients often lack timely awareness and appropriate management strategies, leading to deteriorated health outcomes, preventable complications, and higher medical costs.

Key Features:

• Continuous Monitoring: Collecting and analyzing patient health data from wearable devices and sensors.
• Timely Communication: Real-time alerts for preventive actions and adherence to treatments.
• Personalized Interventions: Recommendations tailored to individual needs and medical history.
• Improved Disease Control: Proactive management through predictive analytics to enhance quality of life.

Expected Outcomes:

• Reduction in hospitalization rates by X%.
• Improvement in medication adherence by Y%.
• Decrease in healthcare costs by Z%.
• Increase in patient-reported quality of life scores.

Behavior Pattern Analysis Through AI Methodology

Problem Statement:

Understanding and predicting human behavior is a complex challenge due to the diversity of actions, contexts, and influencing factors. Traditional methods of behavior analysis often rely on manual observation or incomplete datasets, limiting their effectiveness.

Expected Outcomes:

• Improved Decision-Making: Providing individuals, businesses, or organizations with actionable insights.
• Personalized Experiences: Enhancing user satisfaction and engagement.
• Behavior Prediction: Anticipating future actions for better resource planning.
• Risk Mitigation: Identifying potential problems before they escalate, such as health risks or security breaches.

AI for Analyzing Alcohol Consumption Impacts

Problem Statement:

Alcohol consumption can lead to physical, mental, and social consequences, especially in chronic drinkers. Current methods lack real-time data and comprehensive insights. AI can address these gaps by analyzing patterns, predicting outcomes, and enabling timely care.

Expected Outcomes:

• Early Detection and Intervention: Identify early signs of irreversible damage or critical health conditions.
• Personalized Treatment Plans: Tailor rehabilitation strategies to individual behavior patterns and physiological conditions.
• Behavioral Modification Insights: Provide patients and families with actionable insights to reduce alcohol dependency and mitigate long-term harm.
• Reduction in Healthcare Costs: Minimize hospitalizations and emergency care by predicting and preventing severe complications.
• Support for End-of-Life Care: Improve quality of life by integrating AI insights into palliative care planning.

Using AI to Prepare a Family Budget

Problem Statement:

Families often struggle to plan budgets for children’s education, marriage expenses, and future savings. Accurate forecasting and adaptable strategies are essential to ensure financial stability.

Expected Outcomes:

• Accurate Expense Forecasting: AI-driven tools can analyze historical spending patterns, current income, inflation rates, and future projections to estimate education and marriage costs.
• Optimized Savings Plans: AI can suggest personalized savings strategies, such as monthly contributions, investment options, or fixed deposits, to meet future financial goals efficiently.
• Scenario Planning: AI simulations can help families evaluate different scenarios (e.g., increased education costs or earlier marriage plans) and adjust budgets accordingly.
• Risk Mitigation: AI can identify potential financial risks, such as economic downturns or unexpected expenses, and suggest contingency plans or insurance options.
• Dynamic Budget Recommendations: AI systems can provide ongoing recommendations to adjust budgets in real-time based on changes in income, expenses, or financial priorities.
• Enhanced Financial Discipline: By offering visual dashboards and regular alerts, AI tools can encourage families to stay on track with their savings goals.

Analysis of the Most Suitable Field of Study

Problem Statement:

Selecting the right field of study is complex due to evolving industry demands and unpredictable job market trends. Traditional methods lack personalized insights, leading to mismatched career paths.

Expected Outcomes:

• Informed Decision-Making: AI provides accurate insights into trending industries and future skill demands, ensuring a well-informed choice of study.
• Personalized Recommendations: Tailored suggestions based on individual strengths, interests, and career aspirations improve satisfaction and engagement in the chosen field.
• Career Readiness: Aligning studies with industry demands equips students with relevant skills, enhancing employability and career growth.
• Future-Proofing Education: AI-driven projections ensure that chosen fields of study remain relevant amidst technological and market shifts.
• Optimized Resource Allocation: By identifying suitable fields, families and individuals can make better investments in education and training.
• Increased Financial Returns: Choosing an in-demand field reduces the risk of unemployment and boosts earning potential, ensuring a higher return on educational investments.
• Improved Confidence and Clarity: A clear understanding of study options and their outcomes leads to confident decision-making and reduced anxiety about the future.

Spatial Computing for Smart City Planning

Description Develop a spatial computing platform that allows urban planners to visualize, simulate, and optimize city layouts, traffic flows, and infrastructure projects in a virtual 3D environment.

Key Aspects:

• 3D City Models: Use LiDAR data and other geographic information systems (GIS) to create accurate 3D models of urban areas.
• Simulation: Simulate the impact of new infrastructure projects, such as roads, parks, or buildings, on traffic patterns, environmental factors, and community well-being.
• Public Participation: Allow citizens to interact with the virtual city model, providing feedback on proposed developments, and fostering community involvement in urban planning.

Expected Outcomes:

• Improved decision-making in urban planning through better visualization and simulation tools.
• Enhanced public engagement in city planning processes.

Smart Traffic Management

Description Develop a spatial computing system that optimizes traffic flow and reduces congestion in urban areas using real-time data from various sources.

Key Aspects:

• Real-Time Traffic Monitoring: Integrate data from cameras, sensors, and GPS devices across the city to create a live 3D map of traffic conditions.
• Predictive Analytics: Use AI and machine learning to analyze historical traffic data and predict future congestion points, allowing for pre-emptive traffic signal adjustments.
• Dynamic Routing: Provide drivers with real-time route recommendations via a mobile app, considering current traffic conditions, road closures, and accidents.

Expected Outcomes:

• Reduced traffic congestion and travel times.
• Improved efficiency in public transportation systems through better coordination and scheduling.

Carbon Emission Monitoring

Description Once users log in to a web application, the system needs to capture their behavior based on data volume, color codes, images, styles, and screen rendering time. During logout, the application displays a popup message with CO2 usage.

Expected Outcomes: Efficient usage of the application with less carbon usage.

Cloud-Based Infrastructure Monitoring for CO2 Emission

Description: Develop a tool to monitor the complete infrastructure carbon emission for any cloud environments (AWS / GCP), including consumption by applications (idle, transaction, weekend usage).

Expected Outcomes: Effective usage of the cloud infrastructure with reduced carbon emissions.