prof-freakenstein/SIH2025 huggingface dataset:
Design and Implementation of Solar-Powered Dewatering in Mining Operations
Organization: Ministry of Mines
Category: Hardware | Theme: Renewable / Sustainable Energy

Background

The current dewatering system at our copper mining site relies heavily on electrically operated pumps, often supplemented by diesel generators during power outages. This traditional setup not only leads to high operational expenses due to electricity and fuel consumption, but also contributes to increased carbon emissions, operational complexity, and maintenance challenges.

In light of rising energy costs and the need for reliable and sustainable mine dewatering solutions, there is a pressing need to explore alternative approaches. Solar-powered dewatering systems offer a viable solution, leveraging renewable energy to reduce costs, ensure consistent pump operation, and support broader environmental and sustainability goals. Implementing such systems under an OPEX (Operational Expenditure) model aligns with the strategy to minimize capital expenditure while transitioning to cleaner and more efficient mining practices.

Problem

This outlines the implementation of solar-powered dewatering pumps at the copper mining site under an OPEX model. The system will integrate solar photovoltaic (PV) modules with energy-efficient submersible or surface pumps, designed to meet site-specific dewatering requirements.

A third-party vendor will be engaged to install, operate, and maintain the system through a Power Purchase Agreement (PPA) or operational lease, eliminating the need for upfront capital investment by HCL. The system can be configured as stand-alone solar or hybrid (solar with grid/diesel backup) to ensure uninterrupted pumping. The proposed solution aims to reduce operational energy costs, enhance dewatering reliability, and support environmental commitments by minimizing diesel use and lowering carbon emissions.

Technical specifications are attached.

Expected Solution

Deployment of solar-powered, energy-efficient dewatering pumps under an OPEX model to ensure reliable, low-cost, and eco-friendly mine dewatering. The system will reduce diesel and electricity usage, include hybrid backup, and feature vendor-managed operation and maintenance.
AI-Powered Mobile Platform for Democratizing Sports Talent Assessment
Organization: Ministry of Youth Affairs and Sports
Category: Software | Theme: Fitness & Sports

Background

Identifying and assessing athletic talent in a country as vast and diverse as India is a significant challenge. Aspiring athletes, particularly from rural and remote areas, often lack access to standardized assessment facilities or opportunities to showcase their talent. The absence of reliable and scalable talent assessment models hinders the discovery of potential athletes who could benefit from Government support. A set of standard fitness assessment tests - including height, weight, vertical jump, shuttle run, sit-ups, and endurance runs (Annexure A) - provides a scientific method to evaluate talent. However, the reach and implementation of such tests remain limited due to infrastructure constraints.

Problem Description

The Sports Authority of India (SAI) requires an innovative, mobile-based solution to democratize sports talent assessment. The proposed platform should:

1. Enable athletes to download an app and record videos of their performance in the prescribed fitness assessment tests.
2. Use AI/ML-based on-device verification to analyze the recorded videos for accuracy and authenticity (e.g., detecting jump height, counting sit-ups, or measuring time/distance in runs).
3. Securely submit verified data to SAI servers for further evaluation and athlete profiling.
4. Be low-cost and lightweight, ensuring accessibility even on entry-level smartphones and low-bandwidth networks.

Innovative Features

1. AI-based Cheat Detection: Identify anomalies or manipulations (e.g., tampered videos or incorrect movements) to ensure fair assessments.
2. Offline Video Analysis: Perform preliminary performance analysis directly on the device without requiring continuous internet connectivity.
3. Performance Benchmarking: Compare athlete performance against age/gender-based benchmarks, providing instant feedback.
4. Gamified User Interface: Use progress badges, leaderboards, and interactive visuals to engage athletes and encourage participation.
5. Auto-Test Segmentation: Automatically detect and segment performance clips (e.g., counting reps in sit-ups or analyzing vertical jumps) to reduce manual effort.

Expected Deliverables

1. A mobile application (Android/iOS) that allows video recording and assessment of athletes performance across the test batteries.
2. AI/ML modules for on-device video analysis, verification of test results, and cheat detection.
3. A secure backend system to transmit data to the Sports Authority of India for further processing.
4. A dashboard for officials to view and evaluate verified performance data.

Expected Impact

1. Democratization of sports talent assessment, reaching even remote areas.
2. Low-cost, scalable solution enabling mass participation in talent identification initiatives.
3. Improved efficiency and transparency in evaluating and discovering potential athletes.
AI-Powered Personal Farming Assistant for Kerala Farmers
Organization: Government of Kerala
Category: Software | Theme: Agriculture FoodTech & Rural Development

Problem

Kerala’s smallholder farmers often lack access to personalized, timely agricultural advice. Generic advisories fail to account for local crop choices, weather, soil conditions, or farming practices. Many farmers also don’t maintain records of their activities, which limits learning from past seasons and accessing scheme benefits.

Challenge

Build an AI-powered personal farming assistant that acts like a digital companion for each farmer—understanding their specific context, guiding their actions, and learning over time.

Core Features

1. Farmer & Farm Profiling: Capture key details—location, land size, crop, soil type, irrigation.
2. Conversational Interface: Enable farmers to interact in Malayalam via voice or text.
3. Activity Tracking: Let farmers log events like sowing, irrigation, input use, or pest issues in simple language.
4. Personalized Advisory: Use AI to give proactive, contextual guidance—e.g., “Rain expected, avoid spraying tomorrow,” or “Pest outbreak reported nearby—inspect your brinjal crop.”
5. Reminders & Alerts: Send timely nudges for crop operations, scheme deadlines, and price trends.
6. Knowledge Engine: Pull from local crop calendars, pest data, and best practices to continuously improve recommendations.

Expected Impact

- Empowers farmers with personalized, on-demand support.
- Enhances productivity and sustainability through timely actions.
- Bridges the knowledge gap using AI + local context.

This solution aims to create a “Krishi Sakhi”—a digital friend who walks with the farmer throughout the crop cycle. The fund availability is subject to availability through government sanction.
Gamified Platform to Promote Sustainable Farming Practices
Organization: Government of Kerala
Category: Software | Theme: Agriculture FoodTech & Rural Development

Problem

Despite numerous awareness campaigns, many farmers still rely on unsustainable practices—excessive chemical use, over-irrigation, or mono-cropping—due to habit, lack of training, or limited engagement. Traditional training methods often fail to inspire lasting behavioural change, especially among younger farmers.

Challenge

Design a gamified digital platform that educates and motivates farmers to adopt sustainable agricultural practices through interactive challenges, rewards, and community participation.

Core Features

- Learning Through Play: Convert best practices—such as organic input use, mixed cropping, soil health management—into engaging missions or tasks.
- Personalized Quests: Tailor challenges based on the farmer’s crop, location, and farm size—e.g., “Complete 3 weeks of mulching on banana fields” or “Switch to bio-pesticides this season.”
- Progress Tracker: Visual dashboards showing farmer progress, sustainability score, and learning badges.
- Peer Sharing & Leaderboards: Allow farmers to share progress and earn recognition locally or panchayat-wise.
- Incentive System: Link rewards to real-world benefits like scheme eligibility points, training credits, or public recognition.

Expected Impact

- Encourages adoption of eco-friendly practices in a fun, engaging way.
- Builds a digitally connected community of progressive farmers.
- Makes sustainable farming accessible, especially for youth and early adopters.

This platform transforms agricultural extension from top-down instruction to a bottom-up, gamified experience—making sustainable farming a goal, a game, and a movement.
AI-Based Farmer Query Support and Advisory System
Organization: Government of Kerala
Category: Software | Theme: Agriculture FoodTech & Rural Development

Problem

Farmers often face critical questions related to pests, weather, inputs, subsidies, and market trends—but timely expert advice is rarely accessible. Agri officers and helplines are overburdened, and existing services don’t scale to support individual needs across diverse regions, languages, and literacy levels.

Challenge

Build an AI-powered advisory system that allows farmers to ask queries in their own language (preferably Malayalam) and receive accurate, context-aware answers instantly.

Core Features

- Natural Language Query Handling: Farmers can ask questions via voice or text in Malayalam—e.g., “Which pesticide for leaf spot in my banana?”
- Multimodal Inputs: Support for image uploads (e.g., diseased crop photo) or voice notes.
- AI-Powered Knowledge Engine: Use LLMs and fine-tuned agri datasets to provide reliable, tailored answers—drawing from local crop calendars, pest advisories, weather, and scheme guidelines.
- Context Awareness: Factor in the farmer’s location, crop, season, and history (if known) to give personalized advice.
- Escalation System: For complex or unclear queries, escalate to local agri officers with context and suggestions.
- Learning Loop: Continuously improve the system using real queries, feedback, and local expert inputs.

Expected Impact

- Makes expert-level farming advice instantly accessible to all.
- Bridges the communication gap between farmers and extension systems.
- Supports Krishibhavans and Agri Departments by automating first-level support.

This system aims to become a “Digital Krishi Officer”—always available, always learning, and always farmer-first.
Document Overload at Kochi Metro Rail Limited (KMRL)-An automated solution
Organization: Government of Kerala
Category: Software | Theme: Smart Automation

Since its first commercial run in 2017, KMRL has grown into a complex, multidisciplinary enterprise that stretches far beyond train operations. Every business day the organization generates and receives thousands of pages of material: engineering drawings, maintenance job cards, incident reports, vendor invoices, purchase-order correspondence, regulatory directives, environmental-impact studies, safety circulars, HR policies, legal opinions, and board-meeting minutes. These arrive through e-mail, Maximo exports, SharePoint repositories, WhatsApp PDFs, hard-copy scans, and ad-hoc cloud links—often in both English and Malayalam, sometimes in bilingual hybrids, frequently with embedded tables, photos, or signatures. The sheer diversity and volume have created a silent productivity tax: ● Information latency: Front-line managers spend hours skimming lengthy documents for the few actionable lines that affect their shift, delaying decisions on train availability, contractor payments, or staffing reallocations. ● Siloed awareness: Procurement may negotiate a spare-parts contract without realizing that Engineering has already flagged an upcoming design change HR may schedule refresher training unaware of a new safety bulletin released the previous evening. ● Compliance exposure: Regulatory updates from the Commissioner of Metro Rail Safety and the Ministry of Housing & Urban Affairs are buried in inboxes, risking missed deadlines or audit non-conformities. ● Knowledge attrition: Institutional memory remains locked in static files when key personnel transfer or retire, hard-won insights vanish with them. ● Duplicated effort: Different teams independently create summaries or slide decks of the same source documents, multiplying manual work and version-control headaches. As KMRL prepares to expand its corridor, add two new depots, and integrate emerging technologies such as Unified Namespace (UNS) data streams and IoT condition monitoring, the documentary burden will only intensify. Without an organization-wide mechanism to condense, contextualize, and route critical information, the metro risks slower decision cycles, avoidable operating costs, diminished service reliability, and heightened safety and legal vulnerabilities. The challenge, therefore, is to equip every stakeholder—from station controllers and rolling-stock engineers to finance officers and executive directors—with rapid, trustworthy snapshots of the documents that matter to them, while preserving traceability to the original source. Solving this problem will unlock faster cross-department coordination, strengthen regulatory compliance, safeguard institutional knowledge, and ultimately support KMRL’s mission of delivering safe, efficient, and passenger-centric urban transit to Kochi.
AI-Driven Train Induction Planning & Scheduling for Kochi Metro Rail Limited (KMRL)
Organization: Government of Kerala
Category: Software | Theme: Smart Automation

Kochi Metro must decide every night which of its 25 four-car trainsets will enter revenue service at dawn, which remain on standby, and which are held back in the Inspection Bay Line (IBL) for maintenance. The decision hinges on six inter-dependent variables: 1. Fitness Certificates – validity windows issued by Rolling-Stock, Signalling and Telecom departments. 2. Job-Card Status – open vs. closed work orders exported from IBM Maximo. 3. Branding Priorities – contractual commitments that dictate exterior wrap exposure hours. 4. Mileage Balancing – kilometre allocation to equalise bogie, brake-pad and HVAC wear. 5. Cleaning & Detailing Slots – available manpower and bay occupancy for interior deep-cleaning. 6. Stabling Geometry – physical bay positions that minimise nightly shunting and morning turn-out time. At present these data points reside in siloed spreadsheets, manual logbooks, and daily WhatsApp updates. Supervisors reconcile them in a time-compressed window (21:00–23:00 IST) using ad-hoc filters and experience-based heuristics. The process is opaque, non-repeatable, and highly error-prone: ● Missing a single telecom clearance can force an unscheduled rake withdrawal, eroding the 99.5 % punctuality KPI. ● Uneven mileage assignment accelerates component fatigue, inflating maintenance cost. ● Inadequate visibility into branding priorities risks breaching advertiser SLAs, exposing KMRL to revenue penalties. ● Excessive night-time shunting to rearrange rakes increases energy consumption and track-occupancy safety risk. With fleet size slated to grow to 40 trainsets and two depots by 2027, the existing manual workflow cannot scale linearly—neither in staffing nor in cognitive load. Therefore, KMRL requires an integrated, algorithm-driven decision-support platform that can: ● Ingest heterogeneous inputs (Maximo exports, IoT fitness sensors, UNS streams, manual overrides) in near-real-time. ● Enforce rule-based constraints and multi-objective optimisation (service readiness, reliability, cost, branding exposure). ● Generate a ranked induction list with explainable reasoning, conflict alerts, and “what-if” simulation. ● Learn from historical outcomes via machine-learning feedback loops to improve forecast accuracy over time. Such a system will transform induction planning from a manual reconciliation exercise into a reproducible, auditable, data-driven process—delivering higher fleet availability, lower lifecycle cost, and an enhanced passenger experience while freeing operations staff to focus on strategic exceptions rather than routine data wrangling.
Development of a travel related software app that can be installed on mobile phones that could capture trip related information
Organization: Government of Kerala
Category: Software | Theme: Travel & Tourism

NATPAC is into transportation planning but accurate data collection is a tideous task. Household data collection using manual survey is also time consuming and does not cover even a small percentage of the population making any planning exercise inadequate. Therefore, the problem statement covers development of a travel related software app that can be installed on mobile phones that could capture trip related information - trip number, origin, time, mode, destination etc - such that a travel related activity and trip chain can be established. Also the number and details of the accompanying travellers also can be captured if possible. Consent from the user is required. Some of these details can be automatically detected and some details can be nudged to be filled by the user. Once these trip details are complete, it can be saved in a server/ database which can be accessed by NATPAC Scientists for planning purposes.
Digital Health Record Management System for migrant workers in Kerala aligned with sustainable development goals
Organization: Government of Kerala
Category: Software | Theme: MedTech / BioTech / HealthTech

 
Kerala hosts a significant migrant population lacking comprehensive health record systems. These individuals often serve as a carrier for infectious diseases, posing serious public health risks to local communities. A dedicated software solution for maintaining migrant health records would support SDG achievement, prevent disease transmission and enhance public health surveillance and assist in elimination of diseases while ensuring fair and impartial healthcare access.
Earthquake stabilised dialysis system for patient safety during seismic events
Organization: Government of Kerala
Category: Hardware | Theme: Disaster Management

India's substantial chronic kidney disease patient population relies on dialysis services available from sub divisional level hospitals to specialist Centres including mobile units. Current dialysis infrastructure lacks earthquake resistant technology, potentially resulting in treatment interruption and patient mortality during seismic events. A stabilisation system would ensure continuous dialysis delivery during earthquake, safeguarding patient's life
AI-Based Timetable Generation System aligned with NEP 2020 for Multidisciplinary Education Structures
Organization: Government of Jammu and Kashmir
Category: Software | Theme: Smart Automation

 
Background:
With the implementation of the National Education Policy (NEP) 2020, all higher education institutions-including Institutes of Teacher Education and general degree colleges-have transitioned to Four-Year Undergraduate Programmes (FYUP) and integrated teacher education programs like B.Ed., M.Ed., and ITEP. These programs feature flexible, creditbased, multidisciplinary structures allowing students to choose Major, Minor, Skill-Based, Ability Enhancement, and Value-Added courses. Manual timetable creation under this new framework has become extremely complex, involving numerous subject combinations, varying credit hours, student preferences, and faculty workload distribution. To manage this, colleges usually form dedicated timetable committees, but even then, clashes, underutilization of faculty, and scheduling inefficiencies persist.

Description:
This problem statement envisions the development of a web-based or hybrid intelligent system that can generate automated, conflict-free, optimized academic timetables alignment with the NEP 2020 course structure. The system must integrate:
- Student data (elective choices, enrolled credits)
- Curriculum structure (course codes, credits, theory/practical split)
- Faculty workload, availability, and expertise
- Room/lab availability and capacity
- Teaching practice schedules (especially relevant for B.Ed. and M.Ed.)
- Field work, internships, and project components
The system should also allow dynamic editing, scenario simulation, and scalability for upcoming semesters. A user-friendly admin interface is essential to allow real-time inputs and updates.

Expected Solution:
A deployable AI/ML-assisted Timetable Generator that:
- Accepts structured inputs from academic, student, and faculty databases
- Generates a semester-wise timetable for multiple programs (B.Ed., M.Ed., FYUP, ITEP)
- Prevents scheduling conflicts across faculty and infrastructure
- Accommodates future course additions and changing NEP guidelines
- Offers exportable formats (PDF, Excel) for student and faculty sharing
- Can be integrated with existing Academic Management Systems
Development of a Digital Mental Health and Psychological Support System for Students in Higher Education
Organization: Government of Jammu and Kashmir
Category: Software | Theme: MedTech / BioTech / HealthTech

Problem Statement:

Context:
Mental health issues among college students have significantly increased in recent years, including anxiety, depression, burnout, sleep disorders, academic stress, and social isolation. However, there is a major gap in the availability, accessibility, and stigma-free delivery of mental health support in most higher education institutions, especially in rural and semi-urban colleges.

Problem Faced:
- Absence of a structured, scalable, and stigma-free psychological intervention system.
- Lack of early detection and preventive mental health tools.
- Under-utilization of college counselling centres due to fear of judgment or lack of awareness.
- No centralized mental health monitoring or data-driven policy framework within institutions.

Proposed Technological Challenge:
Develop a Digital Psychological Intervention System (web-based and/or mobile app) with the following capabilities:
1. AI-guided First-Aid Support: Interactive chat box that offers coping strategies and refers students to professionals when needed.
2. Confidential Booking System: For appointment with on-campus counsellor or mental health helpline.
3. Psychoeducational Resource Hub: Videos, relaxation audio, mental wellness guides in regional languages.
4. Peer Support Platform: Moderated peer-to-peer support forum with trained student volunteers.
5. Admin Dashboard: Anonymous data analytics for authorities to recognize trends and plan interventions.

Department/Section Owning the Problem:
Department of Student Welfare / Department of Psychology / Internal Quality Assurance Cell (IQAC).

Problem Explanation Video:
To be prepared by students.

Need for Digital Platform for Psychological Support:
Most available apps are generic, Western-oriented, or paid. They do not integrate:
• Regional cultural context and language
• Institution-specific customization
• Offline support mapping (e.g., linking with college counsellors)
• Real-time analytics for admin
Hence, a tailored open-source solution is needed.

Sample Data/Data Structures for Participants:
• Level of problem through standard psychological screening tools (PHQ-9 / GAD-7 / GHQ, etc.)
• Mock student profiles (anonymized)
• Institutional support structure (counsellor availability, resources, helpline)