Background Land records form the backbone of land administration, property ownership, taxation, land acquisition, dispute resolution, and infrastructure planning. Across India, a significant portion of historical land records continues to exist in the form of handwritten registers, scanned documents, maps, cadastral records, and legacy PDF files maintained at various administrative levels. An intelligent digitization system can significantly improve data quality while accelerating the modernization of India's land administration ecosystem. Description of the Study Develop an AI-powered Intelligent Land Record Digitization and Validation System capable of automatically extracting structured information from scanned land records, handwritten documents, maps, and legacy PDF files. The proposed solution should utilize advanced OCR, Computer Vision, and Natural Language Processing techniques to recognize printed as well as handwritten text in multiple Indian languages. The extracted information should be intelligently classified into predefined fields such as landowner details, survey number, khasra number, khata number, plot area, village, tehsil, district, land classification, ownership details, mutation records, and registration information. The platform should provide a user-friendly interface for document upload, automated processing, manual verification where required, audit tracking, and seamless integration with existing Land Records Management Systems (LRMS), Digital India Land Records Modernization Programme (DILRMP), GIS platforms, and other government databases. Scope of Study Recent advancements in Artificial Intelligence (AI), Optical Character Recognition (OCR), Computer Vision, Natural Language Processing (NLP), and Machine Learning (ML) provide an opportunity to automate the extraction, digitization, and validation of legacy land records with greater speed and accuracy. Add 'Scope of Study' Table here Problems Records often suffer from issues such as poor image quality, inconsistent formats, faded text, damaged pages, multiple regional languages, and handwritten annotations, making manual digitization a time-consuming and error-prone process. The lack of standardized and accurate digital land records creates challenges in maintaining reliable databases, verifying ownership, integrating records with modern land information systems, and delivering citizen-centric services. Manual data entry not only increases operational costs but also introduces inconsistencies that affect decision-making and governance. Expected Solution The proposed solution should be an intelligent AI-based platform capable of automating the digitization and validation of legacy land records while minimizing manual intervention and ensuring high data accuracy. The system should provide 7. Support for multilingual document recognition across major Indian languages. 8. Automatic extraction of structured land record information from scanned PDFs, images, and historical documents. 9. Intelligent classification of extracted data into predefined land record fields. 10. Automated validation using business rules, cross-database verification, and duplicate detection. 11. Confidence scoring for extracted information with automatic identification of uncertain fields. 12. Human-assisted verification workflow for low-confidence records. 13. AI-driven learning mechanism that improves extraction accuracy over time. 14. Integration with existing Land Records Management Systems (LRMS), DILRMP databases, GIS platforms, and cadastral maps. 15. Secure document repository with metadata management and audit trails. Interactive dashboards displaying Number of documents processed, Extraction accuracy, Validation status, Pending verification cases, Error statistics, State-wise and district-wise digitization progress 7. APIs for seamless integration with government applications and digital governance platforms. 8. Role-based access control ensuring secure access to sensitive land record information. The solution should significantly reduce manual effort, improve the accuracy and reliability of digital land records, accelerate modernization of land administration, and support transparent, data-driven governance. Add 'Suggested components-wise technology' table here

Expected Solution Create a digital marketplace that: Connects farmers/FPOs directly with consumers and bulk buyers. Provides logistics support. Uses AI for demand forecasting and route optimization. Benefits Better prices for farmers. Lower prices for consumers. Reduced supply chain inefficiencies.

Background Victims of atrocities frequently experience prolonged psychological distress after complaint registration due to threats, intimidation, repeated court appearances, delays in investigation and trial, social ostracism, economic hardship, and rehabilitation challenges.Existing mechanisms focus primarily on legal and financial support and do not provide continuous monitoring of victim well-being. Problem Statement Develop an AI-based Dynamic Mental Health Monitoring and Distress Prediction System that continuously monitors and predicts psychological distress among victims and complainants registered through NHAA (14566), the Integrated Portal, chatbot, mobile application, IVRS, or other approved communication channels throughout the investigation,trial, rehabilitation, and compensation process. Expected Solution The system should Conduct periodic interactions with victims through chatbot, IVRS calls, SMS, mobile applications, web portal, or helpline follow-up mechanisms. Analyse voice, text, behavioural responses, and engagement patterns using NLP, Sentiment Analysis, and Emotion AI. Generate a Dynamic Distress Score and longitudinal trend analysis. Predict escalation of psychological distress before a crisis situation emerges. Trigger alerts to counsellors, district authorities, and designated officials when predefined risk thresholds are crossed. Recommend appropriate interventions such as counselling, medical treatment, witness protection, relocation support, financial assistance, legal aid, or rehabilitation measures. Provide dashboards at district, State, and national levels for monitoring vulnerable victims and high-risk cases. Ensure explainable AI, privacy protection, data security, and compliance with applicable legal and ethical standards. Expected Outcomes Continuous monitoring of victim well-being. Early detection and prevention of mental health crises. Timely deployment of counselling and rehabilitation services. Strengthened victim confidence in the justice delivery system. Evidence-based decision-making for policymakers and administrators. Improved coordination among welfare, counselling, and law-enforcement agencies. Innovation Components Emotion AI Voice Stress Analytics Sentiment Analysis Predictive Risk Modelling Multilingual Conversational AI Explainable AI Automated Case Prioritisation Real-Time Risk Alerts Priority Use Cases Victims of rape and gang rape. Victims of murder, grievous hurt, and arson. Witnesses facing intimidation or threats. Families affected by caste-based violence. Beneficiaries receiving relief, compensation, rehabilitation, and protection under the provisions of the Scheduled Castes and Scheduled Tribes (Prevention of Atrocities) Act, 1989.

Description Healthcare facilities generate large volumes of biomedical waste that require safe, compliant, and efficient handling. Manual collection and segregation increase the risk of contamination, operational inefficiencies, and regulatory challenges. Design and develop an AI-powered, battery-electric autonomous mobile system that automates the collection, identification, segregation, and digital tracking of biomedical waste across hospitals. The solution should leverage AI-enabled vision systems to classify waste, intelligently segregate it into designated compartments, and provide end-to-end traceability while minimizing human exposure to hazardous materials and improving safety, operational efficiency, and regulatory compliance. Using Autodesk Fusion, students must demonstrate a complete product development lifecycle—from concept ideation to manufacturing-ready product, delivering an innovative, scalable, and solution for next-generation healthcare waste management. Participation Guidelines For Idea Submission Each student team will submit a PowerPoint presentation (5-7 Slides) with conceptual sketches, research, and relevant images. NO design files are required at this stage. The actual design must be created ONLY during the Grand Finale. Designs should be created using ONLY Autodesk Fusion and not copied or taken from any other source. AI Generated content is NOT ALLOWED. For Grand Finale Students must use Autodesk Fusion within the given time period and present: PPT explaining the final project Public link of the fully developed Autodesk Fusion design model Use of Generative Design for optimization will be an added advantage. Simulation and Analysis will be advantageous Motion study and exploded assembly view Hi-res rendered images Design should be capable of developing a prototype with a focus on Cost, Manufacturability, Scalability, and Quality. Note Teams coming with pre-designed files will be disqualified. Attach Marking Criteria Table here* Faculty (SIH SPOC) Form Teams choosing to submit idea for Autodesk’s problem statement are required to request their faculty (SIH SPOC) to fill this 'Mandatory Form'. Autodesk Fusion Autodesk Fusion is a cloud-based 3D modeling, CAD, CAM, CAE, and PCB software platform for professional product design and manufacturing. Students and educators can click here to get FREE access to Fusion.

Problem Description Rural and underserved communities may face long travel distances,shortages of specialists, irregular diagnostics, fragmented medical records, delayed referrals and limited awareness of available services. Primary health facilities may have constrained staff and equipment, while patients may move between sub-centres, primary health centres, rural hospitals and district hospitals without continuity of information. Connectivity, language,health literacy and affordability further affect access. The challenge is to improve timely access, continuity, quality and accountability while strengthening—not replacing—the public-health system. Expected Solution / Outcome An integrated care-access and quality support solution that may combine assisted teleconsultation, appointment and queue management, digital triage,longitudinal patient records, referral tracking, diagnostic coordination,medicine availability, high-risk patient follow-up and facility dashboards. It should support frontline health workers, low-connectivity environments,multilingual interaction, emergency escalation and interoperable health records based on approved standards.Expected outcomes include reduced travel and waiting time, earlier consultation, improved referral completion, better follow-up for maternal, child and chronic conditions,improved medicine/diagnostic availability visibility and enhanced quality monitoring.

Background Early and accurate detection of diseases significantly improves treatment outcomes and reduces healthcare costs. Classical machine learning models have achieved notable success in medical diagnosis; however, they often face limitations when dealing with high-dimensional, noisy, and complex biomedical data (e.g., genomics, medical imaging, and electronic health records). Quantum machine learning (QML) offers the potential to capture intricate patterns through quantum superposition and entanglement. Due to current hardware constraints, a hybrid quantum-classical approach provides a practical pathway to leverage quantum advantages while remaining executable on existing quantum simulators and near-term quantum devices. Description This problem focuses on designing and developing a hybrid quantum machine learning platform for early disease detection. The platform will integrate classical pre-processing and feature engineering with quantum-enhanced learning models (such as quantum support vector machines, quantum neural networks, or variational quantum classifiers). It will be applied to biomedical datasets for the early identification of diseases (e.g., cancer, cardiovascular disorders, or neurological conditions). The system should support data ingestion, hybrid model training, prediction, explainability, and performance evaluation against purely classical baselines. Objectives Design a hybrid quantum-classical machine learning architecture suitable for early disease detection. Develop quantum-enhanced classification/regression models that can process high-dimensional biomedical data. Improve detection accuracy, sensitivity, and specificity compared with classical machine learning baselines. Ensure the platform is scalable, interpretable, and compatible with near-term quantum hardware and simulators. Incorporate data pre-processing, feature selection, and model explainability modules. Benchmark the hybrid approach against classical models in terms of accuracy,computational efficiency, and generalization performance. Expected Solution A fully functional hybrid quantum machine learning software platform capable of performing early disease detection on real or benchmark biomedical datasets. The solution must include data handling pipelines, hybrid quantum-classical model implementation, training and inference workflows, performance evaluation, explainability features, and comprehensive documentation. Add 'Delivery Table (Expected Deliverables)' here

Background Personnel serving in Central Armed Police Forces (CAPFs), Armed Forces, and other uniformed services operate under physically demanding, psychologically stressful, and often hazardous conditions.Extended deployments, operational pressures, separation from families,irregular working hours, and exposure to traumatic incidents can significantly impact mental well-being.Currently, stress identification largely depends on manual observation and self-reporting, which may delay timely intervention. There is a need for a proactive, technology-driven solution that can identify early indicators of stress, burnout, and psychological distress while maintaining privacy and organizational trust. Description The proposed solution aims to develop an AI-powered Personnel Stress and Welfare Monitoring System capable of identifying potential indicators of stress, burnout, emotional fatigue, and welfare concerns through analysis of organizational and voluntarily provided wellness data.The system should: Analyze HR-related indicators such as leave patterns,deployment history, duty schedules, transfer frequency, training commitments, and workload trends. Support optional self-reporting and wellness assessments through a secure mobile application. Incorporate voluntary biometric and wellness data, where authorized and legally permissible. Detect behavioral patterns associated with elevated stress risk. Generate risk assessments and welfare recommendations for authorized welfare officers and commanders. Enable proactive counseling, welfare interventions, and workload balancing measures. The system must be designed with strong privacy safeguards and focus on welfare support rather than disciplinary actions. Expected Solution Develop an AI-driven predictive analytics platform comprising: Personnel Wellness Monitoring Dashboard. Mobile-based Wellness and Self-Assessment Application. Predictive Behavioral Analytics Engine. Stress and Burnout Risk Prediction Models. Welfare Intervention Recommendation System. Role-based Access Control and Privacy Management Framework. Automated Alerts for authorized welfare personnel. Data anonymization and secure storage mechanisms. The solution should identify trends and risk factors while ensuring that individual dignity, confidentiality, and data protection requirements are maintained. Expected Benefits 1. Early identification of personnel requiring welfare support. 2. Reduction in stress-related incidents and operational fatigue. 3. Improved mental well-being and workforce resilience. 4. Enhanced readiness and operational effectiveness. 5. Better workload distribution and personnel management. 6. Improved retention and job satisfaction. 7. Data-driven welfare planning and resource allocation. 8. Reduction in incidents arising from prolonged occupational stress. Preliminary Scope 1. Development of predictive behavioral analytics algorithms. 2. Mobile-based wellness self-reporting platform. 3. AI-driven stress and burnout risk assessment engine. 4. Commander and Welfare Officer dashboard. 5. Automated intervention recommendation system. 6. Secure integration with HRMS and personnel management systems. 7. Privacy-preserving analytics and role-based access controls. Key Technical Challenges 1. Ensuring privacy and confidentiality of sensitive personnel data. 2. Preventing stigmatization of personnel identified as potentially at risk. 3. Minimizing false positives and false negatives in risk prediction. 4. Ensuring ethical and transparent AI decision-making. 5. Securing highly sensitive psychological and welfare-related information against cyber threats. 6. Building trust among personnel regarding system usage and data protection. Strategic Importance Enhances force readiness and personnel welfare. Supports evidence-based welfare management. Strengthens organizational resilience and operational effectiveness. Promotes preventive mental health care rather than reactive interventions. Creates an indigenous capability tailored to the unique operational and cultural environment of Indian CAPFs and Armed Forces. Potential Market 1. Central Armed Police Forces (CAPFs). 2. Indian Armed Forces. 3. State Police Organizations. 4. Disaster Response and Emergency Services. 5. Government Organizations with high-stress workforces. 6. Corporate Human Resource and Employee Wellness Platforms. 7. International security and workforce welfare markets. Expected Impact The proposed AI-enabled Personnel Stress and Welfare Monitoring System will help transform welfare management from a reactive process to a proactive and preventive framework. By enabling early identification of stress indicators and facilitating timely interventions,the solution can improve personnel well-being, enhance operational effectiveness, and strengthen the long-term resilience of uniformed services while maintaining the highest standards of privacy, ethics, and data security.

Cutting-edge technology in these sectors continues to be in demand. Recent shifts in healthcare trends, growing populations also present an array of opportunities for innovation.

There is a need to design drones and robots that can solve some of the pressing challenges of India such as handling medical emergencies, search and rescue operations, etc.