Background The current approach to vessel chartering for bulk cargo procurement to India's East Coast ports often involves daily market exploration, leading to reactive decision-making and likely missed opportunities for cost savings and efficiency. The highly volatile nature of global freight markets, coupled with varying supply and demand dynamics from key origins like Australia, the US, Mozambique, Russia and Indonesia, makes it challenging to identify optimal entry points for short-term or mid-term charter contracts. Furthermore, without a robust future forecasting mechanism, determining the most suitable vessel type (e.g., Handysize, Supramax, Panamax,Capesize) for specific cargo parcels and routes, while accounting for port infrastructure limitations at both origin and destination, results in suboptimal utilization and increased idle time. This manual, market-dependent approach requires analytics to mitigate risks associated with freight fluctuations and port-specific constraints, directly impacting overall logistics costs and supply chain reliability. Detailed HEADING Description The problem statement addresses the critical need for a sophisticated freight forecasting model to revolutionize vessel chartering and bulk cargo procurement for East Coast Indian ports. Currently, our operations are heavily reliant on daily engagements with the freight market. This traditional method leads to several inefficiencies a lack of predictive insight into future freight rates, making it difficult to secure favorable short-term or mid-term charter contracts; an inability to proactively identify the optimal time to enter the market for specific vessel types and cargo sizes; and significant challenges in minimizing vessel idle time due to inadequate planning regarding port-specific infrastructure restrictions. For instance, procuring bulk cargo (such as coal) from Australia, the US,Mozambique, and Indonesia presents unique logistical challenges. Each origin-destination pair has distinct sailing distances, trade lane dynamics, and, crucially,varying port capabilities. East Coast Indian ports, like Paradip, Vizag, Gangavaram,Gopalpur, Dhamra, Sagar- Sandheads and Haldia, each possess specific draft restrictions, berthing limitations, and cargo handling capacities that dictate the maximum permissible vessel size and turnaround time.The proposed system should therefore integrate multiple data points for comprehensive analysis. This includes historical freight rate data for various vessel sizes across relevant trade routes, global economic indicators, commodity price trends, seasonal variations in demand and supply, and real-time port congestion information for both origin and destination ports. Furthermore, it must incorporate detailed infrastructure constraints of Indian East Coast ports, such as maximum LOA (Length Overall), beam, draft, and cargo handling rates, along with similar data for the loading ports in Australia, the US, Mozambique, and Indonesia. Expected Solution The expected solution is the development and implementation of an intelligent, datadriven Freight Forecasting Model. This model should leverage advanced analytical techniques, potentially including machine learning algorithms (e.g., time series forecasting, regression models) and artificial intelligence, to predict future freight rates with a high degree of accuracy for various vessel types and trade routes. The solution should offer actionable insights by providing recommendations on: a. Optimal Market Entry Timing Identify ideal windows to secure short-term or mid-term vessel charter contracts for specific cargo requirements, minimizing freight costs. b. Vessel Type Optimization Recommend the most suitable vessel type (e.g.,Handysize, Supramax, Panamax, Capesize) for a given cargo volume and origin-destination pair, considering all known port infrastructure limitations at both loading and discharge ports on India's East Coast. This includes factoring in draft restrictions, LOA, and cargo handling capabilities to prevent idle time and ensure efficient turnaround. c. Idle Scenario Management Propose strategies for minimizing vessel idle time by forecasting periods of low demand and suggesting alternative employment opportunities or optimized positioning to reduce deadheading. d. Risk Mitigation Provide early warnings for potential market volatility, port congestion, or other disruptions that could impact chartering decisions. The model should be user-friendly, perhaps with a dashboard interface, allowing logistics managers to input cargo details, origin/destination ports, and desired contract duration to receive comprehensive freight forecasts and actionable recommendations.The ultimate goal is to move from a reactive, daily market approach to a proactive, predictive chartering strategy, leading to significant cost reductions, improved supply chain efficiency, and enhanced decision-making capabilities. Objective Development of model to facilitate moving from multiple single spot contracts being entered into currently to short term / medium term multiple voyage contracts.

Background Railway maintenance for fixed infrastructure of Engineering, Traction Distribution, and Signal & Telecommunication departments is currently planned independently. Each department requests maintenance blocks/disconnections via the BDMS system. This planning process is decentralized and manual. This often leads to inefficient block utilization, poor coordination, and suboptimal scheduling,which may reduce asset availability and impact train operations. Detailed HEADING Description Maintenance data-such as defects and overdue tasks—is maintained separately in systems like Track Management System (TMS), Signalling Maintenance & Management System (SMMS), and Traction Distribution Management System (TDMS). Meanwhile, the Control Office Application (COA) manages block corridor availability. Without integration and coordinated scheduling, maintenance blocks/disconnections are not optimally planned, resulting in asset downtime and reduced availability of fixed infrastructure for train operation.Your task is to develop an Automatic Block Planning system that integrates maintenance, defects and corridor data to generate optimized block schedules. The system should prioritize maintenance activities to minimize asset downtime and maximize the availability of critical infrastructure, ensuring uninterrupted train operations. Expected Solution Participants should build an Al system that includes: 1. Integration of maintenance data (defects, overdue maintenance) from TMS, SMMS, and TDMS with corridor block and block availability as per the Train Time Table and the goods trains forecast from the Control Office. 2. Uses AI/ML algorithms to prioritize and schedule maintenance tasks based on criticality, urgency, and impact on asset availability. 3. Optimize block scheduling to maximize asset uptime by minimizing downtime and efficiently coordinating multi-department activities. 4. Provides block plans over multiple time horizons-weekly and monthly—to support both short-term and long-term maintenance. The solution should transform current decentralized and manual block planning into a data-driven, coordinated process that maximizes asset availability, improves safety, and supports reliable train operations.

Background Under the Legal Metrology Act, 2009 and the Legal Metrology (General) Rules, 2011, every weighing and measuring instrument used in transaction or protection is required to be periodically verified and stamped before being put into use. Verification activities are carried out by Legal Metrology Officers (LMOs) of the State Legal Metrology Departments and Government Approved Test Centres (GATCs) notified by the Government.These verification activities presently involve substantial manual processes including submission of applications, scheduling of verification, recording of observations, issuance of verification certificates, maintenance of records and monitoring of validity periods. In many cases, records are maintained physically or through isolated local systems, resulting in delays,and difficulty in monitoring verification status across jurisdictions. There is also a need for digital verification certificates, centralized databases, and online access to verification history for consumers, regulators and businesses. Accordingly, there is a requirement for development of a unified online verification and digital certification system for weighing and measuring instruments to improve transparency,efficiency, and ease of compliance within the Legal Metrology ecosystem. Description Develop a secure web-based and/or mobile-enabled software platform for online verification,certification and lifecycle management of weighing and measuring instruments used under Legal Metrology regulations. The system should be capable of Online registration of stakeholders users of weights and measures, State LMOs, GATCs etc. Online submission of applications for verification and re-verification of weighing and measuring instruments. Scheduling and allocation of verification activities to Legal Metrology Officers or GATCs. Generation of digital verification certificates with QR codes. Recording inspection observations and verification results digitally. Tracking validity and due dates for re-verification. Generating alerts and reminders for expiring verification validity. - Providing dashboard for monitoring verification status, pendency and enforcement activities. Supporting integration with mobile devices for field verification activities. Expected Solution The proposed solution should include: User-friendly web and mobile application for all stakeholders. Online workflow management for verification and re-verification processes. Digital repository of verification certificates and instrument records. Digital verification certificates with QR code and authentication system. Automated alerts for verification expiry and renewal. Dashboards for users, LMOs, GATCs and administrators etc. Search and retrieval facility for verification records and certificates. Role-based secure login system for different stakeholders. Technical documentation describing software architecture, security framework and deployment methodology. Key Functional Requirements Online registration and profile management for stakeholders. Application submission for verification/re-verification of instruments. Verification scheduling and workflow management. Entry of instrument specifications and verification details. Generation of QR-enabled digital verification certificates. Dashboard for monitoring applications, verification status and pendency. Upload and attachment of photographs and supporting documents. Export and printing facility for certificates and reports. Mobile application support for field verification officers.

Background Autonomous navigation depends on the ability of a vehicle to perceive its surroundings with high precision. While 3D Lidar point clouds provide rich spatial data, processing millions of points in real-time creates immense computational bottlenecks and memory latency. Conversely, standard 2D occupancy grids lose critical height information necessary for detecting curbs, potholes, or overhanging obstacles. To balance precision and performance, there is a need for a 'foveated' mapping approach—similar to human vision— where the immediate vicinity is rendered in high detail for safety, and distant areas are simplified to reduce the processing load. Description The goal is to build a deep learning pipeline that transforms raw Lidar point clouds into a variable resolution 2. 5D grid (an elevation map with semantic layers). The system must perform three primary tasks 1. Terrain Analysis Distinguish between drivable surfaces and non-drivable terrain. 2. Object Detection Identify and classify static obstacles (walls, poles) and dynamic objects (pedestrians, other vehicles). 3. Adaptive Spatial Representation Implement a non-uniform grid where the cell size increases as the distance from the sensor increases. This requires a sophisticated data structure that can handle variable resolution without causing alignment errors or data loss during the projection from 3D to 2. 5D. Expected Solution A software framework consisting of: A Deep Learning Model A network (e.g., PointNet++ or a Sparse Convolutional Neural Network) capable of semantic segmentation of point clouds into terrain, static obstacles, and moving objects. Variable Resolution Grid Engine An algorithm that projects classified 3D points into a 2. 5D grid where the resolution is high (e.g., 5cm cells) within a 10m radius and decreases (e.g., 50cm cells) up to a 100m radius. Real-time Visualization A dashboard showing the 2. 5D map with distinct color-coding for terrain and objects, demonstrating a significant reduction in memory usage compared to a uniform high-resolution 3D map. Performance Metrics Evidence of low latency (high FPS) and high accuracy in object classification across varying distances.

Background Modern urban centers deploy vast networks of CCTV and Automatic Number Plate Recognition(ANPR) cameras to manage traffic, enforce traffic laws, and maintain public security. However,most existing systems process these feeds in isolated silos, performing basic license plate detection without effectively linking data across space and time. This lack of integration prevents city authorities from automatically tracking high-interest vehicles across different sectors and limits their ability to extract macro-level traffic movement trends from the existing camera infrastructure. Description The objective is to develop a robust, centralized AI software platform that processes multicamera feeds across a city-wide ANPR network to accomplish three core functionalities. First, the platform must feature a High-Accuracy ANPR and OCR Engine, which utilizes an advanced Optical Character Recognition model capable of achieving greater than 90% accuracy across diverse realworld conditions such as varying lighting, poor weather, angled shots, motion blur, and dirty or damaged license plates. Second, it requires a Single Plate Trajectory Tracking module to build a spatial-temporal tracking system capable of reconstructing the complete travel trajectory of any specific vehicle plate across the entire city network. This system will map a vehicle's movement history, timestamps, direction, and route on a GIS map using inputs from geographically distributed ANPR cameras. Third, the system must perform Macro Traffic Flow and Movement Analytics by analyzing aggregated camera data to compute and visualize general city-wide traffic dynamics. This includes measuring traffic density, identifying origin-destination patterns,detecting congestion bottlenecks, and providing real-time heatmaps of city traffic movement. Expected Solution The expected solution is a scalable, enterprise-grade software platform equipped with four key components. It will feature a High-Precision OCR Module powered by a deep-learning model exceeding 90% recognition accuracy for license plates in multi-lane traffic streams. It will include a Trajectory Reconstruction Engine providing a query-based tracking interface that plots a vehicle's historical path chronologically across the city map with accurate timestamps and camera locations. Furthermore, it will integrate a City Traffic Analytics Dashboard to serve as a centralized, GIS-integrated web platform displaying heatmaps, average vehicle speeds, route densities, and traffic flow trends across all camera nodes. Finally, the platform will incorporate an Alert System capable of flagging blacklisted vehicles and suspicious route anomalies in real time.

Background Bitcoin's pseudonymous, peer-to-peer design lets criminal actors move, layer, and cash out illicit funds — ransomware payments, darknet-market proceeds, extortion, and laundering — while evading traditional financial surveillance. The objective of problem statement is to design and build a complete system (offline) that ingests bulk Bitcoin transaction/network metadata (in CSV/JSON/XML), correlates network-layer (IP/port/timing) observations with blockchain-layer (wallet/TXID/amount) data, and applies AI/ML to detect anomalies, cluster entities, and generate prioritized, explainable investigative leads. Description i. Challenge Objectives- Ingest & parse a bulk metadata dataset (timestamp, src/dst IP & port, TXID, input/output wallet addresses, amounts, fee, script type). Build an entity/transaction graph linking IPs, wallets, and transactions. Implement AI/ML detection use case (see Section 4) with a working model — not just rules. Generate a ranked, explainable alert list (why a wallet/transaction was flagged, with a confidence score). Present findings via a simple dashboard or link-analysis visualization. ii. Suggested AI/ML Focus Areas Attach Table Here of AI/ML Focus Areas iii. Dataset: Parameters & Synthetic Generation Participants will work with a synthetic dataset modelled on real Bitcoin P2P/transaction fields (no real seized or live-intercept data will be provided). Minimum fields timestamp, src_ip, dst_ip, src_port, dst_port, txid, input_addresses[], output_addresses[], input_amounts[], output_amounts[], geo_country/asn (integrate open source downloadable Geo IP database). Expected Solution Workable complete offline solution for linux platform. Working prototype (code repo) with ingestion, correlation, and AI/ML model. Short technical write-up approach, model choice, and explain ability method. Dashboard/visualization showing flagged entities and evidence for each flag.

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