Artificial Intelligence & Machine Learning

Solving complex real-world challenges using data-driven intelligence and adaptive systems. Click on the respective problem statement to view it's description.

Problem

Despite increased awareness of physical fitness, long-term adherence remains low due to generic workout plans, static diet recommendations, lack of calorie transparency, and insufficient engagement. Most existing solutions fail to adapt to individual behavior, lifestyle, and motivation patterns, leading to early disengagement and poor outcomes.

Description

The challenge is to design an AI-driven fitness ecosystem that continuously learns from user activity, nutrition, energy expenditure, and lifestyle signals. The system should dynamically personalize workout plans, diet strategies, and calorie targets based on real-time progress and feedback. Additionally, motivation analytics must detect disengagement early and activate personalized interventions such as reminders, challenges, and incentives to promote sustained commitment.

Problem

Early detection of lung and breast cancer is critical for improving patient survival rates. However, manual analysis of chest X-rays and mammogram images is time-consuming and depends heavily on expert availability. There is a need for an AI-based system that can assist in detecting cancer at an early stage using medical imaging.

Description

Build an AI-driven model that analyzes chest X-ray images for lung cancer and mammogram images for breast cancer to classify cases as cancer or non-cancer. The system should use deep learning techniques to automatically extract features and provide accurate, easy-to-understand predictions. The focus should be on early screening support, simplicity, and real-world applicability rather than final diagnosis.

Problem

Supply chains today face frequent disruptions from demand fluctuations, supplier failures, transportation delays, and external shocks. Traditional centralized systems struggle to respond in real time, leading to inefficiencies, stockouts, and higher operational costs. Companies need adaptive solutions that can dynamically adjust to changing conditions and maintain smooth logistics operations.

Description

The challenge is to design an agentic AI framework where autonomous agents represent suppliers, warehouses, transport units, and retailers. These agents should continuously monitor real-time data, predict potential risks, and collaborate to optimize decision-making. By learning from historical patterns and live updates, the system can dynamically rebalance resources, reroute shipments, and mitigate bottlenecks. The goal is to create a resilient, self-adjusting supply chain that reduces manual intervention and improves overall efficiency.

Problem

Misinformation rarely exists as a single false claim; it evolves through changes in language, tone, and context. Existing detection systems, which focus on binary classification, often fail to capture the dynamic nature of narratives. This leaves platforms, journalists, and fact-checkers struggling to identify and prioritize harmful content effectively.

Description

The challenge is to develop an AI/ML system that can track narrative evolution across multiple platforms and cluster related misinformation. The system should assign risk scores instead of simply labeling content as true or false, enabling prioritization for human review. It must detect subtle changes in tone, context, and framing while providing explainable insights. By capturing these dynamics, the platform can help fact-checkers, journalists, and researchers understand how misinformation spreads and evolves, enabling timely interventions.

Problem

Current assistive navigation systems often overwhelm visually impaired users with excessive or non-contextual information. Critical environmental cues are missed, reducing safety and independence. There is a need for systems that provide guidance selectively and meaningfully, adapting to real-world contexts.

Description

The challenge is to design an AI-based navigation system that understands environmental context, urgency, and user intent. The system should selectively provide relevant guidance to improve situational awareness and safety. It must integrate real-time data from surroundings, prioritize important cues, and minimize information overload. By adapting instructions to each scenario, the platform can enhance mobility and independence for visually impaired individuals, making navigation safer and more intuitive.