It focuses on the design and optimization of biomedical sensors to effectively extract physiological signals from the human body, with performance enhanced through machine learning techniques.

My research on the design and optimization of biomedical sensors using machine learning aligns with several Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being) by enhancing early disease detection and improving healthcare service quality through accurate physiological signal extraction. It also supports SDG 9 (Industry, Innovation, and Infrastructure) by contributing to technological innovation and advanced research in medical devices. Additionally, it relates to SDG 4 (Quality Education) by promoting research-based learning and skill development in higher education.

It focuses on extracting the primary signal from complex biomedical or environmental data and accurately classifying it to generate commands for hardware actuation systems.

This work contributes to SDG 3 (Good Health and Well-being) by enabling assistive technologies and real-time health monitoring, and SDG 9 (Industry, Innovation, and Infrastructure) by fostering innovation in intelligent systems and automated control technologies.

It focuses on extracting energy from solar and other renewable sources in alignment with the goals of the blue economy.

This supports SDG 7 (Affordable and Clean Energy) by promoting clean, renewable energy solutions and SDG 14 (Life Below Water) by integrating energy systems that minimize environmental impact in marine environments. It also contributes to SDG 13 (Climate Action) by reducing dependence on fossil fuels and supporting climate-resilient energy infrastructure in coastal and offshore areas.

It focuses on developing intelligent models for data-driven decision-making across domains such as healthcare, energy, and environmental monitoring.

This aligns with SDG 9 (Industry, Innovation, and Infrastructure) by advancing smart technologies and automation, SDG 3 (Good Health and Well-being) through improved diagnostics and predictive analytics, and SDG 13 (Climate Action) by enabling data-driven solutions for climate resilience and resource management.

It involves extracting meaningful insights from complex datasets to support informed decision-making in areas like healthcare, energy, and environmental sustainability.

This aligns with SDG 9 (Industry, Innovation, and Infrastructure) by promoting data-driven innovation, SDG 3 (Good Health and Well-being) through improved public health analytics, and SDG 13 (Climate Action) by enabling effective environmental data analysis for sustainable planning and climate resilience.

This thesis aims to design and develop a solar-electric vehicle (SEV) integrated with an intelligent MPPT-based solar tracking and power management system to maximize solar energy utilization.

This study proposes a reinforcement learning (RL) framework combined with artificial neural networks (ANN) to optimize yaw angles in multi-turbine wind farms, aiming to boost energy output by 10–15% and reduce wake losses by 20–25%.

This thesis aims to predict corrosion rates in offshore structures using advanced machine learning techniques, focusing on their impact on structural fatigue life.

This work explores the use of marine robotics for eco-friendly ocean waste management, comparing various robotic platforms from manual to autonomous systems

This work delineates future trends and offers insights for clinical translation, system integration, and standardization of next-generation EMG technologies.

This project aims to develop transfer learning models to predict antibiotic resistance in Tuberculosis

This project focuses on the early detection of Parkinson's disease (PD), a degenerative neurological disorder that impairs movement, speech, and posture, mainly in individuals over 50.

This project aims to develop a basic reinforcement learning (RL) framework for traffic light control, where the queue length serves as the reward and the state is defined by the number of vehicles on incoming lanes and the current traffic light state.