Health Sciences Projects - AI, IoT, Edge Computing, Cloud Computing, Blockchain

Disease Prediction

Disease prediction is essential to improve the quality of life. It enables early diagnosis, preventive healthcare, and effective treatment planning.

Our research on diabetes prediction has been pioneering the field of AI-powered diabetes prediction.

 

Read more about this research here.

  

Patient Healthcare and Management Systems

Efficient healthcare management relies on secure, interoperable systems that allow seamless data access and integrity.

Our research on AI, IoT, edge computing, cloud computing, and blockchain technology is transforming the way patient records and healthcare management systems operate by providing a decentralized, efficient, secure, and transparent framework among the different stakeholders.

 

Read more about this research here.

 

Epidemiology and Public Health 

It is critical to manage and mitigate global health crises effectively. Our research leverages AI and big data analytics, edge and cloud computing, to study outbreaks such as COVID-19, offering valuable insights into disease transmission and intervention effectiveness. 

By analyzing real-time epidemiological data, we contribute to evidence-based public health strategies that enhance preparedness, optimize response measures, and support data-driven decision-making in healthcare.

We build an AI-driven mental health framework to enable public health professionals, policymakers, and mental health epidemiology researchers to detect population emotions on social media whenever an event is introduced by policy decision-makers.

 

Read more about this research here.

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Medical Imaging

Artificial intelligence has significantly advanced medical imaging, enabling healthcare professionals to analyze medical scans. Such AI-driven models facilitate the automation of complex imaging tasks, accelerating diagnostic processes and enhancing precision.

Our ongoing research focuses on AI-driven medical imaging for the detection of diseases.

 

Precision Medicine

The future of healthcare lies in precision medicine, where treatments are tailored to individual patient profiles, considering several factors, including genetics, environment, and clinical data.

Our ongoing research focuses on integrating AI-driven analytics to develop personalized and effective treatment plans by identifying patient-specific responses to drugs and diseases. 

 

Computer Vision for Pain-Level Detection

One of our key contributions is in pain level detection, where we investigate the capabilities of AI models in assessing pain intensity based on facial expressions. These innovations offer objective, non-invasive pain assessments and improve pain management strategies in clinical settings. This allows healthcare professionals to allocate more time to patient care and decision-making.

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Read more about this research here.

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AI-Powered Sustainable Solutions to Rising Residential Energy Consumption

In recent years, residential energy consumption has been escalating at an alarming rate due to demographic and behavioral changes such as population growth and the widespread adoption of work-from-home practices following the COVID-19 pandemic.
The International Energy Agency reports that global residential energy consumption has skyrocketed from 1,203 Terawatt hours (TWh) in 1974 to 6,072 TWh in 2019, marking a staggering increase of 404.74%.
In 2021, the residential sector accounted for 27% of the final energy consumption in the European Union, with a significant portion allocated to space heating (64.4%), followed by water heating (14.5%), lighting and electrical appliances (13.6%), cooking (6.0%), other uses (1.1%), and space cooling (0.5%).
Similarly, the Australian Government Department of Climate Change, Energy, the Environment and Water reports that residential buildings in Australia consume 24% of the overall electricity and contribute to over 10% of the country’s total carbon emissions.
This surge in consumption results in substantial emissions of carbon dioxide and other greenhouse gases, exacerbating global warming. Therefore, it is imperative to develop accurate methods for predicting residential energy loads, aligning with the United Nations’ Sustainable Development Goals and Framework Convention on Climate Change to reduce emissions of greenhouse gases.

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Read more about this research here.

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ML-NLPEmot Project: Mental Health Detection in Social Media with AI (From 2023)

• Mental health initiative 

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Generative AI Project (From 2022)

• GPT-Based Sign Language Translation

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Smart Healthcare Project: AI and Blockchain-Enabled and Quality-of-Service (QoS) Driven Smart Healthcare Systems (From 2018)

• AI for COVID-19 Infection Prediction     

• Blockchain for Healthcare

• Chronic Disease Management

  • Diabetes Prediction 

 

Sustainable and Secure Internet of Things (IoT)-Edge-Cloud Computing Projects (From 2008)

This project aims to provide solutions to achieve the sustainable development goals of the United Nations for sustainable cities and communities, and climate action. This project includes:

AI for Cloud Computing 

• AI for Cloud scheduling and energy efficiency

• Cloud data center power modeling

 

AI for IoT, Edge, and Cloud Computing

• AI for Edge and Cloud scheduling and resource management

• AI for QoS and energy efficiency 

• AI for mobile offloading 

 

AI for Green and QoS Smart Transportation Systems

• AI for task offloading and scheduling in vehicular networks

 

AI for smart healthcare systems

• HealthEdge project

 

Cloud Computing

• Cloud resource management and allocations