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Time of India
a day ago
- Health
- Time of India
UAEU and IIT Madras Zanzibar create AI system to track and forecast malaria outbreaks
In a significant step forward for global public health, researchers from United Arab Emirates University (UAEU) and the Indian Institute of Technology Madras' Zanzibar campus have introduced a cutting-edge, data-driven framework that accurately models and forecasts malaria transmission. Tired of too many ads? go ad free now By integrating artificial intelligence with mathematical modelling, this new approach aims to support early intervention and improve disease control strategies in malaria-prone regions. A new approach to malaria modelling A collaborative research team led by Adithya Rajnarayanan, Manoj Kumar, and Professor Abdessamad Tridane has introduced a novel methodology that enhances how malaria outbreaks can be forecasted. Their work, published in Scientific Reports by Nature, presents a comprehensive model that combines artificial intelligence (AI) with classical epidemiological frameworks to simulate malaria dynamics with higher precision. The study, titled 'Analysis of a Mathematical Model for Malaria Using a Data-Driven Approach' , brings a fresh perspective to disease modelling. It incorporates temperature- and altitude-dependent variables into compartmental disease models, a method that makes the simulations more realistic and region-specific. This is particularly crucial for climate-sensitive and vulnerable areas where environmental factors heavily influence malaria transmission patterns. Technologies at the core: AI and dynamic systems To boost the predictive capability of their model, the researchers employed a suite of advanced AI tools. These included: Artificial Neural Networks (ANNs) Recurrent Neural Networks (RNNs) Physics-Informed Neural Networks (PINNs) Each of these tools was used to improve the accuracy of disease forecasting, enabling the model to detect patterns in the complex interplay between environmental conditions and malaria spread. Additionally, the study introduced Dynamic Mode Decomposition (DMD), a mathematical technique that helps break down complex systems into simpler, understandable components. Tired of too many ads? go ad free now This was used to create a real-time infection risk metric, offering public health officials a powerful resource for early detection and targeted response. Implications for global health Professor Abdessamad Tridane of UAEU emphasized the importance of this integration of AI with epidemiological modelling, stating: This research demonstrates the power of AI when combined with classical epidemiological models,' said Prof. Abdessamad Tridane of UAEU. 'By embedding environmental dependencies directly into the transmission functions, our model captures the complex, real-world behaviour of malaria spread, providing a more accurate and timely method for disease tracking.' The implications of this research are especially relevant for regions like sub-Saharan Africa, which accounts for 94% of global malaria cases. With over half a million malaria-related deaths each year, the need for accurate forecasting models is critical. This work offers a valuable step towards improved surveillance, early warning systems, and data-driven policymaking in the fight against malaria. Institutional collaboration and background This study represents a collaboration between two institutions that are expanding their global health research footprint: United Arab Emirates University (UAEU), established in 1976 in Al Ain, is the UAE's oldest public research university. Founded by Sheikh Zayed bin Sultan Al Nahyan, it offers a wide range of undergraduate and postgraduate programs across multiple disciplines. IIT Madras Zanzibar Campus, inaugurated in November 2023, is the first international campus of the Indian Institute of Technology Madras. Located in the Bweleo district of Zanzibar, Tanzania, the campus currently offers programs in Data Science and Artificial Intelligence. It aims to cater to a diverse student population from India, Tanzania, and other African nations, with plans to broaden its academic scope in the coming years.
Time of India
2 days ago
- Health
- Time of India
UAEU and IIT Madras Zanzibar develop AI-based framework to forecast malaria outbreaks
Image: File In a significant step forward for global public health, researchers from United Arab Emirates University (UAEU) and the Indian Institute of Technology Madras' Zanzibar campus have introduced a cutting-edge, data-driven framework that accurately models and forecasts malaria transmission. By integrating artificial intelligence with mathematical modelling, this new approach aims to support early intervention and improve disease control strategies in malaria-prone regions. A new approach to malaria modelling A collaborative research team led by Adithya Rajnarayanan, Manoj Kumar, and Professor Abdessamad Tridane has introduced a novel methodology that enhances how malaria outbreaks can be forecasted. Their work, published in Scientific Reports by Nature, presents a comprehensive model that combines artificial intelligence (AI) with classical epidemiological frameworks to simulate malaria dynamics with higher precision. The study, titled 'Analysis of a Mathematical Model for Malaria Using a Data-Driven Approach' , brings a fresh perspective to disease modelling. It incorporates temperature- and altitude-dependent variables into compartmental disease models, a method that makes the simulations more realistic and region-specific. This is particularly crucial for climate-sensitive and vulnerable areas where environmental factors heavily influence malaria transmission patterns. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like Retro Lanterns: A Halloween Staple topgadgetlife Shop Now Undo Technologies at the core: AI and dynamic systems To boost the predictive capability of their model, the researchers employed a suite of advanced AI tools. These included: Artificial Neural Networks (ANNs) Recurrent Neural Networks (RNNs) Physics-Informed Neural Networks (PINNs) Each of these tools was used to improve the accuracy of disease forecasting, enabling the model to detect patterns in the complex interplay between environmental conditions and malaria spread. Additionally, the study introduced Dynamic Mode Decomposition (DMD), a mathematical technique that helps break down complex systems into simpler, understandable components. This was used to create a real-time infection risk metric, offering public health officials a powerful resource for early detection and targeted response. Implications for global health Professor Abdessamad Tridane of UAEU emphasized the importance of this integration of AI with epidemiological modelling, stating: This research demonstrates the power of AI when combined with classical epidemiological models,' said Prof. Abdessamad Tridane of UAEU. 'By embedding environmental dependencies directly into the transmission functions, our model captures the complex, real-world behaviour of malaria spread, providing a more accurate and timely method for disease tracking.' The implications of this research are especially relevant for regions like sub-Saharan Africa, which accounts for 94% of global malaria cases. With over half a million malaria-related deaths each year, the need for accurate forecasting models is critical. This work offers a valuable step towards improved surveillance, early warning systems, and data-driven policymaking in the fight against malaria. Institutional collaboration and background This study represents a collaboration between two institutions that are expanding their global health research footprint: United Arab Emirates University (UAEU), established in 1976 in Al Ain, is the UAE's oldest public research university. Founded by Sheikh Zayed bin Sultan Al Nahyan, it offers a wide range of undergraduate and postgraduate programs across multiple disciplines. IIT Madras Zanzibar Campus, inaugurated in November 2023, is the first international campus of the Indian Institute of Technology Madras. Located in the Bweleo district of Zanzibar, Tanzania, the campus currently offers programs in Data Science and Artificial Intelligence. It aims to cater to a diverse student population from India, Tanzania, and other African nations, with plans to broaden its academic scope in the coming years.
Yahoo
13-03-2025
- Business
- Yahoo
Google DeepMind Launches Gemini Robotics AI Model - Interview Kickstart's Advanced Machine Learning Course Addresses Demand for ML Engineers
Santa Clara, March 13, 2025 (GLOBE NEWSWIRE) -- Santa Clara, California - Google's latest breakthrough, Gemini Robotics, is pushing the boundaries of AI-driven automation. By integrating advanced large language models (LLMs) into robotics, Gemini enables machines to understand and interact with their environments in more human-like ways. These AI-powered robots can execute complex tasks, adapt to dynamic environments, and even demonstrate reasoning capabilities. As the field evolves, the demand for engineers with deep expertise in machine learning and AI is surging, making it essential for professionals to equip themselves with specialized knowledge to work in the new, exciting world of AI and robotics. To address this growing need, Interview Kickstart offers its Advanced Machine Learning course with Interview Prep, designed to prepare engineers for cutting-edge roles in AI at companies like Google. This intensive program covers the essential skills needed to work on sophisticated AI-driven systems like Gemini Robotics, enabling learners to contribute to the next generation of intelligent machines. The curriculum teaches fundamental machine learning concepts, including supervised and unsupervised learning, feature engineering, and model evaluation. Participants then progress to deep learning techniques, covering neural networks, backpropagation, and optimization methods that form the backbone of modern AI. The course covers key AI architectures, including transformers, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), which have applications in robotics and automation. Learners gain hands-on experience with reinforcement learning, a technique used in AI-driven robotics for decision-making through trial and error, alongside other methods like supervised and imitation learning. The program also explores generative models such as GANs and diffusion models, which contribute to synthetic data generation and visual processing in robotics. These align with AI research initiatives, including those at Google and DeepMind. A core feature of Interview Kickstart's training is its structured, hands-on approach. The program includes live sessions with FAANG+ instructors, interactive problem-solving exercises, and real-world case studies that mirror challenges faced in AI and robotics development. Learners spend 10-12 hours per week mastering concepts through expert-led lectures, coding assignments, and personalized mentorship. Sundays are dedicated to in-depth live sessions covering critical ML topics, while weekdays focus on solving real-world AI problems, engaging in case studies, and refining technical skills with direct guidance from industry leaders. For information visit: To enhance practical learning, Interview Kickstart's Advanced Machine Learning course includes a capstone project where participants apply their knowledge to build and deploy machine learning models on platforms like AWS and Google Cloud. This real-world experience prepares learners to tackle complex AI engineering challenges and positions them for roles at top tech companies, including Google's AI and robotics teams. With expert guidance, an industry-aligned curriculum, and rigorous hands-on training, Interview Kickstart's Advanced Machine Learning course provides the ideal foundation for professionals looking to break into AI and robotics. Interview Kickstart's Advanced Machine Learning course is designed for software engineers, developers, and STEM graduates looking to transition into AI/ML roles. Taught by FAANG+ ML/AI experts, it offers a 360° curriculum with individualized coaching, hands-on capstone projects, and real-world exposure. Learners receive 1:1 mentorship, interview prep modules, and mock interviews with industry professionals. Career development support includes resume building, LinkedIn optimization, and behavioral workshops, ensuring participants are fully equipped for AI/ML careers in top tech companies. The rise of AI-driven robotics will lead to a transformative shift in technology, offering new opportunities and challenges for ML/AI engineers. Google's Gemini Robotics is just the beginning, and professionals with expertise in machine learning will be at the forefront of this revolution. Interview Kickstart's Advanced Machine Learning course is designed to equip engineers with the knowledge, skills, and industry insights needed to excel in this exciting new realm. To learn more visit: About Interview Kickstart Founded in 2014, Interview Kickstart is a leading upskilling platform that empowers aspiring tech professionals to land their dream roles in FAANG and top tech companies. With a proven track record, Interview Kickstart has helped 20,000+ learners achieve their career aspirations at leading tech organizations. What sets Interview Kickstart apart is its pool of 700+ FAANG instructors, comprising hiring managers and tech leads who design and teach the comprehensive curriculum. They offer practical insights, the latest interview prep strategies, and mock interviews to excel in technical interviews and on the job. ### For more information about Interview Kickstart, contact the company here:Interview KickstartBurhanuddin Pithawala+1 (209) 899-1463aiml@ Patrick Henry Dr Bldg 25, Santa Clara, CA 95054, United States CONTACT: Burhanuddin Pithawala
