相关论文: Reshaping the use of digital tools to fight malari…

The fourth Industrial Revolution(4IR), together with the COVID-19 pandemic have made a loud call for digitizing diagnosis processes. The world is now convinced that it is imperative to digitize the diagnosis of long standing diseases such…

To have the greatest impact, public health initiatives must be made using evidence-based decision-making. Machine learning Algorithms are created to gather, store, process, and analyse data to provide knowledge and guide decisions. A…

Through this paper, we call for a distributed, internet-based collaboration to address one of the worst plagues of our present world, malaria. The spirit is a non-proprietary peer-production of information-embedding goods. And we propose to…

Malaria remains a significant global health challenge, necessitating rapid and accurate diagnostic methods. While computer-aided diagnosis (CAD) tools utilizing deep learning have shown promise, their generalization to diverse clinical…

Malaria can be prevented, diagnosed, and treated; however, every year, there are more than 200 million cases and 200.000 preventable deaths. Malaria remains a pressing public health concern in low- and middle-income countries, especially in…

Malaria is a serious infectious disease that is responsible for over half million deaths yearly worldwide. The major cause of these mortalities is late or inaccurate diagnosis. Manual microscopy is currently considered as the dominant…

Disaggregation modelling is a method of predicting disease risk at high resolution using aggregated response data. High resolution disease mapping is an important public health tool to aid the optimisation of resources, and is commonly used…

Malaria is usually diagnosed by a microbiologist by examining a small sample of blood smear. Reducing mortality from malaria infection is possible if it is diagnosed early and followed with appropriate treatment. While the WHO has set…

Malaria is a major health issue worldwide, and its diagnosis requires scalable solutions that can work effectively with low-cost microscopes (LCM). Deep learning-based methods have shown success in computer-aided diagnosis from microscopic…

We propose the Semantics, Interoperability, and Evolution for Malaria Analytics (SIEMA) platform for use in malaria surveillance based on semantic data federation. Using this approach, it is possible to access distributed data, extend and…

The paper provides an overview of core functionalities that digital democracy software needs to provide in order to support democratic deliberative processes at scale. Developing these functionalities poses novel computational challenges…

Malaria is an infectious disease affecting a large population across the world, and interventions need to be efficiently applied to reduce the burden of malaria. We develop a framework to help policy-makers decide how to allocate limited…

Malaria remains a significant global health burden, particularly in resource-limited regions where timely and accurate diagnosis is critical to effective treatment and control. Deep Learning (DL) has emerged as a transformative tool for…

Telecom data is rich on mobility information and as such can be used to identify mobility patterns of people in near real time, enabling to build epidemiological models for understanding where epidemics might spread over time. Based on…

Malaria is a mosquito-borne, lethal disease that affects millions and kills hundreds of thousands of people each year. In this paper, we develop a model for allocating malaria interventions across geographic regions and time, subject to…

Malaria is mosquito-borne blood disease caused by parasites of the genus Plasmodium. Conventional diagnostic tool for malaria is the examination of stained blood cell of patient in microscope. The blood to be tested is placed in a slide and…

The evolution of communication technology and the proliferation of electronic devices have rendered adversaries powerful means for targeted attacks via all sorts of accessible resources. In particular, owing to the intrinsic interdependency…

Malware are becoming a major problem to every individual and organization in the cyber world. They are advancing in sophistication in many ways. Besides their advanced abilities to penetrate and stay evasive against detection and…

On-device machine learning (ML) moves computation from the cloud to personal devices, protecting user privacy and enabling intelligent user experiences. However, fitting models on devices with limited resources presents a major technical…

Malware constitutes a major global risk affecting millions of users each year. Standard algorithms in detection systems perform insufficiently when dealing with malware passed through obfuscation tools. We illustrate this studying in detail…