AIMC Topic: Ecosystem

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A general deep learning model for bird detection in high-resolution airborne imagery.

Ecological applications : a publication of the Ecological Society of America
Advances in artificial intelligence for computer vision hold great promise for increasing the scales at which ecological systems can be studied. The distribution and behavior of individuals is central to ecology, and computer vision using deep neural...

Using Explainable Artificial Intelligence to Discover Interactions in an Ecological Model for Obesity.

International journal of environmental research and public health
Ecological theories suggest that environmental, social, and individual factors interact to cause obesity. Yet, many analytic techniques, such as multilevel modeling, require manual specification of interacting factors, making them inept in their abil...

Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions.

Sensors (Basel, Switzerland)
The recent upsurge of smart cities' applications and their building blocks in terms of the Internet of Things (IoT), Artificial Intelligence (AI), federated and distributed learning, big data analytics, blockchain, and edge-cloud computing has urged ...

Evaluating the Forest Ecosystem through a Semi-Autonomous Quadruped Robot and a Hexacopter UAV.

Sensors (Basel, Switzerland)
Accurate and timely monitoring is imperative to the resilience of forests for economic growth and climate regulation. In the UK, forest management depends on citizen science to perform tedious and time-consuming data collection tasks. In this study, ...

Detecting the sources of chemicals in the Black Sea using non-target screening and deep learning convolutional neural networks.

The Science of the total environment
The Black Sea is an important ecosystem, which is affected by various anthropogenic pressures, such as shipping activities and wastewater inputs from large coastal cities. Significant loads of chemical pollutants are being continuously brought in by ...

Graph neural network modelling as a potentially effective method for predicting and analyzing procedures based on patients' diagnoses.

Artificial intelligence in medicine
BACKGROUND: Currently, the healthcare sector strives to improve the quality of patient care management and to enhance/increase its economic performance/efficiency (e.g., cost-effectiveness) by healthcare providers. The data stored in electronic healt...

The Impact of Data Augmentations on Deep Learning-Based Marine Object Classification in Benthic Image Transects.

Sensors (Basel, Switzerland)
Data augmentation is an established technique in computer vision to foster the generalization of training and to deal with low data volume. Most data augmentation and computer vision research are focused on everyday images such as traffic data. The a...

, Enhancing Long-Term Consistency of Object-Oriented Semantic Maps in Robotics.

Sensors (Basel, Switzerland)
This paper proposes , a method for building object-oriented semantic maps that remain consistent in the long-term operation of mobile robots. Among the different challenges that compromise this aim, focuses on two of the more relevant ones: preventi...

Performance assessment of ontology matching systems for FAIR data.

Journal of biomedical semantics
BACKGROUND: Ontology matching should contribute to the interoperability aspect of FAIR data (Findable, Accessible, Interoperable, and Reusable). Multiple data sources can use different ontologies for annotating their data and, thus, creating the need...

Use of machine learning to identify patients at risk of sub-optimal adherence: study based on real-world data from 10,929 children using a connected auto-injector device.

BMC medical informatics and decision making
BACKGROUND: Our aim was to develop a machine learning model, using real-world data captured from a connected auto-injector device and from early indicators from the first 3 months of treatment, to predict sub-optimal adherence to recombinant human gr...