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Artificial intelligence (AI) and machine learning (ML) present revolutionary opportunities to enhance our understanding of animal behaviour and conservation strategies. Using elephants, a crucial species in Africa and Asia's protected areas, as our focal point, we delve into the role of AI and ML in their conservation. Given the increasing amounts of data gathered from a variety of sensors like cameras, microphones, geophones, drones and satellites, the challenge lies in managing and interpreting this vast data. New AI and ML techniques offer solutions to streamline this process, helping us extract vital information that might otherwise be overlooked. This paper focuses on the different AI-driven monitoring methods and their potential for improving elephant conservation. Collaborative efforts between AI experts and ecological researchers are essential in leveraging these innovative technologies for enhanced wildlife conservation, setting a precedent for numerous other species.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10645515 | PMC |
http://dx.doi.org/10.1098/rsif.2023.0367 | DOI Listing |
Ann Med
December 2025
Department of Cardiovascular Surgery at Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College/National Center for Cardiovascular Diseases, Beijing, China.
Objectives: During emergency surgery, patients with acute type A aortic dissection (ATAAD) experience unfavourable outcomes throughout their hospital stay. The combination of total aortic arch replacement (TAR) and frozen elephant trunk (FET) implantation has become a dependable choice for surgical treatment. The objective of this research was to utilize a machine learning technique based on artificial intelligence to detect the factors that increase the risk of mortality within 30 days after surgery in patients who undergo TAR in combination with FET.
View Article and Find Full Text PDFJ Breath Res
August 2025
G.A.S Gesellschaft für analytische Sensorsysteme mbH, Otto-Hahn-Str. 15, 44227 Dortmund, Germany.
The rapid transfer of volatiles from alveolar blood into the lungs and then out of the body in exhaled breath leads to the common and natural conclusion that these volatiles provide information on health and metabolic processes, with considerable potential as biomarkers for use in the screening, diagnosis and monitoring of diseases. Whilst these exhaled volatiles could well serve as biomarkers for human metabolic processes, thereby providing insights into the clinical and nutritional status of individuals, there exist various confounding factors that limit their easy application. A major confounding factor is the introduction of microbially produced oral volatiles into the exhaled breath, yet these volatiles are often ignored in discovery volatile research studies.
View Article and Find Full Text PDFSci Rep
July 2025
Deparment of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, 641407, India.
Mobile Ad-Hoc Networks (MANETs) are specialized networks that operate in a decentralized manner. As it has no centralized control over communication, the overall network is generally vulnerable to various security threats such as Blackhole, Wormhole, Flooding and Unauthorized access. Although several studies have analysed these issues, ensuring security in MANETs remains a significant challenge.
View Article and Find Full Text PDFComput Biol Med
September 2025
Department of Library & Information Science, Hindustan Institute of Technology and Science, Chennai, India. Electronic address:
Identification of Alzheimer's Disease (AD), especially in its early phases, presents significant challenges due to the nonexistence of reliable biomarkers and effective treatments. Clinical trials for AD medications also suffer from high failure rates. Accurate diagnosis, prognosis determination, progression monitoring, and treatment effect assessment depend heavily on analysing various brain regions, including the Corpus Callosum (CC), Grey Matter (GM), Hippocampus (HC), Ventricle, and White Matter (WM).
View Article and Find Full Text PDFSci Rep
July 2025
Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, 430022, China.
A machine learning model was developed and validated to predict postoperative complications in patients with acute type A aortic dissection (ATAAD) who underwent total arch replacement combined with frozen elephant trunk (TAR + FET), with the goal of improving postoperative survival quality and guiding clinical treatment. We retrospectively analyzed data from 635 ATAAD patients who underwent TAR + FET surgery at our institution between January 2018 and October 2023. Based on the International Aortic Arch Surgery Study Group definition of Major Adverse Outcomes (MAO), the entire dataset was divided into 160 patients with MAO and 475 patients without MAO.
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