Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data.

Camera traps offer enormous new opportunities in ecological studies, but current automated image analysis methods often lack the contextual richness needed to support impactful conservation outcomes. Integrating vision-language models into these workflows could address this gap by providing enhanced contextual understanding and enabling advanced queries across temporal and spatial dimensions. Here, we present an integrated approach that combines deep learning-based vision and language models to improve ecological reporting using data from camera traps.

Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine Learning.

Using machine learning (ML) to automate camera trap (CT) image processing is advantageous for time-sensitive applications. However, little is currently known about the factors influencing such processing. Here, we evaluate the influence of occlusion, distance, vegetation type, size class, height, subject orientation towards the CT, species, time-of-day, colour, and analyst performance on wildlife/human detection and classification in CT images from western Tanzania.

Anomaly Detection Using Autoencoder Reconstruction upon Industrial Motors.

Rotary machine breakdown detection systems are outdated and dependent upon routine testing to discover faults. This is costly and often reactive in nature. Real-time monitoring offers a solution for detecting faults without the need for manual observation.

An Evaluation of the Factors Affecting 'Poacher' Detection with Drones and the Efficacy of Machine-Learning for Detection.

Drones are being increasingly used in conservation to tackle the illegal poaching of animals. An important aspect of using drones for this purpose is establishing the technological and the environmental factors that increase the chances of success when detecting poachers. Recent studies focused on investigating these factors, and this research builds upon this as well as exploring the efficacy of machine-learning for automated detection.

A First Implementation of Underwater Communications in Raw Water Using the 433 MHz Frequency Combined with a Bowtie Antenna.

In 2016, there were 317 serious water pollution incidents in the UK, with 78,000 locations where businesses discharge controlled quantities of pollutants into rivers; therefore, continuous monitoring is vital. Since 1998, the environment agency has taken over 50 million water samples for water quality monitoring. The Internet of Things has grown phenomenally in recent years, reaching all aspects of our lives, many of these connected devices use wireless sensor networks to relay data to internet-connected nodes, where data can be processed, analyzed and consumed.

Utilizing Deep Learning and Genome Wide Association Studies for Epistatic-Driven Preterm Birth Classification in African-American Women.

Genome-Wide Association Studies (GWAS) are used to identify statistically significant genetic variants in case-control studies. The main objective is to find single nucleotide polymorphisms (SNPs) that influence a particular phenotype (i.e.

Machine learning ensemble modelling to classify caesarean section and vaginal delivery types using Cardiotocography traces.

Human visual inspection of Cardiotocography traces is used to monitor the foetus during labour and avoid neonatal mortality and morbidity. The problem, however, is that visual interpretation of Cardiotocography traces is subject to high inter and intra observer variability. Incorrect decisions, caused by miss-interpretation, can lead to adverse perinatal outcomes and in severe cases death.

Classification of caesarean section and normal vaginal deliveries using foetal heart rate signals and advanced machine learning algorithms.

Background: Visual inspection of cardiotocography traces by obstetricians and midwives is the gold standard for monitoring the wellbeing of the foetus during antenatal care. However, inter- and intra-observer variability is high with only a 30% positive predictive value for the classification of pathological outcomes. This has a significant negative impact on the perinatal foetus and often results in cardio-pulmonary arrest, brain and vital organ damage, cerebral palsy, hearing, visual and cognitive defects and in severe cases, death.

Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques.

The epilepsies are a heterogeneous group of neurological disorders and syndromes characterised by recurrent, involuntary, paroxysmal seizure activity, which is often associated with a clinicoelectrical correlate on the electroencephalogram. The diagnosis of epilepsy is usually made by a neurologist but can be difficult to be made in the early stages. Supporting paraclinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and investigate treatment earlier.

A novel method of early diagnosis of Alzheimer's disease based on EEG signals.

Studies have reported that electroencephalogram signals in Alzheimer's disease patients usually have less synchronization than those of healthy subjects. Changes in electroencephalogram signals start at early stage but, clinically, these changes are not easily detected. To detect this perturbation, three neural synchrony measurement techniques: phase synchrony, magnitude squared coherence, and cross correlation are applied to three different databases of mild Alzheimer's disease patients and healthy subjects.

Prediction of preterm deliveries from EHG signals using machine learning.

There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems.

Monitoring and reducing sedentary behavior in the elderly with the aid of human digital memories.

A healthy lifestyle has the ability not only to give you more energy and help you look and feel better, but it also has the ability to help you live longer and prevent disease, such as obesity and pressure ulcers. This is particularly important for the elderly population, as a healthier lifestyle would enable independent living to occur for a longer period of time. However, providing a direct link between increasing physical activity and positive health outcomes is a problem.