In the area of ecology, counting animals to estimate population size and types of species is important for the wildlife conservation. This includes analysing massive volumes of image, video or audio/acoustic data and traditional counting techniques. Automating the process of identifying, classifying and counting animals would be helpful to researchers as it will phase out the tedious human–labour tasks of manual counting and labelling. The intention of this work is to address manual identification and counting methods of images by implementing an automated solution using computer vision and deep learning. This study applies a classification model to classify species and trains an object detection model using deep convolutional neural networks to automatically identify and determine the count of four mammal species in 3304 images extracted from camera traps. The image classification model reports a classification accuracy of 98%, and the YOLOv8 object detection model automatically detects buffalo, elephant, rhino and zebra school mean average precision of 50 of 89% and mean average precision of 50–95 of 72.2% and provides an accurate count over all animal classes. Furthermore, it performs well across various image scenarios such as blurriness, day, night and images displaying multiple species compared to the RT‐DETR model. The results of the study display that the application of computer vision and deep learning methods on data‐scarce and data‐enriched scenarios, respectively, can conserve biologists and ecologists an enormous amount of time used on time‐consuming human tasks methods of analysis and counting. The high‐performing deep learning models developed capable of accurately classifying and localising multiple species can be integrated into the existing conservation workflows to process large volumes of camera trap images in real time. This integration can significantly reduce the manual labour required for labelling and counting, improve the consistency and speed of wildlife surveys and enable timely decision‐making in habitat protection, population assessment and antipoaching initiatives. Additionally, these automated identification techniques can contribute towards enhancing wildlife conservation and future studies.

Ultrasound imaging is a widely adopted method for noninvasive examination of internal structures, valued for its cost‐effectiveness, real‐time imaging capability, and absence of ionizing radiation. Its applications, including peripheral nerve blocking (PNB) procedures, benefit from the direct visualization of nerve structures. However, the inherent distortions in ultrasound images, arising from echo perturbations and speckle noise, pose challenges to the accurate localization of nerve structures, even for experienced practitioners. Computational techniques, particularly Bayesian inference, offer a promising solution by providing uncertainty estimates in model predictions. This article focused on developing and implementing an optimal Bayesian U‐Net for nerve segmentation in ultrasound images, presented through a user‐friendly application. Bayesian convolution layers and the Monte Carlo dropout method were the two Bayesian techniques explored and compared, with a specific emphasis on facilitating medical professionals’ decision‐making processes. The research revealed that integrating the Monte Carlo dropout technique for Bayesian inference yields the most optimal results. The Bayesian model demonstrates an average binary accuracy of 98.99%, an average dice coefficient score of 0.72, and an average IOU score of 0.57 when benchmarked against a typical U‐Net. The culmination of this work is an application designed for practical use by medical professionals, providing an intuitive interface for Bayesian nerve segmentation in ultrasound images. This research contributes to the broader understanding of Bayesian techniques in medical imaging models and offers a comprehensive solution that combines advanced methodology with user‐friendly accessibility.

The National Disaster Management Center has declared a drought disaster in the Northern Cape, South Africa, due to persistent dry conditions that impact regions such as the Western, Eastern, and Northern Cape provinces. Accurate drought predictions are vital for decision‐making and planning in vulnerable areas. This study introduces a hybrid intelligence model, combining long short‐term memory (LSTM) and convolutional neural networks (CNNs), to forecast short‐term meteorological droughts using the Standardized Precipitation Evapotranspiration Index (SPEI). Applied to Kimberley and Upington in the Northern Cape, the model predicts 1‐month and 3‐month SPEI indices (SPEI‐1 and SPEI‐3). The hybrid model’s performance, compared to benchmark models such as artificial neural networks (ANNs), LSTM, and CNN, is measured through statistical analysis. In Kimberley, the CNN–LSTM model displayed a robust positive correlation of 0.901573 and a low mean absolute error (MAE) of 0.082513. Similarly, in Upington, the CNN–LSTM model exhibited strong performance, achieving a correlation coefficient of 0.894805 and a MAE of 0.085212. These results highlight the model’s remarkable precision and effectiveness in predicting drought conditions in both regions, underscoring its superiority over other forecasting techniques. SPEI, incorporating potential evapotranspiration and rainfall, is superior for drought analysis amidst climate change. The findings enhance understanding of drought patterns and aid mitigation efforts. The CNN–LSTM hybrid model demonstrated noteworthy results, outperforming ANN, CNN, and LSTM, emphasizing its potential for precise meteorological drought predictions.