Forecasting Shark Attack Risk Using AI: A Deep Learning Approach

This study aimed to develop a predictive model utilizing available data to forecast the risk of future shark attacks, making this critical information accessible for everyday public use. Employing a deep learning/neural network methodology, the system was designed to produce a binary output that is subsequently classified into categories of low, medium, or high risk. A significant challenge encountered during the study was the identification and procurement of appropriate historical and forecasted marine weather data, which is integral to the model’s accuracy. Despite these challenges, the results of the study were startlingly optimistic, showcasing the model’s ability to predict with impressive accuracy. In conclusion, the developed forecasting tool not only offers promise in its immediate application but also sets a robust precedent for the adoption and adaptation of similar predictive systems in various analogous use cases in the marine environment and beyond.

Artificial intelligence-based predictive model of nanoscale friction using experimental data

A recent systematic experimental characterisation of technological thin films,based on elaborated design of experiments as well as probe calibration and correction procedures,allowed for the first time the determination of nanoscale friction under the concurrent influence of several process parameters,comprising normal forces,sliding velocities,and temperature,thus providing an indication of the intricate correlations induced by their interactions and mutual effects.This created the preconditions to undertake in this work an effort to model friction in the nanometric domain with the goal of overcoming the limitations of currently available models in ascertaining the effects of the physicochemical processes and phenomena involved in nanoscale contacts.Due to the stochastic nature of nanoscale friction and the relatively sparse available experimental data,meta-modelling tools fail,however,at predicting the factual behaviour.Based on the acquired experimental data,data mining,incorporating various state-of-the-art machine learning(ML)numerical regression algorithms,is therefore used.The results of the numerical analyses are assessed on an unseen test dataset via a comparative statistical validation.It is therefore shown that the black box ML methods provide effective predictions of the studied correlations with rather good accuracy levels,but the intrinsic nature of such algorithms prevents their usage in most practical applications.Genetic programming-based artificial intelligence(AI)methods are consequently finally used.Despite the marked complexity of the analysed phenomena and the inherent dispersion of the measurements,the developed AI-based symbolic regression models allow attaining an excellent predictive performance with the respective prediction accuracy,depending on the sample type,between 72%and 91%,allowing also to attain an extremely simple functional description of the multidimensional dependence of nanoscale friction on the studied variable process parameters.An effective tool for nanoscale friction prediction,adaptive control purposes,and further scientific and technological nanotribological analyses is thus obtained.

Development of a 32-gene signature using machine learning for accurate prediction of inflammatory bowel disease

Inflammatory bowel disease(IBD)is a chronic inflammatory condition caused by multiple genetic and environmental factors.Numerous genes are implicated in the etiology of IBD,but the diagnosis of IBD is challenging.Here,XGBoost,a machine learning prediction model,has been used to distinguish IBD from healthy cases following elaborative feature selection.Using combined unsupervised clustering analysis and the XGBoost feature selection method,we successfully identified a 32-gene signature that can predict IBD occurrence in new cohorts with 0.8651 accuracy.The signature shows enrichment in neutrophil extracellular trap formation and cytokine signaling in the immune system.The probability threshold of the XGBoost-based classification model can be adjusted to fit personalized lifestyle and health status.Therefore,this study reveals potential IBD-related biomarkers that facilitate an effective personalized diagnosis of IBD.