Expert Systems最新文献

Machine Learning (ML) algorithms have demonstrated remarkable performance in dysphonia detection using speech samples. However, their efficacy often diminishes when tested on languages different from the training data, raising questions about their suitability in clinical settings. This study aims to develop a robust method for cross- and multi-lingual dysphonia detection that overcomes the limitation of language dependency in existing ML methods. We propose an innovative approach that leverages speech embeddings from speaker verification models, especially ECAPA and x-vector and employs a majority voting ensemble classifier. We utilize speech features extracted from ECAPA and x-vector embeddings to train three distinct classifiers. The significant advantage of these embedding models lies in their capability to capture speaker characteristics in a language-independent manner, forming fixed-dimensional feature spaces. Additionally, we investigate the impact of generating synthetic data within the embedding feature space using the Synthetic Minority Oversampling Technique (SMOTE). Our experimental results unveil the effectiveness of the proposed method for dysphonia detection. Compared to results obtained from x-vector embeddings, ECAPA consistently demonstrates superior performance in distinguishing between healthy and dysphonic speech, achieving accuracy values of 93.33% and 96.55% in both cross-lingual and multi-lingual scenarios, respectively. This highlights the remarkable capabilities of speaker verification models, especially ECAPA, in capturing language-independent features that enhance overall detection performance. The proposed method effectively addresses the challenges of language dependency in dysphonia detection. ECAPA embeddings, combined with majority voting ensemble classifiers, show significant potential for improving the accuracy and reliability of dysphonia detection in cross- and multi-lingual scenarios.

Global temperature is increasing at an alarming rate, which increases the number of heatwaves. Heatwaves have significant impacts, both directly and indirectly, on human and natural systems and can create considerable risk to public health. Predicting the occurrence of a heatwave can save lives, increase the production of crops, improve water quality, and reduce transportation restrictions. Because of its geographical location, Bangladesh is particularly vulnerable to cyclones, droughts, earthquakes, floods, and heatwaves. The Bangladesh Meteorological Department collects temperature data at multiple weather stations, and we use data from 10 weather stations in this research. Data show that most heatwaves occur in the summer months, namely, April, May, and June. In this research, we develop Classification and Regression Tree (CART) models that use daily temperature data for the months of March, April, May, and June to predict the likelihood of a heatwave within the next 7 days, the next 28 days, and on any particular day based on daily high temperatures from the previous 14 days. We also use different model parameters to evaluate the accuracy of the models. Finally, we develop treed Stepwise Logistic Regression models to predict the probability of heatwaves occurring. Even though this research uses data from Bangladesh Meteorological Department, the developed modeling approach can be used in other geographic regions.

Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impressive results across a large spectrum of applications ranging from biology, medicine, education, legislation, computer science, and finance. As one strives for enhanced safety, efficiency, and sustainability, generative AI indeed emerges as a key differentiator and promises a paradigm shift in the field. This article explores the potential applications of generative AI and large language models in geoscience. The recent developments in the field of machine learning and deep learning have enabled the generative model's utility for tackling diverse prediction problems, simulation, and multi-criteria decision-making challenges related to geoscience and Earth system dynamics. This survey discusses several GAI models that have been used in geoscience comprising generative adversarial networks (GANs), physics-informed neural networks (PINNs), and generative pre-trained transformer (GPT)-based structures. These tools have helped the geoscience community in several applications, including (but not limited to) data generation/augmentation, super-resolution, panchromatic sharpening, haze removal, restoration, and land surface changing. Some challenges still remain, such as ensuring physical interpretation, nefarious use cases, and trustworthiness. Beyond that, GAI models show promises to the geoscience community, especially with the support to climate change, urban science, atmospheric science, marine science, and planetary science through their extraordinary ability to data-driven modelling and uncertainty quantification.