Speech is a cost-effective and non-intrusive data source for identifying acute and chronic heart failure (HF). However, there is a lack of research on whether Chinese syllables contain HF-related information, as observed in other well-studied languages. This study presents the first Chinese speech database of HF patients, featuring paired recordings taken before and after hospitalisation. The findings confirm the effectiveness of the Chinese language in HF detection using both standard'patient-wise' and personalised'pair-wise' classification approaches, with the latter serving as an ideal speaker-decoupled baseline for future research. Statistical tests and classification results highlight individual differences as key contributors to inaccuracy. Additionally, an adaptive frequency filter (AFF) is proposed for frequency importance analysis. The data and demonstrations are published at https://github.com/

Inhalation injuries present a challenge in clinical diagnosis and grading due to Conventional grading methods such as the Abbreviated Injury Score (AIS) being subjective and lacking robust correlation with clinical parameters like mechanical ventilation duration and patient mortality. This study introduces a novel deep learning-based diagnosis assistant tool for grading inhalation injuries using bronchoscopy images to overcome subjective variability and enhance consistency in severity assessment. Our approach leverages data augmentation techniques, including graphic transformations, Contrastive Unpaired Translation (CUT), and CycleGAN, to address the scarcity of medical imaging data. We evaluate the classification performance of two deep learning models, GoogLeNet and Vision Transformer (ViT), across a dataset significantly expanded through these augmentation methods. The results demonstrate GoogLeNet combined with CUT as the most effective configuration for grading inhalation injuries through bronchoscopy images and achieves a classification accuracy of 97.8%. The histograms and frequency analysis evaluations reveal variations caused by the augmentation CUT with distribution changes in the histogram and texture details of the frequency spectrum. PCA visualizations underscore the CUT substantially enhances class separability in the feature space. Moreover, Grad-CAM analyses provide insight into the decision-making process; mean intensity for CUT heatmaps is 119.6, which significantly exceeds 98.8 of the original datasets. Our proposed tool leverages mechanical ventilation periods as a novel grading standard, providing comprehensive diagnostic support.

Due to economic and logistic Agriculture 4.0 is using many modern research and technologies reasons, soil sampling are not frequent enough to understand in different aspects of agriculture including genomics, nanotechnology, its impact on annual yield. For example P, K, Mg are tested synthetic proteins, Internet of Things, automation once for three years. However, altitude, soil texture data are not and machine learning [1]. As an important pillar in this space, changed or changed slowly. Based on our data management experience data-driven agriculture has gain a momentum in last twenty in UK farms, yield maps are being collected by many years as a retrofitting mechanism for the available technologies farmers in the last two decades. Most of the analyses have been to feed 9 billion population in 2050. It has become more realistic focused on spatial variability of individual maps. Due to lack of than ever due to wider use of sensors, cloud computing and consecutive number of yield maps and crop rotation complexities, their integration with cyber-physical-social farming systems to both spatio-temporal analysis has been limited so far [? ]. use big data for intuition, intelligence and insights. However, Therefore, many farmers, agronomists and scientists are interested data-driven agriculture is challenging for small actors but important in looking at the relations of those data layers, deriving for global sustainability compared to others industries compound new data layers and accordingly make site-specific such as healthcare, fin-tech and manufacturing.

The COVID-19 epidemic has had a great impact on social media conversation, especially on sites like Twitter, which has emerged as a hub for public reaction and information sharing. This paper deals by analyzing a vast dataset of Twitter messages related to this disease, starting from January 2020. Two approaches were used: a statistical analysis of word frequencies and a sentiment analysis to gauge user attitudes. Word frequencies are modeled using unigrams, bigrams, and trigrams, with power law distribution as the fitting model. The validity of the model is confirmed through metrics like Sum of Squared Errors (SSE), R-squared ($R^2$), and Root Mean Squared Error (RMSE). High $R^2$ and low SSE/RMSE values indicate a good fit for the model. Sentiment analysis is conducted to understand the general emotional tone of Twitter users messages. The results reveal that a majority of tweets exhibit neutral sentiment polarity, with only 2.57\% expressing negative polarity.

In 2021 300 mm of rain, nearly half the average annual rainfall, fell near Catania (Sicily island, Italy). Such events took place in just a few hours, with dramatic consequences on the environmental, social, economic, and health systems of the region. This is the reason why, detecting extreme rainfall events is a crucial prerequisite for planning actions able to reverse possibly intensified dramatic future scenarios. In this paper, the Affinity Propagation algorithm, a clustering algorithm grounded on machine learning, was applied, to the best of our knowledge, for the first time, to identify excess rain events in Sicily. This was possible by using a high-frequency, large dataset we collected, ranging from 2009 to 2021 which we named RSE (the Rainfall Sicily Extreme dataset). Weather indicators were then been employed to validate the results, thus confirming the presence of recent anomalous rainfall events in eastern Sicily. We believe that easy-to-use and multi-modal data science techniques, such as the one proposed in this study, could give rise to significant improvements in policy-making for successfully contrasting climate changes.

Earthquakes have a deep impact on wide areas, and emergency rescue operations may benefit from social media information about the scope and extent of the disaster. Therefore, this work presents a text miningbased approach to collect and analyze social media data for early earthquake impact analysis. First, disasterrelated microblogs are collected from the Sina microblog based on crawler technology. Then, after data cleaning a series of analyses are conducted including (1) the hot words analysis, (2) the trend of the number of microblogs, (3) the trend of public opinion sentiment, and (4) a keyword and rule-based text classification for earthquake impact analysis. Finally, two recent earthquakes with the same magnitude and focal depth in China are analyzed to compare their impacts. The results show that the public opinion trend analysis and the trend of public opinion sentiment can estimate the earthquake's social impact at an early stage, which will be helpful to decision-making and rescue management.

This method exposes fake images created by computer algorithms rather than by humans. They look deceptively real, but they are made by computers: so-called deep-fake images are generated by machine learning algorithms, and humans are pretty much unable to distinguish them from real photos. Researchers at the Horst Görtz Institute for IT Security at Ruhr-Universität Bochum and the Cluster of Excellence "Cyber Security in the Age of Large-Scale Adversaries" (Casa) have developed a new method for efficiently identifying deep-fake images. To this end, they analyze the objects in the frequency domain, an established signal processing technique. Credit: RUB, Marquard The team presented their work at the International Conference on Machine Learning (ICML) on 15 July 2020, one of the leading conferences in the field of machine learning.

This method exposes fake images created by computer algorithms rather than by humans. They look deceptively real, but they are made by computers: so-called deep-fake images are generated by machine learning algorithms, and humans are pretty much unable to distinguish them from real photos. Researchers at the Horst Görtz Institute for IT Security at Ruhr-Universität Bochum and the Cluster of Excellence "Cyber Security in the Age of Large-Scale Adversaries" (Casa) have developed a new method for efficiently identifying deep-fake images. To this end, they analyze the objects in the frequency domain, an established signal processing technique. The team presented their work at the International Conference on Machine Learning (ICML) on 15 July 2020, one of the leading conferences in the field of machine learning.

Statistics can be a powerful tool when performing the art of Data Science (DS). From a high-level view, statistics is the use of mathematics to perform technical analysis of data. A basic visualisation such as a bar chart might give you some high-level information, but with statistics we get to operate on the data in a much more information-driven and targeted way. The math involved helps us form concrete conclusions about our data rather than just guesstimating. Using statistics, we can gain deeper and more fine grained insights into how exactly our data is structured and based on that structure how we can optimally apply other data science techniques to get even more information. Today, we're going to look at 5 basic statistics concepts that data scientists need to know and how they can be applied most effectively!

The recent availability of complex and high-dimensional datasets has spurred the need for new data analysis methods. One prominent research direction in signal processing has been the focus on data supported over graphs [1]. Graph signals, i.e., signals taking values on the nodes of combinatorial graphs, represent a convenient solution to model data exhibiting complex and nonuniform properties, such as those found in social, biological, and transportation networks, among others. Arguably, the most fundamental tool in the analysis of graph signals is the graph Fourier transform (GFT) [1]-[3]. In an analogous manner to the discrete Fourier transform (DFT), using GFT one may examine graph signals in the graph frequency domain, and, for instance, remove noise by attenuating high graph-frequencies. GFT has also lead to significant new insights in problems such as smoothing and denoising [4]-[6], segmentation [7], sampling and approximation [8]-[10], and classification [11]-[13] of graph data.