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Fahim Sufi
GPT (Generative Pre-trained Transformer) represents advanced language models that have significantly reshaped the academic writing landscape. These sophisticated language models offer invaluable support throughout all phases of research work, facilitatin...
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Hossein Hassani and Emmanuel Sirmal Silva
ChatGPT, a conversational AI interface that utilizes natural language processing and machine learning algorithms, is taking the world by storm and is the buzzword across many sectors today. Given the likely impact of this model on data science, through t...
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Sunghae Jun
In big data analysis, various zero-inflated problems are occurring. In particular, the problem of inflated zeros has a great influence on text big data analysis. In general, the preprocessed data from text documents are a matrix consisting of the documen...
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Ali Mirzaei, Hossein Bagheri and Iman Khosravi
Crop classification using remote sensing data has emerged as a prominent research area in recent decades. Studies have demonstrated that fusing synthetic aperture radar (SAR) and optical images can significantly enhance the accuracy of classification. Ho...
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Daiho Uhm and Sunghae Jun
Due to the expansion of the internet, we encounter various types of big data such as web documents or sensing data. Compared to traditional small data such as experimental samples, big data provide more chances to find hidden and novel patterns with big ...
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Mauricio Herrera and Alex Godoy-Faúndez
The COVID-19 crisis has shown that we can only prevent the risk of mass contagion through timely, large-scale, coordinated, and decisive actions. This pandemic has also highlighted the critical importance of generating rigorous evidence for decision-maki...
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Martina Cignetti, Diego Guenzi, Francesca Ardizzone, Paolo Allasia and Daniele Giordan
Nowadays, the increasing demand to collect, manage and share archives of data supporting geo-hydrological processes investigations requires the development of spatial data infrastructure able to store geospatial data and ground deformation measurements, ...
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Sahan Yoruç Selçuk, Perin Ünal, Özlem Albayrak and Moez Jomâa
Digital twins, virtual representations of real-life physical objects or processes, are becoming widely used in many different industrial sectors. One of the main uses of digital twins is predictive maintenance, and these technologies are being adapted to...
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Yee Sye Lee, Ali Rashidi, Amin Talei and Daniel Kong
In recent years, mixed reality (MR) technology has gained popularity in construction management due to its real-time visualisation capability to facilitate on-site decision-making tasks. The semantic segmentation of building components provides an attrac...
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Luigi Gianpio Di Maggio, Eugenio Brusa and Cristiana Delprete
The Intelligent Fault Diagnosis of rotating machinery calls for a substantial amount of training data, posing challenges in acquiring such data for damaged industrial machinery. This paper presents a novel approach for generating synthetic data using a G...
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Darin Majnaric, Sandi Baressi ?egota, Nikola Andelic and Jerolim Andric
One of the main problems in the application of machine learning techniques is the need for large amounts of data necessary to obtain a well-generalizing model. This is exacerbated for studies in which it is not possible to access large amounts of data?fo...
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Viktoriya Tsyganskaya, Sandro Martinis and Philip Marzahn
Synthetic Aperture Radar (SAR) is particularly suitable for large-scale mapping of inundations, as this tool allows data acquisition regardless of illumination and weather conditions. Precise information about the flood extent is an essential foundation ...
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Lino Comesaña-Cebral, Joaquín Martínez-Sánchez, Antón Nuñez Seoane and Pedro Arias
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Bata Hena, Ziang Wei, Luc Perron, Clemente Ibarra Castanedo and Xavier Maldague
Industrial radiography is a pivotal non-destructive testing (NDT) method that ensures quality and safety in a wide range of industrial sectors. Conventional human-based approaches, however, are prone to challenges in defect detection accuracy and efficie...
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Ekaterina Kopets, Tatiana Shpilevaya, Oleg Vasilchenko, Artur Karimov and Denis Butusov
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Jinhong Wu, Konstantinos Plataniotis, Lucy Liu, Ehsan Amjadian and Yuri Lawryshyn
Synthetic data, artificially generated by computer programs, has become more widely used in the financial domain to mitigate privacy concerns. Variational Autoencoder (VAE) is one of the most popular deep-learning models for generating synthetic data. Ho...
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Panagiotis Skondras, Panagiotis Zervas and Giannis Tzimas
In this article, we investigate the potential of synthetic resumes as a means for the rapid generation of training data and their effectiveness in data augmentation, especially in categories marked by sparse samples. The widespread implementation of mach...
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Efrem Yohannes Obsie, Hongchun Qu, Yong-Jiang Zhang, Seanna Annis and Francis Drummond
Early detection and accurately rating the level of plant diseases plays an important role in protecting crop quality and yield. The traditional method of mummy berry disease (causal agent: Monilinia vaccinii-corymbosi) identification is mainly based on f...
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Kayal Lakshmanan, Matt Roach, Cinzia Giannetti, Shubham Bhoite, David George, Tim Mortensen, Manduhu Manduhu, Behzad Heravi, Sharadha Kariyawasam and Xianghua Xie
Vehicle detection in parking areas provides the spatial and temporal utilisation of parking spaces. Parking observations are typically performed manually, limiting the temporal resolution due to the high labour cost. This paper uses simulated data and tr...
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Sandi Baressi ?egota, Vedran Mrzljak, Nikola Andelic, Igor Poljak and Zlatan Car
Machine learning applications have demonstrated the potential to generate precise models in a wide variety of fields, including marine applications. Still, the main issue with ML-based methods is the need for large amounts of data, which may be impractic...
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