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Atefe Sedaghat, Homayoon Arbabkhah, Masood Jafari Kang and Maryam Hamidi
This research introduces an online system for monitoring maritime traffic, aimed at tracking vessels in water routes and predicting their subsequent locations in real time. The proposed framework utilizes an Extract, Transform, and Load (ETL) pipeline to...
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Bochen Duan, Shengping Wang, Changlong Luo and Zhigao Chen
In recent years, the surge in marine activities has increased the frequency of submarine pipeline failures. Detecting and identifying the buried conditions of submarine pipelines has become critical. Sub-bottom profilers (SBPs) are widely employed for pi...
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Mahammad Khalid Shaik Vadla, Mahima Agumbe Suresh and Vimal K. Viswanathan
Understanding customer emotions and preferences is paramount for success in the dynamic product design landscape. This paper presents a study to develop a prediction pipeline to detect the aspect and perform sentiment analysis on review data. The pre-tra...
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MohammadHossein Reshadi, Wen Li, Wenjie Xu, Precious Omashor, Albert Dinh, Scott Dick, Yuntong She and Michael Lipsett
Anomaly detection in data streams (and particularly time series) is today a vitally important task. Machine learning algorithms are a common design for achieving this goal. In particular, deep learning has, in the last decade, proven to be substantially ...
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C. Tamilselvi, Md Yeasin, Ranjit Kumar Paul and Amrit Kumar Paul
Denoising is an integral part of the data pre-processing pipeline that often works in conjunction with model development for enhancing the quality of data, improving model accuracy, preventing overfitting, and contributing to the overall robustness of pr...
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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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Sara Rajaram and Cassie S. Mitchell
The ability to translate Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) into different modalities and data types is essential to improve Deep Learning (DL) for predictive medicine. This work presents DACMVA, a novel framework ...
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Hugo Valayer, Nathalie Bartoli, Mauricio Castaño-Aguirre, Rémi Lafage, Thierry Lefebvre, Andrés F. López-Lopera and Sylvain Mouton
In aerodynamics, characterizing the aerodynamic behavior of aircraft typically requires a large number of observation data points. Real experiments can generate thousands of data points with suitable accuracy, but they are time-consuming and resource-int...
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Firas Alghanim, Ibrahim Al-Hurani, Hazem Qattous, Abdullah Al-Refai, Osamah Batiha, Abedalrhman Alkhateeb and Salama Ikki
Identifying menopause-related breast cancer biomarkers is crucial for enhancing diagnosis, prognosis, and personalized treatment at that stage of the patient?s life. In this paper, we present a comprehensive framework for extracting multiomics biomarkers...
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Francisco Altimiras, Leonardo Pavéz, Alireza Pourreza, Osvaldo Yañez, Lisdelys González-Rodríguez, José García, Claudio Galaz, Andrés Leiva-Araos and Héctor Allende-Cid
In agricultural production, it is fundamental to characterize the phenological stage of plants to ensure a good evaluation of the development, growth and health of crops. Phenological characterization allows for the early detection of nutritional deficie...
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