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Alper Taner, Mahtem Teweldemedhin Mengstu, Kemal Çagatay Selvi, Hüseyin Duran, Ibrahim Gür and Nicoleta Ungureanu
Having the advantages of speed, suitability and high accuracy, computer vision has been effectively utilized as a non-destructive approach to automatically recognize and classify fruits and vegetables, to meet the increased demand for food quality-sensin...
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Benjamin Burrichter, Juliana Koltermann da Silva, Andre Niemann and Markus Quirmbach
This study employs a temporal fusion transformer (TFT) for predicting overflow from sewer manholes during heavy rainfall events. The TFT utilised is capable of forecasting overflow hydrographs at the manhole level and was tested on a sewer network with 9...
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Jing Liu, Xuesong Hai and Keqin Li
Massive amounts of data drive the performance of deep learning models, but in practice, data resources are often highly dispersed and bound by data privacy and security concerns, making it difficult for multiple data sources to share their local data dir...
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Mihael Gudlin, Miro Hegedic, Matija Golec and Davor Kolar
In the quest for industrial efficiency, human performance within manufacturing systems remains pivotal. Traditional time study methods, reliant on direct observation and manual video analysis, are increasingly inadequate, given technological advancements...
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Norah Fahd Alhussainan, Belgacem Ben Youssef and Mohamed Maher Ben Ismail
Brain tumor diagnosis traditionally relies on the manual examination of magnetic resonance images (MRIs), a process that is prone to human error and is also time consuming. Recent advancements leverage machine learning models to categorize tumors, such a...
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Waseem Abbas, Zuping Zhang, Muhammad Asim, Junhong Chen and Sadique Ahmad
In the ever-expanding online fashion market, businesses in the clothing sales sector are presented with substantial growth opportunities. To utilize this potential, it is crucial to implement effective methods for accurately identifying clothing items. T...
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Rokaya Eltehewy, Ahmed Abouelfarag and Sherine Nagy Saleh
Rapid damage identification and classification in disastrous situations and natural disasters are crucial for efficiently directing aid and resources. With the development of deep learning techniques and the availability of imagery content on social medi...
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Angelo Casolaro, Vincenzo Capone, Gennaro Iannuzzo and Francesco Camastra
A time series is a sequence of time-ordered data, and it is generally used to describe how a phenomenon evolves over time. Time series forecasting, estimating future values of time series, allows the implementation of decision-making strategies. Deep lea...
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Hong-Hua Huang, Jian-Fei Luo, Feng Gan and Philip K. Hopke
Small data sets make developing calibration models using deep neural networks difficult because it is easy to overfit the system. We developed two deep neural network architectures by revising two existing network architectures: the U-Net and the attenti...
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Qianjing Li, Jia Tian and Qingjiu Tian
The combination of multi-temporal images and deep learning is an efficient way to obtain accurate crop distributions and so has drawn increasing attention. However, few studies have compared deep learning models with different architectures, so it remain...
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