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James Simon Flynn, Cinzia Giannetti and Hessel Van Dijk
In many manufacturing systems, anomaly detection is critical to identifying process errors and ensuring product quality. This paper proposes three semi-supervised solutions to detect anomalies in Direct Current (DC) Nut Runner engine assembly processes. ...
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Zhiguo Liang, Lijun Zhang and Xizhe Wang
Since failure of steam turbines occurs frequently and can causes huge losses for thermal plants, it is important to identify a fault in advance. A novel clustering fault diagnosis method for steam turbines based on t-distribution stochastic neighborhood ...
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Pilar Rosado-Rodrigo and Ferran Reverter
In the context of a society saturated in images, convolutional neural networks (CNNs), pre-trained using from the visual information contained in many thousands of images, constitute a tool that is of great use in helping us to organize the visual herita...
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Luis Ariosto Serna Cardona, Hernán Darío Vargas-Cardona, Piedad Navarro González, David Augusto Cardenas Peña and Álvaro Ángel Orozco Gutiérrez
The recurrent use of databases with categorical variables in different applications demands new alternatives to identify relevant patterns. Classification is an interesting approach for the recognition of this type of data. However, there are a few amoun...
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Noor Kamal Al-Qazzaz, Iyden Kamil Mohammed, Halah Kamal Al-Qazzaz, Sawal Hamid Bin Mohd Ali and Siti Anom Ahmad
Countless women and men worldwide have lost their lives to breast cancer (BC). Although researchers from around the world have proposed various diagnostic methods for detecting this disease, there is still room for improvement in the accuracy and efficie...
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Sardar Mehboob Hussain, Domenico Buongiorno, Nicola Altini, Francesco Berloco, Berardino Prencipe, Marco Moschetta, Vitoantonio Bevilacqua and Antonio Brunetti
Computer-aided diagnosis (CAD) systems can help radiologists in numerous medical tasks including classification and staging of the various diseases. The 3D tomosynthesis imaging technique adds value to the CAD systems in diagnosis and classification of t...
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Danfeng Liu, Liguo Wang and Jón Atli Benediktsson
Most of the available hyperspectral image (HSI) visualization methods can be considered as data-oriented approaches. These approaches are based on global data, so it is difficult to optimize display of a specific object. Compared to data-oriented approac...
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Bardia Rafieian, Pedro Hermosilla and Pere-Pau Vázquez
In data science and visualization, dimensionality reduction techniques have been extensively employed for exploring large datasets. These techniques involve the transformation of high-dimensional data into reduced versions, typically in 2D, with the aim ...
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Mario Trouillier, Marieke Van der Maaten-Theunissen, Jill E. Harvey, David Würth, Martin Schnittler and Martin Wilmking
Averaging tree-ring measurements from multiple individuals is one of the most common procedures in dendrochronology. It serves to filter out noise from individual differences between trees, such as competition, height, and micro-site effects, which ideal...
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Zhaoyang Tong, Shirui Zhang, Jingxin Yu, Xiaolong Zhang, Baijuan Wang and Wengang Zheng
The growth and yield of crops are highly dependent on irrigation. Implementing irrigation plans that are tailored to the specific water requirements of crops can enhance crop yield and improve the quality of tomatoes. The mastery and prediction of transp...
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Tulsi Patel, Mark W. Jones and Thomas Redfern
We present a novel approach to providing greater insight into the characteristics of an unlabelled dataset, increasing the efficiency with which labelled datasets can be created. We leverage dimension-reduction techniques in combination with autoencoders...
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L. G. Divyanth, D. S. Guru, Peeyush Soni, Rajendra Machavaram, Mohammad Nadimi and Jitendra Paliwal
Applications of deep-learning models in machine visions for crop/weed identification have remarkably upgraded the authenticity of precise weed management. However, compelling data are required to obtain the desired result from this highly data-driven ope...
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Pranita Patil and Kevin Purcell
Although deep learning has proven to be tremendously successful, the main issue is the dependency of its performance on the quality and quantity of training datasets. Since the quality of data can be affected by biases, a novel deep learning method based...
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Ayaka Miyamoto, Mayank V. Bendarkar and Dimitri N. Mavris
With the growth in commercial aviation traffic and the need for improved environmental performance, strategies to lower emissions that can be implemented in the near term are necessary. Since novel technology takes time to enter the market, operational i...
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Yiwei Zhong, Baojin Huang and Chaowei Tang
Cassava is a typical staple food in the tropics, and cassava leaf disease can cause massive yield reductions in cassava, resulting in substantial economic losses and a lack of staple foods. However, the existing convolutional neural network (CNN) for cas...
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Jiyeon Kim and Youngok Kang
With the rise of social media platforms, tourists tend to share their experiences in the form of texts, photos, and videos on social media. These user-generated contents (UGC) play an important role in shaping tourism destination images (TDI) and directl...
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Hyeon Kang, Jang-Sik Park, Kook Cho and Do-Young Kang
Conventional data augmentation (DA) techniques, which have been used to improve the performance of predictive models with a lack of balanced training data sets, entail an effort to define the proper repeating operation (e.g., rotation and mirroring) acco...
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Rodrigo L. Rose, Tejas G. Puranik and Dimitri N. Mavris
The complexity of commercial aviation operations has grown substantially in recent years, together with a diversification of techniques for collecting and analyzing flight data. As a result, data-driven frameworks for enhancing flight safety have grown i...
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