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Know Where to Start – Select the Right Project
Published in James Luke, David Porter, Padmanabhan Santhanam, Beyond Algorithms, 2022
James Luke, David Porter, Padmanabhan Santhanam
Labelled data is a key enabler for supervised ML technologies and labelling data can be a difficult and laborious task. In cases where you have an existing business process, it is usually possible to obtain labelled data based on historical decisions. In cases where a completely new business process is being defined, then obtaining the data will require resources to be assigned to manually label data. A really important point to remember is the need for the data to be consistently labelled. This may seem obvious, but in practice, it is rarely considered. It is not uncommon to review a failing ML project and to find that the ML is being trained with data that is inconsistent and contradictory. For example, there may be two identical images of a cat where one image is labelled “cat” and the other is labelled “Siamese”. Even worse, you may find one of the images labelled “dog”.
A Novel Model for Weather Forecasting Using Deep Learning
Published in S. S. Nandhini, M. Karthiga, S. B. Goyal, Computational Intelligence in Robotics and Automation, 2023
S. K. Nivetha, R. C. Suganthe, C. S. Kanimozhiselvi, N. Senthilkumaran, Senthil Kumar Muthusamy, S. Ashwini, P. Harinitha, B. Aishvarya
Rodrigues et al. (2018) proposed a novel method based on DNNs to achieve a high-resolution representation from low-resolution prediction mainly considering weather forecasting as a case study. The authors considered supervised learning approach in order to do automatic labeling of data. Both linear regression and NN architecture are used for weather forecasting.
Image Measurements
Published in Ravishankar Chityala, Sridevi Pudipeddi, Image Processing and Acquisition using Python, 2020
Ravishankar Chityala, Sridevi Pudipeddi
Labeling is used to identify different objects in an image. The image has to be segmented before labeling can be performed. In a labeled image, all pixels in a given object have the same value. For example, if an image comprises four objects, then in the labeled image, all pixels in the first object have a value 1, etc.
Study on the classification problem of the coping stances in the Satir model based on machine learning
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2023
Xi Wang, Yu Zhao, Guangping Zeng, Peng Xiao, Zhiliang Wang
Individuals with only one type of coping stance are rare, and most people belong to a mixture of different proportions. Coping stances change based on the presence of different individuals and situations. However, there are frequently used coping positions for specific people and situations. According to the above description, we believe that whether a person belongs to a certain type of coping stance can be judged by taking the attention of self, others, and the situation as the classification feature. Therefore, we had psychological counsellors manually label videos to form a numerical feature set. Data labelling is a crucial part of the supervised learning classification algorithm. In our present study, five types of coping stances were labelled with values ranging from 1 to 5 that denoted ‘definitely not,’ ‘probably not,’ ‘difficult to judge,’ ‘probably,’ and ‘definitely,’ respectively.
A visual–textual fused approach to automated tagging of flood-related tweets during a flood event
Published in International Journal of Digital Earth, 2019
Xiao Huang, Cuizhen Wang, Zhenlong Li, Huan Ning
With the development of machine learning, automatic labeling of a picture via its visual characteristics becomes possible. Common approaches include Support Vector Machines (SVMs) which learn visual features by constructing a set of hyperplanes (Suykens and Vandewalle 1999; Wu and Yap 2006); decision trees which learn features by applying a hierarchical decisive structure (Fakhari and Moghadam 2013; Jancsary et al. 2012); and Naïve Bayes which is a probabilistic learning approach by assuming independent contributions of different features to a corresponding class label (Boiman, Shechtman, and Irani 2008; McCann and Lowe 2012). Convolutional Neural Network (CNNs), a more state-of-the-art approach (Simard, Steinkraus, and Platt 2003; Simonyan and Zisserman 2014; Pinheiro and Collobert 2014), has been recently recognized as a promising technique in picture classification and labeling (Krizhevsky, Sutskever, and Hinton 2012; Wei et al. 2016). Ciresan et al. (2011) presented a flexible CNN for classifying benchmarks and handwritten digits with errors of 2.53% and 0.35%, respectively. Ciregan, Meier, and Schmidhuber (2012) developed a deep CNN that firstly achieved near-human labeling performance. Wang et al. (2016) proposed a deep CNN–RNN on multi-labeling Microsoft COCO dataset.
Differential diagnosis of Interstitial Lung Diseases using Deep Learning networks
Published in The Imaging Science Journal, 2020
V. N. Sukanya Doddavarapu, Giri Babu Kande, B. Prabhakara Rao
After obtaining feature descriptors, the next step is to image classification to assign labels to the descriptors. For labelling, machine learning algorithms are used. In the current work, we focus on supervised approaches. Among them the most frequently used classification methods are Support Vector Machine (SVM) [10], K- Nearest Neighbour (KNN) [9,11,12,15], Bayesian Classifiers [6], Linear Discriminant Analysis (LDA) [13,17], and Artificial Neural Network (ANN) [4]. Among these classifiers SVM gives improved true positive rates and accuracy.