Soft trees with neural components as image-processing technique for archeological excavations

There are situations when someone finds a certain object or its remains. Particularly the second case is complicated, because having only a part of the element, it is difficult to identify the full object. In the case of archeological excavations, the fragment should be classified in order to know what we are looking at. Unfortunately, such classification may be a difficult task. Hence, it is essential to focus on certain features which define it, and then to classify the complete object. In this paper, we proposed creating a novel soft tree decision structure. The idea is based on soft sets. In addition, we have introduced convolutional networks to the nodes to make decisions based on graphic files. A new archeological item can be photographed and evaluated by the proposed technique. As a result, the object will be classified depending on the amount of information obtained to the appropriate class. If the object cannot be classified, the method will return individual features and possible class.

[1]  Bo Tang,et al.  Intelligent Fault Diagnosis of the High-Speed Train With Big Data Based on Deep Neural Networks , 2017, IEEE Transactions on Industrial Informatics.

[2]  Fuchun Sun,et al.  Multi-Modal Local Receptive Field Extreme Learning Machine for object recognition , 2016, IJCNN.

[3]  Qingxiang Wu,et al.  Image super-resolution using a dilated convolutional neural network , 2018, Neurocomputing.

[4]  Harish Garg,et al.  A robust correlation coefficient measure of dual hesitant fuzzy soft sets and their application in decision making , 2018, Eng. Appl. Artif. Intell..

[5]  Matthias Hein,et al.  Variants of RMSProp and Adagrad with Logarithmic Regret Bounds , 2017, ICML.

[6]  David Delgado-Gómez,et al.  Computerized adaptive test and decision trees: A unifying approach , 2019, Expert Syst. Appl..

[7]  Xiuqin Ma,et al.  Data Analysis Approaches of Interval-Valued Fuzzy Soft Sets Under Incomplete Information , 2019, IEEE Access.

[8]  Ronald R. Yager,et al.  Another View on Generalized Intuitionistic Fuzzy Soft Sets and Related Multiattribute Decision Making Methods , 2019, IEEE Transactions on Fuzzy Systems.

[9]  Piotr Duda,et al.  New Splitting Criteria for Decision Trees in Stationary Data Streams , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[10]  Zhaofeng Yang,et al.  Image spam filtering using convolutional neural networks , 2018, Personal and Ubiquitous Computing.

[11]  Qing Wang,et al.  Distance metric optimization driven convolutional neural network for age invariant face recognition , 2018, Pattern Recognit..

[12]  Li Shen,et al.  A Sufficient Condition for Convergences of Adam and RMSProp , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Muhammad Akram,et al.  Group decision-making methods based on hesitant N-soft sets , 2019, Expert Syst. Appl..

[14]  Xinping Yan,et al.  Modeling human-like decision-making for inbound smart ships based on fuzzy decision trees , 2019, Expert Syst. Appl..

[15]  Fuyuan Xiao,et al.  A Hybrid Fuzzy Soft Sets Decision Making Method in Medical Diagnosis , 2018, IEEE Access.

[16]  Burak Kantarci,et al.  Multimedia recommendation and transmission system based on cloud platform , 2017, Future Gener. Comput. Syst..

[17]  Leszek Rutkowski,et al.  Stream Data Mining: Algorithms and Their Probabilistic Properties , 2019, Studies in Big Data.

[18]  Marta Wlodarczyk-Sielicka,et al.  The Use of an Artificial Neural Network to Process Hydrographic Big Data during Surface Modeling , 2019, Comput..

[19]  Zheng Liu,et al.  RGB-D-Based Object Recognition Using Multimodal Convolutional Neural Networks: A Survey , 2019, IEEE Access.

[20]  S. Thorpe,et al.  STDP-based spiking deep convolutional neural networks for object recognition , 2018 .

[21]  Daniel P. Bigman,et al.  Processing considerations and improved interpretation for ground‐penetrating radar imaging of a relict archaeological excavation unit , 2018 .

[22]  Leszek Rutkowski,et al.  Decision Trees in Data Stream Mining , 2020 .

[23]  D. Molodtsov.  Soft set theory—First results , 1999 .

[24]  Philippe De Smedt,et al.  On introducing an image-based 3D reconstruction method in archaeological excavation practice , 2014 .

[25]  David Dagan Feng,et al.  Atlas registration and ensemble deep convolutional neural network-based prostate segmentation using magnetic resonance imaging , 2018, Neurocomputing.

[26]  Paul F. Whelan,et al.  Convolutional neural network on three orthogonal planes for dynamic texture classification , 2017, Pattern Recognit..

[27]  Huseyin Ozkan,et al.  Nonlinear regression via incremental decision trees , 2019, Pattern Recognit..

[28]  Wen J. Li,et al.  An assertive reasoning method for emergency response management based on knowledge elements C4.5 decision tree , 2019, Expert Syst. Appl..

[29]  Marcel van Gerven,et al.  Convolutional neural network-based encoding and decoding of visual object recognition in space and time , 2017, NeuroImage.

[30]  Atsuto Maki,et al.  A systematic study of the class imbalance problem in convolutional neural networks , 2017, Neural Networks.