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Waleed Albattah and Saleh Albahli
Handwritten character recognition is a computer-vision-system problem that is still critical and challenging in many computer-vision tasks. With the increased interest in handwriting recognition as well as the developments in machine-learning and deep-le...
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Paul Samuel Ignacio, Jay-Anne Bulauan and David Uminsky
Stability of persistence diagrams under slight perturbations is a key characteristic behind the validity and growing popularity of topological data analysis in exploring real-world data. Central to this stability is the use of Bottleneck distance which e...
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Alejandro Baldominos, Yago Saez and Pedro Isasi
This paper summarizes the top state-of-the-art contributions reported on the MNIST dataset for handwritten digit recognition. This dataset has been extensively used to validate novel techniques in computer vision, and in recent years, many authors have e...
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Uraiwan Buatoom and Muhammad Usman Jamil
In image classification, various techniques have been developed to enhance the performance of principal component analysis (PCA) dimension reduction techniques with guiding weighting features to remove redundant and irrelevant features. This study propos...
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Jeongmin Lee, Younkyoung Yoon and Junseok Kwon
We propose a novel generative adversarial network for class-conditional data augmentation (i.e., GANDA) to mitigate data imbalance problems in image classification tasks. The proposed GANDA generates minority class data by exploiting majority class infor...
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Nazmus Saqib, Khandaker Foysal Haque, Venkata Prasanth Yanambaka and Ahmed Abdelgawad
Neural networks have made big strides in image classification. Convolutional neural networks (CNN) work successfully to run neural networks on direct images. Handwritten character recognition (HCR) is now a very powerful tool to detect traffic signals, t...
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Angona Biswas,Md. Saiful Islam
Pág. 42 - 55
Background: Handwriting recognition becomes an appreciable research area because of its important practical applications, but varieties of writing patterns make automatic classification a challenging task. Classifying handwritten digits with a higher acc...
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Angona Biswas,Md. Saiful Islam
Pág. 42 - 55
Background: Handwriting recognition becomes an appreciable research area because of its important practical applications, but varieties of writing patterns make automatic classification a challenging task. Classifying handwritten digits with a higher acc...
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Nebojsa Bacanin, Timea Bezdan, Eva Tuba, Ivana Strumberger and Milan Tuba
Computer vision is one of the most frontier technologies in computer science. It is used to build artificial systems to extract valuable information from images and has a broad range of applications in various areas such as agriculture, business, and hea...
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Imad Eddine Ibrahim Bekkouch, Youssef Youssry, Rustam Gafarov, Adil Khan and Asad Masood Khattak
Domain adaptation is a sub-field of transfer learning that aims at bridging the dissimilarity gap between different domains by transferring and re-using the knowledge obtained in the source domain to the target domain. Many methods have been proposed to ...
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Tameem Adel and Mark Levene
We investigate the utility of side information in the context of machine learning and, in particular, in supervised neural networks. Side information can be viewed as expert knowledge, additional to the input, that may come from a knowledge base. Unlike ...
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Rahul Sharma, Amar Singh
Pág. 105 - 117
In image processing, developing efficient, automated, and accurate techniques to classify images with varying intensity level, resolution, aspect ratio, orientation, contrast, sharpness, etc. is a challenging task. This study presents an integrated appro...
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Fernando Leonel Aguirre, Nicolás M. Gomez, Sebastián Matías Pazos, Félix Palumbo, Jordi Suñé and Enrique Miranda
In this paper, we extend the application of the Quasi-Static Memdiode model to the realistic SPICE simulation of memristor-based single (SLPs) and multilayer perceptrons (MLPs) intended for large dataset pattern recognition. By considering ex-situ traini...
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Ivana Marin, Ana Kuzmanic Skelin and Tamara Grujic
The main goal of any classification or regression task is to obtain a model that will generalize well on new, previously unseen data. Due to the recent rise of deep learning and many state-of-the-art results obtained with deep models, deep learning archi...
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Stefan Klus and Patrick Gelß
Interest in machine learning with tensor networks has been growing rapidly in recent years. We show that tensor-based methods developed for learning the governing equations of dynamical systems from data can, in the same way, be used for supervised learn...
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Danilo Pau, Andrea Pisani and Antonio Candelieri
In the context of TinyML, many research efforts have been devoted to designing forward topologies to support On-Device Learning. Reaching this target would bring numerous advantages, including reductions in latency and computational complexity, stronger ...
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Bashar Saadoon Mahdi, Mustafa Jasim Hadi and Ayad Rodhan Abbas
Computer security depends mainly on passwords to protect human users from attackers. Therefore, manual and alphanumerical passwords are the most frequent type of computer authentication. However, creating these passwords has significant drawbacks. For ex...
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Jiazhu Dai and Siwei Xiong
Capsule networks are a type of neural network that use the spatial relationship between features to classify images. By capturing the poses and relative positions between features, this network is better able to recognize affine transformation and surpas...
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Fekhr Eddine Keddous and Amir Nakib
Convolutional neural networks (CNNs) have powerful representation learning capabilities by automatically learning and extracting features directly from inputs. In classification applications, CNN models are typically composed of: convolutional layers, po...
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Xianfeng Gao, Yu-an Tan, Hongwei Jiang, Quanxin Zhang and Xiaohui Kuang
These years, Deep Neural Networks (DNNs) have shown unprecedented performance in many areas. However, some recent studies revealed their vulnerability to small perturbations added on source inputs. Furthermore, we call the ways to generate these perturba...
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