Vinayaka: A semi-supervised projected clustering method using differential evolution

ABSTRACT a semi-supervised projected clustering method based on DE. In this method DE optimizes a hybrid cluster validation index. Subspace Clustering Quality Estimate index (SCQE index) is used for internal cluster validation and Gini index gain is used for external cluster validation in the proposed hybrid cluster validation index. Proposed method is applied on Wisconsin breast cancer dataset

Projected Clustering Particle Swarm Optimization and Classification

Abstract. Supervised learning algorithms are trained with labeled data only. But labeling the data can be costly and hence the amount of labeled data available may be limited. Training the classifiers with limited amount of labeled data can lead to low classification accuracy. Hence pre-processing the data is required for getting better classification accuracy. Full dimensional clustering has been used in literature as preprocessing step to classification methods. But in high dimensional data different clusters may exist in different subspaces of the dataset. Projected Clustering Particle Swarm Optimization (PCPSO) finds optimal centers of subspace clusters by optimizing a subspace cluster validation index. In this paper we use PCPSO method to find subspace clusters present in the dataset. The subspace clusters found and limited amount of available labeled data are used to label the large amount of unlabelled data that is present in the dataset. Various classification methods are then applied on the data pre-processed by using PCPSO. In this paper we propose PCPSO-Classification method. Various new classification methods like PCPSO-Naive bayes, PCPSO-Multi layer perceptron and PCPSO-Decision table can be obtained by using different classification methods like Naive bayes, Multi layer perceptron and Decision table respectively in the classification stage of proposed PCPSO-Classification method. When the dataset contains subspace clusters and labeling the data is costly due to which available labeled data is limited then the structure of data may be used along with available limited labeled data to label the large amount of unlabeled data. After pre-processing the data the amount of labeled data is not limited. We applied PCPSO-Naive bayes, PCPSO-Multi layer perceptron and PCPSO-Decision table methods on synthetic datasets and found classification accuracy improved significantly compared to using Naive bayes, Multi layer perceptron and Decision table for classification with limited available labeled data for training classifiers. The subspace clusters found by PCPSO can be used for different types of pre-processing for solving different problems before applying classification methods on datasets. In this paper we considered the problem of limited labeled data and using PCPSO to find subspace clusters which are used for labeling large amount of unlabeled data with the help of available limited labeled data

RDD2022: A multi-national image dataset for automatic Road Damage Detection

The data article describes the Road Damage Dataset, RDD2022, which comprises 47,420 road images from six countries, Japan, India, the Czech Republic, Norway, the United States, and China. The images have been annotated with more than 55,000 instances of road damage. Four types of road damage, namely longitudinal cracks, transverse cracks, alligator cracks, and potholes, are captured in the dataset. The annotated dataset is envisioned for developing deep learning-based methods to detect and classify road damage automatically. The dataset has been released as a part of the Crowd sensing-based Road Damage Detection Challenge (CRDDC2022). The challenge CRDDC2022 invites researchers from across the globe to propose solutions for automatic road damage detection in multiple countries. The municipalities and road agencies may utilize the RDD2022 dataset, and the models trained using RDD2022 for low-cost automatic monitoring of road conditions. Further, computer vision and machine learning researchers may use the dataset to benchmark the performance of different algorithms for other image-based applications of the same type (classification, object detection, etc.).Comment: 16 pages, 20 figures, IEEE BigData Cup - Crowdsensing-based Road damage detection challenge (CRDDC'2022

Deep Loving - The Friend of Deep Learning

Artificial Intelligence and Deep Learning are good fields of research. Recently, the brother of Artificial Intelligence titled "Artificial Satisfaction" was introduced in literature [10]. In this article, we coin the term 201C;Deep Loving201D;. After the publication of this article, "Deep Loving" will be considered as the friend of Deep Learning. Proposing a new field is different from proposing a new algorithm. In this paper, we strongly focus on defining and introducing "Deep Loving Field" to Research Scientists across the globe. The future of the "Deep Loving" field is predicted by showing few future opportunities in this new field. The definition of Deep Learning is shown followed by a literature review of the "Deep Loving" field. The World's First Deep Loving Algorithm (WFDLA) is designed and implemented in this work by adding Deep Loving concepts to Particle Swarm Optimization Algorithm. Results obtained by WFDLA are compared with the PSO algorithm