31 research outputs found
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
Crowdsensing-based Road Damage Detection Challenge (CRDDC-2022)
This paper summarizes the Crowdsensing-based Road Damage Detection Challenge
(CRDDC), a Big Data Cup organized as a part of the IEEE International
Conference on Big Data'2022. The Big Data Cup challenges involve a released
dataset and a well-defined problem with clear evaluation metrics. The
challenges run on a data competition platform that maintains a real-time online
evaluation system for the participants. In the presented case, the data
constitute 47,420 road images collected from India, Japan, the Czech Republic,
Norway, the United States, and China to propose methods for automatically
detecting road damages in these countries. More than 60 teams from 19 countries
registered for this competition. The submitted solutions were evaluated using
five leaderboards based on performance for unseen test images from the
aforementioned six countries. This paper encapsulates the top 11 solutions
proposed by these teams. The best-performing model utilizes ensemble learning
based on YOLO and Faster-RCNN series models to yield an F1 score of 76% for
test data combined from all 6 countries. The paper concludes with a comparison
of current and past challenges and provides direction for the future.Comment: 9 pages 2 figures 5 tables. arXiv admin note: text overlap with
arXiv:2011.0874