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- research-articleJuly 2024
Kernel correlation–dissimilarity for Multiple Kernel k-Means clustering
AbstractThe main objective of the Multiple Kernel k-Means (MKKM) algorithm is to extract non-linear information and achieve optimal clustering by optimizing base kernel matrices. Current methods enhance information diversity and reduce redundancy by ...
Highlights- We propose a MKKM method that assesses kernel correlation–dissimilarity consistency.
- We utilize Manhattan distance and Frobenius inner product for kernel similarity.
- Integrating these measures improves performance and ...
- ArticleOctober 2023
A Privacy-Preserving Takeaway Delivery Service Scheme
AbstractMore and more applications based on location services continue to make our lives rich and convenient. However, location information may also quietly expose our privacy, such as work location, eating habits, etc. In this article, we have designed a ...
- research-articleSeptember 2023
A masked-face detection algorithm based on M-EIOU loss and improved ConvNeXt
Expert Systems with Applications: An International Journal (EXWA), Volume 225, Issue Chttps://doi.org/10.1016/j.eswa.2023.120037AbstractFeature extraction networks play a crucial role in classification algorithms. However, most convolutional neural networks use the form of residual block stacking to extract downstream features of images, and this simple form is not sufficient to ...
Highlights- Designing a deep neural network to use for masked-face detection.
- Anchor-free model is used to avoid time-consuming post-processing.
- The improved ConvNeXt network improves classification performance.
- Adopting Manhattan distance-...
- research-articleMarch 2024
Evaluating the Performance of Subjective Weighting Methods for Multi-Criteria Decision-Making using a novel Weights Similarity Coefficient
Procedia Computer Science (PROCS), Volume 225, Issue CPages 4785–4794https://doi.org/10.1016/j.procs.2023.10.478AbstractIn every decision-making problem which involves two or more criteria, there is to identify the relative importance of those criteria in order to make a proper decision. Very often, a decision-makers employee, for this purpose, subjective weighting ...
- research-articleJanuary 2023
An extended ITARA-TOPSIS method for multi-criteria group decision-making problems based on R-number
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology (JIFS), Volume 45, Issue 5Pages 8889–8905https://doi.org/10.3233/JIFS-232393With the continuous improvement and development of various decision-making methods, it has led to the widespread use of fuzzy sets and fuzzy numbers. At the same time, the application of decision-making methods in different fuzzy environments has been ...
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- research-articleJanuary 2023
An approach to linguistic q-rung orthopair fuzzy multi-attribute decision making with LINMAP based on Manhattan distance measure
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology (JIFS), Volume 45, Issue 1Pages 1341–1355https://doi.org/10.3233/JIFS-221750In order to estimate the deficiency of a city in its ability to prevent and control risks, as well as to evaluate the corresponding measures, this paper focuses on multi-attribute decision making based on LINMAP method and Manhattan distance at linguistic ...
- research-articleOctober 2022
A localization algorithm for DV-Hop wireless sensor networks based on manhattan distance
- Xiaohu Huang,
- Dezhi Han,
- Tien-Hsiung Weng,
- Zhongdai Wu,
- Bing Han,
- Junxiang Wang,
- Mingming Cui,
- Kuan-Ching Li
Telecommunications Systems (TESY), Volume 81, Issue 2Pages 207–224https://doi.org/10.1007/s11235-022-00943-wAbstractWSNs (Wireless Sensor Networks) are critical components of the Internet of Things (IoT). With the internationalization of the IoT and the widespread use of apps, it is crucial to increase WSNs localization algorithms' accuracy and their ...
- research-articleSeptember 2022
A new distance between multivariate clusters of varying locations, elliptical shapes, and directions
Highlights- Proposing of new method for measuring the distance between pairs of clusters in the dataset.
- The proposed distance accurately captures both the variability of the cluster centers as well as the variability of shapes and directions of ...
Clustering methods are based on the computations of both the distances between every pair of the n observations in a multivariate dataset as well as the distances between every pair of clusters in the dataset. The clusters can have different ...
- research-articleJanuary 2022
Euclidean distance versus Manhattan distance for skin detection using the SFA database
International Journal of Biometrics (IJOB), Volume 14, Issue 1Pages 46–60https://doi.org/10.1504/ijbm.2022.119553Skin detection is very challenging because of the differences in illumination, cameras characteristics, the range of skin colours due to different ethnicities and many other variations. New effective and accurate methodologies are developed for skin ...
- research-articleJanuary 2022
Latent fingerprint enhancement and matching using intuitionistic type-2 fuzzy
International Journal of Artificial Intelligence and Soft Computing (IJAISC), Volume 7, Issue 4Pages 313–328https://doi.org/10.1504/ijaisc.2022.130558Latent fingerprints are obtained from crime places by law enforcement and forensic agencies to identify the suspect. The latent fingerprints have vague ridge structures and various overlapping valley-based structures that result in low image quality. ...
- research-articleJanuary 2022
An improved method of linear spectral clustering
Multimedia Tools and Applications (MTAA), Volume 81, Issue 1Pages 1287–1311https://doi.org/10.1007/s11042-021-11459-xAbstractSuperpixel segmentation is a popular image preprocessing technology in image processing. Among the various methods used to calculate uniform superpixel, the performance of linear spectral clustering (LSC) is better than the state-of-the-art ...
- research-articleDecember 2021
Unsupervised textile defect detection using convolutional neural networks
AbstractIn this study, we propose a novel motif-based approach for unsupervised textile anomaly detection that combines the benefits of traditional convolutional neural networks with those of an unsupervised learning paradigm. It consists of ...
Highlights- A novel unsupervised motif-based approach for textile/fabric defect detection.
- ...
- research-articleDecember 2021
- research-articleSeptember 2021
Dispersing and grouping points on planar segments
Theoretical Computer Science (TCSC), Volume 886, Issue CPages 169–177https://doi.org/10.1016/j.tcs.2021.08.011AbstractMotivated by (continuous) facility location, we study the problem of dispersing and grouping points on a set of segments (of streets) in the plane. In the former problem, given a set of n disjoint line segments in the plane, we investigate the ...
Highlights- A non-trivial reduction from planar rectilinear monotone 3-SAT to the 2D dispersion problem.
- As a byproduct of (1), the maximum independent set problem on Colored Linear Unit Disk Graph remains NP-hard.
- MIS on Colored Linear Unit ...
- rapid-communicationJuly 2021
Upper bounds for Rao distance on the manifold of multivariate elliptical distributions
Automatica (Journal of IFAC) (AJIF), Volume 129, Issue Chttps://doi.org/10.1016/j.automatica.2021.109604AbstractAs a natural intrinsic measure on the manifold of probability distributions, Rao distance has received a lot of attentions and been applied successfully to many fields. However, the closed form of Rao distance on the manifold of ...
- research-articleJune 2021
Estimation of resemblance and risk level of a breast cancer patient by prognostic variables using microarray gene expression data
Innovations in Systems and Software Engineering (SPISSE), Volume 17, Issue 2Pages 73–88https://doi.org/10.1007/s11334-020-00367-2AbstractBreast cancer is a common type of cancer affecting women worldwide. Continuous efforts are being made for the identification of significant genes/biomarkers for prognosis of breast cancer. These prognostic biomarkers are very useful to predict the ...
- rapid-communicationFebruary 2021
Quantization based clustering: An iterative approach
Pattern Recognition Letters (PTRL), Volume 142, Issue CPages 51–57https://doi.org/10.1016/j.patrec.2020.12.007Highlights- Efficient quantification based clustering.
- Clustering of functional data.
In this paper we propose a simple new algorithm to perform clustering, based on the Alter algorithm proposed in [1] but lowering significantly the algorithmic complexity with respect to the number of clusters. An empirical study states ...
- research-articleJanuary 2021
Efficient data clustering algorithm designed using a heuristic approach
International Journal of Data Analysis Techniques and Strategies (IJDATS), Volume 13, Issue 1-2Pages 3–14https://doi.org/10.1504/ijdats.2021.114666Information retrieval from a large amount of information available in a database is a major issue these days. The relevant information extraction from the voluminous information available on the web is being done using various techniques like natural ...
- research-articleNovember 2020
Partial inverse min–max spanning tree problem
Journal of Combinatorial Optimization (SPJCO), Volume 40, Issue 4Pages 1075–1091https://doi.org/10.1007/s10878-020-00656-3AbstractThis paper addresses a partial inverse combinatorial optimization problem, called the partial inverse min–max spanning tree problem. For a given weighted graph G and a forest F of the graph, the problem is to modify weights at minimum cost so that ...