Algorithms in Data Classification (3rd Edition)

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Databases and Data Structures".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 434

Special Issue Editor


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Guest Editor
Department of Informatics and Telecommunications, University of Ioannina, 45110 Ioannina, Greece
Interests: optimization; neural networks; genetic algorithms; genetic programming
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Special Issue Information

Dear Colleagues,

I am pleased to invite submissions to the MDPI journal Algorithms for the forthcoming Special Issue entitled “Algorithms in Data Classification”. With this Special Issue, we aim to showcase recent advancements in the field of data classification and demonstrate their practical applications in solving real-world problems.

We welcome submissions focusing on the various methods employed in classification, including but not limited to Bayes methods, stochastic gradient descent, K-NN, decision trees, support vector machines, and neural networks. Furthermore, we encourage authors to explore the application of data classification in areas such as sentiment analysis, spam classification, document classification, and image classification.

This Special Issue presents a unique opportunity to contribute to the ever-evolving field of data classification and its real-world implications, and your expertise and research can make a constructive contribution to enriching the knowledge base and fostering advancements in this dynamic domain.

We invite you to submit your original research articles, literature reviews, or methodology papers to this Special Issue. We aim to gather a well-rounded collection of high-quality manuscripts that will serve as a valuable resource for both academia and industry. Appropriate topics include but are not limited to the following:

  • Binary classification;
  • Multi-class classification;
  • Multi-label classification;
  • Imbalanced classification;
  • Feature selection for classification;
  • Probabilistic models for classification;
  • Big data classification;
  • Text classification;
  • Multimedia classification;
  • Uncertain data classification.

Prof. Dr. Ioannis Tsoulos
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • binary classification
  • multi-label classification
  • decision trees
  • neural networks
  • big data
  • Bayes methods
  • K-NN methods
  • feature selection
  • machine learning
  • supervised learning

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Published Papers (1 paper)

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Research

18 pages, 3202 KiB  
Article
DScanNet: Packaging Defect Detection Algorithm Based on Selective State Space Models
by Yirong Luo, Yanping Du, Zhaohua Wang, Jingtian Mo, Wenxuan Yu and Shuihai Dou
Algorithms 2025, 18(6), 370; https://doi.org/10.3390/a18060370 - 19 Jun 2025
Viewed by 192
Abstract
With the rapid development of e-commerce and the logistics industry, the importance of logistics packaging defect detection as a key link in product quality control is becoming increasingly prominent. However, existing target detection models often face the problems of difficulty in improving detection [...] Read more.
With the rapid development of e-commerce and the logistics industry, the importance of logistics packaging defect detection as a key link in product quality control is becoming increasingly prominent. However, existing target detection models often face the problems of difficulty in improving detection accuracy and high model complexity when dealing with small-scale targets in logistics packaging. For this reason, an improved target detection model, DScanNet, is proposed in this paper. To address the problem that the model’s detailed feature extraction for small target defects is not sufficient and thus leads to low detection accuracy, the MEFE module, the local feature extraction module (LFEM Block), and the PCR module of the multi-scale convolution and feature enhancement strategy are proposed to enhance the model’s capability of capturing defective features and focusing on specific features, and to improve the detection accuracy. To address the problem of excessive model complexity, a Mamba module incorporating a channel attention mechanism is proposed to optimize the model via its linear complexity. Through experiments on its own dataset, BIGC-LP, DScanNet achieves a high accuracy of 96.8% on the defect detection task compared with the current mainstream detection algorithms, while the number of model parameters and the computational volume are effectively controlled. Full article
(This article belongs to the Special Issue Algorithms in Data Classification (3rd Edition))
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