Algorithms

Journal Browser

► Journal Browser

Special Issue Information

Dear Colleagues,

I am pleased to invite submissions to the MDPI journal Algorithms for the forthcoming Special Issue on “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, image classification, and others.

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 together 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:

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.

Please note that all submissions will undergo a rigorous peer-review process to ensure the highest standard of academic excellence. Accepted papers will be published online in the MDPI journal Algorithms, providing authors with far-reaching exposure to the research community.

Dr. Ioannis G. 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.

Related Special Issue

Published Papers (2 papers)

Download All Papers

Research

18 pages, 1892 KiB

Open AccessArticle

Research on Efficient Feature Generation and Spatial Aggregation for Remote Sensing Semantic Segmentation

by Ruoyang Li, Shuping Xiong, Yinchao Che, Lei Shi, Xinming Ma and Lei Xi

Algorithms 2024, 17(4), 151; https://doi.org/10.3390/a17040151 - 04 Apr 2024

Viewed by 689

Abstract

Semantic segmentation algorithms leveraging deep convolutional neural networks often encounter challenges due to their extensive parameters, high computational complexity, and slow execution. To address these issues, we introduce a semantic segmentation network model emphasizing the rapid generation of redundant features and multi-level spatial aggregation. This model applies cost-efficient linear transformations instead of standard convolution operations during feature map generation, effectively managing memory usage and reducing computational complexity. To enhance the feature maps’ representation ability post-linear transformation, a specifically designed dual-attention mechanism is implemented, enhancing the model’s capacity for semantic understanding of both local and global image information. Moreover, the model integrates sparse self-attention with multi-scale contextual strategies, effectively combining features across different scales and spatial extents. This approach optimizes computational efficiency and retains crucial information, enabling precise and quick image segmentation. To assess the model’s segmentation performance, we conducted experiments in Changge City, Henan Province, using datasets such as LoveDA, PASCAL VOC, LandCoverNet, and DroneDeploy. These experiments demonstrated the model’s outstanding performance on public remote sensing datasets, significantly reducing the parameter count and computational complexity while maintaining high accuracy in segmentation tasks. This advancement offers substantial technical benefits for applications in agriculture and forestry, including land cover classification and crop health monitoring, thereby underscoring the model’s potential to support these critical sectors effectively. Full article

(This article belongs to the Special Issue Algorithms in Data Classification (2nd Edition))

► Show Figures

Graphical abstract

11 pages, 374 KiB

Open AccessCommunication

Numerical Algorithms in III–V Semiconductor Heterostructures

by Ioannis G. Tsoulos and V. N. Stavrou

Algorithms 2024, 17(1), 44; https://doi.org/10.3390/a17010044 - 19 Jan 2024

Viewed by 1234

Abstract

In the current research, we consider the solution of dispersion relations addressed to solid state physics by using artificial neural networks (ANNs). Most specifically, in a double semiconductor heterostructure, we theoretically investigate the dispersion relations of the interface polariton (IP) modes and describe the reststrahlen frequency bands between the frequencies of the transverse and longitudinal optical phonons. The numerical results obtained by the aforementioned methods are in agreement with the results obtained by the recently published literature. Two methods were used to train the neural network: a hybrid genetic algorithm and a modified version of the well-known particle swarm optimization method. Full article

(This article belongs to the Special Issue Algorithms in Data Classification (2nd Edition))

► Show Figures