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Algorithms
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Algorithms in Data Classification (2nd Edition)
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Algorithms in Data Classification (2nd Edition)

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

Deadline for manuscript submissions: 20 January 2025 | Viewed by 7024

Special Issue Editor

Prof. Ioannis Tsoulos

E-Mail Website
Guest Editor
Department of Informatics and Telecommunications, University of Ioannina, 45110 Ioannina, Greece
Interests: Optimization; Neural networks; Genetic Algorithms; Genetic Programming
Special Issues, Collections and Topics in MDPI journals

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.

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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Further information on MDPI's Special Issue polices can be found here.

Related Special Issue

Published Papers (5 papers)

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Research

16 pages, 2729 KiB  
Article
Hybrid RFSVM: Hybridization of SVM and Random Forest Models for Detection of Fake News
by Deepali Goyal Dev and Vishal Bhatnagar
Algorithms 2024, 17(10), 459; https://doi.org/10.3390/a17100459 - 16 Oct 2024
Viewed by 609
Abstract
The creation and spreading of fake information can be carried out very easily through the internet community. This pervasive escalation of fake news and rumors has an extremely adverse effect on the nation and society. Detecting fake news on the social web is [...] Read more.
The creation and spreading of fake information can be carried out very easily through the internet community. This pervasive escalation of fake news and rumors has an extremely adverse effect on the nation and society. Detecting fake news on the social web is an emerging topic in research today. In this research, the authors review various characteristics of fake news and identify research gaps. In this research, the fake news dataset is modeled and tokenized by applying term frequency and inverse document frequency (TFIDF). Several machine-learning classification approaches are used to compute evaluation metrics. The authors proposed hybridizing SVMs and RF classification algorithms for improved accuracy, precision, recall, and F1-score. The authors also show the comparative analysis of different types of news categories using various machine-learning models and compare the performance of the hybrid RFSVM. Comparative studies of hybrid RFSVM with different algorithms such as Random Forest (RF), naïve Bayes (NB), SVMs, and XGBoost have shown better results of around 8% to 16% in terms of accuracy, precision, recall, and F1-score. Full article
(This article belongs to the Special Issue Algorithms in Data Classification (2nd Edition))
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20 pages, 5263 KiB  
Article
Correlation Analysis of Railway Track Alignment and Ballast Stiffness: Comparing Frequency-Based and Machine Learning Algorithms
by Saeed Mohammadzadeh, Hamidreza Heydari, Mahdi Karimi and Araliya Mosleh
Algorithms 2024, 17(8), 372; https://doi.org/10.3390/a17080372 - 22 Aug 2024
Viewed by 868
Abstract
One of the primary challenges in the railway industry revolves around achieving a comprehensive and insightful understanding of track conditions. The geometric parameters and stiffness of railway tracks play a crucial role in condition monitoring as well as maintenance work. Hence, this study [...] Read more.
One of the primary challenges in the railway industry revolves around achieving a comprehensive and insightful understanding of track conditions. The geometric parameters and stiffness of railway tracks play a crucial role in condition monitoring as well as maintenance work. Hence, this study investigated the relationship between vertical ballast stiffness and the track longitudinal level. Initially, the ballast stiffness and track longitudinal level data were acquired through a series of experimental measurements conducted on a reference test track along the Tehran–Mashhad railway line, utilizing recording cars for geometric track and stiffness recordings. Subsequently, the correlation between the track longitudinal level and ballast stiffness was surveyed using both frequency-based techniques and machine learning (ML) algorithms. The power spectrum density (PSD) as a frequency-based technique was employed, alongside ML algorithms, including linear regression, decision trees, and random forests, for correlation mining analyses. The results showed a robust and statistically significant relationship between the vertical ballast stiffness and longitudinal levels of railway tracks. Specifically, the PSD data exhibited a considerable correlation, especially within the 1–4 rad/m wave number range. Furthermore, the data analyses conducted using ML methods indicated that the values of the root mean square error (RMSE) were about 0.05, 0.07, and 0.06 for the linear regression, decision tree, and random forest algorithms, respectively, demonstrating the adequate accuracy of ML-based approaches. Full article
(This article belongs to the Special Issue Algorithms in Data Classification (2nd Edition))
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21 pages, 597 KiB  
Article
MVACLNet: A Multimodal Virtual Augmentation Contrastive Learning Network for Rumor Detection
by Xin Liu, Mingjiang Pang, Qiang Li, Jiehan Zhou, Haiwen Wang and Dawei Yang
Algorithms 2024, 17(5), 199; https://doi.org/10.3390/a17050199 - 8 May 2024
Cited by 1 | Viewed by 1323
Abstract
In today’s digital era, rumors spreading on social media threaten societal stability and individuals’ daily lives, especially multimodal rumors. Hence, there is an urgent need for effective multimodal rumor detection methods. However, existing approaches often overlook the insufficient diversity of multimodal samples in [...] Read more.
In today’s digital era, rumors spreading on social media threaten societal stability and individuals’ daily lives, especially multimodal rumors. Hence, there is an urgent need for effective multimodal rumor detection methods. However, existing approaches often overlook the insufficient diversity of multimodal samples in feature space and hidden similarities and differences among multimodal samples. To address such challenges, we propose MVACLNet, a Multimodal Virtual Augmentation Contrastive Learning Network. In MVACLNet, we first design a Hierarchical Textual Feature Extraction (HTFE) module to extract comprehensive textual features from multiple perspectives. Then, we fuse the textual and visual features using a modified cross-attention mechanism, which operates from different perspectives at the feature value level, to obtain authentic multimodal feature representations. Following this, we devise a Virtual Augmentation Contrastive Learning (VACL) module as an auxiliary training module. It leverages ground-truth labels and extra-generated virtual multimodal feature representations to enhance contrastive learning, thus helping capture more crucial similarities and differences among multimodal samples. Meanwhile, it performs a Kullback–Leibler (KL) divergence constraint between predicted probability distributions of the virtual multimodal feature representations and their corresponding virtual labels to help extract more content-invariant multimodal features. Finally, the authentic multimodal feature representations are input into a rumor classifier for detection. Experiments on two real-world datasets demonstrate the effectiveness and superiority of MVACLNet on multimodal rumor detection. Full article
(This article belongs to the Special Issue Algorithms in Data Classification (2nd Edition))
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18 pages, 1892 KiB  
Article
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 - 4 Apr 2024
Viewed by 1575
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 [...] Read more.
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))
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Graphical abstract

11 pages, 374 KiB  
Communication
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 1671
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 [...] Read more.
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))
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