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Applied Sciences
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AI-Based Supervised Prediction Models
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AI-Based Supervised Prediction Models

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 December 2026 | Viewed by 4676

Editor

Dr. Maria Trigka

E-Mail Website
Guest Editor
Industrial Systems Institute, Patra, Greece
Interests: artificial intelligence; signal processing; internet of things; machine learning; data analysis; deep learning

Special Issue Information

Dear Colleagues,

This Special Issue, titled "AI-based Supervised Prediction Models", focuses on the development, analysis, and application of supervised machine learning (ML) techniques in diverse real-world domains. Supervised learning, one of the most widely used branches of artificial intelligence (AI), involves training algorithms on labeled datasets to make accurate predictions or classifications. This Special Issue provides a platform for researchers and practitioners to explore innovative methodologies, architectures, and evaluation strategies that enhance the predictive capabilities and interpretability of AI models.

This Special Issue welcomes contributions that cover a wide range of supervised learning models, including classical algorithms, as well as modern approaches such as deep learning, ensemble models, and hybrid frameworks. Special emphasis is placed on applications in healthcare, education, finance, cybersecurity, and smart systems, where predictive accuracy and robustness are critical. Moreover, this Special Issue encourages submissions addressing challenges, such as feature selection, imbalanced datasets, model explainability, and the integration of domain knowledge into predictive modeling.

Papers that demonstrate the use of supervised AI models to uncover insights from complex datasets, improve decision-making, or personalize user experiences are particularly encouraged. Additionally, comparative studies highlighting the performance of various supervised techniques and papers proposing novel metrics for evaluating model effectiveness are of high interest. Overall, this Special Issue aims to showcase state-of-the-art research that advances the field of supervised AI and contributes to the creation of intelligent, adaptive, and trustworthy predictive systems.

Dr. Maria Trigka
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • supervised learning
  • predictive modeling
  • machine learning
  • feature selection
  • classification algorithms
  • model interpretability
  • artificial intelligence

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Published Papers (4 papers)

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Research

19 pages, 1151 KB  
Article
A Hybrid Framework for Real-Time Saudi Riyal Banknote Recognition in Assistive Applications
by Nora Alhammad, Aljawharah Alsubaie, Rama Alomair, Fajer Alamro and Mashael Alammar
Appl. Sci. 2026, 16(12), 6166; https://doi.org/10.3390/app16126166 - 18 Jun 2026
Viewed by 342
Abstract
Currency recognition is a vital pillar for the financial independence of visually impaired individuals, yet existing solutions often struggle with the trade-off between architectural complexity and real-time performance. This paper introduces a lightweight hybrid framework specifically engineered for Saudi Riyal banknote identification. The [...] Read more.
Currency recognition is a vital pillar for the financial independence of visually impaired individuals, yet existing solutions often struggle with the trade-off between architectural complexity and real-time performance. This paper introduces a lightweight hybrid framework specifically engineered for Saudi Riyal banknote identification. The primary contribution lies in the strategic integration of MobileNetV2 for deep feature extraction with a kernel-based Support Vector Machine to enhance classification boundaries. Furthermore, this study addresses a significant data gap by curating an updated dataset that includes the 20 SR denomination, which is largely missing from current public repositories. Methodologically, the framework emphasizes computational efficiency without compromising precision, achieving a robust test accuracy of 98.16. By prioritizing a streamlined architecture, this work provides a scalable and effective solution for mobile-based assistive technologies, fostering greater accessibility and autonomy for the visually impaired community in Saudi Arabia. Full article
(This article belongs to the Special Issue AI-Based Supervised Prediction Models)
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26 pages, 2458 KB  
Article
An Adaptive Audiovisual Fusion Method Based on Prediction Confidence for Fine Granularity Bird Species Recognition
by Xinliang Xu, Qiming Liu, Xin Wen, Heng Zhao, Zhenhao Wang and Chong Wang
Appl. Sci. 2026, 16(10), 5113; https://doi.org/10.3390/app16105113 - 20 May 2026
Viewed by 468
Abstract
To address the inherent limitations of single-modality approaches in fine-grained bird species recognition, this paper proposes an adaptive audiovisual fusion method based on prediction confidence. The proposed framework comprises three core components: an image classification branch, an audio classification branch, and a confidence–adaptive [...] Read more.
To address the inherent limitations of single-modality approaches in fine-grained bird species recognition, this paper proposes an adaptive audiovisual fusion method based on prediction confidence. The proposed framework comprises three core components: an image classification branch, an audio classification branch, and a confidence–adaptive fusion module. The image branch employs EfficientNet-B3 to extract fine-grained visual features through compound scaling and squeeze-and-excitation (SE) attention. The audio branch utilizes ResNet-50 to classify Mel spectrograms converted from bird vocalizations, incorporating a dense sampling inference strategy to fully exploit complete audio information. For multimodal integration, a confidence–adaptive fusion strategy is introduced that jointly considers information entropy and probability gap to dynamically assess the reliability of each modality’s prediction, thereby assigning fusion weights at the sample level without any additional trainable parameters. Experiments on the SSW60 multimodal bird recognition dataset show that the image branch achieves a Top-1 accuracy of 91.55%, outperforming ResNet-50 (89.75%) and VGG-16 (83.81%); the audio branch reaches 68.20%, surpassing AST (63.29%) and VGG-16 (53.48%); and the fused model attains 95.30% Top-1 accuracy, a 3.75 percentage-point improvement over the image-only baseline and a 0.21 percentage-point gain over the learning-based TMC fusion baseline without introducing any trainable parameters, confirming the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue AI-Based Supervised Prediction Models)
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16 pages, 8250 KB  
Article
Predicting Borsa Istanbul Bank Indices Using Deep Neural Networks and Text Mining
by Cansu Altunbas, Olgun Aydin and Elvan Hayat
Appl. Sci. 2026, 16(9), 4377; https://doi.org/10.3390/app16094377 - 30 Apr 2026
Viewed by 847
Abstract
This study investigates the forecasting of the XBANK banking index traded on Borsa Istanbul by integrating financial and textual data within a deep learning framework. Unlike the majority of existing studies that focus on stable market environments, this paper explicitly examines a period [...] Read more.
This study investigates the forecasting of the XBANK banking index traded on Borsa Istanbul by integrating financial and textual data within a deep learning framework. Unlike the majority of existing studies that focus on stable market environments, this paper explicitly examines a period of heightened political uncertainty, namely the cancellation and re-run of the 2019 Istanbul local elections. This setting provides a unique opportunity to analyze how political events and news-driven information flows influence financial market dynamics. The empirical analysis is based on a comprehensive dataset that includes daily price indicators (opening, closing, high, and low values), technical indicators, selected macroeconomic variables, and Turkish-language news headlines. Textual data are processed using topic modeling techniques to extract latent information embedded in financial news, allowing for the incorporation of qualitative signals into the forecasting framework. From a methodological perspective, this study employs a feedforward deep neural network model designed to capture nonlinear relationships across heterogeneous and contemporaneous features. Feature selection is conducted using the Boruta algorithm, while hyperparameters are optimized via grid search. The model structure reflects a deliberate design choice aimed at capturing short-term, news-driven shocks and cross-feature interactions, which are particularly relevant during periods of political uncertainty. The results indicate that incorporating textual information significantly improves forecasting performance and that news topics related to political decisions, central bank policies, and geopolitical developments have a measurable impact on the XBANK index. Furthermore, the findings suggest that the political uncertainty surrounding the 2019 local elections led to increased market sensitivity and volatility, highlighting the role of information shocks in emerging financial markets. Overall, this study contributes to the literature by combining financial and textual data in an emerging market context, utilizing Turkish-language news sources, and providing empirical evidence on the impact of political uncertainty on the BIST bank index. Full article
(This article belongs to the Special Issue AI-Based Supervised Prediction Models)
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17 pages, 1253 KB  
Article
Wavelet-Enhanced Transformer for Adaptive Multi-Period Time Series Forecasting
by Ping Yu, Hoiio Kong and Zijun Li
Appl. Sci. 2025, 15(23), 12698; https://doi.org/10.3390/app152312698 - 30 Nov 2025
Cited by 5 | Viewed by 2377
Abstract
Time series analysis is of critical importance in a wide range of applications, including weather forecasting, anomaly detection, and action recognition. Accurate time series forecasting requires modeling complex temporal dependencies, particularly multi-scale periodic patterns. To address this challenge, we propose a novel Wavelet-Enhanced [...] Read more.
Time series analysis is of critical importance in a wide range of applications, including weather forecasting, anomaly detection, and action recognition. Accurate time series forecasting requires modeling complex temporal dependencies, particularly multi-scale periodic patterns. To address this challenge, we propose a novel Wavelet-Enhanced Transformer (Wave-Net). Wave-Net transforms 1D time series data into 2D matrices based on periodicity, enhancing the capture of temporal patterns through convolutional filters. This paper introduces Wave-Net, a model that incorporates wavelet and Fourier transforms for feature extraction, along with an enhanced cycle offset and optimized dynamic K for improved robustness. The Transformer layer is further refined to bolster long-term modeling capabilities. Evaluations on real-world benchmarks demonstrate that Wave-Net consistently achieves state-of-the-art performance across mainstream time series analysis tasks. Full article
(This article belongs to the Special Issue AI-Based Supervised Prediction Models)
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