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Innovative Machine Learning Technologies and Applications
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Innovative Machine Learning Technologies and Applications

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 503

Editors

Dr. Zhou Yang

E-Mail Website
Guest Editor
Department of Statistics, George Washington University, Washington, DC 20052, USA
Interests: machine learning; deep learning; explainable learning; natural language processing
Dr. Fang Jin

E-Mail Website
Guest Editor
Department of Statistics, George Washington University, Washington, DC 20052, USA
Interests: deep learning interpretation; explainable AI; machine learning; data mining; deep reinforcement learning; social network analysis

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and machine learning (ML) continue to reshape research and industry by enabling systems that learn from data, make informed decisions, and automate complex tasks. Recent advances in algorithms, computational power, and data availability have driven rapid progress across domains such as computer vision, natural language processing, robotics, healthcare, finance, and engineering. Despite this momentum, many challenges remain—particularly in ensuring the robustness, efficiency, interpretability, and reliability of AI and ML systems deployed in real-world environments.

As applications expand, the integration of domain knowledge, responsible AI practices, and efficient learning paradigms has become increasingly important. Addressing issues such as model transparency, fairness, data scarcity, generalization, and adaptability will be key to advancing the next generation of AI-driven technologies.

This Special Issue of Information, “Innovative Machine Learning Technologies and Applications”, aims to showcase recent developments, innovative methodologies, and practical applications of AI and ML across diverse fields. We welcome contributions that explore novel algorithms, architectures, and use cases that demonstrate the transformative potential of intelligent systems.

Topics of interest include, but are not limited to, the following:

  • Artificial Intelligence Applications;
  • Machine Learning and Deep Learning;
  • Reinforcement Learning;
  • Explainable and Interpretable AI;
  • Responsible, Fair, and Ethical AI;
  • Edge AI and Embedded Intelligence;
  • Computer Vision and Image Processing;
  • Natural Language Processing and Speech Technologies;
  • Robotics, Autonomous Systems, and Control;
  • Data Mining and Knowledge Discovery;
  • Time Series Forecasting and Predictive Analytics;
  • Medical and Healthcare AI Applications;
  • Industrial, Financial, and Smart City Applications;
  • Multimodal Learning and Fusion Techniques;
  • Generative Models and Foundation Models;
  • Optimization Algorithms for AI/ML;
  • Efficient, Lightweight, and Green AI.

This Special Issue seeks to advance the literature through the following:

  • Presenting innovative AI and ML applications that address real-world challenges across disciplines.
  • Bridging methodological advances with practical implementations supported by empirical evidence.
  • Promoting transparency, interpretability, and fairness in AI systems to ensure trustworthy deployment.
  • Providing insights into emerging trends, unresolved challenges, and promising directions for future research.

We look forward to receiving your high-quality submissions and to curating a collection that contributes to the continued evolution of AI and ML technologies.

Sincerely,

Dr. Zhou Yang
Dr. Fang Jin
Guest Editors

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. Information 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 1800 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

  • artificial intelligence
  • machine learning
  • deep learning
  • predictive analytics
  • reinforcement learning
  • generative models
  • explainable and transparent AI

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

Published Papers (2 papers)

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Research

31 pages, 1937 KB  
Article
Benchmarking Energy Efficiency of Supervised Machine Learning Models on Multi-Domain Classification Datasets
by Aamir Ali, Rohail Qamar, Raheela Asif and Saman Hina
Information 2026, 17(7), 652; https://doi.org/10.3390/info17070652 (registering DOI) - 4 Jul 2026
Abstract
Machine learning should be judged by how well it predicts, and computational resources are not accounted for in predictive accuracy. Given the growing emphasis on energy consumption and resource efficiency, decision-supporting frameworks should go beyond accuracy. This study presents an energy-based benchmarking approach [...] Read more.
Machine learning should be judged by how well it predicts, and computational resources are not accounted for in predictive accuracy. Given the growing emphasis on energy consumption and resource efficiency, decision-supporting frameworks should go beyond accuracy. This study presents an energy-based benchmarking approach for supervised learning models. Ten classical algorithms were evaluated on three textual and tabular datasets. The energy consumption of preprocessing, training, and inference was monitored with Intel RAPL via pyRAPL along with the runtime, peak memory usage, and predictive performance statistics (accuracy, precision, recall, F1-score, and AUC). Experiments were conducted in a controlled CPU-based environment to ensure comparability. The computational role of this feature is found to be appreciably diverse. Results show that Random Forest achieved the highest overall balance between predictive performance and efficiency (CI = 0.950, PPI = 0.907), while Logistic Regression provided a competitive trade-off (CI = 0.905, EI = 0.998). Gaussian Naïve Bayes was the most energy-efficient model with a mean energy consumption of 127 J, whereas Support Vector Classifier (SVC) incurred the highest computational cost, consuming 45,758 J and requiring 3925 s on average. The Pareto analysis identified Random Forest, Logistic Regression, Passive Aggressive, and Decision Tree as non-dominated solutions. These findings demonstrate that accuracy alone can be misleading for model evaluation and that integrating energy, runtime, and memory metrics enables more sustainable and resource-aware machine learning model selection. The proposed framework provides practical guidance for Green AI, Tiny Machine Learning (TinyML), edge computing, and other resource-constrained deployment environments. Full article
(This article belongs to the Special Issue Innovative Machine Learning Technologies and Applications)
49 pages, 4337 KB  
Article
Synthetic Data Augmentation for Robust Classification of Diabetic vs. Non-Diabetic Blood FTIR Spectra
by Ahmed Fadlelmoula, Kirill N. Boldyrev, Margarida Gonçalves, Helena Torres, Susana O. Catarino, Graça Minas and Vitor Carvalho
Information 2026, 17(7), 638; https://doi.org/10.3390/info17070638 - 29 Jun 2026
Viewed by 163
Abstract
Early detection of diabetes mellitus (DM) is essential for preventing disease progression and improving clinical outcomes. However, developing robust machine learning (ML) models for diabetes diagnosis is often constrained by limited data availability, privacy regulations, and challenges with data sharing. This study investigates [...] Read more.
Early detection of diabetes mellitus (DM) is essential for preventing disease progression and improving clinical outcomes. However, developing robust machine learning (ML) models for diabetes diagnosis is often constrained by limited data availability, privacy regulations, and challenges with data sharing. This study investigates a privacy-preserving synthetic data augmentation framework for classifying diabetic and non-diabetic blood serum samples using Fourier Transform Infrared (FTIR) spectroscopy. Two deep generative approaches, Autoencoders (AEs) and Generative Adversarial Networks (GANs), were evaluated for their ability to generate realistic synthetic FTIR spectra while preserving the statistical and biochemical characteristics of the original dataset. Synthetic datasets generated by the AE and GAN models were assessed using six ML classifiers: Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Gradient Boosting (GB), Logistic Regression (LoR), and Decision Tree (DT). Model performance was evaluated using accuracy, precision, recall, F1-score, Receiver Operating Characteristic (ROC) curves, and Area Under the Curve (AUC). Results showed that AE-generated spectra retained stronger discriminative characteristics and were more easily distinguished from the original spectra, whereas GAN-generated spectra exhibited lower classifier separability, suggesting closer alignment with the original data distribution and greater realism for privacy-oriented data augmentation. Correlation analysis demonstrated high spectral fidelity for both approaches. Compared with the original spectra, AE-generated spectra achieved r = 0.9990 and R2 = 0.9999, whereas GAN-generated spectra achieved r = 0.9982 and R2 = 0.9965. The most prominent diabetes related spectral variations were observed in the carbohydrate (1000–1200 cm−1), Amide I (~1650 cm−1), and lipid-associated (3000–3500 cm−1) regions. To explore the transferability of the proposed framework, a preliminary experimental feasibility study was conducted using independently acquired whole blood FTIR spectra. The generated spectra showed strong agreement with the measured whole blood spectra, demonstrating the potential applicability of the framework under alternative sampling conditions. Because the experimental cohort included only one diabetic volunteer, this analysis was intended solely as a proof-of-concept assessment of spectral feasibility and methodological transferability, rather than as a validation of diabetes classification performance. Overall, the findings demonstrate that synthetic data generation can effectively augment limited FTIR datasets while preserving privacy and key spectral characteristics. The proposed framework provides a promising foundation for privacy-aware biomedical data augmentation and future development of robust FTIR diabetes screening systems. The results should be interpreted as methodological evidence of feasibility and synthetic data utility rather than as evidence of clinical diagnostic readiness, as the serum dataset remains modest in size and the independent whole-blood experiment was intentionally exploring. Full article
(This article belongs to the Special Issue Innovative Machine Learning Technologies and Applications)
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