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Information
Special Issues
Machine Learning and Artificial Intelligence with Applications
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Machine Learning and Artificial Intelligence with Applications

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 2790

Special Issue Editors

Dr. Jae-Mo Kang

E-Mail Website
Guest Editor
Department of Artificial Intelligence, Kyungbook National University, Daegu 41566, Republic of Korea
Interests: unmanned aerial vehicles; AI-inspired perception, navigation, and control; signal-processing-based perception, navigation, and control; autonomous driving and navigation; recognition and perception techniques for unmanned aerial vehicles; velocity, energy, and trajectory controls for unmanned aerial vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Vikas Palakonda

E-Mail Website
Guest Editor
Department of Artificial Intelligence, Kyungpook National University, Daegu 41566, Republic of Korea
Interests: computational intelligence; evolutionary computation; complex network analysis; reinforcement learning; computer vision

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) and Machine Learning (ML) techniques have become increasingly prominent in scientific disciplines such as computer vision, natural language processing, and speech recognition. These methodologies are now being implemented across various domains, including industrial, energy, vehicular technologies, financial, healthcare, manufacturing, transportation, agricultural, and logistic systems. This Special Issue aims to report on recent advances in state-of-the-art research on artificial intelligence and machine learning fields. Further, this Special Issue also focuses on the development of novel AI and ML algorithms for engineering applications.

The research domains may include (but are not limited to):

  • Neural architecture search;
  • AutoML;
  • Evolutionary deep learning;
  • Hyperparameter optimization;
  • Deep neuroevolution;
  • Deep reinforcement learning;
  • AI/ML algorithms for Cloud computing;
  • AI/ML algorithms for communication and sensing;
  • AI/ML algorithms for smart energy applications to smart cities;
  • AI/ML algorithms for wireless IoT;
  • AI/ML algorithms for e-Governance, socio-political, and economic systems.

Dr. Jae-Mo Kang
Dr. Vikas Palakonda
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 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. 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 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

  • artificial intelligence
  • machine learning
  • deep learning
  • reinforcement learning
  • evolutionary computation
  • optimization

Published Papers (2 papers)

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Research

16 pages, 21213 KiB  
Article
A Lightweight Face Detector via Bi-Stream Convolutional Neural Network and Vision Transformer
by Zekun Zhang, Qingqing Chao, Shijie Wang and Teng Yu
Information 2024, 15(5), 290; https://doi.org/10.3390/info15050290 - 20 May 2024
Viewed by 434
Abstract
Lightweight convolutional neural networks are widely used for face detection due to their ability to learn local representations through spatial induction bias and translational invariance. However, convolutional face detectors have limitations in detecting faces under challenging conditions like occlusion, blurring, or changes in [...] Read more.
Lightweight convolutional neural networks are widely used for face detection due to their ability to learn local representations through spatial induction bias and translational invariance. However, convolutional face detectors have limitations in detecting faces under challenging conditions like occlusion, blurring, or changes in facial poses, primarily attributed to fixed-size receptive fields and a lack of global modeling. Transformer-based models have advantages on learning global representations but are insensitive to capture local patterns. To address these limitations, we propose an efficient face detector that combines convolutional neural network and transformer architectures. We introduce a bi-stream structure that integrates convolutional neural network and transformer blocks within the backbone network, enabling the preservation of local pattern features and the extraction of global context. To further preserve the local details captured by convolutional neural networks, we propose a feature enhancement convolution block in a hierarchical backbone structure. Additionally, we devise a multiscale feature aggregation module to enhance obscured and blurred facial features. Experimental results demonstrate that our method has achieved improved lightweight face detection accuracy with an average precision of 95.30%, 94.20%, and 87.56% across the easy, medium, and hard subdatasets of WIDER FACE, respectively. Therefore, we believe our method will be a useful supplement to the collection of current artificial intelligence models and benefit the engineering applications of face detection. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence with Applications)
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17 pages, 1313 KiB  
Article
Using Generative AI to Improve the Performance and Interpretability of Rule-Based Diagnosis of Type 2 Diabetes Mellitus
by Leon Kopitar, Iztok Fister, Jr. and Gregor Stiglic
Information 2024, 15(3), 162; https://doi.org/10.3390/info15030162 - 12 Mar 2024
Viewed by 1346
Abstract
Introduction: Type 2 diabetes mellitus is a major global health concern, but interpreting machine learning models for diagnosis remains challenging. This study investigates combining association rule mining with advanced natural language processing to improve both diagnostic accuracy and interpretability. This novel approach has [...] Read more.
Introduction: Type 2 diabetes mellitus is a major global health concern, but interpreting machine learning models for diagnosis remains challenging. This study investigates combining association rule mining with advanced natural language processing to improve both diagnostic accuracy and interpretability. This novel approach has not been explored before in using pretrained transformers for diabetes classification on tabular data. Methods: The study used the Pima Indians Diabetes dataset to investigate Type 2 diabetes mellitus. Python and Jupyter Notebook were employed for analysis, with the NiaARM framework for association rule mining. LightGBM and the dalex package were used for performance comparison and feature importance analysis, respectively. SHAP was used for local interpretability. OpenAI GPT version 3.5 was utilized for outcome prediction and interpretation. The source code is available on GitHub. Results: NiaARM generated 350 rules to predict diabetes. LightGBM performed better than the GPT-based model. A comparison of GPT and NiaARM rules showed disparities, prompting a similarity score analysis. LightGBM’s decision making leaned heavily on glucose, age, and BMI, as highlighted in feature importance rankings. Beeswarm plots demonstrated how feature values correlate with their influence on diagnosis outcomes. Discussion: Combining association rule mining with GPT for Type 2 diabetes mellitus classification yields limited effectiveness. Enhancements like preprocessing and hyperparameter tuning are required. Interpretation challenges and GPT’s dependency on provided rules indicate the necessity for prompt engineering and similarity score methods. Variations in feature importance rankings underscore the complexity of T2DM. Concerns regarding GPT’s reliability emphasize the importance of iterative approaches for improving prediction accuracy. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence with Applications)
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