Submit to Special Issue Submit Abstract to Special Issue Review for Information Propose a Special Issue

Journal Menu

Journal Browser

Machine Learning and Artificial Intelligence with Applications

Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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 20971

Share This Special Issue

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

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (10 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

20 pages, 5100 KiB

Open AccessArticle

Neurophysiological Approach for Psychological Safety: Enhancing Mental Health in Human–Robot Collaboration in Smart Manufacturing Setups Using Neuroimaging

by Arshia Arif, Zohreh Zakeri, Ahmet Omurtag, Philip Breedon and Azfar Khalid

Information 2024, 15(10), 640; https://doi.org/10.3390/info15100640 - 15 Oct 2024

Viewed by 697

Abstract

Human–robot collaboration (HRC) has become increasingly prevalent due to innovative advancements in the automation industry, especially in manufacturing setups. Although HRC increases productivity and efficacy, it exposes human workers to psychological stress while interfacing with collaborative robotic systems as robots may not provide visual or auditory cues. It is crucial to comprehend how HRC impacts mental stress in order to enhance occupational safety and well-being. Though academics and industrial interest in HRC is expanding, safety and mental stress problems are still not adequately studied. In particular, human coworkers’ cognitive strain during HRC has not been explored well, although being fundamental to sustaining a secure and constructive workplace environment. This study, therefore, aims to monitor the mental stress of factory workers during HRC using behavioural, physiological and subjective measures. Physiological measures, being objective and more authentic, have the potential to replace conventional measures i.e., behavioural and subjective measures, if they demonstrate a good correlation with traditional measures. Two neuroimaging modalities including electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have been used as physiological measures to track neuronal and hemodynamic activity of the brain, respectively. Here, the correlation between physiological data and behavioural and subjective measurements has been ascertained through the implementation of seven different machine learning algorithms. The results imply that the EEG and fNIRS features combined produced the best results for most of the targets. For subjective measures being the target, linear regression has outperformed all other models, whereas tree and ensemble performed the best for predicting the behavioural measures. The outcomes indicate that physiological measures have the potential to be more informative and often substitute other skewed metrics. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence with Applications)

► Show Figures

Figure 1

22 pages, 3158 KiB

Open AccessArticle

Sensitivity Analysis of Traffic Sign Recognition to Image Alteration and Training Data Size

by Arthur Rubio, Guillaume Demoor, Simon Chalmé, Nicolas Sutton-Charani and Baptiste Magnier

Information 2024, 15(10), 621; https://doi.org/10.3390/info15100621 - 10 Oct 2024

Viewed by 854

Abstract

Accurately classifying road signs is crucial for autonomous driving due to the high stakes involved in ensuring safety and compliance. As Convolutional Neural Networks (CNNs) have largely replaced traditional Machine Learning models in this domain, the demand for substantial training data has increased. This study aims to compare the performance of classical Machine Learning (ML) models and Deep Learning (DL) models under varying amounts of training data, particularly focusing on altered signs to mimic real-world conditions. We evaluated three classical models: Support Vector Machine (SVM), Random Forest, and Linear Discriminant Analysis (LDA), and one Deep Learning model: Convolutional Neural Network (CNN). Using the German Traffic Sign Recognition Benchmark (GTSRB) dataset, which includes approximately 40,000 German traffic signs, we introduced digital alterations to simulate conditions such as environmental wear or vandalism. Additionally, the Histogram of Oriented Gradients (HOG) descriptor was used to assist classical models. Bayesian optimization and k-fold cross-validation were employed for model fine-tuning and performance assessment. Our findings reveal a threshold in training data beyond which accuracy plateaus. Classical models showed a linear performance decrease under increasing alteration, while CNNs, despite being more robust to alterations, did not significantly outperform classical models in overall accuracy. Ultimately, classical Machine Learning models demonstrated performance comparable to CNNs under certain conditions, suggesting that effective road sign classification can be achieved with less computationally intensive approaches. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence with Applications)

► Show Figures

Graphical abstract

47 pages, 17094 KiB

Open AccessArticle

Short-Term Water Demand Forecasting from Univariate Time Series of Water Reservoir Stations

by Georgios Myllis, Alkiviadis Tsimpiris and Vasiliki Vrana

Information 2024, 15(10), 605; https://doi.org/10.3390/info15100605 - 3 Oct 2024

Viewed by 587

Abstract

This study presents an improved data-centric approach to short-term water demand forecasting using univariate time series from water reservoir levels. The dataset comprises water level recordings from 21 reservoirs in Eastern Thessaloniki collected over 15 months via a SCADA system provided by the water company EYATH S.A. The methodology involves data preprocessing, anomaly detection, data imputation, and the application of predictive models. Techniques such as the Interquartile Range method and moving standard deviation are employed to identify and handle anomalies. Missing values are imputed using LSTM networks optimized through the Optuna framework. This study emphasizes a data-centric approach in deep learning, focusing on improving data quality before model application, which has proven to enhance prediction accuracy. This strategy is crucial, especially in regions where reservoirs are the primary water source, and demand distribution cannot be solely determined by flow meter readings. LSTM, Random Forest Regressor, ARIMA, and SARIMA models are utilized to extract and analyze water level trends, enabling more accurate future water demand predictions. Results indicate that combining deep learning techniques with traditional statistical models significantly improves the accuracy and reliability of water demand predictions, providing a robust framework for optimizing water resource management. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence with Applications)

► Show Figures

Figure 1

16 pages, 2706 KiB

Open AccessArticle

Classification of Moral Decision Making in Autonomous Driving: Efficacy of Boosting Procedures

by Amandeep Singh, Yovela Murzello, Sushil Pokhrel and Siby Samuel

Information 2024, 15(9), 562; https://doi.org/10.3390/info15090562 - 11 Sep 2024

Viewed by 792

Abstract

Autonomous vehicles (AVs) face critical decisions in pedestrian interactions, necessitating ethical considerations such as minimizing harm and prioritizing human life. This study investigates machine learning models to predict human decision making in simulated driving scenarios under varying pedestrian configurations and time constraints. Data were collected from 204 participants across 12 unique simulated driving scenarios, categorized into young (24.7 ± 3.5 years, 38 males, 64 females) and older (71.0 ± 5.7 years, 59 males, 43 females) age groups. Participants’ binary decisions to maintain or change lanes were recorded. Traditional logistic regression models exhibited high precision but consistently low recall, struggling to identify true positive instances requiring intervention. In contrast, the AdaBoost algorithm demonstrated superior accuracy and discriminatory power. Confusion matrix analysis revealed AdaBoost’s ability to achieve high true positive rates (up to 96%) while effectively managing false positives and negatives, even under 1 s time constraints. Learning curve analysis confirmed robust learning without overfitting. AdaBoost consistently outperformed logistic regression, with AUC-ROC values ranging from 0.82 to 0.96. It exhibited strong generalization, with validation accuracy approaching 0.8, underscoring its potential for reliable real-world AV deployment. By consistently identifying critical instances while minimizing errors, AdaBoost can prioritize human safety and align with ethical frameworks essential for responsible AV adoption. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence with Applications)

► Show Figures

Figure 1

25 pages, 4636 KiB

Open AccessArticle

Application of Multi-Source Remote Sensing Data and Machine Learning for Surface Soil Moisture Mapping in Temperate Forests of Central Japan

by Kyaw Win, Tamotsu Sato and Satoshi Tsuyuki

Information 2024, 15(8), 485; https://doi.org/10.3390/info15080485 - 15 Aug 2024

Cited by 1 | Viewed by 1990

Abstract

Surface soil moisture (SSM) is a key parameter for land surface hydrological processes. In recent years, satellite remote sensing images have been widely used for SSM estimation, and many methods based on satellite-derived spectral indices have also been used to estimate the SSM content in various climatic conditions and geographic locations. However, achieving an accurate estimation of SSM content at a high spatial resolution remains a challenge. Therefore, improving the precision of SSM estimation through the synergies of multi-source remote sensing data has become imperative, particularly for informing forest management practices. In this study, the integration of multi-source remote sensing data with random forest and support vector machine models was conducted using Google Earth Engine in order to estimate the SSM content and develop SSM maps for temperate forests in central Japan. The synergy of Sentinel-2 and terrain factors, such as elevation, slope, aspect, slope steepness, and valley depth, with the random forest model provided the most suitable approach for SSM estimation, yielding the highest accuracy values (overall accuracy for testing = 91.80%, Kappa = 87.18%, r = 0.98) for the temperate forests of central Japan. This finding provides more valuable information for SSM mapping, which shows promise for precision forestry applications. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence with Applications)

► Show Figures

Figure 1

19 pages, 2777 KiB

Open AccessArticle

Fabric Defect Detection in Real World Manufacturing Using Deep Learning

by Mariam Nasim, Rafia Mumtaz, Muneer Ahmad and Arshad Ali

Information 2024, 15(8), 476; https://doi.org/10.3390/info15080476 - 11 Aug 2024

Cited by 1 | Viewed by 3413

Abstract

Defect detection is very important for guaranteeing the quality and pricing of fabric. A considerable amount of fabric is discarded as waste because of defects, leading to substantial annual losses. While manual inspection has traditionally been the norm for detection, adopting an automatic defect detection scheme based on a deep learning model offers a timely and efficient solution for assessing fabric quality. In real-time manufacturing scenarios, datasets lack high-quality, precisely positioned images. Moreover, both plain and printed fabrics are being manufactured in industries simultaneously; therefore, a single model should be capable of detecting defects in all kinds of fabric. So training a robust deep learning model that detects defects in fabric datasets generated during production with high accuracy and lower computational costs is required. This study uses an indigenous dataset directly sourced from Chenab Textiles, providing authentic and diverse images representative of actual manufacturing conditions. The dataset is used to train a computationally faster but lighter state-of-the-art network, i.e., YOLOv8. For comparison, YOLOv5 and MobileNetV2-SSD FPN-Lite models are also trained on the same dataset. YOLOv8n achieved the highest performance, with a mAP of 84.8%, precision of 0.818, and recall of 0.839 across seven different defect classes. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence with Applications)

► Show Figures

Figure 1

13 pages, 4630 KiB

Open AccessArticle

AquaVision: AI-Powered Marine Species Identification

by Benjamin Mifsud Scicluna, Adam Gauci and Alan Deidun

Information 2024, 15(8), 437; https://doi.org/10.3390/info15080437 - 27 Jul 2024

Viewed by 1875

Abstract

This study addresses the challenge of accurately identifying fish species by using machine learning and image classification techniques. The primary aim is to develop an innovative algorithm that can dynamically identify the most common (within Maltese coastal waters) invasive Mediterranean fish species based on available images. In particular, these include Fistularia commersonii, Lobotes surinamensis, Pomadasys incisus, Siganus luridus, and Stephanolepis diaspros, which have been adopted as this study’s target species. Through the use of machine-learning models and transfer learning, the proposed solution seeks to enable precise, on-the-spot species recognition. The methodology involved collecting and organising images as well as training the models with consistent datasets to ensure comparable results. After trying a number of models, ResNet18 was found to be the most accurate and reliable, with YOLO v8 following closely behind. While the performance of YOLO was reasonably good, it exhibited less consistency in its results. These results underline the potential of the developed algorithm to significantly aid marine biology research, including citizen science initiatives, and promote environmental management efforts through accurate fish species identification. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence with Applications)

► Show Figures

Figure 1

16 pages, 21213 KiB

Open AccessArticle

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 1157

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 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)

► Show Figures

Figure 1

17 pages, 1313 KiB

Open AccessArticle

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 2323

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 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)

► Show Figures

Figure 1

Review

Jump to: Research

14 pages, 1414 KiB

Open AccessReview

The Use of AI in Software Engineering: A Synthetic Knowledge Synthesis of the Recent Research Literature

by Peter Kokol

Information 2024, 15(6), 354; https://doi.org/10.3390/info15060354 - 14 Jun 2024

Cited by 2 | Viewed by 4783

Abstract

Artificial intelligence (AI) has witnessed an exponential increase in use in various applications. Recently, the academic community started to research and inject new AI-based approaches to provide solutions to traditional software-engineering problems. However, a comprehensive and holistic understanding of the current status needs to be included. To close the above gap, synthetic knowledge synthesis was used to induce the research landscape of the contemporary research literature on the use of AI in software engineering. The synthesis resulted in 15 research categories and 5 themes—namely, natural language processing in software engineering, use of artificial intelligence in the management of the software development life cycle, use of machine learning in fault/defect prediction and effort estimation, employment of deep learning in intelligent software engineering and code management, and mining software repositories to improve software quality. The most productive country was China (n = 2042), followed by the United States (n = 1193), India (n = 934), Germany (n = 445), and Canada (n = 381). A high percentage (n = 47.4%) of papers were funded, showing the strong interest in this research topic. The convergence of AI and software engineering can significantly reduce the required resources, improve the quality, enhance the user experience, and improve the well-being of software developers. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence with Applications)

► Show Figures

Journal Menu

Journal Browser

Machine Learning and Artificial Intelligence with Applications

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (10 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI