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Computers
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Emerging Trends in Machine Learning and Artificial Intelligence
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Emerging Trends in Machine Learning and Artificial Intelligence

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 31 August 2025 | Viewed by 2993

Special Issue Editor

Dr. Thuseethan Selvarajah

E-Mail Website1 Website2
Guest Editor
Faculty of Science and Technology, Charles Darwin University, Casuarina, NT 0810, Australia
Interests: machine learning; deep learning; computer vision; emotion recognition; medical imaging; precision agriculture

Special Issue Information

Dear Colleagues,

Machine learning (ML) and artificial intelligence (AI) are rapidly evolving fields that are fundamentally transforming various industries, such as healthcare, finance, agriculture, manufacturing, and education. Traditional approaches to AI and ML have been highly effective, but the ongoing advancements in these areas have led to the emergence of novel trends that push the boundaries of what is possible. These emerging trends are not only enhancing the performance and capabilities of AI systems but are also addressing some of the long-standing challenges in the field. Emerging AI and ML trends enhance image analysis, content creation, language processing, and real-time decision-making across diverse application areas like precision agriculture, emotion recognition, and autonomous systems. In specific, generative AI revolutionizes image, audio, video, and text applications. Despite these advancements, challenges remain. Hence, this Special Issue aims to address these challenges by disseminating recent advances in emerging AI and ML trends. It will focus on the flexibility and adaptability of these new approaches, as well as their potential to surpass traditional methods in terms of their performance. Original contributions as well as benchmarking studies with balanced literature reviews and engineering applications in emerging topics in AI and ML are welcome. Topics of interest for this Special Issue include, but are not limited to: 

  • Generative AI and its applications;
  • AI in healthcare;
  • Multi-modal AI;
  • AI and quantum computing;
  • Federated learning and privacy-preserving AI;
  • Large language models (LLMs) and their applications;
  • Edge AI and TinyML;
  • AI-enhanced cybersecurity;
  • New trends in learning algorithms: self-supervised, active, contrastive, and continual learning;
  • Graph neural networks and their applications;
  • Ethics and responsible AI;
  • Engineering applications of emerging AI and ML methods in biomedical, precision agriculture, affective computing, etc.

Dr. Thuseethan Selvarajah
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. Computers 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
  • neural networks
  • generative artificial intelligence
  • natural language processing
  • computer vision
  • large language models
  • reinforcement learning
  • learning algorithms

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

Published Papers (4 papers)

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Research

19 pages, 2545 KiB  
Article
Distinguishing Human Journalists from Artificial Storytellers Through Stylistic Fingerprints
by Van Hieu Tran, Yakub Sebastian, Asif Karim and Sami Azam
Computers 2024, 13(12), 328; https://doi.org/10.3390/computers13120328 - 5 Dec 2024
Viewed by 657
Abstract
Background: Artificial intelligence poses a critical challenge to the authenticity of journalistic documents. Objectives: This research proposes a method to automatically identify AI-generated news articles based on various stylistic features. Methods/Approach: We used machine learning algorithms and trained five classifiers [...] Read more.
Background: Artificial intelligence poses a critical challenge to the authenticity of journalistic documents. Objectives: This research proposes a method to automatically identify AI-generated news articles based on various stylistic features. Methods/Approach: We used machine learning algorithms and trained five classifiers to distinguish journalistic news articles from their AI-generated counterparts based on various lexical, syntactic, and readability features. BERTopic was used to extract salient keywords from these articles, which were then used to prompt Google’s Gemini to generate new artificial articles on the same topic. Results: The Random Forest classifier performed the best on the task (accuracy = 98.3%, precision = 0.984, recall = 0.983, and F1-score = 0.983). Random Forest feature importance, Analysis of Variance (ANOVA), Mutual Information, and Recursive Feature Elimination revealed the top five important features: sentence length range, paragraph length coefficient of variation, verb ratio, sentence complex tags, and paragraph length range. Conclusions: This research introduces an innovative approach to prompt engineering using the BERTopic modelling technique and identifies key stylistic features to distinguish AI-generated content from human-generated content. Therefore, it contributes to the ongoing efforts to combat disinformation, enhancing the credibility of content in various industries, such as academic research, education, and journalism. Full article
(This article belongs to the Special Issue Emerging Trends in Machine Learning and Artificial Intelligence)
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15 pages, 1050 KiB  
Article
Siamese Network-Based Lightweight Framework for Tomato Leaf Disease Recognition
by Selvarajah Thuseethan, Palanisamy Vigneshwaran, Joseph Charles and Chathrie Wimalasooriya
Computers 2024, 13(12), 323; https://doi.org/10.3390/computers13120323 - 4 Dec 2024
Viewed by 337
Abstract
In this paper, a novel Siamese network-based lightweight framework is proposed for automatic tomato leaf disease recognition. This framework achieves the highest accuracy of 96.97% on the tomato subset obtained from the PlantVillage dataset and 95.48% on the Taiwan tomato leaf disease dataset. [...] Read more.
In this paper, a novel Siamese network-based lightweight framework is proposed for automatic tomato leaf disease recognition. This framework achieves the highest accuracy of 96.97% on the tomato subset obtained from the PlantVillage dataset and 95.48% on the Taiwan tomato leaf disease dataset. Experimental results further confirm that the proposed framework is effective with imbalanced and small data. The backbone network integrated with this framework is lightweight with approximately 2.9629 million trainable parameters, which is second to SqueezeNet and significantly lower than other lightweight deep networks. Automatic tomato disease recognition from leaf images is vital to avoid crop losses by applying control measures on time. Even though recent deep learning-based tomato disease recognition methods with classical training procedures showed promising recognition results, they demand large labeled data and involve expensive training. The traditional deep learning models proposed for tomato disease recognition also consume high memory and storage because of a high number of parameters. While lightweight networks overcome some of these issues to a certain extent, they continue to show low performance and struggle to handle imbalanced data. Full article
(This article belongs to the Special Issue Emerging Trends in Machine Learning and Artificial Intelligence)
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31 pages, 3335 KiB  
Article
Unified Ecosystem for Data Sharing and AI-Driven Predictive Maintenance in Aviation
by Igor Kabashkin and Vitaly Susanin
Computers 2024, 13(12), 318; https://doi.org/10.3390/computers13120318 - 28 Nov 2024
Viewed by 686
Abstract
The aviation industry faces considerable challenges in maintenance management due to the complexities of data standardization, data sharing, and predictive maintenance capabilities. This paper introduces a unified ecosystem for data sharing and AI-driven predictive maintenance designed to address these challenges by integrating real-time [...] Read more.
The aviation industry faces considerable challenges in maintenance management due to the complexities of data standardization, data sharing, and predictive maintenance capabilities. This paper introduces a unified ecosystem for data sharing and AI-driven predictive maintenance designed to address these challenges by integrating real-time and historical data from diverse sources, including aircraft sensors, maintenance logs, and operational records. The proposed ecosystem enables predictive analytics and anomaly detection, enhancing decision-making processes for airlines, maintenance, repair, and overhaul providers, and regulatory bodies. Key elements of the ecosystem include a modular design with feedback loops, scalable AI models for predictive maintenance, and robust data-sharing frameworks. This paper outlines the architecture of a unified aviation maintenance ecosystem built around multiple data sources, including aircraft sensors, maintenance logs, flight data, weather data, and manufacturer specifications. By integrating various components and stakeholders, the system achieves its full potential through several key use cases: monitoring aircraft component health, predicting component failures, receiving maintenance alerts, performing preventive maintenance, and generating compliance reports. Each use case is described in detail and supported by illustrative dataflow diagrams. The findings underscore the transformative impact of such an ecosystem on aviation maintenance practices, marking a significant step toward safer, more efficient, and sustainable aviation operations. Full article
(This article belongs to the Special Issue Emerging Trends in Machine Learning and Artificial Intelligence)
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19 pages, 1382 KiB  
Article
On the Robustness of Compressed Models with Class Imbalance
by Baraa Saeed Ali, Nabil Sarhan and Mohammed Alawad
Computers 2024, 13(11), 297; https://doi.org/10.3390/computers13110297 - 16 Nov 2024
Viewed by 522
Abstract
Deep learning (DL) models have been deployed in various platforms, including resource-constrained environments such as edge computing, smartphones, and personal devices. Such deployment requires models to have smaller sizes and memory footprints. To this end, many model compression techniques proposed in the literature [...] Read more.
Deep learning (DL) models have been deployed in various platforms, including resource-constrained environments such as edge computing, smartphones, and personal devices. Such deployment requires models to have smaller sizes and memory footprints. To this end, many model compression techniques proposed in the literature successfully reduce model sizes and maintain comparable accuracy. However, the robustness of compressed DL models against class imbalance, a natural phenomenon in real-life datasets, is still under-explored. We present a comprehensive experimental study of the performance and robustness of compressed DL models when trained on class-imbalanced datasets. We investigate the robustness of compressed DL models using three popular compression techniques (pruning, quantization, and knowledge distillation) with class-imbalanced variants of the CIFAR-10 dataset and show that compressed DL models are not robust against class imbalance in training datasets. We also show that different compression techniques have varying degrees of impact on the robustness of compressed DL models. Full article
(This article belongs to the Special Issue Emerging Trends in Machine Learning and Artificial Intelligence)
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