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Computers
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Machine Learning: Techniques, Industry Applications, Code Sharing, and Future Trends
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Machine Learning: Techniques, Industry Applications, Code Sharing, and Future Trends

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "AI-Driven Innovations".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 12467

Special Issue Editors

Dr. Hussain Mohammed Dipu Kabir

E-Mail Website
Guest Editor
1. Artificial Intelligence and Cyber Futures Institute, Charles Sturt University, Orange, NSW 2800, Australia
2. Rural Health Research Institute, Charles Sturt University, Orange, NSW 2800, Australia
Interests: artificial intelligence; uncertainty quantification; imbalanced data
Special Issues, Collections and Topics in MDPI journals
Dr. Subrota Kumar Mondal

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China
Interests: cloud computing; networks and distributed systems; blockchain; deep learning; natural language processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to highlight the importance of transparency, reproducibility, and openness in machine learning research by encouraging solutions accompanied by publicly shared codes. The goal is to promote best practices in sharing codes and datasets, making it easier for the research community to reproduce and build upon existing works.

Potential authors are encouraged to submit new concepts according to the submission guidelines. We also encourage researchers to share their codes in public repositories and implement them in open platforms like Kaggle, Code Ocean, etc. Editors and reviewers will aim to improve the presented concepts by providing effective feedback to researchers. This Special Issue can potentially bring about technological advances and an improved understanding of concepts among everyone involved, including readers. 

Scope and Topics of Interest:

We invite original research papers, reviews, and case studies that demonstrate innovative applications of machine learning and provide public access to the codebases used for the research. The topics of interest include, but are not limited to, the following:

  • Open-source machine learning frameworks and tools;
  • New machine learning models with publicly available implementation;
  • Benchmarking studies with open access datasets and codes;
  • Case studies and applications of machine learning in various domains with shared codes;
  • Best practices for reproducibility in machine learning research;
  • Public repositories and tools for collaborative machine learning development;
  • Studies on the impact of code sharing in AI research;
  • Efficient data preprocessing, feature extraction, and model evaluation using shared codes;
  • Reusable machine learning pipelines and workflows.

Dr. Hussain Mohammed Dipu Kabir
Dr. Subrota Kumar Mondal
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-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

  • open-source machine learning
  • reproducible research
  • code sharing in AI
  • machine learning frameworks
  • publicly available datasets
  • transparent machine learning
  • benchmarking in machine learning
  • collaborative machine learning
  • open science in AI
  • code-based research validation
  • machine learning algorithms with codes
  • open repositories in ML
  • GitHub for machine learning
  • computational experiment reproducibility
  • best practices in code sharing

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  • 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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (6 papers)

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Research

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16 pages, 1649 KB  
Article
The Seed Optimization Method for Fuzz Testing Based on Neural Network-Guided Genetic Algorithm
by Yongbo Jiang, Zhitao Li, Baofeng Duan and Tao Feng
Computers 2026, 15(3), 170; https://doi.org/10.3390/computers15030170 - 6 Mar 2026
Viewed by 431
Abstract
To address the issues of low initial seed efficiency and a large number of ineffective mutations, this paper proposes an innovative fuzz testing seed optimization method combining neural networks and genetic algorithms. Traditional fuzz testing seed generation typically relies on random selection and [...] Read more.
To address the issues of low initial seed efficiency and a large number of ineffective mutations, this paper proposes an innovative fuzz testing seed optimization method combining neural networks and genetic algorithms. Traditional fuzz testing seed generation typically relies on random selection and the number of covered paths. In contrast, our method significantly improves seed generation efficiency and coverage by incorporating neural network models and genetic algorithms. First, the AFL tool is used to generate seed coverage path data, which is then used to train the neural network model. This model is employed to construct a fitness function to assess the potential of each seed. Subsequently, new seeds are generated through genetic algorithm crossover and mutation operations, with fitness evaluations based on the predictions of the neural network. Ultimately, the genetic algorithm optimizes the seeds through multiple generations, progressively improving coverage and vulnerability discovery capabilities. The experimental results demonstrate that the proposed method achieves significant improvements in fuzz testing performance, with path coverage increased by 28% compared to AFL and 23% compared to AFL++, and vulnerability discovery enhanced by over 200%. Full article
(This article belongs to the Special Issue Machine Learning: Techniques, Industry Applications, Code Sharing, and Future Trends)
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30 pages, 2239 KB  
Article
Exploring Risk Factors of Mycotoxin Contamination in Fresh Eggs Using Machine Learning Techniques
by Eman Omar, Eman Alsaidi, Abdullah Aref, Sharaf Omar, Wafa’ Bani Mustafa and Hind Milhem
Computers 2026, 15(1), 34; https://doi.org/10.3390/computers15010034 - 7 Jan 2026
Viewed by 568
Abstract
Mycotoxins are toxic compounds produced by certain fungi, whose health effects may be significant when they contaminate fresh eggs. Conventional methods of mycotoxin analysis, while accurate, are labor-intensive, time-consuming, and impractical for large-scale screening applications. This study attempts to use using machine learning [...] Read more.
Mycotoxins are toxic compounds produced by certain fungi, whose health effects may be significant when they contaminate fresh eggs. Conventional methods of mycotoxin analysis, while accurate, are labor-intensive, time-consuming, and impractical for large-scale screening applications. This study attempts to use using machine learning techniques to predict the concentration and presence of deoxynivalenol (DON), aflatoxin B1 (AFB1), and ochratoxin A (OTA) in fresh eggs from Jordan. Rather than replacing analytical detection methods, the proposed approach can enable a risk-based prioritization of samples for laboratory testing by identifying high-risk samples based on environmental and production factors. A dataset consisting of 1250 poultry egg samples collected between January and July 2024 under several factors involving environmental conditions and chemical assay results regarding mycotoxin content in eggs was used. Several machine learning algorithms were used in this study to build predictive models, including decision trees, support vector machines, and neural networks. The results indicate that machine learning can accurately and reliably predict mycotoxin contamination, which demonstrates the potential for integrating machine learning into food safety protocols. This study contributes toward developing predictive analytics for food safety and lays the groundwork for future research aimed at improving contamination monitoring systems. Full article
(This article belongs to the Special Issue Machine Learning: Techniques, Industry Applications, Code Sharing, and Future Trends)
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20 pages, 2549 KB  
Article
RD-RE: Reverse Distillation with Feature Reconstruction Enhancement for Industrial Anomaly Detection
by Youjia Fu and Antao Lin
Computers 2026, 15(1), 21; https://doi.org/10.3390/computers15010021 - 4 Jan 2026
Viewed by 792
Abstract
Industrial anomaly detection methods based on reverse distillation (RD) have shown significant potential. However, existing RD approaches struggle to achieve an effective balance between constraining the feature consistency of the teacher–student networks and maintaining differentiated representation capability, which is crucial for precise anomaly [...] Read more.
Industrial anomaly detection methods based on reverse distillation (RD) have shown significant potential. However, existing RD approaches struggle to achieve an effective balance between constraining the feature consistency of the teacher–student networks and maintaining differentiated representation capability, which is crucial for precise anomaly detection. To address this challenge, we propose Reverse Distillation with Feature Reconstruction Enhancement (RD-RE) for Industrial Anomaly Detection. Firstly, we design a cross-stage feature fusion student network to integrate spatial detail information from the encoder with rich semantic information from the decoder. Secondly, we introduce a Locally Aware Dynamic Attention (LDA) module to enhance local detail feature response, thereby improving the model’s robustness in capturing anomalous regions. Finally, a Context-Aware Adaptive Multi-Scale Feature Fusion (CFFMS-FF) module is designed to constrain the consistency of local feature reconstruction. Experiments on the MVTec AD benchmark dataset demonstrate the effectiveness of RD-RE, achieving competitive results of 99.0%, 95.8%, 78.3%, and 99.7% on pixel-level AUROC, PRO, and AP and image-level AUROC metrics, and outperforming existing RD-based approaches. These results conclude that the integration of cross-stage fusion and local attention effectively mitigates the representation-consistency trade-off, providing a more robust solution for industrial anomaly localization. Full article
(This article belongs to the Special Issue Machine Learning: Techniques, Industry Applications, Code Sharing, and Future Trends)
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22 pages, 3451 KB  
Article
LSTM-Based Music Generation Technologies
by Yi-Jen Mon
Computers 2025, 14(6), 229; https://doi.org/10.3390/computers14060229 - 11 Jun 2025
Cited by 1 | Viewed by 3452
Abstract
In deep learning, Long Short-Term Memory (LSTM) is a well-established and widely used approach for music generation. Nevertheless, creating musical compositions that match the quality of those created by human composers remains a formidable challenge. The intricate nature of musical components, including pitch, [...] Read more.
In deep learning, Long Short-Term Memory (LSTM) is a well-established and widely used approach for music generation. Nevertheless, creating musical compositions that match the quality of those created by human composers remains a formidable challenge. The intricate nature of musical components, including pitch, intensity, rhythm, notes, chords, and more, necessitates the extraction of these elements from extensive datasets, making the preliminary work arduous. To address this, we employed various tools to deconstruct the musical structure, conduct step-by-step learning, and then reconstruct it. This article primarily presents the techniques for dissecting musical components in the preliminary phase. Subsequently, it introduces the use of LSTM to build a deep learning network architecture, enabling the learning of musical features and temporal coherence. Finally, through in-depth analysis and comparative studies, this paper validates the efficacy of the proposed research methodology, demonstrating its ability to capture musical coherence and generate compositions with similar styles. Full article
(This article belongs to the Special Issue Machine Learning: Techniques, Industry Applications, Code Sharing, and Future Trends)
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21 pages, 2758 KB  
Article
Enhancing Cognitive Workload Classification Using Integrated LSTM Layers and CNNs for fNIRS Data Analysis
by Mehshan Ahmed Khan, Houshyar Asadi, Mohammad Reza Chalak Qazani, Adetokunbo Arogbonlo, Siamak Pedrammehr, Adnan Anwar, Hailing Zhou, Lei Wei, Asim Bhatti, Sam Oladazimi, Burhan Khan and Saeid Nahavandi
Computers 2025, 14(2), 73; https://doi.org/10.3390/computers14020073 - 17 Feb 2025
Cited by 5 | Viewed by 4547
Abstract
Functional near-infrared spectroscopy (fNIRS) is employed as a non-invasive method to monitor functional brain activation by capturing changes in the concentrations of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR). Various machine learning classification techniques have been utilized to distinguish cognitive states. However, conventional [...] Read more.
Functional near-infrared spectroscopy (fNIRS) is employed as a non-invasive method to monitor functional brain activation by capturing changes in the concentrations of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR). Various machine learning classification techniques have been utilized to distinguish cognitive states. However, conventional machine learning methods, although simpler to implement, undergo a complex pre-processing phase before network training and demonstrate reduced accuracy due to inadequate data preprocessing. Additionally, previous research in cognitive load assessment using fNIRS has predominantly focused on differentiating between two levels of mental workload. These studies mainly aim to classify low and high levels of cognitive load or distinguish between easy and difficult tasks. To address these limitations associated with conventional methods, this paper conducts a comprehensive exploration of the impact of Long Short-Term Memory (LSTM) layers on the effectiveness of Convolutional Neural Networks (CNNs) within deep learning models. This is to address the issues related to spatial feature overfitting and the lack of temporal dependencies in CNNs discussed in the previous studies. By integrating LSTM layers, the model can capture temporal dependencies in the fNIRS data, allowing for a more comprehensive understanding of cognitive states. The primary objective is to assess how incorporating LSTM layers enhances the performance of CNNs. The experimental results presented in this paper demonstrate that the integration of LSTM layers with convolutional layers results in an increase in the accuracy of deep learning models from 97.40% to 97.92%. Full article
(This article belongs to the Special Issue Machine Learning: Techniques, Industry Applications, Code Sharing, and Future Trends)
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Review

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52 pages, 2937 KB  
Review
Federated Learning: A Survey of Core Challenges, Current Methods, and Opportunities
by Madan Baduwal, Priyanka Paudel and Vini Chaudhary
Computers 2026, 15(3), 155; https://doi.org/10.3390/computers15030155 - 2 Mar 2026
Viewed by 1850
Abstract
Federated learning (FL) has emerged as a transformative distributed learning paradigm that enables collaborative model training without sharing raw data, thereby preserving privacy across large, diverse, and geographically dispersed clients. Despite its rapid adoption in mobile networks, Internet of Things (IoT) systems, healthcare, [...] Read more.
Federated learning (FL) has emerged as a transformative distributed learning paradigm that enables collaborative model training without sharing raw data, thereby preserving privacy across large, diverse, and geographically dispersed clients. Despite its rapid adoption in mobile networks, Internet of Things (IoT) systems, healthcare, finance, and edge intelligence, FL continues to face several persistent and interdependent challenges that hinder its scalability, efficiency, and real-world deployment. In this survey, we present a systematic examination of six core challenges in federated learning: heterogeneity, computation overhead, communication bottlenecks, client selection, aggregation and optimization, and privacy preservation. We analyze how these challenges manifest across the full FL pipeline, from local training and client participation to global model aggregation and distribution, and examine their impact on model performance, convergence behavior, fairness, and system reliability. Furthermore, we synthesize representative state-of-the-art approaches proposed to address each challenge and discuss their underlying assumptions, trade-offs, and limitations in practical deployments. Finally, we identify open research problems and outline promising directions for developing more robust, scalable, and efficient federated learning systems. This survey aims to serve as a comprehensive reference for researchers and practitioners seeking a unified understanding of the fundamental challenges shaping modern federated learning. Full article
(This article belongs to the Special Issue Machine Learning: Techniques, Industry Applications, Code Sharing, and Future Trends)
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