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Efficiency and Scalability of Advanced Machine Learning and Optimization Methods...
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Efficiency and Scalability of Advanced Machine Learning and Optimization Methods for Real-World Applications

A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "AI Systems: Theory and Applications".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 13033

Special Issue Editor

Dr. Mehdi Neshat

E-Mail Website
Guest Editor
Center for Artificial Intelligence Research and Optimisation, Torrens University Australia, Adelaide, SA 5000, Australia
Interests: supervised learning; deep learning; optimisation; evolutionary computations; meta-heuristic algorithms; swarm intelligence; renewable energy systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Compared to synthetic mathematical problems, real-world problems pose distinct challenges and involve complex systems with interdependent components, which require advanced modelling and analysis techniques. Nonlinear behaviour, dependencies, and uncertainties further complicate the task, and require advanced machine learning methods that are capable of capturing such intricacies. Real-world applications must also grapple with massive datasets, leading to computational and memory constraints. To address these limitations, emerging methodologies such as transfer learning, federated learning, and quantum machine learning, must ensure their efficiency and scalability while processing extensive volumes of data. This Special Issue seeks submissions that not only present advanced techniques in this area, but also demonstrate improvements in their scalability and efficiency compared to existing approaches. The deployment of machine learning methods in real-world environments is of particular interest in this Issue. Case studies detailing the practical benefits and implementation challenges of these methods are invited. Discussions on the societal, ethical, and regulatory implications of deploying advanced machine learning systems are encouraged. Additionally, this Special Issue emphasizes techniques that accommodate the computational demands of large-scale datasets; benchmark datasets and evaluation metrics play a crucial role in addressing complex real-world problems.

Dr. Mehdi Neshat
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. AI 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
  • supervised learning
  • reinforcement learning
  • domain adaptation
  • scalability
  • optimisation
  • evolutionary intelligence
  • swarm optimisation methods
  • multiobjective optimisation methods
  • real-world problems

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Published Papers (4 papers)

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Research

18 pages, 3041 KiB  
Article
Enhancing Traffic Accident Severity Prediction Using ResNet and SHAP for Interpretability
by Ilyass Benfaress, Afaf Bouhoute and Ahmed Zinedine
AI 2024, 5(4), 2568-2585; https://doi.org/10.3390/ai5040124 - 29 Nov 2024
Cited by 1 | Viewed by 1996
Abstract
Background/Objectives: This paper presents a Residual Neural Network (ResNet) based framework tailored for structured traffic accident data, aiming to improve accident severity prediction. The proposed model leverages residual learning to effectively model intricate relationships between numerical and categorical variables, resulting in a notable [...] Read more.
Background/Objectives: This paper presents a Residual Neural Network (ResNet) based framework tailored for structured traffic accident data, aiming to improve accident severity prediction. The proposed model leverages residual learning to effectively model intricate relationships between numerical and categorical variables, resulting in a notable increase in prediction accuracy. Methods: A comparative analysis was performed with other Deep Learning (DL) architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Darknet, and Extreme Inception (Xception), showing superior performance of the proposed Resnet. Key factors influencing accident severity were identified, with Shapley Additive Explanations (SHAP) values helping to address the need for transparent and explainable Artificial Intelligence (AI) in critical decision-making areas. Results: The generalizability of the ResNet model was assessed by training it, initially, on a UK road accidents dataset and validating it on a distinct dataset from India. The model consistently demonstrated high predictive accuracy, underscoring its robustness across diverse contexts, despite regional differences. Conclusions: These results suggest that the adapted ResNet model could significantly enhance traffic safety evaluations and contribute to the formulation of more effective traffic management strategies. Full article
(This article belongs to the Special Issue Efficiency and Scalability of Advanced Machine Learning and Optimization Methods for Real-World Applications)
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22 pages, 1720 KiB  
Article
Machine Learning Models Informed by Connected Mixture Components for Short- and Medium-Term Time Series Forecasting
by Andrey K. Gorshenin and Anton L. Vilyaev
AI 2024, 5(4), 1955-1976; https://doi.org/10.3390/ai5040097 - 22 Oct 2024
Cited by 2 | Viewed by 1933
Abstract
This paper presents a new approach in the field of probability-informed machine learning (ML). It implies improving the results of ML algorithms and neural networks (NNs) by using probability models as a source of additional features in situations where it is impossible to [...] Read more.
This paper presents a new approach in the field of probability-informed machine learning (ML). It implies improving the results of ML algorithms and neural networks (NNs) by using probability models as a source of additional features in situations where it is impossible to increase the training datasets for various reasons. We introduce connected mixture components as a source of additional information that can be extracted from a mathematical model. These components are formed using probability mixture models and a special algorithm for merging parameters in the sliding window mode. This approach has been proven effective when applied to real-world time series data for short- and medium-term forecasting. In all cases, the models informed by the connected mixture components showed better results than those that did not use them, although different informed models may be effective for various datasets. The fundamental novelty of the research lies both in a new mathematical approach to informing ML models and in the demonstrated increase in forecasting accuracy in various applications. For geophysical spatiotemporal data, the decrease in Root Mean Square Error (RMSE) was up to 27.7%, and the reduction in Mean Absolute Percentage Error (MAPE) was up to 45.7% compared with ML models without probability informing. The best metrics values were obtained by an informed ensemble architecture that fuses the results of a Long Short-Term Memory (LSTM) network and a transformer. The Mean Squared Error (MSE) for the electricity transformer oil temperature from the ETDataset had improved by up to 10.0% compared with vanilla methods. The best MSE value was obtained by informed random forest. The introduced probability-informed approach allows us to outperform the results of both transformer NN architectures and classical statistical and machine learning methods. Full article
(This article belongs to the Special Issue Efficiency and Scalability of Advanced Machine Learning and Optimization Methods for Real-World Applications)
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14 pages, 2577 KiB  
Article
xLSTMTime: Long-Term Time Series Forecasting with xLSTM
by Musleh Alharthi and Ausif Mahmood
AI 2024, 5(3), 1482-1495; https://doi.org/10.3390/ai5030071 - 23 Aug 2024
Cited by 8 | Viewed by 5860
Abstract
In recent years, transformer-based models have gained prominence in multivariate long-term time series forecasting (LTSF), demonstrating significant advancements despite facing challenges such as high computational demands, difficulty in capturing temporal dynamics, and managing long-term dependencies. The emergence of LTSF-Linear, with its straightforward linear [...] Read more.
In recent years, transformer-based models have gained prominence in multivariate long-term time series forecasting (LTSF), demonstrating significant advancements despite facing challenges such as high computational demands, difficulty in capturing temporal dynamics, and managing long-term dependencies. The emergence of LTSF-Linear, with its straightforward linear architecture, has notably outperformed transformer-based counterparts, prompting a reevaluation of the transformer’s utility in time series forecasting. In response, this paper presents an adaptation of a recent architecture, termed extended LSTM (xLSTM), for LTSF. xLSTM incorporates exponential gating and a revised memory structure with higher capacity that has good potential for LTSF. Our adopted architecture for LTSF, termed xLSTMTime, surpasses current approaches. We compare xLSTMTime’s performance against various state-of-the-art models across multiple real-world datasets, demonstrating superior forecasting capabilities. Our findings suggest that refined recurrent architectures can offer competitive alternatives to transformer-based models in LTSF tasks, potentially redefining the landscape of time series forecasting. Full article
(This article belongs to the Special Issue Efficiency and Scalability of Advanced Machine Learning and Optimization Methods for Real-World Applications)
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14 pages, 1254 KiB  
Article
Optimizing Curriculum Vitae Concordance: A Comparative Examination of Classical Machine Learning Algorithms and Large Language Model Architectures
by Mohammed Maree and Wala’a Shehada
AI 2024, 5(3), 1377-1390; https://doi.org/10.3390/ai5030066 - 6 Aug 2024
Cited by 2 | Viewed by 2283
Abstract
Digital recruitment systems have revolutionized the hiring paradigm, imparting exceptional efficiencies and extending the reach for both employers and job seekers. This investigation scrutinized the efficacy of classical machine learning methodologies alongside advanced large language models (LLMs) in aligning resumes with job categories. [...] Read more.
Digital recruitment systems have revolutionized the hiring paradigm, imparting exceptional efficiencies and extending the reach for both employers and job seekers. This investigation scrutinized the efficacy of classical machine learning methodologies alongside advanced large language models (LLMs) in aligning resumes with job categories. Traditional matching techniques, such as Logistic Regression, Decision Trees, Naïve Bayes, and Support Vector Machines, are constrained by the necessity of manual feature extraction, limited feature representation, and performance degradation, particularly as dataset size escalates, rendering them less suitable for large-scale applications. Conversely, LLMs such as GPT-4, GPT-3, and LLAMA adeptly process unstructured textual content, capturing nuanced language and context with greater precision. We evaluated these methodologies utilizing two datasets comprising resumes and job descriptions to ascertain their accuracy, efficiency, and scalability. Our results revealed that while conventional models excel at processing structured data, LLMs significantly enhance the interpretation and matching of intricate textual information. This study highlights the transformative potential of LLMs in recruitment, offering insights into their application and future research avenues. Full article
(This article belongs to the Special Issue Efficiency and Scalability of Advanced Machine Learning and Optimization Methods for Real-World Applications)
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