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Algorithms
Special Issues
AI Algorithms for Positive Change in Digital Futures
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AI Algorithms for Positive Change in Digital Futures

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 1266

Special Issue Editors

Prof. Dr. Manolya Kavakli-Thorne

E-Mail Website
Guest Editor
Program Leader for Gamification and Simulation Technology, Aston Digital Futures Institute (ADFI), Aston University, Birmingham B4 7ET, UK
Interests: gamification; virtual reality; augmented reality; mixed reality; human information processing; computer game design and development; simulation system design and engineering; human computer interaction
Special Issues, Collections and Topics in MDPI journals
Dr. Zhuangzhuang Dai

E-Mail Website
Guest Editor
School of Engineering and Physical Science, Aston University, Birmingham B4 7ET, UK
Interests: sensor fusion; embedded systems; machine learning; computer vision; propagation modelling; IoT; urban data science

Special Issue Information

Dear Colleagues,

Computer and Automation Engineering continues to evolve at an unprecedented pace, playing a crucial role in sha** our digital future. Automation, driven by machine learning (ML) and artificial intelligence (AI), is transforming traditional industries by improving productivity, enhancing safety, reducing human error, and enabling more sophisticated data analysis. AI refers to the general ability of computers to emulate human thought and perform tasks in real-world environments, while ML refers to the technologies and algorithms that enable systems to identify patterns, make decisions, and improve themselves through experience and data. Developments in this area have led to innovations such as autonomous vehicles, smart homes, automated manufacturing systems, and medical robotics. We invite you to submit your latest research in design, development, application, and integration of intelligent systems driven by AI and ML approaches to this Special Issue entitled “AI Algorithms for Positive Change in Digital Futures”. We are looking for new and innovative approaches for solving real-world problems using novel AI and ML algorithms to implement positive change in society in computer and automation engineering. The global issues we face today are complex, and AI provides us with a valuable tool to augment human efforts to come up with hardware and software solutions to complex problems. High-quality papers are solicited to address both theoretical and practical issues in the use of AI and ML algorithms in computer and automation engineering. Submissions are welcome from both theoretical and applied computing domains. Potential topics include, but are not limited to, emerging applications in healthcare, disaster management, gamification, energy management, climate change, emergency management, smart homes, smart cities, and sustainability.    

Prof. Dr. Manolya Kavakli-Thorne
Dr. Zhuangzhuang Dai
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net 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. Algorithms 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
  • data analytics
  • gamification
  • virtual, augmented and mixed reality
  • computer games
  • neural networks
  • cybersecurity
  • cyberethics
  • bioinformatics
  • human–computer interaction
  • IoT and sensor-based systems
  • computer vision
  • information processing
  • natural language processing
  • embedded systems
  • simulation systems
  • autonomous vehicles
  • smart homes and smart cities
  • automated manufacturing systems
  • robotics

Published Papers (2 papers)

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Research

23 pages, 4962 KiB  
Article
Ensemble Learning with Pre-Trained Transformers for Crash Severity Classification: A Deep N.L.P. Approach
by Shadi Jaradat, Richi Nayak, Alexander Paz and Mohammed Elhenawy
Algorithms 2024, 17(7), 284; https://doi.org/10.3390/a17070284 - 30 Jun 2024
Viewed by 226
Abstract
Transfer learning has gained significant traction in natural language processing due to the emergence of state-of-the-art pre-trained language models (P.L.M.s). Unlike traditional word embedding methods such as TF-IDF and Word2Vec, P.L.M.s are context-dependent and outperform conventional techniques when fine-tuned for specific tasks. This [...] Read more.
Transfer learning has gained significant traction in natural language processing due to the emergence of state-of-the-art pre-trained language models (P.L.M.s). Unlike traditional word embedding methods such as TF-IDF and Word2Vec, P.L.M.s are context-dependent and outperform conventional techniques when fine-tuned for specific tasks. This paper proposes an innovative hard voting classifier to enhance crash severity classification by combining machine learning and deep learning models with various word embedding techniques, including BERT, RoBERTa, Word2Vec, and TF-IDF. Our study involves two comprehensive experiments using motorists’ crash data from the Missouri State Highway Patrol. The first experiment evaluates the performance of three machine learning models—XGBoost (X.G.B.), random forest (R.F.), and naive Bayes (N.B.)—paired with TF-IDF, Word2Vec, and BERT feature extraction techniques. Additionally, BERT and RoBERTa are fine-tuned with a Bidirectional Long Short-Term Memory (Bi-LSTM) classification model. All models are initially evaluated on the original dataset. The second experiment repeats the evaluation using an augmented dataset to address the severe data imbalance. The results from the original dataset show strong performance for all models in the “Fatal” and “Personal Injury” classes but a poor classification of the minority “Property Damage” class. In the augmented dataset, while the models continued to excel with the majority classes, only XGB/TFIDF and BERT-LSTM showed improved performance for the minority class. The ensemble model outperformed individual models in both datasets, achieving an F1 score of 99% for “Fatal” and “Personal Injury” and 62% for “Property Damage” on the augmented dataset. These findings suggest that ensemble models, combined with data augmentation, are highly effective for crash severity classification and potentially other textual classification tasks. Full article
(This article belongs to the Special Issue AI Algorithms for Positive Change in Digital Futures)
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13 pages, 3066 KiB  
Article
Context Privacy Preservation for User Validation by Wireless Sensors in the Industrial Metaverse Access System
by John Owoicho Odeh, **aolong Yang, Cosmas Ifeanyi Nwakanma and Sahraoui Dhelim
Algorithms 2024, 17(6), 225; https://doi.org/10.3390/a17060225 - 23 May 2024
Viewed by 495
Abstract
The Industrial Metaverse provides unparalleled prospects for increasing productivity and efficiency across multiple sectors. As wireless sensor networks play an important role in data collection and transmission within this ecosystem, preserving context privacy becomes critical to protecting sensitive information. This paper investigates the [...] Read more.
The Industrial Metaverse provides unparalleled prospects for increasing productivity and efficiency across multiple sectors. As wireless sensor networks play an important role in data collection and transmission within this ecosystem, preserving context privacy becomes critical to protecting sensitive information. This paper investigates the issue of context privacy preservation for user validation via AccesSensor in the Industrial Metaverse and presents a technological method to address it. We explore the need for context privacy, look at existing privacy preservation solutions, and propose novel user validation methods that are customized to the Industrial Metaverse’s access system. This method is evaluated on time-based efficiency, privacy method and bandwidth utilization. Our method performs better as compared to the DPSensor. Our research seeks to provide insights and recommendations for develo** strong privacy protection methods in wireless sensor networks that operate within the Industrial Metaverse ecosystem. Full article
(This article belongs to the Special Issue AI Algorithms for Positive Change in Digital Futures)
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