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Algorithms
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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: closed (31 October 2024) | Viewed by 12094

Special Issue Editors

Prof. Dr. Manolya Kavakli-Thorne

E-Mail Website
Guest Editor
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 Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computer and Automation Engineering continues to evolve at an unprecedented pace, playing a crucial role in shaping 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 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. 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

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Published Papers (7 papers)

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Editorial

Jump to: Research, Review

6 pages, 164 KiB  
Editorial
AI Algorithms for Positive Change in Digital Futures
by Manolya Kavakli-Thorne and Zhuangzhuang Dai
Algorithms 2025, 18(1), 43; https://doi.org/10.3390/a18010043 - 13 Jan 2025
Viewed by 417
Abstract
Artificial Intelligence (AI) is transforming industries and revolutionizing how we interact with technology at an unprecedented pace, playing a crucial role in shaping our digital future [...] Full article
(This article belongs to the Special Issue AI Algorithms for Positive Change in Digital Futures)

Research

Jump to: Editorial, Review

16 pages, 6381 KiB  
Article
Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods
by Farshad Khodamoradi, Javad Rezazadeh and John Ayoade
Algorithms 2024, 17(12), 544; https://doi.org/10.3390/a17120544 - 2 Dec 2024
Viewed by 3691
Abstract
The Internet of things (IoT) has significantly impacted various sectors, including healthcare, environmental monitoring, transportation, and commerce, by enhancing communication networks through the integration of sensors, software, and hardware. This paper presents an accurate IoT indoor localization system based on IoT devices and [...] Read more.
The Internet of things (IoT) has significantly impacted various sectors, including healthcare, environmental monitoring, transportation, and commerce, by enhancing communication networks through the integration of sensors, software, and hardware. This paper presents an accurate IoT indoor localization system based on IoT devices and fingerprinting methods. We explore indoor localization techniques using Bluetooth Low Energy (BLE) and a Radio Signal Strength Indicator (RSSI) to address the limitations of GPS in indoor environments. The study evaluates the effectiveness of iBeacon transmitters for indoor positioning, comparing the Weighted Centroid Localization (WCL) and Positive Weighted Centroid Localization (PWCL) algorithms, along with fingerprinting methods enhanced by outlier detection and mapping filters. Our methodology includes mapping a real environment onto a coordinate axis, collecting training data from 47 sampling points, and implementing four localization algorithms. The results show that the PWCL algorithm improves accuracy over the WCL algorithm, and hybrid methods further reduce localization errors. The HYBRID-MAPPED method achieves the highest accuracy, with an average error of 1.44 m. Full article
(This article belongs to the Special Issue AI Algorithms for Positive Change in Digital Futures)
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24 pages, 4199 KiB  
Article
Multi-Source Data-Driven Local-Global Dynamic Multi-Graph Convolutional Network for Bike-Sharing Demands Prediction
by Juan Chen and Rui Huang
Algorithms 2024, 17(9), 384; https://doi.org/10.3390/a17090384 - 1 Sep 2024
Viewed by 768
Abstract
The prediction of bike-sharing demand plays a pivotal role in the optimization of intelligent transportation systems, particularly amidst the COVID-19 pandemic, which has significantly altered travel behaviors and demand dynamics. In this study, we examine various spatiotemporal influencing factors associated with bike-sharing and [...] Read more.
The prediction of bike-sharing demand plays a pivotal role in the optimization of intelligent transportation systems, particularly amidst the COVID-19 pandemic, which has significantly altered travel behaviors and demand dynamics. In this study, we examine various spatiotemporal influencing factors associated with bike-sharing and propose the Local-Global Dynamic Multi-Graph Convolutional Network (LGDMGCN) model, driven by multi-source data, for multi-step prediction of station-level bike-sharing demand. In the temporal dimension, we dynamically model temporal dependencies by incorporating multiple sources of time semantic features such as confirmed COVID-19 cases, weather conditions, and holidays. Additionally, we integrate a time attention mechanism to better capture variations over time. In the spatial dimension, we consider factors related to the addition or removal of stations and utilize spatial semantic features, such as urban points of interest and station locations, to construct dynamic multi-graphs. The model utilizes a local-global structure to capture spatial dependencies among individual bike-sharing stations and all stations collectively. Experimental results, obtained through comparisons with baseline models on the same dataset and conducting ablation studies, demonstrate the feasibility and effectiveness of the proposed model in predicting bike-sharing demand. Full article
(This article belongs to the Special Issue AI Algorithms for Positive Change in Digital Futures)
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19 pages, 925 KiB  
Article
Central Kurdish Text-to-Speech Synthesis with Novel End-to-End Transformer Training
by Hawraz A. Ahmad and Tarik A. Rashid
Algorithms 2024, 17(7), 292; https://doi.org/10.3390/a17070292 - 3 Jul 2024
Viewed by 1879
Abstract
Recent advancements in text-to-speech (TTS) models have aimed to streamline the two-stage process into a single-stage training approach. However, many single-stage models still lag behind in audio quality, particularly when handling Kurdish text and speech. There is a critical need to enhance text-to-speech [...] Read more.
Recent advancements in text-to-speech (TTS) models have aimed to streamline the two-stage process into a single-stage training approach. However, many single-stage models still lag behind in audio quality, particularly when handling Kurdish text and speech. There is a critical need to enhance text-to-speech conversion for the Kurdish language, particularly for the Sorani dialect, which has been relatively neglected and is underrepresented in recent text-to-speech advancements. This study introduces an end-to-end TTS model for efficiently generating high-quality Kurdish audio. The proposed method leverages a variational autoencoder (VAE) that is pre-trained for audio waveform reconstruction and is augmented by adversarial training. This involves aligning the prior distribution established by the pre-trained encoder with the posterior distribution of the text encoder within latent variables. Additionally, a stochastic duration predictor is incorporated to imbue synthesized Kurdish speech with diverse rhythms. By aligning latent distributions and integrating the stochastic duration predictor, the proposed method facilitates the real-time generation of natural Kurdish speech audio, offering flexibility in pitches and rhythms. Empirical evaluation via the mean opinion score (MOS) on a custom dataset confirms the superior performance of our approach (MOS of 3.94) compared with that of a one-stage system and other two-staged systems as assessed through a subjective human evaluation. Full article
(This article belongs to the Special Issue AI Algorithms for Positive Change in Digital Futures)
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23 pages, 4962 KiB  
Article
Ensemble Learning with Pre-Trained Transformers for Crash Severity Classification: A Deep NLP 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
Cited by 2 | Viewed by 1779
Abstract
Transfer learning has gained significant traction in natural language processing due to the emergence of state-of-the-art pre-trained language models (PLMs). Unlike traditional word embedding methods such as TF-IDF and Word2Vec, PLMs 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 (PLMs). Unlike traditional word embedding methods such as TF-IDF and Word2Vec, PLMs 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 (XGB), random forest (RF), and naive Bayes (NB)—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, Xiaolong Yang, Cosmas Ifeanyi Nwakanma and Sahraoui Dhelim
Algorithms 2024, 17(6), 225; https://doi.org/10.3390/a17060225 - 23 May 2024
Cited by 1 | Viewed by 1366
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 developing 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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Review

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29 pages, 4333 KiB  
Review
Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review
by Zhuangzhuang Dai, Vincent Gbouna Zakka, Luis J. Manso, Martin Rudorfer, Ulysses Bernardet, Johanna Zumer and Manolya Kavakli-Thorne
Algorithms 2024, 17(12), 560; https://doi.org/10.3390/a17120560 - 6 Dec 2024
Viewed by 1102
Abstract
Human engagement is a vital test research area actively explored in cognitive science and user experience studies. The rise of big data and digital technologies brings new opportunities into this field, especially in autonomous systems and smart applications. This article reviews the latest [...] Read more.
Human engagement is a vital test research area actively explored in cognitive science and user experience studies. The rise of big data and digital technologies brings new opportunities into this field, especially in autonomous systems and smart applications. This article reviews the latest sensors, current advances of estimation methods, and existing domains of application to guide researchers and practitioners to deploy engagement estimators in various use cases from driver drowsiness detection to human–robot interaction (HRI). Over one hundred references were selected, examined, and contrasted in this review. Specifically, this review focuses on accuracy and practicality of use in different scenarios regarding each sensor modality, as well as current opportunities that greater automatic human engagement estimation could unlock. It is highlighted that multimodal sensor fusion and data-driven methods have shown significant promise in enhancing the accuracy and reliability of engagement estimation. Upon compiling the existing literature, this article addresses future research directions, including the need for developing more efficient algorithms for real-time processing, generalization of data-driven approaches, creating adaptive and responsive systems that better cater to individual needs, and promoting user acceptance. Full article
(This article belongs to the Special Issue AI Algorithms for Positive Change in Digital Futures)
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