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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 5066

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

Published Papers (4 papers)

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Research

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 459
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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Figure 1
<p>The mutual impact of demand between stations.</p> Full article ">Figure 2
<p>The dynamic changes in station locations within specific regions of Chicago.</p> Full article ">Figure 3
<p>The relationship between daily demand for shared bicycles and daily confirmed COVID-19 cases.</p> Full article ">Figure 4
<p>The impact of rainy or snowy weather on the demand for shared bicycles.</p> Full article ">Figure 5
<p>The architecture of LGDMGCN. <math display="inline"><semantics> <mrow> <mo>{</mo> <msub> <mi mathvariant="normal">x</mi> <mrow> <msub> <mi>t</mi> <mn>0</mn> </msub> <mo>−</mo> <mi>h</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <mo> </mo> <msub> <mi mathvariant="normal">x</mi> <mrow> <msub> <mi>t</mi> <mn>0</mn> </msub> <mo>−</mo> <mi>h</mi> <mo>+</mo> <mn>2</mn> </mrow> </msub> <mo>,</mo> <mo> </mo> <mo>…</mo> <mo>,</mo> <mo> </mo> <msub> <mi mathvariant="normal">x</mi> <mrow> <msub> <mi>t</mi> <mn>0</mn> </msub> </mrow> </msub> <mo>}</mo> </mrow> </semantics></math>: the input time series. Records: historical ride records between stations. POI: POIs of stations. Dis: distance between stations. <math display="inline"><semantics> <mrow> <mo>{</mo> <msub> <mi>s</mi> <mrow> <msub> <mi>t</mi> <mn>0</mn> </msub> <mo>−</mo> <mi>h</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <mo> </mo> <msub> <mi>s</mi> <mrow> <msub> <mi>t</mi> <mn>0</mn> </msub> <mo>−</mo> <mi>h</mi> <mo>+</mo> <mn>2</mn> </mrow> </msub> <mo>,</mo> <mo> </mo> <mo>…</mo> <mo>,</mo> <mo> </mo> <msub> <mi>s</mi> <mrow> <msub> <mi>t</mi> <mn>0</mn> </msub> </mrow> </msub> <mo>}</mo> </mrow> </semantics></math>: the feature sequence produced by the encoder. <math display="inline"><semantics> <mrow> <mi>A</mi> <mi>t</mi> <mi>t</mi> <mi>n</mi> </mrow> </semantics></math>: the output of Temporal Attention. <math display="inline"><semantics> <mrow> <mo>{</mo> <msub> <mover accent="true"> <mi>y</mi> <mo>^</mo> </mover> <mrow> <msub> <mi>t</mi> <mn>0</mn> </msub> <mo>−</mo> <mi>h</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <mo> </mo> <msub> <mover accent="true"> <mi>y</mi> <mo>^</mo> </mover> <mrow> <msub> <mi>t</mi> <mn>0</mn> </msub> <mo>−</mo> <mi>h</mi> <mo>+</mo> <mn>2</mn> </mrow> </msub> <mo>,</mo> <mo> </mo> <mo>…</mo> <mo>,</mo> <mo> </mo> <msub> <mover accent="true"> <mi>y</mi> <mo>^</mo> </mover> <mrow> <msub> <mi>t</mi> <mn>0</mn> </msub> </mrow> </msub> <mo>}</mo> </mrow> </semantics></math>: the predictive sequence.</p> Full article ">Figure 6
<p>Local-Global Dynamic Spatiotemporal Graph Convolution Module.</p> Full article ">Figure 7
<p>Gated Temporal Convolution Module.</p> Full article ">Figure 8
<p>The metrics of different time granularities.</p> Full article ">Figure 9
<p>Dynamic adjacency matrix weighted heatmaps at different times.</p> Full article ">Figure 10
<p>Relationship between daily confirmed COVID-19 cases and predicted demand for bike-sharing.</p> Full article ">Figure 11
<p>Comparison of MAE for models across different periods.</p> Full article ">
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 1386
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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Figure 1

Figure 1
<p>(<b>A</b>) Initially, a VAE is pre-trained using speech-to-speech data. During this phase, the VAE focuses on reconstructing the input speech waveform. (<b>B</b>) Training Procedure: This is the alignment phase where the pre-trained wave encoder of the VAE is utilized to ensure that the text encoder produces a distribution of latent variables identical to that generated by the wave encoder. (<b>C</b>) Inference Procedure: In this phase, the text encoder is trained to generate distributions that the wave decoder of the VAE can interpret and convert into speech waveforms.</p> Full article ">Figure 2
<p>The text encoder featuring a modified transformer encoder with learnable positional encoding.</p> Full article ">Figure 3
<p>The positional encoder uses a 64-filter grouped 1D convolution to generate relative positional vectors.</p> Full article ">Figure 4
<p>The wave encoder architecture utilizes a transformer structure akin to that of the text encoder, enhancing convergence during the application of KL divergence.</p> Full article ">Figure 5
<p>The feature encoder with a 2200-sample receptive field tokenizing 100 ms segments of raw waveforms for transformer processing.</p> Full article ">Figure 6
<p>The wave decoder architecture inspired by WaveNet [<a href="#B2-algorithms-17-00292" class="html-bibr">2</a>], featuring transposed 1D convolution, and dilated residual blocks, with adversarial training for enhanced waveform generation.</p> Full article ">Figure 7
<p>The architecture of the duration prediction model <math display="inline"><semantics> <msub> <mi>f</mi> <mrow> <mi>d</mi> <mi>u</mi> <mi>r</mi> <mi>a</mi> <mi>t</mi> <mi>i</mi> <mi>o</mi> <mi>n</mi> </mrow> </msub> </semantics></math>.</p> Full article ">Figure 8
<p>Histogram of Kurdish sentences in the dataset.</p> Full article ">
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 1306
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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<p>Timeline for most common deep learning-based models for text embedding and classification models [<a href="#B11-algorithms-17-00284" class="html-bibr">11</a>].</p> Full article ">Figure 2
<p>Research methodology.</p> Full article ">Figure 3
<p>Categories of word embedding methods.</p> Full article ">Figure 4
<p>P.L.M. combined with Bi-LSTM.</p> Full article ">Figure 5
<p>BERT-LSTM performance on the original dataset (5-fold cross-validation).</p> Full article ">Figure 6
<p>RoBERTa-LSTM performance on the original dataset (5-fold cross-validation).</p> Full article ">Figure 7
<p>XGB-TFIDF performance on the original dataset (5-fold cross-validation).</p> Full article ">Figure 8
<p>BERT-LSTM performance on augmented dataset (5-fold cross-validation).</p> Full article ">Figure 9
<p>RoBERTa-LSTM performance on augmented dataset (5-fold cross-validation).</p> Full article ">Figure 10
<p>XGB-TFIDF performance on augmented dataset (5-fold cross-validation).</p> Full article ">Figure 11
<p>Hard voting model.</p> Full article ">Figure 12
<p>The performance metrics of the ensemble model on the original dataset.</p> Full article ">Figure 13
<p>The performance metrics of the ensemble model on the augmented dataset.</p> Full article ">
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
Viewed by 1053
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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Figure 1
<p>The Metaverse Layout [<a href="#B7-algorithms-17-00225" class="html-bibr">7</a>].</p> Full article ">Figure 2
<p>AccesSensor User Validation.</p> Full article ">Figure 3
<p>Metaverse User Validation System.</p> Full article ">Figure 4
<p>Deep Learning Matching Process.</p> Full article ">Figure 5
<p>Metaverse User Validation.</p> Full article ">Figure 6
<p>Masking Procedure.</p> Full article ">Figure 7
<p>Masked User Interface.</p> Full article ">Figure 8
<p>Time-based Efficiency of Sensors.</p> Full article ">Figure 9
<p>Efficiency of Privacy Method of Sensors.</p> Full article ">Figure 10
<p>Bandwidth Utilization for User Validation.</p> Full article ">
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