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Future Trends of Artificial Intelligence (AI) and Big Data

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 July 2025 | Viewed by 35976

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Special Issue Editors

Dr. Sotiris Diplaris

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Guest Editor

Centre for Research and Technology Hellas, Information Technologies Institute, 6th Km Charilaou-Thermi, 57001 Thessaloniki, Greece
Interests: artificial intelligence in media; arts and culture; social media content mining; information retrieval; emergent semantics extraction

Dr. Stefanos Vrochidis

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Guest Editor

Centre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI), 57001 Thessaloniki, Greece
Interests: multimodal AI; multimedia analysis and retrieval; multimodal analytics and fusion; semantics
Special Issues, Collections and Topics in MDPI journals

Dr. Ioannis Yiannis Kompatsiaris

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Guest Editor

Centre for Research and Technology Hellas, Information Technologies Institute, 6th Km Charilaou-Thermi, 57001 Thessaloniki, Greece
Interests: semantic multimedia analysis; indexing and retrieval; social media and big data analysis; knowledge structures; reasoning and personalization for multimedia applications; e-health and environmental applications
Special Issues, Collections and Topics in MDPI journals

Dr. Giuseppe Amato

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Guest Editor

Institute of Information Science and Technologies, Italian National Research Council (ISTI-CNR), Via G. Moruzzi, 1, 56124 Pisa, Italy
Interests: deep learning; multimedia information retrieval; content based image retrieval; large scale similarity search

Special Issue Information

Dear Colleagues,

Recent advances in artificial intelligence (AI) and big data have dramatically changed the way we handle and analyze information. These technologies have become vital in several fields, including healthcare, security, education, media, and entertainment. The application of AI and big data has enabled us to accomplish tasks that were previously impossible, such as identifying complex patterns and trends, predicting outcomes, and automating decision-making processes.

This Special Issue aims to investigate the future trends of AI and big data, as well as their influence on various domains. Extended reality, art and culture, multimodal analysis, and multimedia retrieval are just a few of the areas that will be explored. We welcome original research papers and review articles that delve into topics such as edge computing, personalized medicine, ethics and the responsible use of AI, and natural language understanding, among others. We encourage researchers, scholars, and practitioners to submit their contributions and share their insights to enhance our understanding of the impact of AI and big data on society.

Topics of interest include, but are not limited to, the following:

Emerging trends in AI and big data research;
Applications of AI and big data in Industry 4.0;
The ethics of AI and big data;
Human–AI interaction and collaboration;
Explainable AI and interpretability;
Natural language processing and generation;
Machine learning algorithms and techniques;
Computer vision and image analysis;
Big data analytics and visualization;
Cloud computing and distributed systems for big data;
Edge computing and AI at the edge;
Social media analysis and sentiment analysis;
Recommender systems and personalized recommendations;
Deep learning and neural networks;
Generative models and adversarial learning;
AI and big data in education and e-learning;
AR/VR and extended reality;
AI and art/culture;
AI and big data in environmental sustainability and climate change research;
Impact of AI and big data on urban planning and smart cities;
AI and big data for enhancing cybersecurity and data privacy.

Dr. Sotiris Diplaris
Dr. Stefanos Vrochidis
Dr. Ioannis Yiannis Kompatsiaris
Dr. Giuseppe Amato
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. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

emerging trends in AI and big data research
applications of AI and big data in Industry 4.0
the ethics of AI and big data
human–AI interaction and collaboration
explainable AI and interpretability
natural language processing and generation
machine learning algorithms and techniques
computer vision and image analysis
big data analytics and visualization
cloud computing and distributed systems for big data
edge computing and AI at the edge
social media analysis and sentiment analysis
recommender systems and personalized recommendations
deep learning and neural networks
generative models and adversarial learning
AI and big data on education and e-learning
AR/VR and extended reality
AI and art/culture
AI and big data in environmental sustainability and climate change research
impact of AI and big data on urban planning and smart cities
AI and big data for enhancing cybersecurity and data privacy

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Published Papers (10 papers)

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Research

22 pages, 1541 KiB

Open AccessFeature PaperArticle

A Framework for Integrating Log-Structured Merge-Trees and Key–Value Separation in Tiered Storage

by Charles Jaranilla, Guangxun Zhao, Gunhee Choi, Sohyun Park and Jongmoo Choi

Electronics 2025, 14(3), 564; https://doi.org/10.3390/electronics14030564 - 30 Jan 2025

Viewed by 906

Abstract

This paper presents an approach that integrates tiered storage into the Log-Structured Merge (LSM)-tree to balance Key–Value Store (KVS) performance and storage financial cost trade-offs. The implementation focuses on applying tiered storage to LSM-tree-based KVS architectures, using both vertical and horizontal storage alignment strategies or a combination of both. Additionally, these configurations leverage key–value (KV) separation to further improve performance. Our experiments reveal that this approach reduces storage financial costs while offering trade-offs in write and read performance. For write-intensive workloads, our approach achieves competitive performance compared to a fast NVMe Solid State Drive (SSD)-only approach while storing 96% of data on more affordable SATA SSDs. Additionally, it exhibits lookup performance comparable to BlobDB, and improves range query performance by 1.8x over RocksDB on NVMe SSDs. Overall, the approach results in a 49.5% reduction in storage financial cost compared to RocksDB and BlobDB on NVMe SSDs. The integration of selective KV separation further advances these improvements, setting the stage for future research into offloading remote data in LSM-tree tiered storage systems. Full article

(This article belongs to the Special Issue Future Trends of Artificial Intelligence (AI) and Big Data)

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20 pages, 950 KiB

Open AccessArticle

KG-PLPPM: A Knowledge Graph-Based Personal Learning Path Planning Method Used in Online Learning

by Bo Hou, Yishuai Lin, Yuechen Li, Chen Fang, Chuang Li and Xiaoying Wang

Electronics 2025, 14(2), 255; https://doi.org/10.3390/electronics14020255 - 9 Jan 2025

Cited by 2 | Viewed by 2123

Abstract

In the realm of online learning, where resources are abundant, it is essential to customize recommendations and plans to meet individual learning needs. This involves not only identifying and addressing areas of weakness but also aligning the learning journey with each learner’s cognitive preferences. However, existing methods for suggesting and structuring learning paths have notable limitations. To address these challenges, this paper introduces a knowledge graph-based personalized learning path planning method (KG-PLPPM). By leveraging a knowledge graph and refining cognitive diagnosis models, the proposed method tailors learning paths to individual needs. It evaluates knowledge concept similarity and learner mastery, and employs an algorithm for path planning. In the experiments, two metrics—the concept sequence degree and learning efficiency—are used to assess our work. Experimental results demonstrate that the method presented enhances the coherence and relevance of recommended learning paths, and achieves a higher concept sequence degree, indicating that knowledge concepts are arranged in a manner consistent with the learning sequence, which aligns more closely with learners’ cognitive preferences. Moreover, across various learning progresses and path lengths, it effectively addresses weak knowledge areas, significantly enhancing learning efficiency. Full article

(This article belongs to the Special Issue Future Trends of Artificial Intelligence (AI) and Big Data)

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36 pages, 3115 KiB

Open AccessArticle

The Role of Artificial Intelligence and Big Data Analytics in Shaping the Future of Professions in Industry 6.0: Perspectives from an Emerging Market

by Delia Deliu and Andrei Olariu

Electronics 2024, 13(24), 4983; https://doi.org/10.3390/electronics13244983 - 18 Dec 2024

Cited by 3 | Viewed by 5350

Abstract

Digital technologies are fundamentally transforming professions by altering roles and redefining competencies across all sectors. The progression from computerization to digitization, digitalization, and now digital transformation has been driven by the widespread integration of artificial intelligence (AI) and big data analytics (BDA). Industry 4.0 introduced smart automation and connectivity, Industry 5.0 emphasized human–machine collaboration and personalization, and Industry 6.0 now integrates advanced technologies with sustainability and ethical considerations, exerting a profound influence on many professions. This transformation is especially significant in emerging markets, where AI and BDA are overhauling traditional practices and enhancing efficiency but also introducing new challenges. Focusing on the accounting profession, this paper examines AI’s and BDA’s dual impact on the roles and skill sets of professional accountants (PAs). Specifically, it addresses how these technologies shape the activities, interactions, roles, and competencies of PAs in an Industry 6.0 context, as well as the opportunities and challenges that arise. Given the public interest role of PAs in ensuring accuracy and transparency in financial reporting, understanding their perceptions and experiences of digital transformation is essential. The findings reveal that while AI and BDA drive efficiency gains and open strategic pathways, they also risk eroding core traditional accounting competencies, reducing client engagement, and raising ethical concerns such as data security and privacy—all of which can undermine service quality and, ultimately, public trust. These insights underscore the need for responsible AI and BDA integration, particularly in emerging markets, where digital literacy gaps and regulatory limitations may slow adoption. This study offers actionable recommendations for policymakers, educators, and organizations, highlighting the importance of ethical standards, targeted training, and sustainable practices to preserve the relevance and integrity of the accounting profession in an increasingly technology-driven era. Full article

(This article belongs to the Special Issue Future Trends of Artificial Intelligence (AI) and Big Data)

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18 pages, 1517 KiB

Open AccessArticle

Shaping Tomorrow: Anticipating Skills Requirements Based on the Integration of Artificial Intelligence in Business Organizations—A Foresight Analysis Using the Scenario Method

by Nicolae Bobitan, Diana Dumitrescu, Adriana Florina Popa, Daniela Nicoleta Sahlian and Ioan Codrut Turlea

Electronics 2024, 13(11), 2198; https://doi.org/10.3390/electronics13112198 - 4 Jun 2024

Cited by 4 | Viewed by 4036

Abstract

This study examines the impact of artificial intelligence (AI) on workforce skill requirements as AI becomes increasingly integrated into business operations. Using foresight analysis and scenario-based methods, we anticipate the necessary skills for future AI-integrated workplaces. A SWOT analysis evaluates three potential paths for AI adoption—gradual, aggressive, and selective—to project the evolving skills needed for employee success in changing business environments. The findings emphasize the critical need for both enhanced technical proficiency and soft skills, such as creative problem-solving and interpersonal abilities, across all AI adoption scenarios. The study highlights the importance of strategic reskilling and continuous learning to align employee skills with the new business paradigms shaped by AI. It provides a roadmap for businesses, educators, and policymakers to collaboratively develop a resilient and adaptable workforce for an AI-enhanced future. Full article

(This article belongs to the Special Issue Future Trends of Artificial Intelligence (AI) and Big Data)

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20 pages, 4069 KiB

Open AccessArticle

Industry 4.0 Transformation: Analysing the Impact of Artificial Intelligence on the Banking Sector through Bibliometric Trends

by Alina Georgiana Manta, Roxana Maria Bădîrcea, Nicoleta Mihaela Doran, Gabriela Badareu, Claudia Gherțescu and Jenica Popescu

Electronics 2024, 13(9), 1693; https://doi.org/10.3390/electronics13091693 - 27 Apr 2024

Cited by 13 | Viewed by 5929

Abstract

The importance of artificial intelligence in the banking industry is reflected in the speed at which financial institutions are adopting and implementing AI solutions to improve their services and adapt to new market demands. The aim of this research is to conduct a bibliometric analysis of the involvement of artificial intelligence in the banking sector to provide a comprehensive overview of the current state of research to guide future directions and support the sustainable development of this rapidly expanding field. Another important objective is to identify research gaps and underexplored areas in the field of artificial intelligence in banking. The methodology used is a bibliometric analysis using VOSviewer, analysing 1089 papers from the Web of Science database. The results of the study provide relevant information for banking professionals but also for policy makers. Thus, the study highlights key areas where banks are using artificial intelligence to gain competitive advantage, thereby guiding practitioners in strategic decision making. Moreover, by identifying emerging trends and patterns in AI adoption, the study helps banking practitioners with foresight, enabling them to anticipate and prepare for future developments in the field. In terms of governmental implications, the study can contribute to the development of more nuanced regulatory frameworks that effectively balance the promotion of AI innovation with the protection of ethical standards and consumer protection. Full article

(This article belongs to the Special Issue Future Trends of Artificial Intelligence (AI) and Big Data)

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15 pages, 1627 KiB

Open AccessArticle

Style-Guided Adversarial Teacher for Cross-Domain Object Detection

by Longfei Jia, Xianlong Tian, Yuguo Hu, Mengmeng Jing, Lin Zuo and Wen Li

Electronics 2024, 13(5), 862; https://doi.org/10.3390/electronics13050862 - 23 Feb 2024

Cited by 3 | Viewed by 1317

Abstract

The teacher–student framework is widely employed for cross-domain object detection. However, it suffers from two problems. One is that large distribution discrepancies will cause critical performance drops. The other is that the samples that deviate from the overall distributions of both domains will greatly mislead the model. To solve these problems, we propose a style-guided adversarial teacher (SGAT) method for domain adaptation. Specifically, on the domain level, we generate target-like images based on source images to effectively narrow the gaps between domains. On the sample level, we denoise samples by estimating the probability density ratio of the ‘target-style’ and target distributions, which could filter out the unrelated samples and highlight the related ones. In this way, we could guarantee reliable samples. With these reliable samples, we learn the domain-invariant features through teacher–student mutual learning and adversarial learning. Extensive experiments verify the effectiveness of our method. In particular, we achieve 52.9% mAP on Clipart1k and 42.7% on Comic2k, which are 6.4% and 5.0% higher than the compared baselines. Full article

(This article belongs to the Special Issue Future Trends of Artificial Intelligence (AI) and Big Data)

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22 pages, 5391 KiB

Open AccessArticle

Content Analysis Using Specific Natural Language Processing Methods for Big Data

by Mironela Pirnau, Mihai Alexandru Botezatu, Iustin Priescu, Alexandra Hosszu, Alexandru Tabusca, Cristina Coculescu and Ionica Oncioiu

Electronics 2024, 13(3), 584; https://doi.org/10.3390/electronics13030584 - 31 Jan 2024

Cited by 5 | Viewed by 3495

Abstract

Researchers from different fields have studied the effects of the COVID-19 pandemic and published their results in peer-reviewed journals indexed in international databases such as Web of Science (WoS), Scopus, PubMed. Focusing on efficient methods for navigating the extensive literature on COVID-19 pandemic research, our study conducts a content analysis of the top 1000 cited papers in WoS that delve into the subject by using elements of natural language processing (NLP). Knowing that in WoS, a scientific paper is described by the group Paper = {Abstract, Keyword, Title}; we obtained via NLP methods the word dictionaries with their frequencies of use and the word cloud for the 100 most used words, and we investigated if there is a degree of similarity between the titles of the papers and their abstracts, respectively. Using the Python packages NLTK, TextBlob, VADER, we computed sentiment scores for paper titles and abstracts, analyzed the results, and then, using Azure Machine Learning-Sentiment analysis, extended the range of comparison of sentiment scores. Our proposed analysis method can be applied to any research topic or theme from papers, articles, or projects in various fields of specialization to create a minimal dictionary of terms based on frequency of use, with visual representation by word cloud. Complementing the content analysis in our research with sentiment and similarity analysis highlights the different or similar treatment of the topics addressed in the research, as well as the opinions and feelings conveyed by the authors in relation to the researched issue. Full article

(This article belongs to the Special Issue Future Trends of Artificial Intelligence (AI) and Big Data)

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30 pages, 7237 KiB

Open AccessArticle

Distributed File System to Leverage Data Locality for Large-File Processing

by Erico Correia da Silva, Liria Matsumoto Sato and Edson Toshimi Midorikawa

Electronics 2024, 13(1), 106; https://doi.org/10.3390/electronics13010106 - 26 Dec 2023

Viewed by 2618

Abstract

Over the past decade, significant technological advancements have led to a substantial increase in data proliferation. Both scientific computation and Big Data workloads play a central role, manipulating massive data and challenging conventional high-performance computing architectures. Efficiently processing voluminous files using cost-effective hardware remains a persistent challenge, limiting access to new technologies for individuals and organizations capable of higher investments. In response to this challenge, AwareFS, a novel distributed file system, addresses the efficient reading and updating of large files by consistently exploiting data locality on every copy. Its distributed metadata and lock management facilitate sequential and random I/O patterns with minimal data movement over the network. The evaluation of the AwareFS local-write protocol demonstrated efficiency across various update patterns, resulting in a performance improvement of approximately 13%, while benchmark assessments conducted across diverse cluster sizes and configurations underscored the flexibility and scalability of AwareFS. The innovative distributed mechanisms outlined herein are positioned to contribute to the evolution of emerging technologies related to the computation of data stored in large files. Full article

(This article belongs to the Special Issue Future Trends of Artificial Intelligence (AI) and Big Data)

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15 pages, 2049 KiB

Open AccessEditor’s ChoiceArticle

A Multimodal Late Fusion Framework for Physiological Sensor and Audio-Signal-Based Stress Detection: An Experimental Study and Public Dataset

by Vasileios-Rafail Xefteris, Monica Dominguez, Jens Grivolla, Athina Tsanousa, Francesco Zaffanela, Martina Monego, Spyridon Symeonidis, Sotiris Diplaris, Leo Wanner, Stefanos Vrochidis and Ioannis Kompatsiaris

Electronics 2023, 12(23), 4871; https://doi.org/10.3390/electronics12234871 - 2 Dec 2023

Cited by 6 | Viewed by 3756

Abstract

Stress can be considered a mental/physiological reaction in conditions of high discomfort and challenging situations. The levels of stress can be reflected in both the physiological responses and speech signals of a person. Therefore the study of the fusion of the two modalities is of great interest. For this cause, public datasets are necessary so that the different proposed solutions can be comparable. In this work, a publicly available multimodal dataset for stress detection is introduced, including physiological signals and speech cues data. The physiological signals include electrocardiograph (ECG), respiration (RSP), and inertial measurement unit (IMU) sensors equipped in a smart vest. A data collection protocol was introduced to receive physiological and audio data based on alterations between well-known stressors and relaxation moments. Five subjects participated in the data collection, where both their physiological and audio signals were recorded by utilizing the developed smart vest and audio recording application. In addition, an analysis of the data and a decision-level fusion scheme is proposed. The analysis of physiological signals includes a massive feature extraction along with various fusion and feature selection methods. The audio analysis comprises a state-of-the-art feature extraction fed to a classifier to predict stress levels. Results from the analysis of audio and physiological signals are fused at a decision level for the final stress level detection, utilizing a machine learning algorithm. The whole framework was also tested in a real-life pilot scenario of disaster management, where users were acting as first responders while their stress was monitored in real time. Full article

(This article belongs to the Special Issue Future Trends of Artificial Intelligence (AI) and Big Data)

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19 pages, 9538 KiB

Open AccessArticle

Utilising Artificial Intelligence to Turn Reviews into Business Enhancements through Sentiment Analysis

by Eliza Nichifor, Gabriel Brătucu, Ioana Bianca Chițu, Dana Adriana Lupșa-Tătaru, Eduard Mihai Chișinău, Raluca Dania Todor, Ruxandra-Gabriela Albu and Simona Bălășescu

Electronics 2023, 12(21), 4538; https://doi.org/10.3390/electronics12214538 - 4 Nov 2023

Cited by 3 | Viewed by 3581

Abstract

The use of sentiment analysis methodology has become crucial for e-commerce enterprises in order to optimise their marketing tactics. In the present setting, the authors strive to demonstrate the ethical and efficient use of artificial intelligence in the realm of business. The researchers used qualitative research methodologies to analyse a total of 1687 evaluations obtained from 85 online retailers associated with electronic commerce Europe Trustmark. These stores were linked with 18 different nations and operated over 14 distinct domains. The investigation used the combined power of natural language processing and machine learning, implemented via a Software-as-a-Service (SaaS) platform. The results of the study indicate that consumers often exhibit a neutral emotional tone while leaving one-star ratings. Although the influence of unfavourable evaluations is generally limited, it highlights the need for more attentiveness in their management. The extent to which users interact with goods and services has a substantial impact on the probability of publishing reviews, regardless of whether the encountered experience is unpleasant or favourable. The authors urge for the acquisition of tools and skills in order to boost the efficiency of managers and experts in parallel with expanding technological landscapes, with a particular emphasis on the utilisation of artificial intelligence for sentiment analysis. Full article

(This article belongs to the Special Issue Future Trends of Artificial Intelligence (AI) and Big Data)

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