Future Trends of Artificial Intelligence (AI) and Big Data

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

Deadline for manuscript submissions: 20 November 2024 | Viewed by 6125

Special Issue Editors


E-Mail Website
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

E-Mail Website
Guest Editor

E-Mail Website
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

E-Mail Website
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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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

  • 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

Published Papers (5 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

15 pages, 1627 KiB  
Article
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 1 | Viewed by 402
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 [...] Read more.
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)
Show Figures

Figure 1

22 pages, 5391 KiB  
Article
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
Viewed by 975
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 [...] Read more.
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)
Show Figures

Figure 1

30 pages, 7237 KiB  
Article
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 670
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 [...] Read more.
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)
Show Figures

Figure 1

15 pages, 2049 KiB  
Article
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 - 02 Dec 2023
Cited by 1 | Viewed by 1300
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 [...] Read more.
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)
Show Figures

Figure 1

19 pages, 9538 KiB  
Article
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 - 04 Nov 2023
Viewed by 1640
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 [...] Read more.
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)
Show Figures

Figure 1

Back to TopTop