Submit to Special Issue Submit Abstract to Special Issue Review for Information Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Editorial Board Members’ Collection Series: "Information Processes"

Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 31655

Share This Special Issue

Special Issue Editors

Prof. Emilio Matricciani

E-Mail Website
Guest Editor

Department of Electronics, Information, and Bioengineering, Politecnico di Milano, 20133 Milan, Italy
Interests: satellite and deep space telecommunications; radio propagation; information theory; mathematics of alphabetical texts
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Heming Jia

E-Mail Website
Guest Editor

School of Information Engineering, Sanming University, Sanming, China
Interests: Remora Optimization Algorithm (ROA); Crayfish Optimization Algorithm (COA); Catch Fish Optimization Algorithm (CFOA); bio-inspired computing; nature-inspired computing; swarm intelligence; artificial intelligence; meta-heuristic modeling and optimization algorithms; evolutionary computations; multilevel image segmentation; feature selection; combinatorial problems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Zhigang Chu

E-Mail Website
Guest Editor

College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
Interests: array signal processing; analysis and control on sound and vibration; mechanical systems and signal processing; com-pressive sensing; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Information Processes publishes cutting-edge original research on methods or applications pertaining to a range of areas including, but not limited to, advertising, business, engineering, health, information science, IT marketing, and social computing.

Specifically, there are four types of manuscripts that the Editorial Board Members’ Collection Series is interested in:

The intersection of computing, engineering, and information science;
The application of new methods at the intersection of computer, engineering, and information science;
The critical and in-depth intersection of computer, engineering, and information science, providing information on integration of the prior research, and recommendations for further work in the multidisciplinary area;
Systems design research involving the intersection of computer, engineering, and information science.

Prof. Dr. Emilio Matricciani
Prof. Dr. Heming Jia
Prof. Dr. Zhigang Chu
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. Information 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

knowledge management
social media and social networks
big data and cloud computing
artificial intelligence
Internet of Things/Internet of Everything
digital signal processing
data mining
information extraction
human–machine interface
information in society and social development
business process management
blockchain and emerging technologies
communication systems and networks
wireless sensor network
mobile communication services

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (10 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

18 pages, 1369 KiB

Open AccessArticle

A Hexagon Sensor and A Layer-Based Conversion Method for Hexagon Clusters

by Jun-Ho Kim and Hanul Sung

Information 2024, 15(12), 747; https://doi.org/10.3390/info15120747 - 22 Nov 2024

Viewed by 263

Abstract

In reinforcement learning (RL), precise observations are crucial for agents to learn the optimal policy from their environment. While Unity ML-Agents offers various sensor components for automatically adjusting the observations, it does not support hexagon clusters—a common feature in strategy games due to their advantageous geometric properties. As a result, users can attempt to utilize the existing sensors to observe hexagon clusters but encounter significant limitations. To address this issue, we propose a hexagon sensor and a layer-based conversion method that enable users to observe hexagon clusters with ease. By organizing the hexagon cells into structured layers, our approach ensures efficient handling of observation and spatial coherence. We provide flexible adaptation to varying observation sizes, which enables the creation of diverse strategic map designs. Our evaluations demonstrate that the hexagon sensor, combined with the layer-based conversion method, achieves a learning speed up to 1.4 times faster and yields up to twice the rewards compared to conventional sensors. Additionally, the inference performance is improved by up to 1.5 times, further validating the effectiveness of our approach. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: "Information Processes")

27 pages, 1797 KiB

Open AccessArticle

Assessment of Customers’ Evaluations of Service Quality in Live-Streaming Commerce: Conceptualizing and Testing a Multidimensional and Hierarchical Model

by Chaang-Iuan Ho, Yaoyu Liu and Ming-Chih Chen

Information 2024, 15(9), 510; https://doi.org/10.3390/info15090510 - 23 Aug 2024

Viewed by 817

Abstract

Live-streaming commerce (LSC) is a new shopping method that combines the characteristics of social commerce and e-commerce. Since the global coronavirus disease 2019 (COVID-19) outbreak, the number of branded platforms is growing rapidly, and their competition is fiercer than ever. Understanding consumer needs and improving service quality have become the key issues for survival. This study aims to develop and empirically validate a multidimensional hierarchical model for measuring service quality on LSC platforms. A hierarchical reflective construct was proposed to capture dimensions based on the literature on e-retail and social commerce service quality. The proposed model was rigorously tested using two waves of survey data through the partial least squares method. Results showed that the service quality of LSC is a third-order, reflective construct and includes five primary dimensions (the streamer’s interaction quality, physical environment, website quality, outcome quality, and ordering process) and twelve sub-dimensions (trustworthiness, expertise, responsiveness, telepresence, consumption scenarios, information quality, system operation quality, fulfillment and refund/compensation, privacy/security, contact, and ease of use). Findings also supported the hypothesis that service quality has a significant impact on customers’ satisfaction and their behavioral intentions. Furthermore, we tested an alternative model, and the results showed that the relationship between dimensions and overall assessment is reflective rather than formative. We offered directions for further research on LSC service quality and discussed managerial implications stemming from the empirical findings. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: "Information Processes")

► Show Figures

Figure 1

19 pages, 1050 KiB

Open AccessArticle

Enhancing Biomedical Question Answering with Large Language Models

by Hua Yang, Shilong Li and Teresa Gonçalves

Information 2024, 15(8), 494; https://doi.org/10.3390/info15080494 - 19 Aug 2024

Viewed by 1261

Abstract

In the field of Information Retrieval, biomedical question answering is a specialized task that focuses on answering questions related to medical and healthcare domains. The goal is to provide accurate and relevant answers to the posed queries related to medical conditions, treatments, procedures, medications, and other healthcare-related topics. Well-designed models should efficiently retrieve relevant passages. Early retrieval models can quickly retrieve passages but often with low precision. In contrast, recently developed Large Language Models can retrieve documents with high precision but at a slower pace. To tackle this issue, we propose a two-stage retrieval approach that initially utilizes BM25 for a preliminary search to identify potential candidate documents; subsequently, a Large Language Model is fine-tuned to evaluate the relevance of query–document pairs. Experimental results indicate that our approach achieves comparative performances on the BioASQ and the TREC-COVID datasets. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: "Information Processes")

► Show Figures

Figure 1

20 pages, 5600 KiB

Open AccessArticle

Spatial Analysis of Advanced Air Mobility in Rural Healthcare Logistics

by Raj Bridgelall

Information 2024, 15(7), 397; https://doi.org/10.3390/info15070397 - 10 Jul 2024

Viewed by 1129

Abstract

The transportation of patients in emergency medical situations, particularly in rural areas, often faces significant challenges due to long travel distances and limited access to healthcare facilities. These challenges can result in critical delays in medical care, adversely affecting patient outcomes. Addressing this issue is essential for improving survival rates and health outcomes in underserved regions. This study explored the potential of advanced air mobility to enhance emergency medical services by reducing patient transport times through the strategic placement of vertiports. Using North Dakota as a case study, the research developed a GIS-based optimization workflow to identify optimal vertiport locations that maximize time savings. The study highlighted the benefits of strategic vertiport placement at existing airports and hospital heliports to minimize community disruption and leverage underutilized infrastructure. A key finding was that the optimized mixed-mode routes could reduce patient transport times by up to 21.8 min compared with drive-only routes, significantly impacting emergency response efficiency. Additionally, the study revealed that more than 45% of the populated areas experienced reduced ground travel times due to the integration of vertiports, highlighting the strategic importance of vertiport placement in optimizing emergency medical services. The research also demonstrated the replicability of the GIS-based optimization model for other regions, offering valuable insights for policymakers and stakeholders in enhancing EMS through advanced air mobility solutions. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: "Information Processes")

► Show Figures

Figure 1

14 pages, 1202 KiB

Open AccessArticle

The Impact of Operant Resources on the Task Performance of Learners via Knowledge Management Process

by Quoc Trung Pham, Canh Khiem Le, Dinh Thai Linh Huynh and Sanjay Misra

Information 2024, 15(6), 338; https://doi.org/10.3390/info15060338 - 7 Jun 2024

Viewed by 1375

Abstract

In human resource management, training is considered one of the most effective ways to improve employees’ task performance. However, the effectiveness of training depends mostly on the resources and effort of learners, especially the operant resources. This study investigates the influence of operant resources on individual task performance within the framework of knowledge management. Building on existing research, a quantitative model was developed and tested using data from 296 Vietnamese managers and senior employees. Data analysis employed SPSS 21 and AMOS 24 software. The findings provide strong support for all nine proposed hypotheses, demonstrating a positive impact of operant resources on both learner behavior and subsequent task performance. The research highlights the significant role of individual operant resources in enhancing learning outcomes and employee effectiveness. Managerial implications are derived from these results, offering practical guidance for businesses to improve training activities and ultimately boost employee task performance. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: "Information Processes")

► Show Figures

Figure 1

12 pages, 4072 KiB

Open AccessArticle

Early Parkinson’s Disease Diagnosis through Hand-Drawn Spiral and Wave Analysis Using Deep Learning Techniques

by Yingcong Huang, Kunal Chaturvedi, Al-Akhir Nayan, Mohammad Hesam Hesamian, Ali Braytee and Mukesh Prasad

Information 2024, 15(4), 220; https://doi.org/10.3390/info15040220 - 13 Apr 2024

Cited by 1 | Viewed by 3610

Abstract

Parkinson’s disease (PD) is a chronic brain disorder affecting millions worldwide. It occurs when brain cells that produce dopamine, a chemical controlling movement, die or become damaged. This leads to PD, which causes problems with movement, balance, and posture. Early detection is crucial to slow its progression and improve the quality of life for PD patients. This paper proposes a handwriting-based prediction approach combining a cosine annealing scheduler with deep transfer learning. It utilizes the NIATS dataset, which contains handwriting samples from individuals with and without PD, to evaluate six different models: VGG16, VGG19, ResNet18, ResNet50, ResNet101, and Vit. This paper compares the performance of these models based on three metrics: accuracy, precision, and F1 score. The results showed that the VGG19 model, combined with the proposed method, achieved the highest average accuracy of 96.67%. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: "Information Processes")

► Show Figures

Figure 1

17 pages, 1071 KiB

Open AccessArticle

Leveraging the TOE Framework: Examining the Potential of Mobile Health (mHealth) to Mitigate Health Inequalities

by Salman Bin Naeem, Mehreen Azam, Maged N. Kamel Boulos and Rubina Bhatti

Information 2024, 15(4), 176; https://doi.org/10.3390/info15040176 - 23 Mar 2024

Viewed by 2883

Abstract

(1) Aims and Objectives: Mobile health (mHealth) is increasingly becoming a favorite healthcare delivery solution in underserved areas around the globe. This study aims to identify the influence of technology–organization–environment (TOE) factors on mHealth adoption and to assess the influence of mHealth on the reduction in health disparities in the context of healthcare delivery in low-resource settings. (2) Methods: A cross-sectional survey of physicians and nurses was carried out at six hospitals in the public and private health sectors in Pakistan. The survey’s theoretical foundation is based on the technology–organization–environment (TOE) framework. TOE constructs (relative advantage, compatibility, management support, organizational readiness, external support, and government regulations) were used to develop hypotheses. The hypotheses were tested using structural equation modeling (SEM). (3) Results: Findings from this study show that management support and external support are the two main predictors of mHealth adoption among healthcare professionals. The study proposes an mHealth adoption model that can significantly contribute towards improving medical outcomes, reducing inefficiencies, expanding access, lowering costs, raising quality, making medicine more personalized for patients, and gaining advantages from mHealth solutions in order to reduce health disparities. (4) Conclusion: The study suggests that there is no single approach that could support mHealth adoption. Instead, a holistic approach is required that considers cultural, economic, technological, organizational, and environmental factors for successful mHealth adoption in low-resource settings. Our proposed mHealth model offers guidance to policymakers, health organizations, governments, and political leaders to make informed decisions regarding mHealth implementation plans. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: "Information Processes")

► Show Figures

Figure 1

27 pages, 9431 KiB

Open AccessArticle

Generative Pre-Trained Transformer (GPT) in Research: A Systematic Review on Data Augmentation

by Fahim Sufi

Information 2024, 15(2), 99; https://doi.org/10.3390/info15020099 - 8 Feb 2024

Cited by 15 | Viewed by 14723

Abstract

GPT (Generative Pre-trained Transformer) represents advanced language models that have significantly reshaped the academic writing landscape. These sophisticated language models offer invaluable support throughout all phases of research work, facilitating idea generation, enhancing drafting processes, and overcoming challenges like writer’s block. Their capabilities extend beyond conventional applications, contributing to critical analysis, data augmentation, and research design, thereby elevating the efficiency and quality of scholarly endeavors. Strategically narrowing its focus, this review explores alternative dimensions of GPT and LLM applications, specifically data augmentation and the generation of synthetic data for research. Employing a meticulous examination of 412 scholarly works, it distills a selection of 77 contributions addressing three critical research questions: (1) GPT on Generating Research data, (2) GPT on Data Analysis, and (3) GPT on Research Design. The systematic literature review adeptly highlights the central focus on data augmentation, encapsulating 48 pertinent scholarly contributions, and extends to the proactive role of GPT in critical analysis of research data and shaping research design. Pioneering a comprehensive classification framework for “GPT’s use on Research Data”, the study classifies existing literature into six categories and 14 sub-categories, providing profound insights into the multifaceted applications of GPT in research data. This study meticulously compares 54 pieces of literature, evaluating research domains, methodologies, and advantages and disadvantages, providing scholars with profound insights crucial for the seamless integration of GPT across diverse phases of their scholarly pursuits. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: "Information Processes")

► Show Figures

Figure 1

28 pages, 5371 KiB

Open AccessArticle

Linguistic Communication Channels Reveal Connections between Texts: The New Testament and Greek Literature

by Emilio Matricciani

Information 2023, 14(7), 405; https://doi.org/10.3390/info14070405 - 14 Jul 2023

Cited by 1 | Viewed by 1505

Abstract

We studied two fundamental linguistic channels—the sentences and the interpunctions channels—and showed they can reveal deeper connections between texts. The applied theory does not follow the actual paradigm of linguistic studies. As a study case, we considered the Greek New Testament, with the purpose of determining mathematical connections between its texts and possible differences in the writing style (mathematically defined) of the writers and in the reading skill required of their readers. The analysis was based on deep-language parameters and communication/information theory. To set the New Testament texts in the larger Greek classical literature, we considered texts written by Aesop, Polybius, Flavius Josephus, and Plutarch. The results largely confirmed what scholars have found about the New Testament texts, therefore giving credibility to the theory. The Gospel according to John is very similar to the fables written by Aesop. Surprisingly, the Epistle to the Hebrews and Apocalypse are each other’s “photocopies” in the two linguistic channels and not linked to all other texts. These two texts deserve further study by historians of the early Christian church literature at the level of meaning, readers, and possible Old Testament texts that might have influenced them. The theory can guide scholars to study any literary corpus. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: "Information Processes")

► Show Figures

Figure 1

Review

Jump to: Research

25 pages, 1829 KiB

Open AccessReview

Privacy-Preserving Techniques in Generative AI and Large Language Models: A Narrative Review

by Georgios Feretzakis, Konstantinos Papaspyridis, Aris Gkoulalas-Divanis and Vassilios S. Verykios

Information 2024, 15(11), 697; https://doi.org/10.3390/info15110697 - 4 Nov 2024

Viewed by 2570

Abstract

Generative AI, including large language models (LLMs), has transformed the paradigm of data generation and creative content, but this progress raises critical privacy concerns, especially when models are trained on sensitive data. This review provides a comprehensive overview of privacy-preserving techniques aimed at safeguarding data privacy in generative AI, such as differential privacy (DP), federated learning (FL), homomorphic encryption (HE), and secure multi-party computation (SMPC). These techniques mitigate risks like model inversion, data leakage, and membership inference attacks, which are particularly relevant to LLMs. Additionally, the review explores emerging solutions, including privacy-enhancing technologies and post-quantum cryptography, as future directions for enhancing privacy in generative AI systems. Recognizing that achieving absolute privacy is mathematically impossible, the review emphasizes the necessity of aligning technical safeguards with legal and regulatory frameworks to ensure compliance with data protection laws. By discussing the ethical and legal implications of privacy risks in generative AI, the review underscores the need for a balanced approach that considers performance, scalability, and privacy preservation. The findings highlight the need for ongoing research and innovation to develop privacy-preserving techniques that keep pace with the scaling of generative AI, especially in large language models, while adhering to regulatory and ethical standards. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: "Information Processes")

► Show Figures

Journal Menu

Journal Browser

Editorial Board Members’ Collection Series: "Information Processes"

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (10 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI