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Information
Special Issues
Feature Papers in Information in 2024–2025
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Feature Papers in Information in 2024–2025

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

Deadline for manuscript submissions: 1 December 2024 | Viewed by 1171

Special Issue Editor

Prof. Dr. Willy Susilo

E-Mail Website
Guest Editor
School of Computer Science and Software Engineering, University of Wollongong, Northfields Avenue, Wollongong, NSW 2522, Australia
Interests: cryptography; computer security; design of signature schemes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As Editor-in-Chief of Information, we are pleased to announce the Special Issue entitled "Feature Papers in Information in 2023–2024". This Special Issue will collect high-quality papers from Editorial Board Members and leading researchers invited by the Editorial Office. Both original research articles and comprehensive review papers are welcome. All topics related to information technologies in various fields and applications are welcome.

Prof. Dr. Willy Susilo
Guest Editor

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

  • information theory and methodology
  • information intelligence
  • information processes
  • information applications
  • information and communications technology

Related Special Issue

Published Papers (2 papers)

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Research

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16 pages, 2490 KiB  
Article
Constructing Semantic Summaries Using Embeddings
by Georgia Eirini Trouli, Nikos Papadakis and Haridimos Kondylakis
Information 2024, 15(4), 238; https://doi.org/10.3390/info15040238 - 20 Apr 2024
Viewed by 749
Abstract
The increase in the size and complexity of large knowledge graphs now available online has resulted in the emergence of many approaches focusing on enabling the quick exploration of the content of those data sources. Structural non-quotient semantic summaries have been proposed in [...] Read more.
The increase in the size and complexity of large knowledge graphs now available online has resulted in the emergence of many approaches focusing on enabling the quick exploration of the content of those data sources. Structural non-quotient semantic summaries have been proposed in this direction that involve first selecting the most important nodes and then linking them, trying to extract the most useful subgraph out of the original graph. However, the current state of the art systems use costly centrality measures for identifying the most important nodes, whereas even costlier procedures have been devised for linking the selected nodes. In this paper, we address both those deficiencies by first exploiting embeddings for node selection, and then by meticulously selecting approximate algorithms for node linking. Experiments performed over two real-world big KGs demonstrate that the summaries constructed using our method enjoy better quality. Specifically, the coverage scores obtained were 0.8, 0.81, and 0.81 for DBpedia v3.9 and 0.94 for Wikidata dump 2018, across 20%, 25%, and 30% summary sizes, respectively. Additionally, our method can compute orders of magnitude faster than the state of the art. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2024–2025)
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Review

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23 pages, 471 KiB  
Review
Machine Learning for Smart Irrigation in Agriculture: How Far along Are We?
by Marco Del-Coco, Marco Leo and Pierluigi Carcagnì
Information 2024, 15(6), 306; https://doi.org/10.3390/info15060306 - 24 May 2024
Viewed by 226
Abstract
The management of water resources is becoming increasingly important in several contexts, including agriculture. Recently, innovative agricultural practices, advanced sensors, and Internet of Things (IoT) devices have made it possible to improve the efficiency of water use. However, it is the application of [...] Read more.
The management of water resources is becoming increasingly important in several contexts, including agriculture. Recently, innovative agricultural practices, advanced sensors, and Internet of Things (IoT) devices have made it possible to improve the efficiency of water use. However, it is the application of control strategies based on advanced machine learning techniques that enables the adoption of smart irrigation scheduling and the immediate economic, social, and environmental benefits. This challenging research area has attracted the attention of many researchers worldwide, who have proposed several technological and methodological solutions. Unfortunately, the results of these scientific efforts have not yet been categorized in a thematic survey, making it difficult to understand how far we are from optimal water management based on machine learning. This paper fills this gap by focusing on smart irrigation systems with an emphasis on machine learning. More specifically, the generic structure of a smart agriculture system is presented, and existing machine learning strategies and available datasets are discussed. Furthermore, several open issues are identified, especially in the processing of long-term data, also due to the lack of corresponding annotated datasets. Finally, some interesting future research directions to be pursued in order to build scalable, domain-independent approaches are proposed. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2024–2025)
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