Topical Collection "Featured Reviews of Algorithms"
A topical collection in Algorithms (ISSN 1999-4893).
Viewed by 9753
Complex System and Computational Intelligence Laboratory, TaiYuan University of Science and Technology, Taiyuan 030024, China
Interests: feature extraction; feedforward neural nets; image classification; invasive software; learning (artificial intelligence)
Topical Collection Information
This Topical Collection aims to collect high-quality review papers that highlight the most recent advances on the theory, design, analysis, and implementation of algorithms. We encourage researchers from related fields within the journal’s scope to contribute review papers highlighting the latest developments in their research fields. Reviews provide comprehensive in-depth overviews with no length restrictions, whereas topical reviews focus on a concise and precise “at-a-glance” summary of novel developments in the field. Moreover, the viewpoint of experts should guide the readers through the vast literature and indicate the right direction along which research should move in the near future.
Prof. Dr. Arun Kumar Sangaiah
Dr. Xingjuan Cai
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 collection 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.
- algorithms in biology, chemistry, physics
- approximation algorithms
- combinatorial optimization
- mathematical programming
- operations research
- discrete mathematics and graph theory
- computational geometry
- differential equations
- image processing with applications
- machine learning
- Markov chains and simulation
- metaheuristics and matheuristics
- numerical analysis
- parametrized algorithms
- production planning, scheduling, transport, and timetabling
- quantum algorithms
Published Papers (3 papers)
Investigating Routing in the VANET Network: Review and Classification of Approaches
Cited by 3
| Viewed by 989
Vehicular Ad Hoc Network (VANETs) need methods to control traffic caused by a high volume of traffic during day and night, the interaction of vehicles, and pedestrians, vehicle collisions, increasing travel delays, and energy issues. Routing is one of the most critical problems
[...] Read more.
Vehicular Ad Hoc Network (VANETs) need methods to control traffic caused by a high volume of traffic during day and night, the interaction of vehicles, and pedestrians, vehicle collisions, increasing travel delays, and energy issues. Routing is one of the most critical problems in VANET. One of the machine learning categories is reinforcement learning (RL), which uses RL algorithms to find a more optimal path. According to the feedback they get from the environment, these methods can affect the system through learning from previous actions and reactions. This paper provides a comprehensive review of various methods such as reinforcement learning, deep reinforcement learning, and fuzzy learning in the traffic network, to obtain the best method for finding optimal routing in the VANET network. In fact, this paper deals with the advantages, disadvantages and performance of the methods introduced. Finally, we categorize the investigated methods and suggest the proper performance of each of them.
Algorithms in Low-Code-No-Code for Research Applications: A Practical Review
Cited by 14
| Viewed by 5408
Algorithms have evolved from machine code to low-code-no-code (LCNC) in the past 20 years. Observing the growth of LCNC-based algorithm development, the CEO of GitHub mentioned that the future of coding is no coding at all. This paper systematically reviewed several of the
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Algorithms have evolved from machine code to low-code-no-code (LCNC) in the past 20 years. Observing the growth of LCNC-based algorithm development, the CEO of GitHub mentioned that the future of coding is no coding at all. This paper systematically reviewed several of the recent studies using mainstream LCNC platforms to understand the area of research, the LCNC platforms used within these studies, and the features of LCNC used for solving individual research questions. We identified 23 research works using LCNC platforms, such as SetXRM, the vf-OS platform, Aure-BPM, CRISP-DM, and Microsoft Power Platform (MPP). About 61% of these existing studies resorted to MPP as their primary choice. The critical research problems solved by these research works were within the area of global news analysis, social media analysis, landslides, tornadoes, COVID-19, digitization of process, manufacturing, logistics, and software/app development. The main reasons identified for solving research problems with LCNC algorithms were as follows: (1) obtaining research data from multiple sources in complete automation; (2) generating artificial intelligence-driven insights without having to manually code them. In the course of describing this review, this paper also demonstrates a practical approach to implement a cyber-attack monitoring algorithm with the most popular LCNC platform.
A Synergic Approach of Deep Learning towards Digital Additive Manufacturing: A Review
Cited by 3
| Viewed by 2606
Deep learning and additive manufacturing have progressed together in the previous couple of decades. Despite being one of the most promising technologies, they have several flaws that a collaborative effort may address. However, digital manufacturing has established itself in the current industrial revolution
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Deep learning and additive manufacturing have progressed together in the previous couple of decades. Despite being one of the most promising technologies, they have several flaws that a collaborative effort may address. However, digital manufacturing has established itself in the current industrial revolution and it has slowed down quality control and inspection due to the different defects linked with it. Industry 4.0, the most recent industrial revolution, emphasizes the integration of intelligent production systems and current information technologies. As a result, deep learning has received a lot of attention and has been shown to be quite effective at understanding image data. This review aims to provide a cutting-edge deep learning application of the AM approach and application. This article also addresses the current issues of data privacy and security and potential solutions to provide a more significant dimension to future studies.