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Algorithms
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1st Online Conference on Algorithms (IOCA2021)
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1st Online Conference on Algorithms (IOCA2021)

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 14882

Special Issue Editor

Prof. Dr. Frank Werner

E-Mail Website
Guest Editor
Faculty of Mathematics, Otto-von-Guericke-University, P.O. Box 4120, D-39016 Magdeburg, Germany
Interests: scheduling, in particular development of exact and approximate algorithms; stability investigations is discrete optimization; scheduling with interval processing times; complexity investigations for scheduling problems; train scheduling; graph theory; logistics; supply chains; packing; simulation and applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is my pleasure to invite you to participate in the 1st International Online Conference on Algorithms (IOCA2021). Since the global pandemic has enforced us to reduce our mobility, this event will occur completely online at https://ioca2021.sciforum.net/ from September 27 to October 10, 2021.

IOCA2021 aims to promote and advance the rapidly growing field of all disciplines of the development of algorithms, where both theoretical works and applications are welcome. We plan to bring together both researchers and practitioners working in the area of the design and analysis of algorithms and to present their newest results. Subjects of interest include, but are not limited to:

  • Databases  and Data Structures
  • Combinatorial Optimization, Graph, and Network Algorithms
  • Evolutionary Algorithms and Machine Learning
  • Parallel and Distributed Algorithms
  • Randomized, Online, and Approximation Algorithms
  • Analysis of Algorithms and Complexity Theory
  • Algorithms for Multidisciplinary Applications

Submitted abstracts will be reviewed by the conference committee. The authors of accepted contributions are invited to prepare an extended abstract for the conference proceedings as well as a slide presentation of their work. IOCA2021 will make your presentation accessible to hundreds of researchers worldwide. Participants will also be invited to submit an extended version for publication after the conference in the journal Algorithms, published by MDPI.

I am looking forward to receiving many submissions of your recent work in the field of algorithms and hope that you will join us in this exciting new form of a conference.

 

Prof. Dr. Frank Werner
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. 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.

Published Papers (6 papers)

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Editorial

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2 pages, 156 KiB  
Editorial
Special Issue “1st Online Conference on Algorithms (IOCA2021)”
by Frank Werner
Algorithms 2022, 15(11), 411; https://0-doi-org.brum.beds.ac.uk/10.3390/a15110411 - 04 Nov 2022
Viewed by 943
Abstract
This Special Issue of Algorithms is dedicated to the 1st Online Conference on Algorithms (IOCA 2021), which was held completely online from 27 September to 10 October 2021 [...] Full article
(This article belongs to the Special Issue 1st Online Conference on Algorithms (IOCA2021))

Research

Jump to: Editorial

24 pages, 11828 KiB  
Article
Classification of the Structural Behavior of Tall Buildings with a Diagrid Structure: A Machine Learning-Based Approach
by Pooyan Kazemi, Aldo Ghisi and Stefano Mariani
Algorithms 2022, 15(10), 349; https://0-doi-org.brum.beds.ac.uk/10.3390/a15100349 - 27 Sep 2022
Cited by 7 | Viewed by 2118
Abstract
We study the relationship between the architectural form of tall buildings and their structural response to a conventional seismic load. A series of models are generated by varying the top and bottom plan geometries of the buildings, and a steel diagrid structure is [...] Read more.
We study the relationship between the architectural form of tall buildings and their structural response to a conventional seismic load. A series of models are generated by varying the top and bottom plan geometries of the buildings, and a steel diagrid structure is mapped onto their skin. A supervised machine learning approach is then adopted to learn the features of the aforementioned relationship. Six different classifiers, namely k-nearest neighbour, support vector machine, decision tree, ensemble method, discriminant analysis, and naive Bayes, are adopted to this aim, targeting the structural response as the building drift, i.e., the lateral displacement at its top under the considered external excitation. By focusing on the classification of the structural response, it is shown that some classifiers, like, e.g., decision tree, k-nearest neighbour and the ensemble method, can learn well the structural behavior, and can therefore help design teams to select more efficient structural solutions. Full article
(This article belongs to the Special Issue 1st Online Conference on Algorithms (IOCA2021))
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19 pages, 2550 KiB  
Article
MedicalSeg: A Medical GUI Application for Image Segmentation Management
by Christian Mata, Josep Munuera, Alain Lalande, Gilberto Ochoa-Ruiz and Raul Benitez
Algorithms 2022, 15(6), 200; https://0-doi-org.brum.beds.ac.uk/10.3390/a15060200 - 08 Jun 2022
Cited by 6 | Viewed by 2579
Abstract
In the field of medical imaging, the division of an image into meaningful structures using image segmentation is an essential step for pre-processing analysis. Many studies have been carried out to solve the general problem of the evaluation of image segmentation results. One [...] Read more.
In the field of medical imaging, the division of an image into meaningful structures using image segmentation is an essential step for pre-processing analysis. Many studies have been carried out to solve the general problem of the evaluation of image segmentation results. One of the main focuses in the computer vision field is based on artificial intelligence algorithms for segmentation and classification, including machine learning and deep learning approaches. The main drawback of supervised segmentation approaches is that a large dataset of ground truth validated by medical experts is required. In this sense, many research groups have developed their segmentation approaches according to their specific needs. However, a generalised application aimed at visualizing, assessing and comparing the results of different methods facilitating the generation of a ground-truth repository is not found in recent literature. In this paper, a new graphical user interface application (MedicalSeg) for the management of medical imaging based on pre-processing and segmentation is presented. The objective is twofold, first to create a test platform for comparing segmentation approaches, and secondly to generate segmented images to create ground truths that can then be used for future purposes as artificial intelligence tools. An experimental demonstration and performance analysis discussion are presented in this paper. Full article
(This article belongs to the Special Issue 1st Online Conference on Algorithms (IOCA2021))
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15 pages, 3884 KiB  
Article
An Emotion and Attention Recognition System to Classify the Level of Engagement to a Video Conversation by Participants in Real Time Using Machine Learning Models and Utilizing a Neural Accelerator Chip
by Janith Kodithuwakku, Dilki Dandeniya Arachchi and Jay Rajasekera
Algorithms 2022, 15(5), 150; https://0-doi-org.brum.beds.ac.uk/10.3390/a15050150 - 27 Apr 2022
Cited by 4 | Viewed by 4154
Abstract
It is not an easy task for organizers to observe the engagement level of a video meeting audience. This research was conducted to build an intelligent system to enhance the experience of video conversations such as virtual meetings and online classrooms using convolutional [...] Read more.
It is not an easy task for organizers to observe the engagement level of a video meeting audience. This research was conducted to build an intelligent system to enhance the experience of video conversations such as virtual meetings and online classrooms using convolutional neural network (CNN)- and support vector machine (SVM)-based machine learning models to classify the emotional states and the attention level of the participants to a video conversation. This application visualizes their attention and emotion analytics in a meaningful manner. This proposed system provides an artificial intelligence (AI)-powered analytics system with optimized machine learning models to monitor the audience and prepare insightful reports on the basis of participants’ facial features throughout the video conversation. One of the main objectives of this research is to utilize the neural accelerator chip to enhance emotion and attention detection tasks. A custom CNN developed by Gyrfalcon Technology Inc (GTI) named GnetDet was used in this system to run the trained model on their GTI Lightspeeur 2803 neural accelerator chip. Full article
(This article belongs to the Special Issue 1st Online Conference on Algorithms (IOCA2021))
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16 pages, 1615 KiB  
Article
Mechanical Fault Prognosis through Spectral Analysis of Vibration Signals
by Kang Wang, Zhi-Jiang Xu, Yi Gong and Ke-Lin Du
Algorithms 2022, 15(3), 94; https://0-doi-org.brum.beds.ac.uk/10.3390/a15030094 - 15 Mar 2022
Cited by 1 | Viewed by 2450
Abstract
Vibration signal analysis is the most common technique used for mechanical vibration monitoring. By using vibration sensors, the fault prognosis of rotating machinery provides a way to detect possible machine damage at an early stage and prevent property losses by taking appropriate measures. [...] Read more.
Vibration signal analysis is the most common technique used for mechanical vibration monitoring. By using vibration sensors, the fault prognosis of rotating machinery provides a way to detect possible machine damage at an early stage and prevent property losses by taking appropriate measures. We first propose a digital integrator in frequency domain by combining fast Fourier transform with digital filtering. The velocity and displacement signals are, respectively, obtained from an acceleration signal by means of two digital integrators. We then propose a fast method for the calculation of the envelope spectra and instantaneous frequency by using the spectral properties of the signals. Cepstrum is also introduced in order to detect the unidentifiable periodic signal in the power spectrum. Further, a fault prognosis algorithm is presented by exploiting these spectral analyses. Finally, we design and implement a visualized real-time vibration analyzer on a Raspberry Pi embedded system, where our fault prognosis algorithm is the core algorithm. The real-time signals of acceleration, velocity, displacement of vibration, as well as their corresponding spectra and statistics, are visualized. The developed fault prognosis system has been successfully deployed in a water company. Full article
(This article belongs to the Special Issue 1st Online Conference on Algorithms (IOCA2021))
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17 pages, 326 KiB  
Article
Regularization Algorithms for Linear Copositive Programming Problems: An Approach Based on the Concept of Immobile Indices
by Olga Kostyukova and Tatiana Tchemisova
Algorithms 2022, 15(2), 59; https://0-doi-org.brum.beds.ac.uk/10.3390/a15020059 - 11 Feb 2022
Cited by 1 | Viewed by 1550
Abstract
In this paper, we continue an earlier study of the regularization procedures of linear copositive problems and present new algorithms that can be considered as modifications of the algorithm described in our previous publication, which is based on the concept of immobile indices. [...] Read more.
In this paper, we continue an earlier study of the regularization procedures of linear copositive problems and present new algorithms that can be considered as modifications of the algorithm described in our previous publication, which is based on the concept of immobile indices. The main steps of the regularization algorithms proposed in this paper are explicitly described and interpreted from the point of view of the facial geometry of the cone of copositive matrices. The results of the paper provide a deeper understanding of the structure of feasible sets of copositive problems and can be useful for developing a duality theory for these problems. Full article
(This article belongs to the Special Issue 1st Online Conference on Algorithms (IOCA2021))
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