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Prof. Dr. Marcin Woźniak

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Faculty of Applied Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, Poland

Prof. Dr. Muhammad Fazal Ijaz

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Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea

Computational Intelligence for Virus and Bacteria Detection in Multi Surface Environments

Abstract submission deadline

closed (31 October 2023)

Manuscript submission deadline

closed (31 January 2024)

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23168

Topic Information

Dear Colleagues,

The year 2020 started with a global pandemic, with every nation having to manage the fallout of COVID-19’s spread. COVID-19, SARS, and several other viruses, fungi, and bacteria that are similarly dangerous for humans are present in our daily lives. We must work together to better understand and control them. Scientists are working in chemical laboratories to find a cure. Meanwhile, technologies to detect viruses, fungi, and bacteria on multi-surface environments are being developed. In certain conditions, the presence of biological threads, viruses, fungi, and bacteria can be recorded from metal, glass, paper, plastic, and several derivative materials and environments, whether porous or smooth, or soft or hard materials. Temperature and humidity are other factors with significant influence on the survival of viruses and bacteria.

During this pandemic, scientific methods that help detect viruses, fungi, and bacteria in image and data samples from multi surface environments are necessary. The aim of this Topic is to provide an open platform for scientists and professionals to present the latest achievements in detection methods and sensory apparatuses, by which analysis of information from images or other data samples can quickly and efficiently provide accurate detection to prevent the uncontrolled spread of biological threads, viruses, fungi, and bacteria that are dangerous both for humans and animals..

It is my pleasure to invite you to contribute your innovative research on computational intelligence to this Topic. This issue creates opportunities for dissemination of your research results and cooperation for further innovation.

Topics of interest:

Bio-inspired methods, deep learning, convolutional neural networks, fuzzy systems, cognitive analysis, and hybrid architecture;
Time series, gradient field methods, and surface reconstruction, as well as other mathematical models for intelligent feature detection, extraction, and recognition;
Embedded intelligent computer vision algorithms for microscopic and infrared models;
Human–nature technology and object activity recognition models;
Hyper-parameter learning, transfer learning, automatic calibration, and hybrid and surrogate learning for computational intelligence in vision systems;
Intelligent video and image acquisition techniques applied to medical, transportation, shopping, and delivery;
Sensoric aparatures, devices, electronics, and various equipment to detect potential threads for humans and animals.

Prof. Dr. Marcin Woźniak
Prof. Dr. Muhammad Fazal Ijaz
Topic Editors

Keywords

viruses
fungi
bacteria
image samples
data samples
sensory apparatuses
computational intelligence
bio-inspired methods
deep learning
convolutional neural networks

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Sensors sensors	3.4	7.3	2001	16.8 Days	CHF 2600
Viruses viruses	3.8	7.3	2009	16.1 Days	CHF 2600
Diseases diseases	2.9	0.8	2013	18.9 Days	CHF 1800
Algorithms algorithms	1.8	4.1	2008	15 Days	CHF 1600
Methods and Protocols mps	2.3	3.6	2018	24.9 Days	CHF 1800
Symmetry symmetry	2.2	5.4	2009	16.8 Days	CHF 2400

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Published Papers (5 papers)

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21 pages, 24873 KiB

Open AccessArticle

Malaria Detection Using Advanced Deep Learning Architecture

by Wojciech Siłka, Michał Wieczorek, Jakub Siłka and Marcin Woźniak

Sensors 2023, 23(3), 1501; https://doi.org/10.3390/s23031501 - 29 Jan 2023

Cited by 38 | Viewed by 8837

Abstract

Malaria is a life-threatening disease caused by parasites that are transmitted to humans through the bites of infected mosquitoes. The early diagnosis and treatment of malaria are crucial for reducing morbidity and mortality rates, particularly in developing countries where the disease is prevalent. In this article, we present a novel convolutional neural network (CNN) architecture for detecting malaria from blood samples with a 99.68% accuracy. Our method outperforms the existing approaches in terms of both accuracy and speed, making it a promising tool for malaria diagnosis in resource-limited settings. The CNN was trained on a large dataset of blood smears and was able to accurately classify infected and uninfected samples with high sensitivity and specificity. Additionally, we present an analysis of model performance on different subtypes of malaria and discuss the implications of our findings for the use of deep learning in infectious disease diagnosis. Full article

(This article belongs to the Topic Computational Intelligence for Virus and Bacteria Detection in Multi Surface Environments)
(This article belongs to the Section Intelligent Sensors)

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20 pages, 3327 KiB

Open AccessArticle

Optimization and Modeling of Citrobacter freundii AD119 Growth and 1,3-Propanediol Production Using Two-Step Statistical Experimental Design and Artificial Neural Networks

by Agnieszka Drożdżyńska, Jolanta Wawrzyniak, Piotr Kubiak, Martyna Przybylak, Wojciech Białas and Katarzyna Czaczyk

Sensors 2023, 23(3), 1266; https://doi.org/10.3390/s23031266 - 22 Jan 2023

Cited by 4 | Viewed by 2444

Abstract

1,3-propanediol (1,3-PD) has a wide range of industrial applications. The most studied natural producers capable of fermenting glycerol to 1,3-PD belong to the genera Klebsiella, Citrobacter, and Clostridium. In this study, the optimization of medium composition for the biosynthesis of 1,3-PD by Citrobacter freundii AD119 was performed using the one-factor-at-a-time method (OFAT) and a two-step statistical experimental design. Eleven mineral components were tested for their impact on the process using the Plackett–Burman design. MgSO₄ and CoCl₂ were found to have the most pronounced effect. Consequently, a central composite design was used to optimize the concentration of these mineral components. Besides minerals, carbon and nitrogen sources were also optimized. Partial glycerol substitution with other carbon sources was found not to improve the bioconversion process. Moreover, although yeast extract was found to be the best nitrogen source, it was possible to replace it in part with (NH₄)₂SO₄ without a negative impact on 1,3-PD production. As a part of the optimization procedure, an artificial neural network model of the growth of C. freundii and 1,3-PD production was developed as a predictive tool supporting the design and control of the bioprocess under study. Full article

(This article belongs to the Topic Computational Intelligence for Virus and Bacteria Detection in Multi Surface Environments)
(This article belongs to the Section Intelligent Sensors)

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15 pages, 948 KiB

Open AccessArticle

Convolutional Neural Network Applied to SARS-CoV-2 Sequence Classification

by Gabriel B. M. Câmara, Maria G. F. Coutinho, Lucileide M. D. da Silva, Walter V. do N. Gadelha, Matheus F. Torquato, Raquel de M. Barbosa and Marcelo A. C. Fernandes

Sensors 2022, 22(15), 5730; https://doi.org/10.3390/s22155730 - 31 Jul 2022

Cited by 8 | Viewed by 3578

Abstract

COVID-19, the illness caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus belonging to the Coronaviridade family, a single-strand positive-sense RNA genome, has been spreading around the world and has been declared a pandemic by the World Health Organization. On 17 January 2022, there were more than 329 million cases, with more than 5.5 million deaths. Although COVID-19 has a low mortality rate, its high capacities for contamination, spread, and mutation worry the authorities, especially after the emergence of the Omicron variant, which has a high transmission capacity and can more easily contaminate even vaccinated people. Such outbreaks require elucidation of the taxonomic classification and origin of the virus (SARS-CoV-2) from the genomic sequence for strategic planning, containment, and treatment of the disease. Thus, this work proposes a high-accuracy technique to classify viruses and other organisms from a genome sequence using a deep learning convolutional neural network (CNN). Unlike the other literature, the proposed approach does not limit the length of the genome sequence. The results show that the novel proposal accurately distinguishes SARS-CoV-2 from the sequences of other viruses. The results were obtained from 1557 instances of SARS-CoV-2 from the National Center for Biotechnology Information (NCBI) and 14,684 different viruses from the Virus-Host DB. As a CNN has several changeable parameters, the tests were performed with forty-eight different architectures; the best of these had an accuracy of 91.94 ± 2.62% in classifying viruses into their realms correctly, in addition to 100% accuracy in classifying SARS-CoV-2 into its respective realm, Riboviria. For the subsequent classifications (family, genera, and subgenus), this accuracy increased, which shows that the proposed architecture may be viable in the classification of the virus that causes COVID-19. Full article

(This article belongs to the Topic Computational Intelligence for Virus and Bacteria Detection in Multi Surface Environments)
(This article belongs to the Section Intelligent Sensors)

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13 pages, 433 KiB

Open AccessArticle

Energy Efficient Consensus Approach of Blockchain for IoT Networks with Edge Computing

by Shivani Wadhwa, Shalli Rani, Kavita, Sahil Verma, Jana Shafi and Marcin Wozniak

Sensors 2022, 22(10), 3733; https://doi.org/10.3390/s22103733 - 13 May 2022

Cited by 32 | Viewed by 3512

Abstract

Blockchain technology is gaining a lot of attention in various fields, such as intellectual property, finance, smart agriculture, etc. The security features of blockchain have been widely used, integrated with artificial intelligence, Internet of Things (IoT), software defined networks (SDN), etc. The consensus mechanism of blockchain is its core and ultimately affects the performance of the blockchain. In the past few years, many consensus algorithms, such as proof of work (PoW), ripple, proof of stake (PoS), practical byzantine fault tolerance (PBFT), etc., have been designed to improve the performance of the blockchain. However, the high energy requirement, memory utilization, and processing time do not match with our actual desires. This paper proposes the consensus approach on the basis of PoW, where a single miner is selected for mining the task. The mining task is offloaded to the edge networking. The miner is selected on the basis of the digitization of the specifications of the respective machines. The proposed model makes the consensus approach more energy efficient, utilizes less memory, and less processing time. The improvement in energy consumption is approximately 21% and memory utilization is 24%. Efficiency in the block generation rate at the fixed time intervals of 20 min, 40 min, and 60 min was observed. Full article

(This article belongs to the Topic Computational Intelligence for Virus and Bacteria Detection in Multi Surface Environments)
(This article belongs to the Section Intelligent Sensors)

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20 pages, 14422 KiB

Open AccessArticle

A Novel Blockchain-Based Healthcare System Design and Performance Benchmarking on a Multi-Hosted Testbed

by Nihar Ranjan Pradhan, Akhilendra Pratap Singh, Sahil Verma, Kavita, Navneet Kaur, Diptendu Sinha Roy, Jana Shafi, Marcin Wozniak and Muhammad Fazal Ijaz

Sensors 2022, 22(9), 3449; https://doi.org/10.3390/s22093449 - 30 Apr 2022

Cited by 31 | Viewed by 3788

Abstract

As a result of the proliferation of digital and network technologies in all facets of modern society, including the healthcare systems, the widespread adoption of Electronic Healthcare Records (EHRs) has become the norm. At the same time, Blockchain has been widely accepted as a potent solution for addressing security issues in any untrusted, distributed, decentralized application and has thus seen a slew of works on Blockchain-enabled EHRs. However, most such prototypes ignore the performance aspects of proposed designs. In this paper, a prototype for a Blockchain-based EHR has been presented that employs smart contracts with Hyperledger Fabric 2.0, which also provides a unified performance analysis with Hyperledger Caliper 0.4.2. The additional contribution of this paper lies in the use of a multi-hosted testbed for the performance analysis in addition to far more realistic Gossip-based traffic scenario analysis with Tcpdump tools. Moreover, the prototype is tested for performance with superior transaction ordering schemes such as Kafka and RAFT, unlike other literature that mostly uses SOLO for the purpose, which accounts for superior fault tolerance. All of these additional unique features make the performance evaluation presented herein much more realistic and hence adds hugely to the credibility of the results obtained. The proposed framework within the multi-host instances continues to behave more successfully with high throughput, low latency, and low utilization of resources for opening, querying, and transferring transactions into a healthcare Blockchain network. The results obtained in various rounds of evaluation demonstrate the superiority of the proposed framework. Full article

(This article belongs to the Topic Computational Intelligence for Virus and Bacteria Detection in Multi Surface Environments)
(This article belongs to the Section Intelligent Sensors)

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Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Sensors sensors	3.4	7.3	2001	16.8 Days	CHF 2600
Viruses viruses	3.8	7.3	2009	16.1 Days	CHF 2600
Diseases diseases	2.9	0.8	2013	18.9 Days	CHF 1800
Algorithms algorithms	1.8	4.1	2008	15 Days	CHF 1600
Methods and Protocols mps	2.3	3.6	2018	24.9 Days	CHF 1800
Symmetry symmetry	2.2	5.4	2009	16.8 Days	CHF 2400

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Computational Intelligence for Virus and Bacteria Detection in Multi Surface Environments

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