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Computational Intelligence and Sensoric Aparatures for Virus, Fungus, Bacteria, and Biological Threads Detection in Image and Data Samples from Multi Surface Environments

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 13771

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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
Interests: computational intellgence; neural networks; image processing; expert systems
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Special Issue Information

Dear Colleagues,

The year 2020 started with a global pandemic. Every nation has had to face Cov19’s spread across the globe. Cov19, SARS and several other viruses and bacteria that are similarly dangerous for humans viruses, fungus and bacteria are present in our life. We must work together to better understand and control them. Scientists are working in chemical laboratories to find a cure. Meanwhile, technical minds are developing technologies to detect viruses, fungus, and bacteria on multi surface environments. We know that, at certain times, the presence of viruses biological threads, fungus and bacteria can be recorded from metal, glass, paper, plastic, and several derivative materials and environments, and at different times from porous or smooth, and soft or hard materials. Temperature and humidity are other factors with significant influence on the survival of viruses and bacteria.

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

It is my pleasure to invite you to contribute your innovative research on computational intelligence to this Special Issue. 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. Marcin Woźniak
Guest Editor

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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 (3 papers)

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Research

18 pages, 6025 KiB

Open AccessArticle

Recording the Presence of Peanibacillus larvae larvae Colonies on MYPGP Substrates Using a Multi-Sensor Array Based on Solid-State Gas Sensors

by Beata Bąk, Jakub Wilk, Piotr Artiemjew and Jerzy Wilde

Sensors 2021, 21(14), 4917; https://doi.org/10.3390/s21144917 - 19 Jul 2021

Cited by 3 | Viewed by 1791

Abstract

American foulbrood is a dangerous disease of bee broods found worldwide, caused by the Paenibacillus larvae larvae L. bacterium. In an experiment, the possibility of detecting colonies of this bacterium on MYPGP substrates (which contains yeast extract, Mueller-Hinton broth, glucose, K2HPO4, sodium pyruvate, and agar) was tested using a prototype of a multi-sensor recorder of the MCA-8 sensor signal with a matrix of six semiconductors: TGS 823, TGS 826, TGS 832, TGS 2600, TGS 2602, and TGS 2603 from Figaro. Two twin prototypes of the MCA-8 measurement device, M1 and M2, were used in the study. Each prototype was attached to two laboratory test chambers: a wooden one and a polystyrene one. For the experiment, the strain used was P. l. larvae ATCC 9545, ERIC I. On MYPGP medium, often used for laboratory diagnosis of American foulbrood, this bacterium produces small, transparent, smooth, and shiny colonies. Gas samples from over culture media of one- and two-day-old foulbrood P. l. larvae (with no colonies visible to the naked eye) and from over culture media older than 2 days (with visible bacterial colonies) were examined. In addition, the air from empty chambers was tested. The measurement time was 20 min, including a 10-min testing exposure phase and a 10-min sensor regeneration phase. The results were analyzed in two variants: without baseline correction and with baseline correction. We tested 14 classifiers and found that a prototype of a multi-sensor recorder of the MCA-8 sensor signal was capable of detecting colonies of P. l. larvae on MYPGP substrate with a 97% efficiency and could distinguish between MYPGP substrates with 1–2 days of culture, and substrates with older cultures. The efficacy of copies of the prototypes M1 and M2 was shown to differ slightly. The weighted method with Canberra metrics (Canberra.811) and kNN with Canberra and Manhattan metrics (Canberra. 1nn and manhattan.1nn) proved to be the most effective classifiers. Full article

(This article belongs to the Special Issue Computational Intelligence and Sensoric Aparatures for Virus, Fungus, Bacteria, and Biological Threads Detection in Image and Data Samples from Multi Surface Environments)

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19 pages, 5067 KiB

Open AccessArticle

AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery

by Ahmad Almadhor, Hafiz Tayyab Rauf, Muhammad Ikram Ullah Lali, Robertas Damaševičius, Bader Alouffi and Abdullah Alharbi

Sensors 2021, 21(11), 3830; https://doi.org/10.3390/s21113830 - 01 Jun 2021

Cited by 90 | Viewed by 6034

Abstract

Plant diseases can cause a considerable reduction in the quality and number of agricultural products. Guava, well known to be the tropics’ apple, is one significant fruit cultivated in tropical regions. It is attacked by 177 pathogens, including 167 fungal and others such as bacterial, algal, and nematodes. In addition, postharvest diseases may cause crucial production loss. Due to minor variations in various guava disease symptoms, an expert opinion is required for disease analysis. Improper diagnosis may cause economic losses to farmers’ improper use of pesticides. Automatic detection of diseases in plants once they emerge on the plants’ leaves and fruit is required to maintain high crop fields. In this paper, an artificial intelligence (AI) driven framework is presented to detect and classify the most common guava plant diseases. The proposed framework employs the

Δ

E color difference image segmentation to segregate the areas infected by the disease. Furthermore, color (RGB, HSV) histogram and textural (LBP) features are applied to extract rich, informative feature vectors. The combination of color and textural features are used to identify and attain similar outcomes compared to individual channels, while disease recognition is performed by employing advanced machine-learning classifiers (Fine KNN, Complex Tree, Boosted Tree, Bagged Tree, Cubic SVM). The proposed framework is evaluated on a high-resolution (18 MP) image dataset of guava leaves and fruit. The best recognition results were obtained by Bagged Tree classifier on a set of RGB, HSV, and LBP features (99% accuracy in recognizing four guava fruit diseases (Canker, Mummification, Dot, and Rust) against healthy fruit). The proposed framework may help the farmers to avoid possible production loss by taking early precautions. Full article

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20 pages, 540 KiB

Open AccessArticle

An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm

by Chiranji Lal Chowdhary, Mohit Mittal, Kumaresan P., P. A. Pattanaik and Zbigniew Marszalek

Sensors 2020, 20(14), 3903; https://doi.org/10.3390/s20143903 - 13 Jul 2020

Cited by 84 | Viewed by 4916

Abstract

The herpesvirus, polyomavirus, papillomavirus, and retrovirus families are associated with breast cancer. More effort is needed to assess the role of these viruses in the detection and diagnosis of breast cancer cases in women. The aim of this paper is to propose an efficient segmentation and classification system in the Mammography Image Analysis Society (MIAS) images of medical images. Segmentation became challenging for medical images because they are not illuminated in the correct way. The role of segmentation is essential in concern with detecting syndromes in human. This research work is on the segmentation of medical images based on intuitionistic possibilistic fuzzy c-mean (IPFCM) clustering. Intuitionist fuzzy c-mean (IFCM) and possibilistic fuzzy c-mean (PFCM) algorithms are hybridised to deal with problems of fuzzy c-mean. The introduced clustering methodology, in this article, retains the positive points of PFCM which helps to overcome the problem of the coincident clusters, thus the noise and less sensitivity to the outlier. The IPFCM improves the fundamentals of fuzzy c-mean by using intuitionist fuzzy sets. For the clustering of mammogram images for breast cancer detector of abnormal images, IPFCM technique has been applied. The proposed method has been compared with other available fuzzy clustering methods to prove the efficacy of the proposed approach. We compared support vector machine (SVM), decision tree (DT), rough set data analysis (RSDA) and Fuzzy-SVM classification algorithms for achieving an optimal classification result. The outcomes of the studies show that the proposed approach is highly effective with clustering and also with classification of breast cancer. The performance average segmentation accuracy for MIAS images with different noise level 5%, 7% and 9% of IPFCM is 91.25%, 87.50% and 85.30% accordingly. The average classification accuracy rates of the methods (Otsu, Fuzzy c-mean, IFCM, PFCM and IPFCM) for Fuzzy-SVM are 79.69%, 92.19%, 93.13%, 95.00%, and 98.85%, respectively. Full article

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