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Littérature scientifique sur le sujet « Bacterial classification »

Auteur : Grafiati
Publié le 7 juin 2025
Mis à jour le 2 août 2025

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Articles de revues sur le sujet "Bacterial classification"

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Krieg, Noel R. "Bacterial classification: an overview." Canadian Journal of Microbiology 34, no. 4 (1988): 536–40. http://dx.doi.org/10.1139/m88-091.

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Classification of bacteria evolved from limited subjective groupings to general, more objective arrangements based on overall phenotypic similarities. However, classifications based on phenotypic characters lack stability, whereas those based on genetic relatedness tend to be stable. DNA–DNA hybridization has proven to be extremely useful in resolving taxonomic problems at the species level. Broad relationships among bacteria have been identified by comparing ribosomal RNA cistrons; however, many groups based on ribosomal RNA analysis are not easily definable in terms of phenotypic similaritie
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Hokkinen, Lauri, Artturi Kesti, Jaakko Lepomäki, et al. "Differential mobility spectrometry classification of bacteria." Future Microbiology 15, no. 4 (2020): 233–40. http://dx.doi.org/10.2217/fmb-2019-0192.

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Aim: Rapid identification of bacteria would facilitate timely initiation of therapy and improve cost–effectiveness of treatment. Traditional methods (culture, PCR) require reagents, consumables and hours to days to complete the identification. In this study, we examined whether differential mobility spectrometry could classify most common bacterial species, genera and between Gram status within minutes. Materials & methods: Cultured bacterial sample gaseous headspaces were measured with differential mobility spectrometry and data analyzed using k-nearest-neighbor and leave-one-out cross-va
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Brenner, Don J. "Phylogenetic classification of bacteria and recent developments in bacterial classification and nomenclature." Clinical Microbiology Newsletter 10, no. 20 (1988): 153–56. http://dx.doi.org/10.1016/0196-4399(88)90030-x.

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Tanko, Sani Muhammed, Muhammad Sani, and Abubakar Ahmad. "Enhancing Bacteria Classification using Image Processing and Convolutional Neural Network." Journal of Basics and Applied Sciences Research 2, no. 1 (2024): 156–61. http://dx.doi.org/10.33003/jobasr-2024-v2i1-42.

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Bacteria classification plays a vital role in the medical field, facilitating the diagnosis and treatment of various diseases. Traditionally, clinical specialists have relied on conventional techniques for classification, which lack predictive capabilities. Manual classification of bacteria is a laborious and time-consuming task that demands significant human effort. However, advancements in technology have opened possibilities for microorganism classification through the utilization of novel machine learning algorithms. This research explores the integration of Convolutional Neural Networks (
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Haddad, Mohammed F., Basima A. Abdullah, Hassan AA AlObeidi, Ali M. Saadi, and Mustafa F. Haddad. "Antibiotic classification, mechanisms, and indications: A review." International Journal of Medical and All Body Health Research 5, no. 3 (2024): 39–46. http://dx.doi.org/10.54660/ijmbhr.2024.5.3.39-46.

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An antibiotic was originally a material created by one microorganism that selectively inhibits another’s development. Since then, synthetic antibiotics have been developed that perform comparable tasks, typically chemically similar to natural antibiotics. There are no effects of antibiotics on viral infections. Antibiotics are used to treat bacterial infections in humans and animals. Several proposed classifications of antibiotics including chemical structure, mode of action, or organism of inhibitory activity. Most of the classification systems include two primary categories, the first catego
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Ananya, Das Rojina Khatun Sudeshna Sengupta Malavika Bhattacharya*. "Advances in Bacterial Classification: From Phenotypic Traits to Genomic Signatures." International Journal of Pharmaceutical Sciences 3, no. 5 (2025): 1293–306. https://doi.org/10.5281/zenodo.15367325.

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Our knowledge of the biology of bacterial systems and classification methods has greatly increased as a result of recent developments in bacterial classification using cell imaging. In other instances, imaging has fueled fascinating developments in bacterial cell biology that have resulted in a finer knowledge of the mechanisms behind protein localization and cell growth. Geometric elements taken from digital microscopic pictures have been used to create automated systems for the identification and classification of bacterial cells. The current study's goal is to create an automated system for
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Wonohadidjojo, Daniel Martomanggolo. "Classification of Bacterial Images using Transfer Learning, Optimized Training and Resnet-50." Eduvest - Journal Of Universal Studies 2, no. 2 (2022): 297–305. http://dx.doi.org/10.36418/edv.v2i2.352.

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Bacterial image analysis using traditional laboratory methods encounters bacterial recognition errors and requires extra experience and long processing time. Therefore, the automated classification technique of bacterial images is more useful than traditional visual observations for biologists because of their accurate classification, low cost, and fast diagnosis. In this study, a method to classify bacteria images by implementing the CNN deep learning method using Transfer Learning is proposed. This trained ResNet-50 is implemented as the CNN architecture. In the training of the classificatio
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Wonohadidjojo, Daniel Martomanggolo. "Classification of Bacterial Images using Transfer Learning, Optimized Training and Resnet-50." Eduvest - Journal of Universal Studies 2, no. 2 (2022): 297–305. http://dx.doi.org/10.59188/eduvest.v2i2.352.

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Bacterial image analysis using traditional laboratory methods encounters bacterial recognition errors and requires extra experience and long processing time. Therefore, the automated classification technique of bacterial images is more useful than traditional visual observations for biologists because of their accurate classification, low cost, and fast diagnosis. In this study, a method to classify bacteria images by implementing the CNN deep learning method using Transfer Learning is proposed. This trained ResNet-50 is implemented as the CNN architecture. In the training of the classificatio
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Timofeeva, Anna M., Maria R. Galyamova, and Sergey E. Sedykh. "Bacterial Siderophores: Classification, Biosynthesis, Perspectives of Use in Agriculture." Plants 11, no. 22 (2022): 3065. http://dx.doi.org/10.3390/plants11223065.

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Siderophores are synthesized and secreted by many bacteria, yeasts, fungi, and plants for Fe (III) chelation. A variety of plant-growth-promoting bacteria (PGPB) colonize the rhizosphere and contribute to iron assimilation by plants. These microorganisms possess mechanisms to produce Fe ions under iron-deficient conditions. Under appropriate conditions, they synthesize and release siderophores, thereby increasing and regulating iron bioavailability. This review focuses on various bacterial strains that positively affect plant growth and development through synthesizing siderophores. Here we di
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Sohn, Miryeong, David S. Himmelsbach, Franklin E. Barton, and Paula J. Fedorka-Cray. "Fluorescence Spectroscopy for Rapid Detection and Classification of Bacterial Pathogens." Applied Spectroscopy 63, no. 11 (2009): 1251–55. http://dx.doi.org/10.1366/000370209789806993.

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This study deals with the rapid detection and differentiation of Escherichia coli, Salmonella, and Campylobacter, which are the most commonly identified commensal and pathogenic bacteria in foods, using fluorescence spectroscopy and multivariate analysis. Each bacterial sample cultured under controlled conditions was diluted in physiologic saline for analysis. Fluorescence spectra were collected over a range of 200–700 nm with 0.5 nm intervals on the PerkinElmer Fluorescence Spectrometer. The synchronous scan technique was employed to find the optimum excitation (λex) and emission (λem) wavele
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Thèses sur le sujet "Bacterial classification"

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Chun, Jongsik. "Computer assisted classification and identification of actinomycetes." Thesis, University of Newcastle Upon Tyne, 1995. http://hdl.handle.net/10443/410.

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Three computer software packages were written in the C++ language for the analysis of numerical phenetic, 16S rRNA sequence and pyrolysis mass spectrometric data. The X program, which provides routines for editing binary data, for calculating test error, for estimating cluster overlap and for selecting diagnostic and selective tests, was evaluated using phenotypic data held on streptomycetes. The AL16S program has routines for editing 16S rRNA sequences, for determining secondary structure, for finding signature nucleotides and for comparative sequence analysis; it was used to analyse 16S rRNA
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Mattinson-Rose, A. D. "Classification of amycolate wall chemotype IV actinomycetes." Thesis, University of Newcastle Upon Tyne, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.374849.

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Schmidt, Juliane, Ren Wei, Thorsten Oeser, et al. "Degradation of Polyester Polyurethane by Bacterial Polyester Hydrolases." Universität Leipzig, 2017. https://ul.qucosa.de/id/qucosa%3A21100.

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Polyurethanes (PU) are widely used synthetic polymers. The growing amount of PU used industrially has resulted in a worldwide increase of plastic wastes. The related environmental pollution as well as the limited availability of the raw materials based on petrochemicals requires novel solutions for their efficient degradation and recycling. The degradation of the polyester PU Impranil DLN by the polyester hydrolases LC cutinase (LCC), TfCut2, Tcur1278 and Tcur0390 was analyzed using a turbidimetric assay. The highest hydrolysis rates were obtained with TfCut2 and Tcur0390. TfCut2 also showed a
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Arumugam, Senthil. "Reconstitution of bacterial cytokinesis: the Z-ring." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-99045.

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Prokaryotic cell division is one of the most fundamental processes in biology, but the dynamics and mechanics are far from being understood. In many bacteria, FtsZ, a tubulin homologue assembles into a ring-like structure – Z-ring at precisely the middle of the cell. This accurate site selection is dependent on the Min proteins. Min D and MinE self-organise into waves in vitro, and oscillate pole to pole in vivo. MinC is thought to couple the Min oscillations to FtsZ by direct interaction. The mechanism of inhibitory action of MinC on FtsZ assembly is not known. Critical to the understanding o
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Gibson, Kathryn Elizabeth. "Neorickettsia spp.: Molecular Classification of a Vector and Roles of Bacterial Surface Proteins in Pathogenesis." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1300901970.

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Desai, Gargi Sharad. "Deep Learning for Classification of COVID-19 Pneumonia, Bacterial Pneumonia, Viral Pneumonia and Normal Lungs on CT Images." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627662447914953.

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Tian, Long. "Tackling the current limitations of bacterial taxonomy with genome-based classification and identification on a crowdsourcing Web service." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/103055.

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Bacterial taxonomy is the science of classifying, naming, and identifying bacteria. The scope and practice of taxonomy has evolved through history with our understanding of life and our growing and changing needs in research, medicine, and industry. As in animal and plant taxonomy, the species is the fundamental unit of taxonomy, but the genetic and phenotypic diversity that exists within a single bacterial species is substantially higher compared to animal or plant species. Therefore, the current "type"-centered classification scheme that describes a species based on a single type strain is n
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Paul, Sabu. "Bacterial total maximum daily load (TMDL): development and evaluation of a new classification scheme for impaired waterbodies of Texas." Diss., Texas A&M University, 2003. http://hdl.handle.net/1969.1/1514.

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Under the Clean Water Act (CWA) program the Texas Commission on Environmental Quality (TCEQ) listed 110 stream segments with pathogenic bacteria impairment in 2000. The current study was conducted to characterize the watersheds associated with the impaired waterbodies. The main characteristics considered for the classification of waterbodies were designated use of the waterbody, land use distribution, density of stream network, average distance of a land of a particular use to the closest stream, household population, density of on-site sewage facilities (OSSF), bacterial loading due to the
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Zoghlami, Manel. "Multiple instance learning for sequence data : Application on bacterial ionizing radiation resistance prediction." Thesis, Université Clermont Auvergne‎ (2017-2020), 2019. http://www.theses.fr/2019CLFAC078.

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Dans l’apprentissage multi-instances (MI) pour les séquences, les données d’apprentissage consistent en un ensemble de sacs où chaque sac contient un ensemble d’instances/séquences. Dans certaines applications du monde réel, comme la bioinformatique, comparer un couple aléatoire de séquences n’a aucun sens. En fait, chaque instance de chaque sac peut avoir une relation structurelle et/ou fonctionnelle avec d’autres instances dans d’autres sacs. Ainsi, la tâche de classification doit prendre en compte la relation entre les instances sémantiquement liées à travers les sacs. Dans cette thèse, nou
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Helbig, Ralf, Denise Günther, Jens Friedrichs, Florian Rößler, Andrés Lasagni, and Carsten Werner. "The impact of structure dimensions on initial bacterial adhesion." Royal Society of Chemistry, 2016. https://tud.qucosa.de/id/qucosa%3A35958.

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Substrate topography can have profound effects on initial bacterial adhesion during biofilm formation. We applied Staphylococcus epidermidis and Escherichia coli cells onto periodically structured substrates with different structure dimensions, structure types and wetting properties. We found a strong dependence of cell retention on the structure dimensions of the applied substrates. Periodicities in the range of the cell size increased, whereas smaller periodicities decreased cell retention, independent of contact time (minutes to hours) and hydrophobicity. These novel insights on the role of
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Livres sur le sujet "Bacterial classification"

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Brian, Austin. Modern bacterial taxonomy. Van Nostrand Reinhold, 1986.

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G, Priest F., ed. Modern bacterial taxonomy. Van Nostrand Reinhold (UK), 1986.

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Cullimore, D. Roy. Practical atlas for bacterial identification. 2nd ed. CRC Press, 2010.

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Cullimore, D. Roy. Practical atlas for bacterial identification. 2nd ed. CRC Press, 2010.

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Nakagawa, Yasuyoshi. List of bacterial species approved and validated in the 20th century. Institute for Fermentation, Osaka, 2001.

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Cullimore, D. Roy. Practical atlas for bacterial identification. 2nd ed. CRC Press, 2010.

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A, Cockayne, ed. Molecular methods for microbial identification and typing. Chapman & Hall, 1993.

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Moore, W. E. C. Index of the bacterial and yeast nomenclatural changes: Published in the International journal of systematic bacteriology since the 1980 Approved lists of bacterial names : (1 January 1980 to 1 January 1989). American Society for Microbiology, 1989.

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Moore, W. E. C. Index of the bacterial and yeast nomenclatural changes: Published in the International journal of systematic bacteriology since the 1980 approved lists of bacterial names (1 January 1980 to 1 January 1989). American Society for Microbiology, 1992.

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Petti, Cathy A. Interpretive criteria for identification of bacteria and fungi by DNA target sequencing: Approved guideline. Clinical and Laboratory Standards Institute, 2008.

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Chapitres de livres sur le sujet "Bacterial classification"

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Konwar, Bolin Kumar. "Classification of PHAs." In Bacterial Biopolymers. Apple Academic Press, 2023. http://dx.doi.org/10.1201/9781003331636-5.

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Konwar, Bolin Kumar. "Classification of Biosurfactants." In Bacterial Biosurfactants. Apple Academic Press, 2022. http://dx.doi.org/10.1201/9781003188131-2.

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Konwar, Bolin Kumar. "Biopolymers and Their Classification." In Bacterial Biopolymers. Apple Academic Press, 2023. http://dx.doi.org/10.1201/9781003331636-2.

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Bodey, Gerald P., Danica Milatovic, and Ilja Braveny. "Classification of Bacterial Pathogens." In The Antimicrobial Pocket Book. Vieweg+Teubner Verlag, 1991. http://dx.doi.org/10.1007/978-3-663-05255-5_14.

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Schleifer, Karl-Heinz, and Wolfgang Ludwig. "Molecular Taxonomy: Classification and Identification." In Bacterial Diversity and Systematics. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-1869-3_1.

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Lima-Mendez, Gipsi. "Reticulate Classification of Mosaic Microbial Genomes Using NeAT Website." In Bacterial Molecular Networks. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-61779-361-5_5.

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Torres, Antoni, and Catia Cillóniz. "Diagnosis and classification of Pneumonia." In Clinical Management of Bacterial Pneumonia. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22062-8_4.

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Rosselló-Móra, Ramon, and Erko Stackebrandt. "Bridging 200 years of bacterial classification." In Trends in the systematics of bacteria and fungi. CABI, 2021. http://dx.doi.org/10.1079/9781789244984.0001.

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Abstract This book chapter briefly takes a historical view of the major steps in bacterial systematics leading to the first reconciliation workshop in 1987 and a re-evaluation of the species concept in 2002. New challenges and concepts developed since then will be outlined. Never before has the future of the prokaryotic taxonomy been at such a critical point. The uncertain future, and whether Plan A or Plan B will prevail, depends totally on the wisdom of the ICSP. Perhaps by the time this book is published the situation will have been clarified, but the current situation is as full of uncerta
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Göker, Markus. "What can genome analysis offer for bacteria?" In Trends in the systematics of bacteria and fungi. CABI, 2021. http://dx.doi.org/10.1079/9781789244984.0255.

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Abstract This book chapter is organized as follows: (i) the main approaches to the philosophy of taxonomic classification are recapitulated; (ii) the paradigm of polyphasic taxonomy is discussed in this context; (iii) the causes of conflict between previous classifications and genome-scale analyses are investigated, using examples from recent phylum-wide studies, with a discussion of how markers used in polyphasic taxonomy can be replaced by genome-derived ones; and (iv) the challenges in assigning taxonomic ranks using genome-scale or other data are revisited. The conclusion assesses the chan
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Idris, Abdelmalik O. A. "Bacterial Toxins: Classification, Cellular Biology, Genetics and Applications." In Microbial Toxins in Food Systems: Causes, Mechanisms, Complications, and Metabolism. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62839-9_8.

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Actes de conférences sur le sujet "Bacterial classification"

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Tanveer, Shaik Shifa, Vishali Kumar, and Deva Hema. "Bacterial Classification in Microbiology using BiT." In 2024 International Conference on Communication, Computing and Energy Efficient Technologies (I3CEET). IEEE, 2024. https://doi.org/10.1109/i3ceet61722.2024.10994004.

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Kumar, Deepak, and Abhiraj Malhotra. "YOLO-R50: A Robust Approach for Wheat Ears Detection and Bacterial Black Chaff Classification." In 2024 5th International Conference on Smart Electronics and Communication (ICOSEC). IEEE, 2024. http://dx.doi.org/10.1109/icosec61587.2024.10722403.

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Dpremalatha, R. J., D. Kayethri, R. M. Ddhavamani, D. T. Gokulraj, and G. Jaganathan. "Deep Learning-based Multiclass Classification of Viral, Bacterial, Pneumonia and Healthy Lungs from Chest Radiographs." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10726155.

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Jung, Sung Ho, Yeokyoung Won, Won Seok Song, Ju Hwan Lee, Hakje Yoo, and Dong Hui Lim. "Classification of Bacterial Keratitis Activity with Patch-Based Deep Learning Using Three Anterior Segment Images." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635598.

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Sharma, Rishabh, and Shikhar Gupta. "Harnessing the Power of Neural Networks: A Comprehensive Study on Tea Leaf Bacterial Blight Classification." In 2025 1st International Conference on AIML-Applications for Engineering & Technology (ICAET). IEEE, 2025. https://doi.org/10.1109/icaet63349.2025.10932272.

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Du, Zhihua, Ningyu Zhong, and Jianqiang Li. "Enhancing Gene Cluster Identification and Classification in Bacterial Genomes Through Synonym Replacement and Deep Learning." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821874.

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Lestari, Danur, and Mustakim. "Classification of Bacterial and Viral Pneumonia Using Chest X-Ray Images with Convolutional Neural Network." In 2024 International Conference on Decision Aid Sciences and Applications (DASA). IEEE, 2024. https://doi.org/10.1109/dasa63652.2024.10836528.

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Mannepalli, Praveen Kumar, Ayesha Khan, Priya Chugh, Sadia Patka, and R. Ponmalar. "Expression of Concern for: Classification of Pepper Bell into Healthy and Bacterial Spot Using Deep Learning." In 2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI). IEEE, 2023. http://dx.doi.org/10.1109/iccsai59793.2023.10703756.

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Vargas, Walter A., María Clara Pagliaricci, Juliana Soler Arango, Albert Saavedra, and Walter Morris. "The 1000’s of Microbial Genera Found in Argentina’S O&G Fields: Their Impact on Microbially Induced Corrosion and Integrity of Facilities." In CORROSION 2021. AMPP, 2021. https://doi.org/10.5006/c2021-16754.

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Abstract MIC assessment and management in O&G facilities depends on the accurate and early evaluation of microbial communities involved in material degradation. Traditional monitoring by culture-based methods allow the detection only 0.1% of the microorganisms in the sample, underestimating the real bacterial concentration in the samples, and in some cases, their potentiality for corrosion. However, novel molecular-biology based strategies may overcome these issues offering more reliable and sensitive tools for the study of microbial communities. The novel methodologies have been useful fo
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Trivedi, Sandeep, Nikhil Patel, and Nuruzzaman Faruqui. "Bacterial Strain Classification using Convolutional Neural Network for Automatic Bacterial Disease Diagnosis." In 2023 13th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 2023. http://dx.doi.org/10.1109/confluence56041.2023.10048859.

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Rapports d'organisations sur le sujet "Bacterial classification"

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Becerra-Stasiewicz, Natalie, Peter Stynoski, Clint Arnett, et al. Flood resilience of individual traditional building materials. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49446.

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The Construction Engineering Research Laboratory of the Engineer Research and Development Center is addressing emergency response and disaster relief capabilities in collaboration with the National Park Service, specifically related to the flood hardening and rehabilitation of historical structures. This report describes efforts to enhance the available data for assessing the resilience of individual historical building materials against flood conditions. In the context of official classification language, the experiments described by this report intended to mimic the effects of moving black w
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