Academic literature on the topic 'Biological classifications'

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Journal articles on the topic "Biological classifications"

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Hedegaard, Claus. "Biological Classification—Organizing Organisms." Paleontological Society Special Publications 11 (2002): 63–76. http://dx.doi.org/10.1017/s2475262200009825.

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This is a personal guided tour of biological classification, not a quick-and-dirty manual, nor a comprehensive account of the history of systematic biology. This is a reflection on why and how we classify extinct and extant organisms. I wish to explore alternative views of hierarchy, permanence and delineation of categories, and the use of data. I hope to convince you that biological classifications are necessary tools, but also strong ideological statements. The prevailing approach to classification at any time reflects distinct views on nature, how it is organized, and how you use informatio
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Hedegaard, Claus. "Biological Classification - Organizing Organisms." Paleontological Society Special Publications 9 (1999): 77–94. http://dx.doi.org/10.1017/s2475262200014027.

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This is a personal, guided tour of biological classification, not a quick-and-dirty manual, nor a comprehensive account of the history of systematic biology. This a reflection on why and how we classify extinct and extant organisms. I wish to explore alternative views of hierarchy, permanence and delineation of categories, and the use of data. I hope to convince you that biological classifications are necessary tools, but also strong ideological statements. The prevailing approach to classification at any time reflects distinct views on nature, how it is organized, and how you use information.
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Stuessy, T. F. "Information content for biological classifications." Gardens' Bulletin Singapore 71, no. 2 (2019): 365–76. http://dx.doi.org/10.26492/gbs71(2).2019-04.

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Minelli, Alessandro, Giuseppe Fusco, and Silvia Sartori. "Self-similarity in biological classifications." Biosystems 26, no. 2 (1991): 89–97. http://dx.doi.org/10.1016/0303-2647(91)90040-r.

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Inglis, WG. "Stratigramy - Biological Classifications Through Spontaneous Self-Assembly." Australian Journal of Zoology 34, no. 3 (1986): 411. http://dx.doi.org/10.1071/zo9860411.

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Biological taxonomy is a phenetic process of spontaneous self-assembly because of the intrinsic structural and developmental correlation of organic nature. It is, therefore, independent of any evolutionary- phylogenetic input, and involves six stages. The first three, constituting stratigramy, are procedural and essential, the last three are theoretical and optional, thus: (1) conceptually identical morphological structures, homologues, or equivalent blocks of non-morphological data, are identified in different organisms, and (2) divided into distinct states, here called homolostrata, which su
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Dupré, John. "Disorder." THEORIA. An International Journal for Theory, History and Foundations of Science 40, no. 1 (2025): 5–16. https://doi.org/10.1387/theoria.27510.

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This paper begins with some brief intellectual autobiography, recalling my first engagement with philosophy of biology. The substantive part of the paper then focuses on the plurality of possible classifications central to the theses of scientific disunity and metaphysical disorder developed in my early career. After discussing this in terms of biological classification, and introducing the reasons for thinking of classifications as typically value-laden, I discuss two sets of human classifications bearing on normatively vital questions, those around sex and gender and those involved in the di
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Troitskyi, Mykhailo O., Taisiya B. Troitska, Yurii V. Buydin, Nikita O. Miroshnichenko, and Olha O. Mykhailenko. "Classifications of Iris L. genus at the biological and molecular levels as a basis for modern phylogenetic studies." Journal of Organic and Pharmaceutical Chemistry 19, no. 4(76) (2021): 12–19. http://dx.doi.org/10.24959/ophcj.21.247544.

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Aim. To analyze and summarize literature data on the state of classification and taxonomy of Iris genus plants of Iridaceae family.Results and discussion. The genus Iris L. is one of the largest species of Iridaceae Juss family, which includes 1,800 species of 80 genera. Despite the fact that plants are very common as ornamental plants, there is currently no single classification for both horticultural and wild species of Iris genus. Various classifications and systems of irises have been proposed, the main difference between them is related to the understanding of the genus scope. The taxonom
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Kochnev, Roman L. "Types of Identity and Coordinates of Person." Epistemology & Philosophy of Science 60, no. 2 (2023): 114–32. http://dx.doi.org/10.5840/eps202360228.

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Modern analytical metaphysics contains many theories and approaches regarding the problem of personal identity. This diversity inevitably leads to the emergence of various classifications, the authors of which are trying to develop a compact way of typologizing existing views. Most of the classifications involve a significant simplification of the theories and approaches under consideration, and some of them are not taken into account at all. As such global classifications, one can single out an approach based on the identity criterion used in the theory (psychological, biological, narrative v
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Van Gijn, Marielle E., Isabella Ceccherini, Yael Shinar, et al. "New workflow for classification of genetic variants’ pathogenicity applied to hereditary recurrent fevers by the International Study Group for Systemic Autoinflammatory Diseases (INSAID)." Journal of Medical Genetics 55, no. 8 (2018): 530–37. http://dx.doi.org/10.1136/jmedgenet-2017-105216.

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BackgroundHereditary recurrent fevers (HRFs) are rare inflammatory diseases sharing similar clinical symptoms and effectively treated with anti-inflammatory biological drugs. Accurate diagnosis of HRF relies heavily on genetic testing.ObjectivesThis study aimed to obtain an experts’ consensus on the clinical significance of gene variants in four well-known HRF genes: MEFV, TNFRSF1A, NLRP3 and MVK.MethodsWe configured a MOLGENIS web platform to share and analyse pathogenicity classifications of the variants and to manage a consensus-based classification process. Four experts in HRF genetics sub
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Cronk, Q. C. B. "MEASUREMENT OF BIOLOGICAL AND HISTORICAL INFLUENCES ON PLANT CLASSIFICATIONS." TAXON 38, no. 3 (1989): 357–70. http://dx.doi.org/10.2307/1222266.

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Dissertations / Theses on the topic "Biological classifications"

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VanDerKamp, Martha M. "Modeling and classification of biological signals." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School; Available from the National Technical Information Service, 1992. http://edocs.nps.edu/npspubs/scholarly/theses/1992/Dec/92Dec_VanDerKamp.pdf.

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Andersson, Henrik. "Classification of spectral signatures in biological aerosols." Thesis, Umeå universitet, Institutionen för fysik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-64002.

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In this thesis multivariate methods were used to evaluate pretreatment methods, such as normalization, as well as classification possibilities of data collected with Laser Induced Breakdown Spectroscopy (LIBS). The LIBS system that FOI is currently developing for the purpose of classifying biological airborne threats was used to collect data from ten different samples in a laboratory environment. Principal component analysis (PCA) shows that it is possible to observe differences between samples using the two types of data acquired from the LIBS system, i.e., 2D CCD camera images and 1D spectra
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Stanescu, Ana. "Semi-supervised learning for biological sequence classification." Diss., Kansas State University, 2015. http://hdl.handle.net/2097/35810.

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Doctor of Philosophy<br>Department of Computing and Information Sciences<br>Doina Caragea<br>Successful advances in biochemical technologies have led to inexpensive, time-efficient production of massive volumes of data, DNA and protein sequences. As a result, numerous computational methods for genome annotation have emerged, including machine learning and statistical analysis approaches that practically and efficiently analyze and interpret data. Traditional machine learning approaches to genome annotation typically rely on large amounts of labeled data in order to build quality classifiers. T
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Herndon, Nic. "Domain adaptation algorithms for biological sequence classification." Diss., Kansas State University, 2016. http://hdl.handle.net/2097/35242.

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Doctor of Philosophy<br>Department of Computing and Information Sciences<br>Doina Caragea<br>The large volume of data generated in the recent years has created opportunities for discoveries in various fields. In biology, next generation sequencing technologies determine faster and cheaper the exact order of nucleotides present within a DNA or RNA fragment. This large volume of data requires the use of automated tools to extract information and generate knowledge. Machine learning classification algorithms provide an automated means to annotate data but require some of these data to be manuall
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Tangirala, Karthik. "Unsupervised feature construction approaches for biological sequence classification." Diss., Kansas State University, 2015. http://hdl.handle.net/2097/19123.

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Doctor of Philosophy<br>Department of Computing and Information Sciences<br>Doina Caragea<br>Recent advancements in biological sciences have resulted in the availability of large amounts of sequence data (DNA and protein sequences). Biological sequence data can be annotated using machine learning techniques, but most learning algorithms require data to be represented by a vector of features. In the absence of biologically informative features, k-mers generated using a sliding window-based approach are commonly used to represent biological sequences. A larger k value typically results in bette
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Cheang, Maggie Chon U. "Biological classification of clinical breast cancer using tissue microarrays." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2430.

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Gene expression profiles have identified five major molecular breast cancer subtypes (Luminal A, Luminal B, Basal-like, HER2+/estrogen receptor− , and Normal Breast-like) that show significant differences in survival. The cost and complexity of gene expression technology has impeded its clinical implementation. By comparison, immunohistochemistry is an economical technique applicable to the standard formalin-fixed, paraffin-embedded material commonly used in hospital labs, and has the advantage of simultaneously interpretation with histomorphology. In this thesis, I hypothesize that a surrog
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Rothman, Craig Jeremy. "Tissue-specific classification of alternatively spliced human exons." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/39920.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Biological Engineering Division, 2007.<br>Includes bibliographical references (p. 53-57).<br>Alternative splicing is involved in numerous cellular functions and is often disrupted and involved in disease. Previous research has identified methods to distinguish alternative conserved exons (ACEs) in human and mouse. However, the cellular machinery, the spliceosome, does not use comparative genomics to decide when to include and when to exclude an exon. Human RefSeq exons obtained from the University of California Santa Cruz (UCSC) genome b
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Balleri, A. "Biologically inspired radar and sonar target classification." Thesis, University College London (University of London), 2010. http://discovery.ucl.ac.uk/755812/.

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Classification of targets is a key problem of modern radar and sonar systems. This is an activity carried out with great success by echolocating mammals, such as bats, that have evolved echolocation as a means of detecting, selecting and attacking prey over a period of more than 50 million years. Because they have developed a highly sophisticated capability on which they depend for their survival, it is likely that there is potentially a great deal that can be learnt from understanding how they use this capability and how this might be valuably applied to radar and sonar systems. Bat-pollinate
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Senan, Campos Oriol. "Statistical tools for classification, interpretation and prediction of biological data." Doctoral thesis, Universitat Rovira i Virgili, 2017. http://hdl.handle.net/10803/458361.

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Les tecnologies òmiques prometen una nova aproximació sistèmica a la biologia. Malgrat que ja coneixem el genoma de moltes espècies, i una aproximació del nombre de proteïnes, no sabem quants metabòlits hi ha en una mostra biológica, o en un organisme. Ni tan sols amb la tècnica més efectiva per a detectar el major nombre de metabòlits tenim alguna estimació, sovint només s'aconsegueixen identificar 20-30 metabolíts després d'un llarg treball manual. Em desenvolupat un algorisme, CliqueMS, que soluciona un dels problemes més grans per a anotar un experiment de metabolòmica, la correcta i
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Budach, Stefan [Verfasser]. "Explainable deep learning models for biological sequence classification / Stefan Budach." Berlin : Freie Universität Berlin, 2021. http://d-nb.info/1230407413/34.

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Books on the topic "Biological classifications"

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Hoenigswald, Henry M., and Linda F. Wiener, eds. Biological Metaphor and Cladistic Classification. University of Pennsylvania Press, 1987. http://dx.doi.org/10.9783/9781512802450.

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Kiasaleh, Kamran. Biological Signals Classification and Analysis. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-54879-6.

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Canada, Canada Agriculture, ed. Optimal set covering for biological classification. Agriculture Canada, 1993.

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VanDerKamp, Martha M. Modeling and classification of biological signals. Naval Postgraduate School, 1992.

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Minelli, Alessandro. Biological systematics: The state of the art. Chapman & Hall, 1993.

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Crovello, Theodore J. Careers in biological systematics. American Society of Plant Taxonomists--Society of Systematic Zoology, 1986.

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M, Hoenigswald Henry, and Wiener Linda F, eds. Biological metaphor and cladistic classification: An interdisciplinary perspective. Pinter, 1987.

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1915-, Hoenigswald Henry M., Wiener Linda F, and Interdisciplinary Round-Table on Cladistics and Other Graph Theoretical Representations (1983 : American Philosophical Society Library), eds. Biological metaphor and cladistic classification: An interdisciplinary perspective. University of Pennsylvania Press, 1987.

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Ereshefsky, Marc. The poverty of Linnaean hierarchy: A philosophical study of biological taxonomy. Cambridge University Press, 2001.

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1956-, Catto Norman Rhoderick, and Canada. Dept. of Fisheries and Oceans. Science Branch., eds. Biological and geomorphological classification of Placentia Bay: A preliminary assessment. Science Branch, Dept. of Fisheries and Oceans, 1999.

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Book chapters on the topic "Biological classifications"

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Minelli, Alessandro. "Systems and classifications." In Biological Systematics. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-9643-7_1.

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Reitz, Elizabeth J., and Myra Shackley. "Biological Classifications and Nomenclatures." In Manuals in Archaeological Method, Theory and Technique. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-3339-2_4.

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Nagar, Yiftach, Weaam Shaheen, and Ofer Arazy. "Comparing Competing Approaches to Crowdsourced Classifications of Biological Species." In Lecture Notes in Business Information Processing. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-81325-2_18.

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Yoon, Neville, and John Rose. "An Information Model for the Representation of Multiple Biological Classifications." In Computational Science — ICCS 2001. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45545-0_106.

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Yaremchuk, Vanessa, and Michael R. W. Dawson. "Chord Classifications by Artificial Neural Networks Revisited: Internal Representations of Circles of Major Thirds and Minor Thirds." In Artificial Neural Networks: Biological Inspirations – ICANN 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11550822_94.

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Berkhahn, Magda, Gerald Kremer, Theresa Riedelsheimer, Kai Lindow, and Rainer Stark. "Taxonomy for Biological Transformation Principles in the Manufacturing Industry." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28839-5_109.

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AbstractIndustry and research are seeking answers to current demands in industrial value creation, like resilience of production, sufficient product quality and sustainability of products and processes. A novel line of thought, seeking the accomplishment of those is the Biological Transformation (BT). BT describes the interweaving of biological modes of action, materials and organisms with engineering and information sciences. The conflation of disciplines from natural, technical and social sciences yields in a heterogeneous field of activities with ambiguous technical terms. An ascertainment
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Graziottin, Alessandra, Elisa Maseroli, and Linda Vignozzi. "Female Sexual Dysfunctions: A Clinical Perspective on HSDD, FAD, PGAD, and FOD." In Practical Clinical Andrology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11701-5_8.

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AbstractThe multidimensionality and interpersonal dimension of human sexuality make the study of female sexual dysfunction (FSD) a challenge. The aspiration to pursue a patient-centered, holistic approach collides with the need to establish commonly accepted diagnostic criteria for different disturbances in sexual functioning, involving one or multiple phases of the sexual response cycle and pain associated with sexual activity. According to recently proposed classifications, which are continuously evolving, the most relevant FSDs include hypoactive sexual desire disorder, female arousal disor
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Bolshoy, Alexander, Zeev (Vladimir) Volkovich, Valery Kirzhner, and Zeev Barzily. "Biological Classification." In Genome Clustering. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12952-0_2.

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Kiasaleh, Kamran. "Biological Signals." In Biological Signals Classification and Analysis. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-54879-6_3.

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Hidayati, Syafitri, F. Merlin Franco, Bibi Aminah Abdul Ghani, Beena Giridharan, and Mohd Zafri Hassan. "Folk Fish Names Are Condensed Forms of Traditional Knowledge: Case Study with the Vaie People of Sarawak, Malaysia." In Case Studies in Biocultural Diversity from Southeast Asia. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6719-0_8.

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AbstractFolk names that form the backbone of folk biological classifications are coined using traditional knowledge and linguistic mechanisms. In the process, traditional knowledge on the salient characteristics of the denoted taxa gets encoded in their respective names. In this chapter, we present 132 folk names used by the Vaie community of Sarawak, Malaysia to denote the fish known to them. Based on the apparency of traditional knowledge encoded by these names, we classify them into barefaced and cryptic. We found 61 Vaie fish names encoding traditional knowledge related to ecology, morphol
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Conference papers on the topic "Biological classifications"

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Wei, Yuanyuan, Dehua Hu, Khadija Bibi, et al. "An innovative deep learning-empowered paradigm for precise biological sample quantification." In CLEO: Applications and Technology. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_at.2024.af1b.6.

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We present an innovative deep learning-aided paradigm that enables real-time and automated detection and classification of GFP (Green fluorescence protein)-labeled microreactor, overcoming the limitations of conventional methods.
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Shree, D. Vijaya, B. L. Shiva Kumar, V. Ganesan, M. Sivaraman, and J. Sumitha. "Biological Data Analysis for Disease Prediction and Classification in Bioinformatics." In 2024 8th International Conference on Inventive Systems and Control (ICISC). IEEE, 2024. http://dx.doi.org/10.1109/icisc62624.2024.00080.

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Maier, Patrick, James Rainey, Elena Gheorghiu, Kofi Appiah, and Deepayan Bhowmik. "Digit classification using biologically plausible neuromorphic vision." In Applications of Digital Image Processing XLVII, edited by Andrew G. Tescher and Touradj Ebrahimi. SPIE, 2024. http://dx.doi.org/10.1117/12.3031280.

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Lavanya, Kotha, Gudditi Chetan, Manjunaatt Girish Kumar, and Nagendra Panini Challa. "Ensemble Machine Learning Approach for Biological Bird Species Detection and Classification." In 2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON). IEEE, 2024. https://doi.org/10.1109/delcon64804.2024.10866572.

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Kalpana, T., P. Meghana, M. Spandana, and T. Sai Eswar Kumar. "Biologically Inspired Spiking CNN for Brain Tumor Classification." In 2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN). IEEE, 2024. http://dx.doi.org/10.1109/icipcn63822.2024.00035.

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Fiore, Pierpaolo, Daniele Pirone, Francesco Bardozzo, et al. "Artificial Intelligence for Label-free cells classification in holographic microscopy." In Digital Holography and Three-Dimensional Imaging. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/dh.2024.w4a.29.

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Digital holography in microscopy is one of the emerging technologies to deal with biological specimen imaging without using exogenous agents. Artificial Intelligence approaches are presented here to classify label-free cells also in presence of bias.
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Nkounkou, Brittany, Chih Lee, Chun-Hsi Huang, and Colin Brown. "Biological data classifications with LDA and SPRT." In 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2010. http://dx.doi.org/10.1109/bibmw.2010.5703792.

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Bozhchenko, Alexandr, and Sergey Semenov. "On the classification of damaging factors in forensic medicine." In Issues of determining the severity of harm caused to human health as a result of the impact of a biological factor. Publishing Center RIOR, 2020. http://dx.doi.org/10.29039/conferencearticle_5fdcb03a403b58.93332884.

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The article considers the disadvantages of modern forensic classifications of damaging factors. Attention is drawn to the fact that the mental damaging factor undoubtedly exists, but the assessment of its specific role in the formation of “damage” is the subject of forensic psychiatry, which is an independent medical discipline. The social factor mainly affects the behavior of an individual (population group), but its specific features are also not evaluated by methods and techniques of forensic medicine. There is a discrepancy between general and particular classifications — in particular, th
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Prado-Vázquez, G., L. Trilla-Fuertes, A. Gámez-Pozo, et al. "PO-522 Biological layers identified two independent classifications in melanoma tumours." In Abstracts of the 25th Biennial Congress of the European Association for Cancer Research, Amsterdam, The Netherlands, 30 June – 3 July 2018. BMJ Publishing Group Ltd, 2018. http://dx.doi.org/10.1136/esmoopen-2018-eacr25.537.

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Vakili, V., I. Chiu, L. H. Shu, D. A. McAdams, and R. B. Stone. "Including Functional Models of Biological Phenomena as Design Stimuli." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35776.

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This work explores the representation of biological phenomena as stimuli to designers for biomimetic design. We describe a study where participants were asked to solve a micro-assembly problem given a set of biological representations of leaf abscission for inspiration. The visual aids presented to the designers are investigated, and the use of functional models of biological phenomena in particular is critiqued. The designs resulting from the study are classified and theories drawn as to possible influences of the biological representations. Observations, retrospective conversations with part
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Reports on the topic "Biological classifications"

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Fristrup, Kurt M. Automatic Classification of Biological Sounds in the Arctic. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada329473.

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Schoomaker, Peter J., and Sandra R. Riley. Classification of Former Chemical Warfare, Chemical and Biological Defense, and Nuclear, Biological, and Chemical Contamination Survivability Information. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada440765.

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Munsky, Brian, Scott Sellars, John D. Yeager, Harshini Mukundan, and Brett Ross Nadler. Integration of hyperspectral imagery and biosensors for biological and chemical facility classification. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1079559.

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Munsky, Brian, John D. Yeager, Scott Sellars, Harshini Mukundan, and Brett Ross Nadler. Integration of Hyperspectral Imagery and Biosensors for Biological and Chemical Facility Classification. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1084569.

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Curtis, Rob, Sonia Bingham, and Megan Shaeffer. Natural resource assessment of the proposed Columbia Sensitive Resource Zone, Cuyahoga Valley National Park, Ohio. National Park Service, 2025. https://doi.org/10.36967/2310114.

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From 2021 – 2023, Cuyahoga Valley National Park (CUVA) and Summit Metro Parks (SMP) worked collaboratively through an interagency agreement to inventory and classify natural and cultural resources in an ecologically sensitive zone around Blue Hen Falls and Columbia Picnic area. Increased visitation in recent years created safety concerns, trail erosion and damage to sensitive resources and locally rare habitats. At the time of this study, these areas were located within one of CUVA’s management areas proposed as a Sensitive Resource Zone (SRZ), specifically the Columbia SRZ. The SRZ designatio
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Savosko, V., I. Komarova, Yu Lykholat, E. Yevtushenko, and T. Lykholat. Predictive model of heavy metals inputs to soil at Kryvyi Rih District and its use in the training for specialists in the field of Biology. IOP Publishing, 2021. http://dx.doi.org/10.31812/123456789/4511.

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The importance of our research is due to the need to introduce into modern biological education methods of predictive modeling which are based on relevant factual material. Such an actual material may be the entry of natural and anthropic heavy metals into the soil at industrial areas. The object of this work: (i) to work out a predictive model of the total heavy metals inputs to soil at the Kryvyi Rih ore-mining &amp; metallurgical District and (ii) to identify ways to use this model in biological education. Our study areas are located in the Kryvyi Rih District (Dnipropetrovsk region, Centra
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Савосько, Василь Миколайович, Ірина Олександрівна Комарова, Юрій Васильович Лихолат, Едуард Олексійович Євтушенко,, and Тетяна Юріївна Лихолат. Predictive Model of Heavy Metals Inputs to Soil at Kryvyi Rih District and its Use in the Training for Specialists in the Field of Biology. IOP Publishing, 2021. http://dx.doi.org/10.31812/123456789/4266.

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The importance of our research is due to the need to introduce into modern biological education methods of predictive modeling which are based on relevant factual material. Such an actual material may be the entry of natural and anthropic heavy metals into the soil at industrial areas. The object of this work: (i) to work out a predictive model of the total heavy metals inputs to soil at the Kryvyi Rih ore-mining &amp; metallurgical District and (ii) to identify ways to use this model in biological education. Our study areas are located in the Kryvyi Rih District (Dnipropetrovsk region, Centra
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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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Neeley, Aimee, Stace E. Beaulieu, Chris Proctor, et al. Standards and practices for reporting plankton and other particle observations from images. Woods Hole Oceanographic Institution, 2021. http://dx.doi.org/10.1575/1912/27377.

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This technical manual guides the user through the process of creating a data table for the submission of taxonomic and morphological information for plankton and other particles from images to a repository. Guidance is provided to produce documentation that should accompany the submission of plankton and other particle data to a repository, describes data collection and processing techniques, and outlines the creation of a data file. Field names include scientificName that represents the lowest level taxonomic classification (e.g., genus if not certain of species, family if not certain of genu
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Anderson, Paul, Yakuta Baghat, Brad Bartelme, Nicole Stolic, and Melissa Vaccarino. Cuyahoga Valley National Park headwater stream inventory: Final report. National Park Service, 2024. http://dx.doi.org/10.36967/2302348.

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EnviroScience, Inc. (EnviroScience) was contracted by the National Park Service (NPS) to design and implement a study plan to inventory the headwater stream resources within the Cuyahoga Valley National Park (CUVA). The parameters and expectations of the study are described in a Statement of Work (SOW) developed by NPS staff (NPS, 2021) to collect and analyze data to guide decision-making in the development of management plans to protect these resources within CUVA. The headwater stream inventory was comprised of three components to evaluate the resources as follows: 1. habitat and biological
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