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New York: Dover Publications, Inc; Person K. LIII on lines and planes of closest fit to systems of points in space. Hotelling H. Analysis of a complex of statistical variables into principal components. J Educ Psychol. Algorithm AS a K-means clustering algorithm. CURE: an efficient clustering algorithm for large databases. BIRCH: an efficient data clustering method for very large databases.
Cheng Y. Mean shift, mode seeking, and clustering. TCP ports information. Download references. You can also search for this author in PubMed Google Scholar. Correspondence to R. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Ananda Mohan and Balaji Rajendran. Reprints and Permissions. Niranjana, R. Download citation. Received : 14 April Accepted : 03 August Published : 14 August Anyone you share the following link with will be able to read this content:.
Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search SpringerLink Search. Abstract The cyberspace continues to evolve more complex than ever anticipated, and same is the case with security dynamics there. References 1. Google Scholar 2. Article Google Scholar 6.
Article Google Scholar 7. Article Google Scholar 8. Article Google Scholar Anil Kumar Authors R. Niranjana View author publications. View author publications. Permin, A. The development of Plasmodium gallinaceum infections in chickens following single infections with three different dose levels. Wongsrichanalai, C. A review of malaria diagnostic tools: Microscopy and rapid diagnostic test RDT.
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Loey, M. Cities Soc. Kittichai, V. Deep learning approaches for challenging species and gender identification of mosquito vectors. Liu, L. Deep learning for generic object detection: A survey. Nguyen, N. An evaluation of deep learning methods for small object detection. Joseph, R. Accessed 10 Jul Bolei, Z. Learning deep features for discriminative localization. Christian Matek, S. Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks.
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PeerJ 7 , e Berrar, D. Download references. You can also search for this author in PubMed Google Scholar. Correspondence to Siridech Boonsang. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Reprints and Permissions. Classification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks. Sci Rep 11, Download citation. Received : 28 April Accepted : 11 August Published : 19 August Anyone you share the following link with will be able to read this content:.
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Advanced search. Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily. Skip to main content Thank you for visiting nature. Download PDF. Subjects Mathematics and computing Parasitology Pathology. Abstract The infection of an avian malaria parasite Plasmodium gallinaceum in domestic chickens presents a major threat to the poultry industry because it causes economic loss in both the quality and quantity of meat and egg production. Introduction Avian malaria, a mosquito-borne disease, is one of the most common veterinary threats in tropical regions, including South East Asia and South Asia 1.
Methods Ethics statement Archived Giemsa-stained thin-blood films have been collected from previous studies 1 , Data collections In the present study, blood films were prepared immediately after withdrawal of blood samples from the infected chickens. A hybrid two-stage model: RBC detection and classification models The proposed methodology for classifying the blood stages of avian malaria, P. Dataset preparation Two datasets were developed by a team of experts who labeled all microscopic examination images for RBCs 1 , Figure 1.
Full size image. Performance comparison of classification models In this analysis, the model-wise performance was assessed as to whether the classification model was the best-selected model based on an attention map and used to estimate P.
Table 1 Model-wise comparison and multiclass-wise comparison based on the accuracy from one-class versus total. Full size table. Table 2 Model-wise comparison and multiclass-wise comparison based on the sensitivity from one-class versus total. Table 3 Model-wise comparison and multiclass-wise comparison based on specificity from one-class versus total.
Table 4 Model-wise comparison and multiclass-wise comparison based on misclassification rate from one-class versus total. Table 5 Model-wise comparison and multiclass-wise comparison based on precision from one-class versus total.
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I use darknet to retrain a image classification model sentropesochi.ru I want to convert it for OpenVINO, how could I do? sentropesochi.ru GitHub - AlexeyAB/darknet: YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Text Classification using Neural Networks. Я прошел модельное обучение для обнаружения возражений Yolov4 из пакета AlexeyAB Darknet на Colab. (из sentropesochi.ru) Определены два.