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Mar 22, 2022 · This paper focuses on the recognition of cattle using its primary feature, i.e., muzzle point for identification in real time, and also states the current techniques used for identification like Bluetooth, WiFi, RFID, and traditional methods used for identification of cattle with the challenges faced.
Oct 1, 2017 · The muzzle point image pattern is a primary animal biometric characteristic for the recognition of individual cattle. It is similar to the identification of minutiae points in human fingerprints.
- Santosh Kumar, Sanjay Kumar Singh, Amit Kumar Singh
- 26
- 2017
- 01 October 2017
- Overview
- Underpinnings of computational biology
- Applications of computational biology
- Distinctions among related fields
computational biology, a branch of biology involving the application of computers and computer science to the understanding and modeling of the structures and processes of life. It entails the use of computational methods (e.g., algorithms) for the representation and simulation of biological systems, as well as for the interpretation of experimenta...
The beginnings of computational biology essentially date to the origins of computer science. British mathematician and logician Alan Turing, often called the father of computing, used early computers to implement a model of biological morphogenesis (the development of pattern and form in living organisms) in the early 1950s, shortly before his death. At about the same time, a computer called MANIAC, built at the Los Alamos National Laboratory in New Mexico for weapons research, was applied to such purposes as modeling hypothesized genetic codes. (Pioneering computers had been used even earlier in the 1950s for numeric calculations in population genetics, but the first instances of authentic computational modeling in biology were the work by Turing and by the group at Los Alamos.)
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By the 1960s, computers had been applied to deal with much more-varied sets of analyses, namely those examining protein structure. These developments marked the rise of computational biology as a field, and they originated from studies centred on protein crystallography, in which scientists found computers indispensable for carrying out laborious Fourier analyses to determine the three-dimensional structure of proteins.
Starting in the 1950s, taxonomists began to incorporate computers into their work, using the machines to assist in the classification of organisms by clustering them based on similarities of sets of traits. Such taxonomies have been useful particularly for phylogenetics (the study of evolutionary relationships). In the 1960s, when existing techniques were extended to the level of DNA sequences and amino acid sequences of proteins and combined with a burgeoning knowledge of cellular processes and protein structures, a whole new set of computational methods was developed in support of molecular phylogenetics. These computational methods entailed the creation of increasingly sophisticated techniques for the comparison of strings of symbols that benefited from the formal study of algorithms and the study of dynamic programming in particular. Indeed, efficient algorithms always have been of primary concern in computational biology, given the scale of data available, and biology has in turn provided examples that have driven much advanced research in computer science. Examples include graph algorithms for genome mapping (the process of locating fragments of DNA on chromosomes) and for certain types of DNA and peptide sequencing methods, clustering algorithms for gene expression analysis and phylogenetic reconstruction, and pattern matching for various sequence search problems.
Beginning in the 1980s, computational biology drew on further developments in computer science, including a number of aspects of artificial intelligence (AI). Among these were knowledge representation, which contributed to the development of ontologies (the representation of concepts and their relationships) that codify biological knowledge in “computer-readable” form, and natural-language processing, which provided a technological means for mining information from text in the scientific literature. Perhaps most significantly, the subfield of machine learning found wide use in biology, from modeling sequences for purposes of pattern recognition to the analysis of high-dimensional (complex) data from large-scale gene-expression studies.
Initially, computational biology focused on the study of the sequence and structure of biological molecules, often in an evolutionary context. Beginning in the 1990s, however, it extended increasingly to the analysis of function. Functional prediction involves assessing the sequence and structural similarity between an unknown and a known protein and analyzing the proteins’ interactions with other molecules. Such analyses may be extensive, and thus computational biology has become closely aligned with systems biology, which attempts to analyze the workings of large interacting networks of biological components, especially biological pathways.
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Biochemical, regulatory, and genetic pathways are highly branched and interleaved, as well as dynamic, calling for sophisticated computational tools for their modeling and analysis. Moreover, modern technology platforms for the rapid, automated (high-throughput) generation of biological data have allowed for an extension from traditional hypothesis-driven experimentation to data-driven analysis, by which computational experiments can be performed on genome-wide databases of unprecedented scale. As a result, many aspects of the study of biology have become unthinkable without the power of computers and the methodologies of computer science.
How best to distinguish computational biology from the related field of bioinformatics, and to a lesser extent from the fields of mathematical and theoretical biology, has long been a matter of debate. The terms bioinformatics and computational biology are often used interchangeably, even by experts, and many feel that the distinctions are not useful. Both fields fundamentally are computational approaches to biology. However, whereas bioinformatics tends to refer to data management and analysis using tools that are aids to biological experimentation and to the interpretation of laboratory results, computational biology typically is thought of as a branch of biology, in the same sense that computational physics is a branch of physics. In particular, computational biology is a branch of biology that is uniquely enabled by computation. In other words, its formation was not defined by a need to deal with scale; rather, it was defined by virtue of the techniques that computer science brought to the formulation and solving of challenging problems, to the representation and examination of domain knowledge, and ultimately to the generation and testing of scientific hypotheses.
Computational biology is more easily distinguished from mathematical biology, though there are overlaps. The older discipline of mathematical biology was concerned primarily with applications of numerical analysis, especially differential equations, to topics such as population dynamics and enzyme kinetics. It later expanded to include the application of advanced mathematical approaches in genetics, evolution, and spatial modeling. Such mathematical analyses inevitably benefited from computers, especially in instances involving systems of differential equations that required simulation for their solution. The use of automated calculation does not in itself qualify such activities as computational biology. However, mathematical modeling of biological systems does overlap with computational biology, particularly where simulation for purposes of prediction or hypothesis generation is a key element of the model. A useful distinction in this regard is that between numerical analysis and discrete mathematics; the latter, which is concerned with symbolic rather than numeric manipulations, is considered foundational to computer science, and in general its applications to biology may be considered aspects of computational biology.
Nov 22, 2021 · In the context of muzzle detection, the YOLOv3 framework both detects the presence of a muzzle and provides the rectangular image coordinates of its location within the image. The YOLOv3 framework achieves this task by dividing images into S × S grid, where S represents the size of the grid, and uses each grid to predict the object centered in ...
- Ali Shojaeipour, Greg Falzon, Paul Kwan, Nooshin Hadavi, Frances C. Cowley, David Paul
- 2021
Computational biology refers to the use of data analysis, mathematical modeling and computational simulations to understand biological systems and relationships. [1] An intersection of computer science, biology, and big data, the field also has foundations in applied mathematics, chemistry, and genetics. [2]
Sep 13, 2021 · The field of machine learning essentially attempts to approximate or imitate humans’ ability to recognize patterns, albeit in an objective manner, using computation.
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3 days ago · Previous Edition (6 ed.) Over 6,500 entries. Previously named A Dictionary of Computing, this bestselling dictionary has been fully revised by a team of computer specialists, making it the most up-to-date and authoritative guide to computing available.
