Relevant bibliographies by topics / Animal automatic identification

Academic literature on the topic 'Animal automatic identification'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Animal automatic identification"

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Hu, Xu Ling. "Research on Radio Frequency Identification Technology." Applied Mechanics and Materials 299 (February 2013): 152–55. http://dx.doi.org/10.4028/www.scientific.net/amm.299.152.

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Radio frequency identification (RFID) is an automatic identification technology, is characterized by its greatest non-contact identification. In recent years, RFID technology is widely used in transportation management, logistics management, production automation, security access checking, warehousing management, security management, animal management, and other fields. Apply RFID technology to manage applications for research is important.
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., G. N. Murugananthan. "AUTOMATIC IDENTIFICATION OF ANIMAL USING VISUAL AND MOTION SALIENCY." International Journal of Research in Engineering and Technology 03, no. 04 (April 25, 2014): 64–68. http://dx.doi.org/10.15623/ijret.2014.0304012.

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Gouws, J. "Tegnologie vir ten volle geoutomatiseerde melking van koeie." Suid-Afrikaanse Tydskrif vir Natuurwetenskap en Tegnologie 13, no. 4 (July 10, 1994): 120–24. http://dx.doi.org/10.4102/satnt.v13i4.593.

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Since dairy farming is a very labour intensive, seven-days-per-week activity, increasing emphasis is being placed on the use of advanced technology in dairying throughout the world. Dairy mechanisation has been well established for many years, whereas dairy automation has only started to gain momentum fairly recently. An important milestone was the introduction of systems for automatic animal identification in the 1970’s. That paved the way for all further dairy automation activities. An analysis of the current status of the fully automated milking of cows shows that the automated attachment of a milking machine’s teat cups to a cow ’s teats is the most important task in dairying that remains to be automated.
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Slader, R. W., and A. M. S. Gregory. "Electronic live animal and carcass identification systems." Proceedings of the British Society of Animal Production (1972) 1990 (March 1990): 102. http://dx.doi.org/10.1017/s0308229600018833.

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Unique and secure identification of livestock from birth through to the carcass in the abattoir will become a key feature in many areas of livestock production: disease eradication; quality assurance schemes; pedigree breeding; and producer feedback from abattoirs. There have been many attempts at adapting traditional methods of animal identification to provide a measure of automatic recognition. None has been as successful as the application of radio frequency transponders which transmit a unique code when triggered by a suitable reader.
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Stowell, Dan, Tereza Petrusková, Martin Šálek, and Pavel Linhart. "Automatic acoustic identification of individuals in multiple species: improving identification across recording conditions." Journal of The Royal Society Interface 16, no. 153 (April 10, 2019): 20180940. http://dx.doi.org/10.1098/rsif.2018.0940.

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Many animals emit vocal sounds which, independently from the sounds’ function, contain some individually distinctive signature. Thus the automatic recognition of individuals by sound is a potentially powerful tool for zoology and ecology research and practical monitoring. Here, we present a general automatic identification method that can work across multiple animal species with various levels of complexity in their communication systems. We further introduce new analysis techniques based on dataset manipulations that can evaluate the robustness and generality of a classifier. By using these techniques, we confirmed the presence of experimental confounds in situations resembling those from past studies. We introduce data manipulations that can reduce the impact of these confounds, compatible with any classifier. We suggest that assessment of confounds should become a standard part of future studies to ensure they do not report over-optimistic results. We provide annotated recordings used for analyses along with this study and we call for dataset sharing to be a common practice to enhance the development of methods and comparisons of results.
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Zhou, Meilun, Jared A. Elmore, Sathishkumar Samiappan, Kristine O. Evans, Morgan B. Pfeiffer, Bradley F. Blackwell, and Raymond B. Iglay. "Improving Animal Monitoring Using Small Unmanned Aircraft Systems (sUAS) and Deep Learning Networks." Sensors 21, no. 17 (August 24, 2021): 5697. http://dx.doi.org/10.3390/s21175697.

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In recent years, small unmanned aircraft systems (sUAS) have been used widely to monitor animals because of their customizability, ease of operating, ability to access difficult to navigate places, and potential to minimize disturbance to animals. Automatic identification and classification of animals through images acquired using a sUAS may solve critical problems such as monitoring large areas with high vehicle traffic for animals to prevent collisions, such as animal-aircraft collisions on airports. In this research we demonstrate automated identification of four animal species using deep learning animal classification models trained on sUAS collected images. We used a sUAS mounted with visible spectrum cameras to capture 1288 images of four different animal species: cattle (Bos taurus), horses (Equus caballus), Canada Geese (Branta canadensis), and white-tailed deer (Odocoileus virginianus). We chose these animals because they were readily accessible and white-tailed deer and Canada Geese are considered aviation hazards, as well as being easily identifiable within aerial imagery. A four-class classification problem involving these species was developed from the acquired data using deep learning neural networks. We studied the performance of two deep neural network models, convolutional neural networks (CNN) and deep residual networks (ResNet). Results indicate that the ResNet model with 18 layers, ResNet 18, may be an effective algorithm at classifying between animals while using a relatively small number of training samples. The best ResNet architecture produced a 99.18% overall accuracy (OA) in animal identification and a Kappa statistic of 0.98. The highest OA and Kappa produced by CNN were 84.55% and 0.79 respectively. These findings suggest that ResNet is effective at distinguishing among the four species tested and shows promise for classifying larger datasets of more diverse animals.
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Dela Rue, B. T., C. R. Eastwood, J. P. Edwards, and S. Cuthbert. "New Zealand dairy farmers preference investments in automation technology over decision-support technology." Animal Production Science 60, no. 1 (2020): 133. http://dx.doi.org/10.1071/an18566.

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Dairy farmers are adopting precision technologies to assist with milking and managing their cows due to increased herd sizes and a desire to improve labour efficiency, productivity and sustainability. In the present study, we evaluated the adoption of technologies installed at or near the dairy, and milking practices, on New Zealand dairy farms. These data quantify current use of technology for milking and labour efficiency, and decision-making, and provide insight into future technology adoption. A telephone survey of 500 farmers, randomly selected from a database of New Zealand dairy farms, was conducted in 2018. Adoption for all farms is indicated for six automation technologies, including automatic cup removers (39%), automatic drafting (24%), automatic teat spraying (29%), computer-controlled in-shed feeding (29%), automatic plant wash (18%) and automatic yard wash systems (27%). Five data-capture technologies also included in the survey were electronic milk meters (8%), automatic animal weighing (7%), in-line mastitis detection (7%), automatic heat detection (3%) and electronic animal-identification readers (23%). Analysis by dairy type indicated an adoption level for the automation technologies in rotary dairies of 36–77%, and 7–49% for data-capture technologies, with 10% having none of these 11 technologies installed. This compares with herringbone dairies at 4–21% and 2–11% for automation and data-capture technologies respectively, with 56% having none of these technologies. Rotary dairies, with a combination of automatic cup removers, automatic teat spraying, and automatic drafting, were associated with 43% higher labour efficiency (cows milked/h.person) and 14% higher milking efficiency (cows milked/h) than were rotary dairies without all three technologies. Dairy farmers will increasingly use technologies that deliver value, and the present study has provided information to guide investment decisions, product development and research in areas such as applying technology in new workplaces.
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SEDOV, ALEKSEY. "DEVELOPMENT OF AN INTELLIGENT MACHINE FOR SORTING ANIMALS ACCORDING TO SPECIFIED CRITERIA." Elektrotekhnologii i elektrooborudovanie v APK 4, no. 41 (December 2020): 83–87. http://dx.doi.org/10.22314/2658-4859-2020-67-4-83-87.

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The Federal scientific Agroengineering center VIM has developed technical tools, algorithms and software for the intelligent automatic control system for milking animals “Stimul” on the “Herringbone” milking unit in three versions. The created system does not include automatic selection gates for effective management of zootechnical and veterinary services of animals. (Research purpose) The research purpose is in developing an intelligent machine for automatic sorting of animals for servicing and managing the herd according to specified characteristics. (Materials and methods) The article presents the development of control and management systems in dairy farming based on the conceptual principles of digital transformation. The digital control system is based on a multifunctional panel controller. The created control unit has a port for connecting to the RS 485 network and provides support for network functions via the Modbus Protocol. The programming of the control unit has been made in the SMLogix tool environment, which supports the FBD function block language. (Results and discussion) The article presents an intelligent machine for automatic sorting of animal flows for servicing and managing the herd according to specified characteristics with the unification of hardware, software modules and interface. The article describes the necessary parameters for the automatic remote animal identification system, the basic component of the control system of an intelligent machine for sorting animals according to specified characteristics. (Conclusions) The machine allows to automatically identify, sort and send animals to the specified areas for individual service.
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Barge, P., P. Gay, V. Merlino, and C. Tortia. "Radio frequency identification technologies for livestock management and meat supply chain traceability." Canadian Journal of Animal Science 93, no. 1 (March 2013): 23–33. http://dx.doi.org/10.4141/cjas2012-029.

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Barge, P., Gay, P., Merlino, V. and Tortia, C. 2013. Radio frequency identification technologies for livestock management and meat supply chain traceability. Can. J. Anim. Sci. 93: 23–33. Animal electronic identification could be exploited by farmers as an interesting opportunity to increase the efficiency of herd management and traceability. Although radio frequency identification (RFID) solutions for animal identification have already been envisaged, the integration of a RFID traceability system at farm level has to be carried out carefully, considering different aspects (farm type, number and species of animals, barn structure). The tag persistence on the animal after application, the tag-to-tag collisions in the case of many animals contemporarily present in the reading area of the same antenna and the barn layout play determinant roles in system reliability. The goal of this paper is to evaluate the RFID identification system performance and determine the best practice to apply these devices in livestock management. RFID systems were tested both in laboratory, on the farm and in slaughterhouses for the implementation of a traceability system with automatic animal data capture. For this purpose a complete system for animal identification and tracking, accomplishing regulatory compliance as well as supply chain management requirements, has been developed and is described in the paper. Results were encouraging for identification of calves both in farms and slaughterhouses, while in swine breeding, identification was critical for small piglets. In this case, the design of a RFID gate where tag-to-tag collisions are avoided should be envisaged.
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Manohar, N., Y. H. Sharath Kumar, and G. Hemantha Kumar. "An Approach for the Development of Animal Tracking System." International Journal of Computer Vision and Image Processing 8, no. 1 (January 2018): 15–31. http://dx.doi.org/10.4018/ijcvip.2018010102.

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In this article, the authors propose a system which can identify and track animals. Identification and tracking of animals has got plenty of applications like, avoiding dangerous animal intrusion into residential areas, avoiding animal-vehicle collisions, and behavioral study of animals and so on. Previously, biologists studied videos to detect and identify animals, a time consuming and difficult task. This requires a fully automatic or computer-assisted system to identify and track animals by video. Initially, frames are extracted from the given video. Segmentation is done to the extracted frames using a maximum similarity-based region merging algorithm. Then, the mean shift-based algorithm is used to track the animals. Finally, the animals are classified using Gabor features and a KNN classifier. Experimentation has been conducted on a data set containing more than 150 videos with 15 different classes.
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Dissertations / Theses on the topic "Animal automatic identification"

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Maddali, Hanuma Teja. "Inferring social structure and dominance relationships between rhesus macaques using RFID tracking data." Thesis, Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51866.

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This research address the problem of inferring, through Radio-Frequency Identification (RFID) tracking data, the graph structures underlying social interactions in a group of rhesus macaques (a species of monkey). These social interactions are considered as independent affiliative and dominative components and are characterized by a variety of visual and auditory displays and gestures. Social structure in a group is an important indicator of its members’ relative level of access to resources and has interesting implications for an individual’s health. Automatic inference of the social structure in an animal group enables a number of important capabilities, including: 1. A verifiable measure of how the social structure is affected by an intervention such as a change in the environment, or the introduction of another animal, and 2. A potentially significant reduction in person hours normally used for assessing these changes. The behaviors of interest in the context of this research are those definable using the macaques’ spatial (x,y,z) position and motion inside an enclosure. Periods of time spent in close proximity with other group members are considered to be events of passive interaction and are used in the calculation of an Affiliation Matrix. This represents the strength of undirected interaction or tie-strength between individual animals. Dominance is a directed relation that is quantified using a heuristic for the detection of withdrawal and displacement behaviors. The results of an analysis based on these approaches for a group of 6 male monkeys that were tracked over a period of 60 days at the Yerkes Primate Research Center are presented in this Thesis.
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Books on the topic "Animal automatic identification"

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Lambooij, Ed E. Automatic Electronic Identification Systems for Farm Animals. Unipub, 1991.

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Book chapters on the topic "Animal automatic identification"

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Oswald, Julie N., Christine Erbe, William L. Gannon, Shyam Madhusudhana, and Jeanette A. Thomas. "Detection and Classification Methods for Animal Sounds." In Exploring Animal Behavior Through Sound: Volume 1, 269–317. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97540-1_8.

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AbstractClassification of the acoustic repertoires of animals into sound types is a useful tool for taxonomic studies, behavioral studies, and for documenting the occurrence of animals. Classification of acoustic repertoires enables the identification of species, age, gender, and individual identity, correlations between sound types and behavior, the identification of changes in vocal behavior over time or in response to anthropogenic noise, comparisons between the repertoires of populations living in different geographic regions and environments, and the development of software tools for automated signal processing. Techniques for classification have evolved over time as technical capabilities have expanded. Initially, researchers applied qualitative methods, such as listening and visually discerning sounds in spectrograms. Advances in computer technology and the development of software for the automatic detection and classification of sounds have allowed bioacousticians to quickly find sounds in recordings, thus significantly reducing analysis time and enabling the analysis of larger datasets. In this chapter, we present software algorithms for automated signal detection (based on energy, Teager–Kaiser energy, spectral entropy, matched filtering, and spectrogram cross-correlation) as well as for signal classification (e.g., parametric clustering, principal component analysis, discriminant function analysis, classification trees, artificial neural networks, random forests, Gaussian mixture models, support vector machines, dynamic time-warping, and hidden Markov models). Methods for evaluating the performance of automated tools are presented (i.e., receiver operating characteristics and precision-recall) and challenges with classifying animal sounds are discussed.
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Breth, Björn. "Design and Development of a High-Throughput Platform for Rapid Microbe Identification and Automatic Data Management." In Proceedings of the 21st Annual Meeting of the European Society for Animal Cell Technology (ESACT), Dublin, Ireland, June 7-10, 2009, 307–13. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-0884-6_46.

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Tharwat, Alaa, Tarek Gaber, Aboul Ella Hassanien, Gerald Schaefer, and Jeng-Shyang Pan. "A Fully-Automated Zebra Animal Identification Approach Based on SIFT Features." In Advances in Intelligent Systems and Computing, 289–97. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48490-7_34.

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Choiński, Mateusz, Mateusz Rogowski, Piotr Tynecki, Dries P. J. Kuijper, Marcin Churski, and Jakub W. Bubnicki. "A First Step Towards Automated Species Recognition from Camera Trap Images of Mammals Using AI in a European Temperate Forest." In Computer Information Systems and Industrial Management, 299–310. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84340-3_24.

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AbstractCamera traps are used worldwide to monitor wildlife. Despite the increasing availability of Deep Learning (DL) models, the effective usage of this technology to support wildlife monitoring is limited. This is mainly due to the complexity of DL technology and high computing requirements. This paper presents the implementation of the light-weight and state-of-the-art YOLOv5 architecture for automated labeling of camera trap images of mammals in the Białowieża Forest (BF), Poland. The camera trapping data were organized and harmonized using TRAPPER software, an open-source application for managing large-scale wildlife monitoring projects. The proposed image recognition pipeline achieved an average accuracy of 85% F1-score in the identification of the 12 most commonly occurring medium-size and large mammal species in BF, using a limited set of training and testing data (a total of 2659 images with animals).Based on the preliminary results, we have concluded that the YOLOv5 object detection and classification model is a fine and promising DL solution after the adoption of the transfer learning technique. It can be efficiently plugged in via an API into existing web-based camera trapping data processing platforms such as e.g. TRAPPER system. Since TRAPPER is already used to manage and classify (manually) camera trapping datasets by many research groups in Europe, the implementation of AI-based automated species classification will significantly speed up the data processing workflow and thus better support data-driven wildlife monitoring and conservation. Moreover, YOLOv5 has been proven to perform well on edge devices, which may open a new chapter in animal population monitoring in real-time directly from camera trap devices.
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Exadaktylos, Vasileios, Mitchell Silva, and Daniel Berckmans. "Automatic Identification and Interpretation of Animal Sounds, Application to Livestock Production Optimisation." In Soundscape Semiotics - Localisation and Categorisation. InTech, 2014. http://dx.doi.org/10.5772/56040.

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Pang, Les. "Radio Frequency IdentificationTechnology in Digital Government." In Information Security and Ethics, 2623–33. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-937-3.ch174.

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Following technical strides in radio and radar in the 1930s and 1940s, the 1950s were a period of exploration for radio frequency identity (RFID) technology as shown by the landmark development of the long-range transponder systems for the “identification, friend or foe” for aircraft. Commercial use of RFID appeared in the 1960s, such as electronic article surveillance systems in retail stores to prevent theft. The 1970s were characterized by developmental work resulting in applications for animal tracking, vehicle tracking, and factory automation. RFID technology exploded during the 1980s in the areas of transportation and, to a lesser extent, personnel access and animals. Wider deployment of RFID tags for automated toll collection happened in the 1990s. Also, there was growing interest of RFID for logistics and having it work along side with bar codes. In the beginning of the 21st century, the application of RFID technology has been ubiquitous and now it is practically part of everyday life (Landt, 2001).
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Yang, Bo. "Multimedia Representation." In Encyclopedia of Multimedia Technology and Networking, Second Edition, 995–1007. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-014-1.ch135.

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In recent years, the rapid expansion of multimedia applications, partly due to the exponential growth of the Internet, has proliferated over the daily life of computer users (Yang & Hurson, 2006). The integration of wireless communication, pervasive computing, and ubiquitous data processing with multimedia database systems has enabled the connection and fusion of distributed multimedia data sources. In addition, the emerging applications, such as smart classroom, digital library, habitat/environment surveillance, traffic monitoring, and battlefield sensing, have provided increasing motivation for conducting research on multimedia content representation, data delivery and dissemination, data fusion and analysis, and contentbased retrieval. Consequently, research on multimedia technologies is of increasing importance in computer society. In contrast with traditional text-based systems, multimedia applications usually incorporate much more powerful descriptions of human thought—video, audio, and images (Karpouzis, Raouzaiou, Tzouveli, Iaonnou, & Kollias, 2003; Liu, Bao, Yu, & Xu, 2005; Yang & Hurson, 2005). Moreover, the large collections of data in multimedia systems make it possible to resolve more complex data operations such as imprecise query or content-based retrieval. For instance, the image database systems may accept an example picture and return the most similar images of the example (Cox, Miller, & Minka, 2000; Hsu, Chua, & Pung, 2000; Huang, Chang, & Huang, 2003). However, the conveniences of multimedia applications come with challenges to the existing data management schemes: • Efficiency: Multimedia applications generally require more resources; however, the storage space and processing power are limited in many practical systems, for example, mobile devices and wireless networks (Yang & Hurson, 2005). Due to the large data volume and complicated operations of multimedia applications, new methods are needed to facilitate efficient representation, retrieval, and processing of multimedia data while considering the technical constraints. • Semantic Gap: There is a gap between user perception of multimedia entities and physical representation/access mechanism of multimedia data. Users often browse and desire to access multimedia data at the object level (“entities” such as human beings, animals, or buildings). However, the existing multimedia retrieval systems tend to access multimedia data based on their lower-level features (“characteristics” such as color patterns and textures), with little regard to combining these features into data objects. This representation gap often leads to higher processing cost and unexpected retrieval results. The representation of multimedia data according to human’s perspective is one of the focuses in recent research activities; however, few existing systems provide automated identification or classification of objects from general multimedia collections. • Heterogeneity: The collections of multimedia data are often diverse and poorly indexed. In a distributed environment, because of the autonomy and heterogeneity of data sources, multimedia data objects are often represented in heterogeneous formats. The difference in data formats further leads to the difficulty of incorporating multimedia data objects under a unique indexing framework. • Semantic Unawareness: The present research on content-based multimedia retrieval is based on feature vectors—features are extracted from audio/video streams or image pixels, empirically or heuristically, and combined into vectors according to the application criteria. Because of the application-specific multimedia formats, the feature-based paradigm lacks scalability and accuracy.
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Conference papers on the topic "Animal automatic identification"

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Tacioli, Leandro, Luíz Toledo, and Claudua Medeiros. "An Architecture for Animal Sound Identification based on Multiple Feature Extraction and Classification Algorithms." In XI Brazilian e-Science Workshop. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/bresci.2017.9919.

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Automatic identification of animals is extremely useful for scientists, providing ways to monitor species and changes in ecological communities. The choice of effective audio features and classification techniques is a challenge on any audio recognition system, especially in bioacoustics that commonly uses several algorithms. This paper presents a novel software architecture that supports multiple feature extraction and classification algorithms to help on the identification of animal species from their recorded sounds. This architecture was implemented by the WASIS software, freely available on the Web.
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Kim, Heegon. "Automatic Identification of a Coughing Animal using Audio and Video Data." In The fourth International Conference on Information Science and Cloud Computing. Trieste, Italy: Sissa Medialab, 2016. http://dx.doi.org/10.22323/1.264.0008.

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Nguyen, Hung, Sarah J. Maclagan, Tu Dinh Nguyen, Thin Nguyen, Paul Flemons, Kylie Andrews, Euan G. Ritchie, and Dinh Phung. "Animal Recognition and Identification with Deep Convolutional Neural Networks for Automated Wildlife Monitoring." In 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2017. http://dx.doi.org/10.1109/dsaa.2017.31.

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Diaz Simões, Juan Raphael, Paul Bourgine, Denis Grebenkov, and Nadine Peyriéras. "Cell Trajectory Clustering: Towards the Automated Identification of Morphogenetic Fields in Animal Embryogenesis." In 6th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006259407460752.

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Li, Qingqing, Qiong Wu, Jiajia Yang, Yiyang Yu, Fengxia Wu, Wu Wang, Satoshi Takahashi, Yoshimichi Ejima, and Jinglong Wu. "The Identification and Evaluation for Animal and Other Sounds: The Effect of Presentation Time." In 2019 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2019. http://dx.doi.org/10.1109/icma.2019.8816333.

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