scholarly journals Farm Animals’ Behaviors and Welfare Analysis with IA Algorithms: A Review

2021 ◽  
Vol 35 (3) ◽  
pp. 243-253
Author(s):  
Olivier Debauche ◽  
Meryem Elmoulat ◽  
Saïd Mahmoudi ◽  
Jérôme Bindelle ◽  
Frédéric Lebeau
Keyword(s):  
Machine Learning ◽  
Animal Species ◽  
Technological Development ◽  
Welfare Analysis ◽  
Farm Animals ◽  
Learning Approaches ◽  
Current Trends ◽  
Animal Category

Numerous bibliographic reviews related to the use of AI for the behavioral detection of farm animals exist, but they only focus on a particular type of animal. We believe that some techniques were used for some animals that could also be used for other types of animals. The application and comparison of these techniques between animal species are rarely done. In this paper, we propose a review of machine learning approaches used for the detection of farm animals’ behaviors such as lameness, grazing, rumination, and so on. The originality of this paper is matched classification in the midst of sensors and algorithms used for each animal category. First, we highlight the most implemented approaches for different categories of animals (cows, sheep, goats, pigs, horses, and chickens) to inspire researchers interested to conduct investigation and employ the methods we have evaluated and the results we have obtained in this study. Second, we describe the current trends in terms of technological development and new paradigms that will impact the AI research. Finally, we critically analyze what is done and we draw new pathways of research to advance our understanding of animal’s behaviors.

scholarly journals 7 ASAS-EAAP Exchange Speaker Talk: Increasing animal welfare and efficiency on-farm with the use of precision technology tools: technology development, application, and use

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 12-13
Author(s):  
Jasmeet Kaler
Keyword(s):  
Machine Learning ◽  
Animal Welfare ◽  
Technological Development ◽  
Window Size ◽  
Machine Learning Algorithms ◽  
Farm Animals ◽  
Drinking Behaviour ◽  
Learning Approaches ◽  
Livestock Farming ◽  
Adoption Of Technology

Abstract Recent advances in bio-telemetry technology have made it possible to generate lot of data through sensors, which could be used to monitor welfare and classify behavioural activities in many different farm animals. However, little has been done with regards to evaluating predictive ability and comparing various machine learning approaches for ‘big data’ and also evaluating how this changes depending on sampling frequencies and position of sensors. In this talk, I will discuss technological development covering range of sensor technologies utilising state-of-the-art computation and transmission protocols we have co- developed as part of our research and on how we used these technologies to build machine learning algorithms for lameness in, and drinking behaviour in cows, with an ultimate aim to improve animal welfare. Algorithms could classify behaviours with overall accuracy above 95%; however, the accuracy varied by number of features used, choice of algorithm and window size used for feature generation. The talk will focus on challenges and approaches to build smart systems that are not only technologically advanced, have good accuracy, algorithms that continue to learn and versatile but also energy efficient and practical. While precision livestock farming has been a growing area for the past decade and has huge potential to improve livestock health and welfare, technology adoption has not occurred at the same pace. We need to understand farmers’ perceptions and understanding around technology, its use on farms and in farming. Results from our research with farmers suggest few key areas are important for embedding and adoption of technology on farms: first, utility of the technology, lack of validation and its ability to fit with existing structures and practices and the beliefs held by farmers that the use of the device may result in a loss of skill in future—that of the farmer knowing his animals.

Investigating Machine Learning Techniques for User Sentiment Analysis

2019 ◽  
Vol 11 (3) ◽  
pp. 1-12 ◽  
Author(s):  
Nimesh V Patel ◽  
Hitesh Chhinkaniwala
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Product Reviews ◽  
Learning Approaches ◽  
Current Trends ◽  
Learning Techniques ◽  
Benchmark Datasets

Sentiment analysis identifies users in the textual reviews available in social networking sites, tweets, blog posts, forums, status updates to share their emotions or reviews and these reviews are to be used by market researchers to do know the product reviews and current trends in the market. The sentiment analysis is performed by two methods. Machine learning approaches and lexicon methods which are also known as the knowledge base approach. These. In this article, the authors evaluate the performance of some machine learning techniques: Maximum Entropy, Naïve Bayes and Support Vector Machines on two benchmark datasets: the positive-negative dataset and a Movie Review dataset by measuring parameters like accuracy, precision, recall and F-score. In this article, the authors present the performance of various sentiment analysis and classification methods by classifying the reviews in binary classes as positive, negative opinion about reviews on different domains of dataset. It is also justified that sentiment analysis using the Support Vector Machine outperforms other machine learning techniques.

A Systematic Review of Web Accessibility Metrics

2022 ◽  
pp. 77-108
Author(s):  
Pınar Onay Durdu ◽  
Ömer Naci Soydemir
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Literature Review ◽  
Quantitative Methods ◽  
Web Accessibility ◽  
Learning Approaches ◽  
Internet Applications ◽  
Essential Requirement ◽  
Rich Internet Applications ◽  
Current Trends

Currently, providing accessible websites for all users is an essential requirement. There are various qualitative and quantitative evaluation methods to assure accessibility. Among these, the quantitative methods show the level of accessibility of the website using web accessibility metrics (WAM), which provide a way to understand, control, and improve these websites. This study was aimed to identify current trends and analyze WAMs through a systematic literature review. Therefore, 30 WAM studies that were published since 2008 were determined and investigated according to attributes defined for the metrics such as guideline set used by the metric, coupling level with the guidelines, type of evaluation, site complexity, and validation with the user. Fourteen recently proposed WAMs were determined since 2008. Recently proposed WAMs have begun to consider more elaborate issues such as rich internet applications, website complexity, usability, or user experience issues and implement some machine learning approaches for the metrics.

Machine-learning techniques and their applications in manufacturing

2005 ◽  
Vol 219 (5) ◽  
pp. 395-412 ◽  
Author(s):  
D T Pham ◽  
A A Afify
Keyword(s):  
Machine Learning ◽  
Inductive Learning ◽  
Computer Programs ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Research Issues ◽  
Current Trends ◽  
Learning Techniques ◽  
Recent Developments ◽  
Learning Research

Machine learning is concerned with enabling computer programs automatically to improve their performance at some tasks through experience. Manufacturing is an area where the application of machine learning can be very fruitful. However, little has been published about the use of machine-learning techniques in the manufacturing domain. This paper evaluates several machine-learning techniques and examines applications in which they have been successfully deployed. Special attention is given to inductive learning, which is among the most mature of the machine-learning approaches currently available. Current trends and recent developments in machine-learning research are also discussed. The paper concludes with a summary of some of the key research issues in machine learning.

Registration of animal rabies in Krasnodar region

2020 ◽  
pp. 3-4
Author(s):  
Oleg Yu. Chernykh ◽  
◽  
Vadim A. Bobrov ◽  
Sergey N. Zabashta ◽  
Roman A. Krivonos ◽  
...  
Keyword(s):  
Urban Form ◽  
Animal Species ◽  
Farm Animals ◽  
Natural Focus ◽  
Dead End ◽  
Wide Range ◽  
Epizootic Process ◽  
Krasnodar Region ◽  
Spread Of Infection ◽  
Reporting Data

Rabies remains a constant threat to humanity in many parts of the world. At the same time, scientifically grounded antiepizootic measures should be based on the peculiarities of the regional epizootology of this zooanthroponosis. The authors studied the epizootological and statistical reporting data of the Kropotkin Regional Veterinary Laboratory, presented an analysis of the registration of rabies in animals in Krasnodar region. From the obtained data, it should be noted that despite the wide range of animals involved in the epizootic process of rabies infection in Krasnodar region, dogs, cats and foxes play a major role in the reservation and spread of infection, which account for 78.6. Of the total number of registered cases, 15.5% falls on foxes, that indicates the natural focus of the disease, along with the manifestation of the disease in an urban form. At the same time, stray and neglected dogs and cats, which occupy a significant place among the total number of sick animals, are also sources and spread of the infection. Thus farm animals (8.3% of the total number of infected animals) are a biological dead end for the infection. Isolated cases of the disease were noted in muskrat, donkey, raccoon, raccoon dog, marten, ferret and jackal. The authors also established the specific morbidity of various animal species with rabies infection, that is an important aspect in the development and implementation of antiepizootic measures complex

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

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