Vasseur, Elsa; Dallago, Gabiel M.; Diallo, Abdoulaye Baniré
Perspectives from Canada on Dairy Cows’ Longevity [External] Divers
2025.
Résumé | Liens | BibTeX | Étiquettes: Presentation
@misc{vasseur_perspectives_2025,
title = {Perspectives from Canada on Dairy Cows’ Longevity},
author = {Elsa Vasseur and Gabiel M. Dallago and Abdoulaye Baniré Diallo},
url = {chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://eaap2025.org/Final_Programme_Innsbruck_2025.pdf?v=5},
year = {2025},
date = {2025-08-01},
urldate = {2025-08-01},
address = {Innbruck, Austria},
abstract = {Our focus is ensuring the sustainable life of dairy cattle, or, how to keep healthy, comfortable animals longer in the herd, encompassing research on animal welfare for better management and facilities, together with our understanding of cow longevity. Our approach is to develop methods to remotely assess changes in cow welfare, and to evaluate how health issues and other risk factors are linked to cow longevity. We aimed to exploit the potential of the considerable dataset regularly collected on Canadian dairies via the Canadian Dairy Network (Lactanet). We have assessed the impact of health events such as lameness and mastitis, finding that primiparous cows afflicted with these diseases have reduced milk yields and gross profit, and higher risk of culling than healthy cows. We also unveiled valuable insights on early-life variables that are linked with future productivity and longevity, such as weight at birth and at weaning, weaning age, and serum IgG levels; these represent early indicators that can be used to select replacement animals based on their potential, and reduce economic and environmental losses incurred by keeping animals that will likely never be profitable. Our work has also led to innovations in the remote detection of welfare. On a herd basis, the analysis of DHI indicators routinely collected on dairies was used to develop a composite herd welfare index. On an individual cow basis, we have tested the use of spectral data from milk samples as a source of information on cow-specific changes in welfare status; this approach was successful in linking milk components with improvements in specific outcomes of cow welfare. Overall, these innovations show that the potential to conduct remote assessments is present and could thus be extended to more specific topics (e.g., lameness in individual cows) and other aspects (e.g., economic and environmental performance of cows and farms).},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
Our focus is ensuring the sustainable life of dairy cattle, or, how to keep healthy, comfortable animals longer in the herd, encompassing research on animal welfare for better management and facilities, together with our understanding of cow longevity. Our approach is to develop methods to remotely assess changes in cow welfare, and to evaluate how health issues and other risk factors are linked to cow longevity. We aimed to exploit the potential of the considerable dataset regularly collected on Canadian dairies via the Canadian Dairy Network (Lactanet). We have assessed the impact of health events such as lameness and mastitis, finding that primiparous cows afflicted with these diseases have reduced milk yields and gross profit, and higher risk of culling than healthy cows. We also unveiled valuable insights on early-life variables that are linked with future productivity and longevity, such as weight at birth and at weaning, weaning age, and serum IgG levels; these represent early indicators that can be used to select replacement animals based on their potential, and reduce economic and environmental losses incurred by keeping animals that will likely never be profitable. Our work has also led to innovations in the remote detection of welfare. On a herd basis, the analysis of DHI indicators routinely collected on dairies was used to develop a composite herd welfare index. On an individual cow basis, we have tested the use of spectral data from milk samples as a source of information on cow-specific changes in welfare status; this approach was successful in linking milk components with improvements in specific outcomes of cow welfare. Overall, these innovations show that the potential to conduct remote assessments is present and could thus be extended to more specific topics (e.g., lameness in individual cows) and other aspects (e.g., economic and environmental performance of cows and farms).
Egger-Danner, C.; Klaas, I.; Brito, L.; Bewley, J.; Cabrera, V.; Gengler, N.; Haskel, M.; Heringstad, B.; Hostens, M.; Iwersen, M.; Schodl, A.; Stock, K.; Stygar, A.; Vasseur, E.
ICAR/IDF initiative: Guidelines and reference standards for using rumination sensor data in animal health and welfare recording [External] Divers
2025.
Résumé | Liens | BibTeX | Étiquettes: Presentation
@misc{egger-danner_icaridf_2025,
title = {ICAR/IDF initiative: Guidelines and reference standards for using rumination sensor data in animal health and welfare recording},
author = {C. Egger-Danner and I. Klaas and L. Brito and J. Bewley and V. Cabrera and N. Gengler and M. Haskel and B. Heringstad and M. Hostens and M. Iwersen and A. Schodl and K. Stock and A. Stygar and E. Vasseur},
url = {https://eaap2025.org/},
year = {2025},
date = {2025-08-01},
urldate = {2025-08-01},
address = {Innbruck, Austria},
abstract = {On-farm sensor data offers new opportunities for animal health and welfare assessment when used along the dairy value chain, thus improving transparency and sustainability in the dairy sector. The aim of the joint ICAR IDF initiative is to develop guidelines to facilitate use of sensor data for improving animal health and welfare. The joint initiative consists of different stakeholder groups, scientists, breeding organizations and manufacturers and has the goal to develop: Definitions and terminology for health conditions and behaviors measured with sensor systemsStandards to facilitate data exchange along the dairy value chain following acknowledged ICAR and IDF principles Guidelines for best practices on data collection, handling and analysis for use in genetics, health and welfare assessmentRecommendations and protocols for testing sensor performanceFirst results from the use case sensor-based rumination data will be presented, recommendations on use of rumination data, key performance indicators and references standards for animal health and welfare assessment given. The close collaboration between the relevant stakeholders in the joint ICAR/IDF initiative enables development of guidelines considering industry needs while promoting use of sensor data along the dairy value chain.},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
On-farm sensor data offers new opportunities for animal health and welfare assessment when used along the dairy value chain, thus improving transparency and sustainability in the dairy sector. The aim of the joint ICAR IDF initiative is to develop guidelines to facilitate use of sensor data for improving animal health and welfare. The joint initiative consists of different stakeholder groups, scientists, breeding organizations and manufacturers and has the goal to develop: Definitions and terminology for health conditions and behaviors measured with sensor systemsStandards to facilitate data exchange along the dairy value chain following acknowledged ICAR and IDF principles Guidelines for best practices on data collection, handling and analysis for use in genetics, health and welfare assessmentRecommendations and protocols for testing sensor performanceFirst results from the use case sensor-based rumination data will be presented, recommendations on use of rumination data, key performance indicators and references standards for animal health and welfare assessment given. The close collaboration between the relevant stakeholders in the joint ICAR/IDF initiative enables development of guidelines considering industry needs while promoting use of sensor data along the dairy value chain.
Vasseur, Elsa; Diallo, Abdoulaye Baniré
Responsible Development and Deployment of AI and IoT in the Dairy Industry, bridging gaps between research and implementation: The WELL-E Initiative [External] Divers
2025.
Résumé | Liens | BibTeX | Étiquettes: conférencier invité, Presentation
@misc{vasseur_responsible_2025,
title = {Responsible Development and Deployment of AI and IoT in the Dairy Industry, bridging gaps between research and implementation: The WELL-E Initiative},
author = {Elsa Vasseur and Abdoulaye Baniré Diallo},
url = {https://eaap2025.org/},
year = {2025},
date = {2025-08-01},
urldate = {2025-08-01},
address = {Innbruck, Austria},
abstract = {Responsible AI that properly addresses real-world stakeholder needs is at the heart of the R+I Chair in Animal Welfare and Artificial Intelligence. WELL-E approach relies on the use of IoT, computer vision, and machine learning to improve our ability to detect and monitor changes in animal welfare earlier than possible with visual methods, and to generate predictions to aid on-farm decision-making, ensuring that resources and efforts are focused on the animals most likely to succeed in the long term. Grounded in industry partnerships, members of the dairy community sit on both the scientific and management committees, where they contribute to research orientations, leading to the co-creation of research projects and initiatives. This collaborative approach not only ensures the applicability and relevance of all research carried out, but allows for continuous feedback between the research and industry environments, creating a true digital living lab that can grow with the Chair. Launched in 2023, our team has been conducting pilot research on two research farms and working to build functional and resilient data collection infrastructure for implementation on commercial dairy farms throughout 2025. Through a federated learning approach, this deployment ensures robust continuous monitoring across a network of farms, while respecting data confidentiality and cybersecurity. We have developed a framework for the study of animal behaviours and emotions, presenting a paradigm shift for both annotation and data analysis based on continuouscand heterogeneous data sources. As we grow our network of partner farms, we will continue focussed studies on research farms, allowing for continuous exchange between the two contexts. This collaborative approach promotes the responsible integration of new technologies to the industry and empowers endusers to be at the forefront of these new developments, ensuring their sustainability and reinforcing the importance of stakeholder participation in innovative scientific research.},
keywords = {conférencier invité, Presentation},
pubstate = {published},
tppubtype = {misc}
}
Responsible AI that properly addresses real-world stakeholder needs is at the heart of the R+I Chair in Animal Welfare and Artificial Intelligence. WELL-E approach relies on the use of IoT, computer vision, and machine learning to improve our ability to detect and monitor changes in animal welfare earlier than possible with visual methods, and to generate predictions to aid on-farm decision-making, ensuring that resources and efforts are focused on the animals most likely to succeed in the long term. Grounded in industry partnerships, members of the dairy community sit on both the scientific and management committees, where they contribute to research orientations, leading to the co-creation of research projects and initiatives. This collaborative approach not only ensures the applicability and relevance of all research carried out, but allows for continuous feedback between the research and industry environments, creating a true digital living lab that can grow with the Chair. Launched in 2023, our team has been conducting pilot research on two research farms and working to build functional and resilient data collection infrastructure for implementation on commercial dairy farms throughout 2025. Through a federated learning approach, this deployment ensures robust continuous monitoring across a network of farms, while respecting data confidentiality and cybersecurity. We have developed a framework for the study of animal behaviours and emotions, presenting a paradigm shift for both annotation and data analysis based on continuouscand heterogeneous data sources. As we grow our network of partner farms, we will continue focussed studies on research farms, allowing for continuous exchange between the two contexts. This collaborative approach promotes the responsible integration of new technologies to the industry and empowers endusers to be at the forefront of these new developments, ensuring their sustainability and reinforcing the importance of stakeholder participation in innovative scientific research.
Benserir, Nadjib; Etiabi, Yaya; Sabir, Essaid; Amhoud, Elmehdi; Elbiaze, Halima; Diallo, Abdoulaye Baniré
IRSA Over Spreading Factors for Spatio-Temporal SIC in Scalable LoRaWAN IoT Networks [External] Divers
2025.
Liens | BibTeX | Étiquettes: Presentation
@misc{benserir_irsa_2025,
title = {IRSA Over Spreading Factors for Spatio-Temporal SIC in Scalable LoRaWAN IoT Networks},
author = {Nadjib Benserir and Yaya Etiabi and Essaid Sabir and Elmehdi Amhoud and Halima Elbiaze and Abdoulaye Baniré Diallo},
url = {https://ieee-iscc.computer.org/2025},
year = {2025},
date = {2025-07-01},
urldate = {2025-07-01},
address = {Bologna, Italy},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
Vasseur, Elsa
Plenary Session 2 [External] Présentation
Vila Real, Portugal, 01.07.2025.
Liens | BibTeX | Étiquettes: conférencier invité
@misc{vasseur_plenary_2025,
title = {Plenary Session 2},
author = {Elsa Vasseur},
url = {https://www.esee2025.utad.pt/program/},
year = {2025},
date = {2025-07-01},
urldate = {2025-07-01},
address = {Vila Real, Portugal},
keywords = {conférencier invité},
pubstate = {published},
tppubtype = {presentation}
}
Roche, Steven; Vasseur, Elsa
Advancing Dairy Extension: A Digital Living Laboratory Approach to Knowledge Mobilization in the Canadian Dairy Sector [External] Divers
2025.
Résumé | Liens | BibTeX | Étiquettes: Presentation
@misc{roche_advancing_2025,
title = {Advancing Dairy Extension: A Digital Living Laboratory Approach to Knowledge Mobilization in the Canadian Dairy Sector},
author = {Steven Roche and Elsa Vasseur},
url = {https://www.esee2025.utad.pt/program/},
year = {2025},
date = {2025-07-01},
urldate = {2025-07-01},
address = {Vila Real, Portugal},
abstract = {The WELL-E initiative is a national research program that applies a Digital Living Laboratory (DLL) model to close the gap between scientific innovation and on-farm application in the Canadian dairy sector. With a focus on animal welfare, AI integration, and digital innovation, WELL-E aims to generate actionable knowledge, enable practical adoption, and build system-wide capacity. Early work has centered on developing a robust knowledge translation and transfer (KTT) strategy grounded in literature, systems mapping, and engagement with advisory networks.
WELL-E employs a dual push-pull KTT model, blending structured dissemination (e.g., training modules, videos) with participatory strategies that ensure tools and messages are co-developed with stakeholders. Findings emphasize the importance of trusted messengers, multi-modal delivery, and the integration of pedagogical principles in digital resource design. WELL-E’s mapping of Canada’s dairy KTT ecosystem—hosted on Kumu—has identified fragmentation and opportunities for improved collaboration.
This work offers both practical and theoretical contributions. It provides a scalable model for digital extension grounded in systems thinking, while reinforcing that digital tools alone are insufficient—effective KTT requires trust, contextual fit, and continuous evaluation. WELL-E’s early lessons highlight a path forward for AI-enabled extension services that are credible, collaborative, and impactful.},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
The WELL-E initiative is a national research program that applies a Digital Living Laboratory (DLL) model to close the gap between scientific innovation and on-farm application in the Canadian dairy sector. With a focus on animal welfare, AI integration, and digital innovation, WELL-E aims to generate actionable knowledge, enable practical adoption, and build system-wide capacity. Early work has centered on developing a robust knowledge translation and transfer (KTT) strategy grounded in literature, systems mapping, and engagement with advisory networks.
WELL-E employs a dual push-pull KTT model, blending structured dissemination (e.g., training modules, videos) with participatory strategies that ensure tools and messages are co-developed with stakeholders. Findings emphasize the importance of trusted messengers, multi-modal delivery, and the integration of pedagogical principles in digital resource design. WELL-E’s mapping of Canada’s dairy KTT ecosystem—hosted on Kumu—has identified fragmentation and opportunities for improved collaboration.
This work offers both practical and theoretical contributions. It provides a scalable model for digital extension grounded in systems thinking, while reinforcing that digital tools alone are insufficient—effective KTT requires trust, contextual fit, and continuous evaluation. WELL-E’s early lessons highlight a path forward for AI-enabled extension services that are credible, collaborative, and impactful.
WELL-E employs a dual push-pull KTT model, blending structured dissemination (e.g., training modules, videos) with participatory strategies that ensure tools and messages are co-developed with stakeholders. Findings emphasize the importance of trusted messengers, multi-modal delivery, and the integration of pedagogical principles in digital resource design. WELL-E’s mapping of Canada’s dairy KTT ecosystem—hosted on Kumu—has identified fragmentation and opportunities for improved collaboration.
This work offers both practical and theoretical contributions. It provides a scalable model for digital extension grounded in systems thinking, while reinforcing that digital tools alone are insufficient—effective KTT requires trust, contextual fit, and continuous evaluation. WELL-E’s early lessons highlight a path forward for AI-enabled extension services that are credible, collaborative, and impactful.
Vasseur, Elsa
The WELL-E Initiative: Inclusive Innovation to deliver data-driven solutions with and for the Canadian Dairy Industry [External] Divers
2025.
Résumé | Liens | BibTeX | Étiquettes: Presentation
@misc{vasseur_well-e_2025,
title = {The WELL-E Initiative: Inclusive Innovation to deliver data-driven solutions with and for the Canadian Dairy Industry},
author = {Elsa Vasseur},
url = {https://www.esee2025.utad.pt/program/},
year = {2025},
date = {2025-07-01},
urldate = {2025-07-01},
address = {Vila Real, Portugal},
abstract = {Purpose textbar Evolving theoretical frameworks of responsible and inclusive innovation argue systems change must properly address real-world stakeholder needs and create positive impacts for society and environment. These principles lie at the heart and mission of the Research and Innovation Chair in Animal Welfare and Artificial Intelligence (WELL-E). Our team works to integrate intentionally both stakeholder and domain expert knowledge with cutting-edge AI methods and tools for the improvement of animal (and human) welfare. WELL-E is a Digital Living Laboratory, focused on the needs of animals and end-users, and is currently running at two research facilities: a university training environment and a vocational training environment for incarcerated persons, creating a community of practice allowing co-creation of technology, knowledge, and information with farmers, educators, researchers, and extensionists.
Design/Methodology/Approach textbar Beginning with the university training environment, we have been working directly with farm staff and management to co-develop and pilot new practices for animal housing and management. Specifically through living lab experiments from 2019-2024, we aimed to understand how end users transition herds from a movement-restricted system to having regular outdoor access in a practical way while enhancing animal welfare and ethics. Our next major project focusses on working directly with the vocational training environment staff on the construction of a new dairy as a research location to test cutting-edge technologies and practices for the deployment of responsible AI tools on farms, embracing F.A.I.R. principles and empowering end users to be at the forefront of these innovations.
Findingstextbar While our team’s research on developing new knowledge has led to major changes in practice, we have since been working directly with farm staff and management at the research training farm on how best to introduce increased movement opportunities (group size on exits, handler methods, etc.), ensuring its swift adoption across Canada. Vocational farm staff have been working directly with WELL-E to build training curriculum on stockmanship and clerical work (data entry, use of sensors), as well as to pilot new tools and technologies.},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
Purpose textbar Evolving theoretical frameworks of responsible and inclusive innovation argue systems change must properly address real-world stakeholder needs and create positive impacts for society and environment. These principles lie at the heart and mission of the Research and Innovation Chair in Animal Welfare and Artificial Intelligence (WELL-E). Our team works to integrate intentionally both stakeholder and domain expert knowledge with cutting-edge AI methods and tools for the improvement of animal (and human) welfare. WELL-E is a Digital Living Laboratory, focused on the needs of animals and end-users, and is currently running at two research facilities: a university training environment and a vocational training environment for incarcerated persons, creating a community of practice allowing co-creation of technology, knowledge, and information with farmers, educators, researchers, and extensionists.
Design/Methodology/Approach textbar Beginning with the university training environment, we have been working directly with farm staff and management to co-develop and pilot new practices for animal housing and management. Specifically through living lab experiments from 2019-2024, we aimed to understand how end users transition herds from a movement-restricted system to having regular outdoor access in a practical way while enhancing animal welfare and ethics. Our next major project focusses on working directly with the vocational training environment staff on the construction of a new dairy as a research location to test cutting-edge technologies and practices for the deployment of responsible AI tools on farms, embracing F.A.I.R. principles and empowering end users to be at the forefront of these innovations.
Findingstextbar While our team’s research on developing new knowledge has led to major changes in practice, we have since been working directly with farm staff and management at the research training farm on how best to introduce increased movement opportunities (group size on exits, handler methods, etc.), ensuring its swift adoption across Canada. Vocational farm staff have been working directly with WELL-E to build training curriculum on stockmanship and clerical work (data entry, use of sensors), as well as to pilot new tools and technologies.
Design/Methodology/Approach textbar Beginning with the university training environment, we have been working directly with farm staff and management to co-develop and pilot new practices for animal housing and management. Specifically through living lab experiments from 2019-2024, we aimed to understand how end users transition herds from a movement-restricted system to having regular outdoor access in a practical way while enhancing animal welfare and ethics. Our next major project focusses on working directly with the vocational training environment staff on the construction of a new dairy as a research location to test cutting-edge technologies and practices for the deployment of responsible AI tools on farms, embracing F.A.I.R. principles and empowering end users to be at the forefront of these innovations.
Findingstextbar While our team’s research on developing new knowledge has led to major changes in practice, we have since been working directly with farm staff and management at the research training farm on how best to introduce increased movement opportunities (group size on exits, handler methods, etc.), ensuring its swift adoption across Canada. Vocational farm staff have been working directly with WELL-E to build training curriculum on stockmanship and clerical work (data entry, use of sensors), as well as to pilot new tools and technologies.
Hambly, Helen; Vasseur, Elsa
Learning About Crossing the Lines: The WELL-E Approach to Capacity Development [External] Divers
2025.
Résumé | Liens | BibTeX | Étiquettes: Presentation
@misc{hambly_learning_2025,
title = {Learning About Crossing the Lines: The WELL-E Approach to Capacity Development},
author = {Helen Hambly and Elsa Vasseur},
url = {https://www.esee2025.utad.pt/program/},
year = {2025},
date = {2025-07-01},
urldate = {2025-07-01},
address = {Vila Real, Portugal},
abstract = {Short abstract (200 words): WELL-E, Research and Innovation Chair on Animal Welfare and Artificial Intelligence, is a Digital Living Laboratory, focused on the needs of animals and end-users with deep industry partnerships, as well as multi-disciplinary scientific committees, in which end-users and the research team interact intensively in the co-creation of projects and delivery of high-impact results. WELL-E strategically pursues capacity development (CapDev) as an all-encompassing approach to building capacity, enabling capacities to change ways of knowing and doing, thereby responding and contributing to systems change. WELL-E’s pursuit of CapDev to enable individual students to build direct links with diverse scientists, organizations, and networks across systems of higher education, research partnerships and industry is an important model for future Living Labs. WELL-E’s contribution to transforming agri-food systems and extension services through enhancing capacity crosses many boundaries, not only within science disciplines but twinning science-based industries like dairy and information technology that have not always had sufficient learning opportunities together.},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
Short abstract (200 words): WELL-E, Research and Innovation Chair on Animal Welfare and Artificial Intelligence, is a Digital Living Laboratory, focused on the needs of animals and end-users with deep industry partnerships, as well as multi-disciplinary scientific committees, in which end-users and the research team interact intensively in the co-creation of projects and delivery of high-impact results. WELL-E strategically pursues capacity development (CapDev) as an all-encompassing approach to building capacity, enabling capacities to change ways of knowing and doing, thereby responding and contributing to systems change. WELL-E’s pursuit of CapDev to enable individual students to build direct links with diverse scientists, organizations, and networks across systems of higher education, research partnerships and industry is an important model for future Living Labs. WELL-E’s contribution to transforming agri-food systems and extension services through enhancing capacity crosses many boundaries, not only within science disciplines but twinning science-based industries like dairy and information technology that have not always had sufficient learning opportunities together.
Driss, Maryam Ben; Sabir, Essaid; Elbiaze, Halima; Diallo, Abloulaye Baniré
Fast & Energy Efficient Federated Learning Using Multi-Attribute Client Clustering and Selection [External] Article d'actes
Dans: Oslo, Norway, 2025.
Liens | BibTeX | Étiquettes: Conference paper
@inproceedings{ben_driss_fast_nodate,
title = {Fast & Energy Efficient Federated Learning Using Multi-Attribute Client Clustering and Selection},
author = {Maryam Ben Driss and Essaid Sabir and Halima Elbiaze and Abloulaye Baniré Diallo},
url = {https://ieeexplore.ieee.org/document/11174476},
year = {2025},
date = {2025-06-19},
urldate = {2025-06-19},
address = {Oslo, Norway},
keywords = {Conference paper},
pubstate = {published},
tppubtype = {inproceedings}
}
Arpin, Catherine; Vliet, Rachel; Cellier, Marjorie; Aigueperse, Nadège; Robichaud, Marianne Villettaz; Diallo, Abdoulaye Baniré; Vasseur, Elsa
Udderly important behaviours: The study of the behavioural needs of adult dairy cows across common intensive housing systems [External] Divers
2025.
Résumé | Liens | BibTeX | Étiquettes: Presentation
@misc{arpin_udderly_2025,
title = {Udderly important behaviours: The study of the behavioural needs of adult dairy cows across common intensive housing systems},
author = {Catherine Arpin and Rachel Vliet and Marjorie Cellier and Nadège Aigueperse and Marianne Villettaz Robichaud and Abdoulaye Baniré Diallo and Elsa Vasseur},
url = {https://www.ufaw.org.uk/ufaw-events/ufaw-international-animal-welfare-conference-2025},
year = {2025},
date = {2025-06-01},
urldate = {2025-06-01},
address = {Online},
abstract = {Behavioural needs represent behaviours that animals perform no matter the environment or their physiological state. If these needs are not met, the welfare of the animals has the potential to be compromised. Characterizing the behavioural needs of farm animals is pivotal in understanding how to satisfy them by for example modifying their captive environment. However, behavioural needs are not well understood, and both their definition and application are inconsistent across the literature, particularly in the case of dairy cows. The objectives of this review are to identify papers that study behavioural needs of dairy cows, identify which needs were studied, how they were studied, and how that changed over time. A scoping review (conducted according to PRISMA guidelines) was completed, where 11,512 articles were reduced to 144 through a multi-step screening process. The included papers were published between 1946 and 2024 mostly in Canada (15%), in the United States (14%) and in the UK (11%). Of the remaining articles, only 15% acknowledged the existence of behavioural needs, and there was a lack of consistency in the terms used surrounding this concept. Resting and feeding behaviours were the most studied (in 122 and 86 papers, respectively), as opposed to grooming and exploratory behaviours that were only studied in 16 and 9 studies, respectively. By examining the timeline of when different behavioural categories were studied, it was revealed that the study of behavioural needs in adult dairy cows has evolved greatly in time and was guided by the priorities and the tools available at the time. This review summarises where the concept of behavioural needs fits within the study of dairy cows and highlights the gaps in the literature observed in the past and the present. It is imperative that these concepts be better understood and defined in the literature for the study of dairy cow welfare to move forward.},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
Behavioural needs represent behaviours that animals perform no matter the environment or their physiological state. If these needs are not met, the welfare of the animals has the potential to be compromised. Characterizing the behavioural needs of farm animals is pivotal in understanding how to satisfy them by for example modifying their captive environment. However, behavioural needs are not well understood, and both their definition and application are inconsistent across the literature, particularly in the case of dairy cows. The objectives of this review are to identify papers that study behavioural needs of dairy cows, identify which needs were studied, how they were studied, and how that changed over time. A scoping review (conducted according to PRISMA guidelines) was completed, where 11,512 articles were reduced to 144 through a multi-step screening process. The included papers were published between 1946 and 2024 mostly in Canada (15%), in the United States (14%) and in the UK (11%). Of the remaining articles, only 15% acknowledged the existence of behavioural needs, and there was a lack of consistency in the terms used surrounding this concept. Resting and feeding behaviours were the most studied (in 122 and 86 papers, respectively), as opposed to grooming and exploratory behaviours that were only studied in 16 and 9 studies, respectively. By examining the timeline of when different behavioural categories were studied, it was revealed that the study of behavioural needs in adult dairy cows has evolved greatly in time and was guided by the priorities and the tools available at the time. This review summarises where the concept of behavioural needs fits within the study of dairy cows and highlights the gaps in the literature observed in the past and the present. It is imperative that these concepts be better understood and defined in the literature for the study of dairy cow welfare to move forward.
Amarioarei, Georgiana; Vliet, Rachel; Cellier, Marjorie; Aigueperse, Nadège; Diallo, Abdoulaye Baniré; Vasseur, Elsa
A scoping review of cognitive enrichment for young cattle and its implications for welfare and agricultural practices [External] Divers
2025.
Résumé | Liens | BibTeX | Étiquettes: Presentation
@misc{amarioarei_scoping_2025,
title = {A scoping review of cognitive enrichment for young cattle and its implications for welfare and agricultural practices},
author = {Georgiana Amarioarei and Rachel Vliet and Marjorie Cellier and Nadège Aigueperse and Abdoulaye Baniré Diallo and Elsa Vasseur},
url = {https://www.ufaw.org.uk/ufaw-events/ufaw-international-animal-welfare-conference-2025},
year = {2025},
date = {2025-06-01},
urldate = {2025-06-01},
address = {Online},
abstract = {Cognitive enrichment satisfies the behavioral needs of animals by engaging their natural instincts, providing mental stimulation, and enhancing cognitive skills. It distinguishes itself from other types of enrichment by introducing cognitively challenging tasks to the environment and promoting cognitive stimulation. For young cattle, this form of enrichment is necessary for supporting healthy development, adaptability and overall well-being. This scoping review seeks to critically assess the current state of literature on the effects of cognitive enrichment on young domestic bovine within an agricultural environment. This is substantial for understanding benefits to individual animals in addition to possible implications for the efficiency and sustainability of farming practices. The scoping review specifically focuses on identifying gaps in knowledge related to methodology, terminology, practical implementation, and future directions of cognitive enrichment practices. A comprehensive search of databases Scopus and Web of Science was conducted using PRISMA guidelines to identify records published between 1970 and 2024. Studies were included if they met the criteria examining cognitive enrichment or related interventions for calves and heifers. After a multistep screening process, a total of 32 studies reduced from 13,195 were included in the final analysis. Results of the analysis showed inconsistent definitions of age classification of bovine developmental stages and missing fundamental cognitive enrichment terminology. These results highlight the difficulties in tracking and defining the emergence of cognitive enrichment for young bovines due to lack of standardization. Consequently, non-standardized methodologies impose limitations on cross-study comparisons and hinder the development of evidence-based recommendations for practical implementation. Secondly, methodological elements such as measures and practicality of tests were categorically organized to investigate the potential welfare implications of cognitive enrichment and were found to hold meaningful contributions towards the topic. As a whole, cognitive enrichment holds significant promise as a tool for enhancing the welfare and cognitive development of young bovine.},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
Cognitive enrichment satisfies the behavioral needs of animals by engaging their natural instincts, providing mental stimulation, and enhancing cognitive skills. It distinguishes itself from other types of enrichment by introducing cognitively challenging tasks to the environment and promoting cognitive stimulation. For young cattle, this form of enrichment is necessary for supporting healthy development, adaptability and overall well-being. This scoping review seeks to critically assess the current state of literature on the effects of cognitive enrichment on young domestic bovine within an agricultural environment. This is substantial for understanding benefits to individual animals in addition to possible implications for the efficiency and sustainability of farming practices. The scoping review specifically focuses on identifying gaps in knowledge related to methodology, terminology, practical implementation, and future directions of cognitive enrichment practices. A comprehensive search of databases Scopus and Web of Science was conducted using PRISMA guidelines to identify records published between 1970 and 2024. Studies were included if they met the criteria examining cognitive enrichment or related interventions for calves and heifers. After a multistep screening process, a total of 32 studies reduced from 13,195 were included in the final analysis. Results of the analysis showed inconsistent definitions of age classification of bovine developmental stages and missing fundamental cognitive enrichment terminology. These results highlight the difficulties in tracking and defining the emergence of cognitive enrichment for young bovines due to lack of standardization. Consequently, non-standardized methodologies impose limitations on cross-study comparisons and hinder the development of evidence-based recommendations for practical implementation. Secondly, methodological elements such as measures and practicality of tests were categorically organized to investigate the potential welfare implications of cognitive enrichment and were found to hold meaningful contributions towards the topic. As a whole, cognitive enrichment holds significant promise as a tool for enhancing the welfare and cognitive development of young bovine.
Muszik, Jasmine; Aigueperse, Nadége; Cellier, Marjorie; Diallo, Abdoulay Baniré; Vasseur, Elsa
The Relationship Between Motivation, Anticipation, and Frustration in Animals: A Scoping Review [External] Divers
2025.
Résumé | Liens | BibTeX | Étiquettes: Presentation
@misc{muszik_relationship_2025,
title = {The Relationship Between Motivation, Anticipation, and Frustration in Animals: A Scoping Review},
author = {Jasmine Muszik and Nadége Aigueperse and Marjorie Cellier and Abdoulay Baniré Diallo and Elsa Vasseur},
url = {https://www.ufaw.org.uk/ufaw-events/ufaw-international-animal-welfare-conference-2025},
year = {2025},
date = {2025-06-01},
urldate = {2025-06-01},
address = {Online},
abstract = {Motivation, anticipation, and frustration, are important concepts for understanding animal emotion. Denying or delaying an outcome that an animal is motivated for, expects, and performs specific behavior in preparation of, can cause frustration, impacting the emotional welfare of the animal. However, diversity in the definitions and methodology related to these concepts can lead to confusing or conflicting results, and may complicate replication of previous work. A scoping review (conducted according to PRISMA guidelines) was completed where a multi-step screening process identified 112 final primary research papers. The objectives of the review were to determine the ways in which the three concepts have been studied in previous literature, the relationship between the main concepts, and the gaps in knowledge regarding these concepts. Advantages and drawbacks of tests and variables used in the selected papers were explored, along with the repeatability, the applicability within and between individuals or species, and the interpretation of results. There were no universally accepted definitions of motivation, anticipation, or frustration, and the methods used to find or show evidence of the concepts varied, even within a species using the same test. However, a clear connection between concepts was confirmed, where it was suggested that authors develop a framework to measure the concepts together to understand the full scope of how different decisions or practices may impact animal emotion.},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
Motivation, anticipation, and frustration, are important concepts for understanding animal emotion. Denying or delaying an outcome that an animal is motivated for, expects, and performs specific behavior in preparation of, can cause frustration, impacting the emotional welfare of the animal. However, diversity in the definitions and methodology related to these concepts can lead to confusing or conflicting results, and may complicate replication of previous work. A scoping review (conducted according to PRISMA guidelines) was completed where a multi-step screening process identified 112 final primary research papers. The objectives of the review were to determine the ways in which the three concepts have been studied in previous literature, the relationship between the main concepts, and the gaps in knowledge regarding these concepts. Advantages and drawbacks of tests and variables used in the selected papers were explored, along with the repeatability, the applicability within and between individuals or species, and the interpretation of results. There were no universally accepted definitions of motivation, anticipation, or frustration, and the methods used to find or show evidence of the concepts varied, even within a species using the same test. However, a clear connection between concepts was confirmed, where it was suggested that authors develop a framework to measure the concepts together to understand the full scope of how different decisions or practices may impact animal emotion.
Driss, Maryam Ben; Sabir, Essaid; Elbiaze, Halima; Diallo, Abloulaye Baniré
Fast & Energy Efficient Federated Learning Using Multi-Attribute Client Clustering and Selection [External] Article d'actes
Dans: Oslo, Norway, 2025.
Résumé | Liens | BibTeX | Étiquettes: Article de conference
@inproceedings{ben_driss_fast_2025,
title = {Fast & Energy Efficient Federated Learning Using Multi-Attribute Client Clustering and Selection},
author = {Maryam Ben Driss and Essaid Sabir and Halima Elbiaze and Abloulaye Baniré Diallo},
url = {https://ieeexplore.ieee.org/document/11174476},
year = {2025},
date = {2025-06-01},
address = {Oslo, Norway},
abstract = {Federated Learning (FL) presents a promising paradigm for decentralized model training; however, its real-world adoption is hindered by several critical challenges, including non-independent and identically distributed (non-IID) data across clients, heterogeneous computational capabilities, and significant communication overhead. To address these issues, this paper introduces a novel multi-attribute client clustering and selection framework for FL. The proposed approach groups clients according to data distribution, device capabilities, geographic location, and model update behavior. Within each cluster, an adaptive client selection mechanism leverages dynamic attributes such as residual energy, data freshness, and client participation motivation to identify the most suitable participants. Experimental evaluations on standard FL benchmark datasets demonstrate that the proposed framework achieves faster convergence, higher global model accuracy, and improved energy efficiency compared to state-of-the-art approaches.},
keywords = {Article de conference},
pubstate = {published},
tppubtype = {inproceedings}
}
Federated Learning (FL) presents a promising paradigm for decentralized model training; however, its real-world adoption is hindered by several critical challenges, including non-independent and identically distributed (non-IID) data across clients, heterogeneous computational capabilities, and significant communication overhead. To address these issues, this paper introduces a novel multi-attribute client clustering and selection framework for FL. The proposed approach groups clients according to data distribution, device capabilities, geographic location, and model update behavior. Within each cluster, an adaptive client selection mechanism leverages dynamic attributes such as residual energy, data freshness, and client participation motivation to identify the most suitable participants. Experimental evaluations on standard FL benchmark datasets demonstrate that the proposed framework achieves faster convergence, higher global model accuracy, and improved energy efficiency compared to state-of-the-art approaches.
Vasseur, Elsa; Diallo, Abdoulaye Baniré
Improving welfare through inclusive innovation: The story of WELL-E. [External] Divers
2025.
Résumé | Liens | BibTeX | Étiquettes: Conférence plénière
@misc{vasseur_improving_2025,
title = {Improving welfare through inclusive innovation: The story of WELL-E.},
author = {Elsa Vasseur and Abdoulaye Baniré Diallo},
url = {https://www.adsa.org/Meetings/2025-Annual-Meeting/Program},
year = {2025},
date = {2025-06-01},
urldate = {2025-06-01},
address = {Louisville, Kentucky},
abstract = {The welfare of animals managed by humans is integrally tied to, and ultimately dependent on, the welfare of the humans doing the managing. Any sustainable and effective change is thus dependent on collaborative solutions that address the needs of both the animals and people involved. Our approach relies on the use of IoT, computer vision, and machine learning to improve our ability to detect and monitor changes in animal welfare and longevity earlier than possible with visual methods, and to generate predictions to aid on-farm decision-making, ensuring that resources and efforts are focused on the animals most likely to succeed in the long term. Grounded in industry partnerships, members of the dairy community sit on both the scientific and management committees, where they contribute to research orientations, leading to the co-creation of research projects and initiatives. This collaborative approach not only ensures the applicability and relevance of all research but allows for continuous feedback between the research and industry environments, creating a true digital living lab.
Launched in 2023, our team has been conducting pilot research on two farms and working directly with staff and management to co-develop and pilot new practices and techniques for the management of animal welfare, to be disseminated across Canada and beyond. We work towards major paradigm shifts in both research (changing the way animal behaviour and complex states are understood through new analytic techniques) and practice (our research informing major changes in the Canadian Dairy Code of Practice), with the goal of providing practical, long-term solutions for the improvement of animal welfare, while simultaneously addressing real-world problems farmers face along the way. Our collaborative approach promotes the responsible integration of new technologies to the dairy industry and empowers producers to be at the forefront of positive welfare developments, ensuring their sustainability and reinforcing the importance of stakeholder participation in innovative scientific research.},
keywords = {Conférence plénière},
pubstate = {published},
tppubtype = {misc}
}
The welfare of animals managed by humans is integrally tied to, and ultimately dependent on, the welfare of the humans doing the managing. Any sustainable and effective change is thus dependent on collaborative solutions that address the needs of both the animals and people involved. Our approach relies on the use of IoT, computer vision, and machine learning to improve our ability to detect and monitor changes in animal welfare and longevity earlier than possible with visual methods, and to generate predictions to aid on-farm decision-making, ensuring that resources and efforts are focused on the animals most likely to succeed in the long term. Grounded in industry partnerships, members of the dairy community sit on both the scientific and management committees, where they contribute to research orientations, leading to the co-creation of research projects and initiatives. This collaborative approach not only ensures the applicability and relevance of all research but allows for continuous feedback between the research and industry environments, creating a true digital living lab.
Launched in 2023, our team has been conducting pilot research on two farms and working directly with staff and management to co-develop and pilot new practices and techniques for the management of animal welfare, to be disseminated across Canada and beyond. We work towards major paradigm shifts in both research (changing the way animal behaviour and complex states are understood through new analytic techniques) and practice (our research informing major changes in the Canadian Dairy Code of Practice), with the goal of providing practical, long-term solutions for the improvement of animal welfare, while simultaneously addressing real-world problems farmers face along the way. Our collaborative approach promotes the responsible integration of new technologies to the dairy industry and empowers producers to be at the forefront of positive welfare developments, ensuring their sustainability and reinforcing the importance of stakeholder participation in innovative scientific research.
Launched in 2023, our team has been conducting pilot research on two farms and working directly with staff and management to co-develop and pilot new practices and techniques for the management of animal welfare, to be disseminated across Canada and beyond. We work towards major paradigm shifts in both research (changing the way animal behaviour and complex states are understood through new analytic techniques) and practice (our research informing major changes in the Canadian Dairy Code of Practice), with the goal of providing practical, long-term solutions for the improvement of animal welfare, while simultaneously addressing real-world problems farmers face along the way. Our collaborative approach promotes the responsible integration of new technologies to the dairy industry and empowers producers to be at the forefront of positive welfare developments, ensuring their sustainability and reinforcing the importance of stakeholder participation in innovative scientific research.
Cellier, Marjorie; Gisiger, Thomas; Diallo, Abdoulaye Baniré; Vasseur, Elsa
Enhancing Statistical Power in Animal Behavior Research: A Case Study on Enrichment in Dairy Cows Using Data Augmentation Methods [External] Divers
2025.
Résumé | Liens | BibTeX | Étiquettes: Presentation
@misc{cellier_enhancing_2025,
title = {Enhancing Statistical Power in Animal Behavior Research: A Case Study on Enrichment in Dairy Cows Using Data Augmentation Methods},
author = {Marjorie Cellier and Thomas Gisiger and Abdoulaye Baniré Diallo and Elsa Vasseur},
url = {https://www.youtube.com/watch?v=vRVwPu_lO2Y&list=PLf1GMQ3ilgOwgkl5wD-_dH-E6TeUbj2zA&index=3},
doi = {10.1038/s41598-025-89891-4},
year = {2025},
date = {2025-05-01},
urldate = {2025-03-06},
volume = {15},
number = {1},
pages = {5917},
address = {Guelph, Canada},
abstract = {Research on animals often faces a critical limitation: small sample sizes limit statistical power, making it challenging to detect behavioral differences. Artificial data augmentation offers a promising solution by increasing dataset size. This study explores the potential of data augmentation in behavioral research through a case study on dairy cow enrichment in tie-stall housing.
Ten Holstein dairy cows were tested using a rossover design over two weeks, with a four-day washout
period. Two enrichment types were evaluated: a standard Kong and a modified version with additional visual (white tape) and tactile (chains) elements. Each device was suspended in the stall. Behavioral interactions with the enrichment were recorded using GoPro cameras for 45-minute sessions and annotated using The Observer XT software. A linear mixed model was applied, with interaction type, day, and treatment as fixed effects, and individuals nested within day as a random effect. Preliminary results showed no significant difference in the percentage of time spent interacting with the two Kong types (standard: 6.9±5.3% of their time; modified: 6.6±6.6% of their time},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
Research on animals often faces a critical limitation: small sample sizes limit statistical power, making it challenging to detect behavioral differences. Artificial data augmentation offers a promising solution by increasing dataset size. This study explores the potential of data augmentation in behavioral research through a case study on dairy cow enrichment in tie-stall housing.
Ten Holstein dairy cows were tested using a rossover design over two weeks, with a four-day washout
period. Two enrichment types were evaluated: a standard Kong and a modified version with additional visual (white tape) and tactile (chains) elements. Each device was suspended in the stall. Behavioral interactions with the enrichment were recorded using GoPro cameras for 45-minute sessions and annotated using The Observer XT software. A linear mixed model was applied, with interaction type, day, and treatment as fixed effects, and individuals nested within day as a random effect. Preliminary results showed no significant difference in the percentage of time spent interacting with the two Kong types (standard: 6.9±5.3% of their time; modified: 6.6±6.6% of their time
Ten Holstein dairy cows were tested using a rossover design over two weeks, with a four-day washout
period. Two enrichment types were evaluated: a standard Kong and a modified version with additional visual (white tape) and tactile (chains) elements. Each device was suspended in the stall. Behavioral interactions with the enrichment were recorded using GoPro cameras for 45-minute sessions and annotated using The Observer XT software. A linear mixed model was applied, with interaction type, day, and treatment as fixed effects, and individuals nested within day as a random effect. Preliminary results showed no significant difference in the percentage of time spent interacting with the two Kong types (standard: 6.9±5.3% of their time; modified: 6.6±6.6% of their time
Arpin, Catherine; Cellier, Marjorie; Wolfe, Tania; Almeida, Hayda; Julliot, Célia; Robichaud, Marianne Villettaz; Diallo, Abdoulaye Baniré; Vasseur, Elsa
Hoofing it to New Homes: How the transition towards a new housing system is experienced by dairy cows. [External] Divers
2025.
Résumé | Liens | BibTeX | Étiquettes: Presentation
@misc{arpin_hoofing_2025,
title = {Hoofing it to New Homes: How the transition towards a new housing system is experienced by dairy cows.},
author = {Catherine Arpin and Marjorie Cellier and Tania Wolfe and Hayda Almeida and Célia Julliot and Marianne Villettaz Robichaud and Abdoulaye Baniré Diallo and Elsa Vasseur},
url = {https://ccsaw.uoguelph.ca/upcoming-events/2025-isae-north-american-regional-meeting/},
year = {2025},
date = {2025-05-01},
urldate = {2025-05-01},
address = {Guelph, Canada},
abstract = {In response to a 2023 change in the Dairy Code of Practice that prohibits continuous tethering as of 2027, there is a predicted rise in the construction of free-stall and stall-free barns, and more cows transitioning between housing systems. The transition period of dairy cattle after a housing change is of interest from a welfare standpoint as there are behavioural adjustments, and potential stressors that could influence their ability to adapt to a new environment. Therefore, this study aims to understand how the transition towards a new barn is experienced by lactating dairy cows and is divided into 2 case studies: (1) Examining how the transition to a new housing system affects the cows' time-budget and how long it takes to stabilize, and (2) Assessing how previous housing system influences the behaviour of cows and their reactivity to milking once they have adapted to their new environment.
Thirty-eight cows from various farms were relocated to a bedded-pack barn on an enrollment basis. Initially, cows remained in their original groups for 10 days before being regrouped into larger groups. Data was collected after arrival via video recordings (2-3 hours, three times per day, three times a week, for 5 weeks) and live observations of milking reactivity at the parlour, conducted twice weekly. In the first case study, we will establish the time-budget of two groups (n=8 & 11 cows) using scan sampling at the group level. In the second case study, the individual time-budgets of a subsample of cows from four groups (n=8-11 per group) will be assessed by scan sampling for two weeks following regrouping, and analysed to explore how previous housing system affects the behaviours of dairy cows once adapted to their environment. As for milking reactivity, preliminary results showed that more tie-stall cows were reactive than free-stall cows on the first and last day of the experiment. Behaviours include kicking (1st day: tie stall=12.5% of cows},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
In response to a 2023 change in the Dairy Code of Practice that prohibits continuous tethering as of 2027, there is a predicted rise in the construction of free-stall and stall-free barns, and more cows transitioning between housing systems. The transition period of dairy cattle after a housing change is of interest from a welfare standpoint as there are behavioural adjustments, and potential stressors that could influence their ability to adapt to a new environment. Therefore, this study aims to understand how the transition towards a new barn is experienced by lactating dairy cows and is divided into 2 case studies: (1) Examining how the transition to a new housing system affects the cows' time-budget and how long it takes to stabilize, and (2) Assessing how previous housing system influences the behaviour of cows and their reactivity to milking once they have adapted to their new environment.
Thirty-eight cows from various farms were relocated to a bedded-pack barn on an enrollment basis. Initially, cows remained in their original groups for 10 days before being regrouped into larger groups. Data was collected after arrival via video recordings (2-3 hours, three times per day, three times a week, for 5 weeks) and live observations of milking reactivity at the parlour, conducted twice weekly. In the first case study, we will establish the time-budget of two groups (n=8 & 11 cows) using scan sampling at the group level. In the second case study, the individual time-budgets of a subsample of cows from four groups (n=8-11 per group) will be assessed by scan sampling for two weeks following regrouping, and analysed to explore how previous housing system affects the behaviours of dairy cows once adapted to their environment. As for milking reactivity, preliminary results showed that more tie-stall cows were reactive than free-stall cows on the first and last day of the experiment. Behaviours include kicking (1st day: tie stall=12.5% of cows
Thirty-eight cows from various farms were relocated to a bedded-pack barn on an enrollment basis. Initially, cows remained in their original groups for 10 days before being regrouped into larger groups. Data was collected after arrival via video recordings (2-3 hours, three times per day, three times a week, for 5 weeks) and live observations of milking reactivity at the parlour, conducted twice weekly. In the first case study, we will establish the time-budget of two groups (n=8 & 11 cows) using scan sampling at the group level. In the second case study, the individual time-budgets of a subsample of cows from four groups (n=8-11 per group) will be assessed by scan sampling for two weeks following regrouping, and analysed to explore how previous housing system affects the behaviours of dairy cows once adapted to their environment. As for milking reactivity, preliminary results showed that more tie-stall cows were reactive than free-stall cows on the first and last day of the experiment. Behaviours include kicking (1st day: tie stall=12.5% of cows
Amarioarei, Georgiana; Cellier, Marjorie; Aigueperse, Nadège; Wolfe, Tania; Montigny, Nicolas; Almeida, Hayda; Shepley, Elise; Diallo, Abdoulay Baniré
Stimulating Minds: Investigating the motivation of calves to engage with cognitive enrichment through participation measures [External] Divers
2025.
Résumé | Liens | BibTeX | Étiquettes: Presentation
@misc{amarioarei_stimulating_2025,
title = {Stimulating Minds: Investigating the motivation of calves to engage with cognitive enrichment through participation measures},
author = {Georgiana Amarioarei and Marjorie Cellier and Nadège Aigueperse and Tania Wolfe and Nicolas Montigny and Hayda Almeida and Elise Shepley and Abdoulay Baniré Diallo},
url = {https://ccsaw.uoguelph.ca/upcoming-events/2025-isae-north-american-regional-meeting/},
year = {2025},
date = {2025-05-01},
urldate = {2025-05-01},
address = {Guelph, Canada},
abstract = {Introducing cognitive enrichment from an early age has the potential to enhance an animal's capacity to learn both simple and complex tasks, promote neural plasticity, and support cognitive development. This is applicable for young cattle who are at a critical stage in their development and could benefit from the influence cognitive enrichment has on their ehavioral expression. This study aims to explore the motivation of weaned dairy calves to engage with cognitive enrichment by analyzing measures of participation over time. Furthermore, we will investigate whether calf performance in the form of measured success rates is a factor influencing the motivation to participate. We hypothesize that providing the opportunity to access cognitive enrichment will have an effect on the calves’ motivation to participate in cognitive challenges and engage with the nrichment. Additionally, we expect there to be a correlation between participation and ability to solve the cognitive tasks. Our study involved a total of four pairs of weaned calves (n=8). The experimental groups were presented with three variations of a puzzle box, each equipped with unique challenges that offer different solutions (push, slide, pull). These boxes were provided to the calves over the span of nine days in an isolated corridor behind their pen. We investigated participation through measuring the frequency to visit the enrichment and we measured the success rates of each calf by processing a predetermined pass/fail criteria of daily performance. Our preliminary results show that on day one of the experiment, the average participation was 2.7 ± 1.4 with a success rate of 58 ± 0.4%. By day nine, participation was 2.6 ± 1.6 with an average success rate of 76 ± 0.4%. These results indicate that there may be no notable change in participation level over time such that the animals are consistently motivated to access enrichment. Moreover, these results allude that there may be a progressive increase in performance success, possibly due to consistent participation. For future analysis, these results will be used in conjunction with the durations and latencies of use, to investigate further potential correlations between participation and success rate.},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
Introducing cognitive enrichment from an early age has the potential to enhance an animal's capacity to learn both simple and complex tasks, promote neural plasticity, and support cognitive development. This is applicable for young cattle who are at a critical stage in their development and could benefit from the influence cognitive enrichment has on their ehavioral expression. This study aims to explore the motivation of weaned dairy calves to engage with cognitive enrichment by analyzing measures of participation over time. Furthermore, we will investigate whether calf performance in the form of measured success rates is a factor influencing the motivation to participate. We hypothesize that providing the opportunity to access cognitive enrichment will have an effect on the calves’ motivation to participate in cognitive challenges and engage with the nrichment. Additionally, we expect there to be a correlation between participation and ability to solve the cognitive tasks. Our study involved a total of four pairs of weaned calves (n=8). The experimental groups were presented with three variations of a puzzle box, each equipped with unique challenges that offer different solutions (push, slide, pull). These boxes were provided to the calves over the span of nine days in an isolated corridor behind their pen. We investigated participation through measuring the frequency to visit the enrichment and we measured the success rates of each calf by processing a predetermined pass/fail criteria of daily performance. Our preliminary results show that on day one of the experiment, the average participation was 2.7 ± 1.4 with a success rate of 58 ± 0.4%. By day nine, participation was 2.6 ± 1.6 with an average success rate of 76 ± 0.4%. These results indicate that there may be no notable change in participation level over time such that the animals are consistently motivated to access enrichment. Moreover, these results allude that there may be a progressive increase in performance success, possibly due to consistent participation. For future analysis, these results will be used in conjunction with the durations and latencies of use, to investigate further potential correlations between participation and success rate.
Araujo, Voncarlos; Rili, Ines; Gisiger, Thomas; Gambs, Sebastien; Vasseur, Elsa; Cellier, Marjorie; Diallo, Abdoulaye Baniré
AI-powered cow detection in complex farm environments [External] Article de journal
Dans: Smart Agricultural Technology, vol. 10, no 100770, 2025, ISSN: 2772-3755.
Liens | BibTeX | Étiquettes: Article de journal
@article{araujo_ai-powered_2025,
title = {AI-powered cow detection in complex farm environments},
author = {Voncarlos Araujo and Ines Rili and Thomas Gisiger and Sebastien Gambs and Elsa Vasseur and Marjorie Cellier and Abdoulaye Baniré Diallo},
doi = {doi.org/10.1016/j.atech.2025.100770},
issn = {2772-3755},
year = {2025},
date = {2025-03-01},
journal = {Smart Agricultural Technology},
volume = {10},
number = {100770},
keywords = {Article de journal},
pubstate = {published},
tppubtype = {article}
}
Naghashi, Vahid; Boukadoum, Mounir; Diallo, Abdoulaye Banire
A multiscale model for multivariate time series forecasting [External] Article de journal
Dans: Scientific Reports, vol. 15, no 1, p. 1565, 2025, ISSN: 2045-2322.
Résumé | Liens | BibTeX | Étiquettes: Article de journal
@article{naghashi_multiscale_2025,
title = {A multiscale model for multivariate time series forecasting},
author = {Vahid Naghashi and Mounir Boukadoum and Abdoulaye Banire Diallo},
url = {https://www.nature.com/articles/s41598-024-82417-4},
doi = {10.1038/s41598-024-82417-4},
issn = {2045-2322},
year = {2025},
date = {2025-01-01},
urldate = {2025-03-06},
journal = {Scientific Reports},
volume = {15},
number = {1},
pages = {1565},
abstract = {Transformer based models for time-series forecasting have shown promising performance and during the past few years different Transformer variants have been proposed in time-series forecasting domain. However, most of the existing methods, mainly represent the time-series from a single scale, making it challenging to capture various time granularities or ignore inter-series correlations between the series which might lead to inaccurate forecasts. In this paper, we address the above mentioned shortcomings and propose a Transformer based model which integrates multi-scale patch-wise temporal modeling and channel-wise representation. In the multi-scale temporal part, the input time-series is divided into patches of different resolutions to capture temporal correlations associated with various scales. The channel-wise encoder which comes after the temporal encoder, models the relations among the input series to capture the intricate interactions between them. In our framework, we further design a multi-step linear decoder to generate the final predictions for the purpose of reducing over-fitting and noise effects. Extensive experiments on seven real world datasets indicate that our model (MultiPatchFormer) achieves state-of-the-art results by surpassing other current baseline models in terms of error metrics and shows stronger generalizability.},
keywords = {Article de journal},
pubstate = {published},
tppubtype = {article}
}
Transformer based models for time-series forecasting have shown promising performance and during the past few years different Transformer variants have been proposed in time-series forecasting domain. However, most of the existing methods, mainly represent the time-series from a single scale, making it challenging to capture various time granularities or ignore inter-series correlations between the series which might lead to inaccurate forecasts. In this paper, we address the above mentioned shortcomings and propose a Transformer based model which integrates multi-scale patch-wise temporal modeling and channel-wise representation. In the multi-scale temporal part, the input time-series is divided into patches of different resolutions to capture temporal correlations associated with various scales. The channel-wise encoder which comes after the temporal encoder, models the relations among the input series to capture the intricate interactions between them. In our framework, we further design a multi-step linear decoder to generate the final predictions for the purpose of reducing over-fitting and noise effects. Extensive experiments on seven real world datasets indicate that our model (MultiPatchFormer) achieves state-of-the-art results by surpassing other current baseline models in terms of error metrics and shows stronger generalizability.
Xu, Xu
You've got a friend in me: Exploration of personality concepts and validation of common personality tests in dairy cattle Mémoire de maîtrise
McGill University, Montreal, Canada, 2025.
@mastersthesis{xu_youve_2025,
title = {You've got a friend in me: Exploration of personality concepts and validation of common personality tests in dairy cattle},
author = {Xu Xu},
year = {2025},
date = {2025-01-01},
address = {Montreal, Canada},
school = {McGill University},
keywords = {These},
pubstate = {published},
tppubtype = {mastersthesis}
}
Muszik, Jasmine
Implementing On-Farm Enrichment: Effects on the Motivation of Lambs and Dairy Cows [External] Thèse de PhD
McGill University, 2025.
Résumé | Liens | BibTeX | Étiquettes: These
@phdthesis{muszik_implementing_2025,
title = {Implementing On-Farm Enrichment: Effects on the Motivation of Lambs and Dairy Cows},
author = {Jasmine Muszik},
url = {https://escholarship.mcgill.ca/concern/theses/6t053p36k},
year = {2025},
date = {2025-01-01},
address = {Montreal, Canada},
school = {McGill University},
abstract = {Les pratiques agricoles ont évolué pour offrir aux animaux plus de contrôle sur leur temps, en mettant l'accent sur leur bien-être. L'enrichissement ciblé, conçu pour répondre à des besoins spécifiques, favorise des émotions positives en permettant aux animaux d'agir selon leurs motivations, satisfaisant ainsi leurs besoins. Un enrichissement efficace doit être activement utilisé par l'animal et doit répondre à une motivation d'interaction ou d'accès, ce qui suggère qu'il est perçu positivement. L'enrichissement multimodal, combinant plusieurs types d'enrichissement (ex. physique, cognitif, sensoriel, social, nutritionnel), pourrait répondre à cette problématique en encourageant une gamme de comportements motivés. Les normes futures concernant les soins des animaux d'élevage pourraient également recommander aux producteurs l'inclusion de ces enrichissements fortement motivés dans leurs pratiques quotidiennes afin d'éviter la provocation d’émotions négatives, comme la frustration, liées à l'anticipation d'un événement ou d'une récompense retardée ou inexistante, comme révélé dans une revue de littérature. Cependant, la mise en œuvre de l'enrichissement multimodal et ses interactions avec la motivation animale nécessite un examen plus approfondi, tant au niveau théorique qu’au niveau pratique. Une étude menée sur des agneaux, exposés ou non à un enrichissement multimodal, a démontré que bien que les deux groupes aient appris à naviguer un labyrinthe et aient démontré la même flexibilité pour obtenir la récompense, les agneaux enrichis étaient plus calmes, tandis que ceux du groupe témoin étaient plus agités, ce qui suggère que l'enrichissement peut avoir un impact sur la réactivité. Cette étude n’a pas directement étudié la motivation des animaux vers l'enrichissement, mais vers une récompense dans un autre contexte, et elle s’est concentrée sur les jeunes. Alors, la prochaine étude a cherché à comprendre comment la fréquence d'accès à l'enrichissement influence la motivation des vaches laitières adultes. Des vaches ayant un accès limité à l’exercice ont été réparties en deux groupes : un avec un accès extérieur trois jours par semaine et l'autre un jour par semaine. Leur comportement et celui des membres du personnel ont été observés pendant ces périodes. Les résultats ont démontré que les vaches des deux groupes étaient plus motivées pour sortir que pour retourner à l'étable, bien que les interventions du personnel et les conditions climatiques aient pu influencer cette motivation. Bien que cette étude ait été bénéfique pour comprendre si les vaches laitières accordent de l’importance à l’accès extérieur, peu importe la fréquence d’accès fournie, l'étape suivante consistait à tester la provision d’accès extérieur dans un cadre réaliste pour déterminer comment la taille des groupes et les interventions du personnel influencent la capacité des producteurs à faire la transition vers la provision d’exercice extérieur quotidien pour leur troupeau. 76 vaches en lactation ont été réparties en sept groupes, avec un nouveau groupe étant ajouté à chaque semaine, jusqu'à ce que tous les groupes aient accès à un des deux enclos d'exercice. Il a été constaté qu'augmenter le nombre de vaches n’entraînait pas de besoin supplémentaire en personnel ni de plus de temps par sortie. De plus, le nombre d'interventions du personnel est resté stable (interventions go) ou a diminué (interventions stop) après une semaine. De plus, le comportement des vaches n’a pas différé de manière significative entre les enclos, même après l'introduction d'un nouveau groupe. Cela suggère que les vaches et le personnel s'adaptent rapidement à l'accès extérieur, et que les producteurs peuvent commencer à introduire progressivement des sorties extérieures quotidiennes pour leur troupeau. Toutes les études décrites ont été essentielles pour comprendre comment l'enrichissement multimodal influence la motivation des animaux, tant en matière d'enrichissement que dans d'autres contextes, et comment ces pratiques peuvent être utilisées de manière pratique par les producteurs dans les systèmes de production modernes},
keywords = {These},
pubstate = {published},
tppubtype = {phdthesis}
}
Les pratiques agricoles ont évolué pour offrir aux animaux plus de contrôle sur leur temps, en mettant l'accent sur leur bien-être. L'enrichissement ciblé, conçu pour répondre à des besoins spécifiques, favorise des émotions positives en permettant aux animaux d'agir selon leurs motivations, satisfaisant ainsi leurs besoins. Un enrichissement efficace doit être activement utilisé par l'animal et doit répondre à une motivation d'interaction ou d'accès, ce qui suggère qu'il est perçu positivement. L'enrichissement multimodal, combinant plusieurs types d'enrichissement (ex. physique, cognitif, sensoriel, social, nutritionnel), pourrait répondre à cette problématique en encourageant une gamme de comportements motivés. Les normes futures concernant les soins des animaux d'élevage pourraient également recommander aux producteurs l'inclusion de ces enrichissements fortement motivés dans leurs pratiques quotidiennes afin d'éviter la provocation d’émotions négatives, comme la frustration, liées à l'anticipation d'un événement ou d'une récompense retardée ou inexistante, comme révélé dans une revue de littérature. Cependant, la mise en œuvre de l'enrichissement multimodal et ses interactions avec la motivation animale nécessite un examen plus approfondi, tant au niveau théorique qu’au niveau pratique. Une étude menée sur des agneaux, exposés ou non à un enrichissement multimodal, a démontré que bien que les deux groupes aient appris à naviguer un labyrinthe et aient démontré la même flexibilité pour obtenir la récompense, les agneaux enrichis étaient plus calmes, tandis que ceux du groupe témoin étaient plus agités, ce qui suggère que l'enrichissement peut avoir un impact sur la réactivité. Cette étude n’a pas directement étudié la motivation des animaux vers l'enrichissement, mais vers une récompense dans un autre contexte, et elle s’est concentrée sur les jeunes. Alors, la prochaine étude a cherché à comprendre comment la fréquence d'accès à l'enrichissement influence la motivation des vaches laitières adultes. Des vaches ayant un accès limité à l’exercice ont été réparties en deux groupes : un avec un accès extérieur trois jours par semaine et l'autre un jour par semaine. Leur comportement et celui des membres du personnel ont été observés pendant ces périodes. Les résultats ont démontré que les vaches des deux groupes étaient plus motivées pour sortir que pour retourner à l'étable, bien que les interventions du personnel et les conditions climatiques aient pu influencer cette motivation. Bien que cette étude ait été bénéfique pour comprendre si les vaches laitières accordent de l’importance à l’accès extérieur, peu importe la fréquence d’accès fournie, l'étape suivante consistait à tester la provision d’accès extérieur dans un cadre réaliste pour déterminer comment la taille des groupes et les interventions du personnel influencent la capacité des producteurs à faire la transition vers la provision d’exercice extérieur quotidien pour leur troupeau. 76 vaches en lactation ont été réparties en sept groupes, avec un nouveau groupe étant ajouté à chaque semaine, jusqu'à ce que tous les groupes aient accès à un des deux enclos d'exercice. Il a été constaté qu'augmenter le nombre de vaches n’entraînait pas de besoin supplémentaire en personnel ni de plus de temps par sortie. De plus, le nombre d'interventions du personnel est resté stable (interventions go) ou a diminué (interventions stop) après une semaine. De plus, le comportement des vaches n’a pas différé de manière significative entre les enclos, même après l'introduction d'un nouveau groupe. Cela suggère que les vaches et le personnel s'adaptent rapidement à l'accès extérieur, et que les producteurs peuvent commencer à introduire progressivement des sorties extérieures quotidiennes pour leur troupeau. Toutes les études décrites ont été essentielles pour comprendre comment l'enrichissement multimodal influence la motivation des animaux, tant en matière d'enrichissement que dans d'autres contextes, et comment ces pratiques peuvent être utilisées de manière pratique par les producteurs dans les systèmes de production modernes
Balde, Abdourahmane
Méthode d'extraction de signatures de biomarqueurs métaboliques dans le cadre de prédiction des maladies chez les bovins laitiers [External] Mémoire de maîtrise
UQAM, Montreal, Canada, 2025.
Résumé | Liens | BibTeX | Étiquettes: These
@mastersthesis{balde_methode_2025,
title = {Méthode d'extraction de signatures de biomarqueurs métaboliques dans le cadre de prédiction des maladies chez les bovins laitiers},
author = {Abdourahmane Balde},
url = {http://archipel.uqam.ca/id/eprint/18426},
year = {2025},
date = {2025-01-01},
address = {Montreal, Canada},
school = {UQAM},
abstract = {Les maladies métaboliques chez les bovins laitiers sont une préoccupation majeure pour les producteurs laitiers, car elles affectent la production, la reproduction, le bien-être et la longévité des animaux. Les approches de classification informatique actuelles des maladies métaboliques manquent de précision et de simplicité pour permettre une classification effective en milieu de ferme. Cela est dû principalement à la variabilité naturelle des profils métaboliques entre les vaches et à la distribution non équilibrée des classes (malade, non malade) dans les jeux de données sur les maladies surveillées par l’industrie laitière. Cette disparité entre les classes rend la prédiction des maladies encore plus complexe. Pour permettre aux vétérinaires de mieux suivre les paramètres métaboliques en lien avec les principales maladies suivies en médecine vétérinaire, nous proposons une approche d’extraction des signatures métaboliques qui permettrait de caractériser les maladies, d’identifier une meilleure prédiction des risques associés et d’informer les classifieurs pour le diagnostique des maladies métaboliques. Notre approche est décomposée en trois étapes comme suit : la catégorisation des indicateurs métaboliques clés, l’extraction des signatures métaboliques présentes dans les maladies, la sélection des signatures discriminantes à l’aide de deux mesures statistiques (le test de Fisher et l’intervalle de confiance), et enfin l’entraînement des modèles de classification afin de prédire une maladie métabolique à partir d’un profil métabolique. Cette approche a été appliquée à un jeu de données contenant cinq indicateurs métaboliques pour 623 vaches. Ces indicateurs sont associés aux sept maladies principales à déclaration obligatoire au Québec. Les résultats préliminaires ont permis d’identifier 208 signatures métaboliques et 96 signatures discriminantes. Ces signatures ont ensuite permis de construire un classifieur pour identifier si une vache est diagnostiquée comme malade seulement à partir de son échantillon de données métaboliques. Ces signatures discriminantes constitueront un potentiel de biomarqueurs utiles pour le diagnostic des maladies. Les résultats de ces classifieurs sont intéressants avec des F-mesure avoisinant 0,91.},
keywords = {These},
pubstate = {published},
tppubtype = {mastersthesis}
}
Les maladies métaboliques chez les bovins laitiers sont une préoccupation majeure pour les producteurs laitiers, car elles affectent la production, la reproduction, le bien-être et la longévité des animaux. Les approches de classification informatique actuelles des maladies métaboliques manquent de précision et de simplicité pour permettre une classification effective en milieu de ferme. Cela est dû principalement à la variabilité naturelle des profils métaboliques entre les vaches et à la distribution non équilibrée des classes (malade, non malade) dans les jeux de données sur les maladies surveillées par l’industrie laitière. Cette disparité entre les classes rend la prédiction des maladies encore plus complexe. Pour permettre aux vétérinaires de mieux suivre les paramètres métaboliques en lien avec les principales maladies suivies en médecine vétérinaire, nous proposons une approche d’extraction des signatures métaboliques qui permettrait de caractériser les maladies, d’identifier une meilleure prédiction des risques associés et d’informer les classifieurs pour le diagnostique des maladies métaboliques. Notre approche est décomposée en trois étapes comme suit : la catégorisation des indicateurs métaboliques clés, l’extraction des signatures métaboliques présentes dans les maladies, la sélection des signatures discriminantes à l’aide de deux mesures statistiques (le test de Fisher et l’intervalle de confiance), et enfin l’entraînement des modèles de classification afin de prédire une maladie métabolique à partir d’un profil métabolique. Cette approche a été appliquée à un jeu de données contenant cinq indicateurs métaboliques pour 623 vaches. Ces indicateurs sont associés aux sept maladies principales à déclaration obligatoire au Québec. Les résultats préliminaires ont permis d’identifier 208 signatures métaboliques et 96 signatures discriminantes. Ces signatures ont ensuite permis de construire un classifieur pour identifier si une vache est diagnostiquée comme malade seulement à partir de son échantillon de données métaboliques. Ces signatures discriminantes constitueront un potentiel de biomarqueurs utiles pour le diagnostic des maladies. Les résultats de ces classifieurs sont intéressants avec des F-mesure avoisinant 0,91.
Vasseur, Elsa; Diallo, Abdoulaye Baniré
WELL-E : Research and Innovation Chair in Animal Welfare and Artificial Intelligence [External] Divers
2024.
Liens | BibTeX | Étiquettes: Conférence plénière
@misc{vasseur_well-e_2024,
title = {WELL-E : Research and Innovation Chair in Animal Welfare and Artificial Intelligence},
author = {Elsa Vasseur and Abdoulaye Baniré Diallo},
url = {https://www.mcgill.ca/cdsi/mccais},
year = {2024},
date = {2024-12-01},
urldate = {2024-05-01},
address = {Montreal, Canada},
keywords = {Conférence plénière},
pubstate = {published},
tppubtype = {misc}
}
Zhu, Junsheng
Student Panel - The Next Generation of AI Researchers [External] Divers
2024.
Liens | BibTeX | Étiquettes: Presentation
@misc{zhu_student_2024,
title = {Student Panel - The Next Generation of AI Researchers},
author = {Junsheng Zhu},
url = {https://www.mcgill.ca/cdsi/channels/event/mcgill-collaborative-ai-society-annual-symposium-361276},
year = {2024},
date = {2024-12-01},
urldate = {2024-12-01},
address = {Montreal, Canada},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
Xu, Aimee; Cellier, Marjorie; Aigueperse, Nadège; Vasseur, Elsa
You've Got A Friend In Me: A Scoping Review on How Personality Traits in Dairy Cows Affects Social Enrichment Response [External] Divers
2024.
Liens | BibTeX | Étiquettes: Presentation
@misc{xu_youve_2024,
title = {You've Got A Friend In Me: A Scoping Review on How Personality Traits in Dairy Cows Affects Social Enrichment Response},
author = {Aimee Xu and Marjorie Cellier and Nadège Aigueperse and Elsa Vasseur},
url = {https://www.oplait.org/rvannuel},
year = {2024},
date = {2024-11-01},
urldate = {2024-11-01},
address = {Sherbrooke, Canada},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
Naghashi, Vahid; Boukadoum, Mounir; Diallo, Baniré
Transformer-BEATS: A Transformer model for time series prediction of dairy milk production [External] Divers
2024.
Liens | BibTeX | Étiquettes: Presentation
@misc{naghashi_transformer-beats_2024,
title = {Transformer-BEATS: A Transformer model for time series prediction of dairy milk production},
author = {Vahid Naghashi and Mounir Boukadoum and Baniré Diallo},
url = {https://f1000research.com/posters/13-1400},
year = {2024},
date = {2024-11-01},
urldate = {2024-11-01},
address = {Medellin, Colombia},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
Vasseur, Elsa; Diallo, Abdoulaye Baniré
L'utilisation de l'intelligence artificielle en bien-être animal [External] Divers
2024.
Liens | BibTeX | Étiquettes: Conférence plénière
@misc{vasseur_utilisation_2024,
title = {L'utilisation de l'intelligence artificielle en bien-être animal},
author = {Elsa Vasseur and Abdoulaye Baniré Diallo},
url = {https://asio.oaq.qc.ca/Web/MyCatalog/ViewP?pid=Hz%2BL4eQ7kA0l7a2znDtCow%3D%3D&id=tCNTM8jNtNCLZwucN9Fz7g%3D%3D},
year = {2024},
date = {2024-11-01},
urldate = {2024-11-01},
address = {Drummondville, Canada},
keywords = {Conférence plénière},
pubstate = {published},
tppubtype = {misc}
}
Diallo, Abdoulaye Baniré
Introduction à l’intelligence artificielle [External] Présentation
22.10.2024.
Liens | BibTeX | Étiquettes: Presentation
@misc{banire_diallo_introduction_nodate,
title = {Introduction à l’intelligence artificielle},
author = {Abdoulaye Baniré Diallo},
url = {https://event.fourwaves.com/2024rqrsymposium/pages},
year = {2024},
date = {2024-10-22},
urldate = {2024-10-22},
keywords = {Presentation},
pubstate = {published},
tppubtype = {presentation}
}
Xu, Aimee; Cellier, Marjorie; Aigueperse, Nadège; Vasseur, Elsa
Je suis ton amie : Identification des traits de personnalité chez les vaches laitières pour un regroupement optimisé comme forme d’enrichissement social [External] Divers
2024.
Résumé | Liens | BibTeX | Étiquettes: Presentation
@misc{xu_je_2024,
title = {Je suis ton amie : Identification des traits de personnalité chez les vaches laitières pour un regroupement optimisé comme forme d’enrichissement social},
author = {Aimee Xu and Marjorie Cellier and Nadège Aigueperse and Elsa Vasseur},
url = {https://www.youtube.com/watch?v=K5nIlA2I-Kc},
year = {2024},
date = {2024-10-01},
urldate = {2024-10-01},
address = {Drummondville, Canada},
abstract = {Dans de nombreuses fermes laitières canadiennes, les vaches sont regroupées par âge ou race, des critères pratiques, mais qui ne garantissent pas toujours l’harmonie. Tout comme les humains, ces critères ne garantissent pas une bonne entente entre les vaches. Notre projet explore le regroupement des vaches selon leur personnalité pour améliorer leurs liens sociaux et réduire les conflits. La personnalité, définie comme des différences comportementales stables au fil du temps et dans différents contextes joue un rôle clé : des études montrent que les vaches aux personnalités similaires s’entendent mieux, réduisant le stress et améliorant leur bien-être. Notre objectif est donc double : 1) valider les tests comportementaux appliqués aux vaches logées dans les stabulations entravées pour mesurer la personnalité; 2) déterminer des profils de personnalité afin de mieux comprendre les dynamiques sociales au sein du troupeau. Plusieurs tests comportementaux dont le Test d'Approche Humaine (HAT) et le Test de Soudaineté (SUD) ont été réalisés à trois périodes différentes, sur environ 70 vaches par période (HAT : n=44, SUD : n=48). Nous avons observé la cohérence des comportements des vaches à travers le temps et entre les tests. Nos résultats montrent une forte corrélation entre le HAT et le SUD sur les trois périodes, indiquant une réponse comportementale stable dans divers contextes. Cela apporte une information importante sur la validité des tests pour mesurer la personnalité chez les vaches laitières, en renforçant l’idée que ces deux tests mesurent un aspect partagé de la personnalité. Cependant, la réactivité aux stimuli soudains (SUD) semble plus stable que celle aux interactions humaines (HAT) du fait que ce dernier test mesure aussi l’affinité à l’humain qui peut être modifiée par l’expérience. Ces résultats sont un premier pas permettant de confirmer les tests classiquement utilisés pour mesurer la personnalité des animaux, en particulier la réactivité générale. L’implémentation et la validation d’autres tests complémentaires portant sur des traits de personnalité comme l’agressivité ou la curiosité sont nécessaires pour obtenir des profils plus complets. Cela permettra d’optimiser le regroupement des vaches, améliorant ainsi leur bien-être et pourrait avoir un impact sur leur santé et productivité, tout en répondant aux attentes sociétales pour des pratiques agricoles plus durables.},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
Dans de nombreuses fermes laitières canadiennes, les vaches sont regroupées par âge ou race, des critères pratiques, mais qui ne garantissent pas toujours l’harmonie. Tout comme les humains, ces critères ne garantissent pas une bonne entente entre les vaches. Notre projet explore le regroupement des vaches selon leur personnalité pour améliorer leurs liens sociaux et réduire les conflits. La personnalité, définie comme des différences comportementales stables au fil du temps et dans différents contextes joue un rôle clé : des études montrent que les vaches aux personnalités similaires s’entendent mieux, réduisant le stress et améliorant leur bien-être. Notre objectif est donc double : 1) valider les tests comportementaux appliqués aux vaches logées dans les stabulations entravées pour mesurer la personnalité; 2) déterminer des profils de personnalité afin de mieux comprendre les dynamiques sociales au sein du troupeau. Plusieurs tests comportementaux dont le Test d'Approche Humaine (HAT) et le Test de Soudaineté (SUD) ont été réalisés à trois périodes différentes, sur environ 70 vaches par période (HAT : n=44, SUD : n=48). Nous avons observé la cohérence des comportements des vaches à travers le temps et entre les tests. Nos résultats montrent une forte corrélation entre le HAT et le SUD sur les trois périodes, indiquant une réponse comportementale stable dans divers contextes. Cela apporte une information importante sur la validité des tests pour mesurer la personnalité chez les vaches laitières, en renforçant l’idée que ces deux tests mesurent un aspect partagé de la personnalité. Cependant, la réactivité aux stimuli soudains (SUD) semble plus stable que celle aux interactions humaines (HAT) du fait que ce dernier test mesure aussi l’affinité à l’humain qui peut être modifiée par l’expérience. Ces résultats sont un premier pas permettant de confirmer les tests classiquement utilisés pour mesurer la personnalité des animaux, en particulier la réactivité générale. L’implémentation et la validation d’autres tests complémentaires portant sur des traits de personnalité comme l’agressivité ou la curiosité sont nécessaires pour obtenir des profils plus complets. Cela permettra d’optimiser le regroupement des vaches, améliorant ainsi leur bien-être et pourrait avoir un impact sur leur santé et productivité, tout en répondant aux attentes sociétales pour des pratiques agricoles plus durables.
Diallo, Abdoulaye Baniré; Sirard, Marc-André
Round Table Discussion [External] Divers
2024.
Liens | BibTeX | Étiquettes: Table ronde
@misc{banire_diallo_round_2024,
title = {Round Table Discussion},
author = {Abdoulaye Baniré Diallo and Marc-André Sirard},
url = {https://event.fourwaves.com/2024rqrsymposium/pages},
year = {2024},
date = {2024-10-01},
urldate = {2024-10-01},
address = {Montreal, Quebec},
keywords = {Table ronde},
pubstate = {published},
tppubtype = {misc}
}
Massoua, Armand Bandiang
Using deep learning techniques for survival analyses [External] Divers
2024.
Liens | BibTeX | Étiquettes: Presentation
@misc{bandiang_massoua_using_2024,
title = {Using deep learning techniques for survival analyses},
author = {Armand Bandiang Massoua},
url = {https://event.fourwaves.com/2024rqrsymposium/pages},
year = {2024},
date = {2024-10-01},
urldate = {2024-10-01},
address = {Montreal, Canada},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
Naghashi, Vahid
Introduction to Deep Learning techniques with use case on dairy data [External] Divers
2024.
Liens | BibTeX | Étiquettes: Presentation
@misc{naghashi_introduction_2024,
title = {Introduction to Deep Learning techniques with use case on dairy data},
author = {Vahid Naghashi},
url = {https://event.fourwaves.com/2024rqrsymposium/pages},
year = {2024},
date = {2024-10-01},
urldate = {2024-10-01},
address = {Montreal, Canada},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
Diallo, Abdoulaye Baniré
Introduction à l’intelligence artificielle [External] Divers
2024.
Liens | BibTeX | Étiquettes: Presentation
@misc{banire_diallo_introduction_2024,
title = {Introduction à l’intelligence artificielle},
author = {Abdoulaye Baniré Diallo},
url = {https://event.fourwaves.com/2024rqrsymposium/pages},
year = {2024},
date = {2024-10-01},
urldate = {2024-10-01},
address = {Montreal, Canada},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
Dallago, G. M.; Elsohaby, I.; McClure, J. T.; Lacroix, R.; Vasseur, E.
The associations of early-life health and performance with subsequent dairy cow longevity, productivity, and profitability [External] Article de journal
Dans: animal, vol. 18, no 9, p. 101281, 2024, ISSN: 17517311.
Liens | BibTeX | Étiquettes: Article de journal
@article{dallago_associations_2024,
title = {The associations of early-life health and performance with subsequent dairy cow longevity, productivity, and profitability},
author = {G. M. Dallago and I. Elsohaby and J. T. McClure and R. Lacroix and E. Vasseur},
url = {https://linkinghub.elsevier.com/retrieve/pii/S175173112400212X},
doi = {10.1016/j.animal.2024.101281},
issn = {17517311},
year = {2024},
date = {2024-09-01},
urldate = {2024-09-05},
journal = {animal},
volume = {18},
number = {9},
pages = {101281},
keywords = {Article de journal},
pubstate = {published},
tppubtype = {article}
}
Vasseur, Elsa
Provision of automatic milking-system alerts for potential incidence of mastitis: a comparative study between stationary classification and time-series regression modelling [External] Présentation
Bologna, Italy, 01.09.2024.
Liens | BibTeX | Étiquettes: Presentation
@misc{vasseur_provision_2024,
title = {Provision of automatic milking-system alerts for potential incidence of mastitis: a comparative study between stationary classification and time-series regression modelling},
author = {Elsa Vasseur},
url = {https://www.ecplf2024.it/program/parallel-sessions-11-45-13-00/},
year = {2024},
date = {2024-09-01},
urldate = {2024-09-01},
address = {Bologna, Italy},
keywords = {Presentation},
pubstate = {published},
tppubtype = {presentation}
}
Samaké, Awa; Diallo, Abdoulaye Baniré
Predicting Dairy Milk Yields Using Privileged Information and Heteroscedastic Dropout [External] Divers
2024.
Liens | BibTeX | Étiquettes: Presentation
@misc{samake_predicting_2024,
title = {Predicting Dairy Milk Yields Using Privileged Information and Heteroscedastic Dropout},
author = {Awa Samaké and Abdoulaye Baniré Diallo},
url = {https://wscv-indaba.github.io/2024#program},
year = {2024},
date = {2024-09-01},
urldate = {2024-09-01},
address = {Dakar, Senegal},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
Samaké, Awa; Diallo, Abdoulaye Baniré
Predicting Dairy Milk Yields Using Privileged Information and Heteroscedastic Dropout [External] Divers
2024.
Liens | BibTeX | Étiquettes: Presentation
@misc{samake_predicting_2024-1,
title = {Predicting Dairy Milk Yields Using Privileged Information and Heteroscedastic Dropout},
author = {Awa Samaké and Abdoulaye Baniré Diallo},
url = {https://deeplearningindaba.com/2024/},
year = {2024},
date = {2024-09-01},
urldate = {2024-09-01},
address = {Dakar, Senegal},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
Vasseur, Elsa; Diallo, Abdoulaye Baniré
WELL-E: Enhancing Animal Welfare & Longevity through Artificial Intelligence and Internet of Things Dedicated to Dairy Farming [External] Divers
2024.
Liens | BibTeX | Étiquettes: Conférence plénière
@misc{vasseur_well-e_2024-2,
title = {WELL-E: Enhancing Animal Welfare & Longevity through Artificial Intelligence and Internet of Things Dedicated to Dairy Farming},
author = {Elsa Vasseur and Abdoulaye Baniré Diallo},
url = {https://eaap2024.org/session/m221s60/},
year = {2024},
date = {2024-09-01},
urldate = {2024-12-01},
address = {Florence, Italy},
keywords = {Conférence plénière},
pubstate = {published},
tppubtype = {misc}
}
Vasseur, Elsa
Provision of automatic milking-system alerts for potential incidence of mastitis: a comparative study between stationary classification and time-series regression modelling [External] Présentation
Bologna, Italy, 01.09.2024.
Liens | BibTeX | Étiquettes: Presentation
@misc{vasseur_provision_2024-1,
title = {Provision of automatic milking-system alerts for potential incidence of mastitis: a comparative study between stationary classification and time-series regression modelling},
author = {Elsa Vasseur},
url = {https://www.ecplf2024.it/program/parallel-sessions-11-45-13-00/},
year = {2024},
date = {2024-09-01},
urldate = {2024-09-01},
address = {Bologna, Italy},
keywords = {Presentation},
pubstate = {published},
tppubtype = {presentation}
}
Orchi, H.; Diallo, A. B.; Vasseur, E.; Elbiaze, H.; Sabir, E.; Sadik, M.
Temporal Synchronization of Multi-View Video for Cattle Movement Analysis in Dynamic Farm Settings [External] Divers
2024.
Liens | BibTeX | Étiquettes: Presentation
@misc{orchi_temporal_2024,
title = {Temporal Synchronization of Multi-View Video for Cattle Movement Analysis in Dynamic Farm Settings},
author = {H. Orchi and A. B. Diallo and E. Vasseur and H. Elbiaze and E. Sabir and M. Sadik},
url = {https://www.iscb.org/ismb2024/programme-schedule/scientific-programme/digag},
year = {2024},
date = {2024-07-01},
urldate = {2024-07-01},
address = {Montreal, Canada},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
Araujo, V. M. De; Gisiger, T.; Gambs, S.; Vasseur, E.; Diallo, A. B.
Revolutionizing Livestock Monitoring: AI-Powered Cow Detection in Farm Environments [External] Divers
2024.
Liens | BibTeX | Étiquettes: Presentation
@misc{de_araujo_revolutionizing_2024,
title = {Revolutionizing Livestock Monitoring: AI-Powered Cow Detection in Farm Environments},
author = {V. M. De Araujo and T. Gisiger and S. Gambs and E. Vasseur and A. B. Diallo},
url = {https://www.iscb.org/ismb2024/programme-schedule/scientific-programme/digag},
year = {2024},
date = {2024-07-01},
urldate = {2024-07-01},
address = {Montreal, Canada},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
Jacques, A. A. Boatswain; Lord, E.; Reinhartz, V.; Diallo, A. B.
Precision Farming for Profit: Leveraging Profitability Maps and ILPMZ to Optimize Return on Investment and Soil Conservation of Agricultural Fields [External] Divers
2024.
Liens | BibTeX | Étiquettes: Presentation
@misc{boatswain_jacques_precision_2024,
title = {Precision Farming for Profit: Leveraging Profitability Maps and ILPMZ to Optimize Return on Investment and Soil Conservation of Agricultural Fields},
author = {A. A. Boatswain Jacques and E. Lord and V. Reinhartz and A. B. Diallo},
url = {https://www.iscb.org/ismb2024/programme-schedule/scientific-programme/digag},
year = {2024},
date = {2024-07-01},
urldate = {2024-07-01},
address = {Montreal, Canada},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
Gisiger, T.; Cellier, M.; Vasseur, E.; Diallo, A. B.
Extracting meaningful video segments using a movement detection algorithm applied to dairy cow behavior study and welfare monitoring. [External] Divers
2024.
Résumé | Liens | BibTeX | Étiquettes: Presentation
@misc{gisiger_extracting_2024,
title = {Extracting meaningful video segments using a movement detection algorithm applied to dairy cow behavior study and welfare monitoring.},
author = {T. Gisiger and M. Cellier and E. Vasseur and A. B. Diallo},
url = {https://www.iscb.org/ismb2024/programme-schedule/scientific-programme/digag},
year = {2024},
date = {2024-07-01},
urldate = {2024-07-01},
address = {Montreal, Canada},
abstract = {Precision dairy farming is essential to creating a food production system that is durable and respects animal welfare and the environment.
This approach requires gathering many hours of videos with still cameras, which are then used for research, welfare monitoring and tool training. Extracting the video segments with the most meaningful information would allow us to study larger fractions of the recordings taken while gathering the maximum number of observations. This can be framed as a movement detection problem, or, alternatively, a detection problem using traditional or deep learning techniques. However, the latter process of training in a cluttered farm environment might prove challenging.
Here, we propose an algorithm that estimates cow movement in a robust manner without the need for object detection or training. The resulting movement indices, paired with an independently set movement threshold, can then be used to partition videos into episodes where the cow is either immobile or displaying relevant movements and behaviours. This approach takes advantage of typical cow behaviour features and allows for factoring out video sections with repetitive or little/no movement.
The experimental setting consists of five 15-minute videos and focuses on measuring the extent to which discarding episodes with little to no movements speeds up the process of labelling behaviours by animal science experts.
This approach will allow for more complex experiments, novel angles of investigation and larger data-sets to study cow behaviour and interaction with their environment as well as monitoring for welfare status.},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
Precision dairy farming is essential to creating a food production system that is durable and respects animal welfare and the environment.
This approach requires gathering many hours of videos with still cameras, which are then used for research, welfare monitoring and tool training. Extracting the video segments with the most meaningful information would allow us to study larger fractions of the recordings taken while gathering the maximum number of observations. This can be framed as a movement detection problem, or, alternatively, a detection problem using traditional or deep learning techniques. However, the latter process of training in a cluttered farm environment might prove challenging.
Here, we propose an algorithm that estimates cow movement in a robust manner without the need for object detection or training. The resulting movement indices, paired with an independently set movement threshold, can then be used to partition videos into episodes where the cow is either immobile or displaying relevant movements and behaviours. This approach takes advantage of typical cow behaviour features and allows for factoring out video sections with repetitive or little/no movement.
The experimental setting consists of five 15-minute videos and focuses on measuring the extent to which discarding episodes with little to no movements speeds up the process of labelling behaviours by animal science experts.
This approach will allow for more complex experiments, novel angles of investigation and larger data-sets to study cow behaviour and interaction with their environment as well as monitoring for welfare status.
This approach requires gathering many hours of videos with still cameras, which are then used for research, welfare monitoring and tool training. Extracting the video segments with the most meaningful information would allow us to study larger fractions of the recordings taken while gathering the maximum number of observations. This can be framed as a movement detection problem, or, alternatively, a detection problem using traditional or deep learning techniques. However, the latter process of training in a cluttered farm environment might prove challenging.
Here, we propose an algorithm that estimates cow movement in a robust manner without the need for object detection or training. The resulting movement indices, paired with an independently set movement threshold, can then be used to partition videos into episodes where the cow is either immobile or displaying relevant movements and behaviours. This approach takes advantage of typical cow behaviour features and allows for factoring out video sections with repetitive or little/no movement.
The experimental setting consists of five 15-minute videos and focuses on measuring the extent to which discarding episodes with little to no movements speeds up the process of labelling behaviours by animal science experts.
This approach will allow for more complex experiments, novel angles of investigation and larger data-sets to study cow behaviour and interaction with their environment as well as monitoring for welfare status.
Almeida, H.; Grégoire, N. B.; Bilal, M.; Leduc, M.; Chorif, Y.; Zhao, X.; Dubuc, J.; Diallo, A. B.
Biomarker-based learning for disease prediction in precision dairy farming [External] Divers
2024.
Résumé | Liens | BibTeX | Étiquettes: Presentation
@misc{almeida_biomarker-based_2024,
title = {Biomarker-based learning for disease prediction in precision dairy farming},
author = {H. Almeida and N. B. Grégoire and M. Bilal and M. Leduc and Y. Chorif and X. Zhao and J. Dubuc and A. B. Diallo},
url = {https://www.iscb.org/ismb2024/programme-schedule/scientific-programme/digag},
year = {2024},
date = {2024-07-01},
urldate = {2024-07-01},
address = {Montreal, Canada},
abstract = {Metabolic diseases have great impact on dairy production and animal welfare [1, 2]. Metabolomic profiling has helped identify biomarkers to predict disease risk in dairy cows [3, 4]. Previous studies tend to overlook other biomarkers, like from milk production, which could help predict diseases in cows [5]. Our ensemble learner supports predicting disease risk based on heterogeneous biomarkers from metabolomic and health profiles, milk production history, and herd history.
Our datasets contain biomarkers for over 13,700 health events of 1,200 cows from 50 dairy farms in Canada. Biomarkers are captured for a health event e at timepoint t. Given an upcoming lactation Ln, base predictions are obtained for all health events et occurring during lactation Ln−1.
The ensemble learner averages base predictions for an animal and outputs disease probabilities for lactation Ln. Binary classes are disease or non-disease, based on a curated set of nine most common diseases in dairy cows. Classification performance was evaluated for multiple combinations of biomarkers and classifiers.
Classification models based on Logistic Regression and Random Forest classifiers yield best performances, with an average of 0.6 and 0.77 F-measure for disease and non-disease respectively.},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
Metabolic diseases have great impact on dairy production and animal welfare [1, 2]. Metabolomic profiling has helped identify biomarkers to predict disease risk in dairy cows [3, 4]. Previous studies tend to overlook other biomarkers, like from milk production, which could help predict diseases in cows [5]. Our ensemble learner supports predicting disease risk based on heterogeneous biomarkers from metabolomic and health profiles, milk production history, and herd history.
Our datasets contain biomarkers for over 13,700 health events of 1,200 cows from 50 dairy farms in Canada. Biomarkers are captured for a health event e at timepoint t. Given an upcoming lactation Ln, base predictions are obtained for all health events et occurring during lactation Ln−1.
The ensemble learner averages base predictions for an animal and outputs disease probabilities for lactation Ln. Binary classes are disease or non-disease, based on a curated set of nine most common diseases in dairy cows. Classification performance was evaluated for multiple combinations of biomarkers and classifiers.
Classification models based on Logistic Regression and Random Forest classifiers yield best performances, with an average of 0.6 and 0.77 F-measure for disease and non-disease respectively.
Our datasets contain biomarkers for over 13,700 health events of 1,200 cows from 50 dairy farms in Canada. Biomarkers are captured for a health event e at timepoint t. Given an upcoming lactation Ln, base predictions are obtained for all health events et occurring during lactation Ln−1.
The ensemble learner averages base predictions for an animal and outputs disease probabilities for lactation Ln. Binary classes are disease or non-disease, based on a curated set of nine most common diseases in dairy cows. Classification performance was evaluated for multiple combinations of biomarkers and classifiers.
Classification models based on Logistic Regression and Random Forest classifiers yield best performances, with an average of 0.6 and 0.77 F-measure for disease and non-disease respectively.
Naghashi, V.; Boukadoum, M.; Diallo, A. B.
Empowering Dairy Farmers: A Transformer-Based Framework for Informed Decision Making in Dairy Agriculture [External] Divers
2024.
Résumé | Liens | BibTeX | Étiquettes: Presentation
@misc{naghashi_empowering_2024,
title = {Empowering Dairy Farmers: A Transformer-Based Framework for Informed Decision Making in Dairy Agriculture},
author = {V. Naghashi and M. Boukadoum and A. B. Diallo},
url = {https://www.iscb.org/ismb2024/programme-schedule/scientific-programme/digag},
year = {2024},
date = {2024-07-01},
urldate = {2024-07-01},
address = {Montreal, Canada},
abstract = {In precision livestock, the decision of animal replacement requires an estimation of the lifetime profit of the animal based on multiple factors and operational conditions. In dairy farms, this can be associated with the milk income corresponding to milk production, health condition and herd management costs, which in turn may be a function of other factors including genetics and weather conditions. Estimating the cumulative income from a cow's milk production can be posed as a multivariate time-series prediction task where a late-milk income of a cow can be predicted based on early dairy factors recorded in a sequence of time-steps (lactation months). Furthermore, the predicted milk income would serve as an input to a decision making system for deciding whether to keep or remove an animal in the next lactation period. In this work, a Transformer based model is proposed to predict the cumulative dairy income over the incoming lactation period and further a recommendation procedure is devised for decision making in order to reduce the farmers cost and save their time. In the Transformer, both temporal and inter-variable correlations are captured thanks to the temporal and spatial multi-head attention modules. The proposed framework is assessed using 47749 dairy cows corresponding to more than 5000 herds and the results are compared with the other state-of-the-art models. Our Transformer model outperforms the previous baselines and provides a promising prediction performance with the highest accuracy of 76%, opening the way of better resource management in the dairy industry.},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
In precision livestock, the decision of animal replacement requires an estimation of the lifetime profit of the animal based on multiple factors and operational conditions. In dairy farms, this can be associated with the milk income corresponding to milk production, health condition and herd management costs, which in turn may be a function of other factors including genetics and weather conditions. Estimating the cumulative income from a cow's milk production can be posed as a multivariate time-series prediction task where a late-milk income of a cow can be predicted based on early dairy factors recorded in a sequence of time-steps (lactation months). Furthermore, the predicted milk income would serve as an input to a decision making system for deciding whether to keep or remove an animal in the next lactation period. In this work, a Transformer based model is proposed to predict the cumulative dairy income over the incoming lactation period and further a recommendation procedure is devised for decision making in order to reduce the farmers cost and save their time. In the Transformer, both temporal and inter-variable correlations are captured thanks to the temporal and spatial multi-head attention modules. The proposed framework is assessed using 47749 dairy cows corresponding to more than 5000 herds and the results are compared with the other state-of-the-art models. Our Transformer model outperforms the previous baselines and provides a promising prediction performance with the highest accuracy of 76%, opening the way of better resource management in the dairy industry.
Toure, François Gonothi; Diallo, Abdoulaye Baniré; Boukadoum, Mounir; Araujo,
Optimizing Dairy Cow Health and Productivity with Efficient Movement Monitoring using Data Augmentation and Computer Vision [External] Divers
2024.
Liens | BibTeX | Étiquettes: Presentation
@misc{gonothi_toure_optimizing_2024,
title = {Optimizing Dairy Cow Health and Productivity with Efficient Movement Monitoring using Data Augmentation and Computer Vision},
author = {François Gonothi Toure and Abdoulaye Baniré Diallo and Mounir Boukadoum and Araujo},
url = {https://blackscientists.ca/be-stemm-event/2024-en-stimm/},
year = {2024},
date = {2024-07-01},
urldate = {2024-07-01},
address = {Ottawa, Canada},
keywords = {Presentation},
pubstate = {published},
tppubtype = {misc}
}
Almeida, H.; Grégoire, N. B.; Bilal, M.; Leduc, M.; Chorif, Y.; Zhao, X.; Dubuc, J.; Diallo, A. B.
Biomarker-based learning for disease prediction in precision dairy farming [External] Présentation
Montreal, Canada, 01.07.2024.
Liens | BibTeX | Étiquettes: Presentation
@misc{almeida_biomarker-based_2024-1,
title = {Biomarker-based learning for disease prediction in precision dairy farming},
author = {H. Almeida and N. B. Grégoire and M. Bilal and M. Leduc and Y. Chorif and X. Zhao and J. Dubuc and A. B. Diallo},
url = {https://www.iscb.org/ismb2024/home},
year = {2024},
date = {2024-07-01},
urldate = {2024-07-01},
address = {Montreal, Canada},
keywords = {Presentation},
pubstate = {published},
tppubtype = {presentation}
}
Massoua, Armand Bandiang; Diallo, Abdoulaye Banire; Bouguessa, Mohamed
Towards Robust Time-to-Event Prediction: Integrating the Variational Information Bottleneck with Neural Survival Model [External] Article d'actes
Dans: 2024 International Joint Conference on Neural Networks (IJCNN), p. 1–8, IEEE, Yokohama, Japan, 2024, ISBN: 979-8-3503-5931-2.
Liens | BibTeX | Étiquettes: Article de conference
@inproceedings{massoua_towards_2024,
title = {Towards Robust Time-to-Event Prediction: Integrating the Variational Information Bottleneck with Neural Survival Model},
author = {Armand Bandiang Massoua and Abdoulaye Banire Diallo and Mohamed Bouguessa},
url = {https://ieeexplore.ieee.org/document/10651066/},
doi = {10.1109/IJCNN60899.2024.10651066},
isbn = {979-8-3503-5931-2},
year = {2024},
date = {2024-06-01},
urldate = {2024-10-16},
booktitle = {2024 International Joint Conference on Neural Networks (IJCNN)},
pages = {1–8},
publisher = {IEEE},
address = {Yokohama, Japan},
keywords = {Article de conference},
pubstate = {published},
tppubtype = {inproceedings}
}
Diallo, Abdoulaye Banire
Cas concrèt: WELL-E [External] Divers
2024.
Liens | BibTeX | Étiquettes: conférencier invité
@misc{diallo_cas_2024,
title = {Cas concrèt: WELL-E},
author = {Abdoulaye Banire Diallo},
url = {https://www.obvia.ca/evenements/action-ia-ensemble-pour-le-developpement-et-ladoption-responsable-dans-lindustrie},
year = {2024},
date = {2024-06-01},
urldate = {2024-06-01},
address = {Montreal, Canada},
keywords = {conférencier invité},
pubstate = {published},
tppubtype = {misc}
}
driss, Maryam Ben; Sabir, Essaid; Elbiaze, Halima; banire Diallo, Abdoulaye; Sadik, Mohamed
GWO-Boosted Multi-Attribute Client Selection for Over-The-Air Federated Learning [External] Article d'actes
Dans: IEEE, Montreal, Canada, 2024.
Liens | BibTeX | Étiquettes: Article de conference
@inproceedings{ben_driss_gwo-boosted_2024,
title = {GWO-Boosted Multi-Attribute Client Selection for Over-The-Air Federated Learning},
author = {Maryam Ben driss and Essaid Sabir and Halima Elbiaze and Abdoulaye banire Diallo and Mohamed Sadik},
url = {https://r-libre.teluq.ca/3274/1/1570983885%20stamped-e.pdf},
year = {2024},
date = {2024-05-01},
publisher = {IEEE},
address = {Montreal, Canada},
keywords = {Article de conference},
pubstate = {published},
tppubtype = {inproceedings}
}