Artificial Intelligence as Key Enabler for Sustainable Maintenance in the Manufacturing Industry: Scope &amp; Challenges

Saraswat, Praveen; Agrawal, Rajeev

doi:10.5109/7162012

Cited by 3 publications

(1 citation statement)

References 71 publications

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“…Technological advancements play a pivotal role in aiding poultry farmers in ensuring good poultry welfare 6) . Deep learning algorithms analyze visual input together with associated class labels, revealing a world of varied patterns in the process 9,10) . A comprehensive previous study for chicken health is described in the following section based on chicken sound and image analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Chicken Diseases Detection and Classification Based on Fecal Images Using EfficientNetB7 Model

Vandana,

Kuldeep Kumar Yogi,

Satya Prakash Yadav

2024

Evergreen

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The agriculture sector, particularly the chicken and poultry industries, is under pressure to produce more due to the increased demand for livestock products among consumers. Increased poultry production can lead to the enhanced spread of many infectious diseases in chickens, which can result in high bird fatality rates and significant financial losses. A shortage of trustworthy professionals or delayed diagnosis cause farmers to be losing an extensive number of domestic chickens. Deep learning algorithms can help the early detection of illnesses. This paper proposes a system based on convolutional neural networks to categorize chicken illnesses by identifying healthy and harmful fecal images. Unhealthy images may indicate a poultry illness. Through the use of deep learning algorithms and image analysis of chicken feces, the most common illnesses that affect chickens may be rapidly identified. With the use of a convolutional neural network (CNN) architecture, this research developed a model to identify different chicken ailments by classifying fecal images into two groups: those representing symptoms associated with healthy conditions and those representing symptoms associated with potentially dangerous conditions like Newcastle diseases, Coccidiosis, or Salmonella. To determine if chicken feces fell into one of four categories with the least amount of loss utilized the EfficientNetB7 model with additional layers that extracted the most appropriate features from the fecal images and achieved the highest accuracy. With an accuracy of 97.07%, the new proposed model generated the greatest results when compared to the aforementioned models.

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Section: Literature Reviewmentioning

confidence: 99%

Chicken Diseases Detection and Classification Based on Fecal Images Using EfficientNetB7 Model

Vandana,

Kuldeep Kumar Yogi,

Satya Prakash Yadav

2024

Evergreen

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Challenges and Opportunities in the Implementation of AI in Manufacturing: A Bibliometric Analysis

Espina-Romero,

Gutiérrez Hurtado,

Ríos Parra

et al. 2024

Sci

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This study explores the evolution and impact of research on the challenges and opportunities in the implementation of artificial intelligence (AI) in manufacturing between 2019 and August 2024. By addressing the growing integration of AI technologies in the manufacturing sector, the research seeks to provide a comprehensive view of how AI applications are transforming production processes, improving efficiency, and opening new business opportunities. A bibliometric analysis was conducted, examining global scientific production, influential authors, key sources, and thematic trends. Data were collected from Scopus, and a detailed review of key publications was carried out to identify knowledge gaps and unresolved research questions. The results reveal a steady increase in research related to AI in manufacturing, with a strong focus on automation, predictive maintenance, and supply chain optimization. The study also highlights the dominance of certain institutions and key authors driving this field of research. Despite the progress, significant challenges remain, particularly regarding the scalability of AI solutions and ethical considerations. The findings suggest that while AI holds considerable potential for the manufacturing industry, more interdisciplinary research is needed to address existing gaps and maximize its benefits.

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Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

Le,

Paramasivam,

Adril

et al. 2024

Int. J. Renew. Energy Dev.

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This review article examines the revolutionary possibilities of machine learning (ML) and intelligent algorithms for enabling renewable energy, with an emphasis on the energy domains of solar, wind, biofuel, and biomass. Critical problems such as data variability, system inefficiencies, and predictive maintenance are addressed by the integration of ML in renewable energy systems. Machine learning improves solar irradiance prediction accuracy and maximizes photovoltaic system performance in the solar energy sector. ML algorithms help to generate electricity more reliably by enhancing wind speed forecasts and wind turbine efficiency. ML improves the efficiency of biofuel production by optimizing feedstock selection, process parameters, and yield forecasts. Similarly, ML models in biomass energy provide effective thermal conversion procedures and real-time process management, guaranteeing increased energy production and operational stability. Even with the enormous advantages, problems such as data quality, interpretability of the models, computing requirements, and integration with current systems still remain. Resolving these issues calls for interdisciplinary cooperation, developments in computer technology, and encouraging legislative frameworks. This study emphasizes the vital role of ML in promoting sustainable and efficient renewable energy systems by giving a thorough review of present ML applications in renewable energy, highlighting continuing problems, and outlining future prospects

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Artificial Intelligence as Key Enabler for Sustainable Maintenance in the Manufacturing Industry: Scope & Challenges

Cited by 3 publications

References 71 publications

Chicken Diseases Detection and Classification Based on Fecal Images Using EfficientNetB7 Model

Chicken Diseases Detection and Classification Based on Fecal Images Using EfficientNetB7 Model

Challenges and Opportunities in the Implementation of AI in Manufacturing: A Bibliometric Analysis

Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

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