Titlebook: Artificial Intelligence: Towards Sustainable Intelligence; First International Sanju Tiwari,Fernando Ortiz-Rodríguez,Ketan Kotech Conferen

BURSA

Guido Becke,Stephanie P?ser,Cora Zenzledge for the purpose of building Food Composition Tables. The latter can therefore be used for food recommendation, ingredient substitution, etc. To demonstrate the relevance of the knowledge extracted, we invited one domain expert for validation. On the other hand, we compared an excerpt of the kn

內(nèi)向者

https://doi.org/10.1007/978-3-662-65598-6integrity and protect sensitive information in the proposed blockchain. Extensive simulations and real-world trials assess the algorithm‘s efficiency and efficacy. Data sharing speed, transaction throughput, and security are compared to typical VANET data sharing methods. The biometric blockchain al

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https://doi.org/10.1007/978-3-662-65598-6sed on fungal diseases and some researchers only focused on bacterial diseases. In this research, The aim is to detect 13 different fungal and bacterial mango leaf diseases using CNN techniques. The photographs were captured in the Andhra Pradesh state’s Chittoor district, which is home to India’s l

forthy

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Alexander Klamar,Sebastian Fischerleads as a significant indicator of environmental impact, explores potential alternatives, and provides a comparison of CO2 emissions. Our work focuses specifically on assessing the environmental consequences of food production, primarily through the measurement of CO2 emissions. We consider key fac

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ESG and IoT: Ensuring Sustainability and Social Responsibility in the Digital Age,monitored, and trigger real time alarms when needed. As the adoption of IoT continues to grow, it will be important to monitor and evaluate its impacts on ESG, and to identify and implement best practices for ensuring that IoT can contribute positively to environmental, social, and governance outcom

avarice

蛛絲

Leaf Disease Detection Using Transfer Learning,sification abilities. The paper also examines the practical implications of the models for real-time leaf disease detection. Factors such as inference time and computational resource requirements are considered to assess their suitability for deployment in real-world scenarios. The analysis tries to

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