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Abstract: Artificial Intelligence (AI) is transforming the agricultural sector, enhancing both productivity and sustainability. This paper delves into the impact of AI technologies on agriculture, emphasizing their application in precision farming, predictive analytics, and automation. AI-driven tools facilitate more efficient crop and resource management, leading to higher yields and a reduced environmental footprint. The paper explores key AI technologies, such as machine learning algorithms for crop monitoring, robotics for automated planting and harvesting, and data analytics for optimizing resource use. Additionally, (...) |
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Abstract: As artificial intelligence (AI) technologies rapidly evolve and permeate various aspects of society, the need for effective regulation and governance has become increasingly critical. This paper explores the current landscape of AI regulation, examining existing frameworks and their efficacy in addressing the unique challenges posed by AI. Key issues such as ensuring compliance, mitigating biases, and maintaining transparency are analyzed. The paper also delves into ethical considerations surrounding AI governance, emphasizing the importance of fairness and accountability. Through case studies (...) |
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Abstract: This research paper explores the transformative impact of artificial intelligence (AI) on digital media, examining both the opportunities it presents and the challenges it poses. The integration of AI into digital media has revolutionized content creation, distribution, and analytics, offering unprecedented levels of personalization, efficiency, and insight. Automated journalism, AI- driven recommendation systems, and advanced audience analytics are among the key areas where AI is making significant contributions. However, the adoption of AI also brings ethical considerations, including concerns about (...) |
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Abstract: Vegetables that are popular and versatile over the world are peppers. Precise categorisation of pepper cultivars is vital for multiple uses, such as assessing market trends, regulating quality, and conducting genetic research. Classifying peppers using traditional methods can be subjective and time-consuming. This research proposes an automated pepper variety classification method based on deep learning. A deep convolutional neural network (CNN) model was trained on a dataset of 2,368 photos of peppers. With the purpose of accurately classifying the pepper (...) |
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Abstract. This study investigates the application of convolutional neural networks (CNNs) to the task of classifying male and female eyes. Using a dataset of eye images, the research explores the potential of deep learning to accurately distinguish between the genders based solely on eye features. The proposed CNN model achieved 94% accuracy on the training set and 91% on the validation set. The study addresses the challenges and limitations in feature extraction from eye images and compares the proposed model with (...) |
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