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Switch to: References
Citations of:
Diagnosis of Blood Cells Using Deep Learning
Dissertation, University of Tehran (2022)
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Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. The aim of this study is to develop a deep learning model to predict the gender from retinal fundus images. The proposed model was based on the Xception pre-trained model. The proposed model was trained on 20,000 retinal fundus images from Kaggle depository. The dataset was preprocessed them split into three datasets (training, validation, Testing). After training and cross-validating the proposed model, (...) |
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Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...) |
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Abstract: The integration of artificial intelligence (AI) into mental health care has the potential to revolutionize the field by enhancing diagnostic accuracy, personalizing treatment, and improving access to care. This paper explores the advancements in AI technologies applied to mental health, including machine learning algorithms for diagnosis, natural language processing for therapeutic applications, and predictive analytics for personalized care. It also examines the ethical and practical challenges associated with these technologies, such as privacy concerns, algorithmic bias, and the need for (...) |
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Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...) |
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Abstract: As artificial intelligence (AI) technologies become increasingly integrated into various sectors, ethical considerations surrounding their development and deployment have become paramount. This paper explores the multifaceted ethical landscape of AI, focusing on key challenges such as bias, transparency, privacy, and accountability. It examines how these issues manifest in AI systems and their impact on society. The paper also evaluates current approaches and solutions aimed at mitigating these ethical concerns, including regulatory frameworks, ethical guidelines, and best practices for AI design. (...) |
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Abstract: Abstract: The classification of nuts is crucial for food security; nevertheless, accurate and swift identification continues to be a challenge in numerous areas due to insufficient infrastructure. The rise in smartphone utilization, along with advancements in computer vision driven by deep learning, has facilitated smartphone-assisted nut classification. We trained a deep convolutional neural network to categorize five distinct nut types (Chestnut, Hazelnut, Nut Forest, Nut Pecan, and Walnut) using a public dataset of 2,850 photos gathered under controlled conditions. The (...) |
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