GENERATIVE ADVERSARIAL NETWORKS: TECHNIQUES, AREA COVERED, AND CHALLENGES.
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- i-Manager's Journal on Artificial Intelligence & Machine Learning (JAIM), 2023, v. 1, n. 2, p. 49, doi. 10.26634/jaim.1.2.20025
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- Article
Works matching IS 25839128 AND DT 2023 AND VI 1 AND IP 2
Results: 6
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2
UNLOCKING CLINICAL INSIGHTS FROM MEDICAL IMAGES USING DEEP LEARNING.
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- i-Manager's Journal on Artificial Intelligence & Machine Learning (JAIM), 2023, v. 1, n. 2, p. 37, doi. 10.26634/jaim.1.2.20044
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- Article
3
A NOVEL APPROACH FOR LUNG CANCER DETECTION USING DEEP BELIEF NETWORKS.
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- i-Manager's Journal on Artificial Intelligence & Machine Learning (JAIM), 2023, v. 1, n. 2, p. 30, doi. 10.26634/jaim.1.2.20010
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- Article
4
FIRE AND SMOKE DETECTION USING YOLOv8.
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- i-Manager's Journal on Artificial Intelligence & Machine Learning (JAIM), 2023, v. 1, n. 2, p. 22, doi. 10.26634/jaim.1.2.19849
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- Article
5
A DISTINCTIVE ENSEMBLE DEEP LEARNING MODEL FOR BRAIN TUMOR MRI IMAGE CLASSIFICATION.
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- i-Manager's Journal on Artificial Intelligence & Machine Learning (JAIM), 2023, v. 1, n. 2, p. 12, doi. 10.26634/jaim.1.2.19281
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- Article
6
UNCOVERING THE TRUTH: A DEEP LEARNING APPROACH TO DETECTING FAKE NEWS.
- Published in:
- i-Manager's Journal on Artificial Intelligence & Machine Learning (JAIM), 2023, v. 1, n. 2, p. 1, doi. 10.26634/jaim.1.2.19771
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- Article