Titlebook: Computational Intelligence and Data Analytics; Proceedings of ICCID Rajkumar Buyya,Susanna Munoz Hernandez,T. Hitendra Conference proceedin

恃强凌弱的人

https://doi.org/10.1007/978-3-319-22521-0d efficient recognition system for fruits and vegetables is one major challenge. To solve these challenges, we have examined various feature descriptors based on colour, texture and shape (its combination also). In this paper, Otsu’s thresholding is used for background subtraction. Further, all segm

谄媚于人

Induced Mutations in Plant Breedingof determination may not be adequate on account of a genuine infirmity. Fostering a clinical determination framework dependent on machine learning calculations for forecast of any illness can help in a more exact finding than the regular strategy. We have built a disease predication framework utiliz

Interlocking

Zul Zulkarnain,Tanya Tapingkae,Acram Tajiredictions to identify the optimal medication holds enormous potential for improving a patient’s probability of successful treatment. Unfortunately, the statistical effort of forecasting medication reaction is extremely difficult, partly due to dataset limitations and also due to algorithms flaws. M

偶像

Zul Zulkarnain,Tanya Tapingkae,Acram Tajismooth surface with size variation, indiscriminate shape, and unusual colors. Segmentation plays an essential and crucial role here. When an image is divided into segments, important features can be projected and processed instead of complete image. Some expert dermatologists can see the segmented p

legacy

agglomerate

Fateh Aljane,Awatef Essid,Sabrine Nahdies has been around. However, of late, machine learning (ML) became a useful and appropriate solution to many real-world problems. In this context, usage of machine learning has become an important step forward in improving state of the art in bug detection. It is an artificial intelligence-based (AI

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