Results 211 to 220 of about 168,829 (242)

Machine learning (ML)-based lithography optimizations

2016 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), 2016
Recent lithography optimizations demand higher accuracy and cause longer runtime. Optical proximity correction (OPC) and sub-resolution assist feature (SRAF) insertion, for example, take a few days due to lengthy lithography simulations and high pattern density.
Seongbo Shim, Suhyeong Choi, Youngsoo Shin   +2 more
openaire   +1 more source

Leaf Disease Detection Using Machine Learning (ML)

2023
This method's central idea is the generation of features using grey level co-occurrence matrices (GLCM). The spatial interactions between pixels are to be measured by the matrices. A grey-level co-occurrence matrix is used to extract co-occurrence features. Texture classification can be used for a number of applications, such as pattern identification,
C.V. Suresh Babu, Ambati Swapna, Dama Swathi Chowdary, Burri Sujit Vardhan, Mohd Imran   +4 more
openaire   +1 more source

Machine Learning with Core ML

2020
This chapter introduces the Core ML API and shows how it is now possible to use contemporary machine learning models to implement intelligent image analysis and computer vision solutions, such as object detection and recognition and scene classification.
openaire   +1 more source

ML Deployment Pipeline Using Oracle Machine Learning

2021
As large-scale model-driven applications are being deployed at an ever-increasing pace, enterprises and industries are racing to adopt technology that makes the process of building and maintaining these applications more efficient and less expensive. A machine learning (ML) platform is the data center software and hardware infrastructure that automates
Heli Helskyaho, Jean Yu, Kai Yu
openaire   +1 more source

ML Suite: An Auto Machine Learning Tool

2020
In today’s age, it is important for some businesses to upgrade to machine learning techniques. The aim of this project is to create an autonomous platform for researchers/laymen who operate on data, which would auto-clean the data and suggest machine learning approach to understand and get better value out of data.
Nilesh M. Patil, Tanmay P. Rane, Anmol A. Panjwani   +2 more
openaire   +1 more source

Implementation of Machine Learning (ML) in Biomedical Engineering

Transaction on Biomedical Engineering Applications and Healthcare, 2021
The subfields within AI have been discussed throughout the article and the findings of the article have provided a positive outcome. ML has a huge potential through ML methodologies such as supervised and unsupervised learning as discussed in the article.
Prof. Kshatrapal Singh, Dr.Kamal Kumar Srivastava   +1 more
openaire   +1 more source

Azure Machine Learning (ML) Workbench

2019
Azure ML Workbench is another tool introduced by Microsoft in 2017. Azure Machine Learning services (preview) integrate end-to-end data science with advanced analytics tools. They help professional data scientists prepare data, develop experiments, and deploy models at cloud scale [1]. First in this chapter, a brief introduction into Azure ML Workbench
openaire   +1 more source

Flink-ML: machine learning in Apache Flink

Brazilian Journal of Technology
The emergence of Big Data has spurred the development of various frameworks designed for efficient data storage and processing. Key frameworks include Hadoop, Spark, Flink, Storm, Pig, and Zookeeper. Among these, Apache Flink stands out as a prominent open-source platform known for its powerful stream and batch processing capabilities.
Messaoud Mezati, Ines Aouria
openaire   +1 more source

Machine Learning (ML) Driven Agriculture

2021
Latief Ahmed, Firasath Nabi
openaire   +1 more source

Machine Learning (ML)

2022
openaire   +1 more source