This paper presents approaches to determine a network based pricing for 3D printing services in the context of a two-sided manufacturing-as-a-service marketplace. The intent is to provide cost analytics to enable service bureaus to better compete in the market by moving away from setting ad-hoc and subjective prices. A data mining approach with machine learning methods is used to estimate a price range based on the profile characteristics of 3D printing service suppliers. The model considers factors such as supplier experience, supplier capabilities, customer reviews and ratings from past orders, and scale of operations among others to estimate a price range for suppliers' services. Data was gathered from existing marketplace websites, which was then used to train and test the model. The model demonstrates an accuracy of 65% for US based suppliers and 59% for Europe based suppliers to classify a supplier's 3D Printer listing in one of the seven price categories. The improvement over baseline accuracy of 25% demonstrates that machine learning based methods are promising for network based pricing in manufacturing marketplaces. Conventional methodologies for pricing services through activity based costing are inefficient in strategically pricing 3D printing service offering in a connected marketplace. As opposed to arbitrarily determining prices, this work proposes an approach to determine prices through data mining methods to estimate competitive prices. Such tools can be built into online marketplaces to help independent service bureaus to determine service price rates.
--Additive Manufacturing (AM) is a manufacturing paradigm that builds three-dimensional objects from a computer-aided design model by successively adding material layer by layer . AM has become very popular in the past decade due to its utility for fast prototyping such as 3D printing as well as manufacturing functional parts with complex geometries using processes such as laser metal deposition that would be difficult to create using traditional machining. As the process for creating an intricate part for an expensive metal such as Titanium is prohibitive with respect to cost, computational models are used to simulate the behavior of AM processes before the experimental run. However, as the simulations are computationally costly and time-consuming for predicting multiscale multi-physics phenomena in AM, physics-informed data-driven machine-learning systems for predicting the behavior of AM processes are immensely beneficial. Such models accelerate not only multiscale simulation tools but also empower real-time control systems using in-situ data. In this paper, we design and develop essential components of a scientific framework for developing a data-driven model-based real-time control system. Finite element methods are employed for solving time-dependent heat equations and developing the database. The proposed framework uses extremely randomized trees - an ensemble of bagged decision trees as the regression algorithm iteratively using temperatures of prior voxels and laser information as inputs to predict temperatures of subsequent voxels. The models achieve mean absolute percentage errors below 1% for predicting temperature profiles for AM processes. Additive Manufacturing (AM) is a modern manufacturing approach in which digital 3D design data is used to build parts by sequentially depositing layers of materials . AM techniques are becoming very popular compared to traditional approaches because of their success in building complicated designs, fast prototyping, and low-volume or one-of-a-kind productions across many industries. Direct Metal Deposition (DMD)  is an AM technology where various materials such as steel or Titanium are used to develop the finished product.
The relentless increase in computing power and the accumulation of big data over the years has sparked intense interest in machine learning and its associated techniques. The new SAS Visual Data Mining and Machine Learning software will feed this need for smarter analytics. Advanced analytics offer insight to businesses, but machine learning and deep learning algorithms take it deeper, revealing insights that were previously out of reach. For example, machine learning use can include facial recognition in security systems, speech recognition in customer service applications, accurate product recommendations in e-commerce, self-driving cars and medical diagnostics. "SAS Visual Data Mining and Machine Learning shatters barriers related to data volume and variety, limited analytical depth and computational bottlenecks.
We are pleased to announce the second edition of our book Data Mining and Machine Learning: Fundamental Concepts and Algorithms, Second Edition, by Mohammed J. Zaki and Wagner Meira, Jr., published by Cambridge University Press, 2020. The entire book is available to read online for free and the site includes video lectures and other resources. New to this edition is an entire part devoted to regression and deep learning. The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners.
Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about 1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learning algorithm in classification of phishing attacks, with the major objective of developing an improved phishing email classifier with better prediction accuracy and fewer numbers of features. From a dataset consisting of 2000 phishing and ham emails, a set of prominent phishing email features (identified from the literature) were extracted and used by the machine learning algorithm with a resulting classification accuracy of 99.7% and low false negative (FN) and false positive (FP) rates.