About this course: This course provides an unique opportunity for you to learn key components of text mining and analytics aided by the real world datasets and the text mining toolkit written in Java. Hands-on experience in core text mining techniques including text preprocessing, sentiment analysis, and topic modeling help learners be trained to be a competent data scientists. Empowered by bringing lecture notes together with lab sessions based on the y-TextMiner toolkit developed for the class, learners will be able to develop interesting text mining applications.

Data Mining is the computational process of discovering patterns in large data sets involving methods using the artificial intelligence, machine learning, statistical analysis, and database systems with the goal to extract information from a data set and transform it into an understandable structure for further use. Jubatus, MiningMart, Databionic ESOM, Apache Mahout, TraMineR, ROSETTA, KEEL, ADaM, ML-Flex, Modular toolkit for Data Processing, Dataiku, SenticNet API, LIBSVM and LIBLINEAR, Lattice Miner, Gnome datamine tools, yooreeka, AstroML, jHepWork, ARMiner, and arules are some of the top free data mining software. Orange is an open source data visualization and analysis tool. Orange is developed at the Bioinformatics Laboratory at the Faculty of Computer and Information Science, University of Ljubljana, Slovenia, along with open source community. Data mining is done through visual programming or Python scripting.

The term "industrial Internet of Things" has a more muted-sounding promise of driving operational efficiencies through automation, connectivity and analytics. But the focus of IIoT -- on industry at large -- is broader. Here, we take a comprehensive view, rounding up 20 IIoT leaders and pioneers, drawing on the feedback from industry analysts and consultants. The focus here is not on vendors offering, say, a cloud-based platform for monitoring industrial machines but on the companies that themselves are using IIoT technology to drive their business forward. For the sake of this feature, we focus on organizations that use connected technology in tandem with cloud-based analytics to drive efficiencies and launch new business models.

What's the next $300 billion industry to be disrupted by technology? For background, engineered wood is the technical name for any wood product (like particle board) that is created by bonding wood chips into different shapes using an adhesive. It's much cheaper than using a solid piece of wood, and can be used to make anything from an Ikea desk to kitchen countertops. Materialize.X, launching today at TechCrunch Disrupt SF 2017, has two new products that it thinks will revolutionize the $300 billion a year engineered wood market. A lot of engineered wood is created using an adhesive called urea-formaldehyde, which has recently been labeled by the FDA as a toxic carcinogen.

You'd think computers spend most of their time and energy doing, well, computation. But that's not the case: about 90 percent of a computer's execution time and electrical energy is spent transferring data between the processor and the memory banks, says Subhasish Mitra, a computer scientist at Stanford University. Even if Moore's law continued on indefinitely, computers would still be limited by this memory bottleneck. This week in the journal Nature, Mitra and collaborators describe a new computer architecture they say addresses this problem--and that Mitra believes will improve both the energy efficiency and speed of computers by a factor of 1000. The new 3D architecture is based on novel devices including 2 million carbon nanotube transistors and over 1 million resistive RAM cells, all built on top of a layer of silicon using existing fabrication methods and connected by densely packed metal wiring between the layers. As a demonstration, the team built an electronic nose that can sense and identify several common vapors including lemon juice, rubbing alcohol, vodka, wine, and beer.

On a block in San Francisco's SoMa district, near LinkedIn's headquarters and dozens of startups, a 180-year-old company best-known for making tractors has a gleaming new Silicon Valley office. But inside, instead of building the latest app, John Deere is focused on how to use artificial intelligence to make farming equipment that can meet modern sustainability and food production challenges. John Deere Labs, which opened its doors in the spring, made its first major deal on September 6. The company spent $305 million to acquire Blue River Technology, a startup with computer vision and machine learning technology that can identify weeds–making it possible to spray herbicides only where they're needed. The technology reduces chemical use by about 95%, while also improving yield.

In conventional chemisorption model, the d-band center theory (augmented sometimes with the upper edge of d-band for imporved accuarcy) plays a central role in predicting adsorption energies and catalytic activity as a function of d-band center of the solid surfaces, but it requires density functional calculations that can be quite costly for large scale screening purposes of materials. In this work, we propose to use the d-band width of the muffin-tin orbital theory (to account for local coordination environment) plus electronegativity (to account for adsorbate renormalization) as a simple set of alternative descriptors for chemisorption, which do not demand the ab initio calculations. This pair of descriptors are then combined with machine learning methods, namely, artificial neural network (ANN) and kernel ridge regression (KRR), to allow large scale materials screenings. We show, for a toy set of 263 alloy systems, that the CO adsorption energy can be predicted with a remarkably small mean absolute deviation error of 0.05 eV, a significantly improved result as compared to 0.13 eV obtained with descriptors including costly d-band center calculations in literature. We achieved this high accuracy by utilizing an active learning algorithm, without which the accuracy was 0.18 eV otherwise. As a practical application of this machine, we identified Cu3Y@Cu as a highly active and cost-effective electrochemical CO2 reduction catalyst to produce CO with the overpotential 0.37 V lower than Au catalyst.

The food and agriculture chain is one of the most promising industries where the Internet of Things (IoT) can bring about transformational changes. And this transformation includes building smart farm machines to manage crops at the plant level. To that end, John Deere Labs, which opened its doors earlier this year, made its first major deal on September 6, spending $305 million to acquire Blue River Technology, a startup with computer vision and machine learning technology that can identify weeds–making it possible to spray herbicides only where they're needed. Currently, on a global basis, $25 billion is spent each year on about 3.0 billion pounds of herbicides. This has resulted in over 250 species of weeds now considered resistant to herbicides.

During the Hands Free Hectare project, no human set foot on the field between planting and harvest--everything was done by robots. To make these decisions, robot scouts (including drones and ground robots) surveyed the field from time to time, sending back measurements and bringing back samples for humans to have a look at from the comfort of someplace warm and dry and clean. With fully autonomous farm vehicles, you can use a bunch of smaller ones much more effectively than a few larger ones, which is what the trend has been toward if you need a human sitting in the driver's seat. Robots are only going to get more affordable and efficient at this sort of thing, and our guess is that it won't be long before fully autonomous farming passes conventional farming methods in both overall output and sustainability.

America's agricultural sector faces an unprecedented crisis. Native honeybees, one of the most prolific pollinators in the animal kingdom, are dying off at an unprecedented rate from Colony Collapse Disorder (CCD), threatening an ecosystem service worth about $15 billion. Supported by the National Science Foundation (NSF), the "RoboBees" project looks to minimize the loss of this critical resource with remarkable microbots that can mimic the pollinating role of a honeybee. But the project has a number of challenges to overcome before these robots can take to the skies. The RoboBee is a microrobot inspired by the biology of a honey bee.