Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. "This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining.
Many mathematical algorithms that we use in data science and machine learning require numeric data. And many algorithms tend to be very complex to implement (such as Support Vector Machines or Local Linear Embedding, which we previously discussed). But, association rule mining is perfect for categorical (non-numeric) data and it involves nothing more than simple counting! What we have here is a simple algorithm with not so simplistic results! The ratio of actionable insights discovery potential (high) to algorithm complexity (low) is quite large and atypical, IMHO.
In today's fast-paced world of city living and stressful work-life imbalances, especially on the (hopefully) tail-end of a year of pandemic quarantine measures, many young workers are yearning to get closer to nature and family. In the face of re-emerging commutes and the push-and-pull of back-to-the-office versus hybrid or fully-remote working, many young robots would rather ditch the status quo and return to the countryside to scratch a living from the land like their ancestors before them. And they'll bring lasers, too. Of course, we're not talking about the weary office drones being herded back to the office after a year of blissfully working at home, but of robots armed with deep learning computer vision systems and precision actuators for a new breed of farming automation. This new breed of automated agriculture promises to decrease inputs and the side-effects of modern agriculture, while helping farmers deal with everything from labor shortages to climate change.
In a paper published by Nature Communication's Scientific Reports, a team of chemists from Surrey built a machine learning model based on the information from the DrugAge database to predict whether a compound can extend the life of Caenorhabditis elegans – a translucent worm that shares a similar metabolism to humans. The worm's shorter lifespan gave the researchers the opportunity to see the impact of the chemical compounds. "Ageing is increasingly being recognised as a set of diseases in modern medicine, and we can apply the tools of the digital world, such as AI, to help slow down or protect against ageing and age-related diseases. Our study demonstrates the revolutionary ability of AI to aid the identification of compounds with anti-ageing properties." "This research shows the power and potential of AI, which is a speciality of the University of Surrey, to drive significant benefits in human health."
Come winter this year, robots will be guarding tomato and potato crops from insects, birds and viruses, analyse the soil and spray insecticides in a targeted area at the CS Azad University of Agriculture and Technology, Kanpur. Developed by the Indian Institute of Technology (IIT) Kanpur, the robots can navigate through agricultural fields to help farmers keep an eye on the crops, leaves and flowers and guard them against attacks from insects. "It will treat the crops and also send out instant alerts about the viruses infecting the soil, helping the farmer decide on a future course of action. It's overall interventions will help the farmers get good crop quality and yield," said professor Vishakh Bhattacharya, the maker of this robot. Between October and November, these robots will be deployed at the CSA University fields to give their inputs on soil and crop conditions.
August 31, 2021 Artificial intelligence (AI) has been paired with one of the simplest of organisms--the nematode Caenorhabditis elegans--to enlighten the scientific community about the physical and chemical properties of drug compounds with anti-aging effects, according to Brendan Howlin, reader in computational chemistry at the University of Surrey (U.K.). The predictive power of the methodology has just been demonstrated using an established database of small molecules found to extend life in model organisms. The 1,738 compounds in the DrugAge database were broadly separated into flavonoids (e.g., from fruits and vegetables), fatty acids (e.g, omega-3 fatty acids), and those with a carbon-oxygen bond (e.g., alcohol)--all heavily tied to nutrition and lifestyle choices. Pharmaceuticals could be developed based on that nutraceutical knowledge, including the importance of the number of nitrogen atoms, says Howlin. Unlike prior efforts using AI to identify compounds that slow the aging process, Howlin used machine learning to calculate the quantitative structure–activity relationship (QSAR) of molecules.
Steel is very much prone to get defects during the manufacturing or shipping process, and it is very difficult for large manufacturing companies to detect these defects with help of manpower. Hence, there is scope to train a machine learning or deep learning model to detect these defects. Severstal is a Russian company mainly operating in the steel and mining industry, headquartered in Cherepovets. Severstal conducted a kaggle competition by providing the data of defective steel images. This story is about my work as a response to the above Kaggle competition.
FURA Gems has announced a partnership with India-based Cognecto to improve operational efficiency, sustainability, productivity and decrease the carbon footprint of its Australian mining operation. Cognecto, which calls itself India's leading artificial intelligence-based heavy equipment monitoring company, has deployed an integrated custom-built hardware sensor and remote telemetry data protocol for FURA to share the data from its Sapphire mining operations in Queensland to company headquarters in Dubai. This collaborative effort forges a solution combining heavy equipment monitoring and analytics to empower operational visibility and control wherever and whenever, according to Cognecto. In addition, FURA employees can access real-time fleet updates via a "well-integrated, easy-to-implement, and zero-tech footprint AI platform created by Cognecto to improve operational conditions and enhances safety", it said. Operational insights for real-time tracking are delivered using a web interface, while the alerts can be relayed on any commonly used messaging platform.
China is planning to build miles-wide'megastructures' in orbit, including solar power plants, tourism complexes, gas stations and even asteroid mining facilities. The National Natural Science Foundation of China (NSFC) announced a new five-year plan, directing researchers to develop technologies and techniques. The structures will require lightweight materials to allow larger objects to get into orbit with existing rockets. Researchers will also need to adopt technology to allow for in-orbit assembly and control. The Chinese government said there is an'urgent need' for megaprojects in space that would require ultra-large spacecraft to keep them in orbit.
To the naked eye, a sheet of stainless steel presents a smooth, polished, homogenous surface. The same material when viewed at 400 times magnification reveals its true jumbled structure--different crystal shapes, joined at wildly different angles. Researchers at the University of Illinois Urbana-Champaign used data from high-resolution images of stainless-steel samples to train neural networks that make predictions about how the material will behave at places where the crystals meet, when strained. John Lambros explained, when studying the properties of a material such as stainless steel, it is impossible to conduct separate experiments at such high magnifications that subject it to every conceivable parameter--every temperature, every loading angle, every amount of pressure. So we often rely on models.