Mediaboss Marketing

A data driven tool that predicts with 99.9 percent accuracy the safest and most successful windows for crew transfers to offshore wind platforms WAVES is the first technology of its kind and is already being used by RWE across its Robin Rigg and Rampion windfarms.

The collaboration has now seen RWE integrate Weatherquests API into the already operational WAVES platform on Robin Rigg to work alongside other forecast data to enable in-day and week-ahead O&M decision-support for turbine specific accessibility.

The integration of WAVES with Weatherquests API allows us to develop our unique technology yet further to make it an even more trusted tool for windfarm owners and operators to plan and schedule their O&M programmes said MD at Cognitive Business, Ty Burridge Oakland, speaking about the upgrades to its WAVES technology. WAVES is already a hugely accurate and relied upon technology in the industry for effectively, efficiently and safely deploying crews onto windfarms to conduct repairs and maintenance and by integrating weather forecast data, we can confidently say we have made an already highly valued technology an even more robust tool for managing and planning offshore wind repair and maintenance programmes.

Developed by Nottingham and London based, Cognitive Business in 2020, WAVES was funded in the same year by the Offshore Wind Growth Partnership to better predict safer and more successful windows for crew transfers to offshore wind platforms.

Steve Dorling, Chief Executive at Weatherquest, added that WAVES has developed a reputation within the offshore wind industry, over a number of years, for enabling owners and operators to deploy their crews with real accuracy and has been working to great effect on some of the UKs largest windfarms.

It therefore made absolute sense for us both, as data analysis businesses focused on supporting safety and productivity, to combine our expertise in this innovative way said Mr Dorling. Its great that we can further enhance the WAVES technology together in a market where it is already a trusted technology for identifying optimal windows for offshore wind crew transfers.

Cognitive Business is an industry leader in machine learning and applied A.I, developing a wide range of decision support, performance monitoring, and predictive maintenance solutions for offshore wind operations and maintenance applications.

Weatherquest is a privately owned weather forecasting and weather analysis company headquartered at the University of East Anglia providing weather forecasting support services across sectors in the UK and Northern Europe including onshore and offshore wind energy and ports.

For additional information:

Cognitive Business

Weatherquest

Read more:
Wind - Machine learning and AI specialist Cognitive Business collaborates with Weatherquest on weather forecasts for offshore wind platform -...

Advertiser disclosure: We may be compensated by vendors who appear on this page through methods such as affiliate links or sponsored partnerships. This may influence how and where their products appear on our site, but vendors cannot pay to influence the content of our reviews. For more info, visit ourTerms of Use page.

Machine learning (ML) books are a valuable resource for IT professionals looking to expand their ML skills or pursue a career in machine learning. In turn, this expertise helps organizations automate and optimize their processes and make data-driven decisions. Machine learning books can help ML engineers learn a new skill or brush up on old ones.

Beginners and seasoned experts alike can benefit from adding machine learning books to their reading lists, though the right book depends on the learners goals. Some books serve as an entry point to the world of machine learning, while others build on existing knowledge.

The books in this list are roughly ranked in order of difficultybeginners should avoid pursuing the books toward the end until theyve mastered the concepts introduced in the books at the top of the list.

Machine Learning for Absolute Beginners is an excellent introduction to the machine learning field of study. Its a clear and concise overview of the high-level concepts that drive machine learning, so its ideal for beginners. The e-book format has free downloadable resources, code exercises, and video tutorials to satisfy a variety of learning styles.

Readers will learn the basic ML libraries and other tools needed to build their first model. In addition, this book covers data scrubbing techniques, data preparation, regression analysis, clustering, and bias/variance. This book may be a bit too basic for readers who are interested in learning more about coding, deep learning, or other advanced skills.

As the name implies, The Hundred-Page Machine Learning Book provides a brief overview of machine learning and the mathematics involved. Its suitable for beginners, but some knowledge of probability, statistics, and applied mathematics will help readers get through the material faster.

The book covers a broad range of ML topics at a high level and focuses on the aspects of ML that are of significant practical value. These include:

Several reviewers said that the text explains complicated topics in a way that is easy for most readers to understand. It doesnt dive into any one topic too deeply, but it provides several practice exercises and links to other resources for further reading.

Introduction to Machine Learning with Python is a starting point for aspiring data scientists who want to learn about machine learning through Python frameworks. It doesnt require any prior knowledge of machine learning or Python, though familiarity with NumPy and matplotlib libraries will enhance the learning experience.

In this book, readers will gain a foundational understanding of machine learning concepts and the benefits and drawbacks of using standard ML algorithms. It also explains how all of the algorithms behind various Python libraries fit together in a way thats easy to understand for even the most novice learners.

Python Machine Learning by Example builds on existing machine learning knowledge for engineers who want to dive deeper into Python programming. Each chapter demonstrates the practical application of common Python ML skills through concrete examples. These skills include:

This book walks through each problem with a step-by-step guide for implementing the right Python technique. Readers should have prior knowledge of both machine learning and Python, and some reviewers recommended supplementing this guide with more theoretical reference materials for advanced comprehension.

Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow provides a practical introduction to machine learning with a focus on three Python frameworks. Readers will gain an understanding of numerous machine learning concepts and techniques, including linear regression, neural networks, and deep learning. Then, readers can apply what they learn to practical exercises throughout the book.

Though this book is marketed toward beginners, some reviewers said it requires a basic understanding of machine learning principles. With this in mind, it may be better suited for readers who want to refresh their existing knowledge through concrete examples.

Machine learning for Hackers is written for experienced programmers who want to maximize the impact of their data. The text builds on existing knowledge of the R programming language to create basic machine learning algorithms and analyze datasets.

Each chapter walks through a different machine learning challenge to illustrate various concepts. These include:

This book is best suited for intermediate learners who are fluent in R and want to learn more about the practical applications of machine learning code. Students looking to delve into machine learning theory should opt for a more advanced book like Deep Learning, Hands-on Machine Learning, or Mathematics for Machine Learning.

Pattern Recognition and Machine Learning is an excellent reference for understanding statistical methods in machine learning. It provides practical exercises to introduce the reader to comprehensive pattern recognition techniques.

The text is broken into chapters that cover the following concepts:

Readers should have a thorough understanding of linear algebra and multivariable calculus, so it may be too advanced for beginners. Familiarity with basic probability theory, decision theory, and information theory will make the material easier to understand as well.

Mathematics for Machine Learning teaches the fundamental mathematical concepts necessary for machine learning. These topics include:

Some reviewers said this book leans more into mathematical theorems than practical application, so its not recommended for those without prior experience in applied mathematics. However, its one of the few resources that bridge the gap between mathematics and machine learning, so its a worthwhile investment for intermediate learners.

For advanced learners, Deep Learning covers the mathematics and concepts that power deep learning, a subset of machine learning that makes human-like decisions. This book walks through deep learning computations, techniques, and research including:

There are about 30 pages that cover practical applications of deep learning like computer vision and natural language processing, but the majority of the book deals with the theory behind deep learning. With this in mind, readers should have a working knowledge of machine learning concepts before delving into this text.

Read next: Ultimate Machine Learning Certification Guide

Go here to see the original:
Best Machine Learning Books to Read This Year [2022 List] - CIO Insight

The University of Torontos Geoffrey Hinton has been honoured withthe Royal Societysprestigious Royal Medal for his pioneering work in deep learning a field of artificial intelligence that mimics the way humans acquire certain types of knowledge.

TheU.K.s national academy of sciencessaid it is recognizing Hinton,a University Professor Emeritus in the department of computer science in the Faculty of Arts & Science, for pioneering work on algorithms that learn distributed representations in artificial neural networks and their application to speech and vision, leading to a transformation of the international information technology industry.

Its the latest in along list of accolades for Hinton, who is alsochief scientific adviser at theVector Institute for Artificial Intelligenceand a vice-president and engineering fellow at Google. Others includethe Association for Computing Machinerys A. M. Turing Award, widely considered the Nobel Prize of computing.

It is a great honour to receive the Royal Medal a medal previously awarded to intellectual giants like Darwin, Faraday, Boole and G.I. Taylor, Hinton says.

But unlike them, my success was the result of recruiting and nurturing an extraordinarily talented set of graduate students and post-docs who were responsible for many of the breakthroughs in deep learning that revolutionized artificial intelligence over the last 15 years.

Royal Medalshave been awarded annually since 1826 for advancements in the physical and biological sciences. A third medal for applied sciences has been awarded since 1965.

Previous U of T winners of the Royal Medalinclude Anthony Pawson andNobel Prize-winner John Polanyi.

Hinton, meanwhile,has been a Fellow of the Royal Society since 1998 and a Fellow of the Royal Society of Canada since 1996.

The Royal Medal is one of the most significant acknowledgements of an individuals research and career, says Melanie Woodin, dean of the Faculty of Arts & Science. And Professor Hinton is truly deserving of the distinction for his foundational research and for the exceptional contribution hes made toward shaping the modern world and the future. I am thrilled to congratulate him on this award.

I want to congratulate Geoff on this spectacular achievement, adds Eyal de Lara, chair of the department of computer science. We are very proud of the seminal contributions he has made to field of computer science, which are fundamentally reshaping our discipline and impacting society at large.

Deep learning is a typeof machine learningthat relies on a neural network modelled on the network of neurons in the human brain. In 1986, Hinton and his collaborators developed the breakthrough approach based on the backpropagation algorithm, a central mechanism by which artificial neural networks learn that would realize the promise of neural networks and form the current foundation of that technology.

Hinton and his colleagues in Toronto built on that initial work with a number of critical developments that enhanced the potential of AI and helped usher in todays revolution in deep learning with applications in speech and image recognition, self-driving vehicles, automated diagnosis of images and language, and more.

I believe that the spectacular recent progress in large language models, image generation and protein structure prediction is evidence that the deep learning revolution has only just started, Hinton says.

See the original post here:
Deep learning pioneer Geoffrey Hinton receives prestigious Royal Medal from the Royal Society - University of Toronto

The Karlsruhe Institute of Technology in Germany has an open PhD position for a project that will use machine learning to model scaling formation in cascade geothermal operations.

The Karlsruhe Institute of Technology (KIT) in Germany currently has an open PhD position in the upcoming Machine Learning for Enhancing Geothermal energy production (MALEG) project. Interested applicants may visit the official KIT page for more details on the application. Submissions will be accepted only until September 30, 2022.

The target of the MALEG project is the design and optimization of cascade production schemes aiming for the highest possible energy output in geothermal energy facilities by preventing scaling. The enhanced scaling potential of lower return temperatures is one key challenge as geothermal cascade use becomes a more common strategy to increase efficiency.

The research will be focusing on the development of a machine learning tool to quantify the impact of the enhanced cooling on the fluid-mineral equilibrium and to optimize the operations economically. The tool will be based on results from widely-applied deterministic models and experimental data collected at geothermal plants in Germany, Austria and Turkey by our international project partners. Once fully implemented the MALEG-tool will work as a digital twin of the power plant, ready to assess and predict scaling formation processes for geothermal production from different geological settings.

The ideal candidate should hold a masters degree in geosciences or geophysics with sound interest in aqueous geochemistry and experience in numerical modeling.

Source: Karlsruhe Institute of Technology

Read more:
PhD Position - Machine learning to increase geothermal energy efficiency, Karlsruhe Institute - ThinkGeoEnergy

Participants

This study was conducted as part of the ongoing Study on the Design of a Comprehensive Medical System for Chronic Kidney Disease (CKD) Based on Individual Risk Assessment by Specific Health Examination (J-SHC Study). A specific health checkup is conducted annually for all residents aged 4074years, covered by the National Health Insurance in Japan. In this study, a baseline survey was conducted in 685,889 people (42.7% males, age 4074years) who participated in specific health checkups from 2008 to 2014 in eight regions (Yamagata, Fukushima, Niigata, Ibaraki, Toyonaka, Fukuoka, Miyazaki, and Okinawa prefectures). The details of this study have been described elsewhere11. Of the 685,889 baseline participants, 169,910 were excluded from the study because baseline data on lifestyle information or blood tests were not available. In addition, 399,230 participants with a survival follow-up of fewer than 5years from the baseline survey were excluded. Therefore, 116,749 patients (42.4% men) with a known 5-year survival or mortality status were included in this study.

This study was conducted in accordance with the Declaration of Helsinki guidelines. This study was approved by the Ethics Committee of Yamagata University (Approval No. 2008103). All data were anonymized before analysis; therefore, the ethics committee of Yamagata University waived the need for informed consent from study participants.

For the validation of a predictive model, the most desirable way is a prospective study on unknown data. In this study, the data on health checkup dates were available. Therefore, we divided the total data into training and test datasets to build and test predictive models based on health checkup dates. The training dataset consisted of 85,361 participants who participated in the study in 2008. The test dataset consisted of 31,388 participants who participated in this study from 2009 to 2014. These datasets were temporally separated, and there were no overlapping participants. This method would evaluate the model in a manner similar to a prospective study and has an advantage that can demonstrate temporal generalizability. Clipping was performed for 0.01% outliers for preprocessing, and normalization was performed.

Information on 38 variables was obtained during the baseline survey of the health checkups. When there were highly correlated variables (correlation coefficient greater than 0.75), only one of these variables was included in the analysis. High correlations were found between body weight, abdominal circumference, body mass index, hemoglobin A1c (HbA1c), fasting blood sugar, and AST and alanine aminotransferase (ALT) levels. We then used body weight, HbA1c level, and AST level as explanatory variables. Finally, we used the following 34 variables to build the prediction models: age, sex, height, weight, systolic blood pressure, diastolic blood pressure, urine glucose, urine protein, urine occult blood, uric acid, triglycerides, high-density lipoprotein cholesterol (HDL-C), LDL-C, AST, -glutamyl transpeptidase (GTP), estimated glomerular filtration rate (eGFR), HbA1c, smoking, alcohol consumption, medication (for hypertension, diabetes, and dyslipidemia), history of stroke, heart disease, and renal failure, weight gain (more than 10kg since age 20), exercise (more than 30min per session, more than 2days per week), walking (more than 1h per day), walking speed, eating speed, supper 2h before bedtime, skipping breakfast, late-night snacks, and sleep status.

The values of each item in the training data set for the alive/dead groups were compared using the chi-square test, Student t-test, and MannWhitney U test, and significant differences (P<0.05) were marked with an asterisk (*) (Supplementary Tables S1 and S2).

We used two machine learning-based methods (gradient boosting decision tree [XGBoost], neural network) and one conventional method (logistic regression) to build the prediction models. All the models were built using Python 3.7. We used the XGBoost library for GBDT, TensorFlow for neural network, and Scikit-learn for logistic regression.

The data obtained in this study contained missing values. XGBoost can be trained to predict even with missing values because of its nature; however, neural network and logistic regression cannot be trained to predict with missing values. Therefore, we complemented the missing values using the k-nearest neighbor method (k=5), and the test data were complemented using an imputer trained using only the training data.

The parameters required for each model were determined for the training data using the RandomizedSearchCV class of the Scikit-learn library and repeating fivefold cross-validation 5000 times.

The performance of each prediction model was evaluated by predicting the test dataset, drawing a ROC curve, and using the AUC. In addition, the accuracy, precision, recall, F1 scores (the harmonic mean of precision and recall), and confusion matrix were calculated for each model. To assess the importance of explanatory variables for the predictive models, we used SHAP and obtained SHAP values that express the influence of each explanatory variable on the output of the model4,12. The workflow diagram of this study is shown in Fig.5.

Workflow diagram of development and performance evaluation of predictive models.

Read more here:
Prediction of mortality risk of health checkup participants using machine learning-based models: the J-SHC study | Scientific Reports - Nature.com