Accurately estimating the impact of road maintenance schedules on traffic conditions is important because maintenance operations can substantially worsen congestion if not carefully planned. Reliable estimates allow planners to avoid excessive delays during periods of roadwork. Since the exact increase in congestion is difficult to predict analytically, traffic simulations are commonly used to assess the redistribution of the flow of traffic. However, when applied to long-term maintenance planning involving many overlapping projects and scheduling alternatives, these simulations must be run thousands of times, resulting in a significant computational burden. This paper investigates the use of machine learning-based surrogate models to predict network-wide congestion caused by simultaneous road renovations. We frame the problem as a supervised learning task, using one-hot encodings, engineered traffic features, and heuristic approximations. A range of linear, ensemble-based, probabilistic, and neural regression models is evaluated under an online learning framework in which data progressively becomes available. The experimental results show that the Costliest Subset Heuristic provides a reasonable approximation when limited training data is available, and that most regression models fail to outperform it, with the exception of XGBoost, which achieves substantially better accuracy. In overall performance, XGBoost significantly outperforms alternatives in a range of metrics, most strikingly Mean Absolute Percentage Error (MAPE) and Pinball loss, where it achieves a MAPE of 11% and outperforms the next-best model by 20% and 38% respectively. This modeling approach has the potential to reduce the computational burden of large-scale traffic assignment problems in maintenance planning.

Couplings connect the spinning shafts of driving and driven machines in the industry. A coupling guard encloses the coupling to protect personnel from the high-speed rotating coupling. The American Petroleum Institute API publishes standards that restrict the overheating of the coupling guards due to windage caused by the spinning shaft. Based on the most recent version of API 671, the peak temperature for the coupling guard should not exceed 60 C. This paper proposes a machine learning (ML) model and an empirical formula to predict the maximum guard temperature and power loss.

Operational Processes are defined as the core business of companies and firms: drug companies consider them to be drug testing and approval, manufacturing firms identify them in the product assembly process, while banks and financial firms have their own core business in risk management and evaluation. In order to be able to concede loans, financial institutions are compelled to predict whether an applicant is likely to repay the debit. In such a framework, Credit Scoring plays a huge role in ranking applicants based on their likelihood to pay back the loan. Each person is associated with a credit score value, namely a "number that summarizes its credit risk, based on a snapshot of its credit report at a particular point in time" [1]. Behind the scenes, CRM is employed to reach the goal: scoring models, or "scorecards", are generated from historical data, employing well-established statistical techniques. The cornerstones of a reliable scorecard are well depicted by Loretta Mester in [2]: "the model should give a higher percentage of high scores to borrowers whose loans will perform well and a higher percentage of low scores to borrowers whose loans won't perform well". Several advantages stem from risk modelling, among the most important there are an increased profitability of financial corporations due to more reliable loans conceded, the chance of evaluating new loan programs based on the data collected and the enhancement of the credit-loss management capability [1]. Therefore, over the years, some institutions arose to accomplish the task. CRIF is a global company specialized in credit bureau and business information, outsourcing and processing services, and credit solutions.

As stated by the author on the Github page -- "SHAP (SHapley Additive exPlanations) is a game-theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions". As mentioned above, Shapley values are based on classic game theory. There are many game types such as cooperative/non-cooperative, symmetric/non-symmetric, zero-sum/non zero-sum etc. But Shapley values are based on the cooperative (coalition) game theory.

The main goal of the LIME package is to explain any black-box machine learning models. It is used for both classification and regression problems. Let's try to understand why we need to explain machine learning predictions. Consider you are working for a housing finance or bank client. You are tasked with building a machine learning model to predict loan defaults.

It's needless to say: machine learning is powerful. At the most basic level, machine learning algorithms can be used to classify things. Given a collection of cute animal pictures, a classifier can separate the pictures into buckets of'dog' and'not a dog'. Given data about customer restaurant preferences, a classifier can predict what restaurant a user goes to next. However, the role of humans is overlooked in the technology.

Machine learning (ML), an application of computer programs, makes algorithms and is capable of making decisions and generating outputs without any human involvement. Hailed as one of the most impactful and significant technological developments that we have seen in recent times, machine learning has already helped us perform key real-world calculations and analytics that conventional computing would take years to solve. When it comes to the budding IT engineers and software developers, ML has been quite popular as a career choice. A lot of students have been suggested to take up a Machine Learning course and get industry-ready for the upcoming technological trend. As a matter of fact, jobs related to machine learning has seen incredible growth over the last couple of years.

It is common practice to test the performance of ML models, but it is not so common to test the reliability of the predictions. Training a model, test its performance and hoping that it will produce good quality predictions is not the right approach if we are concerned with reliable ML. Hence, in this talk, we will discuss the concept of conformal predictions which quantify quality in predictions.

Machine learning (ML), an application of computer programs, makes algorithms and is capable of making decisions and generating outputs without any human involvement. Hailed as one of the most impactful and significant technological developments that we have seen in recent times, machine learning has already helped us perform key real-world calculations and analytics that conventional computing would take years to solve. When it comes to the budding IT engineers and software developers, ML has been quite popular as a career choice. A lot of students have been suggested to take up a Machine Learning course and get industry-ready for the upcoming technological trend. As a matter of fact, jobs related to machine learning has seen incredible growth over the last couple of years.

Mark Needham is a Support Engineer for Neo4j. Amy is the Analytics and AI Program Manager at Neo4j. She believes a thriving graph ecosystem is essential to catalyze new types of insights. Accordingly, she helps ensure Neo4j partners are successful. In her career, Amy has consistently helped teams break into new markets at startups and large companies including EDS, Microsoft, and Hewlett-Packard (HP).