A central role in process improvement is played by the process analyst [2], who is responsible for'monitoring, measuring, and providing feedback on the performance of a business process' [3, p.45]. The ongoing implementation of information systems in organisations, along with the subsequently enhanced availability of event log data, have enabled process analysts to discover as-is models of processes with process mining with relative ease [4]. However, the crucial challenge lies in identifying potential areas for process improvements (i.e., process analysis) with respect to a strategic goal [5]; this requires analytical capabilities such as Pareto or root cause analysis [2]. A business process can be defined as a'completely closed, timely, and logical sequence of activities' [6, p.3] that realises an outcome valuable to a customer [7]. The effectiveness (i.e., customer value) and efficiency (e.g., timely, logical sequence, resource utilisation) of a business process are monitored using key performance indicators (KPIs) as aggregated measures of process outcomes; in the context of BPM, these are often referred to as process performance indicators (PPIs) [8]. Thus, to improve a business process, it is essential for a process analyst to understand the relevance of individual process activities in terms of their impact on the dimensions expressed by these performance measures.

The Receiver Operating Characteristic (ROC) curve is a popular tool used with binary classifiers. It is very similar to the precision/recall curve. Still, instead of plotting precision versus recall, the ROC curve plots the true positive rate (another name for recall) against the false positive rate (FPR). The FPR is the ratio of negative instances that are incorrectly classified as positive. It is equal to 1 – the true negative rate (TNR), which is the ratio of negative cases that are correctly classified as negative.

In Machine Learning, Precision and Recall are the two most important metrics for Model Evaluation. Precision represents the percentage of the results of your model, which are relevant to your model. The recall represents the percentage total of total pertinent results classified correctly by your machine learning algorithm. In this article, I will show you how you can apply Precision and Recall to evaluate the performance of your Machine Learning model. See Full Article -- thecleverprogrammer.com.

Comparing model performances on benchmark datasets is an integral part of measuring and driving progress in artificial intelligence. A model's performance on a benchmark dataset is commonly assessed based on a single or a small set of performance metrics. While this enables quick comparisons, it may also entail the risk of inadequately reflecting model performance if the metric does not sufficiently cover all performance characteristics. Currently, it is unknown to what extent this might impact current benchmarking efforts. To address this question, we analysed the current landscape of performance metrics based on data covering 3867 machine learning model performance results from the web-based open platform 'Papers with Code'. Our results suggest that the large majority of metrics currently used to evaluate classification AI benchmark tasks have properties that may result in an inadequate reflection of a classifiers' performance, especially when used with imbalanced datasets. While alternative metrics that address problematic properties have been proposed, they are currently rarely applied as performance metrics in benchmarking tasks. Finally, we noticed that the reporting of metrics was partly inconsistent and partly unspecific, which may lead to ambiguities when comparing model performances.

Machine learning best practice statements have proliferated, but there is a lack of consensus on what the standards should be. For fairness standards in particular, there is little guidance on how fairness might be achieved in practice. Specifically, fairness in errors (both false negatives and false positives) can pose a problem of how to set weights, how to make unavoidable tradeoffs, and how to judge models that present different kinds of errors across racial groups. This paper considers the consequences of having higher rates of false positives for one racial group and higher rates of false negatives for another racial group. The paper examines how different errors in justice settings can present problems for machine learning applications, the limits of computation for resolving tradeoffs, and how solutions might have to be crafted through courageous conversations with leadership, line workers, stakeholders, and impacted communities.

Network attacks have been very prevalent as their rate is growing tremendously. Both organization and individuals are now concerned about their confidentiality, integrity and availability of their critical information which are often impacted by network attacks. To that end, several previous machine learning-based intrusion detection methods have been developed to secure network infrastructure from such attacks. In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique to tune the parameters of Support Vector Machine with Gaussian Kernel (SVM-RBF), Random Forest (RF), and k-Nearest Neighbor (k-NN) algorithms. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.

Has the NFL put the proper COVID-19 protocols in place to protect players for the 2020 season? Former NFL player Jack Brewer reacts. The Detroit Lions removed Matthew Stafford from the COVID-IR list, saying he received a false-positive test result -- and drawing the ire of the quarterback's wife toward the NFL. The list was created for players who either test positive for COVID-19 or have been in close contact with an infected person. Stafford was listed on it Saturday, but the team said Tuesday his testing sequence for the pre-entry period was: negative, negative, false positive -- then the next three tests were all negative.

To carry out our mathematical study, we have used the standard Decimal Code (R,G,B) to represent the 12 colours of [12], a depiction of which is in Figure 1. The relation between colours and human emotion has been studied for more than a century (e.g., see for instance [1-8]). Even longer ago, colours were commonly associated to emotions in a universal manner that allowed populations to understand quickly the given emotions. Figure 1: A depiction of the 12 colors used in [12]. For example, for centuries in many cultures it has been said that someone "had the blues" [29] or "is feeling In the last decades colours have also been studied in blue" when being down or sad. As explained in [9], the terms of emotional reactions to color hue, saturation, and phrase "feeling blue" comes from deepwater sailing ships: brightness (e.g., [14, 15]). Here, we shall put the two If a ship lost the captain or any of the officers during its approaches together to consider a novel path, where we voyage, then blue flags would be shown, and a blue band let the colour association within our neural network take would be painted along the entire hull when returning to a continuum of colours, hence considering a continuous home port. RGB analysis [30], depicted in Figure 2. Inspired by [10, 11] we consider their data base [12] to analize the correlation between colours and emotions via a deep learning approach. Whilst machine learning techniques have been used before in this direction (e.g.

This article considers "sketched learning," or "compressive learning," an approach to large-scale machine learning where datasets are massively compressed before learning (e.g., clustering, classification, or regression) is performed. In particular, a "sketch" is first constructed by computing carefully chosen nonlinear random features (e.g., random Fourier features) and averaging them over the whole dataset. Parameters are then learned from the sketch, without access to the original dataset. This article surveys the current state-of-the-art in sketched learning, including the main concepts and algorithms, their connections with established signal-processing methods, existing theoretical guarantees---on both information preservation and privacy preservation, and important open problems.

While global cybersecurity spending now surpasses $100 billion annually, 64 percent of enterprises were compromised in 2018, according to a study by the Ponemon Institute. The standard answer is that wily cyber-criminals are employing ever-evolving, increasingly sophisticated attack methods, part of a never-ending game of cat-and-mouse in which they all too often outsmart the good guys. This is undoubtedly true – but the root of the problem is that traditional machine learning-based cybersecurity solutions fail to keep up with the growing sophistication of today's cyber threats, both those that are created by hackers and AI alike. Why does machine learning so often come up short – and how should cybersecurity evolve to meet the scale and complexity of the challenge? There's no question that machine learning has driven significant improvements in cybersecurity.