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The overall equipment effectiveness (OEE) is a performance measurement metric widely used. Its calculation provides to the managers the possibility to identify the main losses that reduce the machine effectiveness and then take the necessary decisions in order to improve the situation. However, this calculation is done a-posterior which is often too late. In the present research, we implemented different Machine Learning algorithms namely; Support vector machine, Optimized Support vector Machine (using Genetic Algorithm), Random Forest, XGBoost and Deep Learning to predict the estimate OEE value. The data used to train our models was provided by an automotive cable production industry. The results show that the Deep Learning and Random Forest are more accurate and present better performance for the prediction of the overall equipment effectiveness in our case study.

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Machine Learning (ML) is a science dealing with the study and development of computational models of learning and discovery process, alongwith building learning programs for specific applications. In the present study, ML techniques have been used to develop correlations for predicting geotechnical parameters for Civil Engineering design. For the determination of design values, strength and compressibility tests need to be carried out on undisturbed samples of soil. It is difficult to obtain an undisturbed sample every time, due to handling, transportation, the release of overburden pressure and poor laboratory conditions. ML techniques can predict fairly accurate values of various geotechnical parameters like in-situ density, compression index (Cc) and shear strength parameters (c and ϕ), if accurate datasets of laboratory and field results are used to develop the models. Several ML techniques like Linear Regression (LR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF) and M5 tree (M5P) have been used for the analysis. In the present study, relationships between in-place density using SPT N-value, compression index (Cc) using liquid limit (LL) and void ratio (e), and cohesion (c) and angle of internal friction (ϕ) using SPT N-value have been developed. Geotechnical data up to a depth of 50 m from 1053 borehole locations covering almost every district in the state of Haryana have been considered to develop models and statistical correlations. A general trend has been recorded in the observed data and accordingly, the outliers have been excluded. Several models have been developed to establish functional correlations. These correlations have been ranked on the basis their coefficient of determination (R2) value and mean absolute error (MAE). Subsequently, the model with the highest R2 value and minimum mean absolute error has been considered for the development of correlations. Sensitivity analysis has also been carried out for all the developed correlations to assess their individual performance. For this purpose, all the developed models have been evaluated by fitting a straight line between observed and modelled values, and in all the cases, a good value of R2 has been observed. The R2 values obtained for all the models range from 0.798 to 0.988. On comparison, it has been observed that the values of geotechnical parameters obtained are in close agreement with the existing work.

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

A comprehensive introduction to Support Vector Machines and related kernel methods.
In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels—for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics.
Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

The results of counting the size of programs in terms of Lines-of-Code (LOC) depends on the rules used for counting (i.e. definition of which lines should be counted). In the majority of the measurement tools, the rules are statically coded in the tool and the users of the measurement tools do not know which lines were counted and which were not. The goal of our research is to investigate how to use machine learning to teach a measurement tool which lines should be counted and which should not. Our interest is to identify which parameters of the learning algorithm can be used to classify lines to be counted. Our research is based on the design science research methodology where we construct a measurement tool based on machine learning and evaluate it based on open source programs. As a training set, we use industry professionals to classify which lines should be counted. The results show that classifying the lines as to be counted or not has an average accuracy varying between 0.90 and 0.99 measured as Matthew's Correlation Coefficient and between 95% and nearly 100% measured as the percentage of correctly classified lines. Based on the results we conclude that using machine learning algorithms as the core of modern measurement instruments has a large potential and should be explored further.

Background: Modern software development companies increasingly rely on quantitative data in their decision-making for product releases, organizational performance assessment and monitoring of product quality. KPIs (Key Performance Indicators) are a critical element in the transformation of raw data (numbers) into decisions (indicators). The goal of the paper is to develop, document and evaluate a quality model for KPIs – addressing the research question of What characterizes a good KPI? In this paper we consider a KPI to be "good" when it is actionable and supports the organization in achieving its strategic goals. We use an action research collaborative project with an infrastructure provider company and an automotive OEM to develop and evaluate the model. We analyze a set of KPIs used at both companies and verify whether the organization's perception of these evaluated KPIs is aligned with the KPI's assessment according to our model. The results show that the model organizes good practices of KPI development and that it is easily used by the stakeholders to improve the quality of the KPIs or reduce the number of the KPIs. Using the KPI quality model provides the possibility to increase the effect of the KPIs in the organization and decreases the risk of wasting resources for collecting KPI data which cannot be used in practice.

Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.

We show the lower bound of the capacity of a hierarchical neural network, having multiple hidden layers whose node unit takes the value of a real number between zero and one as the output of a sigmoid function. It is shown that \(n.\left \lceil \frac{h_1}{2} \right \rceil+\left \lfloor \frac{h_1}{2} \right \rfloor\cdot \left \lceil \frac{h_2}{2}-1 \right \rceil+\cdots +\left \lfloor \frac{h_{N-1}}{2} \right \rfloor\cdot\left \lceil \frac{h_N}{2}-1 \right \rceil\) examples in the general position (i.e. no subset of n or less input vectors degenerate) can be memorized by the network which has n input units in the input layer, h
ℓ hidden units in the ℓ-th layer of N hidden layers, and a single output unit in the output layer.

We introduce Adam, an algorithm for first-order gradient-based optimization
of stochastic objective functions. The method is straightforward to implement
and is based an adaptive estimates of lower-order moments of the gradients. The
method is computationally efficient, has little memory requirements and is well
suited for problems that are large in terms of data and/or parameters. The
method is also ap- propriate for non-stationary objectives and problems with
very noisy and/or sparse gradients. The method exhibits invariance to diagonal
rescaling of the gradients by adapting to the geometry of the objective
function. The hyper-parameters have intuitive interpretations and typically
require little tuning. Some connections to related algorithms, on which Adam
was inspired, are discussed. We also analyze the theoretical convergence
properties of the algorithm and provide a regret bound on the convergence rate
that is comparable to the best known results under the online convex
optimization framework. We demonstrate that Adam works well in practice when
experimentally compared to other stochastic optimization methods.

In recent years, deep neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical
survey compactly summarises relevant work, much of it from the previous
millennium. Shallow and deep learners are distinguished by the depth of their
credit assignment paths, which are chains of possibly learnable, causal links
between actions and effects. I review deep supervised learning (also
recapitulating the history of backpropagation), unsupervised learning,
reinforcement learning & evolutionary computation, and indirect search for
short programs encoding deep and large networks.