Product whole life-cycle and omni-channels data convergence oriented enterprise networks integration in a sensing environment

With the process of globalisation and the development of management models and information technologies, enterprise cooperation and collaboration meets a new business and technical environment. The Internet of things, mobile Internet, cloud computing and big data technologies build a sensing environment for all kinds of businesses. Inter enterprise collaboration is also meeting the new challenges of omni-channel marketing, closed-loop supply chain and enterprise networks integration. A data convergence oriented enterprise networks integration architecture with relative enabling technologies is developed in the paper. In order to collect, transfer and fuse data from different data sources, Data Portal (DP) and Collaboration Agent (CA) concepts are introduced, which present a lightweight and loosely coupled infrastructure for enterprise networks integration. How to use the developed technologies to solve problems of product lifecycle management and omni-channel marketing management are discussed in detailed cases studies.     

An overview on fault diagnosis and nature-inspired optimal control of industrial process applications

Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted.     

Particle Swarm Optimization inspired by starling flock behavior

Swarm intelligence is a meta-heuristic algorithm which is widely used nowadays for efficient solution of optimization problems. Particle Swarm Optimization (PSO) is one of the most popular types of swarm intelligence algorithm. This paper proposes a new Particle Swarm Optimization algorithm called Starling PSO based on the collective response of starlings. Although PSO performs well in many problems, algorithms in this category lack mechanisms which add diversity to exploration in the search process. Our proposed algorithm introduces a new mechanism into PSO to add diversity, a mechanism which is inspired by the collective response behavior of starlings. This mechanism consists of three major steps: initialization, which prepares alternative populations for the next steps; identifying seven nearest neighbors; and orientation change which adjusts velocity and position of particles based on those neighbors and selects the best alternative. Because of this collective response mechanism, the Starling PSO explores a wider area of the search space and thus avoids suboptimal solutions. We tested the algorithm with commonly used numerical benchmarking functions as well as applying it to a real world application involving data clustering. In these evaluations, we compared Starling PSO with a variety of state of the art algorithms. The results show that Starling PSO improves the performance of the original PSO and yields the optimal solution in many numerical benchmarking experiments. It also gives the best results in almost all clustering experiments.     

Automating data exchange in process choreographies

Communication between organizations is formalized as process choreographies in daily business. While the correct ordering of exchanged messages can be modeled and enacted with current choreography techniques, no approach exists to describe and automate the exchange of data between processes in a choreography using messages. This paper describes an entirely model-driven approach for BPMN introducing a few concepts that suffice to model data retrieval, data transformation, message exchange, and correlation – four aspects of data exchange. For automation, this work utilizes a recent concept to enact data dependencies in internal processes. We present a modeling guideline to derive local process models from a given choreography; their operational semantics allows to correctly enact the entire choreography from the derived models only including the exchange of data. Targeting on successful interactions, we discuss means to ensure correct process choreography modeling. Finally, we implemented our approach by extending the camunda BPM platform with our approach and show its feasibility by realizing all service interaction patterns using only model-based concepts.     

ECG heart beat classification method based on modified ABC algorithm

Electrocardiogram is the most commonly used tool for the diagnosis of cardiologic diseases. In order to help cardiologists to diagnose the arrhythmias automatically, new methods for automated, computer aided ECG analysis are being developed. In this paper, a Modified Artificial Bee Colony (MABC) algorithm for ECG heart beat classification is introduced. It is applied to ECG data set which is obtained from MITBIH database and the result of MABC is compared with seventeen other classifier's accuracy.In classification problem, some features have higher distinctiveness than others. In this study, in order to find higher distinctive features, a detailed analysis has been done on time domain features. By using the right features in MABC algorithm, high classification success rate (99.30%) is obtained. Other methods generally have high classification accuracy on examined data set, but they have relatively low or even poor sensitivities for some beat types. Different data sets, unbalanced sample numbers in different classes have effect on classification result. When a balanced data set is used, MABC provided the best result as 97.96% among all classifiers.Not only part of the records from examined MITBIH database, but also all data from selected records are used to be able to use developed algorithm on a real time system in the future by using additional software modules and making adaptation on a specific hardware.     

A novel approach for efficient stance detection in online social networks with metaheuristic optimization

In the 19th and 20th centuries, social networks have been an important topic in a wide range of fields from sociology to education. However, with the advances in computer technology in the 21st century, significant changes have been observed in social networks, and conventional networks have evolved into online social networks. The size of these networks, along with the large amount of data they generate, has introduced new social networking problems and solutions. Social network analysis methods are used to understand social network data. Today, several methods are implemented to solve various social network analysis problems, albeit with limited success in certain problems. Thus, the researchers develop new methods or recommend solutions to improve the performance of the existing methods. In the present paper, a novel optimization method that aimed to classify social network analysis problems was proposed. The problem of stance detection, an online social network analysis problem, was first tackled as an optimization problem. Furthermore, a new hybrid metaheuristic optimization algorithm was proposed for the first time in the current study, and the algorithm was compared with various methods. The analysis of the findings obtained with accuracy, precision, recall, and F-measure classification metrics demonstrated that our method performed better than other methods.     

Preprocessed dynamic classifier ensemble selection for highly imbalanced drifted data streams

This work aims to connect two rarely combined research directions, i.e., non-stationary data stream classification and data analysis with skewed class distributions. We propose a novel framework employing stratified bagging for training base classifiers to integrate data preprocessing and dynamic ensemble selection methods for imbalanced data stream classification. The proposed approach has been evaluated based on computer experiments carried out on 135 artificially generated data streams with various imbalance ratios, label noise levels, and types of concept drift as well as on two selected real streams. Four preprocessing techniques and two dynamic selection methods, used on both bagging classifiers and base estimators levels, were considered. Experimentation results showed that, for highly imbalanced data streams, dynamic ensemble selection coupled with data preprocessing could outperform online and chunk-based state-of-art methods.     

Wind of change: Small-scale electricity production and distribution-grid efficiency in Sweden

In this paper, we measure the technical efficiency for local electricity distribution firms in Sweden, and in particular how small and micro-scale generation affects efficiency scores. Using a two-stage data envelopment analysis to model the technical efficiency and a double bootstrap approach to estimate the determinants of inefficiencies, we show that firms are heterogeneous in terms of inefficiency, but that a large share of small and micro-scale generation is not associated with more inefficient operations.     

Two-stage optimization for machine learning workflow

Machine learning techniques play a preponderant role in dealing with massive amount of data and are employed in almost every possible domain. Building a high quality machine learning model to be deployed in production is a challenging task, from both, the subject matter experts and the machine learning practitioners.For a broader adoption and scalability of machine learning systems, the construction and configuration of machine learning workflow need to gain in automation. In the last few years, several techniques have been developed in this direction, known as AutoML.In this paper, we present a two-stage optimization process to build data pipelines and configure machine learning algorithms. First, we study the impact of data pipelines compared to algorithm configuration in order to show the importance of data preprocessing over hyperparameter tuning. The second part presents policies to efficiently allocate search time between data pipeline construction and algorithm configuration. Those policies are agnostic from the metaoptimizer. Last, we present a metric to determine if a data pipeline is specific or independent from the algorithm, enabling fine-grain pipeline pruning and meta-learning for the coldstart problem.     

Potential genomic biomarkers of obesity and its comorbidities for phthalates and bisphenol A mixture: <i>In silico</i> toxicogenomic approach

This in silico toxicogenomic study aims to explore the relationship between phthalates and bisphenol A (BPA) co-exposure and obesity, as well as its comorbid conditions, in order to construct a possible set of genomic biomarkers. The Comparative Toxicogenomics Database (CTD; http://ctd.mdibl.org) was used as the main data mining tool, along with GeneMania (https://genemania.org), ToppGene Suite (https://toppgene.cchmc.org) and DisGeNET (http://www.disgenet.org). Among the phthalates, bis(2-ethylhexyl) phthalate (DEHP) and dibutyl phthalate (DBP) were chosen as the most frequently curated phthalates in CTD, which also share similar mechanisms of toxicity. DEHP, DBP and BPA interacted with 84, 90 and 194 obesity-related genes/proteins, involved in 67, 65 and 116 pathways, respectively. Among these, 53 genes/proteins and 42 pathways were common to all three substances. 31 genes/proteins had matching interactions for all three investigated substances, while more than half of these genes/proteins (56.49%) were in co-expression. 7 of the common genes/proteins (6 relevant to humans: CCL2, IL6, LPL, PPARG, SERPINE1, and TNF) were identified in all the investigated obesity comorbidities, while PPARG and LPL were most closely linked to obesity. These genes/proteins could serve as a target for further in vitro and in vivo studies of molecular mechanisms of DEHP, DBP and BPA mixture obesogenic properties. Analysis reported here should be applicable to any mixture of environmental chemicals and any disease present in CTD.