Single row layout models

Coronary artery disease (CAD) is one of the main causes of cardiac death around the world. Due to its significant impact on the society, early and accurate detection of CAD is essential. This study proposes a novel nested ensemble nu-Support Vector Classification (NE-nu-SVC) model which combines several traditional machine learning methods and ensemble learning techniques for effective diagnosis of CAD. We validated our model using two well-known CAD datasets (Z-Alizadeh Sani and Cleveland). To improve the performance of the model, we selected clinically significant features from the datasets using a genetic search algorithm. To further improve our results, we applied a multi-level filtering technique to balance the data using the ClassBlancer and Resample methods. Our base algorithm, nu-SVC, is performed using four well-known kernel functions (linear, polynomial, radial basis (RBF) and sigmoid). The proposed NE-nu-SVC model provided the highest accuracy of 94.66% and 98.60% to predict CAD entities in the Z-Alizadeh Sani and Cleveland CAD datasets, respectively. Our system can aid the clinicians to diagnose CAD accurately and may probably replace other invasive diagnostic techniques.

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NE-nu-SVC: A New Nested Ensemble Clinical Decision Support System for Effective Diagnosis of Coronary Artery Disease

Proceedings of the World Congress on Engineering 2017

Data mining (DM) is an instrument of pattern detection and retrieval of knowledge from a large quantity of data. Many robust early detection services and other health-related technologies have developed from clinical and diagnostic evidence in both the DM and healthcare sectors. Artificial Intelligence (AI) is commonly used in the research and health care sectors. Classification or predictive analytics is a key part of AI in machine learning (ML). Present analyses of new predictive models founded on ML methods demonstrate promise in the area of scientific research. Healthcare professionals need accurate predictions of the outcomes of various illnesses that patients suffer from. In addition, timing is another significant aspect that affects clinical choices for precise predictions. In this regard, the authors have reviewed numerous publications in this area in terms of method, algorithms, and performance. This review paper summarized the documentation examined in accordance with approaches, styles, activities, and processes. The analyses and assessment techniques of the selected papers are discussed and an appraisal of the findings is presented to conclude the article. Present statistical models of healthcare remedies have been scientifically reviewed in this article. The uncertainty between statistical methods and ML has now been clarified. The study of related research reveals that the prediction of existing forecasting models differs even if the same dataset is used. Predictive models are also essential, and new approaches need to be improved.

Smart healthcare disease diagnosis and patient management: Innovation, improvement and skill development

Long-term decisions, such as the design of infrastructure systems and buildings or the planning of risk mitigation measures, should be made in consideration of the uncertain future. The initial design of a system determines its flexibility, i.e. its ability to cope with potential future changes. Increasing flexibility is generally considered to be a good approach to dealing with future uncertainty, such as climate change uncertainty, but its effects have not been systematically investigated. We propose the use of Markov Decision Processes combined with Influence Diagrams to solve adaptation planning problems. This framework can identify the optimal system type and capacity and determine the value of flexibility. It is here applied to two numerical examples: Planning of a waste water treatment plant under uncertainty in future population growth and planning of a flood protection system under uncertain climate change scenarios. Based on these idealized examples, it is shown that for flexible systems...

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Long-term adaption decisions via fully and partially observable Markov decision processes

Heart disease is the most important public health problem for many countries. Early diagnosis of heart disease is extremely crucial for the survival of the patient. At this point, classification algorithms are widely used for medical diagnosis. In this study, firstly, artificial neural network (ANN) with default parameters is used to diagnose heart disease. Then, a hybrid approach, combining artificial neural network (ANN) and genetic algorithm (GA), is proposed to improve classification accuracy. Finally, the effectiveness of the proposed approach is illustrated with ‘Cleveland’ dataset taken from UCI machine learning repository. Experimental results show that the proposed hybrid ANN - GA approach outperforms Naive Bayes, K- Nearest Neighbor and C4.5 algorithms in terms of accuracy rate, precision, recall and F-measure.

Diagnosis of Heart Disease Using an Intelligent Method: A Hybrid ANN – GA Approach

In this research, an entropy based evaluation is adapted for the quantification of manufacturing flexibility. The aim is to evaluate the manufacturing flexibility in scope of a job shop model and to reach a single numeric value that would represent the overall flexibility level of operations performed in that manufacturing unit. The model proposed is applied to a job shop in which manufacturing is characterized by type of machinery, number and type of operations and routing for each product as well as time spend for operations, machine set up activities and material handling. In the paper, the flexibility level of the job shop is found and the association between the total entropy value and flexibility level is discussed.

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On Quantifying Manufacturing Flexibility: An Entropy Based Approach

Intelligent automation systems support business efforts through the collaborative interactions of humans and machines. In the realm, partial or integrative transformations of conventional techniques into digital practices is both applicable. Hereby, automation systems directly determine the task sharing between the manpower and software. Robotic Process Automations (RPAs) are emerging technological applications in order to replicate human effort for accomplishing inter-related sets of activities and tasks by accessing one or multiple unintegrated systems and even making sophisticated decisions. Management success of the utilized automation is crucial in this context. This study mainly investigates RPAs as of intelligent automation. Study presents the definitional and conceptive nuances on the topic initially. Then, an RPA Ecosystem Model is proposed for an augmented automation process. Technical tips for RPA implementation are clarified, and RPA lifecycle is presented. This study is one of the first ones that RPA implementation is presented by an overall approach, in an industrial engineering perspective, for both researchers and implementers serving for the organizations which are recently in the transformation process into RPAs. The study becomes prominent by giving the fundamental information in a nutshell for an extremely novel research area.

A Conceptual Study on RPAs as of Intelligent Automation

Copula is a distribution function on the unit hypercube with uniform margins. The margin is directly related to the stochastic behaviour of one variable, while joint distribution function covers the holistic character of more. In multivariate (and particularly bivariate) analysis, using copulas is an elegant way to solve the missing information problem between joint distribution function and the total of the margins. Hereby, the intention of this paper is twofold. Firstly, the paper intends to emphasize the advantages of copulas in practice. In order to encourage potential researchers to diversify their subject of work with these functions, authors give the essential introductory details for a clear understanding of copulas associated with their basic mathematical and statistical preliminaries. Secondly, the study exemplifies the practical usage of copulas in statistical process control area. In this context, process parameters are estimated in order to calculate the control limits of a typical Shewhart type control chart. Parameter estimation is performed by Maximum Likelihood Estimation (MLE) for the bivariate Clayton copula in univariate AR (1) time series with several different levels of high dependence. Since monitoring autocorrelated data in control charts is known as being one of the main causes of producing tighter control limits than required, false alarm rate may be increased and accordingly, the performance of control charts may be dramatically decreased. This study shows that copulas may alternatively be used for getting the same or little wider acceptable region between upper and lower limits. This recognition of the properness of copulas may help to decrease some of the negative effects of dependent data being monitored on charts for further studies.

On Copula Based Serial Dependence in Statistical Process Control

Facility layout is the arrangement of machines, equipments, or other resources in a manufacturing environment to designate an ideal configuration for minimizing the total cost by affecting the production flow. Layout design has a significant impact on the performance of manufacturing systems, and the layout problems are generally regarded as NP-Hard problems. In the literature, a considerable amount of attention is granted to biology-inspired metaheuristic algorithms in order to find efficient solutions to deal with many optimization problems. In this study, the general features and the mechanism of the Firefly Algorithm are presented initially. In order to illustrate how to adapt the proposed algorithm to a real manufacturing problem, a numerical application is shown for the solution of single row facility layout problem. A candidate solution array for 15 departments is obtained through the presumptions of the proposed algorithm. For a sample size of 500 iterations, 95 % confidence interval is constructed between the values of 8,306.53 and 8,378.22 with a standard error value of 18.288.

Özlen Erkal Sönmez

Papers

Diagnosis of Heart Disease Using an Intelligent Method: A Hybrid ANN – GA Approach

On Quantifying Manufacturing Flexibility: An Entropy Based Approach

A Conceptual Study on RPAs as of Intelligent Automation

On Copula Based Serial Dependence in Statistical Process Control

Application of Firefly Metaheuristic Algorithm for the Single Row Facility Layout Problem