Data Mining - Concepts and Techniques.

Manufacturing industry profoundly impact economic and societal progress. As being a commonly accepted term for research centers and universities, the Industry 4.0 initiative has received a splendid attention of the business and research community. Although the idea is not new and was on the agenda of academic research in many years with different perceptions, the term “Industry 4.0” is just launched and well accepted to some extend not only in academic life but also in the industrial society as well. While academic research focuses on understanding and defining the concept and trying to develop related systems, business models and respective methodologies, industry, on the other hand, focuses its attention on the change of industrial machine suits and intelligent products as well as potential customers on this progress. It is therefore important for the companies to primarily understand the features and content of the Industry 4.0 for potential transformation from machine dominant manufacturing to digital manufacturing. In order to achieve a successful transformation, they should clearly review their positions and respective potentials against basic requirements set forward for Industry 4.0 standard. This will allow them to generate a well-defined road map. There has been several approaches and discussions going on along this line, a several road maps are already proposed. Some of those are reviewed in this paper. However, the literature clearly indicates the lack of respective assessment methodologies. Since the implementation and applications of related theorems and definitions outlined for the 4th industrial revolution is not mature enough for most of the reel life implementations, a systematic approach for making respective assessments and evaluations seems to be urgently required for those who are intending to speed this transformation up. It is now main responsibility of the research community to developed technological infrastructure with physical systems, management models, business models as well as some well-defined Industry 4.0 scenarios in order to make the life for the practitioners easy. It is estimated by the experts that the Industry 4.0 and related progress along this line will have an enormous effect on social life. As outlined in the introduction, some social transformation is also expected. It is assumed that the robots will be more dominant in manufacturing, implanted technologies, cooperating and coordinating machines, self-decision-making systems, autonom problem solvers, learning machines, 3D printing etc. will dominate the production process. Wearable internet, big data analysis, sensor based life, smart city implementations or similar applications will be the main concern of the community. This social transformation will naturally trigger the manufacturing society to improve their manufacturing suits to cope with the customer requirements and sustain competitive advantage. A summary of the potential progress along this line is reviewed in introduction of the paper. It is so obvious that the future manufacturing systems will have a different vision composed of products, intelligence, communications and information network. This will bring about new business models to be dominant in industrial life. Another important issue to take into account is that the time span of this so-called revolution will be so short triggering a continues transformation process to yield some new industrial areas to emerge. This clearly puts a big pressure on manufacturers to learn, understand, design and implement the transformation process. Since the main motivation for finding the best way to follow this transformation, a comprehensive literature review will generate a remarkable support. This paper presents such a review for highlighting the progress and aims to help improve the awareness on the best experiences. It is intended to provide a clear idea for those wishing to generate a road map for digitizing the respective manufacturing suits. By presenting this review it is also intended to provide a hands-on library of Industry 4.0 to both academics as well as industrial practitioners. The top 100 headings, abstracts and key words (i.e. a total of 619 publications of any kind) for each search term were independently analyzed in order to ensure the reliability of the review process. Note that, this exhaustive literature review provides a concrete definition of Industry 4.0 and defines its six design principles such as interoperability, virtualization, local, real-time talent, service orientation and modularity. It seems that these principles have taken the attention of the scientists to carry out more variety of research on the subject and to develop implementable and appropriate scenarios. A comprehensive taxonomy of Industry 4.0 can also be developed through analyzing the results of this review.

Literature review of Industry 4.0 and related technologies

Article history: Received June25, 2015 Received in revised format: September 22, 2015 Accepted September 24, 2015 Available online September 25 2015 The teaching-learning-based optimization (TLBO) algorithm is finding a large number of applications in different fields of engineering and science since its introduction in 2011. The major applications are found in electrical engineering, mechanical design, thermal engineering, manufacturing engineering, civil engineering, structural engineering, computer engineering, electronics engineering, physics, chemistry, biotechnology and economics. This paper presents a review of applications of TLBO algorithm and a tutorial for solving the unconstrained and constrained optimization problems. The tutorial is expected to be useful to the beginners.

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Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems

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Ad Hoc Networks: Technologies and Protocols

This survey gives state-of-the-art of genetic algorithm (GA) based clustering techniques. Clustering is a fundamental and widely applied method in understanding and exploring a data set. Interest in clustering has increased recently due to the emergence of several new areas of applications including data mining, bioinformatics, web use data analysis, image analysis etc. To enhance the performance of clustering algorithms, Genetic Algorithms (GAs) is applied to the clustering algorithm. GAs are the best-known evolutionary techniques. The capability of GAs is applied to evolve the proper number of clusters and to provide appropriate clustering. This paper present some existing GA based clustering algorithms and their application to different problems and domains.

Genetic Algorithm Based Clustering: A Survey

Improvised prophecy using regularization method of machine learning algorithms on medical data

Summary In Image Processing Model Based Image Segmentation plays a dominant role in Image Analysis and Image Retrieval . Recently much work has been reported regarding Image Segmentation based on Finite Gaussian Mixture Models using EM algorithm. (Yiming Wu et al (2003)) , (Yamazaki.T (1998)). However, in some images the pixel intensities inside the image regions may not be Meso- Kurtic or Bell Shaped, because of the NonGaussian nature . Hence there are some situations where Image Segmentation is to be done with a more Generalized Finite Mixture Distribution. In this article we develop and analyze an image segmentation method based on Finite Generalized Gaussian Mixture Model using EM and K-Means algorithm. The K-Means algorithm is utilized to obtain the number of regions and the initial estimates of the model parameters. The update equations of the model parameters are obtained by using the EM algorithm. The segmentation of the pixels in the image is done by maximizing the component likelihood function. The performance of this method is evaluated through real time data on 3 images by calculating misclassification rate and image quality metrics. It is observed that the proposed method performs much superior to the earlier image segmentation methods.

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Unsupervised Image Segmentation Method based on Finite Generalized Gaussian Distribution with EM & K-Means Algorithm

Stock market prediction is the act of trying to determine the future value of a company stock of other financial Instrument traded on a financial exchange. This paper presents a comparison between, PSO and CSO trained Neural Network to predict the stock rates by preparing data which acts as input. The data is prepared in such a way that the external factors like traditional issues can be mitigated. Earlier Neural Network was trained using Back Propagation algorithm but it converges to local optima and cannot be applied to discrete functions. Sow we have chosen PSO and CSO optimization algorithm to train the Neural Network. The results show that training neural network with such data gives a better performance.

A Comparative Study of CSO and PSO Trained Artificial Neural Network for Stock Market Prediction

This paper presents a comparative analysis of three algorithms namely K-means, Particle swarm Optimization (PSO) and Self-Organizing PSO (SOPSO) for image clustering problems. The traditional K-means algorithm found to be trapped in local minima. However, PSO and SOPSO overcome the problem of local minima and provide better results. In this work gbest model is used in PSO and both West and gbest models are used in SOPSO based on self-Organizing rules. It is shown that PSO and SOPSO produce better results compared to K-means with respect to the quantization error, inter- and intra-cluster distances.

P. V. G. D. Prasad Reddy

Papers

Improvised prophecy using regularization method of machine learning algorithms on medical data

Unsupervised Image Segmentation Method based on Finite Generalized Gaussian Distribution with EM & K-Means Algorithm

A Comparative Study of CSO and PSO Trained Artificial Neural Network for Stock Market Prediction

A Comparative Analysis of Unsupervised K-Means, PSO and Self-Organizing PSO for Image Clustering

Hybrid deep learning approaches for the detection of diabetic retinopathy using optimized wavelet based model