Data Mining - Concepts and Techniques.

A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements

A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT

Automated surface-anomaly detection using machine learning has become an interesting and promising area of research, with a very high and direct impact on the application domain of visual inspection. Deep-learning methods have become the most suitable approaches for this task. They allow the inspection system to learn to detect the surface anomaly by simply showing it a number of exemplar images. This paper presents a segmentation-based deep-learning architecture that is designed for the detection and segmentation of surface anomalies and is demonstrated on a specific domain of surface-crack detection. The design of the architecture enables the model to be trained using a small number of samples, which is an important requirement for practical applications. The proposed model is compared with the related deep-learning methods, including the state-of-the-art commercial software, showing that the proposed approach outperforms the related methods on the specific domain of surface-crack detection. The large number of experiments also shed light on the required precision of the annotation, the number of required training samples and on the required computational cost. Experiments are performed on a newly created dataset based on a real-world quality control case and demonstrates that the proposed approach is able to learn on a small number of defected surfaces, using only approximately 25–30 defective training samples, instead of hundreds or thousands, which is usually the case in deep-learning applications. This makes the deep-learning method practical for use in industry where the number of available defective samples is limited. The dataset is also made publicly available to encourage the development and evaluation of new methods for surface-defect detection.

pdf/segmentation-based-deep-learning-approach-for-surface-defect-m56iact1ln.pdf

Segmentation-based deep-learning approach for surface-defect detection

The three previous industrial revolutions profoundly transformed agriculture industry from indigenous farming to mechanized farming and recent precision agriculture. Industrial farming paradigm greatly improves productivity, but a number of challenges have gradually emerged, which have exacerbated in recent years. Industry 4.0 is expected to reshape the agriculture industry once again and promote the fourth agricultural revolution. In this article, first, we review the current status of industrial agriculture along with lessons learned from industrialized agricultural production patterns, industrialized agricultural production processes, and the industrialized agri-food supply chain. Furthermore, five emerging technologies, namely the Internet of Things, robotics, artificial intelligence, big data analytics, and blockchain, toward Agriculture 4.0 are discussed. Specifically, we focus on the key applications of these emerging technologies in the agricultural sector and corresponding research challenges. This article aims to open up new research opportunities for readers, particularly industrial practitioners.

From Industry 4.0 to Agriculture 4.0: Current Status, Enabling Technologies, and Research Challenges

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

Industry 4.0 and related technologies will remain at the top agenda of manufacturing systems until respective digital transformation is completed. In order to increase the speed of the transformation and the respective performance, a taxonomy of industry 4.0 is proposed in this chapter. The taxonomy is defined through four aspects including strategic understanding, managerial practices, technological infrastructure and developments, as well as human intervention with respective skills and competencies. Each aspect of these is defined and further sub-categorized in order to reveal the real dynamics of industry 4.0 and respective implementations. Generating the taxonomy would also easy the categorization of the respective efforts and make the assessment processes to be carried out more effectively. It is also believed that the proposed taxonomy will be the source of generating a maturity model of industry 4.0. Note that the proposed taxonomy and respective components are defined by reviewing 620 papers, maturity models, and industry 4.0 projects.

/pdf/a-taxonomy-of-industry-4-0-and-related-technologies-5ekvlv9jg9.pdf

A Taxonomy of Industry 4.0 and Related Technologies

Bilisim sistemlerinin ve dijitallesme hareketinin dogal bir sonucu olarak gunumuz dunyasi 4. toplumsal donusum surecini yasamaktadir. Bu donusum sureci ozellikle endustriyel yasami koklu degisiklikleri uygulama zorunda birakacak sekilde gelismektedir. Gelecegin imalat vizyonunun 4 temel bileseni bulunmaktadir. Bunlar “urun”, “zeka”, “iletisim”, “bilgi agi” olacak sekilde gelismektedir. Bu 4 temel bileseni hareketlendirmek uzere Siber fiziksel sistemler, sensorler, nesnelerin interneti, makineler arasi iletisim (M2M), zeki bilgi aglari ve guvenli bulut bilisim ortamlari yaygin olarak gelismektedir. Bu gidisat dogal olarak urun tasarimi, surec tasarimi, kalite yonetimi, pazarlama gibi tum surecleri de etkisi altina almakta ve kapsamli bir surec inovasyonu ile surec donusumu gerceklesmektedir.     Isletmelerin tum sureclerini etkiledigi gibi bu degisim ve donusum tedarik sureclerinin de yeniden yapilandirilmasini gerektirmektedir. Ozellikle 4 Parti, 5 Parti ve 6 Parti Lojistik hizmetlerinin yaygin olarak kullanim alani bulmasina yol acacak is modellerinin gelisecegi gorulmektedir.   Bu makale dorduncu endustri devrimi ile olusan firsatlarin ve yeniliklerin lojistik yonetimine etkisini irdelemektedir. Ozellikle, lojistik yonetimi uzerindeki yenilikci cozumler, IT cozumlerinin sektor uzerindeki etkilerine dikkatler cekilmis ve Lojistik sektorde dorduncu donusumun sinirlari cizilmeye calisilmistir. Diger taraftan sektor aktorlerinin 4. Donusumu ve lojistik dunyasindaki etkilerini nasil algiladiklarina yonelik bir calisma gerceklestirilmistir. Anket yolu ile bilgi toplanmis, gerekli analizler yapilarak neticeleri raporlanmistir.Bu calismanin ulkemizde lojistik sektorunun yeni toplumsal donusum surecinde aktif olarak yol alabilmesi icin rehberlik edecek bulgular sunulmaktadir. Bu kapsamda genel hatlari ile islemelerin uygulayabilecekleri bir yol haritasinin olusturulmasi hedeflenmistir. Bu yol haritasinin temel felsefesini gunumuzde lojistik sektorunde hangi teknolojilerin daha oncelikli olarak uygulanmasi ve bu kapsamda yatirim stratejilerinin belirlenmesi icin gerekli olan unsurlara isaret edilmesi olusturacaktir.

/pdf/turkiye-de-lojistik-yonetiminde-endustri-4-0-etkileri-ve-1nupvnn98o.pdf

Türkiye’de Lojistik Yönetiminde Endüstri 4.0 Etkileri ve Yatırım İmkanlarına Bakış Üzerine Anket Uygulaması

Individual Pension System (IPS) is a personal future investment system that allows individuals to regularly save for their retirement. IPS is enacted by the law and supported by the government through state contribution. In Turkey, IPS entered into force on October 27, 2003 and it achieved an impressive progress over the last years. This improvement has caused increase in amount of raw data stored in databases. However, accumulated data are complicated and big to be processed and cannot be analyzed by classical methods. Data mining is becoming an essential tool to discover hidden and potentially useful knowledge from raw data. For this reason, application of data mining techniques on Individual Pension Savings and Investment system is necessary. In this study, one of the data mining techniques, decision tree classification, was used to determine customers’ profile. SPSS Clementine 12.0 software was used to develop a classification model. Analyses were performed by various decision tree algorithms. Some customer information of a pension company operating in Turkey were extracted from system. The significant rules about customers were revealed by analysis. The results of analysis indicated that the CHAID algorithm showed the best prediction with an accuracy of 85.64% among C5.0, C&R Tree, QUEST.

/pdf/an-application-of-data-mining-in-individual-pension-savings-48yb5uqiwm.pdf

An Application of Data Mining in Individual Pension Savings and Investment System

The information sector is changing and developing very rapidly with the effect of the technologies it contains. It is very important for companies to keep up with this change in order to compete with their competitors and provide customer satisfaction. Therefore, in order for companies to continue their activities, they need to progress smoothly and innovatively in information technology infrastructure. This is especially critical for companies operating in the banking and finance sectors. In these sectors where competition is intense, it is important that the type and quality of the service provided is easily accessible. Mobile applications, the ability to make transactions over the internet, chatbotlar increase customer satisfaction of companies in this sector, as well as self-service applications saves the cost of operational labor. In this study, the characteristics and success criteria of 4 different software development projects which use different project management methods such as Scrum, Kanban, and Waterfall and located in the different functions of a mobile application of a leading company in operating in the banking sector in Turkey were analyzed. The main and sub-criteria that these four projects need to be successful are determined according to expert opinions. The weighting coefficient for the criteria was determined by AHP method, which is one of the multi-criteria decision making methods frequently used in the literature, and then success ranking was performed by TOPSIS method. As a result of the study, it is seen that the projects that implement agile project management methods such as Scrum and Kanban in the software development department are highly successful in terms of all criteria.

/pdf/ahp-ve-topsis-yontemleri-ile-bilgi-teknolojileri-2yu6ee06q5.pdf

AHP ve TOPSIS Yöntemleri ile Bilgi Teknolojileri Projelerinde Scrum-Kanban-Şelale Uygulamaları Karşılaştırması

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