Polyp Detection in Colonoscopy Images using Deep Learning and Bootstrap Aggregation

Computer-aided polyp detection is playing an increasingly more important role in the colonoscopy procedure. Although many methods have been proposed to tackle the polyp detection problem, their out-of-distribution test results, which is an important indicator of their clinical readiness, are not demonstrated. In this study, we propose an ensemble-based polyp detection pipeline for detecting polyps in colonoscopy images. We train various models from EfficientDet family on both the EndoCV2021 and the Kvasir-SEG datasets, and evaluate their performances on these datasets both in- and out-of-distribution manner. The proposed architecture works in near real-time due to the efficiency of the EfficientDet architectures even when used in an ensemble setting

Assessment of process capabilities in transition to a data-driven organisation: A multidisciplinary approach

The ability to leverage data science can generate valuable insights and actions in organisations by enhancing data-driven decision-making to find optimal solutions based on complex business parameters and data. However, only a small percentage of the organisations can successfully obtain a business value from their investments due to a lack of organisational management, alignment, and culture. Becoming a data-driven organisation requires an organisational change that should be managed and fostered from a holistic multidisciplinary perspective. Accordingly, this study seeks to address these problems by developing the Data Drivenness Process Capability Determination Model (DDPCDM) based on the ISO/IEC 330xx family of standards. The proposed model enables organisations to determine their current management capabilities, derivation of a gap analysis, and the creation of a comprehensive roadmap for improvement in a structured and standardised way. DDPCDM comprises two main dimensions: process and capability. The process dimension consists of five organisational management processes: change management, skill and talent management, strategic alignment, organisational learning, and sponsorship and portfolio management. The capability dimension embraces six levels, from incomplete to innovating. The applicability and usability of DDPCDM are also evaluated by conducting a multiple-case study in two organisations. The results reveal that the proposed model is able to evaluate the strengths and weaknesses of an organisation in adopting, managing, and fostering the transition to a data-driven organisation and providing a roadmap for continuously improving the data-drivenness of organisations

Open-Source Big Data Analytics Architecture for Businesses

Unaware of existing big data technologies, organizations fail to develop a big data capability despite its disruptive impact on today's competitive business environment. To determine the shortcomings and strengths of developing a big data architecture with open-source tools from technical and managerial perspectives, this study (1) systematically reviews the available open-source big data technologies to present a comprehensive picture, and (2) proposes an open-source architecture for businesses to take as a reference while developing big data analytics capabilities. Lastly, we discuss technical, domain-specific, and firm-specific soft challenges related to establishing a big data architecture in an organization, and how these challenges are reshaping the big data research domain

Bulut Tabanlı Kurumsal Yönetim Bilişim Sistemlerinin Kullanımının İncelenmesi

Bu proje kapsamında, bulut tabanlı kurumsal yönetim bilişim sistemlerinin (KYBS) kullanıcılar tarafından kullanımı ve kabulünü teşvik edici ve engelleyici faktörlerin araştırılması amaçlanmaktadır. Böylece, bulut servis sağlayıcılarına, ürünlerinin kabul edilebilirliğini ve kullanımını arttırmak için, ürünlerini geliştirirken nelere dikkat etmeleri gerektiği konusunda bilgi sağlanması hedeflenmektedir. Proje kapsamında, öncelikle bulut bilişim ve KYBS alanında kullanıcı kabulü ile ilgili literatürde yer alan çalışmalar incelenecektir. Ardından kullanıcılarla yapılacak olan görüşmelerden elde edilen bilgiler ışığında bulut tabanlı KYBS’lerinin kabulünü ve kullanımını etkileyen faktörleri içeren bir model oluşturulacaktır. Elde edilen modele “sistematik düşünme” yaklaşımı uygulanarak, bulut tabanlı KYBS üreticileri için ürünlerini nasıl geliştirmeleri gerektiğine dair bir rehber oluşturulması hedeflenmektedir

A Workflow and Cloud Based Service-Oriented Architecture for Distributed Manufacturing in Industry 4.0 Context

In Industry 4.0, manufacturing organizations have to become more flexible and agile to stay competitive in the marketplace. Industry and academia focus on sharing globally distributed manufacturing resources and capabilities. This shift to collaborative environment calls for a service dominant logic in which reusable services model entire production processes. Accordingly, service-oriented architectures have been under spotlight by researchers to integrate distributed capabilities and resources of manufacturing. In this article, we identify rapidly developing relevant IT fields, state-of-the-art service-oriented manufacturing approaches, and discuss related challenges with those approaches. Then we introduce a higher-level workflow and cloud based service-oriented architecture for (1) individuals to design and prototype products, (2) manufacturing organizations to focus on their core competencies and find complementary services for part of their production processes, and (3) manufacturing organizations to publish their idle capabilities and resources as services. We investigate these potential impacts and more in the next manufacturing era

Data science roadmapping: An architectural framework for facilitating transformation towards a data-driven organization

Leveraging data science can enable businesses to exploit data for competitive advantage by generating valuable insights. However, many industries cannot effectively incorporate data science into their business processes, as there is no comprehensive approach that allows strategic planning for organization-wide data science efforts and data assets. Accordingly, this study explores the Data Science Roadmapping (DSR) to guide organizations in aligning their business strategies with data-related, technological, and organizational resources. The proposed approach is built on the widely adopted technology roadmapping framework and customizes its context, architecture, and process by synthesizing data science, big data, and data-driven organization literature. Based on industry collaborations, the framework provides a hybrid and agile methodology comprising the recommended steps. We applied DSR with a research group with sector experience to create a comprehensive data science roadmap to validate and refine the framework. The results indicate that the framework facilitates DSR initiatives by creating a comprehensive roadmap capturing strategy, data, technology, and organizational perspectives. The contemporary literature illustrates prebuilt roadmaps to help businesses become data-driven. However, becoming data-driven also necessitates significant social change toward openness and trust. The DSR initiative can facilitate this social change by opening communication channels, aligning perspectives, and generating consensus among stakeholders

The development of the data science capability maturity model: a survey-based research

Purpose The purpose of this paper is to investigate social and technical drivers of data science practices and develop a standard model for assisting organizations in their digital transformation by providing data science capability/maturity level assessment, deriving a gap analysis, and creating a comprehensive roadmap for improvement in a standardized way. Design/methodology/approach This paper systematically reviews and synthesizes the existing literature-related to data science and 183 practitioners' considerations by employing a survey-based research method. By blending the findings of this research with a well-established process capability maturity model standard, International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 330xx, and following a methodological maturity development framework, a theoretically grounded model, entitled as the data science capability maturity model (DSCMM) was developed. Findings It was found that organizations seek a capability/maturity model standard to evaluate and improve their current data science capabilities. To close this research gap, the DSCMM is developed. It consists of six capability maturity levels and twenty-seven processes categorized under five process areas: organization, strategy management, data analytics, data governance and technology management. Originality/value This paper validates the need for a process capability maturity model for the data science domain and develops the DSCMM by integrating literature findings and practitioners' considerations into a well-accepted process capability maturity model standard to continuously assess and improve the maturity of data science capabilities of organizations

Büyük Veri Çağında İşletmelerde Veri Bilimi

İçinde bulunduğumuz büyük veri çağında bilgi teknolojisi servislerinden ve nesnelerin interneti kaynaklarından üretilen veri miktarındaki üstel artış ile birlikte şirketlerin veriden elde edebileceği fayda da her geçen gün hızla artmaktadır. Ancak bu mevcut verileri etkin şekilde kullanmak, stratejik üstünlük elde etmek ve kendi iş süreçlerini iyileştirmek isteyen kuruluşların büyük veri ve veri biliminden elde edebilecekleri faydaları doğru tanımlamaları ve şirketlerini bu doğrultuda veri odaklı yönetime hazır hale getirmeleri gerekmektedir. Bu nedenle, bu çalışma kapsamında, büyük veri ve veri biliminin tanımı, mevcut durumu ve işletmelerin büyük veri çağında veri bilimini iş süreçlerine dahil ederken karşılaşacakları zorluklar incelenmiştir