Organizational knowledge in the I4:0 using BPMN: a case study
دانش سازمانی در I4:0 با استفاده از BPMN: مطالعه موردی-2021
expert operators is not transferred quickly and easily to newly arrived operators. This sharing of knowledge could help in the faster adaptation of humans to workstations and could bring the more agile accommodation of artificial intelligence techniques to allow the self-learning. The Business Process Management (BPM) is a technique which enables the representation and analysis of processes, has been already mention in the literature as a useful tool that can facilitate the Knowledge Management. A process repository can be accomplished with BPM, thus promoting agile and fast knowledge transfer in a context where new skills emerge and must be quickly taken up. This paper intends to show the development of the working instructions maps, with workers’ tacit knowledge, using the BPMN 2.0, in a chemical industry. This representation allowed the creation of a knowledge’s repository which will help the company (in a I4.0 environment) to deal with the most existing workforce rotation, thus preserving most of the knowledge within the company itself. © 2019 The Authors. Published by Elsevier B.V.
Keywords: Knowledge Management | Industry 4.0 | Business Process Management | BPMN 2.0 | Organizational Knowledge
Feature based classification of voice based biometric data through Machine learning algorithm
طبقه بندی مبتنی بر ویژگی داده های بیومتریک مبتنی بر صدا از طریق الگوریتم یادگیری ماشین-2021
In the era of big data and growing artificial intelligence, the requirement and necessity of biometric identification increase in a rapid manner. The digitalization and recent Pandemic crisis gives a boost to need to authorized identification which get fulfilled with biometric identification. Our paper focuses on same concept of checking the identification accuracy of machine learning algorithm REPTree on selected bio- metric dataset which is being deployed and evaluated on a data mining tool WEKA. Our target is to achieve more or equal to 95 percentages in order to predict the given sample data is accurately classified into our target variables values i.e. male female. The selected algorithm REPTree is a kind of decision tree classification algorithm which works on same concept as C4.5 and decision tree algorithm with speciality of generation of both kind of output i.e. discrete and continuous. The selection of algorithm gives us ben- efits with achievement of higher accuracy and selection of dataset also become easy with some required modification and pre-processing of data with some dimension reduction filters.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 1st International Con- ference on Computations in Materials and Applied Engineering – 2021.
Keywords: Prediction | Biometric data | Voice samples | Male | Female | Cost complexity pruning (CCP) | Dimension reduction
X-PHM: Prognostics and health management knowledge-based framework for SME
X-PHM: پیش آگهی و چارچوب دانش مبتنی بر مدیریت سلامت برای SME-2021
Prognostics and Health Management (PHM) is an emerging concept based on industrial data management. The implementation of PHM in small and medium-sized enterprises (SMEs) is currently limited due to data accessibility difficulties. In order to overcome this pitfall, one could formalize the operators’ knowledge and integrate it in the SME’s information system. Thus, the implementation of the PHM will be based on this information system associating data with knowledge. To this end, we propose a collaborative PHM approach (X-PHM) to ensure the extraction of operators’ knowledge and its integration into the PHM process. The decision resulting from this approach is restituted with a concern of explainability. This paper details the proposed approach while focusing on the data management process and its integration in explainable decisions. This new framework is applied in a French SME to understand its production process and facilitate its digital transformation.
Keywords: PHM | Knowledge formalization and integration | Explainable artificial intelligence | SME | Data analysis.
Facilitating artificial intelligence powered supply chain analytics through alliance management during the pandemic crises in the B2B context
تسهیل تجزیه و تحلیل های زنجیره تامین مجهز به هوش مصنوعی از طریق مدیریت اتحاد در طول بحران های همه گیر در زمینه B2B-2021
The COVID-19 pandemic has disrupted global supply chains and exposed weak links in the chains far beyond what most people have witnessed in their living memory. The scale of disruption affects every nation and industry, and the sudden and dramatic changes in demand and supply that have occurred during the pandemic crisis clearly differentiate its impact from other crises. Using the dynamic capabilities view, we studied alliance management capability (AMC) and artificial intelligence (AI) driven supply chain analytics capability (AI-SCAC) as dynamic capabilities, under the moderating effect of environmental dynamism. We tested our four research hypotheses using survey data collected from the Indian auto components manufacturing industry. For data analysis we used Warp PLS 7.0 (a variance-based structural equation modelling tool). We found that alliance management capability under the mediating effect of artificial intelligence-powered supply chain analytics capability enhances the operational and financial performance of the organization. Moreover, we also observed that the alliance management capability has a significant effect on artificial intelligence-powered supply chain analytics capability under the moderating effect of environmental dynamism. The results of our study provide a nuanced understanding of the dynamic capabilities and the relational view of organization. Finally, we noted the limitations of our study and provide numerous research directions that may help answer some of the questions that arise from our study.
Keywords: Artificial intelligence | Supply chain analytics | Alliance management | Environmental dynamism | Dynamic capability view
Management Transformation with a Single Digital Platform as Exemplified by Accounting
تحول مدیریت با یک پلتفرم دیجیتالی واحد به عنوان نمونه با حسابداری-2021
The paper considers the solution to the problem of accounting method transformation under the influence of the accelerating economy digitalization that requires transition to new methods of working with information: in addition to reflecting the history of business activities, accounting statements should allow making decisions for the future at any time and at any production process stage; that is, it should be re-oriented from the control function to the informative one. This, in turn, requires integration based on digital standards of reporting information with information reflecting other aspects of the business and the external environment through developing new indicators, new methods of collecting and processing data. We have shown that a solution to this problem could be creating a single digital economy management platform consisting of two specialized sub-platforms: an aggregator subplatform for collecting and accumulating primary data and an application sub-platform for production management tasks. In this case, a widespread introduction of a single digital platform in any production process allows to migrate to a new type of manufacturing enterprises: from the quality control phase after the production phase towards the principle of current control over all production operation, which should also affect the entire taxation system. This system will become more efficient by increasing tax collection, reducing the cost of maintaining the financial and accounting service and actively including it in the production management system. In addition, such a digital platform will release a significant number of IT specialists, who are sorely lacking in the country, especially in agriculture, from the financial and accounting service with their reorientation to the introduction of new digital technologies in the form of mathematical models, artificial intelligence, big data, neural networks, which will give an additional impetus to the accelerated digitalization of the economy.
Keywords: accounting | digital standards | information resources | digital platform | management.
Computer vision in surgery
بینایی ماشین در جراحی-2021
The fields of computer vision (CV) and artificial intelligence (AI) have undergone rapid advancements in the past decade, many of which have been applied to the analysis of intraoperative video. These advances are driven by wide-spread application of deep learning, which leverages multiple layers of neural networks to teach computers complex tasks. Prior to these advances, applications of AI in the operating room were limited by our relative inability to train computers to accurately understand images with traditional machine learning (ML) techniques. The development and refining of deep neural networks that can now accurately identify objects in images and remember past surgical events has sparked a surge in the applications of CV to analyze intraoperative video and has allowed for the accurate identification of surgical phases (steps) and instruments across a variety of procedures. In some cases, CV can even identify operative phases with accuracy similar to surgeons. Future research will likely expand on this foundation of surgical knowledge using larger video datasets and improved algorithms with greater accuracy and interpretability to create clinically useful AI models that gain widespread adoption and augment the surgeon’s ability to provide safer care for patients everywhere.
Automated classification of fauna in seabed photographs: The impact of training and validation dataset size, with considerations for the class imbalance
طبقه بندی خودکار جانوران در عکس های بستر دریا: تأثیر اندازه مجموعه داده های آموزش و اعتبار سنجی ، با ملاحظاتی برای عدم تعادل کلاس-2021
Machine learning is rapidly developing as a tool for gathering data from imagery and may be useful in identifying (classifying) visible specimens in large numbers of seabed photographs. Application of an automated classifi- cation workflow requires manually identified specimens to be supplied for training and validating the model. These training and validation datasets are generally generated by partitioning the available manual identified specimens; typical ratios of training to validation dataset sizes are 75:25 or 80:20. However, this approach does not facilitate the desired scalability, which would require models to successfully classify specimens in hundreds of thousands to millions of images after training on a relatively small subset of manually identified specimens. A second problem is related to the ‘class imbalance’, where natural community structure means that fewer spec- imens of rare morphotypes are available for model training. We investigated the impact of independent variation of the training and validation dataset sizes on the performance of a convolutional neural network classifier on benthic invertebrates visible in a very large set of seabed photographs captured by an autonomous underwater vehicle at the Porcupine Abyssal Plain Sustained Observatory. We tested the impact of increasing training dataset size on specimen classification in a single validation dataset, and then tested the impact of increasing validation set size, evaluating ecological metrics in addition to computer vision metrics. Computer vision metrics (recall, precision, F1-score) indicated that classification improved with increasing training dataset size. In terms of ecological metrics, the number of morphotypes recorded increased, while diversity decreased with increasing training dataset size. Variation and bias in diversity metrics decreased with increasing training dataset size. Multivariate dispersion in apparent community composition was reduced, and bias from expert-derived data declined with increasing training dataset size. In contrast, classification success and resulting ecological metrics did not differ significantly with varying validation dataset sizes. Thus, the selection of an appropriate training dataset size is key to ensuring robust automated classifications of benthic invertebrates in seabed photographs, in terms of ecological results, and validation may be conducted on a comparatively small dataset with confidence that similar results will be obtained in a larger production dataset. In addition, our results suggest that automated classification of less common morphotypes may be feasible, providing that the overall training dataset size is sufficiently large. Thus, tactics for reducing class imbalance in the training dataset may produce improvements in the resulting ecological metrics.
Keywords: Computer vision | Deep learning | Benthic ecology | Image annotation | Marine photography | Artificial intelligence | Convolutional neural networks | Sample size
Digital interoperability in logistics and supply chain management: state-of-the-art and research avenues towards Physical Internet
قابلیت همکاری دیجیتال در تدارکات و مدیریت زنجیره تأمین: پیشرفته ترین و روشهای تحقیقاتی به سمت اینترنت فیزیکی-2021
Interoperability is playing an increasing role for today’s logistics and supply chain management (LSCM) because of the trends of cooperation or coopetition. Especially, digital interoperability concerning data or information exchange becomes a key enabler for the next evolutions that will massively rely upon digitalization, artificial intelligence, and autonomous systems. The notion of Physical Internet (PI) is one such evolution, an innovative worldwide logistic paradigm aimed at interconnecting and coordinating logistics networks for efficiency and sustainability. This paper investigates how digital interoperability can help interconnect logistics and supply networks as well as the operational solutions for sustainable development, and examines the new challenges and research opportunities for digital interoperability under the PI paradigm. To this end, we study the most relevant technologies for digital interoperability in LSCM, via a bibliometric analysis based on 208 papers published during 2010−2020. The results reveal that the present state-of-the-art solutions of digital interoperability are not fully aligned with PI requirements and show new challenges, research gaps and opportunities that need further discussion. Accordingly, several research avenues are suggested to advance research and applications in this area, and to achieve interconnection in logistics and supply networks for sustainability.
Keywords: Interoperability | Interconnection | Physical internet | Digitalization | Logistics | Supply Chain management | Bibliometric review | State-of-the-art | Research avenues
Data-driven detection and characterization of communities of accounts collaborating in MOOCs
شناسایی و توصیف مبتنی بر داده جوامع حسابهایی که در MOOC همکاری میکنند-2021
Collaboration is considered as one of the main drivers of learning and it has been broadly studied across numerous contexts, including Massive Open Online Courses (MOOCs). The research on MOOCs has risen exponentially during the last years and there have been a number of works focused on studying collaboration. However, these previous studies have been restricted to the analysis of collaboration based on the forum and social interactions, without taking into account other possibilities such as the synchronicity in the interactions with the platform. Therefore, in this work we performed a case study with the goal of implementing a data-driven approach to detect and characterize collaboration in MOOCs. We applied an algorithm to detect synchronicity links based on their submission times to quizzes as an indicator of collaboration, and applied it to data from two large Coursera MOOCs. We found three different profiles of user accounts, that were grouped in couples and larger communities exhibiting different types of associations between user accounts. The characterization of these user accounts suggested that some of them might represent genuine online learning collaborative associations, but that in other cases dishonest behaviors such as free-riding or multiple account cheating might be present. These findings call for additional research on the study of the kind of collaborations that can emerge in online settings.
keywords: تجزیه و تحلیل یادگیری | داده کاوی آموزشی | یادگیری مشارکتی | دوره های آنلاین گسترده باز | هوش مصنوعی | Learning analytics | Educational data mining | Collaborative learning | Massive open online courses | Artificial intelligence
Blockchain-based royalty contract transactions scheme for Industry 4:0 supply-chain management
طرح معاملات قرارداد حق امتیاز مبتنی بر بلاکچین برای مدیریت زنجیره تأمین صنعت 4:0-2021
Industry 4.0-based oil and gas supply-chain (OaG-SC) industry automates and efficiently executes most of the processes by using cloud computing (CC), artificial intelligence (AI), Internet of things (IoT), and industrial Internet of things (IIoT). However, managing various operations in OaG-SC industries is a challenging task due to the involvement of various stakeholders. It includes landowners, Oil and Gas (OaG) company operators, surveyors, local and national level government bodies, financial institutions, and insurance institutions. During mining, OaG company needs to pay incentives as a royalty to the landowners. In the traditional existing schemes, the process of royalty transaction is performed between the OaG company and landowners as per the contract between them before the start of the actual mining process. These contracts can be manipulated by attackers (insiders or outsiders) for their advantages, creating an unreliable and un-trusted royalty transaction. It may increase disputes between both parties. Hence, a reliable, cost-effective, trusted, secure, and tamper-resistant scheme is required to execute royalty contract transactions in the OaG industry. Motivated from these research gaps, in this paper, we propose a blockchain-based scheme, which securely executes the royalty transactions among various stakeholders in OaG industries. We evaluated the performance of the proposed scheme and the smart contracts’ functionalities and compared it with the existing state-of-the-art schemes using various parameters. The results obtained illustrate the superiority of the proposed scheme compared to the existing schemes in the literature.
Keywords: Blockchain | Smart contract | Oil and gas industry | Supply chain management | Royalty