با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت مقاله خود را دریافت کنید (تا مشکل رفع گردد). با تشکر از صبوری شما!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Cognitive computing, Big Data Analytics and data driven industrial marketing
محاسبات شناختی ، تحلیل داده های بزرگ و بازاریابی صنعتی مبتنی بر داده ها-2020
The integration of cognitive computing and big data analytics leads to a new paradigm that enables the application of the most sophisticated advances in information and communication technology (ICT) in business, including industry, business to business, and related decision-making process. The same paradigm will lead to several breakthroughs in the subfield of industrial marketing: a field both promising and extremely challenging. This special issue makes a case that cognitive computing and big data are a source of a new competitive advantage that, if properly embraced, will further consolidate industrial marketing management position in the of core the decision-making process of businesses operating locally and globally. In this vein, the value added of this special issue is twofold. On the one hand, this special issue communicates high quality research on big data analytics and data science as it is applied in industrial marketing management; On the other hand, it proposes a multidisciplinary approach to the study of the design, implementation and provision of sophisticated applications and systems necessary for data-driven industrial marketing decisions.
The impact of entrepreneurship orientation on project performance: A machine learning approach
تأثیر گرایش کارآفرینی بر عملکرد پروژه: یک رویکرد یادگیری ماشین-2020
Recent studies in project management have shown the important role of entrepreneurship orientation of the individuals in project performance. Although identifying the role of entrepreneurship orientation as a critical success factor in project performance has been considered as an important issue, it is also important to develop a measurement system for predicting performance based on the degree of an individual’s entrepreneurial orientation. In this study, we use predictive analytics by proposing a machine learning approach to predict individuals’ project performance based on measures of several aspects of entrepreneurial orientation and entrepreneurial attitude of the individuals. We investigated this relationship using a sample of 185 observations and a range of machine learning algorithms including lasso, ridge, support vector machines, neural networks, and random forest. Our results showed that the best method for predicting project performance is lasso. After identifying the best predictive model, we then used the Bayesian Information Criterion and the Akaike Infor mation Criterion to identify the most significant factors. Our results identify all three aspects of entrepreneurial attitude (social self-efficacy, appearance self-efficacy, and comparativeness) and one aspect of entrepreneurial orientation (proactiveness) as the most important factors. This study contributes to the relationship between entrepreneurship skills and project performance and provides insights into the application of emerging tools in data science and machine learning in operations management and project management research.
Keywords: Project performance | Entrepreneurship orientation | Machine learning | Supervised learning | Predictive analytics
Deep learning for continuous manufacturing of pharmaceutical solid dosage form
یادگیری عمیق برای تولید مداوم فرم دوز جامد دارویی-2020
Continuous Manufacturing (CM) of pharmaceutical drug products is a new approach within the pharmaceutical industry. In the presented paper, a GMP continuous wet granulation line for production of solid dosage forms was investigated. The line was composed of the subsequent continuous unit: operations feeding – twin-screw wet-granulation – fluid-bed drying – sieving and tableting. The formulation of a commercial entity was selected for this study. Several critical process parameters were evaluated in order to probe the process and to characterize the impact on quality attributes. Seven critical process parameters have been selected after a risk analysis: API and excipient mass flows of the two feeders, liquid feed rate and rotation speed of the extruder and rotation speed, temperature and airflow of the dryer. Eight quality attributes were controlled in real time by Process Analytical Technologies (PAT): API content after blender, after dryer, in tablet press feed frame and of tablet, LOD after dryer and PSD after dryer (three PSD parameters: x10 x50 x90). The process parameter values were changed during production in order to detect the impact on the quality of the final product. The deep learning techniques have been used in order to predict the quality attribute (output) with the process parameters (input). The use of deep learning reduces the noise and simplify the data interpretation for a better process understanding. After optimization, three hidden layers neural network were selected with 6 hidden neurons. The activation function ReLU (Rectified Linear Unit) and the ADAM optimizer were used with 2500 epochs (number of learning cycle). API contents, PSD values and LOD values were estimated with an error of calibration lower than 10%. The level of error allow an adequate process monitoring by DNN and we have proven that the main critical process parameters can be identified at a higher levelof process understanding. The synergy between PAT and process data science creates a superior monitoring framework of the continuous manufacturing line and increase the knowledge of this innovative production line and the products that it makes.
Keywords: Continuous manufacturing | Solid dosage form | Process monitoring | Process analytical technology | Deep learning | Process data science | Process data analytics
Big data and the electricity sector in African countries
داده های بزرگ و بخش برق در کشورهای آفریقایی-2020
A number of “disruptive” data science and sensor technologies are creating new opportunities for addressing global challenges. The emergence of abundant computing power made possible the generation and storage of “big data,” enabled the explosion of sensors and networked devices, and powered major breakthroughs in the application of Artificial Intelligence, and Machine Learning techniques. These developments have led to a new trend best described as the seamless interplay between the physical and the digital world—also known as the Fourth Industrial Revolution (Industry 4.0) (Deloitte, 2015). This has paved the way for potential radical transformation of whole sectors and industries across the globe. Perhaps somewhat hidden from the hype surrounding these advancements are the opportunities they present for challenges in emerging and frontier markets, and sub-Saharan African countries in particular.
A digital twin to train deep reinforcement learning agent for smart manufacturing plants: Environment, interfaces and intelligence
یک دوقلوی دیجیتال برای آموزش عامل یادگیری تقویتی عمیق برای کارخانه های تولید هوشمند: محیط ، رابط ها و هوش-2020
Filling the gaps between virtual and physical systems will open new doors in Smart Manufacturing. This work proposes a data-driven approach to utilize digital transformation methods to automate smart manufacturing systems. This is fundamentally enabled by using a digital twin to represent manufacturing cells, simulate system behaviors, predict process faults, and adaptively control manipulated variables. First, the manufacturing cell is accommodated to environments such as computer-aided applications, industrial Product Lifecycle Management solutions, and control platforms for automation systems. Second, a network of interfaces between the environments is designed and implemented to enable communication between the digital world and physical manufacturing plant, so that near-synchronous controls can be achieved. Third, capabilities of some members in the family of Deep Reinforcement Learning (DRL) are discussed with manufacturing features within the context of Smart Manufacturing. Trained results for Deep Q Learning algorithms are finally presented in this work as a case study to incorporate DRL-based artificial intelligence to the industrial control process. As a result, developed control methodology, named Digital Engine, is expected to acquire process knowledges, schedule manufacturing tasks, identify optimal actions, and demonstrate control robustness. The authors show that integrating a smart agent into the industrial platforms further expands the usage of the system-level digital twin, where intelligent control algorithms are trained and verified upfront before deployed to the physical world for implementation. Moreover, DRL approach to automated manufacturing control problems under facile optimization environments will be a novel combination between data science and manufacturing industries.
Keywords: Smart manufacturing systems | Robotics | Artificial intelligence | Digital transformation | Virtual commissioning
Setting up standards: A methodological proposal for pediatric Triage machine learning model construction based on clinical outcomes
تنظیم استانداردها: یک پیشنهاد روش شناختی برای ساخت مدل یادگیری ماشین تراشی کودکان براساس نتایج بالینی-2019
Triage is a critical process in hospital emergency departments (ED). Specifically, we consider how to achieve fast and accurate patient Triage in the ED of a pediatric hospital. The goal of this paper is to establish methodological best practices for the application of machine learning (ML) to Triage in pediatric ED, providing a comprehensive comparison of the performance of ML techniques over a large dataset. Our work is among the first attempts in this direction. Following very recent works in the literature, we use the clinical outcome of a case as its label for supervised ML model training, instead of the more uncertain labels provided by experts. The experimental dataset contains the records along 3 years of operation of the hospital ED. It consists of 189,718 patients visits to the hospital. The clinical outcome of 9271 cases (4.98%) wa hospital admission, therefore our dataset is highly class imbalanced. Our reported performance comparison results focus on four ML models: Deep Learning (DL), Random Forest (RF), Naive Bayes (NB) and Support Vector Machines (SVM). Data preprocessing includes class imbalance correction, and case re-labeling. We use different well known metrics to evaluate performance of ML models in three different experimental settings: (a) classification of each case into the standard five Triage urgency levels, (b) discrimination of high versus low case severity according to its clinical outcome, and (c) comparison of the number of patients assigned to each standard Triage urgency level against the Triage rule based expert system currently in use at the hospital. RF achieved greater AUC, accuracy, PPV and specificity than the other models in the dychotomic classification experiments. On the implementation side, our study shows that ML predictive models trained according to clinical outcomes, provide better Triage performance than the current rule based expert system in operation at the hospital.
Keywords: Machine learning | Emergency department | Triage | Data science | Clinical decision support systems
Big Data Analysis and Machine Learning in Intensive Care Units
تجزیه و تحلیل داده های بزرگ و یادگیری ماشین در بخش مراقبت های ویژه-2019
Intensive care is an ideal environment for the use of Big Data Analysis (BDA) andMachine Learning (ML), due to the huge amount of information processed and stored in elec-tronic format in relation to such care. These tools can improve our clinical research capabilitiesand clinical decision making in the future.The present study reviews the foundations of BDA and ML, and explores possible applicationsin our field from a clinical viewpoint. We also suggest potential strategies to optimize thesenew technologies and describe a new kind of hybrid healthcare-data science professional witha linking role between clinicians and data.
KEYWORDSBig Data Analysis | Machine Learning | Artificial intelligence | Secondary electronichealth record dataanalysis
Predictive model of cardiac arrest in smokers using machine learning technique based on Heart Rate Variability parameter
مدل پیش بینی ایست قلبی در افراد سیگاری با استفاده از روش یادگیری ماشین بر اساس پارامتر تنوع ضربان قلب-2019
Cardiac arrest is a severe heart anomaly that results in billions of annual casualties. Smoking is a specific hazard factor for cardiovascular pathology, including coronary heart disease, but data on smoking and heart death not earlier reviewed. The Heart Rate Variability (HRV) parameters used to predict cardiac arrest in smokers using machine learning technique in this paper. Machine learning is a method of computing experience based on automatic learning and enhances performances to increase prognosis. This study intends to compare the performance of logistical regression, decision tree, and random forest model to predict cardiac arrest in smokers. In this paper, a machine learning technique implemented on the dataset received from the data science research group MITU Skillogies Pune, India. To know the patient has a chance of cardiac arrest or not, developed three predictive models as 19 input feature of HRV indices and two output classes. These model evaluated based on their accuracy, precision, sensitivity, specificity, F1 score, and Area under the curve (AUC). The model of logistic regression has achieved an accuracy of 88.50%, precision of 83.11%, the sensitivity of 91.79%, the specificity of 86.03%, F1 score of 0.87, and AUC of 0.88. The decision tree model has arrived with an accuracy of 92.59%, precision of 97.29%, the sensitivity of 90.11%, the specificity of 97.38%, F1 score of 0.93, and AUC of 0.94. The model of the random forest has achieved an accuracy of 93.61%, precision of 94.59%, the sensitivity of 92.11%, the specificity of 95.03%, F1 score of 0.93 and AUC of 0.95. The random forest model achieved the best accuracy classification, followed by the decision tree, and logistic regression shows the lowest classification accuracy.
Keywords: Cardiac arrest | Heart Rate Variability | Machine learning | Accuracy | Precision | Area under the curve
Weaving seams with data: Conceptualizing City APIs as elements of infrastructures
بافتن با داده ها: اندیشه سازی رابط های برنامه های کاربردی (API) شهری به عنوان عناصر زیرساخت-2019
This article addresses the role of application programming interfaces (APIs) for integrating data sources in the context of smart cities and communities. On top of the built infrastructures in cities, application programming interfaces allow to weave new kinds of seams from static and dynamic data sources into the urban fabric. Contributing to debates about ‘‘urban informatics’’ and the governance of urban information infrastructures, this article provides a technically informed and critically grounded approach to evaluating APIs as crucial but often overlooked elements within these infrastructures. The conceptualization of what we term City APIs is informed by three perspectives: In the first part, we review established criticisms of proprietary social media APIs and their crucial function in current web architectures. In the second part, we discuss how the design process of APIs defines conventions of data exchanges that also reflect negotiations between API producers and API consumers about affordances and mental models of the underlying computer systems involved. In the third part, we present recent urban data innovation initiatives, especially CitySDK and OrganiCity, to underline the centrality of API design and governance for new kinds of civic and commercial services developed within and for cities. By bridging the fields of criticism, design, and implementation, we argue that City APIs as elements of infrastructures reveal how urban renewal processes become crucial sites of socio-political contestation between data science, technological development, urban management, and civic participation.
Keywords: Application Programming Interface (API) | infrastructure | Internet of Things (IoT) | interface design | social urban data | smart city
‘‘You Social Scientists Love Mind Games’’: Experimenting in the ‘‘divide’’ between data science and critical algorithm studies
دانشمندان اجتماعی شما بازی های ذهنی را دوست دارند: آزمایش در "تقسیم" بین علم داده ها و مطالعات الگوریتم بحرانی-2019
In recent years, many qualitative sociologists, anthropologists, and social theorists have critiqued the use of algorithms and other automated processes involved in data science on both epistemological and political grounds. Yet, it has proven difficult to bring these important insights into the practice of data science itself. We suggest that part of this problem has to do with under-examined or unacknowledged assumptions about the relationship between the two fields—ideas about how data science and its critics can and should relate. Inspired by recent work in Science and Technology Studies on interventions, we attempted to stage an encounter in which practicing data scientists were asked to analyze a corpus of critical social science literature about their work, using tools of textual analysis such as co-word and topic modelling. The idea was to provoke discussion both about the content of these texts and the possible limits of such analyses. In this commentary, we reflect on the planning stages of the experiment and how responses to the exercise, from both data scientists and qualitative social scientists, revealed some of the tensions and interactions between the normative positions of the different fields. We argue for further studies which can help us understand what these interdisciplinary tensions turn on—which do not paper over them but also do not take them as given.
Keywords: Algorithms | data science | intervention | reflexivity | interdisciplinarity | Science and Technology Studies