با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
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.
Deep belief network and linear perceptron based cognitive computing for collaborative robots
شبکه باور عمیق و محاسبات شناختی مبتنی بر پرسپترون خطی برای روبات های مشترک-2020
Objective: This paper is to analyze the performance of the control system of collaborative robots based on cognitive computing technology. Methods: This study combines cognitive computing and deep belief network algorithms with collaborative robots to construct a cognitive computing system model based on deep belief networks, which is applied to the control system of collaborative robots. Further, the simulation is used to compare and analyze the algorithm performance of deep belief network (DBN), multilayer perceptron (MLP) and the cognitive computing system model of deep belief network and linear perceptron (DBNLP) proposed in this study. Results: The results show that compared with the DBN and MLP algorithms, the DBNLP algorithm model has a significantly lower error rate in the number of repetitions of the training set, the number of hidden neurons, and the number of network layers. And the number of task backlog, the number of resources to be allocated and the time consumption are less, as well as the accuracy is high. After comparing and analyzing the changes in the estimated value of Ex (expected value), En (entropy value) and He (hyper entropy value), it is found that the estimated value of the DBNLP algorithm model is closer to the true value than that of the DBN and MLP algorithms. Conclusion: The application of the DBNLP algorithm model to collaborative robots can significantly improve its accuracy and safety, providing an experimental basis for the performance improvement of later collaborative robots.
Keywords: Collaborative robot | Cognitive computing | Deep belief network | Simulation | Multilayer perceptron
Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda
هوش مصنوعی برای تصمیم گیری در عصر داده های بزرگ - تکامل ، چالش ها و دستور کار تحقیق-2019
Artificial intelligence (AI) has been in existence for over six decades and has experienced AI winters and springs. The rise of super computing power and Big Data technologies appear to have empowered AI in recent years. The new generation of AI is rapidly expanding and has again become an attractive topic for research. This paper aims to identify the challenges associated with the use and impact of revitalised AI based systems for decision making and offer a set of research propositions for information systems (IS) researchers. The paper first provides a view of the history of AI through the relevant papers published in the International Journal of Information Management (IJIM). It then discusses AI for decision making in general and the specific issues regarding the interaction and integration of AI to support or replace human decision makers in particular. To advance research on the use of AI for decision making in the era of Big Data, the paper offers twelve research propositions for IS researchers in terms of conceptual and theoretical development, AI technology-human interaction, and AI implementation.
Keywords: Artificial intelligence | AI | Big data | Cognitive computing | Decision making | Expert system | Machine learning | Recommender system | Research agenda
Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy
هوش مصنوعی (AI): چشم اندازهای چند رشته ای در مورد چالش ها ، فرصت ها و دستور کار برای تحقیق ، تمرین و سیاست های نوظهور-2019
As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance, healthcare, manufacturing, retail, supply chain, logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology. This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development.
Keywords: Artificial intelligence | AI | Cognitive computing | Expert systems | Machine learning | Research agenda
Bridging learning analytics and Cognitive Computing for Big Data classification in micro-learning video collections
پل تجزیه و تحلیل یادگیری و پردازش شناختی برای طبقه بندی داده های بزرگ در مجموعه های ویدئویی میکرو یادگیری-2018
Moving towards the next generation of personalized learning environments requires intelligent ap proaches powered by analytics for advanced learning contexts with enriched digital content. Micro Learning through Massive Open Online Courses is riding the wave of popularity as a novel paradigm for delivering short educational videos in small pre-organized chunks over time, so that learners can get knowledge in a manageable way. However, with the ever-increasing number of videos, it has become challenging to arrange and search them according to specific categories. In this paper, we get around the problem by bridging Learning Analytics and Cognitive Computing to analyze the content of large video collections, going over traditional term-based methods. We propose an efficient and effective approach to automatically classify a collection of educational videos on pre-existing categories which uses (i) a Speech-to-Text tool to get video transcripts, (ii) Natural Language Processing and Cognitive Computing methods to extract semantic concepts and keywords from video transcripts for their representation, and (iii) Apache Spark as Big Data technology for scalability. Several classifiers are trained on the feature vectors extracted by Cognitive Computing tools. Then, we compared our approach with other combi nations of state-of-the-art feature types and classifiers over a large-scale dataset we collected from Coursera. Considering the experimental results, we expect our approach can facilitate the development of Learning Analytics tools powered by Cognitive Computing to support content managers on micro learning video management while improving how learners search videos.
Keywords: Cognitive Computing ، Big Data technologies ، Micro-learning video ، Multi-class classification ، Learning Analytics ، Video classification
Using Cognitive Computing for Learning Parallel Programming: An IBM Watson Solution
استفاده از رایانه شناختی برای یادگیری برنامه نویسی موازی: راه حل آی بی ام واتسون-2017
While modern parallel computing systems provide high performance resources, utilizing them to the highest extent requires advanced programming expertise. Programming for parallel com puting systems is much more difficult than programming for sequential systems. OpenMP is an extension of C++ programming language that enables to express parallelism using compiler directives. While OpenMP alleviates parallel programming by reducing the lines of code that the programmer needs to write, deciding how and when to use these compiler directives is up to the programmer. Novice programmers may make mistakes that may lead to performance degradation or unexpected program behavior. Cognitive computing has shown impressive re sults in various domains, such as health or marketing. In this paper, we describe the use of IBM Watson cognitive system for education of novice parallel programmers. Using the dialogue service of the IBM Watson we have developed a solution that assists the programmer in avoid ing common OpenMP mistakes. To evaluate our approach we have conducted a survey with a number of novice parallel programmers at the Linnaeus University, and obtained encouraging results with respect to usefulness of our approach.
Keywords: Cognitive Computing | Parallel Programming Education | IBM Watson | OpenMP