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.
Incremental concept-cognitive learning based on attribute topology
یادگیری مفهوم شناختی افزایشی مبتنی بر توپولوژی ویژگی-2020
Incremental learning is an alternative approach for maintaining knowledge by utilizing previous computational results of dynamic data contexts. As a new and important part of incremental learning, incremental Concept-cognitive learning (CCL) is an emerging field of concerning evolution of object or attributes sets and dynamic knowledge processing in the dynamic big data. However, existing incremental CCL algorithms still face some challenges that improve the generalization ability of new concepts, and the previously acquired knowledge should be efficiently utilized to reduce the computational complexity of the algorithm. At the same time, formal concept analysis has become a potential direction of cognitive computing, which can describe the processes of CCL. Attribute topology (AT) as a new representation of formal concepts can clearly display the relationship between new data and original data for reducing the complexity of the CCL process; therefore, we present an incremental concept-cognitive algorithm based on AT for incremental concept calculation, which is expressed by a concept tree. First, a relationship between the new object and some of the original objects is established. Then, on the basis of this finding, we propose an algorithm for updating the concept and presenting them through a concept tree. The algorithm determines the position and subtree of the new object by the relationship between the object and the original objects. Finally, an example is presented to demonstrate that the concept update algorithm based on AT is feasible and effective, and different orders of increments will result in different concept tree structures.
Keywords: Concept-cognitive learning | Concept tree | Formal concept analysis | Incremental algorithm | Attribute topology
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
Improved Cohort Intelligence: A high capacity, swift and secure approach on JPEG image steganography
بهبود هوش گروهی: رویکردی با ظرفیت بالا ، سریع و ایمن در استگانوگرافی تصویر JPEG -2019
In the recent high level of information security was attained by combining the concepts of cryptogra- phy, steganography along with the nature inspired optimization algorithms. However, in today’s world computational speed plays a vital role for the success of any scientific method. The optimization algo- rithms, such as cohort Intelligence with Cognitive Computing (CICC) and Modified-Multi Random Start Local Search (M-MRSLS) were already implemented and applied for JPEG image steganography for 8 ×8 as well as 16 ×16 quantization table, respectively. Although results were satisfactory in terms of image quality and capacity, the computational time was high for most of the test images. To overcome this challenge, the paper proposes modified version of cohort intelligence (CI) algorithm referred to as Im- proved Cohort Intelligence (CI). The Improved CI algorithm was considered as a cryptography technique and implemented to generate optimized cipher text. Improved CI algorithm was further employed for JPEG image steganography to propose a reversible data hiding scheme. Experimentation was done on grey scale image, of size 256 ×256; both for 8 ×8 and 16 ×16 quantization table. Results validation of the proposed work exhibited very encouraging improvements in the computational cost
Keywords: Information security | Cryptography | Steganography | JPEG compression | Grey scale image | Improved CI
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
Implementation of a web based universal exchange and inference language for medicine: Sparse data, probabilities and inference in data mining of clinical data repositories
پیاده سازی تبادل جهانی و زبان استنتاج مبتنی بر وب برای پزشکی: اطلاعات پراکنده، احتمال و استنباط در داده کاوی از مخازن داده های بالینی-2015
We extend Q-UEL, our universal exchange language for interoperability and inference in healthcare and biomedicine, to the more traditional ﬁelds of public health surveys. These are the type associated with screening, epidemiological and cross-sectional studies, and cohort studies in some cases similar to clinical trials. There is the challenge that there is some degree of split between frequentist notions of probability as (a) classical measures based only on the idea of counting and proportion and on classical biostatistics as used in the above conservative disciplines, and (b) more subjectivist notions of uncertainty, belief, reliability, or conﬁdence often used in automated inference and decision support systems. Samples in the above kind of public health survey are typically small compared with our earlier “Big Data” mining efforts. An issue addressed here is how much impact on decisions should sparse data have. We describe a new Q-UEL compatible toolkit including a data analytics application DiracMiner that also delivers more standard biostatistical results, DiracBuilder that uses its output to build Hyperbolic Dirac Nets (HDN) for decision support, and HDNcoherer that ensures that probabilities are mutually consistent. Use is exempliﬁed by participating in a real word health-screening project, and also by deployment in a industrial platform called the BioIngine, a cognitive computing platform for health management.& 2015 Elsevier Ltd. All rights reserved.
Keywords: Universal exchange language | Dirac notation | Bayes | Watson | Electronic health record | Public health reporting | Probability theory | Zeta function | Cognitive computing