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ردیف | عنوان | نوع |
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51 |
An extensive study on the evolution of context-aware personalized travel recommender systems
یک مطالعه گسترده در مورد تکامل سیستمهای توصیه گر سفر شخصی آگاه از متن-2020 Ever since the beginning of civilization, travel for various causes exists as an essential part of
human life so as travel recommendations, though the early form of recommendations were the
accrued experiences shared by the community. Modern recommender systems evolved along
with the growth of Information Technology and are contributing to all industry and service
segments inclusive of travel and tourism. The journey started with generic recommender engines
which gave way to personalized recommender systems and further advanced to contextualized
personalization with advent of artificial intelligence. Current era is also witnessing a boom in
social media usage and the social media big data is acting as a critical input for various analytics
with no exception for recommender systems. This paper details about the study conducted on the
evolution of travel recommender systems, their features and current set of limitations. We also
discuss on the key algorithms being used for classification and recommendation processes and
metrics that can be used to evaluate the performance of the algorithms and thereby the recommenders. Keywords: Recommender system | Personalization | Context aware | Big data | Travel and tourism |
مقاله انگلیسی |
52 |
Towards a real-time processing framework based on improved distributed recurrent neural network variants with fastText for social big data analytics
به سمت یک چارچوب پردازش در زمان واقعی بر اساس بهبود انواع شبکه عصبی مکرر توزیع شده با fastText برای تجزیه و تحلیل داده های بزرگ اجتماعی-2020 Big data generated by social media stands for a valuable source of information, which offers an
excellent opportunity to mine valuable insights. Particularly, User-generated contents such as
reviews, recommendations, and users’ behavior data are useful for supporting several marketing
activities of many companies. Knowing what users are saying about the products they bought or
the services they used through reviews in social media represents a key factor for making decisions.
Sentiment analysis is one of the fundamental tasks in Natural Language Processing.
Although deep learning for sentiment analysis has achieved great success and allowed several
firms to analyze and extract relevant information from their textual data, but as the volume of
data grows, a model that runs in a traditional environment cannot be effective, which implies the
importance of efficient distributed deep learning models for social Big Data analytics. Besides, it
is known that social media analysis is a complex process, which involves a set of complex tasks.
Therefore, it is important to address the challenges and issues of social big data analytics and
enhance the performance of deep learning techniques in terms of classification accuracy to obtain
better decisions.
In this paper, we propose an approach for sentiment analysis, which is devoted to adopting
fastText with Recurrent neural network variants to represent textual data efficiently. Then, it
employs the new representations to perform the classification task. Its main objective is to enhance
the performance of well-known Recurrent Neural Network (RNN) variants in terms of
classification accuracy and handle large scale data. In addition, we propose a distributed intelligent
system for real-time social big data analytics. It is designed to ingest, store, process,
index, and visualize the huge amount of information in real-time. The proposed system adopts
distributed machine learning with our proposed method for enhancing decision-making processes.
Extensive experiments conducted on two benchmark data sets demonstrate that our
proposal for sentiment analysis outperforms well-known distributed recurrent neural network
variants (i.e., Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory
(BiLSTM), and Gated Recurrent Unit (GRU)). Specifically, we tested the efficiency of our approach
using the three different deep learning models. The results show that our proposed approach
is able to enhance the performance of the three models. The current work can provide
several benefits for researchers and practitioners who want to collect, handle, analyze and visualize
several sources of information in real-time. Also, it can contribute to a better understanding
of public opinion and user behaviors using our proposed system with the improved
variants of the most powerful distributed deep learning and machine learning algorithms.
Furthermore, it is able to increase the classification accuracy of several existing works based on
RNN models for sentiment analysis. Keywords: Big data | FastText | Recurrent neural networks | LSTM | BiLSTM | GRU | Natural language processing | Sentiment analysis | Social big data analytics |
مقاله انگلیسی |
53 |
Understanding the impact of business analytics on innovation
درک تأثیر تحلیل های تجاری بر نوآوری-2020 Advances in Business Analytics in the era of Big Data have provided unprecedented opportunities for or- ganizations to innovate. With insights gained from Business Analytics, companies are able to develop new or improved products/services. However, few studies have investigated the mechanism through which Business Analytics contributes to a firm’s innovation success. This research aims to address this gap by theoretically and empirically investigating the relationship between Business Analytics and innovation. To achieve this aim, absorptive capacity theory is used as a theoretical lens to inform the development of a research model. Absorptive capacity theory refers to a firm’s ability to recognize the value of new, external information, assimilate it and apply it to commercial ends. The research model covers the use of Business Analytics, environmental scanning, data-driven culture, innovation (new product newness and meaningfulness), and competitive advantage. The research model is tested through a questionnaire survey of 218 UK businesses. The results suggest that Business Analytics directly improves environmental scan- ning which in turn helps to enhance a company’s innovation. Business Analytics also directly enhances data-driven culture that in turn impacts on environmental scanning. Data-driven culture plays another important role by moderating the effect of environmental scanning on new product meaningfulness. The findings demonstrate the positive impact of business analytics on innovation and the pivotal roles of en- vironmental scanning and data-driven culture. Organizations wishing to realize the potential of Business Analytics thus need changes in both their external and internal focus. Keywords: Analytics | Innovation | Big Data | Data-driven culture | Absorptive capacity |
مقاله انگلیسی |
54 |
An analytic infrastructure for harvesting big data to enhance supply chain performance
یک زیرساخت تحلیلی برای برداشت داده های بزرگ به منظور افزایش عملکرد زنجیره تأمین-2020 Big data has already received a tremendous amount of attention from managers in every industry, policy and decision makers in governments, and researchers in many different areas. However, the current big data analytics have conspicuous limitations, especially when dealing with information silos. In this pa- per, we synthesise existing researches on big data analytics and propose an integrated infrastructure for breaking down the information silos, in order to enhance supply chain performance. The analytic infras- tructure effectively leverages rich big data sources (i.e. databases, social media, mobile and sensor data) and quantifies the related information using various big data analytics. The information generated can be used to identify a required competence set (which refers to a collection of skills and knowledge used for specific problem solving) and to provide roadmaps to firms and managers in generating actionable supply chain strategies, facilitating collaboration between departments, and generating fact-based opera- tional decisions. We showcase the usefulness of the analytic infrastructure by conducting a case study in a world-leading company that produces sports equipment. The results indicate that it enabled managers: (a) to integrate information silos in big data analytics to serve as inputs for new product ideas; (b) to capture and interrelate different competence sets to provide an integrated perspective of the firm’s op- erations capabilities; and (c) to generate a visual decision path that facilitated decision making regarding how to expand competence sets to support new product development. Keywords: Decision support systems | Big data | Analytic infrastructure | Competence set | Deduction graph |
مقاله انگلیسی |
55 |
AI Aided Noise Processing of Spintronic Based IoT Sensor for Magnetocardiography Application
پردازش نویز به کمک هوش مصنوعی مبتنی بر حسگر اینترنت اشیا بر Spintronic برای کاربرد مغناطیسی قلب-2020 As we are about to embark upon the highly hyped
“Society 5.0”, powered by the Internet of Things (IoT), traditional
ways to monitor human heart signals for tracking cardio-vascular
conditions are challenging, particularly in remote healthcare
settings. On the merits of low power consumption, portability,
and non-intrusiveness, there are no suitable IoT solutions that
can provide information comparable to the conventional Electrocardiography
(ECG). In this paper, we propose an IoT device
utilizing a spintronic-technology-based ultra-sensitive Magnetic
Tunnel Junction (MTJ) sensor that measures the magnetic fields
produced by cardio-vascular electromagnetic activity, i.e. Magentocardiography
(MCG). We treat the low-frequency noise
generated by the sensor, which is also a challenge for most
other sensors dealing with low-frequency bio-magnetic signals.
Instead of relying on generic signal processing techniques such
as moving average, we employ deep-learning training on biomagnetic
signals. Using an existing dataset of ECG records, MCG
signals are synthesized. A unique deep learning model, composed
of a one-dimensional convolution layer, Gated Recurrent Unit
(GRU) layer, and a fully-connected neural layer, is trained using
the labeled data moving through a striding window, which is able
to smartly capture and eliminate the noise features. Simulation
results are reported to evaluate the effectiveness of the proposed
method that demonstrates encouraging performance. Index Terms: Smart health | IoT | ECG | MCG | deep learning | noise | spintronic sensor | convolution | GRU | medical analytics |
مقاله انگلیسی |
56 |
Hiding Private Information in Images From AI
پنهان کردن اطلاعات خصوصی در تصاویر از هوش مصنوعی-2020 Privacy protection attracts increasing concerns these
days. People tend to believe that large social platforms will
comply with the agreement to protect their privacy. However,
photos uploaded by people are usually not treated to achieve
privacy protection. For example, Facebook, the world’s largest
social platform, was found leaking photos of millions of users
to commercial organizations for big data analytics. A common
analytical tool used by these commercial organizations is the Deep
Neural Network (DNN). Today’s DNN can accurately identify
people’s appearance, body shape, hobbies and even more sensitive
personal information, such as addresses, phone numbers, emails,
bank cards and so on. To enable people to enjoy sharing
photos without worrying about their privacy, we propose an
algorithm that allows users to selectively protect their privacy
while preserving the contextual information contained in images.
The results show that the proposed algorithm can select and
perturb private objects to be protected among multiple optional
objects so that the DNN can only identify non-private objects in
images. Index Terms: privacy | object detection | deep learning |
مقاله انگلیسی |
57 |
Transparency and accountability in AI decision support: Explaining and visualizing convolutional neural networks for text information
شفافیت و پاسخگویی در پشتیبانی تصمیم گیری هوش مصنوعی : توضیح و تجسم شبکه های عصبی کانولوشن برای اطلاعات متن-2020 Proliferating applications of deep learning, along with the prevalence of large-scale text datasets, have revolutionized
the natural language processing (NLP) field, thereby driving the recent explosive growth.
Nevertheless, it is argued that state-of-the-art studies focus excessively on producing quantitative performances
superior to existing models, by playing “the Kaggle game.” Hence, the field requires more effort in solving new
problems and proposing novel approaches and architectures. We claim that one of the promising and constructive
efforts would be to design transparent and accountable artificial intelligence (AI) systems for text
analytics. By doing so, we can enhance the applicability and problem-solving capacity of the system for realworld
decision support. It is widely accepted that deep learning models demonstrate remarkable performances
compared to existing algorithms. However, they are often criticized for being less interpretable, i.e., the “black
box.” In such cases, users tend to hesitate to utilize them for decision-making, especially in crucial tasks. Such
complexity obstructs transparency and accountability of the overall system, potentially debilitating the deployment
of decision support systems powered by AI. Furthermore, recent regulations are emphasizing fairness
and transparency in algorithms to a greater extent, turning explanations more compulsory than voluntary. Thus,
to enhance the transparency and accountability of the decision support system and preserve the capacity to
model complex text data at the same time, we propose the Explaining and Visualizing Convolutional neural networks
for Text information (EVCT) framework. By adopting and ameliorating cutting-edge methods in NLP and
image processing, the EVCT framework provides a human-interpretable solution to the problem of text classification
while minimizing information loss. Experimental results with large-scale, real-world datasets show that
EVCT performs comparably to benchmark models, including widely used deep learning models. In addition, we
provide instances of human-interpretable and relevant visualized explanations obtained from applying EVCT to
the dataset and possible applications for real-world decision support. Keywords: Convolutional neural network | Machine learning interpretability | Class activation mapping | Explainable artificial intelligence |
مقاله انگلیسی |
58 |
Actualizing big data analytics affordances: A revelatory case study
واقعی سازی هزینه های تحلیلی داده های بزرگ: یک مطالعه موردی الهامی-2020 Drawing on a revelatory case study, we identify four big data analytics (BDA) actualization mechanisms: (1)
enhancing, (2) constructing, (3) coordinating, and (4) integrating, which manifest in actions on three sociotechnical
system levels, i.e., the structure, actor, and technology levels. We investigate the actualization of four
BDA affordances at an automotive manufacturing company, i.e., establishing customer-centric marketing, provisioning
vehicle-data-driven services, data-driven vehicle developing, and optimizing production processes.
This study introduces a theoretical perspective to BDA research that explains how organizational actions contribute
to actualizing BDA affordances. We further provide practical implications that can help guide practitioners
in BDA adoption. Keywords: Big data analytics | Affordance theory | Socio-technical approach | Organizational transformation | Organizational benefits | Affordance actualization |
مقاله انگلیسی |
59 |
Firm-level capabilities towards big data value creation
قابلیت های سطح شرکت برای ایجاد ارزش کلان داده-2020 Big data has played an increasingly important role in using data to improve business value. In response to several big data challenges, the purpose of this study is to identify firm-level capabilities required to create value from big data. The adjacent theories of business process management and IT business value underpinned the study, together with an in-depth case study that led to the identification of twenty-four types of capabilities related to IT, process, performance, human, strategic, and organizational practices. The findings confirmed the application of practices and capabilities of adjacent theories, as well as certain practices and attributes that were both changed and reinforced at the intersection of big data. As an outstanding additional support to the extant big data studies, this work empirically confirms and portrays hitherto unexplored capabilities of big data and set their roles, thus providing a holistic overview of firm-level capabilities that are required for big data value creation. Keywords: Big data | Supply chain management | Operations management | Value creation | Business analytics | Capabilities |
مقاله انگلیسی |
60 |
Leveraging internet of things and big data analytics initiatives in European and American firms: Is data quality a way to extract business value?
اعمال اینترنت اشیاء و ابتکارات تحلیلی داده های بزرگ در شرکت های اروپایی و آمریکایی: آیا کیفیت داده راهی برای استخراج ارزش تجارت است؟-2020 Big data analytics (BDA) and the Internet of Things (IoT) tools are considered crucial investments for firms to
distinguish themselves among competitors. Drawing on a strategic management perspective, this study proposes
that BDA and IoT capabilities can create significant value in business processes if supported by a good level of
data quality, which will lead to a better competitive advantage. Responses are collected from 618 European and
American firms that use IoT and BDA applications. Partial least squares results reveal that better data quality is
needed to unlock the value of IoT and BDA capabilities. Keywords: Big data analytics | Internet of things | Strategic management | Knowledge-based theory | Dynamics capability theory |
مقاله انگلیسی |