دانلود و نمایش مقالات مرتبط با Big data analysis::صفحه 1
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نتیجه جستجو - Big data analysis

تعداد مقالات یافته شده: 77
ردیف عنوان نوع
1 Nine-nine-six work system and people’s movement patterns: Using big data sets to analyse overtime working in Shanghai
سیستم کار نه-نه-شش و الگوهای حرکت مردم: استفاده از مجموعه داده های بزرگ برای تحلیل اضافه کاری در شانگهای-2020
Although topics regarding “996 work system” and overtime working have aroused hot arguments, there is scant literature that analyses the spatial distribution and movement patterns of people who work overtime. This article fills this gap by adopting big data analysis and examining the mobile phone signal data which allow the calculation of the approximate spatial position of the mobile-phone user, and the generation of transportation flows and individuals’ origin-destination (OD) flows. The findings show that no less than one third of employees in Shanghai work overtime, and that overtime workers face higher job-housing imbalance than workers who have normal work durations or flexible schedules. This corroborates David Harvey’s time-space compression theory. Going beyond that, we further discover the interchangeability between exploitation in the time dimension, and that in the spatial dimension, resulting in dual exploitation. This article has important policy implications for optimizing the urban spatial system of Shanghai, as it advocates that in addition to strengthening the enforcement of labor law, the government also needs to improve the public service such as strengthening the underground system’s capacity, and construct affordable houses, so as to alleviate the employees’ sufferings caused by temporal and spatial exploitation. Moreover, the research points out the necessity for Chinese cities to enhance the vertical mixing, in order to shorten the job-housing distance.
Keywords: Overtime working | Human activity patterns | Big data | Mobile phone Signal data | Shanghai | OD | Time-space compression | Vertical mixing of land use
مقاله انگلیسی
2 Data mining of customer choice behavior in internet of things within relationship network
داده کاوی رفتار انتخاب مشتری در اینترنت اشیایی که در شبکه ارتباطی قرار دارند-2020
Internet of Things has changed the relationship between traditional customer networks, and traditional information dissemination has been affected. Smart environment accelerates the changes in customer behaviors. Apparently, the new customer relationship network, benefitted from the Internet of Things technology, will imperceptibly influence customer choice behaviors for the cyber intelligence. In this work, we selected 298 customers click browsing records as training data, and collected 50 customers who used the platform for the first time as research objects. and use the smart customer relationship network correspond to cyber intelligence to build the customer intelligence decision model in Internet of Things. The results showed that the MAE (Mean Absolute Deviation) of the customer trust evaluation model constructed in this study is 0.215, 45% improvement over the traditional equal assignment method. In addition, customers consumer experience can be enhanced with the support of data mining technology in cyber intelligence. Our work indicated the key to build eliminates confusion in customer choice behavior mechanism is to establish a consumer-centric, effective network of customers and service providers, and to be supported by the Internet of Things, big data analysis, and relational fusion technologies.
Keywords: Internet of things | Customer relationship network | Decision making | Recommendation | Fusion algorithm
مقاله انگلیسی
3 Paradigm change in Indian agricultural practices using Big Data: Challenges and opportunities from field to plate
تغییر پارادایم در شیوه های کشاورزی هند با استفاده از داده های بزرگ: چالش ها و فرصت ها از زمینه ای به صفحه دیگر-2020
Agriculture is the backbone of the Indian Economy. However, statistics show that the rural population and arable land per person is declining. This is an ominous development for a country with a population of more than one billion, with over sixty-six percent living in rural areas. This paper aims to review current studies and research in agriculture, employing the recent practice of Big Data analysis, to address various problems in this sector. To execute this review, this article outline a framework for Big Data analytics in agriculture and present ways in which they can be applied to solve problems in the present agricultural domain. Another goal of this review is to gain insight into state-of-the-art Big Data appli- cations in agriculture and to use a structural approach to identify challenges to be addressed in this area. This review of Big Data applications in the agricultural sector has also revealed several collection and analytics tools that may have implications for the power relationships between farmers and large corporations.© 2020 China Agricultural University. Production and hosting by Elsevier B.V. on behalf of KeAi. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Contents
Keywords: Agriculture | Data | Governance | Precision agriculture | Smart farming
مقاله انگلیسی
4 A novel intelligent option price forecasting and trading system by multiple kernel adaptive filters
رویکرد پیش بینی قیمت و گزینه سیستم تجاری با فیلترهای انطباقی چند هسته ای-2020
Derivatives such as options are complex financial instruments. The risk in option trading leads to the demand of trading support systems for investors to control and hedge their risk. The nonlinearity and non-stationarity of option dynamics are the main challenge of option price forecasting. To address the problem, this study develops a multi-kernel adaptive filters (MKAF) for online option trading. MKAF is an improved version of the adaptive filter, which employs multiple kernels to enhance the richness of nonlinear feature representation. The MKAF is a fully adaptive online algorithm. The strength of MKAF is that the weights to the kernels are simultaneous optimally determined in filter coefficient updates. We do not need to design the weights separately. Therefore, MKAF is good at tracking nonstationary nonlinear option dynamics. Moreover, to reduce the computation time in updating the filter, and prevent overadaptation, the number of kernels is restricted by using coherence-based sparsification, which constructs a set of dictionary and uses a coherence threshold to restrict the dictionary size. This study compared the new method with traditional ones, we found the performance improvement is significant and robust. Especially, the cumulated trading profits are substantially increased
Keywords: Artificial intelligence | Adaptive filter | Multiple Kernel Machine | Big data analysis | Data mining | Financial forecasting
مقاله انگلیسی
5 Use of a big data analysis technique for extracting HRA data from event investigation reports based on the Safety-II concept
استفاده از روش تجزیه و تحلیل داده های بزرگ برای استخراج داده های مجموعه فعالان حقوق بشر از رویداد گزارش تحقیقات بر اساس مفهوم ایمنی-II-2020
The safe operation of complex socio-technical systems including NPPs (Nuclear Power Plants) is a determinant for ensuring their sustainability. From this concern, it should be emphasized that a large portion of safety significant events were directly and/or indirectly caused by human errors. This means that the role of an HRA (Human Reliability Analysis) is critical because one of its applications is to systematically distinguish error-prone tasks triggering safety significant events. To this end, it is very important for HRA practitioners to access diverse HRA data which are helpful for understanding how and why human errors have occurred. In this study, a novel approach is suggested based on the Safety-II concept, which allows us to collect HRA data by considering failure and success cases in parallel. In addition, since huge amount of information can be gathered if the failure and success cases are simultaneously involved, a big data analysis technique called the CART (Classification And Regression Tree) is applied to deal with this problem. As a result, it seems that the novel approach proposed by combining the Safety-II concept with the CART technique is useful because HRA practitioners are able to get HRA data with respect to diverse task contexts.
Keywords: Human reliability analysis | Nuclear power plant | Safety-II | Classification and regression tree | Event investigation report
مقاله انگلیسی
6 A hybrid deep learning model for efficient intrusion detection in big data environment
یک مدل یادگیری عمیق ترکیبی برای تشخیص نفوذ موثر در محیط داده های بزرگ-2020
The volume of network and Internet traffic is expanding daily, with data being created at the zettabyte to petabyte scale at an exceptionally high rate. These can be character- ized as big data, because they are large in volume, variety, velocity, and veracity. Security threats to networks, the Internet, websites, and organizations are growing alongside this growth in usage. Detecting intrusions in such a big data environment is difficult. Various intrusion-detection systems (IDSs) using artificial intelligence or machine learning have been proposed for different types of network attacks, but most of these systems either cannot recognize unknown attacks or cannot respond to such attacks in real time. Deep learning models, recently applied to large-scale big data analysis, have shown remarkable performance in general but have not been examined for detection of intrusions in a big data environment. This paper proposes a hybrid deep learning model to efficiently detect network intrusions based on a convolutional neural network (CNN) and a weight-dropped, long short-term memory (WDLSTM) network. We use the deep CNN to extract mean- ingful features from IDS big data and WDLSTM to retain long-term dependencies among extracted features to prevent overfitting on recurrent connections. The proposed hybrid method was compared with traditional approaches in terms of performance on a publicly available dataset, demonstrating its satisfactory performance.
Keywords: Big data | Intrusion | detection Deep learning | Convolution neural network | Weight-dropped long short-term memory | network
مقاله انگلیسی
7 Does artificial intelligence dream of non-terrestrial techno-signatures?
آیا هوش مصنوعی رویای امضاهای فنی غیر زمینی را می بیند؟-2020
Today, we live in the midst of a surge in the use of artificial intelligence in many scientific and technological applications, including the Search for Extraterrestrial Intelligence (SETI). However, human perception and decision-making is still the last part of the chain in any data analysis or interpretation of results or outcomes. One of the potential applications of artificial intelligence is not only to assist in big data analysis but to help to discern possible artificiality or oddities in patterns of either radio signals, megastructures or techno-signatures in general. In this study, we review the comparative results of an experiment based on geometric patterns reconnaissance and a perception task, performed by 163 human volunteers and an artificial intelligence convolutional neural network (CNN) computer vision model. To test the model, we used an image of the famous bright spots on the Occator crater on Ceres. We wanted to investigate how the search for techno-signatures or oddities might be influenced by our cognitive skills and consciousness, and whether artificial intelligence could help or not in this task. This article also discusses how unintentional human cognitive bias might affect the search for extraterrestrial intelligence and techno-signatures compared with artificial intelligence models, and how such artificial intelligence models might perform in this type of task. We discuss how searching for unexpected, irregular features might prevent us from detecting other nearside or in-plain-sight rare and unexpected signs. The results strikingly showed that a CNN trained to detect triangles and squares scored positive hits on these two geometric shapes as some humans did
مقاله انگلیسی
8 Upcoming Scenarios for the Comprehensive Management of Obstructive Sleep Apnea: An Overview of the Spanish Sleep Network
سناریوهای آینده برای مدیریت جامع Apnea خواب انسدادی: مروری بر شبکه خواب اسپانیایی-2020
Sleep is considered an essential part of life and plays a vital role in good health and well-being. Equally important as a balanced diet and adequate exercise, quality and quantity of sleep are essential for maintaining good health and quality of life. Sleep-disordered breathing is one of the most prevalent conditions that compromises the quality and duration of sleep, with obstructive sleep apnea (OSA) being the most prevalent disorder among these conditions. OSA is a chronic and highly prevalent disease that is considered to be a true public health problem. OSA has been associated with increased cardiovascular, neurocognitive, metabolic and overall mortality risks, and its management is a challenge facing the health care system. To establish the main future lines of research in sleep respiratory medicine, the Spanish Sleep Network (SSN) promoted the 1st World Café experts’ meeting. The overall vision was established by consensus as “Sleep as promoter of health and the social impact of sleep disturbances”. Under this leitmotiv and given that OSA is the most prevalent sleep disorder, five research lines were established to develop a new comprehensive approach for OSA management: (1) an integrated network for the comprehensive management of OSA; (2) the biological impact of OSA on comorbidities with high mortality, namely, cardiovascular and metabolic diseases, neurocognitive diseases and cancer; (3) Big Data Analysis for the identification of OSA phenotypes; (4) personalized medicine in OSA; and (5) OSA in children: current needs and future perspectives. Keywords: Obstructive sleep apnea | Continuous positive airway pressure | Clinical management | Personalized medicine
مقاله انگلیسی
9 Use of big data analysis to investigate the relationship between natural radiation dose rates and cancer incidences in Republic of Korea
استفاده از تجزیه و تحلیل داده های بزرگ برای بررسی رابطه بین میزان دوز پرتوی طبیعی و بروز سرطان در جمهوری کره-2020
In this study, we investigated whether there is a significant relationship between the natural radiation dose rate and the cancer incidents in Korea by using a big data analysis. The natural dose rate data for this analysis were the measurement data obtained from the 171 monitoring posts of the 113 administrative districts in Korea over the 10 years from 2007 and 2016. The relative cancer incidences for this analysis were the difference in the cancer patients per hundred thousand people year-on-year in the administrative districts with the five highest and the five lowest natural gamma dose rates each year over the same period. To analyze the correlation between the two variables, Spearman’s rank correlation coefficient between the two rates was derived using R, a well-known big data analysis tool. The analysis showed that Spearman’s rank correlation coefficient was more than 0.05 and that the correlation between the two variables was not statistically significant.
Keywords: Natural radiation dose rate | Cancer incident | Big data analysis | Relative cancer incidence | Spearman’s rank correlation analysis
مقاله انگلیسی
10 Paradigm change in Indian agricultural practices using Big Data: Challenges and opportunities from field to plate
تغییر پارادایم در شیوه های کشاورزی هند با استفاده از داده های بزرگ: چالش ها و فرصت ها از زمینه به صفحه دیگر-2020
Agriculture is the backbone of the Indian Economy. However, statistics show that the rural population and arable land per person is declining. This is an ominous development for a country with a population of more than one billion, with over sixty-six percent living in rural areas. This paper aims to review current studies and research in agriculture, employing the recent practice of Big Data analysis, to address various problems in this sector. To execute this review, this article outline a framework for Big Data analytics in agriculture and present ways in which they can be applied to solve problems in the present agricultural domain. Another goal of this review is to gain insight into state-of-the-art Big Data applications in agriculture and to use a structural approach to identify challenges to be addressed in this area. This review of Big Data applications in the agricultural sector has also revealed several collection and analytics tools that may have implications for the power relationships between farmers and large corporations.
Keywords: Agriculture | Data | Governance | Precision Agriculture | Smart Farming
مقاله انگلیسی
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