As the world has entered the era of big data, the digital economy, as an important part of the development of the real economy and innovation-driven development, has become a key factor in China’s implementation of m...
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There are more and more applications involve the big dataanalytics. The research method of big data is particularly important in the study of time-varying volatilities. In the article, we analysis the tests for a con...
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Internet of Things (IoT) is one of the prominent domains of research in the segment of wireless technologies whereby the smart objects are connected with each other using wireless signals. Despite the enormous areas o...
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With the advent of Internet-of-Things (IoT) devices, including smart meters and sensors in the smart grid, there has been immense research interest in big data management, analytics, and parallel processing of data. H...
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ISBN:
(纸本)9781728143811
With the advent of Internet-of-Things (IoT) devices, including smart meters and sensors in the smart grid, there has been immense research interest in big data management, analytics, and parallel processing of data. However, complex hardware and software parameters configurations and in-depth understanding of the data processing design are essential for efficient utilization of big dataanalytics platforms. In this work, we analyze the parallelization of load prediction by utilizing spark regression python library to assess the performance with workloads of up to 8 nodes. The results of different configurations have been studied and analyzed against the performance of Apache Spark. It was found that a trade-off between the number of nodes and cores is necessary to perform efficient parallel computing. Multiple sets of combinations of nodes and cores are considered in this paper to evaluate the performance. The work also signifies the importance of high-performance computing capability for smart meters big data management. The obtained results indicate that the computational time is not only dependent on the data size but also on the number of compute nodes and the number of cores assigned to run the job.
Over the previous few periods, global warming, driven by contamination, the emission of conservatory gases, and the use of non-environmentally friendly dynamism sources, has become a pressing issue. This phenomenon is...
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ISBN:
(数字)9798331516284
ISBN:
(纸本)9798331516291
Over the previous few periods, global warming, driven by contamination, the emission of conservatory gases, and the use of non-environmentally friendly dynamism sources, has become a pressing issue. This phenomenon is causing a steady increase in the Earth's overall temperature, which is having profound and detrimental effects on both human populations and ecosystems. Millions of people are at risk of losing their livelihoods due to these changes. One significant consequence of global warming is the escalation of high temperatures during the summer months, resulting in heat-related illnesses and even premature fatalities. To mitigate these risks and better understand climate patterns, researchers have turned to machine learning techniques. Specifically, they have employed algorithms like autoregressive combined poignant averages, collaborative learning, and extensive short-term remembrance networks to forecast temperature trends. This article introduces a novel approach known as CPSO to optimize the hyper parameters within Long Short-Term Remembrance networks. These hyper parameters are fine-tuned by minimizing the mean squared error during the validation of LSTM models. The resulting optimized LSTM hyper parameters are then used to predict temperatures in Chennai, a city heavily impacted by rising temperatures. The CPSO-LSTM model's effectiveness is assessed using a publicly available 25-year dataset of temperatures in Chennai. The evaluation is conducted using MATLABR2020a, with the mean squared error and mean absolute error serving as key performance metrics for the temperature predictions. Significantly, the CPSO-LSTM model outperforms traditional LSTM algorithms in several aspects. Notably, it significantly reduces training time to just 25 minutes while using 200 training epochs and 150 hidden neurons. Moreover, it achieves a lower RMSE of 0.250, as opposed to the outdated LSTM's RMSE of 0.35. This improvement in accuracy is promising, as it can have a positive
intelligent manufacturing system (IMS) has been the focus of most industries since Industry 4.0 revolution. IMS is being implemented through the integration of Internet of Things, (IoT), Cyber-Physical Systems (CPS), ...
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The proceedings contain 15 papers. The special focus in this conference is on Artificial Intelligence for Knowledge Management. The topics include: Holistic Approach to smart Factory;development of Big dataanalytics ...
ISBN:
(纸本)9783030808464
The proceedings contain 15 papers. The special focus in this conference is on Artificial Intelligence for Knowledge Management. The topics include: Holistic Approach to smart Factory;development of Big dataanalytics in a Multi-site Enterprise on the Example of Supply Chain Management;analysing Natural Gas Prices for Turkey in the Light of a Possible Hub;predicting Power Deviation in the Turkish Power Market Based on Adaptive Factor Impacts;machine Learning Methods in the Inclinometers Readings Anomaly Detection Issue on the Example of Tailings Storage Facility;ontologies Cooperation to Model the Needs of Disabled Persons;a Conceptual Framework of intelligent Management Control System for Higher Education;Curriculum Vitae (CVs) Evaluation Using Machine Learning Approach;university Students’ Research on Artificial Intelligence and Knowledge Management. A Review and Report of Multi-case Studies;Customer Churn Prediction in FMCG Sector Using Machine Learning Applications;crowdsourcing as a Tool Supporting Intra-city Communication;the Importance of the Internet of Things for smart Cities;developing a Knowledge Base on Climate Change for Metropolitan Cities.
Times are developing, technology is innovating, and big data thinking and methods promote the progress of human civilization, triggering all-round deep thinking and change in society. In the age of Internet communicat...
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To understand the comprehensive quality evaluation of high school students, the first thing is to go out of the three major misunderstandings and recognize the value of comprehensive quality evaluation, the quantifiab...
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With a deluge of data from various sources, sensors being resource constrained with less computing power, small memory and battery life leads them into getting compromised with various attacks and malfunction. Failure...
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