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检索条件"任意字段=8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023"

共 155 条记录，以下是71-80 订阅

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Utilizing Monitoring Systems for Information Management in Rooftop Solar Photovoltaic Plants

Utilizing Monitoring Systems for Information Management in R...

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big data, cloud computing, data science & Engineering (bcd)

作者： thoa Kim thi Ninh Faculty of Library and Information Science University of Social Sciences and Humanities Vietnam National University Ho Chi Minh City Vietnam

the rapid expansion of rooftop solar photovoltaic plants necessitates advanced management strategies to ensure efficiency and sustainability. this article explores the integration of monitoring systems in rooftop solar plants, focusing on the case of NVH Company in Vietnam. By employing sophisticated monitoring tools like FusionSolar and iSolarcloud, the company collects real-time data on energy output, system performance metrics, and errors. through in-depth analysis and application of various software solutions, the company optimizes operational decisions, enabling timely maintenance and maximizing energy generation. the study highlights the crucial role of energy monitoring systems in data collection, storage, and analysis, providing insights for efficient plant management. Ethical considerations, confidentiality, and secure data practices are emphasized, ensuring the integrity of the research. the findings underscore the significance of integrating monitoring technologies in rooftop solar plants, offering valuable insights for the broader renewable energy sector.

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Innovative Approach of data Encryption Algorithm for Securing big data 8

Innovative Approach of Data Encryption Algorithm for Securin...

引用

8th international conference on Communication and Electronics Systems, ICCES 2023

作者： Awasthi, Shivani Kohli, Narendra Harcourt Butler Technical University Department of Computer Science Kanpur India

ISBN: (纸本)9798350396638

Nowadays, a large amount of data that is growing exponentially is known as big data. In other words, big data contains more variety and comes in big volume with more velocity. Traditional processing software cannot handle complex and big amounts of data, but this data addresses a lot of business problems, that could not be able to tackle before so to deal with this data, open-source software HDFS (Hadoop Distributed File System) is used as a cloud storage system. big data Security is a collective term used for all measures and tools that are used to guard data and analytics processes used for accessing the data. big data is a joint term and tool for data and data management systems against attacks, thefts, or other malicious activities that could harm or negatively affect them so server and serverless systems both are major concerned with high security of data. the author's research presents data security in the cloud computing environment in this paper. HDFS does not provide any scheme of encryption and decryption for securing the data, HDFS act as a storage medium that stores data in Avro format. Users use the encryption key and encryption zone to encrypt their data into HDFS. © 2023 ieee.

关键词： big data

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A Comparative Study of Transformers Embeddings for Question Answering in Arabic Private Documents

A Comparative Study of Transformers Embeddings for Question ...

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big data, cloud computing, data science & Engineering (bcd)

作者： Essam Al Daoud Ghassan Samara Laith Al Daoud Mohammad Al Jaidi Computer Science Department Zarqa University Jordan Computer Information System Department Jordan University of Science and Technology Jordan

Question answering (QA) tasks in natural language processing (NLP) are tricky, particularly when used with Arabic private documents. this is due to the complexity of Arabic language and the lack of sufficient annotated datasets for Arabic QA. Recent research findings have demonstrated the effectiveness of transformer embeddings for English QA. their efficiency for QA in Arabic private documents, however, has not been fully investigated. In this study, various transformer embeddings for QA in Arabic private documents are compared. the effectiveness of these embeddings is evaluated by assessing a golden dataset extracted from private documents. the results show that the accuracy of QA in Arabic private documents can be significantly improved by combining transformer embeddings with count vectorization. Furthermore, different transformer embeddings have distinct strengths and weaknesses.

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Personalized Recommendation: A novel approach based on Hybrid methods and fog computing architecture

Personalized Recommendation: A novel approach based on Hybri...

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big data, cloud computing, data science & Engineering (bcd)

作者： Noura Abdaoui Ismahene Hadj Khalifa Sami Faiz National School for Computer Science (ENSI) University of Manouba Manouba Tunisia Higher Institute of Multimedia Arts of La Manouba (ISAMM) University of Manouba Manouba Tunisia

A personalized recommender system meticulously analyzes users’ data encompassing their purchases, ratings, and interactions with other users. this thorough analysis allows the system to provide personalized and tailor-made recommendations aligned with users’ preferences. the advent of IoT-connected sensors has significantly augmented the availability of contextual information, prompting the rapid development of Context-Aware Recommendation Systems. Consequently, we have embraced a cloud computing approach, specifically leveraging fog architecture, which optimizes shared resources and offers a stable environment for consistent performance measurement. the primary objective of this paper is to propose an intelligent context-aware recommender system based on fog architecture, employing hybrid machine learning approaches. this innovative system involves gathering data from fog nodes and employing a process that learns users’ profiles by screening and analyzing reflective feedback loops. Ultimately, this approach enables the prediction of personalized items.

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Audio Spectrogram Transformer-based Audio Classification using Voice data of Dementia Patients

Audio Spectrogram Transformer-based Audio Classification usi...

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big data, cloud computing, data science & Engineering (bcd)

作者： Younjeong Lee Chanho Park Yeeun Moon Jongpil Jeong Department of Smart Factory Convergence Sungkyunkwan University Suwon Republic of Korea

Globally, aging is ongoing, and the probability of senile disease and dementia patients continue to increase as human lifespan and the proportion of the elderly increase. Dementia is a disease that affects the depression and quality of life of the elderly, and it is a socially and economically dangerous disease that cannot live independently. Currently, the only treatment for dementia is through training if the disease is delayed or detected early. Diagnosing dementia early is the most important step at this point, and studies are currently being conducted non-face-to-face or through voice. this study aims to predict dementia early through human voice. the study was conducted with the aim of optimizing and lightening the characteristics of dementia patients by applying the Audio Spectrogram Transformer analysis model as a voice classification model. the model used in this paper classifies dementia with an accuracy of 89%. In future research, we will try a study that combines Explainable AI (XAI), an interpretable artificial intelligence of deep learning.

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Basic system design in the PBNM Scheme for Multiple Domains as Cyber Physical System Using data science and AI

Basic system design in the PBNM Scheme for Multiple Domains ...

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big data, cloud computing, data science & Engineering (bcd)

作者： Kazuya Odagiri Shogo Shimizu Naohiro Ishii Sugiyama Jogakuen University Aichi Japan Gakushuin Women’s College Tokyo Japan Advanced Institute of Industrial Technology Tokyo Japan

In the current Internet system, there are many problems using anonymity of the network communication such as personal information leaks and crimes using the Internet system. this is why TCP/IP protocol used in Internet system does not have the user identification information on the communication data, and it is difficult to supervise the user performing the above acts immediately. As a study for solving the above problem, there is the study of Policy Based Network Management (PBNM). this is the scheme for managing a whole Local Area Network (LAN) through communication control for every user. In this PBNM, two types of schemes exist. As one scheme, we have studied theoretically about the Destination Addressing Control System (DACS) Scheme with affinity with existing internet. By applying this DACS Scheme to Internet system management, we will realize the policy-based Internet system management. In this paper, basic system design for PBNM scheme for multi-domain management utilizing data science and AI is proposed.

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Digital Electronic Electricity Meter Reading Using AlexNet

Digital Electronic Electricity Meter Reading Using AlexNet

引用

big data, cloud computing, data science & Engineering (bcd)

作者： Yu-Quan Xu Ruey-Maw Chen Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung Taiwan

Traditionally, electricity usage is based on manual reading and recording. However, this manual process is prone to errors and can result in a waste of manpower. To increase the reading and recording efficiency, a reading scheme based on image recognition is proposed. the preprocessing, including Gaussian blur, binarization, grayscale conversion, and Canny Edge detection was processed followed by a deep learning model. A deep learning AlexNet model trained by MNIST handwriting recognition dataset is employed for digital meter reading, the model is based on a ratio of 5:1:1 data for training, validation, and testing. three image resolutions, 84x84, 56x56, and 28x28, were tested. the 56x56 resolution image was applied for the electricity meter reading due to the accuracy and time complexity considerations. the experimental test was conducted on electricity meter images, and the obtained mean average precision is more than 90%, which indicates the suggested AlexNet model for digital meter reading is feasible for real applications.

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Benchmarking OpenStack for edge computing applications

Benchmarking OpenStack for edge computing applications

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big data, cloud computing, data science & Engineering (bcd)

作者： Jean-François Dollinger Kaouther Bouhouch Imen Bouzarkouna CESI LINEACT Strasbourg France CESI LINEACT Nanterre France

In this paper, we focus on the identification and evaluation of performance factors of OpenStack-based edge computing platforms. Such infrastructure relies on the deployment of additional computing resources close to the data source, to alleviate low throughput, latencies and network congestion. While cloud data centres offer numerous compute-intensive processing units, the edge layer leverages heterogeneous, dynamic and performance-constrained computing devices. the edge avoids the systematic use of low Quality of Service (QoS) of Wide Area Network (WAN) links to perform computations. Many edge platforms incorporate cloud software environment provisioning and elasticity capabilities. In this regard, we investigate the performance of the state-of-the-art OpenStack framework to set up an edge computing infrastructure. To achieve our goal, we first evaluate the impact of the deployment type, whether bare metal, containerised, virtualized or emulated on compute-intensive kernels. Within the same deployment scenarios, we examine the OpenStack virtual network infrastructure latency and throughput. We also cover the duration of several OpenStack primitives involved in the deployment of applications. Finally, we execute an MPI (Message Passing Interface) distributed application and highlight the performance impact of our infrastructure. Our study provides a comprehensive analysis of parameters influencing performance in an edge computing infrastructure based on OpenStack.

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Development of parking space certificate management system based on blockchain approach

Development of parking space certificate management system b...

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big data, cloud computing, data science & Engineering (bcd)

作者： Yu-Che Tsai Ming-Te Chen Yi-Ying Chang Computer Science and Information Engineering National Chin-Yi University of Science and Technology Taichung Taiwan

During rush hours in modern cities, drivers often encounter no available parking spaces and spend 5 to 30 minutes searching for parking spaces. During this time, not only is driver’s time wasted but air pollution and traffic jams are exacerbated. this is because people cannot immediately check whether there are empty spaces in other parking lots. However, parking lots are controlled by a centralized server, bringing the risk of a single point of failure and potential attacks from internal and external parties. this paper proposes a smart parking space certificate management system based on blockchain technology. this system mainly uses the Ethereum alliance chain to establish a parking space certificate management system. this allows data to be securely and decentralized stored on the chain. It then allows parking lot operators to interact with consumers through decentralized application (DApp) to save time finding parking spaces. the transaction process utilizes smart contract technology to automate delivery and save the digital signatures of parking space certificates. this realizes a decentralized and transparent system of management of parking space information.

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Predicting the Movement of Index (Bank Nifty) Using Day’s First Support & Resistance with Pivot Points Standard Indicator

Predicting the Movement of Index (Bank Nifty) Using Day’s F...

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big data, cloud computing, data science & Engineering (bcd)

作者： Vaibhav Aggarwal Ansh Arora Pankaj Kumar Jindal Global Business School O P Jindal Global University Sonipat India O P Jindal Global University Sonipat India Deenbandhu Chhotu Ram University Of Science & Technology Sonipat India

Predicting stocks and stock indexes movement has remained a primary focus of scholars in nearly all emerging and developed countries because interest to investors and policymakers. Predictive modelling for stock prices refer to models that can accurately interpret and anticipate events based on examining historical data to speculate on possible future prices. this paper analyses the internal representation of a system that predicts the best times to purchase and sell equities on the BSE & the NSE. First, support and resistance are used, which are established by candlesticks of contrasting colours, and the point of control is combined with open-interest data to complete the depiction. For NIFTY, BANK NIFTY, and FNO stocks, a variety of learning algorithms and prediction methodologies are formed. Simulations of stock trading demonstrated a high potential for profit using the prediction method, and the process created accurate predictions. We have examined the trend of intraday trading volume on 2 Indian exchanges (the BSE & the NSE), and replicate our study over the course of 264 trading days to ensure that our findings are consistent.

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