Статті в журналах: "BASED DATA"

1

Gültepe, Yasemin. "Querying Bibliography Data Based on Linked Data." Journal of Software 10, no. 8 (August 2015): 1014–20. http://dx.doi.org/10.17706//jsw.10.8.1014-1020.

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2

N.J., Anjala. "Algorithmic Assessment of Text based Data Classification in Big Data Sets." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 1231–34. http://dx.doi.org/10.5373/jardcs/v12sp4/20201598.

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3

C, Ajitha, DivyaLakshmi K, and Jothi Jayashree M. "MicroRNA-Disease Predictions Based On Genomic Data." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1646–51. http://dx.doi.org/10.31142/ijtsrd11386.

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4

Mohite, Ms Nikita S., and Mr H. P. Khandagale. "Social Group Recommendation based on Big Data." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1118–21. http://dx.doi.org/10.31142/ijtsrd7097.

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5

Baidari, Dr Ishwar, and S. P. Sajjan. "Location Based Crime Detection Using Data Mining." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (October 31, 2016): 208–12. http://dx.doi.org/10.9756/bijsesc.8279.

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6

K., Ashfaq Ahmed, and Dr Shaheda Akthar. "Ridge Regression based Missing Data Estimation with Dimensionality Reduction: Microarray Gene Expression Data." Webology 19, no. 1 (January 20, 2022): 4113–28. http://dx.doi.org/10.14704/web/v19i1/web19271.

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Анотація:

Data is considered to be the important element in the field of Data Science and Machine Learning. Performance of Machine Learning and Data Mining algorithms greatly influenced by the characteristics of data and data with missing values. Performance of all these Machine Learning algorithms greatly improved and they can give accurate results when the data is in full without missing values. So before applying these algorithms; dataset and its missing values are completely filled. To impute these missing values in the dataset there are numerous methods were proposed. In this paper we used micro array gene expression dataset; by introducing various percentages of missing values a new methodology is proposed to impute these missing values in the data set. The nature of micro array gene expression dataset is huge in dimensionality, so at first, we used recursive feature elimination method to select the best features which contributes much for model was selected then we apply the Ridge Regression for imputation. Imputations with other methods are compared. We evaluate the performance of all models by using the metrics like MSE, MAE, R-square. To select the best model in the set of models we used Normalized Criteria Distance (NCD) to rank the models under proposed metrics. The model with least NCD rank selected as the best model among other models, in our paper proposed model has got the lowest value among other models and considered to be the best model among other models.

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7

Pantos, C., M, Schroeder, and P. Ivanova. "Open Access to Quality Biomedical Experimental and Clinical Data and Data-based Models." Biomedical Data Journal 01, no. 1 (January 2015): 1–2. http://dx.doi.org/10.11610/bmdj.01100.

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8

Yong-Qiong Zhu, Yong-Qiong Zhu, Ye-Ming Cai Yong-Qiong Zhu, and Fan Zhang Ye-Ming Cai. "Motion Capture Data Denoising Based on LSTNet Autoencoder." 網際網路技術學刊 23, no. 1 (January 2022): 011–20. http://dx.doi.org/10.53106/160792642022012301002.

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This paper proposes a novel deep learning-based optical motion capture denoising model encoder-LSTNet- decoder (ELD). ELD uses an autoencoder for manifold learning and decoder to remove jitter noise and missing noise effectively. It uses recurrent units in LSTNet to effectively obtain the spatial-temporal information of motion sequences, especially the periodic long-term and short-term features. In the denoising procedure, the kinetical characteristics of the motion are also considered so that the reconstructed deviation is smaller and can more accurately reflect the real action. We simulated ELD with the CMU database and compared it with the art-of-state methods. The experiment shows that ELD is a very effective denoising technique with lower reconstruction error, stronger robustness, and shorter running time.

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9

S, Revathy, Sangavi A, Surekha U, Sushmitha R, and Nandhagopal N. "Li-Fi Based Data Transfer in Underwater System." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 05, no. 04 (August 4, 2017): 08–10. http://dx.doi.org/10.9756/sijcsea/v5i4/05010190101.

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10

N, Rajkumar, Janhavi V, and Rajendra M.A.B. "Cryptography Policy-Based Data Communication in Trusted Environment." Indian Journal of Science and Technology 12, no. 48 (December 20, 2019): 11–4. http://dx.doi.org/10.17485/ijst/2019/v12i48/149385.

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11

P., Poonguzhali, Prajyot Dhanokar, M. K. Chaithanya, and Mahesh U. Patil. "Secure Storage of Data on Android Based Devices." International Journal of Engineering and Technology 8, no. 3 (March 2016): 177–82. http://dx.doi.org/10.7763/ijet.2016.v6.880.

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12

P., Poonguzhali, Prajyot Dhanokar, M. K. Chaithanya, and Mahesh U. Patil. "Secure Storage of Data on Android Based Devices." International Journal of Engineering and Technology 8, no. 3 (March 2016): 177–82. http://dx.doi.org/10.7763/ijet.2016.v8.880.

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13

MARTYNIUK, Tatiana, Andrii KOZHEMIAKO, Bohdan KRUKIVSKYI, and Antonina BUDA. "ASSOCIATIVE OPERATIONS BASED ON DIFFERENCE-SLICE DATA PROCESSING." Herald of Khmelnytskyi National University. Technical sciences 311, no. 4 (August 2022): 159–63. http://dx.doi.org/10.31891/2307-5732-2022-311-4-159-163.

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Анотація:

Associative operations are effectively used to solve such application problems as sorting, searching for certain features, and identifying extreme (maximum/minimum) elements in data sets. Thus, determining the maximum number as a result of sorting a numerical array is an acceptable operation in implementing the competition mechanism in neural networks. In addition, determining the average number in a numerical series by sorting significantly speeds up the process of median filtering of images and signals. In this case, the implementation of median filtering requires the use of sorting with the ranking of the elements of the number array. This paper analyses the possibilities of associative operations implementing the elements of a vector (one-dimensional) array of numbers based on processing by difference slices (DS). A simplified description of DS processing with a selection of the common part of the elements of the vector and the difference slice formed from its elements is given. In addition, elements of the binary mask matrix are used as an example of a topological feature matrix. The proposed approach allows for the formation of the ranks of the elements of the initial vector, as a result of sorting in ascending order of their numerical values. The paper shows a schematic representation of the process of DS processing, as well as an example of DS processing of a number vector in the form of a table, which shows the formation sequence of numbers of the sorted array and the ranks of numbers of the initial array. Therefore, the proposed use of topological features allows to determine the comparative relations between the elements of the numerical array in the process of spatially distributed DS processing, as well as to confirm the versatility of this approach.

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14

Sim, Kwan-Hua, Kwan-Yong Sim, and Valliappan Raman. "Scalable Pattern Recognition in Financial Time Series Data with Double-Cycled Value Based Data Representation." International Journal of Machine Learning and Computing 10, no. 3 (May 2020): 416–22. http://dx.doi.org/10.18178/ijmlc.2020.10.3.951.

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15

Raval, Hitesh R., and Dr Vikram Kaushik. "Data Mining: Performance Tuning Of Temporal Data Mining Based On Frequent Inter-Transaction Itemsets Discovery." International Journal of Scientific Research 3, no. 2 (June 1, 2012): 78–82. http://dx.doi.org/10.15373/22778179/feb2014/25.

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16

An, Cheol-Jin, Hyun-Chul Yi, Hyoung-Woo Kim, Sung-Mun Park, and Joon Young Choi. "Preventing Data Loss in Linux-Based Ethe rCAT Master." International Journal of Future Computer and Communication 4, no. 4 (2015): 246–49. http://dx.doi.org/10.7763/ijfcc.2015.v4.394.

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17

Kalaivani, S., B. Priyadharshini, and B. Selva Nalini. "Analyzing Student’s Academic Performance Based on Data Mining Approach." International Journal of Innovative Research in Computer Science & Technology 5, no. 1 (January 31, 2017): 194–97. http://dx.doi.org/10.21276/ijircst.2017.5.1.4.

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18

Kumar.R, Dr Prasanna, Porselvan G, Prem Kumar S, and Robinlash F. "Security and Privacy Based Data Sharing in Cloud Computing." International Journal of Innovative Research in Engineering & Management 5, no. 1 (January 2018): 42–49. http://dx.doi.org/10.21276/ijirem.2018.5.1.9.

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19

Chen, Yingying, Bo Liu, Hongbo Liu, and Yudong Yao. "VLC-based Data Transfer and Energy Harvesting Mobile System." Journal of Ubiquitous Systems and Pervasive Networks 15, no. 01 (March 1, 2021): 01–09. http://dx.doi.org/10.5383/juspn.15.01.001.

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Анотація:

This paper explores a low-cost portable visible light communication (VLC) system to support the increasing needs of lightweight mobile applications. VLC grows rapidly in the past decade for many applications (e.g., indoor data transmission, human sensing, and visual MIMO) due to its RF interference immunity and inherent high security. However, most existing VLC systems heavily rely on fixed infrastructures with less adaptability to emerging lightweight mobile applications. This work proposes Light Storage, a portable VLC system takes the advantage of commercial smartphone flashlights as the transmitter and a solar panel equipped with both data reception and energy harvesting modules as the receiver. Light Storage can achieve concurrent data transmission and energy harvesting from the visible light signals. It develops multi-level light intensity data modulation to increase data throughput and integrates the noise reduction functionality to allow portability under various lighting conditions. The system supports synchronization together with adaptive error correction to overcome both the linear and non-linear signal offsets caused by the low time-control ability from the commercial smartphones. Finally, the energy harvesting capability in Light Storage provides sufficient energy support for efficient short range communication. Light Storage is validated in both indoor and outdoor environments and can achieve over 98% data decoding accuracy, demonstrating the potential as an important alternative to support low-cost and portable short range communication.

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20

Baklanova, Olga E., and Vladimir A. Vasilenko. "Data compression with $\Sigma\Pi$-approximations based on splines." Applications of Mathematics 38, no. 6 (1993): 405–10. http://dx.doi.org/10.21136/am.1993.104563.

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21

Modi, Prof Bharat P., and Dr Jayesh M. Patel. "Exploiting Consumptive Behavior Based Data Mining of Mobile Environment." Indian Journal of Applied Research 4, no. 1 (October 1, 2011): 146–48. http://dx.doi.org/10.15373/2249555x/jan2014/44.

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22

Benila S, Benila S., and Usha Bhanu N. Benila S. "Fog Managed Data Model for IoT based Healthcare Systems." 網際網路技術學刊 23, no. 2 (March 2022): 217–26. http://dx.doi.org/10.53106/160792642022032302003.

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Анотація:

In Internet of things enabled healthcare system, sensors create vast volumes of data that are analyzed in the cloud. Transferring data from the cloud to the application takes a long time. An effective infrastructure can reduce latency and costs by processing data in real-time and close to the user devices. Fog computing can solve this issue by reducing latency by storing, processing, and analyzing patient data at the network edge. Placing the resources at fog layer and scheduling tasks is quite challenging in Fog computing. This paper proposes a Fog Managed Data Model (FMDM) with three layers namely Sensor, Fog and cloud to solve the aforementioned issue. Sensors generate patient data and that are managed and processed by Fog and cloud layers. Tasks are scheduled using a Weighted Fog Priority Job Scheduling algorithm (WFPJS) and fog nodes are allocated based on Priority based Virtual Machine Classification Algorithm (PVCA). The performance of this model is validated with static scheduling techniques with variable patient counts and network configurations. The proposed FMDM with WFPJS reduces response time, total execution cost, network usage, network latency, computational latency and energy consumption.

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23

More, Prof Vijay, Ms Ankita Shetty, and Ms Aishwarya Mapara Mr Rahul Ghuge Mr Rohit Sharma. "Employee Data Mining Based on Text and Image Processing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 379–81. http://dx.doi.org/10.31142/ijtsrd10791.

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24

G, Aravind, Varun K, and Manjunath C. R. Soumya K. N. "Application of Big Data Analytics with Evidence Based Medicine." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 440–44. http://dx.doi.org/10.31142/ijtsrd12979.

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25

Aung, Swe Swe, and Naing Kyaw Soe. "Zigbee Based Smart Farm Data Logging and Monitoring System." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (August 31, 2018): 2173–77. http://dx.doi.org/10.31142/ijtsrd18295.

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26

Balachander, Bhuvaneswari. "Laser Based Data Signal Transmission Using Free Space Optics." International Journal of Scientific Research 2, no. 12 (June 1, 2012): 204–5. http://dx.doi.org/10.15373/22778179/dec2013/63.

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27

BASYSTIUK, Oleh, and Nataliia MELNYKOVA. "MULTIMODAL SPEECH RECOGNITION BASED ON AUDIO AND TEXT DATA." Herald of Khmelnytskyi National University. Technical sciences 313, no. 5 (October 27, 2022): 22–25. http://dx.doi.org/10.31891/2307-5732-2022-313-5-22-25.

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Анотація:

Systems of machine translation of texts from one language to another simulate the work of a human translator. Their performance depends on the ability to understand the grammar rules of the language. In translation, the basic units are not individual words, but word combinations or phraseological units that express different concepts. Only by using them, more complex ideas can be expressed through the translated text. The main feature of machine translation is different length for input and output. The ability to work with different lengths of input and output provides us with the approach of recurrent neural networks. A recurrent neural network (RNN) is a class of artificial neural network that has connections between nodes. In this case, a connection refers to a connection from a more distant node to a less distant node. The presence of connections allows the RNN to remember and reproduce the entire sequence of reactions to one stimulus. From the point of view of programming, such networks are analogous to cyclic execution, and from the point of view of the system, such networks are equivalent to a state machine. RNNs are commonly used to process word sequences in natural language processing. Usually, a hidden Markov model (HMM) and an N-program language model are used to process a sequence of words. Deep learning has completely changed the approach to machine translation. Researchers in the deep learning field has created simple solutions based on machine learning that outperform the best expert systems. In this paper was reviewed the main features of machine translation based on recurrent neural networks. The advantages of systems based on RNN using the sequence-to-sequence model against statistical translation systems are also highlighted in the article. Two machine translation systems based on the sequence-to-sequence model were constructed using Keras and PyTorch machine learning libraries. Based on the obtained results, libraries analysis was done, and their performance comparison.

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28

Savadjiev, Peter, Caroline Reinhold, Diego Martin, and Reza Forghani. "Knowledge Based Versus Data Based." Neuroimaging Clinics of North America 30, no. 4 (November 2020): 401–15. http://dx.doi.org/10.1016/j.nic.2020.06.002.

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29

Idol, Lorna, and Shirley Ritter. "Data-Based Instruction." Teacher Education and Special Education: The Journal of the Teacher Education Division of the Council for Exceptional Children 10, no. 2 (April 1987): 65–70. http://dx.doi.org/10.1177/088840648701000203.

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30

Yilmaz, Mustafa, and Sangit Chatterjee. "Data-Based Dominance." American Journal of Mathematical and Management Sciences 10, no. 3-4 (February 1990): 341–66. http://dx.doi.org/10.1080/01966324.1990.10737289.

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31

Raheja, D., J. Llinas, R. Nagi, and C. Romanowski. "Data fusion/data mining-based architecture for condition-based maintenance." International Journal of Production Research 44, no. 14 (July 15, 2006): 2869–87. http://dx.doi.org/10.1080/00207540600654509.

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32

M., Inbavalli. "An Intelligent Agent based Mining Techniques for Distributed Data Mining." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 610–17. http://dx.doi.org/10.5373/jardcs/v12sp4/20201527.

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33

Gajmal, Yogesh M., and Udayakumar R. "Analysis of Authentication based Data Access Control Systems in Cloud." Journal of Advanced Research in Dynamical and Control Systems 12, no. 07-SPECIAL ISSUE (June 30, 2020): 2961–67. http://dx.doi.org/10.5373/jardcs/v12sp7/20202441.

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34

Madrazo, L., M. Massetti, A. Sicilia, G. Wadel, and M. Ianni. "SEíS: A semantic-based system for integrating building energy data." Informes de la Construcción 67, no. 537 (March 30, 2015): e060. http://dx.doi.org/10.3989/ic.13.048.

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35

Zhang, Xu, Min Huang, Jiajie Pan, and Jian Zheng. "Data Modeling Method Based on CAD Intersection and Its Application." International Journal of Modeling and Optimization 5, no. 2 (April 2015): 109–13. http://dx.doi.org/10.7763/ijmo.2015.v5.445.

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36

Seitov, Azamat. "On Development of Entrepreneurship in Uzbekistan: Based on Sociological Data." International Journal of Psychosocial Rehabilitation 24, no. 02 (February 20, 2020): 325–31. http://dx.doi.org/10.37200/ijpr/v24i2/pr200339.

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37

Wang, Yanhong, and Dandan Ji. "Data- and Rule-Based Integrated Mechanism for Job Shop Scheduling." International Journal of Computer and Communication Engineering 4, no. 3 (2015): 180–86. http://dx.doi.org/10.17706/ijcce.2015.4.3.180-186.

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38

Diachok, Roman, and Halyna Klym. "DATA CLEANING METHOD IN WIRELESS SENSOR-BASED ON INTELLIGENCE TECHNOLOGY." Measuring Equipment and Metrology 83, no. 2 (2022): 5–10. http://dx.doi.org/10.23939/istcmtm2022.02.005.

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Анотація:

The method of cleaning management data in wireless sensor networks based on intelligence technology has been studied. Specific forms of application of wireless sensor networks are analyzed. The characteristics of the structure of wireless sensor networks are presented and the data cleaning technology based on the clustering model is offered. An algorithm for deleting a cluster-based replication record is proposed and the accuracy of data cleaning methods is tested. The obtained results testify to the efficiency of using the studied method.

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39

Zi-Yang Ye, Zi-Yang Ye, Xuan Ji Zi-Yang Ye, Ming-Zi Ye Xuan Ji, Yu-Tong Shan Ming-Zi Ye, and Xiang-Rong Shi Yu-Tong Shan. "Data Analysis of Amazon Product Based on LSTM and GPR." 電腦學刊 33, no. 4 (August 2022): 015–27. http://dx.doi.org/10.53106/199115992022083304002.

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Анотація:

In this paper, we propose a method that combines models such as GPR with PSO optimization to predict the time series data. We use LSTM and TOPSIS with entropy weight method modification to process vari-ous types of data from various aspects, taking into account both tabular and textual data, and to mine valuable contents from them. Based on shopping data, we analyze the historical situation and predict the future sales of products. So that we can recommend the most suitable products for customers. At the same time, for merchants, this paper provides directions for product optimization and improvement of advertising and marketing strategies.

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40

Aiyan Wu, Aiyan Wu, and Yongmei Zhang Aiyan Wu. "Data Analysis Method of Talent Cultivation Based on Relational Graph." 電腦學刊 32, no. 6 (December 2021): 168–75. http://dx.doi.org/10.53106/199115992021123206014.

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41

S, Sowmiya, and Dr T. Priyaradhikadevi. "Key-Aggregate Based Vulnerable Data Access Control in Cloud Computing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1962–65. http://dx.doi.org/10.31142/ijtsrd11633.

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42

Xiaofeng Li, Xiaofeng Li, Jun Xu Jun Xu, Jijun Luo Jijun Luo, Lijia Cao Lijia Cao, and Shengxiu Zhang Shengxiu Zhang. "Ground target recognition based on imaging LADAR point cloud data." Chinese Optics Letters 10, s1 (2012): S11002–311005. http://dx.doi.org/10.3788/col201210.s11002.

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43

R. Q. Leithardt*, Valderi, Luiz Henrique Andrade Correia, Guilherme A. Borges, Anubis G.M. Rossetto, Carlos O. Rolim, Claudio F. R. Geyer, and Jorge M. Sá Silva. "Mechanism for Privacy Management Based on Data History (UbiPri-His)." Journal of Ubiquitous Systems and Pervasive Networks 10, no. 1 (March 7, 2018): 11–19. http://dx.doi.org/10.5383/juspn.10.01.002.

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44

Anitha, M. P., and S. S. Lijina. "Encryption Based Hybrid DWT-SVD Watermarking Technique for Data Hiding." Bonfring International Journal of Data Mining 6, no. 3 (June 30, 2016): 30–33. http://dx.doi.org/10.9756/bijdm.7205.

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45

Chakrabarti, P. "Information Security: An Artificial Intelligence and Data Mining Based Approach." International Journal of Engineering and Technology 1, no. 5 (2009): 448–53. http://dx.doi.org/10.7763/ijet.2009.v1.83.

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46

Wu, Chien Ho. "A Web-Based Multiple Regressor for Data Analysis Using R." International Journal of Computer and Communication Engineering 3, no. 3 (2014): 216–20. http://dx.doi.org/10.7763/ijcce.2014.v3.323.

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47

Nguyen, Bao T., Emanuele Olivetti, and Paolo Avesani. "Multiple-Scale Visualization of Large Data Based on Hierarchical Clustering." International Journal of Computer and Electrical Engineering 6, no. 2 (2014): 77–82. http://dx.doi.org/10.7763/ijcee.2014.v6.798.

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48

Shengjuan Guo, Shengjuan Guo, and Mei Yang Shengjuan Guo. "A Private Storage System of Medical Data Based on Blockchain." 電腦學刊 33, no. 6 (December 2022): 073–82. http://dx.doi.org/10.53106/199115992022123306006.

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Анотація:

At present, most medical institutions use electronic medical records (EMRs) to save medical and health data in the database, which take certain security risks on sharing the medical and health data. To solve this problem, this paper presents a novel storage method for health data using blockchain technology, which can share and avoid tampering the medical data between different hospitals and patients and keep avoid tampering the medical data between different hospitals and patients and keep the privacy at the same time. The schema of health data structure, the generation of block, the data access, peer management, system design and access controlling are presented in this paper.

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49

Hong Zhang, Hong Zhang, Kun Jiang Hong Zhang, Chuanqi Cheng Kun Jiang, Jie Cao Chuanqi Cheng, and Wenyue Zhang Jie Cao. "Multi-source Heterogeneous Data Fusion Model Based on FC-SAE." 網際網路技術學刊 23, no. 7 (December 2022): 1473–81. http://dx.doi.org/10.53106/160792642022122307003.

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Анотація:

Multi-source heterogeneous data has different degrees of data correlation or data conflict. How to fuse this data and fully mine its inherent meanings to obtain more accurate decision information is a problem that needs to be solved urgently. This paper proposes a multi-source heterogeneous data fusion model based on fully connected layers and sparse autoencoders (short for FC-SAE) to solve the above problem. This model can effectively improve the time series forecasting performance compared with the traditional time series forecasting model. The MAE value is reduced by 4.4% and the RMSE value is reduced by 3.7%. In terms of fusion strategy, the method that uses the sparse autoencoder as the fusion strategy reduces the MAE value by 1.7% and the RMSE value by 2.3% compared with the method that uses the fully connected layer as the fusion strategy.

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SHIN, Doh Kyoum, Sang Hwa LEE, Junghyun KANG, and Eun Mi PARK. "Data Catalogue Standards Based on DCAT for Transportation Data: DCAT-Trans." Journal of Korean Society of Transportation 37, no. 5 (October 31, 2019): 430–44. http://dx.doi.org/10.7470/jkst.2019.37.5.430.

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